may 2007, volume 18, issue 3,pp.287-413 2007 prizelib.ajaums.ac.ir/booklist/827570.pdf · may 2007,...

May 2007, Volume 18, Issue 3,pp.287-413 2007 Prize

287 Rothman Epidemiology Prize.

Acknowledgments 288 Thanks to Our Reviewers.

Epidemiology & Society 290 Studying Vulnerable Populations: Lessons From the Roma

Minority. Karolina Kósa; Róza Ádány

Original Article 300 Environmental Exposure to Confined Animal Feeding

Operations and Respiratory Health of Neighboring Residents. Katja Radon; Anja Schulze; Vera Ehrenstein; Rob T. van Strien; Georg Praml; Dennis Nowak

Commentary 309

FREE Environmental Exposure and Health Effects From Concentrated Animal Feeding Operations. Frank M. Mitloehner; Marc B. Schenker

Original Article 312 Neurobehavioral Development in Children With Potential

Exposure to Pesticides. Alexis J. Handal; Betsy Lozoff; Jaime Breilh; Siobán D. Harlow

321 Combining Internal and External Validation Data to Correct for Exposure Misclassification: A Case Study. Robert H. Lyles; Fan Zhang; Carolyn Drews-Botsch

329 The Identification of Synergism in the Sufficient-Component-Cause Framework. Tyler J. VanderWeele; James M. Robins

340 Risk Factors for Positive Tuberculin Skin Test in Guinea-Bissau. Per Gustafson; Ida Lisse; Victor Gomes; Cesaltina S. Vieira; Christian Lienhardt; Anders Nauclér; Henrik Jensen; Peter Aaby

Commentary 348

FREE Tuberculin Testing to Detect Latent Tuberculosis in Developing Countries. Kenrad Nelson

Original Article 350 Work Schedule During Pregnancy and Spontaneous Abortion.

Elizabeth A. Whelan; Christina C. Lawson; Barbara Grajewski; Eileen N. Hibert; Donna Spiegelman; Janet W. Rich-Edwards

356 Maternal Stressful Life Events and Risks of Birth Defects. Suzan L. Carmichael; Gary M. Shaw; Wei Yang; Barbara Abrams; Edward J. Lammer

362 Vitamin B12 and the Risk of Neural Tube Defects in a Folic-Acid-Fortified Population. Joel G. Ray; Philip R. Wyatt; Miles D. Thompson; Marian J. Vermeulen; Chris Meier; Pui-Yuen Wong; Sandra A. Farrell; David E. C. Cole

Commentary 367

FREE When Will We Eliminate Folic Acid-Preventable Spina Bifida? Godfrey P. Oakley Jr

Original Article 369 Temperature and Cardiovascular Deaths in the US Elderly:

Changes Over Time. Adrian Gerard Barnett

373 Cooked Meat and Risk of Breast Cancer-Lifetime Versus Recent Dietary Intake. Susan E. Steck; Mia M. Gaudet; Sybil M. Eng; Julie A. Britton; Susan L. Teitelbaum; Alfred I. Neugut; Regina M. Santella; Marilie D. Gammon

383 Occupational Exposures and Breast Cancer Among Women Textile Workers in Shanghai. Roberta M. Ray; Dao Li Gao; Wenjin Li; Karen J. Wernli; George Astrakianakis; Noah S. Seixas; Janice E. Camp; E Dawn Fitzgibbons; Ziding Feng; David B. Thomas; Harvey Checkoway

393 A Prospective Study of Dietary Patterns and Mortality in Chinese Women. Hui Cai; Xiao Ou Shu; Yu-Tang Gao; Honglan Li; Gong Yang; Wei Zheng

402 Effect of Soy Isoflavones on Endometriosis: Interaction With Estrogen Receptor 2 Gene Polymorphism. Masaki Tsuchiya; Tsutomu Miura; Tomoyuki Hanaoka; Motoki Iwasaki; Hiroshi Sasaki; Tadao Tanaka; Hiroyuki Nakao; Takahiko Katoh; Tsuyomu Ikenoue; Michinori Kabuto; Shoichiro Tsugane

Vignette 409 The Halifax Explosion.

Warren Winkelstein Jr

Letters to the Editor 410 Lower Limb Cellulitis After a Typhoon and Flood.

Hung-Jung Lin; Chien-Chin Hsu; How-Ran Guo

410 SSRIs and Birth Defects. Vivien Burt; Laura Miller; Adrienne Einarson

411 SSRIs and Birth Defects. Monique B. Kelly; Katherine L. Wisner; Marie D. Cornelius

412 SSRIs and Birth Defects. Pia Wogelius; Mette Nørgaard; Mette Gislum; Lars Pedersen; Estrid Munk; Preben Bo Mortensen; Loren Lipworth; Henrik Toft Sørensen

413 Janet Lane-Claypon. Alfredo Morabia

ACKNOWLEDGMENTS

Thanks to Our ReviewersReviewers are the backbone of peer-reviewed journals. As Editors, we depend on the help of these experts in selecting the best from

among the hundreds of manuscripts that come to our desks. In the process, we see how papers can benefit in substantial ways from theinsights of reviewers. We are glad to acknowledge our otherwise-unsung colleagues in this annual list (this year covering July 2005 toDecember 2006). Those who names are marked by an asterisk have provided 3 or more reviews. Special thanks to Enrique Schisterman,who led the pack with 7 reviews.

We are deeply grateful to all for their contributions to EPIDEMIOLOGY.

Habibul AhsanMichael AlavanjaAnthony Alberg*Robert AllardCande Ananth*Henry AndersonSamuel ArbesBen Armstrong*Alberto AscherioSusan AssmannBrad AstorJacques AugerHarland AustinPeter C. AustinDonna Baird*Dean BakerKi Moon BangOlga Basso*Rupa Basu*Thomas BatesonChris BauchLydia BazzanoTerri BeatyJames BeaumontMelissa BeggMichelle BellDavid BellingerTrude BennettShirley BeresfordJesse BerlinSonja BerndtLeslie BernsteinMarianne Berwick*Aaron Blair*Margit BleeckerJ. T. BoermaPaolo BoffettaSimon BondLorenzo BottoHeather BoydColeen BoyleAbee BoylesPaul Brandt-RaufMichael BrauerHermann BrennerNaomi BreslauLouise BrintonM. Alan Brookhart

Bert Brunekreef*Germaine Buck LouisTimothy BuckleyJames BuehlerPierre Buekens*Greta BuninEsteban BurchardAnn BurchellRick BurnettRobin M. BushTimothy ByersRebecca CalderonKenneth CantorJohn CarlinSuzan CarmichaelPhil CastleJames CerhanLisa Chasan-TaberNilanjan Chatterjee*Harvey Checkoway*Aimin ChenBingshu Eric ChenEric ChenHonglei ChenK. F. ChengMary Chipman*Kaare ChristensenDavid ChristianiAaron Cohen*Stephen Cole*George Comstock*Jennie ConnorGlinda Cooper*Adolfo Correa-Villasenor*Ciprian CrainiceanuLisa CroenAmanda CrossRosa CrumDana DabeleaJulie Daniels*George Davey SmithBill DavisScott DavisVictor DeGruttolaRalph DelfinoPaul DemersLisa DeRoo*William Dietz

Ana Diez-Roux*Douglas DockeryJennifer DodsonNancy DoleChristine Dunkel SchetterDavid DunsonJohanna DwyerMartin EichnerJoseph EisenbergLawrence EngelLucinda EnglandJohn EvansDani FallinVern FarewellNeil FergusonMaria FeychtingDianne FinkelsteinKatherine FlegalTerry FonthamFrancesco ForastiereBetsy FoxmanEduardo FrancoTom FriedenChristine Friedenreich*Sandro GaleaGeoffrey GarnettMia GaudetDionne Gesink LawAlison GeyhDavid GibbonsFrank Gilliland*Beth GladenRobert GlynnDiane GoldMark GoldbergLynn GoldinJean GoldingLynn GoldmanMarlene GoldmanDavid GoldsmithAlisa GoldsteinJames GoodwinPhilippe GrandjeanRonald GraySander GreenlandMarie GriffinFrancine GrodsteinLaura Grosso

Eliseo GuallarHarry GuessPaul GustafsonRichard HammanJean HankinJohnni HansenLinda HarlanSusan HarlapD. O. HarleyBernard HarlowSam HarperJohn HarrisPatricia HartgeElizabeth HatchMaureen HatchRuss HauserDick HeederikTine Brink HenriksenMiguel Hernan*Mauricio Hernandez-AvilaSonia Hernandez-DiazAmy HerringIrva Hertz-PicciottoGloria HoCarol JR HogueJane Hoppin*David HosmerPenelope HowardsHoward HuR. J. HungSally HunsbergerMichel IbrahimClaire Infante-Rivard*Peter InskipJohn IoannidisLorentz IrgensJouni Jaakkola*David Jacobs*Eric JacobsKevin JacobsBjarne JacobsenMichael JoffeFreya KamelNorma KanarekEdward KaplanMargaret KaragasJoanne KatzJay Kaufman*

Epidemiology • Volume 18, Number 3, May 2007288

Claudia KawasLeeka Kheifets*Howard KipenRussell KirbyNeil KlarMark KlebanoffJennie KlineThomas KoepsellLaurence KolonelJim Koopman*Mary Grace KovarPeter KraftMichael KramerJess KrausMirjam KretzschmarAlan KristalPetter KristensenStephen KritchevskyNino KuenzliMartin KulldorffJohn KusekJim LaceyDaniel Lackland*Francine LadenTimothy Lash*Graham LawAlain Le TertreBonita E LeeKyung-Yil LeePeter LeesMichael Leitzmann*Richard LevineRolv Lie*Julian LittleJames Lloyd-SmithStephanie London*Matthew Longnecker*Dana LoomisGina LovasiJay Lubin*Russell LuepkerCourtney Lynch*Thomas MackMalcolm Maclure*Per MagnusGeorge MaldonadoMichele MarcusGary MarshEdwin MartensFernando MartinezThomas MasonSusan McCannRobert McConnellRobert McKeownSumi MehtaAdam MeijerPauline Mendola*

James MerchantSteven MeshnickLynne MesserGabor MezeiWilliam MillerRobert MillikanDawn MisraStacey MissmerAllen MitchellBraxton MitchellMaurice MittelmarkAnnette MolinaroLynn MoorePatricia MoormanPeter MorfeldHal MorgensternHoward MorrisonKirsten MoysichNancy MuellerStephanie Mulherin EngelRon Munger*Lucas NeasKenrad Nelson*Pablo NepomnaschyC. Ineke NeutelRaymond NeutraCraig NewschafferJavier NietoMark NieuwenhuijsenMonica NordbergJ. Michael OakesPatricia O’CampoJørn OlsenAndrew Olshan*Michael O’SheaBart OstroRuth OttmanMyunghee PaikJulie PalmerNigel PanethChristine ParksJonathan PatzNina PaynterRoger PengThomas PernegerSally Perreault-DarneyAnnette PetersUlrike PetersHerbert PetersonMaya PetersenDiana PetittiRuth PfeifferLinda PickleJennifer Pinto-MartinElizabeth PlatzCharles Poole*Arden Pope

Garth RauscherJohn ReifPeggy ReynoldsBeate Ritz*James RochonBeverly RockhillWalter RoganThomas RohanIsabelle RomieuKathryn RoseLynn RosenbergBernard RosnerJulie RossAndrew RowlandDavid RushLouise RyanSharon Sagiv*Markku Sallmen*Robert SandlerStefanie SarnatMarc SchenkerMark SchiffmanEnrique Schisterman*Mario SchootmanJane Schroeder*Kellogg SchwabEyal ShaharGary ShawChris ShawLianne Sheppard*Philip ShermanRoy ShoreAnna Maria Siega-RizJack SiemiatyckiAllan SmithNancy SonnenfeldHenrik SorensenGary SorockFrank SpeizerAnthony StainesLorann StallonesMeir StampferJacqueline Starr*Leslie StaynerKyle Steenland*Aryeh SteinRichard SteinKaren SteindorfMariana SternRachael Stolzenberg-SolomonRoslyn StoneDaniel StramBrian StromDonna StroupSusan SturgeonTil Sturmer*Jordi Sunyer

Ezra SusserGeorge SwinglerMoyses SzkloIra TagerRobert Tarone*Jeremy TaylorPaul TerryAnne ThiebautMichael ThunM. D. TobinMaurizio TrevisanDavid UmbachAnjel VahratianJohn VenaRoel VermeulenBarbara VisscherDavid Vlahov*Suma VupputuriPathik WadhwaRodrick WallaceSylvan WallensteinLance WallerStephen WalterMary WardMichael WardDan WartenbergLarry WebberClarice Weinberg*Scott WeissNoel Weiss*Leah WeltyHelena WennborgMartha WerlerElizabeth Whelan*Alice WhiteEmily WhiteRon WhiteErich WichmannCharles WigginsGregg WilkinsonWalter WillettMichelle WilliamsGayle WindhamSteven WingWarren Winkelstein JrJohn WitteRosalind WrightYin YaoLeland YeeKai YuMimi YuShelia ZahmAntonella ZanobettiJun Zhang*Sally Zierler

Epidemiology • Volume 18, Number 3, May 2007 Acknowledgments

© 2007 Lippincott Williams & Wilkins 289

EPIDEMIOLOGY & SOCIETY

Editors’ Note: EPIDEMIOLOGY and SOCIETY provides a broad forum for epidemiologicperspectives on health research, public policy, and global health.

Studying Vulnerable PopulationsLessons From the Roma Minority

Karolina Kosa* and Roza Adany†

Abstract: There are important disparities in health outcomes be-tween racial/ethnic minorities and majorities in all countries whereminority health has been investigated. This holds true for the largestminority population of Europe, the Roma, although research datarelated to Roma are scarcer and more contested than for otherminorities. We discuss major obstacles that hinder or prevent thecollection of reliable data in Roma and other minorities. Thedefinitions and classification systems on race/ethnicity vary widely,pointing to the social construction of both race and ethnicity.Imprecision in taxonomy and definition of target groups is com-pounded by challenges in data collection, analysis, and interpreta-tion, along with ethnocentricity that shapes the perspectives andapproaches of the researchers. However, administrative data collec-tion on race/ethnicity serves legitimate purposes although such datamust comply with less-stringent quality requirements as opposed todata meant for scientific analysis. Research on minorities shouldconsider race/ethnicity as proxy indicators of complex health deter-minants, and should aim at dissecting these determinants into sep-arate items. Careful documentation of methodology and activeinvolvement of the minorities themselves can increase trust betweenthe investigators and the research subjects, which can in turnimprove research on minority health.

(Epidemiology 2007;18: 290–299)

Health inequalities (or health disparities)1,2 constitute a majorfocus of attention for both epidemiologic research and

health policy. The number of English-language papers inPubMed under the medical subject headings of “socioeconomicfactors” and “health” increased from 1279 in 1968–1980 to9352 in 1993–2005.3 The documentation of strong associationsbetween health and economic development has in turn led toincreased interest in health inequalities among policy makers.4

Research into health inequalities according to race andethnicity has shown consistent disadvantages in health status,morbidity, and mortality for various racial/ethnic groups inminority positions.5–8 The causes of inequalities betweenblacks and whites in the United States and among a range ofethnic groups in the United Kingdom have been attributed invarying degree to racism and to cultural and socioeconomicdifferences.9–12 The extent and determinants of health inequal-ities in other minorities (particularly those among the Romaminority in Europe) have been less extensively investigated.

Roma people (also known as Gypsy, Sinti, or Tzigane)constitute Europe’s largest minority. Their number is esti-mated between 6.3 and 8.5 million people,13 roughly thepopulation of Sweden or Austria. Roma migrated in severalwaves from northern India, their presence in Europe beingdocumented as early as the 12th century.14 They have beenamong the poorest people in Europe, and the collapse ofsocialist regimes around 1990 in central and eastern Europeancountries—where the largest populations of Roma live—hashad an especially harsh impact, worsening their alreadyunfavorable living conditions and health.15

Literature reviews have documented unfavorable healthstatus, lower life expectancy, and greater communicable dis-ease burden for Roma compared with non-Roma in the Czechand Slovak Republics,16 Spain,17 and Hungary.18,19 Romahave higher infant mortality rates,20 with higher rates of lowbirth weight, preterm birth, and intrauterine growth retarda-tion.21 Average blood lead levels in Spain were found to besubstantially higher in Gypsy compared with white chil-dren.22 Access to health care and provision of services forRoma in several countries has been burdened by limitedcoverage and discrimination.23

Several factors have contributed to heightened interestin the welfare of Roma in Europe24,25 including increasingmarginalization of Roma (in both economic and socialterms),26 higher birth rates compared with majority popula-tions, and increased mobility within the European Union.This has led to the launch, in 2005, of a large-scale initiative,

Submitted 21 July 2006; accepted 4 December 2006.From the *Division of Health Promotion and †Department of Preventive

Medicine, Faculty of Public Health, University of Debrecen, Hungary.Supported by grants ISZKF-206/2004 of the Ministry of Environmental

Protection; ETT 445/2003 of the Ministry of Health, Social andFamily Affairs; and NKFP-1B/0013/2002 of the Ministry of Educa-tion, Hungary.Supplemental material for this article is available with the online versionof the journal at www.epidem.com; click on “Article Plus.”

Correspondence: Karolina Kosa, Division of Health Promotion, Departmentof Preventive Medicine, Faculty of Public Health, University of Debre-cen, POB 2., Debrecen 4012, Hungary. E-mail: [email protected].

Copyright © 2007 by Lippincott Williams & WilkinsISSN: 1044-3983/07/1803-0290DOI: 10.1097/01.ede.0000258919.15281.4f


“The Decade of Roma Inclusion,” involving 8 national gov-ernments and several international organizations, includingWorld Bank, United Nations Development Program, OpenSociety Institute, and European Roma Rights Center.27

Despite the vulnerability of Roma and increased polit-ical interest in their welfare, there remains a dearth of re-search data on their health status.17,20,24,28,29 This work pre-sents an overview of issues that hamper the collection ofreliable health data for Roma and other minorities.

Who Are The Subjects?Definition, Taxonomy, and Data Collectionon Ethnicity

“Ethnic” groups are people classed according to commonracial, national, tribal, religious, linguistic, or cultural back-ground.30,31 In this context, “minority” people are “part of apopulation differing from others in some characteristics andoften subjected to differential treatment.”30 Collection of data onethnicity and race has been a controversial issue. Such data areladen with historical examples of abuse, and contemporaryexamples of misuse. For example, crime statistics reported on anethnic basic can reinforce prejudice.11,32 However, controversyaround such data is not due simply to their potential for abusebut also to legitimate questions as to what race/ethnicity is, andhow people should be classified. As demonstrated by Table 1,there is no theoretical grounding and no standard classificationof racial/ethnic/minority groups. The taxonomy is always con-text-dependent and subject to change with context.33–41

Designation of the same people can change from onecountry to another, making identification or comparison dif-ficult. The following names have been used to identify Romapeople: Sinti/Zigeuner (in Germany), Tsigan (Bulgaria, Ro-mania), Zingari (Italy), Cigany (Hungary), Gitano (Spain),Manoush/Bohemian (France), Tinker/Tinkler (United King-dom), and Kalderash (worldwide).42 The US National Li-brary of Medicine offers the term “Gypsies” as a medicalsubject heading in its PubMed database.3 Health researchprojects have included Roma in the heterogeneous group ofTravelers in the United Kingdom and Ireland, although Trav-elers, having similar traditions, are ethnically distinct fromRoma.43,44 These people themselves prefer to be calledRoma, the name adopted in 1971 by the first World RomaniCongress to denote them as a nation.42

The changing designation of minorities has usually beenlinked to changes in official (government) recognition, whichmakes the trend-analysis of minority data, even within the samepopulation, challenging. For example, “Roma” was not amongthe options for ethnic identity in the census of 1980 in Hungary,although it was an option in censuses before and after that year(ethnicity itself was not an item in the census of 1970).45 AsianIndians were counted as “Hindus” in US censuses from 1920 to1940, as “white” from 1950 to 1970, and as “Asians or PacificIslanders” in 1980 and 1990.46

Self-Reporting of Minority StatusRacial/ethnic classifications offer socially constructed

categories that minorities can choose from to identify them-selves. One question that arises is whether ethnic or racialgroups wish to report themselves as minorities at all. Roma

people and their representatives have been divided on thisquestion. Some regard such reporting as necessary for develop-ing sound minority policy, while others are opposed to any datacollection by ethnicity on the grounds of past and present misuseof such data.47 Indeed, there has been a long-recognized practiceamong Roma people not to identify themselves as ethnic minor-ities in census or official data collection exercises. This hasresulted in their chronic underenumeration.13,20,47–49 For thisreason, even those countries in which data on ethnicity arecollected must resort to estimation when counting Roma.

Reliable population numbers are important in definingsampling frames for research studies, as well as for calculat-ing health indices such as disease rates. Estimates of the totalnumber of Roma people in European countries can vary up to50%,13,50 which understandably complicates assessment oftheir health and health needs.

The underestimation that can occur with self-identifi-cation of racial/ethnic status was recognized early on insociological research. An alternative is ethnic/racial identifi-cation by an observer. Three constructs for minority statushave been described.51 There is internal racial/ethnic identity(the individual’s belief), which might or might not be thesame as expressed identity (conveyed by speech and deed).These 2 are related but not necessarily the same as externalidentity (an observer’s belief about an individual).51

The problem of underreporting of minority identity iscompounded by the fact that self-perception (and self–reporting) of race/ethnicity is fluid. It can shift with ageand with changes over time in societal attitudes towards racerelations and multiculturality (described in various ethnicgroups).20,52–54 Self-reported ethnic identity might even beinfluenced by the order of relevant questions asked.55

Three generations of a Roma family, in a colony on theoutskirts of Debrecen, Hungary.

Epidemiology • Volume 18, Number 3, May 2007 Studying Vulnerable Populations


TABLE 1. Data Collected on Race/Ethnicity in Census in Some Countries

Country Race of Self

Ethnicity of Self

Cultural and Ethnic Groups (189 Units)Broad Groups Narrow Groups

Australia No Oceanian Australian Peoples Australian, Australian Aboriginal, Australian South Sea Islander,Torres Strait Islander

New Zealand Peoples Maori, New Zealander

Melanesian andPapuan

New Caledonian, Ni-Vanuatu, Papua New Guinean, SolomonIslander, Melanesian and Papuan, n.e.c. (includes Bisorio,Bougainvillian, Huli)

Micronesian I-Kiribati, Nauruan, Micronesian, n.e.c. (includes Marianas Islander,Marshallese, Palauan)

Polynesian Cook Islander, Fijian, Niuean, Samoan, Tongan, Polynesian, n.e.c.(includes Hawaiian, Pitcairn Islander, Tahitian)

North-West European British English, Scottish, Welsh, British n.e.c. (includes Channel Islander,Guernsey Islander, Manx)

Irish Irish

Western European Austrian, Breton, Dutch, Flemish, French, German, Swiss, Walloon,Western European, n.e.c. (includes Alsatian, Frisian, Luxembourgish)

Northern European Danish, Finnish, Icelandic, Norwegian, Swedish, Northern European,n.e.c. (includes Faeroese, Greenlandic, Saami)

Southern and EasternEuropean

Southern European Basque, Catalan, Italian, Maltese, Portuguese, Spanish, SouthernEuropean, n.e.c. (includes Andorran, Galician, Ladin)

South EasternEuropean

Albanian, Bosnian, Bulgarian, Croatian, Greek, Macedonian,Moldovan, Montenegrin, Romanian, Roma/Gypsy, Serbian,Slovene, South Eastern European, n.e.c. (includes Aromani,Karakachani, Vlach)

Eastern European Belarusan, Czech, Estonian, Hungarian, Latvian, Lithuanian, Polish,Russian, Slovak, Ukrainian, Eastern European, n.e.c. (includesAdygei, Khanty, Sorb/Wend)

North-African andMiddle Eastern

Arab Algerian, Egyptian, Iraqi, Jordanian, Kuwaiti, Lebanese, Libyan,Moroccan, Palestinian, Saudi Arabian, Syrian, Tunisian, Arab,n.e.c. (includes Baggara, Bedouin, Yemeni)

Jewish Jewish

Other North Africanand Middle Eastern

Assyrian/Chaldean, Berber, Coptic, Iranian, Kurdish, Sudanese,Turkish, Other North African and Middle Eastern, n.e.c. (includesAzande, Beja, Nubian)

South-East Asian Mainland South-EastAsian

Anglo-Burmese, Burmese, Hmong, Khmer, Lao, Thai, Vietnamese,Mainland South-East Asian, n.e.c. (includes Arakanese, Karen,Mon)

Maritime South-EastAsian

Filipino, Indonesian, Javanese, Madurese, Malay, Sundanese,Timorese, Maritime South-East Asian, n.e.c. (includes Balinese,Irian Jayan, Sumatran)

North-East Asian Chinese Asian Chinese, Taiwanese, Chinese Asian, n.e.c. (includes Hui, Manchu, Yi)

Other North-EastAsian

Japanese, Korean, Mongolian, Tibetan, Other North-East Asian, n.e.c.(includes Ainu, Menba, Xiareba)

Southern and CentralAsian

Southern Asian Anglo-Indian, Bengali, Burgher, Gujarati, Gurkha, Indian, Malayali,Marathi, Nepalese, Pakistani, Punjabi, Sikh, Sinhalese, Tamil,Southern Asian, n.e.c. (includes Bhote, Kashmiri, Sherpa)

Central Asian Afghan, Armenian, Georgian, Kazakh, Pathan, Uzbek, Central Asian,n.e.c. (includes Azerbaijani, Chechen, Tatar)

People of the Americas North American African American, American, Canadian, French Canadian, Hispanic(North American), Native North American Indian, NorthAmerican, n.e.c. (includes Bermudan, Inuit, Metis)

South American Argentinian, Bolivian, Brazilian, Chilean, Colombian, Ecuadorian,Guyanese, Peruvian, Uruguayan, Venezuelan, South American,n.e.c. (includes Arawak, Carib, Surinamese)

Central American Mexican, Nicaraguan, Salvadoran, Central American, n.e.c. (includesBelizean, Costa Rican, Mayan)

Caribbean Islander Cuban, Jamaican, Trinidadian (Tobagonian), Caribbean Islander,n.e.c. (includes Bahamian, Haitian, Puerto Rican)

(Continued)

Kosa and Adany Epidemiology • Volume 18, Number 3, May 2007

© 2007 Lippincott Williams & Wilkins292

TABLE 1. (Continued)


Ethnicity of Self


Sub-Saharan African Central and WestAfrican

Akan, Fulani, Ghanaian, Nigerian, Yoruba, Central and WestAfrican, n.e.c. (includes Fang, Kongo, Liberian)

Southern and EastAfrican

Afrikaner, Angolan, Eritrean, Ethiopian, Kenyan, Malawian,Mauritian, Mozambican, Namibian, Oromo, Seychellois, Somali,South African, Tanzanian, Ugandan, Zambian, Zimbabwean,Southern and East African, n.e.c. (includes Afar, Tutsi, Zulu)

Canada No Aboriginal Indian Band/FirstNation

Treaty Indian/Registered Indian

North American Indian Yes/No Yes/No

Métis Yes/No Yes/NoInuit (Eskimo) Yes/No Yes/NoNon-AboriginalWhiteChineseSouth Asian East Indian, Pakistani,

Sri Lankan, etc.BlackFilipinoLatin AmericanSoutheast Asian Cambodian,

Indonesian, Laotian,Vietnamese, etc.

ArabWest Asian Afghan, Iranian, etc.JapaneseKoreanOther

New Zealand No Level 1 Level 2 Level 3

European European n.f.d. European n.f.d.

New ZealandEuropean

New Zealand European

Other European British and Irish, Dutch, Greek, Polish, South Slav, Italian, German,Australian, Other European

Maori Maori MaoriPacific Peoples Pacific Peoples n.f.d. Pacific Peoples n.f.d.

Samoan SamoanCook Islands Maori Cook Islands MaoriTongan TonganNiuean NiueanTokelauan TokelauanFijian Fijian

Other Pacific PeoplesAsian Asian n.f.d. Asian n.f.d.

Southeast Asian Southeast Asian n.f.d., Filipino, Cambodian, Vietnamese, OtherSoutheast Asian

Chinese ChineseIndian IndianOther Asian Sri Lankan, Japanese, Korean, Other Asian

Middle Eastern/LatinAmerican/African

Middle Eastern Middle EasternLatin American Latin AmericanAfrican African

Other ethnicity Other ethnicity Other ethnicity

UK (Englandand Wales)

Level 1 Level 2

No White BritishIrish

(Continued)



One consequence of this fluidity of minority identity isthat “population growth” or “decline” of minorities can occurwithout changes in birth rates or immigration trends. Thenumber of self-identified Roma in Hungary increased 29-foldbetween the censuses of 1980 and 2001,56 while the �Native�Indian population in the United States increased 6-fold be-tween 1960 and 1990.57 Such large changes presumablyrepresent major shifts in self-identification.

Observer Reporting of Minority StatusCompared with self-identification, observer identifica-

tion has consistently been found to estimate higher numbersof Roma.20 An observer-based estimate by the Slovak state

administration in 1989 found 3.2 times more Roma than wasreported in the 1991 census based on self-identification.58

Results were similar in Hungary, comparing observer identi-fication of Roma people in a 2003 nationally representativeresearch sample with self-report in the 2001 census.59

However, observer classification of race/ethnicity hasits own subjectivity. Observer classifications of race for USinfants who die within a year of birth often differ on birth anddeath certificates for the same baby. Differential racial clas-sification in such cases has been found to be more than 31times as likely with different-race than with same-race par-ents and has also been related to the states’ infant mortalityrate by race.60



Ethnicity of Self


Other Whitebackground

All White groupsMixed White and Black

Caribbean

White and BlackAfrican

White and Asian

Other Mixedbackground

All Mixed groups

Asian/Asian British Indian

Pakistani

Bangladeshi

Other Asianbackground

All Asian groups

Black/Black British Caribbean

African

Other Blackbackground

All Black groups

Chinese/other Chinese

Other ethnic group

All Chinese or Othergroups

United States AmericanIndian orAlaskaNative

Hispanic or Latino

Asian

Black orAfricanAmerican

NativeHawaiianor OtherPacificIslander

White

n.e.c. indicates not elsewhere classified; n.f.d., not further defined.



Observer identification is subject to other limitations. Aresearch project on observer identification of race usingphotographs of multiracial faces found that observed racevaries among observers and is influenced by the sex andracial identity of the observer.61 White and Asian men takerelatively little time to categorize others, are the least likely toidentify people as multiracial, and the most likely to identifypeople as black. On the other hand, multiracial observers tendto take a longer time to classify race and are more likely todesignate people as multiracial.

Identification by Self Versus ObserverData collection for census or official purposes necessarily

rely on self-reporting of identity, while research projects canapply either method of identification. The American NationalElection Studies, a US research center for collecting data onvoting, public opinion, and political participation, used observeridentification of respondents’ race until 2002.62

The classification of ancestry by various means (self,proxy, interviewer) in a sample of the US population surveyed inthe First National Health and Nutrition Examination Survey(NHANES, 1971–1975) was more likely to vary for personswith multiracial backgrounds.63 Self- and observer classificationwas found to have low correspondence for American Indians.

The variability of racial identity by self- versus observer-reporting was demonstrated in the US Health Interview Sur-vey of 1978, during which both methods were applied. Sixpercent of self-reported black, 29% of self-reported Asian,62% of self-identified American Indians, and 80% of thosewho perceived themselves as “other” were classified by theobserver (interviewer) as white.64

Invisible GroupsA special category of minority citizens are those with-

out documentation. Illegal immigrants do not identify them-selves and will not be available for identification by others.While the numbers of minorities without documentation areunknown, they are believed to be numerous in some coun-tries; there are approximately 7 million illegal immigrants inthe United States, representing approximately 10% of ethnicminorities in that country.65

Large groups of Roma in Europe lack either citizenship ordocumentation necessary for being officially or statistically “vis-ible” (birth certificates, personal identity documents, local resi-dence permits).25 Lack of documents makes not only researchprojects but also service provision exceedingly cumbersome,even though these groups are likely to have as many healthproblems (or more) as minorities with documentation.23,66

How To Collect Data?Communication is a vital issue when working with

minorities. Sometimes there is a language barrier betweenresearchers and the target population. The use of interpreterscan increase rapport and communication, although at theexpense of reduced structure. Conversely, a strict control ofthe communication process can reduce the volume and qual-ity of information collected.67

When the target group is heterogeneous in terms oflanguage and literacy, structured face-to-face interviewsmight yield the best approach, as shown in the survey by theUN Development Program of Roma in 5 countries in EasternEurope.20 Difficulties of communication can occur evenwhen researchers and respondents ostensibly share the samelanguage. An attempt in the 1970s to include a direct questionon ethnicity in the British census provoked such publicresistance (due to improper wording) that such a question wasnot asked again until 1991.68

Tools validated in a given setting might not be appro-priate for use in certain minority groups. For example, bodymass index calculated from self-reported weight and heightgreatly underestimates the prevalence of obesity among Mex-ican Americans compared with European Americans andAfrican Americans.69 An analysis of the validity of self-reported hypertension in the NHANES III showed that self-report was not appropriate for estimating trends in hyperten-sion prevalence among Mexican-Americans.70 Principles ofquestionnaire validation in cross-cultural settings71 should beapplied in cross-ethnic settings as well.72

Even if the barriers of identifying minorities and com-municating with minorities can be overcome, the questionstill remains as to whether minorities will participate in healthresearch projects. Racial/ethnic minorities have low partici-pation rates in medical research projects.73–75 A major reasonfor nonparticipation is a lack of trust in institutions of medicalcare, research, and personnel—influenced by the infamousTuskegee study, and shaped by prior personal encounterswith medical care.76,77 Documented racial discriminationtowards Roma people in the health care systems of severalEuropean countries has limited not only service provision23

but involvement of Roma in health research projects.Such attitudes may be changing—at least in US minor-

ities.77 Minority groups, mostly African-Americans and His-panics, are as likely as nonminorities to participate in variousintervention trials if they are invited to do so.78 Involvingblack Americans as implementers of research has been shownto be critically important in increasing the participation ofblack Americans in research studies.77 Trust and participationof minorities can be increased by the active involvement oftheir representatives not only in policy design and interven-tional projects47 but also in health research, especially if theyparticipate in the preparation, conduct and analysis.15,79

Challenges in Data Analysis and InterpretationAnalysis and interpretation of health data by race/

ethnicity should be approached by attention to the importanceof methodology, starting with the classification system.Changes in taxonomy require sophisticated methods to com-pare data recorded in one census with data from another.80,81

Researchers should also take into account the shiftingmeaning of racial/ethnic categories over time. For example,the term “Hispanic” was introduced in the US census in 1980,with the precise meaning contested ever since.82,83

It is a challenge to separate the effects of known healthdeterminants and risk factors from the effects of unknowndeterminants represented by race/ethnicity.84 Socioeconomicstatus (SES) is a well-recognized confounder in relation to



race/ethnicity and health outcomes. However, even if SES istaken into account, there can be variations in socioeconomicstatus categories (eg, quality differences between equal-levelschooling), measurement error, or incomparable SES indicatorsthat give rise to residual confounding.52,85 Furthermore, esti-mates of racial income inequality can depend on whether race isbased on self-classification or observer classification.86

Diet in racial/ethnic groups might also appear as a con-founder. For example a higher exposure to persistent pollutantsand biotoxins among certain groups of Asian and Pacific Island-ers was linked to much higher consumption of seafood.87

Epidemiologists aim to uncover etiologic relations be-tween various biologic, environmental, cultural, and socialfactors, and health outcomes. Given the scientifically-un-grounded, heterogeneous, and fluid nature of race/ethnicity,race and ethnicity cannot be treated as if they were riskfactors such as smoking or cholesterol level.37,39,79,83,84

Rather, race and ethnicity are proxy measures for a range ofcritically important health determinants including culturalfactors, attitudes, beliefs, values, social network, social sup-port, languages spoken, religion, diet, family traditions, ru-rality, and a sense of social exclusion.39,41,48,51,88–92 Epide-miologic research that includes racial/ethnic variations inhealth should aim to collect information on all these potentialrisk factors, rather than be content with the umbrella term of“race/ethnicity.” Multivariate analysis should be conducted toadjust for all factors known and hypothesized to be etiolog-ically related to the outcome in question.84

Comprehensive analyses that include a wide range ofrisk factors have sometimes been able to account for racial/ethnic differences in health outcomes. For example, an inter-national comparison of 8 white and 3 black populations didnot find higher blood pressure levels in blacks, pointing to theweight of environmental factors.93

Furthermore, considerable differences in blood pres-sure and risk factors for coronary heart disease were uncov-ered within a supposedly homogenous ethnic group (“SouthAsian”) in the United Kingdom, revealing the shortcomingsof aggregating data in ethnic groups.94,95

Who Is Doing The Research?Ethnocentricity is an important influence in health re-

search. In the United States, debates on the innate versusimposed nature of health disparities between blacks andwhites were taking place even 150 years ago, with differentviewpoints depending on the racial identity of the parties.Black doctors generally argued that slavery and poor livingconditions were the major causes of health disparities, whilewhites proposed genetic inferiority.39,96

The role of the ethnicity of the researcher was vividlyillustrated by Senior and Bhopal52 in their analysis of mortalitydata in England and Wales. A research group of nonminorityresearchers had compared mortality in male Indian immigrantsand the male population of England and Wales in 5 categories ofdisease, for which the standardized mortality ratios (SMRs) were1.7–3.4 times higher in immigrants. The researchers failed tonote that those 5 categories of death comprised only 4% of alldeaths in immigrants. Another 5 causes of death—the SMRs ofwhich were only marginally increased or lower among the

immigrants—comprised 60% of all deaths in the immigrants, asnoted by minority researchers.52

There have been examples for which Roma people havebeen successfully incorporated into research design and im-plementation. A recent survey of segregated living areaspopulated by Roma people in Hungary was modified onrecommendations of participating Roma field workers whosuggested surveying all segregated areas regardless of theethnicity of its inhabitants. A recent health interview surveyamong Roma groups employing Roma interviewers resultedin a 92% response rate.92

These examples do not imply that one perspective isbetter than another; we believe any perspective based on validdata and open to being reshaped is acceptable. However,especially when working with minorities, researchers shouldbe aware of their own perspectives and should aim at wid-ening those perspectives by inviting persons into the projectwho identify with those to be investigated. Active participa-tion of minorities in research projects is the optimal means toimplement social justice, increase trust, and ensure the incor-poration of minority perspectives, both in the research projectand in the potential utilization of its results.32,47,79

CONCLUSIONSExternal race/ethnicity (as reported by an observer) has

been prone to variations according to location, time, the ob-server, and the observed persons. Self-reported race/ethnicity isfluid, and inherently linked to the basic human right of people toreveal—or not reveal—their own racial/ethnic identity.

Classifications of race/ethnicity are socially constructedand depend on the social and geographic context. There is noscientific basis for standardizing these categories across allsettings. Nevertheless, race and ethnicity will in all probabil-ity remain in use in census and official statistics. Theirrelative simplicity of application enables data-gathering forpurposes of policy-making, such as developing and enforcingmeasures of positive discrimination. These benefits overrideshortcomings in data quality.

Even so, the use of “race,” “ethnicity,” or “minority” asvariables in research projects should be justified explicitly.Utmost care should be taken to describe the methodology bywhich the target groups are defined, individuals accessed, anddata collected. Analysis should adjust for all relevant confound-ers, among them socioeconomic position, environmental factors,rurality, diet, social capital, access to health care, and quality ofcare received. Race and ethnicity in epidemiologic researchshould be recognized as proxy measures for an as-yet-unknownset of health determinants of primary importance. Scientificinvestigation should aim at dissecting these proxy measures intoseparate, operationalizable and interpretable indicators.

To increase trust and implement the principle of socialjustice, persons racially/ethnically identifying with the targetminority group should be involved in the research as co-workers; the results should be fed back to representatives ofthe target group or to the entire target group if possible.Finally, it should be made clear how the research projectserves the interests of those researched.



In short, simplistic categorizations such as “race” and“ethnicity” lead to simplistic research conclusions and, sooner orlater, to simplistic policy measures. These are certainly inappro-priate in our world of ever increasing complexity.

ACKNOWLEDGMENTSThe authors thank Monika Devay, Birgit Fillies, Zaida

Herrera-Ortiz, and Thierry Louvet for their help with trans-lation of documents in French, Spanish, and German.

REFERENCES1. Whitehead M. The Concepts and Principles of Equity and Health.

Copenhagen: World Health Organization Regional Office for Europe;1990.

2. Kawachi I, Subramanian SV, Almeida-Filho N. A glossary of healthinequalities. J Epidemiol Community Health. 2002;56:647–652.

3. National Library of Medicine, PubMed. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db�PubMed. Accessed January 23, 2007.

4. Commission on Macroeconomics and Health. Macroeconomics andHealth: Investing in Health for Economic Development. 20 December2001. Available at http://www.cid.harvard.edu/cidcmh/CMHReport.pdf.Accessed January 23, 2007.

5. James SA, Almeida-Filho N, Kaufman JS. Hypertension in Brazil: areview of the epidemiological evidence. Ethn Dis. 1991;1:91–98.

6. Young TK, Reading J, Elias B, et al. Type 2 diabetes mellitus in Canada’sfirst nations: status of an epidemic in progress. CMAJ. 2000;163:561–566.

7. Ring I, Brown N. The health status of indigenous peoples and others.BMJ. 2003;327:404–405.

8. Krieger N. Stormy weather: race, gene expression, and the science ofhealth disparities. Am J Public Health. 2005;95:2155–2160.

9. Bassett MT, Krieger N. Social class and black-white differences inbreast cancer survival. Am J Public Health. 1986;76:1400–1403.

10. Navarro V. Race or class versus race and class: mortality differentials inthe United States. Lancet. 1990;336:1238–1240.

11. Krieger N, Rowley DL, Herman AA, et al. Racism, sexism, and socialclass: implications for studies of health, disease, and well-being. Am JPrev Med. 1993;9(6 Suppl):82–122.

12. Nazroo JY. The structuring of ethnic inequalities in health: economicposition, racial discrimination, and racism. Am J Public Health. 2003;93:277–284.

13. Legal situation of the Roma in Europe. Doc. 9397 revised. Council ofEurope, 19 April 2002. Available at: http://assembly.coe.int/Documents/WorkingDocs/doc02/EDOC9397.htm. Accessed January 23, 2007.

14. Crowe DM. A History of the Gypsies of Eastern Europe and Russia.London: I.B. Tauris Publishers; 1995.

15. Ringold D, Orenstein MA, Wilkens E. Roma in an Expanding Europe.Breaking the Poverty Cycle. Washington, DC: The World Bank; 2005.Available at: http://siteresources.worldbank.org/EXTROMA/Resources/roma_in_expanding_europe.pdf. Accessed January 23, 2007.

16. Koupilova I, Epstein H, Holcik J, et al. Health needs of the Romapopulation in the Czech and Slovak Republics. Soc Sci Med. 2001;53:1191–1204.

17. Hajioff S, McKee M. The health of the Roma people: a review of thepublished literature. J Epidemiol Community Health. 2000;54:864–869.

18. Kosa K, Lenart B, Adany R. Health status of the roma population inHungary (in Hungarian). Orv Hetil. 2002;143:2419–2426.

19. Kovac C. Overall health of Hungary’s Roma worse than averagepopulation. BMJ. 2002;324:755.

20. The United Nations Development Programme. The Roma in Central andEastern Europe: Avoiding the Dependency Trap. A Regional HumanDevelopment Report. UNDP, 2003. Available at: http://roma.undp.sk/.Accessed January 23, 2007.

21. Bobak M, Dejmek J, Solansky I, et al. Unfavourable birth outcomes ofthe Roma women in the Czech Republic and the potential explanations:a population-based study. BMC Public Health. 2005;5:106.

22. Redondo MJ, Guisasola FJ. An unknown risk group of lead poisoning:the gypsy children. Eur J Pediatr. 1995;154:197–200.

23. Danova-Russinova S. Ambulance Not on the Way: The Disgrace of HealthCare for Roma in Europe. Budapest: European Roma Rights Center; 2006.Available at: http://www.errc.org/db/01/E6/m000001E6.pdf Accessed Jan-uary 23, 2007.

24. European Commission. Directorate General for Employment and SocialAffairs. The situation of Roma in an enlarged European Union. Lux-embourg: Office for Official Publications of the European Communities;2005. Available at: http://ec.europa.eu/employment_social/publications/2005/ke6204389_en.html Accessed January 23, 2007.

25. Judge K, Platt S, Costongs C, et al. Health Inequalities: a Challenge forEurope. UK Presidency of the EU, 2006. Available at: http://ec.europa.eu/health/ph_determinants/socio_economics/documents/ev_060302_rd05_en.pdf. Accessed January 23, 2007.

26. Emigh RJ, Szelenyi I, eds. Poverty, Ethnicity, and Gender in EasternEurope During the Market Transition. Westport, CT: Praeger Publish-ers; 2000.

27. The Decade of Roma Inclusion 2005–2015. Open Society Institute,Roma Initiatives Office, 2005. Available at: www.romadecade.org/.Accessed January 23, 2007.

28. Sepkowitz KA. Health of the world’s Roma population. Lancet. 2006;367:1707–1708.

29. Zeman CL, Depken DE, Senchina DS. Roma health issues: a review ofthe literature and discussion. Ethn Health. 2003;8:223–249.

30. Merriam-Webster Online Dictionary. Available at: http://www.m-w.com/Accessed January 23, 2007.

31. National Library of Medicine, 2006. Available at: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd�Retrieve&db�mesh&list_uids�68005006&dopt�Full. Accessed July 10, 2006.

32. Negrin K. Collecting ethnic data: an old dilemma, the new challenges.Monitoring human rights and the rule of law in Europe, EUMAP. Availableat: http://www.eumap.org/journal/features/2003/april/oldilemma AccessedJanuary 23, 2007.

33. Cooper R. A note on the biologic concept of race and its application inepidemiologic research. Am Heart J. 1984;108:715–722.

34. Crews DE, Bindon JR. Ethnicity as a taxonomic tool in biomedical andbiosocial research. Ethn Dis. 1991;1:42–49.

35. Jackson FL. Race and ethnicity as biological constructs. Ethn Dis.1992;2:120–125.

36. Bhopal R, Donaldson L. White, European, Western, Caucasian, or what?Inappropriate labeling in research on race, ethnicity, and health. Am JPublic Health. 1998;88:1303–1307.

37. Chaturvedi N. Ethnicity as an epidemiological determinant—crudelyracist or crucially important? Int J Epidemiol. 2001;30:925–927.

38. Krieger N. A glossary for social epidemiology. J Epidemiol CommunityHealth. 2001;55:693–700.

39. Oppenheimer GM. Paradigm lost: race, ethnicity, and the search for anew population taxonomy. Am J Public Health. 2001;91:1049–1055.

40. Kaufman JS, Hall SA. The slavery hypertension hypothesis: disseminationand appeal of a modern race theory. Epidemiology. 2003;14:111–118.

41. Duster T. Race and reification in science. Science. 2005;307:1050–1051.42. Rishi WR. Roma - The Panjabi Emigrants in Europe, Central and Middle

Asia, the USSR, and the Americas. Patiala, India: Punjabi University; 1996.Available at: http://www.romani.org/rishi/rnames.html Accessed January23, 2007.

43. Van Cleemput P. Health care needs of Travellers. Arch Dis Child.2000;82:32–37.

44. Green A, Lynch SA. Health of the world’s Roma population. Lancet.2006;368:575.

45. Statistical Yearbook of Hungary 2000. Budapest: Central StatisticalOffice; 2001.

46. Standards for the Classification of Federal Data on Race and Ethnicity(interim notice). Office of Management and Budget; August 1995. Avail-able at http://www.whitehouse.gov/omb/fedreg/race-ethnicity.html. Ac-cessed January 23, 2007.

47. Roma and statistics. Council of Europe, 2000. Available at http://www.eumap.org/library/static/libr0/9/e/19xoxov.pdf. Accessed January 23, 2007.

48. Krizsan A (ed) Ethnic Monitoring and Data Protection. The EuropeanContext. Budapest: Central European University Press–INDOK; 2001.

49. Swanson B. State’s Roma monitoring criticized. �Dzeno Association.�http://www.dzeno.cz/?c_id�9607. Accessed January 23, 2007.



50. Liegeois JP, Gheorghe N. Roma/Gypsyes: A European minority. Minor-ity Rights Group International, 1995. http://www.minorityrights.org/admin/Download/Pdf/Romareport.pdf. Accessed January 23, 2007.

51. Nagel J. Constructing ethnicity: Creating and recreating ethnic identity andculture. Social Problems. Special Issue on Immigration, Race, and Ethnicityin America. 1994;41(1):152–176. Available at: http://links.jstor.org/sici?sici�0037-7791%28199402%2941%3A1%3C152%3ACECARE%3E2.0.CO%3B2-U&size�SMALL. Accessed January 23, 2007.

52. Senior PA, Bhopal R. Ethnicity as a variable in epidemiological re-search. BMJ. 1994;309:327–330.

53. Harris DM, Sim JJ. Who is multiracial? Assessing the complexity oflived race. American Sociological Review. 2002;67:614–627. Availableat: http://www.jstor.org/view/00031224/sp030008/03x0086h/0?searchUrl�http%3a//www.jstor.org/search/BasicResults%3fhp%3d25%26so%3dNewestFirst%26si%3d26%26Query%3dau%253A%2522harris%2522&frame�noframe&currentResult�00031224%2bsp030008%2b03x0086h%2b0%2c00&userID�[email protected]/01cce44035138bf10c6285d63d&dpi�3&config�jstor. Accessed January 23, 2007.

54. Ahmed P, Feliciano C, Emigh RJ. Ethnic classification in Eastern Europe.California Center for Population Research, UCLA 2001. Available at: http://www.ccpr.ucla.edu/ccprwpseries/ccpr_007_01.pdf Accessed January 23, 2007.

55. Greenberg E, Cohenn J, Skidmore D. Maintaining race and ethnicitytrend lines in U.S. government surveys. Journal of Official Statistics.2004;20:687–703. Available at: http://www.jos.nu/Articles/abstract.asp?article�204687. Accessed January 23, 2007.

56. Statistical Yearbook of Hungary 2002. Budapest: Central StatisticalOffice; 2003.

57. Williams D. The Quality of Racial Data. Office of Minority Health Re-search Center, US Department of Health and Human Services, 1998.Available at: http://www.omhrc.gov/assets/pdf/checked/The%20Quality%20of%20Racial%20Data.pdf Accessed January 23, 2007.

58. Orgovanova K. Roma in Slovakia. http://www.slovakia.org/society-roma.htm Accessed January 23, 2007

59. Kemeny I, Janky B. On the Gipsy survey of 2003. Demographic, languageand ethnic data. (in Hungarian) Beszel˝o, October 2003. Available at:http://beszelo.c3. hu/03/10/07kemeny.htm. Accessed January 23, 2007.

60. Hahn RA Why race is differentially classified on U.S. birth and infantdeath certificates: an examination of two hypotheses. Epidemiology.1999;10:108–111.

61. Harris DR. In the Eye of the Beholder: Observed Race and ObserverCharacteristics. PSC Research Report. Report No. 02&cjs0025;5222002 Population Studies Center at the Intsitute for Social ResearchUniversity of Michigan, 2002. Available at: http://www.psc.isr.u-mich.edu/pubs/pdf/rr02-522.pdf. Accessed January 23, 2007.

62. The ANES Guide to public opinion and electoral behavior. Available at:http://www.umich.edu/�nes/nesguide/toptable/tab1a_3.htm. AccessedJanuary 23, 2007.

63. Hahn RA, Truman BI, Barker ND. Identifying ancestry: the reliability ofancestral identification in the United States by self, proxy, interviewerand funeral director. Epidemiology. 1996;7:75–80.

64. Williams D. The Quality of Racial Data. Office of Minority HealthResearch Center, U.S. Department of Health and Human Services, 1998.http://www.omhrc.gov/assets/pdf/checked/The%20Quality%20of%20Racial%20Data.pdf. Accessed January 23, 2007.

65. Sabri AA, Ahad Quayyum M. Increasing the participation: another factor. PLoSMedicine. 2006;3(5):e24-e250. doi: 10. 1371/journal/pmed. 0030250 Availableat: http://medicine.plosjournals.org/perlserv/?request�get-document&doi�10.1371/journal.pmed.0030250. Accessed January 23, 2007.

66. Sullivan M, Rehm R. Mental health of undocumented Mexican immi-grants: a review of the literature advances in nursing science. HealthDisparities. 2005;28:240–251.

67. Pitchforth E, van Teijlingen E. International public health research involv-ing interpreters: a case study from Bangladesh BMC Public Health 2005,5:71 doi:10. 1186/1471-2458-5-71 Available at: http://www.biomedcentral.com/1471&cjs0025;2458/5/71/abstract. Accessed January 23, 2007.

68. Bulmer M. A Controversial Census Topic: Race and Ethnicity in the BritishCensus. J Official Stat. 1986;2:471–480. Available at: http://www.jos.nu/Articles/abstract.asp?article�24471.Accessed January 23, 2007.

69. Gillum RF, Sempos CT Ethnic variation in validity of classification ofoverweight and obesity using self-reported weight and height in Amer-ican women and men: the Third National Health and Nutrition Exami-

nation Survey. Nutr J. 2005;4:27 doi:10. 1186/1475-2891-4-27 Avail-able at: www.nutritionj.com/content/4/1/27. Accessed January 23, 2007.

70. Vargas CM, Burt VL, Gillum RF, Pamuk ER. Validity of self-reportedhypertension in the National Health and Nutrition Examination SurveyIII, 1988–1991. Prev Med. 1997;26:678–685.

71. Guillemin F, Bombardier C, Beaton D. Cross-cultural adaptation ofhealth-related quality of life measures: literature review and proposedguidelines. J Clin Epidemiol. 1993;46:1417–1432.

72. The IEA European Questionnaire Group. Epidemiology Deserves BetterQuestionnaires. Available at: http://www.dundee.ac.uk/iea/EuroQuests.htm#FOUR%20ARGUMENTS. Accessed January 23, 2007.

73. Green BL, Partridge EE, Fouad MN, et al. African-American attitudesregarding cancer clinical trials and research studies: results from focusgroup methodology. Ethn Dis. 2000;10:76–86.

74. Shavers VL, Lynch CF, Burmeister LF. Factors that influence African-Americans’ willingness to participate in medical research studies. Can-cer. 2001;91(1 Suppl):233–236.

75. Hussain-Gambles M, Atkin K, Leese B. Why ethnic minority groups areunder-represented in clinical trials: a review of the literature. Health &Social Care Community. 2004;12:382–388.

76. Shavers VL, Lynch CF, Burmeister LF. Racial differences in factors thatinfluence the willingness to participate in medical research studies. AnnEpidemiol. 2002;12:248–256.

77. Herring P, Montgomery S, Yancey AK, et al. Understanding the chal-lenges in recruiting blacks to a longitudinal cohort study: the Adventisthealth study. Ethn Dis. 2004;14:423–430.

78. Wendler D, Kington R, Madans J, et al. Are racial and ethnic minoritiesless willing to participate in health research? PLoS Med. 2006;3(2):e19.doi:10.1371/journal.pmed.0030019.

79. Haug W. Ethnic, Religious and Language Groups: Towards a Set of Rulesfor Data Collection and Statistical Analysis. Monitoring human rights andthe rule of law in Europe, EUMAP. Available at: www.eumap.org/journal/features/2003/april/ethrellangroups/. Accessed January 23, 2007.

80. Bosveld K, Connolly H, Rendall MS. A guide to comparing 1991 and2001 Census ethnic group data. Office for National Statistics, UnitedKingdom. Available at: http://www.statistics.gov.uk/articles/nojournal/GuideV9.pdf. Accessed January 23, 2007.

81. Ethnic monitoring. A guide for public authorities. Committee for RacialEquality, United Kingdom 2002. Available at: http://www.cre.gov.uk/duty_ethmon.pdf. Accessed January 23, 2007.

82. Revisions to the Standards for the Classification of Federal Data on Raceand Ethnicity. Office of Management and Budget Available at: http://www.whitehouse.gov/omb/fedreg/1997standards.html. Accessed January 23, 2007.

83. Borak J, Fiellin M, Chemerynski S. Who is Hispanic? Implications forepidemiologic research in the United States. Epidemiology. 2004;15:240–244.

84. Kaplan JB, Bennett T. Use of race and ethnicity in biomedical publica-tion. JAMA. 2003;289:2709–2716.

85. Kaufman JS, Cooper RS, McGee DL. Socioeconomic status and healthin blacks and whites: the problem of residual confounding and theresiliency of race. Epidemiology. 1997;8:609–611.

86. Telles EE, Limm N. Does it matter who answers the race question?Racial classification and income inequality in Brazil. Demography.1998;35:465–474.

87. Judd NL, Griffith WC, Faustman EM. Consideration of cultural andlifestyle factors in defining susceptible populations for environmentaldisease. Toxicology. 2004;198:121–133.

88. Krieger N, Sidney S. Racial discrimination and blood pressure: theCARDIA Study of young black and white adults. Am J Public Health.1996;86:1370–1378.

89. Trewin D. Australian Standard Classification of Cultural and Ethnic Groups.Australian Bureau of Statistics, 2000. Available at: http://www.ausstats.abs.gov.au/ausstats/free.nsf/0/CAFD9A578C421AEFCA 256C0F0001D603/$File/12490_2000&cjs0025;01.pdf. Accessed January 23, 2007.

90. Probst JC, Moore CG, Glover SH, et al. Person and place: the com-pounding effects of race/ethnicity and rurality on health. Am J PublicHealth. 2004;94:1695–1703.

91. Krieger N. Racial and gender discrimination: risk factors for high bloodpressure? Soc Sci Med. 1990;30:1273–1281.



92. Kosa Zs, Adany R, Szeles Gy, et al. Health of the inhabitants of Romasettlements in Hungary – a comparative health survey. Am J PublicHealth., in press.

93. Cooper RS, Wolf-Maier K, Luke A, et al. An international comparativestudy of blood pressure in populations of European vs. African descent.BMC Med. 2005;3::2 doi:10. 1186/1741-7015-3-2 http://www.biomedcentral.com/1741-7015/3/2. Accessed January 23, 2007.

94. Bhopal R, Unwin N, White M, et al. Heterogeneity of coronary heart

disease risk factors in Indian, Pakistani, Bangladeshi, and Europeanorigin populations: cross sectional study. BMJ. 1999;319:215–220.

95. Agyemang C, Bhopal RS. Is the blood pressure of South Asian adults inthe UK higher or lower than that in European white adults? A review ofcross-sectional data. J Hum Hypertens. 2002;16:739–751.

96. Krieger N. Shades of difference: theoretical underpinnings of the med-ical controversy on black/white differences in the United States, 1830–1870. Int J Health Serv. 1987;17:259–278.



ORIGINAL ARTICLE

Environmental Exposure to Confined Animal FeedingOperations and Respiratory Health of

Neighboring ResidentsKatja Radon,* Anja Schulze,*† Vera Ehrenstein,*‡ Rob T. van Strien,*§ Georg Praml,* and

Dennis Nowak*

Background: Despite public concern about potential adverse healtheffects of concentrated animal feeding operations, objectively as-sessed data on environmental exposure to concentrated animalfeeding operations and respiratory health are sparse. We aimed toassess respiratory health in neighbors of confined animal feedingoperations.Methods: A survey was done in 2002–2004 among all adults(18–45 years old) living in 4 rural German towns with a highdensity of confined animal feeding operations. Questionnaire datawere available for 6937 (68%) eligible subjects. In a random samplewe measured the following outcomes: specific IgE to common andfarm-specific allergens, lung function, and bronchial hyperrespon-siveness to methacholine. Exposure was measured by collecting dataon odor annoyance and geo-coded data on the number of animalhouses within 500 m of the home. Locally optimal estimating andsmoothing scatter plots were used to model the association betweenexposure and outcome. Analyses were restricted to subjects withoutprivate or professional contact with farming environments.Results: The prevalence of self-reported asthma symptoms andnasal allergies increased with self-reported odor annoyance. Thenumber of animal houses was a predictor of self-reported wheezeand decreased forced expiratory volume in 1 second, but not allergicrhinitis or specific sensitization. Self-reported exposure and resultsof clinical measurements were poorly correlated.Conclusions: Confined animal feeding operations may contribute tothe burden of respiratory disease among their neighbors. Our find-ings underline the importance of objective assessment of exposureand outcome in environmental epidemiology.

(Epidemiology 2007;18: 300–308)

Exposures inside animal houses include gases (eg, ammoniaand hydrogen sulfide) and organic dusts containing fungi,

bacteria, and their constituents (eg, beta-(1,3-) glucans, en-dotoxins).1,2 The adverse effects of these exposures on respi-ratory health of farmers and farm workers have long beenestablished (as reviewed by Radon2 and by Kirkhorn andGarry3). Occupationally exposed persons have increased risksof chronic bronchitis and asthma-like syndrome,3–5 bronchialhyperresponsiveness,6 and sensitization against farm-specificallergens,7,8 as well as inflammation of the upper and lowerrespiratory tract.2,3

In recent years, animal production in North Americaand many European countries has shifted from smallfamily-owned farms to confined animal feeding operationsthat house large numbers of animals. In Lower Saxony innorth-west Germany, the number of confined animal feed-ing operations has increased substantially over the last20 –30 years. The major animal production in this areaconsists of poultry (74 million animals in 2001) and swine(6.5 million animals in 2001) housed in about 30,000production facilities.

Emissions from confined animal feeding operations andthe spraying of the animal wastes on the surrounding fieldscan result in environmental exposure to gases, organic dusts,bacteria, fungi, endotoxins, and residues of veterinary antibi-otics.9,10 Neighbors of large-scale animal production facilitiesare frequently annoyed by the associated odor.11–13 Accord-ing to several studies, this annoyance may decrease thequality of life,9,11,12,14,15 impair mental health9,15,16 and re-duce immune function.17

Neighbors are frequently concerned about negativeeffects of confined animal feeding operations on their respi-ratory health. A number of surveys have been conducted onthe association between environmental exposures to emissionof confined animal feeding operations and respiratory healthin children18–20 and adults15,16 living in close proximity tothese facilities. These surveys suggest a higher prevalence ofasthma symptoms in subjects potentially exposed to emis-sions of confined animal feeding operations.

At the same time, many studies have indicated alower prevalence of respiratory allergies among subjectswith farm-animal contact in early infancy.21–23 These stud-ies, however, were mainly conducted in areas with tradi-tional farming.

Submitted 25 August 2006; accepted 12 December 2006.From the *Institute for Occupational and Environmental Medicine, LMU

Munich, Germany; †GSF-National Research Centre for Environment andHealth, Neuherberg, Germany; ‡Boston University School of PublicHealth, Boston, MA; and §Municipal Health Service Amsterdam, Am-sterdam, Netherlands.

Editors’ note: A commentary on this article appears on page 309.Correspondence: Katja Radon, Institute for Occupational and Environmental

Medicine, Ziemssenstr. 1, 80336 Munich, Germany. E-mail: [email protected].

Copyright © 2007 by Lippincott Williams & WilkinsISSN: 1044-3983/07/1803-0300DOI: 10.1097/01.ede.0000259966.62137.84


One major challenge of studying health effects ofenvironmental exposure to confined animal feeding opera-tions is objective assessment of exposure and outcome. Sub-jects concerned about potential health effects may be bothmore aware of symptoms and more likely to report exposure,compared with less concerned neighbors or persons with aneconomic interest in confined animal feeding operations (eg,farm workers).24–26 In addition the validity of self-reportedrespiratory symptoms varies largely by socioeconomicstatus.27 These methodologic limitations may bias studies thatrely solely on self-report of symptoms or exposures.

The aim of the Lower Saxony Lung Study was to studypotential adverse effects of environmental exposures to emis-sions from confined animal feeding operations on respiratoryhealth. Exposure and outcome were ascertained using self-reports as well as objective measurement.

METHODS

Study SubjectsThe study was conducted in 4 rural towns in Lower

Saxony, northwestern Germany, with a high density of ani-mal feeding operations (Table 1). The animal productionfocused primarily on pigs and poultry. All adults age 18 to 44years with German citizenship, registered in the populationregistries of these towns, formed the target population (n �10,252). The registry provided information on home ad-dresses, age, and sex of the target population. The study wasperformed consecutively in the 4 communities between 2002and 2004, using the same instruments and measurementsthroughout the study period. To reduce reporting bias thestudy was introduced as a study on respiratory health in ruralareas.

TABLE 1. Description of the Study Towns and Characteristics of the Study Population by Town, Lower Saxony, Germany

Town 1 Town 2 Town 3 Town 4

Area; km2 42 79 100 113

No. of inhabitants 2652 5805 7562 12,577

No. of animals

Cattle 960 11,836 11,554 17,610

Pigs 24,300 98,926 45,958 87,448

Chicken 1,382,000 1,884,647 176,527 506,790

Turkey 161,600 n/a 397,244 642,369

Study populationParticipants*; no. (%) 630 (23.8) 1114 (19.2) 1432 (18.9) 2380 (18.9)

Descriptive dataAge (yrs); mean � SD 33.6 � 7.4 32.9 � 7.7 33.0 � 7.3 33.7 � 7.3

Sex (female); no. (%) 303 (48.1) 540 (48.5) 720 (50.3) 1190 (50.0)

Farm subjects†; no. (%) 363 (57.6) 623 (55.9) 755 (52.7) 1390 (58.4)

Education �12 yrs; no. (%) 127 (20.5) 377 (34.2) 315 (22.2) 484 (20.6)

Measures of exposureAmbient endotoxin concentration (EU/m3) measured at 32

study sites28; geometric mean � SD3.0 � 2.2 n/a n/a n/a

Number of animal houses within 500 m; median (range) 7 (0 to 18) 3 (0 to 15) 3 (0 to 12) 4 (0 to 20)

Self-reported odor annoyance; no. (%)

Not at all 118 (19.1) 531 (48.1) 611 (43.3) 851 (36.4)

Somewhat 236 (38.1) 482 (43.7) 631 (44.7) 1124 (48.1)

Moderately 126 (20.4) 62 (5.6) 121 (8.6) 244 (10.4)

Strongly 139 (22.5) 29 (2.6) 49 (3.5) 119 (5.1)

Outcomes: questionnaire-basedWheezing without having a cold; no. (%) 108 (17.2) 123 (11.1) 182 (12.8) 266 (11.2)

Physician diagnosed asthma; no. (%) 52 (8.3) 62 (5.6) 86 (6.1) 134 (5.7)

Allergic rhinitis; no. (%) 82 (13.2) 151 (13.6) 196 (13.8) 307 (13.1)

Outcomes: clinical measurementsSpecific IgE to common allergens �0.35 IU/mL; no. (%) 64 (27.1) 139 (24.0) 124 (26.4) 267 (21.8)

Specific IgE to agricultural allergens �0.35 IU/mL; no. (%) 10 (4.2) 8 (1.4) 15 (3.2) 26 (2.1)

Bronchial hyperresponsiveness to methacholine; no. (%) 57 (42.9) 176 (42.6) 128 (40.3) 400 (41.7)

FEV1 (% predicted); mean � SD 99.3 � 14.4 102.7 � 14.0 98.8 � 13.0 104.3 � 13.2

n/a indicates not available.*Participants in the questionnaire part of the study born in the former western part of Germany; % of inhabitants.†Lived on a farm in first 3 years of life or had regular farm animal contact during childhood, lived or worked on a farm at the time of the study.

Epidemiology • Volume 18, Number 3, May 2007 Respiratory Health Effects of Confined Animal Feeding Operations


Before the study, the target population of each town wasdivided at random into 2 groups. All residents were sent amail-in questionnaire (Fig. 1). In addition, parts of the popula-tion were randomly selected and invited to take part in theclinical examinations (n � 7080). Nonresponders of both groupsreceived up to 2 postal reminders and a phone call. To assesspotential selection bias, subjects declining to participate at phonecontact were asked 10 items of the main questionnaire. In thefirst town, 59 home visits were also done in attempt to reduceattrition. However, this measure (which was extremely time-consuming) resulted in only 3 additional returned question-naires, and so was dropped. Overall, 68% of the eligible popu-lation completed the questionnaire. The study was approved bythe Medical Ethical Committee of the Ludwig-Maximilians-University Munich, and the Lower Saxony Medical Board.

QuestionnaireThe 74 items of the questionnaire were taken mainly

from existing, validated questionnaire instruments (question-naire available from the authors by request). The question-naire covered 6 main areas:

Sociodemographic DataThese included occupational exposures, smoking patterns,

and childhood environment. Questions were taken from the

European Community Respiratory Health Survey question-naire.29

Respiratory SymptomsThe items of the European Community Respiratory

Health Survey questionnaire were used to assess symptoms ofasthma, allergic rhinitis, atopic eczema, and chronic bronchitis.29

Farm-Animal Contact During Childhood and atthe Time of the Survey

These items were taken from the Allergy and Endo-toxin study.21

Odor and Noise AnnoyanceThese items were taken from the German National

Health Survey.30 Irrelevant items on noise annoyance wereincluded to reduce reporting bias.

Confined Animal Feeding Operations Within500 m of the Home and Work Environment, asWell as During Childhood

These questions were developed for the present studyand included items on type, number, and proximity of con-fined animal feeding operations.

FIGURE 1. Flow chart of the study. Farm contact during childhood (living on a farm or regular contact to farm animals during thefirst 3 years of life) or at the time of the study (living or working on a farm). (Does not add up to 100% due to missing data onfarm contact.)

Radon et al Epidemiology • Volume 18, Number 3, May 2007


Prior to the study, the reliability of the questionnaire wasassessed among 53 inhabitants of another rural town in the studyarea.31 This pilot study indicated a moderate to good reliabilityof most questionnaire items. Only a few questions had poorreliability and were thus not used in the analysis: details ofexposure to confined animal feeding operations in childhood,and in the current home and work environment.

Medical ExaminationMedical examination consisted of blood sampling and

pulmonary function testing—followed by bronchial challengewith methacholine. Procedures were done according to theEuropean Community Respiratory Health Survey protocol.29

Only subjects born in former West Germany were eligible formedical examination, to ensure similar childhood environ-ments. Informed consent for medical examination was ob-tained for 66% of eligible subjects (Fig. 1).

We measured specific IgE against a mix of inhalantallergens (Timothy grass, rye, mugwort, birch, Dermatopha-goides pteronyssinus, Cladosporium herbarum, cat, and dog)in serum samples (Pharmacia, Freiburg, Germany). Thisgroup of allergens are summarized here as SX1. In addition,samples were tested for specific IgE against a mix of commonagricultural antigens (chicken, turkey, pig, cattle, Aspergillusfumigatus) summarized here as AX1.

Lung function was measured with a spirometer (Jaeger,Wurzburg, Germany) according to American Thoracic Soci-ety criteria,32 and is shown as percent of predicted function,derived from sex, height, and age standards.33 The EuropeanCommunity Respiratory Health Survey protocol for stepwisemethacholine challenge was adapted for the APS dosimeter(Jaeger, Wurzburg, Germany).34 Briefly, doubling or quadru-pling doses were used until a drop in forced expiratoryvolume in 1 second (FEV1) of 20% occurred (maximumcumulative dose: 1.2 mg). One study nurse did all pulmonaryfunction testing and bronchial challenges throughout thestudy period.

Exposure DefinitionExposure to confined animal feeding operations was de-

fined by the self-reported level of odor annoyance in the homeenvironment (“How annoyed are you by odor in and aroundyour home?”). The question on odor annoyance was assessed ona 4-point Likert scale from “not at all” to “strongly.” Ninetypercent of subjects reporting to be at least somewhat annoyedby odors in the home environment reported that agricul-tural sources (spraying of the fields, confined animal feed-ing operations) were the major source of odor.

Separate exposure estimates were developed on thebasis of number of animal houses within 500 m (0.3 miles)around participants’ home. This distance was chosen becausemicrobial emissions can be measured up to 500 m fromconfined animal feeding operations.35 For this approach eachhome address was geo-coded. The number of animal houseswithin 500 m of each home was provided by local authorities.The information was based on the most recent (year 2000)mandatory information about farming facilities. Owing toconfidentiality issues, the actual number, type of animals, andgeographic coordinates of the animal houses could not be used.

Outcome DefinitionBased on the questionnaire data, we used the following

conditions as self-reported outcomes: wheeze without a coldduring the last 12 months, physician diagnosis of asthma(ever), and symptoms of allergic rhinitis (“Do you have nasalallergies, eg, hay fever”). Allergic sensitization was definedas a specific IgE concentration of 0.35 kU/L or higher inserum samples.29 Age-, sex- and height-standardized FEV1was used to evaluate bronchial obstruction. Finally, bronchialhyperresponsiveness to methacholine challenge was definedas more than a 20% drop in FEV1.

Statistical MethodsAnalyses were restricted to subjects born in the former

West Germany. Group differences were assessed using �2 testfor categorical variables. Continuous variables were com-pared using Mann-Whitney-U test (2 group comparisons) orKruskal-Wallis-ANOVA (multiple group comparison).

TABLE 2. General Characteristics of the Rural StudyPopulation* Stratified by Farm Contact†

Farm(n � 3131)

Nonfarm(n � 2425)

Sex (female); no. (%) 1539 (49.2) 1214 (50.1)

Age (yrs); mean � SD 34.0 � 7.3 32.5 � 7.5

Active and passive smoke exposure; no. (%)

Not at all 938 (30.4) 661 (27.7)

Only ETS 379 (12.3) 329 (13.8)

Ex smoker 659 (21.4) 478 (20.0)

Current smoker 1108 (35.9) 919 (38.5)

Education �12 yrs; no. (%) 695 (22.5) 608 (25.3)

Family history of allergic disease; no. (%) 898 (31.6) 698 (32.2)

Three or more siblings; no. (%) 1819 (58.9) 1026 (43.3)

ExposuresSelf-reported odor annoyance; no. (%)

Not at all 1180 (38.3) 931 (38.9)

Somewhat 1411 (45.8) 1062 (44.4)

Moderately 302 (9.8) 251 (10.5)

Strongly 186 (6.0) 150 (6.3)

Number of animal houses within 500 m ofthe home; median (range)

4 (0 to 19) 3 (0 to 20)

Outcomes: questionnaire-basedWheezing without having a cold; no. (%) 357 (11.5) 322 (13.4)

Doctors’ diagnosed asthma; no. (%) 157 (5.1) 177 (7.3)

Allergic rhinitis; no. (%) 327 (10.6) 409 (17.0)

Outcomes: clinical measurementsSpecific IgE to common allergens �0.35

IU/mL; no. (%)285 (19.5) 309 (29.6)

Specific IgE to agricultural allergens �0.35IU/mL; no. (%)

34 (2.3) 25 (2.4)

Bronchial hyperresponsiveness tomethacholine; no. (%)

439 (40.4) 322 (43.7)

FEV1 (% predicted); no. (%) 103.1 � 13.6 101.4 � 13.7

ETS indicates environmental tobacco smoke exposure.*Born in the former western part of Germany.†Lived on a farm in first 3 years of life or had regular farm animal contact during

childhood, lived or worked on a farm at the time of the study.



All multiple regression models were adjusted a priorifor sex, age, passive and active smoking, level of education,family history of allergic disease, and the number of siblings.

To assess the linearity of the association between num-ber of animal houses within 500 m of the home and the healthoutcomes under study, we used locally optimal estimatingand smoothing scatter (LOESS) plots using bandwidth of 0.6.These models were adjusted for the above-mentioned poten-tial confounders. Based on the results of the analyses for theoutcome of wheezing, the number of animal houses in thehome environment was categorized at the resulting cut-offvalues (�5, �10, �12, and �12 animal houses).

Logistic regression analysis was used to calculate out-come odds ratios (ORs) with 95% confidence intervals (CIs)for the animal house categories, as well as the self-assessedodor annoyance in the home environment. Linear regressionanalysis was used to calculate differences in lung functionparameters between different groups. Analyses were per-formed using SAS v 9.02 (SAS, Cary NC) and S-Plus(Insightful Corporation, Seattle, WA).

RESULTS

Participation and NonresponderCharacteristics

Based on information from the population registry, par-ticipants were more likely than nonparticipants to be female(51% vs. 43%) while the 2 groups did not differ on age (mean33 years). Comparing the 433 subjects who answered only the

short questionnaire with other participants, the former were lesslikely to have been born in former West Germany (81% vs.86%), more likely to have lived on a farm during the first 3 yearsof life (46% vs. 37%), and more likely to ever have smoked(61% vs. 56%). No meaningful differences were seen withrespect to prevalence of asthma or allergic rhinitis.

Descriptive DataCharacteristics of the Study Population andExposure by Town

The median number of animal houses within 500 m ofthe home environment was highest in Town 1 (7; range 0–18)and lowest in Town 3 (3; range 0–12; Table 1). In line withthis finding, subjects living in Town 1 were on average moreannoyed by odor in the home environment. Endotoxin mea-surements at 32 study sites in Town 1 indicated endotoxinlevels of up to 23 EU/m3 (geometric mean 3.0 EU/m3;geometric standard deviation 2.2 EU/m3).28

The 4 towns differed with respect to level of education(Table 1). The percentage of subjects with regular farmcontact was highest in Town 4.

Questionnaire-based outcomes indicated a higher prev-alence of wheezing in Town 1 (Table 1). The mean FEV1 waslowest in Towns 1 and 3.

Comparison of Farm and Nonfarm SubjectsThe general characteristics of subjects with and without

regular farm contact are compared in Table 2. Regular farm

TABLE 3. Prevalence and Adjusted Odds Ratios of Respiratory Symptoms and Disease by Level of Odor Annoyance forSubjects Without Regular Farm Contact

Level of OdorAnnoyance No.*

Prevalence% OR† (95% CI)



Symptoms

Wheezing Without ColdPhysician-Diagnosed

Asthma Allergic Rhinitis

Not at all‡ 788 10.8 1.00 5.8 1.00 15.2 1.00

Somewhat 919 12.2 1.23 (0.90 to 1.68) 8.0 1.40 (0.95 to 2.06) 17.0 1.09 (0.83 to 1.42)

Moderately 215 19.8 2.19 (1.42 to 3.37) 8.4 1.51 (0.84 to 2.73) 22.2 1.49 (1.00 to 2.22)

Strongly 116 26.7 2.96 (1.80 to 4.86) 12.9 2.51 (1.32 to 4.75) 25.0 1.81 (1.11 to 2.97)

Clinical Measurements

Specific IgE to CommonAllergens >0.35 IU/mL

BronchialHyperresponsiveness to

Methacholine FEV1% Predicted§

Not at all‡ 289 28.1 1.00 41.2 1.00 101.9 � 12.8¶ 0.0�

Somewhat 452 29.5 1.11 (0.79 to 1.57) 46.6 1.21 (0.83 to 1.76) 100.8 � 13.9¶ �1.5 (�4.0 to 1.0)�

Moderately 102 37.4 1.71 (1.02 to 2.87) 41.8 0.92 (0.50 to 1.69) 102.3 � 14.8¶ 0.2 (�3.7 to 4.2)�

Strongly 53 27.5 1.02 (0.51 to 2.03) 42.4 1.12 (0.50 to 2.49) 102.6 � 12.8¶ �0.1 (�5.2 to 5.0)�

*No. missing for wheezing, 9; asthma, 3; allergic rhinitis, 17; IgE, 25; bronchial hyperresponsiveness, 274; and FEV1, 43.†Adjusted for age (5 categories), sex, active and passive smoke exposure, level of education, number of siblings, and parental allergies.‡Reference category.§Additionally adjusted for passive smoke exposure during childhood¶Mean � SD.�Adjusted mean difference in % predicted (95% CI).



contact was defined as living on a farm during the first 3 yearsof life (n � 2560), regular farm animal contact duringchildhood (n � 2810), living (n � 1060), or working on afarm at the time of the study (n � 490) (overall n � 3131;56%; Fig. 1).

Compared with the nonfarm population, the farm pop-ulation was, on average, older, and had lower prevalence ofsmokers, lower average level of education, and more siblings(Table 2). The prevalence of wheezing without a cold, phy-sician-diagnosed asthma, allergic rhinitis, and sensitizationagainst common allergens were lower among farm subjectsthan among the nonfarm population. Likewise, mean FEV1values were higher among farm subjects. There was nodifference between subjects with and without farm contactwith respect to bronchial hyperresponsivness.

Overall, only 59 subjects were sensitized to agricul-tural allergens. None of them was sensitized exclusively toagricultural allergens. Therefore, only specific IgE to com-mon allergen was considered relevant for the multivariateanalyses.

The level of odor annoyance did not differ betweenfarm and nonfarm subjects. The mean number of animalhouses within 500 m of the home environment was slightlyhigher among farm subjects (median 4; range 0–19) than

among nonfarm subjects (3; 0 –20). The remaining analy-ses were restricted to subjects without regular farm contact(n � 2425), since for them environmental exposures to farmemissions were considered relevant.

Respiratory HealthThe odds for all respiratory symptoms and for physi-

cian-diagnosed asthma increased with increasing self-re-ported level of odor annoyance (Table 3). In contrast, noassociations were seen between self-reported odor annoyanceand any of the clinical outcomes.

While constant at lower exposure levels, the LOESSsmoothers suggested an increase in wheezing without a coldwhen there were large numbers of animal houses in the homevicinity (Fig. 2A). In addition, FEV1 dropped with increasingnumber of animal houses (Fig. 2B). In contrast, for sensiti-zation to common allergens the prevalence was slightlydecreased at high exposure levels; however, confidence in-tervals were wide (Fig. 2C).

The odds for wheezing without a cold was increased forsubjects having more than 12 animal houses within 500 m oftheir home (Table 4). These subjects also had lower meanFEV1 values as compared with subjects with 5 or feweranimal houses within 500 m of their home (mean difference

FIGURE 2. Smoothed plots (solid lines) with 95% confidence intervals (dashed lines) of number of animal houses within 500 mof the home and (A) wheezing without cold, % (B) FEV1% predicted, and (C) sensitization to common allergens. Adjusted for age,sex, active and passive smoke exposure, level of education, number of siblings, and parental allergies. FEV1% predictedadditionally adjusted for passive smoke exposure during childhood.



� 7.1%; 95% CI � 2.9%–11.9%). In contrast, no importantassociations were seen with the number of animal houses forallergic rhinitis, sensitization, doctor’s diagnosed asthma orbronchial hyperresponsiveness.

DISCUSSIONOur study suggests that a high density of confined animal

feeding operations close to a residential area adversely affectsrespiratory health. The observed health effects point to asthma-like syndrome similar to those seen in farmers. In addition, ourstudy underlines the importance of using objective measure-ments for exposure and disease in environmental epidemiology.

The strengths of our study were objective assessment ofexposure and outcome, in addition to self-reported data of theparticipants. Furthermore, there was a reasonable responserate among a large population-based sample of rural subjects.Nonparticipants were mainly persons born outside formerWest Germany and thus would have been excluded from theanalyses. To further reduce selection bias, a random sampleof the population was asked to undergo a medical examina-tion. The clinical measurements were done according tostandardized procedures with thorough quality control.

At the same time, failure to test a considerable propor-tion of subjects for bronchial hyperresponsiveness could haveintroduced selection bias. As the proportion of asthmaticswas the same (6%) among those who participated in theclinical measurements and those who did not, no major biasis anticipated. The subjects’ lack of awareness of the number

of animal houses in the home vicinity is also expected to helpavoid selection bias.

Comparing self-reported data on number of confinedanimal feeding operations within 500 m of the home withdata provided by the local authorities yielded a low level ofagreement (17%; data not shown). Furthermore, in our pilotstudy the intraindividual test–retest reliability for the numberof animal houses in the home environment was low.31 There-fore, self-reported number of confined animal feeding oper-ations in the home environment seems to be a poor indicatorof actual exposure, which might bias results.24–26 This issupported by our finding that self-reported odor annoyancewas associated only with self-reported symptoms and disease,but not with clinical measurements.

One problem with the government data on number ofanimal houses in the home vicinity is that they cover allanimal houses regardless of size, type and number of animalskept, and type of ventilation. For confidentiality reasons wewere unable to obtain more detailed data. Therefore, somemisclassification of exposure has to be anticipated, whichmight lead to an underestimation of effects. Nevertheless, ourendotoxin measurements in 1 town indicated a moderateagreement between number of animal houses within 500 m ofthe home environment and level of endotoxin exposure mea-sured in the home environment (Spearman’s rho � 0.31).28

Unfortunately, the number of measurements done (n � 32)does not allow use of these measurements as markers ofexposure. Exposure to confined animal feeding operations is

TABLE 4. Prevalence and Adjusted Odds Ratios of Respiratory Symptoms and Disease by Number of Animal Houses Within500 m for Subjects Without Regular Farm Contact

No. of animalhouses within500 m No.*




Symptoms

Wheezing Without ColdPhysician-Diagnosed

Asthma Allergic Rhinitis

�5‡ 1343 12.3 1.00 7.9 1.00 17.4 1.00

�10 416 11.9 1.00 (0.70 to 1.42) 5.3 0.69 (0.42 to 1.11) 15.4 0.91 (0.66 to 1.24)

�12 48 18.8 1.62 (0.74 to 3.53) 8.3 1.23 (0.43 to 3.54) 18.8 1.20 (0.56 to 2.57)

�12 48 27.1 2.45 (1.22 to 4.90) 10.4 1.18 (0.45 to 3.10) 22.9 1.29 (0.64 to 2.60)

Clinical Measurements

Specific IgE to CommonAllergens >0.35 IU/mL

BronchialHyperresponsiveness to

Methacholine FEV1% Predicted§

�5‡ 580 29.4 1.00 46.1 1.00 101.5 � 13.2� 0.0¶

�10 186 28.0 0.95 (0.65 to 1.39) 40.5 0.72 (0.47 to 1.10) 101.5 � 13.6� �0.1 (�2.8 to 2.6)¶

�12 22 36.4 1.38 (0.55 to 3.47) 29.4 0.50 (0.17 to 1.49) 103.7 � 12.8� 0.2 (�6.9 to 7.3)¶

�12 22 19.1 0.54 (0.17 to 1.69) 33.3 0.38 (0.11 to 1.31) 93.8 � 12.6� �7.4 (�14.4 to �0.4)¶

*No. missing for wheezing, 9; asthma, 3; allergic rhinitis, 17; IgE, 25; bronchial hyperresponsiveness, 274; and FEV1, 43.†Adjusted for age (5 categories), sex, active and passive smoke exposure, level of education, number of siblings, and parental allergies.‡Reference category.§Additionally adjusted for passive smoke exposure during childhood.�Mean � SD.¶Adjusted mean difference in % predicted (95% CI).



determined by emission both from the confined animal feed-ing operations and from spraying of manure on the surround-ing fields. Emissions are thus predicted not by the animalhouses alone, and may vary from day to day. This variabilityis also indicated by the moderate test–retest reliability of thequestion on the level of odor annoyance in the home envi-ronment (kappa � 0.51).31 To overcome this problem, long-term measurements of such chemicals as ammonia at severalpoints in the study area would be required. Using suchexposure data, the individual exposure at the participants’home could be modeled by GIS models.

Another source of error might stem from the fact thatthe registry of animal houses was last updated in 2000, whilethe study was done between 2002 and 2004. Furthermore,only data on animal houses in the vicinity of participants’homes were available. Personal exposure assessment wouldbe necessary to overcome this problem. Our study populationreported spending, on average, 102 (�33) hours per week athome. Adjusting our analyses for number of hours at homedid not change our results.

In addition, our results might be biased by havingdrawn samples from different towns. Stratifying the data bytown did not indicate effect modification by town. Neverthe-less, due to small sample sizes, especially in the higherexposure categories, confidence intervals by town were wide(data not shown). Likewise, neither adjusting the analyses forstudy town nor the use of GLM mixed-effect models changedthe results considerably (data not shown).

The association between number of animal houses nearthe home and wheezing, as well as FEV1, are similar to thoseobserved in farmers and farm workers and might be anindication of asthma-like syndrome.2,4,36–38 We restricted ourstudy population to those without private or occupationalcontact to farming environments. This was done because theenvironmental exposures to emissions from confined animalfeeding operations are considered to be small compared withexposures inside animal houses or exposures levels experi-enced of those who live on a farm,1,8,23 and thus of minorimportance for respiratory health. Repeating our analyses forsubjects with farm contact, no association between number ofanimal houses in home vicinity and respiratory symptoms anddisease could be shown. However, the association betweenself-reported odor annoyance and self-reported respiratorysymptoms and disease were similar to those in subjectswithout farm contact (data not shown).

Adverse effects were seen only among subjects with ahigh number of animal houses in the immediate home vicin-ity. As this was the first study using number of animal housesas an exposure proxy, and clinical measurements to assess theoutcome, we did not have the benefit of predefined cut-offvalues. In addition, due to the higher population density inEurope and different farming practices, the exposure isthought to differ considerably from that in the USA.10,17,39

Therefore, LOESS smoothers have been used to elicit cut-offlevels. Furthermore, due to exposure misclassification, theexact cut-off point cannot be identified from our data. Toconfirm our results, further epidemiologic studies are neededin areas with intensive animal production.

REFERENCES1. Radon K, Danuser B, Iversen M, et al. Air contaminants in different

European farming environments. Ann Agric Environ Med. 2002;9:41– 48.

2. Radon K. The two sides of the “endotoxin coin”. Occup Environ Med.2006;63:73–78 10.

3. Kirkhorn SR, Garry VF. Agricultural lung diseases. Environ HealthPerspect. 2000;108(Suppl 4):705–712.

4. Schenker M. Exposures and health effects from inorganic agriculturaldusts. Environ Health Perspect. 2000;108(Suppl 4):661–664.

5. Melbostad E, Eduard W, Magnus P. Chronic bronchitis in farmers.Scand J Work Environ Health. 1997;23:271–280.

6. Vogelzang PF, van der Gulden JW, Preller L, et al. Bronchial hyperre-sponsiveness and exposure in pig farmers. Int Arch Occup EnvironHealth. 1997;70:327–333.

7. Monso E, Magarolas R, Badorrey I, et al. Occupational asthma ingreenhouse flower and ornamental plant growers. Am J Respir Crit CareMed. 2002;165:954–960.

8. Radon K, Schottky A, Garz S, et al. Distribution of dust-mite allergens(Lep d 2, Der p 1, Der f 1, Der 2) in pig-farming environments andsensitization of the respective farmers. Allergy. 2000;55:219–225.

9. Schiffman SS. Livestock odors: implications for human health andwell-being. J Anim Sci. 1998;76:1343–1355.

10. Mirabelli M, Wing S, Marshall SW, et al. Race, poverty, and potentialexposure of middle-school students to air emissions from confined swinefeeding operations. Environ Health Perspect. 2006;114:591–596.

11. Radon K, Peters A, Praml G, et al. Livestock odours and quality of lifeof neighbouring residents. Ann Agric Environ Med. 2004;11:59–62.

12. Nimmermark S. Odour influence on well-being and health with specificfocus on animal production emissions. Ann Agric Environ Med. 2004;11:163–173.

13. Dalton P. Upper airway irritation, odor perception and health risk due toairborne chemicals. Toxicol Lett. 2003;140–141:239–248.

14. Kirkhorn SR. Community and environmental health effects of concen-trated animal feeding operations. Minn Med. 2002;85:38–43.

15. Wing S, Wolf S. Intensive livestock operations, health, and quality oflife among eastern North Carolina residents. Environ Health Perspect.2000;108:233–238.

16. Thu KM. Public health concerns for neighbors of large-scale swineproduction operations. J Agric Saf Health. 2002;8:175–184.

17. Avery RC, Wing S, Marshall SW, et al. Odor from industrial hogfarming operations and mucosal immune function in neighbors. ArchEnviron Health. 2004;59:101–108.

18. Merchant JA, Naleway AL, Svendsen ER, et al. Asthma and farmexposures in a cohort of rural Iowa children. Environ Health Perspect.2005;113:350–356.

19. Mirabelli MC, Wing S, Marshall SW, et al. Asthma symptoms amongadolescents who attend public schools that are located near confinedswine feeding operations. Pediatrics. 2006;118:e66–e75.

20. Sigurdarson ST, Kline JN. School proximity to concentrated animalfeeding operations and prevalence of asthma in students. Chest. 2006;129:1486–1491.

21. Riedler J, Braun-Fahrlander C, Eder W, et al. Exposure to farming inearly life and development of asthma and allergy: a cross-sectionalsurvey. Lancet. 2001;358:1129–1133.

22. Ernst P, Cormier Y. Relative scarcity of asthma and atopy among ruraladolescents raised on a farm. Am J Respir Crit Care Med. 2000;161:1563–1566.

23. Braun-Fahrlander C. Environmental exposure to endotoxin and other mi-crobial products and the decreased risk of childhood atopy: evaluating devel-opments since April 2002. Curr Opin Allergy Clin Immunol. 2003;3:325–329.

24. Kaye WE, Hall HI, Lybarger JA. Recall bias in disease status associatedwith perceived exposure to hazardous substances. Ann Epidemiol. 1994;4:393–397.

25. Ahlborg GA Jr. Validity of exposure data obtained by questionnaire.Two examples from occupational reproductive studies. Scand J WorkEnviron Health. 1990;16:284–288.

26. Vrijheid M, Deltour I, Krewski D, et al. The effects of recall errors andof selection bias in epidemiologic studies of mobile phone use andcancer risk. J Expo Sci Environ Epidemiol. 2006;16:371–384.

27. Smeeton NC, Rona RJ, Oyarzun M, et al. Agreement between responses toa standardized asthma questionnaire and a questionnaire following a demon-



stration of asthma symptoms in adults. Am J Epidemiol. 2006;163:384–391.28. Schulze A, van Strien RT, Schierl R, et al. Ambient endotoxin level in

an area with intensive livestock production. Ann Agric Environ Med.2006;13:87–91.

29. Nowak D, Heinrich J, Jorres R, et al. Prevalence of respiratory symp-toms, bronchial hyperresponsiveness and atopy among adults: west andeast Germany. Eur Respir J. 1996;9:2541–2552.

30. Schulz C, Becker K, Helm D, et al. �1998 environment survey—initialresults�. Gesundheitswesen. 1999;61:S213–S215.

31. Entorf H. Reliabilitat eines Fragebogens zur Atemwegsgesundheit undAllergiestatus bei jungen Erwachsenen in landlichen Regionen Nied-ersachsens: “Die Niedersachsische Lungenstudie NiLS” �Doctorate�.Munich: Ludwig-Maximilians-University; 2005.

32. American Thoracic Society. Standardization of spirometry, 1994 update.Am J Respir Crit Care Med. 1995;152:1107–1136.

33. Quanjer PH, Tammeling GJ, Cotes JE, et al. Lung volumes and forcedventilatory flows. Report Working Party Standardization of Lung Func-tion Tests, European Community for Steel and Coal. Official Statement

of the European Respiratory Society. Eur Respir J Suppl. 1993;16:5–40.34. Praml G, Scharrer E, de la Motte D, et al. Physical dose is not the same

as biological dose: comparison of the Mefar and the APS nebulizers.Chest. 2005;128:3585–3589.

35. Hoopmann M, Csicsaky M, Schulze A, et al. Gesundheitliche Bewer-tung von Bioaerosolen aus der Intensivtierhaltung in Niedersachsen.UMID. 2005;(4/2005):3–6.

36. Radon K, Monso E, Weber C, et al. Prevalence and risk factors forairway diseases in farmers—summary of results of the European Farm-ers’ Project. Ann Agric Environ Med. 2002;9:207–213.

37. Linaker C, Smedley J. Respiratory illness in agricultural workers. OccupMed (Lond). 2002;52:451–459.

38. Von Essen S, Donham K. Illness and injury in animal confinementworkers. Occup Med. 1999;14:337–350.

39. Cole D, Todd L, Wing S. Concentrated swine feeding operations andpublic health: a review of occupational and community health effects.Environ Health Perspect. 2000;108:685–699.



COMMENTARY

Environmental Exposure and Health Effects FromConcentrated Animal Feeding Operations

Frank M. Mitloehner* and Marc B. Schenker†

Abstract: Modern concentrated animal feeding operations generatesizeable amounts of manure and related emissions into water and air.These present potential harm to human health. Adverse respiratoryeffects have been documented among workers in these feedingoperations, but there has been little research on wider environmentaleffects. Few conclusions are possible at this time but recent studies(including a report in this issue of EPIDEMIOLOGY by Radon andcolleagues) suggest possible adverse effects. Respiratory outcomesof greatest concern include nasal allergies, airflow obstruction andasthma. Another concern among residents near concentrated animalfeeding operations is adverse effects from malodors. The potentialimpact of these operations on quality of life and health needs to bedocumented.

(Epidemiology 2007;18: 309–311)

Historically, livestock was raised on small family farmsspread throughout agricultural regions. Over the past 4

decades, the total number of livestock farms has sharplydeclined, while the number of confined animals being raisedin concentrated feeding operations has increased. Since 1960,the number of cattle farms in the United States has fallen59%, the number of dairy farms has fallen 94%, and thenumber of hog farms has fallen 95%—even though the totalnumber of livestock has been relatively constant.1 Today,concentrated animal feeding operations make up 5% of alllivestock farms in the nation but raise 54% of all livestock.2,3

This trend is exacerbated by the nation’s “cheap food policy”in which consumer demand for inexpensive food leads toincreasing production efficiencies utilizing larger livestockfacilities.4 Modern livestock industries simply follow HenryFord’s rule of producing the highest quality goods at thelowest costs possible.

Modern animal feeding operations produce large amountsof animal waste (288 million tons in the United States annu-ally).2,3,5 Disposing of this sizeable amount of manure is a

challenge. There are potential adverse human health effects ofmanure and its related emissions into water and air.

Communities in regions with large-scale animal pro-duction can range from small family farms (crop- or animal-producing), to rural nonfarm residents to urban residents inneighboring towns. In some parts of the United States, thereis a shift of populations from cities to the countryside wherethey experience the nuisances of concentrated livestock pro-duction.6 This can lead to complaints, conflict, and ultimatelya need for legislative and regulatory actions.

Until recently, most of the attention to human healthrisks posed by concentrated animal feeding operations wasrelated to water quality (eg, nitrate leaching in ground water).However, air emissions from livestock facilities present agrowing challenge. Concentrated animal feeding operationsemit several compounds of concern, including endotoxin,particulate matter, ammonia, hydrogen sulfide, volatile or-ganic compounds, and various greenhouse gases.7 Studies areneeded to investigate the emissions from concentrated animalfeeding operations and their potential health effects, to iden-tify vulnerable worker and neighborhood groups, and ifwarranted, to identify and implement options for mitigation.

Occupational Health ConcernsWork in the animal agricultural industries continues to

rank among the most hazardous of all occupations.8–12 The 2main contributors to worker injury are machinery-related andanimal-related incidents.11,12 Air pollutants also pose a risk.13,14

Harmful air emissions in animal farming arise from thehandling of feed, movement of animals on manure, and thestorage and removal of manure. The composition of airemissions (gases and particulate matter) can differ widelyaccording to farm layouts, management type, region, andspecies of animals housed. This variability makes it difficultto identify specific practices and recommend changes.7

As with other occupational hazards, higher exposuresoccur to workers than to neighboring residents. Thus, research tounderstand the effects of exposures from animal feeding opera-tions often progresses from studies of workers to studies ofneighbors who experience lower exposures. The respiratoryeffects of agricultural occupational exposures have been welldocumented in recent years.13,14 Agricultural exposures, partic-ularly those from animal farming, are associated with a widerange of airway diseases including mucous membrane irritation,bronchitis, asthma, asthma-like syndrome and chronic obstruc-tive pulmonary disease. Acute toxicity from high-dose gasexposures (eg, nitrogen oxides, hydrogen sulfide, ammonia) andinterstitial diseases (hypersensitivity pneumonitis, interstitial fi-

From the *Department of Animal Science and Agricultural Air QualityCenter; †Department of Public Health Sciences and Western Center forAgricultural Health and Safety, University of California, Davis.

Supported by NIH/NIOSH grant 2 U50 OH07550-06.Correspondence: Marc Schenker, Department of Public Health Sciences, TB

168, One Shields Avenue, Davis, CA 95616. E-mail: [email protected].

Copyright © 2007 by Lippincott Williams & WilkinsISSN: 1044-3983/07/1803-0309DOI: 10.1097/01.ede.0000260490.46197.e0

Epidemiology • Volume 18, Number 3, May 2007 309

brosis) also are well documented. Many adverse respiratoryeffects of farming result from the wide spectrum of respiratorytoxicants (eg, organic and inorganic dusts, gases, agrochemicals,biologic agents) as well as the exposure to high concentrations.

Questions have been raised about pesticide drift, dustexposure, plant antigens, and other agricultural agents thatmight affect neighboring populations. The capacity for ad-verse community effects from agricultural exposures wasdemonstrated in the Barcelona epidemic of asthma fromexposure to soybean dust.15 However, few studies haveinvestigated the environmental effects of agricultural expo-sures in general, and those from concentrated animal feedingoperations in particular, on local communities.

Environmental Health EffectsRecent work has begun to focus on the potential envi-

ronmental health effects of concentrated animal feeding op-erations. A first step is the measurement of contaminantconcentrations in the vicinity of such operations. This sam-pling needs to address temporal variability, plume dispersion,and individual exposures, taking into account indoor–outdoorgradients, physical activity, and other determinants. The pol-lutants of primary concern are ammonia, hydrogen sulfide,particulate matter and its contaminants (microorganisms, en-dotoxin), volatile organic compounds, and odors. Severalstudies have shown high concentrations of microbial organ-isms16 and of endotoxin13,17 in these feeding operations. Forexample, concentrations of endotoxin from hundreds to sev-eral thousand EU/m3 may occur in some swine confinementoperations,18 while environmental endotoxin concentrationshave been reported in single digits in residences near theseoperations.19 More definitive exposure studies, epidemiologicstudies, and modeling are needed to predict downwind con-centrations and resulting health effects from concentratedanimal feeding operations.20

What is currently known about adverse effects to thelocal communities? There are few studies on the potentialhealth effects of environmental exposures from concentratedanimal feeding operations, but this is changing.21,22 Majoroutcomes of concern are those observed among workers, includ-ing respiratory and systemic effects,23 reduced lung function andincreased decline in lung function,24–26 and asthma.27,28 Thereare ample data showing an association of agriculture or concen-trated animal feeding operations with asthma,13,29 and endotoxinexposure alone may cause or exacerbate asthma.30 However,little is known about environmental exposures from concen-trated animal feeding operations and asthma. This question isparticularly interesting because of studies showing a reduction ofatopic sensitization with agricultural exposure to endotoxin.31

Odors are a product of concentrated animal feedingoperations. While odors are not highly correlated with respi-ratory toxicants, self-assessed level of odor annoyance is astrong predictor of negative quality-of-life scores.32,33 Animmunosuppressive effect of odor on mucosal immunity hasbeen hypothesized.34 Studies of the health effects of odors areparticularly challenging because objective outcome measuresare required to reduce reporting bias.

The article in this issue by Radon and colleagues35 addsimportant new data to our understanding of environmental

health effects of CAFOs. They have shown an association ofresidence near CAFOs and self-reported wheeze and airflowobstruction (decreased forced expiratory volume in one sec-ond), but no association with asthma or allergic rhinitis.However, many questions remain before their observationscan be accepted as causal. Specifically, replication is neededin different populations with better exposure characterizationand careful selection of controls. Differences between Euro-pean and larger U.S. CAFOs should also be studied. Thecomplex relationship of agricultural exposures, atopy andasthma needs further elucidation, and may result in modelssuggesting different effects depending on the age and dura-tion of an individual’s exposure.

Adverse health effects of exposures from concentratedanimal feeding operations have not been addressed by tradi-tional ambient air quality studies or regulations. The sametools of air pollution research are needed to provide a scien-tific basis for regulatory decision-making. Careful studiesneed to evaluate individual exposures to neighboring resi-dents, preferably coupled with real-time measurements ofobjective outcomes. The complex mixture of emissions fromconcentrated animal feeding operations needs to be studied tounderstand the etiologic agents. This should preferably berelated to animal and exposure chamber studies to understandunderlying mechanisms. Health effect studies are required toevaluate suspect adverse respiratory effects, with particularattention to dissecting the positive and negative effects ofendotoxin exposure.36 As with other air pollution healtheffects, meteorological conditions must be taken into account.Studies should also consider the impact of odor on health.Epidemiologic studies need to pay particular attention toacute as well as lifetime exposure histories. Finally, regula-tory efforts will require assessment of the specific farmingconditions and practices that produce harmful exposures.

ABOUT THE AUTHORSFRANK MITLOEHNER is the Air Quality Cooperative

Extension Specialist in the Department of Animal Science atthe University of California, Davis. He serves as Director ofthe UC Davis Agricultural Air Quality Center and carries outresearch on occupational health in large dairies in Califor-nia. MARC SCHENKER is the Professor and Chairman ofthe Public Health Sciences Department at the University ofCalifornia, Davis. His research includes studies on environ-mental and occupational hazards. He is the Director of theWestern Center for Agricultural Health and Safety and theCenter for Occupational and Environmental Health in Davis.

REFERENCES1. Centner TJ. Regulating concentrated animal feeding operations to en-

hance the environment. Environ Sci Policy. 2003;6:433–440.2. American Public Health Association. Policy Statements Database. As-

sociation news, 2003 Policy statements. Precautionary moratorium onnew concentrated animal feed operations. 2003. Available at: http://www.apha.org/advocacy/policy/policysearch/default.htm?id�1243. Ac-cessed January 22, 2007.

3. Institute for Agriculture and Trade Policy. Food and health program:factory farms and health. 2006. Available at: http://www.iatp.org/foodandhealth/issues_factoryfarms.cfm. Accessed September 12, 2006.

Mitloehner and Schenker Epidemiology • Volume 18, Number 3, May 2007


4. Environmental Protection Agency. Risk assessment evaluation for con-centrated animal feeding operations: air transport and deposition. UnitedStates Environmental Protection Agency. 2004. Available at: http://www.epa.gov/nrmrl/pubs/600r04042/600r04042.pdf. Accessed Septem-ber 20, 2006.

5. Federal Register. National pollutant discharge elimination system permitregulation and effluent limitation guidelines and standards for concen-trated animal feeding operations (CAFOs): final rule. Federal Register.2003;68:7175–7184.

6. Donham K, Wing S, Osterberg D, et al. Community health and socio-economic issues surrounding concentrated animal feeding operations.Environ Health Perspect. 2007;115:317–320.

7. National Research Council. Air Emissions from Animal Feeding Oper-ations: Current Knowledge, Future Needs . Washington, DC: Commit-tee on Animal Nutrition, National Research Council. National AcademyPress.

8. Osorio AM, Beckman J, Geiser CR, et al. California farm survey ofoccupational injuries and hazards (special issue). J Agric Safety Health1998;(1):99–108.

9. Hendricks KJ, Adekoya N. Non-fatal animal related injuries to youthoccurring on farms in the United States, 1998. Inj Prev. 2001;7:307–311.

10. Layde PM, Nordstrom DL, Stueland D, et al. Animal-related occupa-tional injuries in farm residents. J Agric Safety Health. 1996;2:27–37.

11. McCurdy SA, Carroll DJ. Agricultural injury. Am J Ind Med. 2000;38:463–480.

12. Miller RL, Webster JK, Mariger SC. Nonfatal injury rates of Utahagricultural producers. J Agric Safety Health. 2004;10:285–293.

13. Schenker M. Respiratory health hazards in agriculture, Am J Respir CritCare Med. 1998;158:S1–S76.

14. Omland O. Exposure and respiratory health in farming in temperatezones—-a review of the literature. Ann Agric Environ Med. 2002;9:119–136.

15. Anto JM, Sunyer J, Rodriguez-Roisin R, et al. Community outbreaks ofasthma associated with inhalation of soybean dust. N Engl J Med.1989;320:1097–1102.

16. Keikhaefer M, Donham K, Whitten P, et al. Cross seasonal studies ofairborne microbial populations and environment in swine buildings:implications for worker and animal health. Ann Agric Environ Med.1995;2:37–41.

17. Zhiping W, Malmberg P, Larsson BM, et al. Exposure to bacteria inswine-house dust and acute inflammatory reactions in humans. Am JRespir Crit Care Med. 1996;154:1261–1266.

18. Douwes J, Heederik D. Epidemiologic investigations of endotoxins. IntJ Occ Env Health. 1997;3(Suppl 1):S26–S31.

19. Schulze A, Van Strien R, Ehrenstein V, et al. Ambient endotoxin levelin an area with intensive livestock production. Ann Agric Environ Med.2006;13:87–91.

20. Bunton B, O’Shaugnhessy P, Fitzsimmons S, et al. Monitoring and

modeling of emissions from CAFOs: overview of methods. EnvironHealth Perspect. 2007;115:303–307.

21. Cole D, Todd L, Wing S. Concentrated swine feeding operations andpublic health: a review of occupational and community health effects.Environ Health Perspect. 2000;108:685–699.

22. Heederik D, Sigsgaard T, Thorne P, et al. Health effects of airborneexposures from concentrated animal feeding operations. Environ HealthPerspect. 2007;115:298–302.

23. Rylander R, Donham KJ, Hjort C, et al. Effects of exposure to dust inswine confinement buildings—a working group report. Scand J WorkEnviron Health. 1989;15:309–312.

24. Donham KJ, Zavala DC, Merchant JA. Respiratory symptoms and lungfunction among workers in swine confinement buildings: a cross-sec-tional epidemiological study. Arch Environ Health. 1984;39:96–101.

25. Vogelzang PF, van der Gulden JW, Folgering H, et al. Longitudinalchanges in lung function associated with aspects of swine-confinementexposure. J Occup Environ Med. 1998;40:1048–1052.

26. Senthilselvan A, Dosman JA, Kirychuk SP, et al. Accelerated lungfunction decline in swine confinement workers. Chest. 1997;111:1733–1741.

27. Eduard W, Omenaas E, Bakke PS, et al. Atopic and non-atopic asthmain a farming and a general population. Am J Ind Med. 2004;46:396–399.

28. Melbostad E, Eduard W, Magnus P. Determinants of asthma in afarming population. Scand J Work Environ Health. 1998;24:262–269.

29. Kogevinas M, Anto JM, Sunyer J, et al. European Community Respi-ratory Health Survey Study Group. Occupational asthma in Europe andother industrialised areas: a population-based study. Lancet. 1999;353:1750–1754.

30. Thorne PS, Kulhankova K, Yin M, et al. Endotoxin exposure is a riskfactor for asthma: the national survey of endotoxin in United Stateshousing. Am J Respir Crit Care Med. 2005;172:1371–1377.

31. Portengen L, Preller L, Tielen M, et al. Endotoxin exposure and atopicsensitization in adult pig farmers. J Allergy Clin Immunol. 2005;115:797–802.

32. Wing S, Wolf S. Intensive livestock operations, health, and quality oflife among eastern North Carolina residents. Environ Health Perspect.2000;108:233–238.

33. Radon K, Peters A, Praml G, et al. Livestock odours and quality of lifeof neighbouring residents. Ann Agric Environ Med. 2004;11:59–62.

34. Avery RC, Wing S, Marshall SW, et al. Odor from industrial hogfarming operations and mucosal immune function in neighbors. ArchEnviron Health. 2004;59:101–108.

35. Radon K, Schutze A, Ehrenstein V, et al. Environmental exposure toconfined animal feeding operations and respiratory health of neighboringresidents. Epidemiology2007;18:300–307.

36. Radon K. The two sides of the ‘endotoxin coin’. Occup Environ Med.2006;63:73–78.

Epidemiology • Volume 18, Number 3, May 2007 Exposure and Health Effects of Large Livestock Operations


ORIGINAL ARTICLE

Neurobehavioral Development in Children With PotentialExposure to Pesticides

Alexis J. Handal,* Betsy Lozoff,† Jaime Breilh,‡ and Sioban D. Harlow*

Background: Children may be at higher risk than adults frompesticide exposure, due to their rapidly developing physiology,unique behavioral patterns, and interactions with the physical envi-ronment. This preliminary study conducted in Ecuador examines theassociation between household and environmental risk factors forpesticide exposure and neurobehavioral development.Methods: We collected data over 6 months in the rural highlandregion of Cayambe, Ecuador (2003–2004). Children age 24–61months residing in 3 communities were assessed with the Ages andStages Questionnaire and the Visual Motor Integration Test. Wegathered information on maternal health and work characteristics,the home and community environment, and child characteristics.Growth measurements and a hemoglobin finger-prick blood testwere obtained. Multiple linear regression analyses were conducted.Results: Current maternal employment in the flower industry wasassociated with better developmental scores. Longer hours playingoutdoors were associated with lower gross and fine motor andproblem solving skills. Children who played with irrigation waterscored lower on fine motor skills (8% decrease; 95% confidenceinterval � �9.31 to �0.53), problem-solving skills (7% decrease;�8.40 to �0.39), and Visual Motor Integration test scores (3%decrease; �12.00 to 1.08).Conclusions: These results suggest that certain environmental riskfactors for exposure to pesticides may affect child development,with contact with irrigation water of particular concern. However,the relationships between these risk factors and social characteristicsare complex, as corporate agriculture may increase risk throughpesticide exposure and environmental contamination, while indi-

rectly promoting healthy development by providing health care,relatively higher salaries, and daycare options.

(Epidemiology 2007;18: 312–320)

Animal studies show that several pesticides including or-ganochlorines, pyrethyroids, organophosphates, and car-

bamates are developmental neurotoxins.1–4 However, fewepidemiologic studies have focused on the effects of pesti-cides on neurobehavioral development and function.5–8 In-fants and young children are more susceptible to environmen-tal toxins due to their developing and still immaturephysiology.9–13 The developing central and peripheral ner-vous systems are especially susceptible to adverse affects ofneurotoxins such as organophosphates and carbamates.

In addition to their increased susceptibility, young chil-dren are also more likely to be exposed to environmentaltoxins through their behaviors. For infants under the age of 6months, inhalation and breast milk are the main potentialroutes of exposure.14 As infants begin to crawl and spendmuch of their day on the floor or soil, dermal exposure andoral ingestion become the principal avenues of exposure.15,16

Sucking, chewing, and biting, are part of normal developmentand contribute to increased exposure. As children begin towalk, they spend less time on the floor, but their feedingpatterns and gender-related behaviors may contribute to ex-posure risk. Children may eat their meals sitting on the flooror outside on the ground. Boys may be more likely to playoutside, whereas girls may be more likely to assist theirmothers in washing contaminated clothing or cleaning con-taminated food sources.15

For young children in agricultural communities, severaladditional factors may influence exposure risk. The proximityof the child’s home to large agricultural industries or farm-land may increase exposure, depending on the direction of thewind, water run-off, and ground contamination. Lu and col-leagues17 found that children whose parents worked withpesticides or who lived in close proximity to farmland treatedwith pesticide had higher exposures to organophosphate pes-ticides compared with other children in the same community.Similarly, Simcox and colleagues18 found that children ofagricultural families had a higher risk of exposure to organo-phosphate pesticides than children of nonagricultural familiesin the same region. Inhalation may be a key exposure routeamong children who live in close proximity to the flowerfarms. Dermal and oral exposure may occur through exposureto pesticide residues on parents’ work clothes and boots.19

Submitted 17 May 2006; accepted 19 December 2006.From the *Department of Epidemiology, University of Michigan School of

Public Health, Ann Arbor, MI; †Center for Human Growth and Devel-opment and Department of Pediatrics and Communicable Diseases,University of Michigan, Ann Arbor, MI; and ‡Health Research andAdvisory Center (CEAS), Quito, Ecuador.

Alexis J. Handal was supported by a Fulbright student grant from the J.William Fulbright Foreign Scholarship Board, grant #D43-TW01276from the Fogarty International Center and the National Institute of ChildHealth and Human Development, grant # R25 GM58641-06 from theNational Institute of General Medical Sciences, and the University ofMichigan Rackham School of Graduate Studies.

Correspondence: Alexis J. Handal, Division of Epidemiology, Statistics, andPrevention Research, National Institute of Child Health and HumanDevelopment, 6100 Executive Boulevard, Room 7B03B, Rockville, MD20852. E-mail: [email protected].

Copyright © 2007 by Lippincott Williams & WilkinsISSN: 1044-3983/07/1803-0312DOI: 10.1097/01.ede.0000259983.55716.bb


Use of pesticides on private crops, as well as the participationof the child in the cultivation and harvest of these crops, alsocontribute to the child’s exposure profile. Fenske and col-leagues20 found that use of pesticides in a home garden wasassociated with an increase of an organophosphate metabolitein children’s urine samples.

In Ecuador, large-scale agricultural products such asbananas and cut-flowers are important contributors to thenational economy. The cut-flower industry is now the coun-try’s third most important export, following petroleum andbananas. Pesticide use in this industry is common, with theorganophosphate, carbamate, and dithiocarbamate classes ofpesticides being the most frequently used.

In 2001, the Centro de Estudios y Asesorıa en Salud(CEAS) in Quito, Ecuador, in collaboration with the Cana-dian International Development Research Center (IDRC),initiated the EcoSalud Project in the Cayambe-Tabacundoregion of Ecuador. This project addresses the social impact ofthe cut-flower industry through a wide range of approaches,including sociology, anthropology, ecology, and epidemiol-ogy. This preliminary study considered household and com-munity risk factors for pesticide exposure and their potentialeffects on neurobehavioral development of young childrenaged 24 to 61 months living in the region.

METHODS

Study PopulationThe sample population for the EcoSalud project was

drawn from 2 northern and 2 southern sections of the Cay-ambe-Tabacundo region. The present study focused on 3northern communities. Communities A and B were at loweraltitudes and were likely to have higher exposures, with morecommunity members employed in the flower industry andliving in closer proximity to the flower plantations. Commu-nity C was at a higher altitude and was likely to have lowerexposure. It was located farther from the flower plantationsand had few flower industry employees. These communitieswere selected based on exposure status and because of closeties between researchers and community leaders, allowing forgreater accessibility to the communities’ members.

A census was taken to establish the sampling frame. Allmothers who had been living in the community for at least ayear and who had any children aged 3 to 61 months wereeligible to participate. Mothers were interviewed about alltheir eligible children, up to 3 children total. Informed con-sent was obtained from the mothers for their participation aswell as that of their children. Consent forms were read to themother and consent was documented by the mother’s signa-ture or fingerprint. In total, 219 mothers (91% of thoseeligible) and 283 children (91% of those eligible) partici-pated. This analysis of household and community risk factorsfor exposure to pesticides includes children 24 to 61 monthsof age (n � 142). Approval for this project was obtained fromthe Institutional Review Board at the University of Michigan,as well as from CEAS in Quito, Ecuador.

ProceduresThe Ages and Stages Questionnaire (ASQ) (2nd Edi-

tion; Paul H. Brookes Publishing Co., Baltimore, MD) is adevelopmental screening test that was directly administeredto the child. We administered an additional test to childrenaged 48 to 61 months, to assess their visual-motor integration(VMI) skills. Two trained testers assessed the participatingchildren in each of the 3 communities. Mothers were admin-istered a questionnaire to obtain information on sociodemo-graphic characteristics, maternal occupational history, maternaland child health characteristics, and the child’s socialization andexposure profiles. Blood by finger-prick was obtained to assessthe child’s hemoglobin levels (HemoCue, Lake Forest, CA).Height (centimeters), weight (kilograms), and head circum-ference (centimeters) of the child were measured. All surveyinstruments were pretested and piloted to ensure clarity andcomprehensibility.

Risk Factors for Pesticide ExposurePotential risk factors for pesticide exposure were clas-

sified within 3 domains: 1) community and household vari-ables; 2) maternal and paternal work variables; and 3) childactivities. As community and household risk factors, we useddistance of the home to a flower farm (�200 m, �200 m),pesticide use on domestic crops (yes/no), pesticide use withinthe home (yes/no), and use of the potentially contaminatedplastics/wood from the flower farms at home (yes/no). Ma-ternal work exposure included whether she had worked in theflower industry in the past 6 years (yes/no, and mean numberof years), whether she had worked in the flower industrywhile pregnant with the child (yes/no), and her current workstatus (currently not working, currently working but not inflowers, currently working in flowers). Mothers also reportedfather’s current work status. Child’s play activities includedthe distance from a flower farm (�200 m, �200 m), numberof hours per day the child plays outdoors (mean number ofhours/day; �5/�5 hours/day), and whether the child playswith irrigation water (no or rarely, sometimes or frequently).

Neurobehavioral DevelopmentAges and Stages Questionnaire

The use of a screening test that combines both parent-report and direct evaluation approaches has been shown to bean effective and valid way to assess a child’s developmentalprogress.21,22 The questionnaire is standardized for use inchildren ages 3 to 61 months and is composed of 19 age-specific questionnaires that cover 5 broad developmentaldimensions: communication, fine and gross motor skills,problem solving, and personal-social skills. Each domainis scored from 0 to 60 points, with 60 being a perfect score.A continuous score is calculated for each age-specificquestionnaire, with scores summarized for each develop-mental domain.

Before administering the ASQ (Spanish version), thetool was adapted into the local vernacular and contextuallyinappropriate questions were removed to prevent cultural andlanguage bias. For example, all references to the baby/childwere changed to the term “guagua,” a Quichua term com-

Epidemiology • Volume 18, Number 3, May 2007 Pesticide-related Activities and Neurodevelopment in Ecuador


monly used in the region. Testing was conducted using thehome-visit procedure outlined in the manual, in which thetester attempts to elicit all behaviors directly from the childduring the assessment.22 This procedure varies slightly fromone that relies solely on parent-report and is more appropriatein a setting where the parent may not be able to complete thequestionnaire on her own. Testers brought all materials re-quired for direct assessment as listed in the manual. Motherswere encouraged to participate in the activities with theirchild throughout the session. When the particular activitycould not be carried out directly, the mother was asked aboutthe child’s behavior at home.

Visual Motor IntegrationThe use of targeted developmental testing may be more

able to detect subtle delays in development in this populationdue to chronic low level exposure to pesticides. To supple-ment the use of a general screening tool, we included onetargeted developmental test that assessed the integration ofvisual perception and motor skills—skills that have beenshown to be affected by pesticide exposure in adults andchildren.5,7,23,24 Children aged 48 to 61 months were testedusing the Beery-Buktenica VMI developmental test (4th Edi-tion, Revised ed., Modern Curriculum Press).25 The VMI is adevelopmental sequence of geometric forms to be copiedwith paper and pencil by the child. It is designed to assess theextent to which the child can integrate his visual and motorabilities. The VMI manual was used as a reference forscoring, with each drawing scored as 0 for incorrect and 1 forcorrect. Points were totaled and the raw score was recorded.A total score of 160 was possible. Based on the age of thechild, the raw score was converted into a standard scoreequivalent. The VMI manual presents a standardized meanscore of 100 and standard deviation (SD) of 15.25

CovariatesStandardized z-scores for anthropometric measures of

chronic malnutrition were calculated using the 1978 Centers forDisease Control (CDC)/World Health Organization (WHO)growth reference curves, which are a normalized version of the1977 National Center for Health Statistics (NCHS) growthreference curves.26 Chronic malnutrition (stunting) was de-fined by a height-for-age z-score 2 SDs below the referencemedian. Presence of anemia (yes/no) was determined aftertaking into account the child’s age and the altitude of thecommunity of residence.27

The child’s exposure to developmentally fostering ex-periences was assessed by attendance at the daycare center(yes/no) and the type and frequency of stimulating activitiesat home. For the latter, a set of 6 questions was adapted froma UNICEF multicountry survey.28 The 6 activities betweenmother and child included reading, counting and drawing,looking at pictures (from any type of media), singing songs,going out of the house together, and playing.

Maternal and sociodemographic characteristics includedmaternal age and education level, father’s education level, moth-er’s ethnicity and predominant language preference (Quichua/Spanish, Spanish only), marital status, monthly household in-come in US dollars ($0–150, $151–250, or �$250), and hous-

ing construction. Maternal age was examined as a continu-ous variable and as a dichotomous variable based on themedian (�25 years old, �25 years old). Maternal educa-tion, categorized as none/partial primary, completed pri-mary school, or partial/completed high school, was used toassess education level and as a proxy for literacy. Mother’seducation and her ability to read were correlated (r � 0.52)as were mother’s education and her ability to write (r �0.54). Father’s education level was categorized similarly.A housing scale (ranging from 0 to 7), was constructedfrom the following housing characteristics: roof composi-tion; floor composition; wall composition; type of waterused in home; bathroom type; and access to electricity.This housing scale was categorized as poorer (�3),midlevel (4 –5), and better (6 –7).

Statistical AnalysisWe examined distributions of the pesticide-related

community and household factors, child play activity, child’shealth and nutrition, maternal characteristics, and the socio-demographic characteristics of the child’s family. Cross-tabulation of all risk factors were examined to assess col-linearity, and �2 tests were conducted to assess differencesacross risk factor categories.

Developmental delay was analyzed separately for eachdevelopmental domain screened by the ASQ, and for theVMI test score. We assessed differences in mean develop-mental scores among the various pesticide-related risk factorsusing ANOVA and t tests. We constructed regression modelsfor each developmental domain to assess the effects ofhousehold environmental exposures to pesticides on devel-opment after controlling for key confounders. Prior analyseshave examined the sociodemographic and health characteris-tics associated with neurobehavioral development in thispopulation.29,30 In this paper, we consider those variablesassociated with development (as measured by the ASQ) inour population and associated with our exposure variables aspotential confounders. Due to the limited sample size, onlythose variables found to be associated with each ASQ domainand the exposure variable of interest were included in theregression model for a given domain.

We report potential associations, with confidence inter-vals and the percent change in developmental scores betweenexposure groups. Effect size was calculated to compare themagnitude of effect of the main exposure variables on thedevelopmental scores across exposure groups.31 The measureof effect size, Cohen’s d, is calculated by taking the differ-ence in the mean score of each exposure group divided by theSD; this parameter is independent of sample size. Effect sizeis cautiously interpreted as small for d � 0.2, medium for d �0.5, and large for d � 0.8. Data were entered into SPSS 11.5(SPSS, Chicago, IL) and were analyzed in SPSS and SASVersion 8 (SAS Institute, Cary, NC). Nutritional data wereanalyzed in EpidInfo’s NutStat program software (CDC,Division of Public Health Surveillance and Informatics,2003).

Handal et al Epidemiology • Volume 18, Number 3, May 2007


RESULTSA total of 142 children aged 24 to 61 months were

included in this analysis, with 57 children aged 48 to 61months included in the VMI testing group. Table 1 displaysthe maternal and health and sociodemographic characteristicsof the study population. Three-quarters of the mothers in thispopulation identified as indigenous, and 89% reported theirpredominant language as Spanish. Approximately 80% of themothers reported being married or living with their partner ina free union. About half (56%) of the mothers had completedprimary schooling and 21% of the fathers achieved a partialor complete high school education. Almost half (45%) of themothers reported a monthly household income of $150 or lessand about half reported a midlevel housing quality.

There was a high prevalence of both anemia (51%) andstunting (60%) among the children. About a quarter (22%) ofthe children had suffered from at least one infection in thepast 3 months. Nearly two-thirds (63%) participated in 3 ormore developmentally stimulating activities with their motherat home. A lower percentage (24%) of children were engagedin such activities outside the home, specifically at a daycarecenter.

Table 2 displays the risk factors for pesticide exposureincluded in the analyses. Approximately half of the mothersreported working in the flower industry in the previous 6years (53%), working an average of 45 months. Thirty-fivepercent of the mothers reported currently being employed inthe flower industry, and 39% reported having worked in theflower industry during their pregnancy. More than half of thechildren resided in a household where pesticides were usedon domestic crops (78%) and inside the home (57%). Overhalf of the children spent 5 or more hours playing outdoors(mean � 5.6 hours), and about half of the children occasion-ally or frequently played with irrigation water (49%), with nosubstantial difference between boys and girls.

Table 3 presents the unadjusted mean developmentaloutcome score (with SD) and the percent change for eachneurobehavioral domain by risk factors for pesticide expo-sure. Given the correlations among several of the exposurevariables, we focused on those variables that describe distinctexposure domains (eg, maternal employment, pesticide usewithin and around the home, and child play activities).

Tables 4 and 5 display the results of adjusted regressionmodels for the ASQ developmental outcomes and the VMIscores, respectively. Results of the unadjusted and adjustedanalyses were similar. Mother’s current employment in theflower industry was associated with better scores among theirchildren for all 5 ASQ domains. Specifically, current employ-ment in flowers was more strongly associated with bettercommunication and problem-solving skills. Employment inthe flower industry during pregnancy, however, showed apositive association only with the problem-solving skillsdomain. Pesticide use on domestic crops was also associatedwith better gross motor and personal-social scores, whileconversely, pesticide use within the home was associatedwith lower communication scores (effect size: d � 0.2).

More hours spent outdoors (�5 hours/d) was associatedwith lower gross and fine motor and problem solving skills.

TABLE 1. Maternal and Child Characteristics of the StudyPopulation, Children 24–61 Months of Age (n � 142),Cayambe-Tabacundo Region, Ecuador (2003)

No. (%)

Mother’s characteristicsMother’s age; yrs

�25 64 (45)25� 78 (55)

Ethnicity of mother*Indigenous 108 (77)Mestizo/white 32 (23)

Language most usedSpanish/Quichua mix 15 (11)Spanish 127 (89)

Marital statusMarried 88 (62)Free union 29 (20)Single/separated/widowed 25 (18)

Mother’s education levelNone or partial elementary 40 (28)Completed elementary school 80 (56)Partial or completed high school 22 (16)

Socioeconomic characteristicsMonthly household income (U.S. dollars)†

$0–150 63 (45)$151–250 41 (29)�$250 37 (26)

Father’s education levelNone or partial elementary 23 (16)Completed elementary school 64 (45)Partial or completed high school 30 (21)No father 25 (18)

Housing constructionPoorer 25 (18)Midlevel 73 (51)Better 44 (31)

Child health characteristicsSex of child

Male 71 (50)Female 71 (50)

Anemia‡

No 69 (49)Yes 73 (51)

StuntingNo 57 (40)Yes 85 (60)

Infection health score†§

0 110 (78)�1 31 (22)

Maternal-child stimulation at home3� 89 (63)�3 activities 52 (37)

Daycare attendanceNo 108 (76)Yes 34 (24)

*Information missing for 2 mothers.†Information missing for 1 mother/child.‡Cut points for anemia were specific for age and community altitude, and ranged

from 12.3 g/dL to 13.2 g/dL.§In last 3 months, cold, sore throat, lung infection, cough, ear infection, or other

infection.



Occasional or frequent playing in irrigation water was asso-ciated with poorer fine motor and problem solving scores. Onaverage, a child who played with irrigation water scoredapproximately 5 points lower on fine motor skills (8.2%decrease; 95% CI� �9.3 to �0.5); effect size, d � 0.4), 4.4points lower on problem solving skills (7.3% decrease; �8.4to �0.4;; effect size, d � 0.3), and 5.5 points less on the VMItest (3.4% decrease; �12.0 to 1.1; effect size, d � 0.2)compared with children who rarely or never played withirrigation water. Current employment in the flower industry,longer hours spent outdoors playing, and contact with irriga-tion water were all associated with lower VMI scores; how-ever, estimates are imprecise, as the VMI was administeredonly to a subset of the study, thus reducing the sample size.

DISCUSSIONThese results suggest that exposures related to the

cut-flower industry may be associated with child develop-ment and health, although these associations are complex.Some factors associated with the presence of the industrymay be harmful, such as exposure to pesticides, whereasother factors such as increased resources and opportunitiesfor creating a stimulating environment may be beneficial.

The environmental impacts of agricultural industry arewell-established, particularly on the contamination of irriga-tion and drainage canals from pesticide residues.32–34 Incommunities where cut-flower farms are located, irrigationcanals are located close to the flower plantations and passthroughout the community, where the population then usesthis water for domestic purposes. It is not uncommon forthese canals to receive waste waters from the farms. Envi-ronmental sampling done in this region showed increasingcontamination in the water systems and river basins sedimentaccording to proximity to the contaminating sources. Systemsclosest to the flower farms showed the highest level ofcontamination with pesticide, metal, and biologic residues.35

Few studies have examined child exposure during playto potentially-contaminated surface water such as irrigationwater. Most studies focus on the effects of exposure viaingesting contaminated drinking water. The results of thepresent study provide evidence that children’s exposure tothis water was associated with lower developmental screen-ing test scores. Future research should incorporate continuedenvironmental sampling of irrigation waters and sediment inthe region to determine the type and the level of contamina-tion in these surfaces.

Pesticide use on domestic crops and their proximity tochildren’s outdoor play area were either not associated withdevelopmental delay or were associated with better develop-mental outcomes. We considered several explanations forthese seemingly anomalous findings. The risk of childrenliving and playing in close proximity to the flower farmsmight be offset by the benefits of having a mother employedin the flower industry, which corresponds to a higher monthlyhousehold income, higher parental educations, and betteraccess to health care and daycare. As we were not able to takedirect measures of pesticide exposure in the physical envi-ronment of the child, we had to rely on indirect or proxymeasures, and they may not correlate well with actual expo-sure. Alternatively, there may be unmeasured confounders.For example, domestic pesticide use may correlate witheconomic security; pesticides are an added expense for thefamily and only those who can afford the chemicals may beusing them at home.

Current maternal employment in the flower industryand maternal employment in the flower industry during preg-nancy were associated with better communication and prob-lem-solving skills. Previous studies have found that take-home exposures via contaminated parental work clothing andequipment contribute to household pesticide residue contam-ination.17,18,36 In this population, however, it is uncommonfor flower farm workers to take home their work clothes and

TABLE 2. Potential Risk Factors for Pesticide Exposure in theStudy Population, Children 24–61 Months of Age (n � 142)

Exposure variablesMother worked in the flower industry in past 6 yrs; no. (%)

No 67 (47)

Yes 75 (53)

Total no. mos worked in flowers in past 6 yr (n � 75); mean 44.7

Mother worked in flower industry during pregnancy; no. (%)

No 87 (61)

Yes 55 (39)

Mother’s current work status; no. (%)

Currently is not working or has not worked in past 6 yrs 83 (59)

Currently works, but not in flower industry 10 (7)

Currently works in flower industry 49 (35)

Father’s current work status; no. (%)

Currently is not working or has not worked in past 6 yrs 26 (19)

Currently works, but not in flower industry 44 (31)

Currently works in flower industry 46 (33)

No father 25 (18)

Use of pesticides on domestic crops; no. (%)

No 32 (23)

Yes 110 (78)

Use of pesticides within the home; no. (%)

No, rarely 61 (43)

Sometimes/frequently 81 (57)

Use of plastics/wood at home; no. (%)

Neither 45 (32)

Either plastics or wood 97 (68)

Child play activitiesDistance between place where child passes day to flower farm

(meters); no. (%)

�200 97 (68)

�200 45 (32)

Number of hours child plays outdoors daily; no. (%)

�5 50 (35)

5� 92 (65)

Mean 5.61

Child plays with irrigation water; no. (%)

No/rarely 73 (51)

Occasionally/frequently 69 (49)



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TABLE 5. Adjusted* Regression Models for VMI Developmental Outcome for Distinct Risk Factorsfor Pesticide Exposure in the Household and Community Environment for Children Age 24–61Months (n � 57)

Household/Community Exposure VariableDevelopmental

Outcome � (SE)† % Change (95% CI)

Mother currently works in flower industry VMI �3.53 (3.94) �2.2 (�11.4 to 4.4)

Mother worked in flower industry during pregnancy VMI 0.19 (3.74) �0.1 (�7.3 to 7.7)

Pesticides on domestic crops VMI �0.39 (3.82) �0.2 (�8.0 to 7.3)

Pesticides in home VMI �2.41 (3.29) �1.5 (�9.0 to 4.2)

Child plays outdoors �5 h/d VMI �3.90 (3.43) �2.4 (�10.8 to 3.0)

Child plays with irrigation water VMI �5.46 (3.26) �3.4 (�12.0 to 1.1)

*Adjusted for daycare attendance and household monthly income.†Mean difference in score.

TABLE 4. Adjusted Regression Models for 5 ASQ Developmental Domains for Distinct Risk Factorsfor Pesticide Exposure in the Household and Community Environment for Children Age 24–61Months (n � 142)

Household/Community Exposure Variable ASQ Domain* � (SE)† % Change (95% CI)

Mother currently works in flower industry Communication 4.13 (2.23) 6.9 (�0.3 to 8.5)

Gross motor 2.38 (2.04) 4.0 (�1.7 to 6.4)

Fine motor 2.12 (2.45) 3.5 (�2.7 to 7.0)

Problem solving 5.03 (2.20) 8.4 (0.7 to 9.4)

Personal–social 1.87 (2.31) 3.1 (�2.7 to 6.5)

Mother worked in flower industry duringpregnancy

Communication �1.71 (2.19) �2.9 (�6.1 to 2.6)

Gross motor �0.86 (1.99) �1.4 (�4.8 to 3.1)

Fine motor 0.59 (2.35) 1.0 (�4.1 to 5.2)

Problem solving 3.57 (2.11) 6.0 (�0.6 to 7.8)

Personal-social 0.15 (2.21) 0.3 (�4.2 to 4.5)

Pesticides on domestic crops Communication �1.03 (2.56) �1.7 (�6.1 to 4.0)

Gross motor 4.86 (2.18) 8.1 (0.6 to 9.2)

Fine motor 0.57 (2.71) 1.0 (�4.8 to 5.9)

Problem solving 0.04 (2.47) 0.1 (�4.8 to 4.9)


Pesticides in home Communication �4.51 (2.15) �7.5 (�8.8 to �0.3)

Gross motor 2.10 (1.87) 3.5 (�1.6 to 5.8)

Fine motor 1.50 (2.28) 2.5 (�3.0 to 6.1)

Problem solving �0.52 (2.09) �1.0 (�4.7 to 3.6)


Child plays outdoors �5 h/d Communication �0.06 (2.24) �0.1 (�4.5 to 4.4)

Gross motor �2.52 (1.94) �4.2 (�6.3 to 1.3)

Fine motor �2.12 (2.41) �3.5 (�6.9 to 2.6)

Problem solving �3.28 (2.16) �5.5 (�7.5 to 1.0)


Child plays with irrigation water Communication �2.23 (2.15) �3.7 (�6.5 to 2.0)

Gross motor 0.72 (1.85) 1.2 (�2.9 to 4.4)

Fine motor �4.92 (2.22) �8.2 (�9.3 to �0.5)

Problem solving �4.39 (2.03) �7.3 (�8.4 to �0.4)

Personal–social �2.09 (2.08) �3.5 (�6.2 to 2.0)

*Communication domain is adjusted for presence of anemia, presence of infection in past 3 months. Gross motor domain is adjusted for ageof child, presence of infection in past 3 months, stimulation at home, and housing construction. Fine motor domain is adjusted for daycareattendance. Problem solving domain is adjusted for age of child, mother’s education. Personal-social domain is adjusted for presence of infectionin past 3 months, stimulation at home.

†Mean difference in score.



equipment. Therefore, assessing take-home pathways for ex-posure via parental work clothing may be less relevant forthis population, whereas other aspects of maternal employ-ment in the flower industry (ie, increased monthly income,more education, better access to daycare and health services)may benefit the child’s health and development.

This preliminary study has several limitations. Werelied on indirect exposure measurement through the use of aquestionnaire, which may lead to exposure misclassification.Additionally, domestic pesticide use is common in this re-gion. We were not able to obtain information on the type orquantity of pesticides used, either in the work environment orat home, therefore leading to another potential source ofmeasurement error. Future investigations should incorporatethe use of biomarkers and environmental sampling to supple-ment information gathered by questionnaire.

There are several limitations of a general screening toolsuch as the ASQ22 in this population. The optimal test forassessing developmental delay is one that directly evaluatesthe child and assesses specific developmental functions anddomains. These domain-specific tests are particularly usefulwhen there are sufficient data to generate hypotheses aboutwhich domains may be affected. However, global tests ofdevelopment can be expensive, difficult to administer in afield setting, and time-consuming. In a developing countrysuch as Ecuador, where assessment of neurobehavioral de-velopment must be conducted in a field setting with minimalcost, a screening test was the most appropriate option in apreliminary investigation. Another limitation of using theASQ is that, although standardized and validated in a largeand multicultural population in the United States, the instru-ment may not be culturally appropriate for rural Andeanpopulations. Validation of these developmental tools in thisand similar cultures is needed.

Our results highlight the importance of social relation-ships and opportunities that may interact with adverse expo-sures to affect neurobehavioral development. Neurobehav-ioral development is a complex and dynamic process that isaffected by numerous factors, including parent-child interac-tion, household relationships, maternal health, physical envi-ronment, the child’s physical and mental health, and socialorganization. Associated social opportunities that increaseaccess to daycare or health care for the child may counteractthe effects of pesticide exposure. In researching effects ofpesticide exposure on child neurobehavioral development,recognition of other environmental factors within a largerecosystem is critical.

Our analysis highlights the complexity of consideringexposure risks that come with new economic endeavors. Onewould expect to see gains in health of a population that hasexperienced the economic growth that the cut-flower industryhas brought to as has this region of Ecuador. The economicbenefits of the flower industry may improve child health insome respects, whereas pesticide exposure and environmentalcontamination may cause harm in others. Both aspects mustbe considered when assessing the impact of specific industrialexposures on child development and health.

ACKNOWLEDGMENTSWe thank the researchers at CEAS, the staff at Casa

Campesina and local community leaders, and the participat-ing mothers and children. We also thank Sandra Jacobson,Rosa Angulo-Barroso, and Tal Shafir for their expertise inthe development of this study.

REFERENCES1. Eriksson P, Ahlbom J, Fredriksson A. Exposure to DDT during a defined

period in neonatal life induces permanent changes in brain muscarinicreceptors and behaviour in adult mice. Brain Res. 1992;582:277–281.

2. Eriksson P, Fredriksson A. Neurotoxic effects of two different pyre-throids, bioallethrin and deltamethrin, on immature and adult mice:changes in behavioral and muscarinic receptor variables. Toxicol ApplPharmacol. 1991;108:78–85.

3. Chanda SM, Pope CN. Neurochemical and neurobehavioral effects ofrepeated gestational exposure to chlorpyrifos in maternal and developingrats. Pharmacol Biochem Behav. 1996;53:771–776.

4. Miller DB. Neurotoxicity of the pesticidal carbamates. NeurobehavToxicol Teratol. 1982;4:779–787.

5. Guillette EA, Meza MM, Aquilar MG, et al. An anthropological ap-proach to the evaluation of preschool children exposed to pesticides inMexico. Environ Health Perspect. 1998;106:347–353.

6. Young JG, Eskenazi B, Gladstone EA, et al. Association between inutero organophosphate pesticide exposure and abnormal reflexes inneonates. Neurotoxicology. 2005;26:199–209.

7. Ruckart PZ, Kakolewski K, Bove FJ, et al. Long-term neurobehavioralhealth effects of methyl parathion exposure in children in Mississippiand Ohio�see comment�. Environ Health Perspect. 2004;112:46–51.

8. Rohlman DS, Arcury TA, Quandt SA, et al. Neurobehavioral performancein preschool children from agricultural and non-agricultural communities inOregon and North Carolina. Neurotoxicology. 2005;26:589.

9. Faustman EM, Silbernagel SM, Fenske RA, et al. Mechanisms under-lying children’s susceptibility to environmental toxicants. EnvironHealth Perspect. 2000;108(Suppl 1):13–21.

10. Whyatt R. Intolerable risk: the physiological susceptibility of children topesticides. J Pest Reform. 1989;9:5–9.

11. Scheuplein R, Charnley G, Dourson M. Differential sensitivity of chil-dren and adults to chemical toxicity. I. Biological basis. Regul ToxicolPharmacol. 2002;35:429–447.

12. Wargo J. Our Children’s Toxic Legacy. New Haven, CT: Yale Press; 1996.13. National Research Council. Pesticides in the Diets of Infants and

Children. Washington, DC: National Academy Press; 1993.14. Eskenazi B, Bradman A, Castorina R. Exposures of children to organo-

phosphate pesticides and their potential adverse health effects. EnvironHealth Perspect. 1999;107(Suppl 3):409–419.

15. Hubal EAC, Sheldon LS, Burke JM, et al. Children’s exposure assess-ment: a review of factors influencing children’s exposure, and the dataavailable to characterize and assess that exposure. Environ HealthPerspect. 2000;108:475–486.

16. Aprea C, Strambi M, Novelli MT, et al. Biologic monitoring of exposureto organophosphorus pesticides in 195 Italian children. Environ HealthPerspect. 2000;108:521–525.

17. Lu C, Fenske RA, Simcox NJ, et al. Pesticide exposure of children in anagricultural community: evidence of household proximity to farmlandand take home exposure pathways. Environ Res. 2000;84:290–302.

18. Simcox NJ, Fenske RA, Wolz SA, et al. Pesticides in household dust andsoil: exposure pathways for children of agricultural families. EnvironHealth Perspect. 1995;103:1126–1134.

19. Loewenherz C, Fenske RA, Simcox NJ, et al. Biological monitoring oforganophosphorus pesticide exposure among children of agriculturalworkers in central Washington state. Environ Health Perspect. 1997;105:1344–1353.

20. Fenske RA, Lu C, Barr D, et al. Children’s exposure to chlorpyrifos andparathion in an agricultural community in central Washington state�seecomment�. Environ Health Perspect. 2002;110:549–553.

21. Glascoe FP. Evidence-based approach to developmental and behaviouralsurveillance using parents’ concerns. Child Care Health Dev. 2000;26:137–149.



22. Squire J, Potter L, Bricker D. The ASQ User’s Guide. 2nd ed. Baltimore,MD: Brooks Publishing Co.; 1999.

23. Cole DC, Carpio F, Julian J, et al. Neurobehavioral outcomes amongfarm and nonfarm rural Ecuadorians. Neurotoxicol Teratol. 1997;19:277–286.

24. Farahat TM, Abdelrasoul GM, Amr MM, et al. Neurobehavioural effectsamong workers occupationally exposed to organophosphorous pesti-cides. Occup Environ Med. 2003;60:279–286.

25. Beery KE. The Beery-Buktenica Developmental Test of Visual-Motor Inte-gration: VMI. 4th ed. Revised ed. Cleveland: Modern Curriculum Press.

26. Dibley MJ, Goldsby JB, Staehling NW, et al. Development of normal-ized curves for the international growth reference: historical and tech-nical considerations. Am J Clin Nutr. 1987;46:736–748.

27. CDC. CDC criteria for anemia in children and childbearing-agedwomen. MMWR Morb Mortal Wkly Rep. 1989;38:400–404.

28. UNICEF. UNICEF Indicators Projects: Family Psychosocial Care Prac-tices Measures. 2003; Version 16.

29. Handal AJ, Lozoff B, Breilh J, et al. Socio-demographic and nutritioncorrelates of neurobehavioral development in Ecuadorian children. PanAm J Public Health. In press.

30. Handal AJ, Lozoff B, Breilh J, et al. Effect of community of residence

on neurobehavioral development in infants and young children in aflower growing region of Ecuador. Environ Health Perspect. 2007;115:128–133.

31. Cohen, J. Statistical Power Analysis For The Behavioral Sciences. 2nded. Hillsdale, NJ: Lawrence Earlbaum Associates; 1988.

32. Lopez-Rios O, Lechuga-Anaya M. �Pollutants in water bodies in thesouth of Sonora.� Salud Publica Mex. 2001;43:298–305.

33. Ritter L, Solomon K, Sibley P, et al. Sources, pathways, and relativerisks of contaminants in surface water and groundwater: a perspectiveprepared for the Walkerton inquiry. J Toxicol Environ Health A. 2002;65:1–142.

34. Hunt JW, Anderson BS, Phillips BM, et al. Ambient toxicity due tochlorpyrifos and diazinon in a central California coastal watershed.Environ Monit Assess. 2003;82:83–112.

35. Breilh J, Campana A, Hidalgo F, et al. Floriculture and the health divide:a struggle for fair and ecological flowers. In: CEAS, ed. Latin AmericanHealth Watch: Alternative Latin American Health Report. Quito: GlobalHealth Watch; 2005;66–79.

36. Thompson B, Coronado GD, Grossman JE, et al. Pesticide take-homepathway among children of agricultural workers: study design, methods,and baseline findings. J Occup Environ Med. 2003;45:42–53.



ORIGINAL ARTICLE

Combining Internal and External Validation Data to Correctfor Exposure Misclassification

A Case Study

Robert H. Lyles,* Fan Zhang,* and Carolyn Drews-Botsch†

Abstract: Internal validation data offer a well-recognized means tohelp correct for exposure misclassification or measurement error.When available, external validation data offer the advantage ofcost-effectiveness. However, external data are a generally inefficientsource of information about misclassification parameters. Further-more, external data are not necessarily “transportable”, for example,if there are differences in the design or target populations of themain and validation studies. Recent work has suggested weightedestimators to simultaneously take advantage of internal and externalvalidation data. We explore efficiency and transportability in thefundamental case of estimating the odds ratio for binary exposure ina case-control setting. Our results support the use of closed-formweighted log odds ratio estimators in place of computationallydemanding maximum likelihood estimators under both types ofvalidation study designs (using internal data only, and combininginternal and external data). We also provide and assess a formal testof the transportability assumption, and introduce a new log oddsratio estimator that is inherently robust to violation of that assump-tion. A case-control study of the association between maternalantibiotic use and sudden infant death syndrome provides a real-dataexample.

(Epidemiology 2007;18: 321–328)

The problem of misclassified binary exposure in a case-control setting is familiar to many epidemiologists, and its

primary analytical aspects are discussed in well-referencedarticles and texts.1–4 Assuming there is a “gold standard”measure of exposure that is typically costly or labor-inten-sive, researchers generally recommend the use of internal orexternal validation data to help correct for misclassificationbias.5–7 Recent research addressed the efficiency of alterna-tive estimators of the true odds ratio, balanced against the

feasibility of computing them and the cost of collecting thedata.7,8 Weighted averages of closed-form log odds ratioestimators have been proposed for situations in which mis-classification is nondifferential and internal validation dataare collected, with recent adaptations also developed for thecase of “hybrid” designs that involve the incorporation ofboth internal and external validation data.5,9,10

Prior work on the combined internal/external validationstudy designs has focused on a continuous mismeasuredexposure, with a binary or continuous outcome.9,10 Our focusis the simple but more classic case of binary exposure and abinary outcome, with one of our goals being to examine theefficiency of closed-form weighted-average log odds ratioestimators relative to less accessible, computationally-inten-sive maximum likelihood estimators (MLEs). A further issuethat we consider is the potential bias in corrected odds ratioestimates due to a lack of “transportability” of externalvalidation data,11 ie, when the external data are obtained fromstudy populations or under conditions that are different inimportant respects from the main study. Given a combinedinternal/external validation study design, we assess a formalstatistical test for transportability, and advocate its use inobtaining odds ratio estimates that are robust to this potentialthreat to validity. We conclude that highly efficient closed-form estimators are available, and that the transportabilityissue merits attention when using combination validationstudy designs.

STUDY DESIGN AND DATA LAYOUTTable 1 displays our notation for the cell counts ob-

tained in the main study/internal validation design consideredhere. We let D (�0,1) denote case status and X (�0,1) denotesurrogate exposure status, while E (�0,1) represents trueexposure status as measured by a gold standard. Note fromTable 1 that internal validation data permit estimation ofthe sensitivity SE � Pr(X�1|E�1) and specificity SP �Pr(X�0|E�0) of X as a surrogate for E, separately for casesand controls. This allows correction for potentially differen-tial exposure misclassification,12 as well as a likelihood ratiotest for whether misclassification is nondifferential.7,8 Werecommend such a test as a precursor to applying the methodspresented here. However, in what follows we assume that SEand SP are the same for cases and controls (nondifferentialmisclassification), due to our focus on combined internal/external validation designs. This stems from the fact that the

Submitted 17 August 2006; accepted 15 January 2007.From the Departments of *Biostatistics and †Epidemiology, The Rollins

School of Public Health of Emory University, Atlanta, Georgia.Robert Lyles was supported in part by an R01 from the National Institute of

Environmental Health Sciences (ES012458).Correspondence: Robert H. Lyles, Department of Biostatistics, The Rollins

School of Public Health of Emory University, 1518 Clifton Rd. N.E.,Atlanta, GA 30322. E-mail: [email protected].



typical external validation study provides data (Table 2) thatare not differentiated with respect to case status. We devoteattention to whether SE and SP are the same in the externalas in the internal validation study (ie, are “transportable”11),assuming nondifferential misclassification.

Define �d � Pr(E�1|D�d) and �d* � Pr(X�1|D�d)

(d�0,1). Interest lies in the true odds ratio, e� � �1(1��0)/{�0(1��1)}. Direct use of the surrogate X in place of E (eg,by confining analysis to the main study data in Table 1) isknown to produce directly quantifiable attenuation, as it leads toestimation of e�* � �1

*(1��0*)/{�0

* (1��1*)} instead of the true

odds ratio.3,13

LOG ODDS RATIO ESTIMATORS

Using Main/Internal Validation Study Data OnlyBased solely on the main study data together with the

(D, X) pairs from the internal validation study (Table 1), the“naive” estimator of the logs odds ratio is

�OBS � ln{n1*1n0

*0/�n1

*0n0

*1�}, (1)

where n11* � n11• � n111 � n110, n00

* � n00• � n001 � n000, n10* �

n10• � n101 � n100, and n0*1 � n01• � n011 � n010, with estimated

variance Var(�OBS) � n11*�1 � n10

*�1 � n01*�1 � n00

*�1.14 Clearly,�OBS is biased in general and estimates �* instead of �. Onthe other hand, a valid but inefficient estimator under non-differential misclassification that takes the same standardform can be derived based solely on the (D,E) pairs in theinternal validation study (Table 1):

�I � ln{n1*1n0

*0/(n1

*0n0

*1)}, (2)

where this time n1*1 � n111 � n101, n0

*0 � n010 � n000, n1

*0 � n110 �

n100, n0*1 � n011 � n001, and again Var(�I) � n1

*1�1 � n1

*0�1 � n0

*1�1 �

n0*0�1. �I forms a component of weighted estimators described be-

low, as suggested in prior references.5,9,10

We consider 2 more efficient analogues to �I that makeuse of all of the data in Table 1. The first is the main/internalvalidation study MLE, denoted as �I, ML. While the MLE isavailable in closed form under differential misclassification,8 wemust resort to numerical optimization to obtain it in the nondif-ferential case considered here. Details regarding the likelihoodfunction for obtaining �I,ML are found in prior literature.7,8 As analternative to �I,ML , we consider a weighted-average estimatorproposed by Greenland and defined as follows5:

�G � wG�I � (1 � wG)�E, (3)

where wG �Var(�I)

�1

Var(�I)�1 � Var(�E)�1

and �E is the log odds

ratio estimator obtained using the main study/internal valida-tion data (Table 1), with the internal validation data treated asif they were external. Appendix A.1 details the calculation of�E. The inverse-variance weight wG aims for a minimum-variance weighted average of �I and �E given their indepen-dence asymptotically, a feature that yields a convenientestimator for the variance of �G as follows5:

Var(�G) �1

Var(�I)�1 � Var(�E)�1

(4)

This estimator treats the weight (wG) as if it were a constant,which is generally accepted.5,9,10 In particular, this conditional(on the weight) variance estimator is valid assuming approxi-mate independence between wG and each of the 2 log odds ratioestimators, �I and �E. The asymptotic normality of �I and �E

makes this assumption defensible. Delta method-based calcula-tions to obtain Var(�E) are outlined in Appendix A.1.

�G combines 2 consistent estimators (�I and �E) into asingle estimator that is potentially much more efficient thaneither one separately. Its availability in closed form is a distinctadvantage over the MLE (�I,ML). One objective of our simulationstudies (to follow) is to evaluate the efficiency of �G relative to�I,ML for the main/internal validation study design.

Combining Main, Internal, and ExternalValidation Study Data

Let SE and SP represent the sensitivity and specificityoperating in the main/internal validation study population,whereas SEE and SPE represent the analogous parametersoperating in the external validation study population. Undertransportability (SE � SEE and SP � SPE), the properincorporation of available external validation data (Table 2)together with the main/internal validation study data (Table1) should provide improved efficiency over any estimatordiscussed in the previous sections.

One obvious but computationally intensive estimator isthe MLE (�C,ML), obtained by maximizing the overall joint

TABLE 1. Main and Internal Validation Study Data for aCase–Control Study With Misclassified Exposure (Entries areCell Counts)

Main Study Internal Validation Study(ndx•; d,x�0,1) (ndxe; d,x,e�0,1)

D�1 D�0

D X�1 X�0 X E�1 E�0 E�1 E�0

1 n11• n10• 1 n111 n110 n011 n010

0 n01• n00• 0 n101 n100 n001 n000

D indicates case status; E, gold standard exposure measure; X, surrogate exposuremeasure.

TABLE 2. External Validation Study Data With NondifferentialMisclassified Exposure (mxe: cell counts, �xe: cell probabilities;x,e�0,1)

E X � 1 X � 0 X � 1 X � 0

1 m11 m01 �11 �01

0 m10 m00 �10 �00

E indicates gold standard exposure measure; X, surrogate exposure measure.

Lyles et al Epidemiology • Volume 18, Number 3, May 2007


log-likelihood of the combined data in Tables 1 and 2. Up toa constant, the log-likelihood may be written as:

l (�, �) � �d�0

1

�x�0

1

ndx•ln (�dx•) �

�d�0

1

�x�0

1

�e�0

1

ndxeln(�dxe) � �x�0

1

�e�0

1

mxeln(�xe), (5)

where the ndx• and ndxe refer to the cell counts in Table 1 andthe mxe and �xe refer to the cell counts and corresponding cellprobabilities in Table 2. The probabilities �dx• and �dxe(d,x,e�0, 1) associated with the cell counts (ndx• and ndxe) inTable 1 are as follows:

�d1• � SE�d � (1�SP)(1��d), �d0• � (1�SE)�d � SP(1��d),

�d11 � SE�d, �d10 � (1�SP)(1��d), �d01 � (1�SE)�d, �d00 � SP(1��d).

Reparameterized in terms of SE, SP, and �d, the cell proba-bilities (�xe) in Table 2 are given by

�11 � SE�E, �10 � (1�SP)(1��E), �01 � (1�SE)�E, �00 � SP(1��E),

(6)

where �E is a nuisance parameter representing the prevalenceof true exposure �Pr(E�1)� in the external validation studypopulation. The joint log-likelihood in (5) may be maximizednumerically to obtain the MLE of the true odds ratio (�) andits standard error using optimization routines available incommercial software,15 and such a program is available fromthe authors.

An appealing alternative to �C,ML is a weighted-averageestimator analogous to �G, but making use of both theexternal and internal validation data. Such an approach wasproposed by Spiegelman and colleagues in continuous co-variate measurement error settings under combination inter-nal/external validation study designs.9,10 Adapting it to oursetting produces the following:

�S � wS�I � (1 � wS)�E*, (7)

where wS�Var(�I)

�1

Var(�I)�1 � Var(�E

*)�1. This estimator is identi-

cal to �G in (3) except that �E* is a more efficient (under

transportability) “external only” estimator than �E. �E* is com-

puted in exactly the same manner as �E, but the externalvalidation data (Table 2) are first lumped together with theinternal validation data (Table 1) and used to estimate SE andSP under the assumption of nondifferential misclassification.Thus, the calculation of �E

* and Var(�E*) follow Appendix A.1

directly, after first replacing nIxe by nI*xe (x,e � 0,1),

where nI*

11 � m11 � n111 � n011, nI*

01 � m01 � n101 � n001,nI

*10 � m10 � n110 � n010, and nI

*00 � m00 � n100 � n000.

Note that Var(�S) is also identical in form to Var(�G) in(4), after replacing �E with �E

*.

Testing for “Transportability”Empirical studies can compare the performance of the

closed-form estimator �S to the numerically-derived MLE�C,ML to recommend a method assuming transportability. Inaddition, one of our key goals is to provide a formal statisticaltest of that assumption. This is because violation of thetransportability assumption subjects both �S and �C,ML tobias, even in certain large-sample situations. The test fortransportability can be framed in terms of the following nulland alternative hypotheses:

H0: SE � SEE AND SP � SPE

vs.

H1: SE SEE AND/OR SP SPE (8)

Ideally, one would make this assessment via a likelihood ratiotest after numerically maximizing (5) with and without im-posing the 2 restrictions under H0 above. The restrictedversion of l(�,�) is as reflected in (5) and (6), while theunrestricted version redefines l(�, �) as l*(�, �) by replacingSE with SEE and SP with SPE in (6). The likelihood ratio teststatistic is then the difference in the minus-2 log-likelihoodsevaluated at the restricted and unrestricted MLEs, i.e.,

�2 �l(�, �) � l (�*, �*)� , referable to a �2 distribution with2 degrees of freedom.

A disadvantage of the likelihood ratio test outlinedabove is the computational challenge of maximizing the jointlog-likelihood for the data in Tables 1 and 2. We thereforepropose an alternative likelihood ratio statistic, computablewithout numeric maximization, to address the test in (8). Thistest ignores the main study portion of the data in Table 1 anduses only the internal (Table 1) and external (Table 2)validation data. There tends to be little sacrifice in the powerof this test to detect nontransportability (details are providedin Appendix A.2). Letting tLR represent the observed value ofthis likelihood ratio (LR) test statistic, the P value follows aspLR � Pr(� 2

2 � tLR), where � 22 denotes a random variable

distributed as central �2 with 2 degrees of freedom. Ratherthan as a direct measure of statistical significance, we pri-marily use pLR as a weight to construct a new robust log oddsratio estimator in the following section.

Estimators Robust to NontransportabilityOf the estimators discussed thus far, �G in equation (3)

and �S in equation (7) are most appealing as closed-form yetpotentially efficient alternatives to their corresponding MLEs.The difference is that �G ignores the external validation data(Table 2), making it fully robust to nontransportability butless efficient than �S when transportability holds. The poten-tial invalidity of �S under nontransportability calls for arobust alternative that remains more efficient than �G.

Epidemiology • Volume 18, Number 3, May 2007 Combining Validation Data


We propose the following robust weighted-averageestimator:

�R � pLR �S � (1 � pLR) �G (9)

Again treating the weight (pLR) as fixed, the estimated vari-ance becomes

Var(�R) � pLR2 Var(�S) � (1 � pLR)2 Var(�G)

� 2pLR (1 � pLR) Cov(�S, �G) (10)

Under the null hypothesis of transportability, the argumentfor this conditional variance estimator is similar to the onegiven below equation (4), this time with the assumption beingapproximate independence between the LR test statistic andthe 2 estimators �S and �G. This assumption may be morequestionable under nontransportability, but in that case theweight pLR approaches 0 asymptotically and supports theresult in equation (10). Empirical studies in the next sectionwill allow us to assess the conditional variance estimators inequations (4) and (10) in practical situations. The covarianceterm Cov(�S, �G) is given by

Cov(�S, �G) � wSwG Var(�I) � (1 � wS)(1 � wG) Cov(�E, �E*)

Although it is available in closed form, the delta-method-based estimator Cov(�E, �E

*) is tedious. Details of that calcu-lation are available from the authors, but are not includedhere because the standard error of an alternative robustestimator is more straightforward to obtain.

To construct the alternative estimator, we simply choose�S or �G based on whether or not the LR test (Appendix A.2) isrejected at a specified significance level (eg, 0.05, 0.10, 0.20).This approach is robust to nontransportability in that, assample sizes become large, �G is chosen with probabilityapproaching 1 if the external validation data are not trans-portable. Although lowering the significance level improvesefficiency slightly under transportability, simulation results(not shown) indicate little difference in the performance ofthe estimator based on the level specified. Selecting 0.10 as areasonable compromise, we define the estimator as follows:

�LR,.10 � { �S if pLR 0.10�G if pLR � 0.10 (11)

We estimate the standard error of �LR,.10 as that of �S or �G,depending on which is selected.

SIMULATION RESULTSThis section details simulation studies to evaluate the

proposed log odds ratio estimators, with particular attention toefficiency and robustness to nontransportability. We simulateddata under a variety of combinations of the exposure probabil-ities (�1 and �0), main study SE and SP, main/internal valida-tion study sample size

�n � �d�0

1

�x�0

1

ndx• � �d�0

1

�x�0

1

�e�0

1

ndxe�,

and external validation sample size

�nE��x�0

1

�e�0

1

mxe�(Tables 1 and 2). In each case, 2500 independent data setswere generated, with a 25% rate of selection (per n subjects)into the internal validation study. Separate simulations wereperformed under nontransportability, under various combina-tions of the external validation study sensitivity and specific-ity (SEE and SPE). Each simulation assumed an equal numberof cases and controls, and an exposure prevalence (�E) of 0.5in the external population.

Under TransportabilityTable 3 summarizes the performance of log odds ratio

(OR) estimators in cases where nE�200, �1�0.6 and�0�0.3, corresponding to a true ln(OR) of 1.253 (OR � 3.5).As expected, the “naive” estimator �OBS is biased severelytoward the null and provides extremely poor confidenceinterval (CI) coverage; thus, �OBS is considered no further.Also as expected, the main/internal validation study MLE(�I,ML) was always more efficient than the estimator �I basedonly on the (D, E) pairs in the internal validation sample, sothat �I is not tabulated. Of much interest, however, is the factthat the closed-form estimator �G is extremely competitive withthe corresponding MLE (�I,ML) for the main study/internal val-idation study design, both in terms of bias and variability.Likewise with respect to the combined main/internal/externalstudy design, the closed-form estimator �S is an excellent alter-native to the MLE (�C,ML), actually outperforming it in terms ofbias and variance in all cases displayed in Table 3. As expected,the 2 robust estimators (�R and �LR,.10) are less efficient than �S

under transportability, although the loss of efficiency is minimal.Table 3 suggests that �LR, .10 enjoys a slight edge over �R whencomparing the robust estimators under transportability. The CIcoverages for �R are not shown, as they were nearly identical tothose for �LR,.10 in all instances.

Table 4 leads to very similar conclusions in the settingof a protective effect, where nE � 200, �1 � 0.2 and �0 �0.3, corresponding to a true ln(OR) of �0.539 (OR � 0.583).In Table 5, we summarize simulations under the same con-ditions as in Table 3 except that the external validationsample size (nE) is increased from 200 to 1000. Despite thelarge-sample optimality of the MLEs, note that �G remains verynearly as efficient as �I,ML, and �S generally continues to outper-form �C,ML under these conditions. The sacrifice in efficiencyassociated with the robust estimator �LR,.10 relative to �S remainsfairly minor in all scenarios considered under transportability(Tables 3–5). This corresponds to the fact that the rejection rate(type I error) of the likelihood ratio test for transportability(Appendix A.2) remained close to the nominal level in thesecases.

Under NontransportabilityIn Table 6, we summarize simulations aimed at evalu-

ating estimators in the event that transportability fails to hold,



ie, where SESEE or SPSPE. We examine several scenar-ios, assuming that SEE and SPE are both high (0.9) or low(0.6). In general, the results highlight the potential fallibility

of the 2 estimators �C,ML and �S that directly combine infor-mation from the external validation sample with that of themain/internal validation study. With SEE and SPE set at 0.9

TABLE 3. Simulation Results Under Transportability (True parameter values: �1 � 0.6, �0 � 0.3, � � 1.253)*

SE � 0.6, SP � 0.7 SE � 0.6, SP � 0.9 SE � 0.8, SP � 0.7 SE � 0.8, SP � 0.9

(n � 200) (n � 500) (n � 200) (n � 500) (n � 200) (n � 500) (n � 200) (n � 500)

�OBS 0.378 (0.290) 0.367 (0.181) 0.699 (0.318) 0.701 (0.194) 0.615 (0.295) 0.612 (0.181) 0.889 (0.294) 0.881 (0.187)

�I,ML† 1.299 (0.596) 1.279 (0.354) 1.300 (0.516) 1.276 (0.303) 1.304 (0.530) 1.277 (0.314) 1.296 (0.437) 1.266 (0.263)

�G 1.207 (0.582) 1.230 (0.356) 1.226 (0.513) 1.249 (0.303) 1.213 (0.524) 1.237 (0.315) 1.244 (0.428) 1.255 (0.261)

�C,ML† 1.299 (0.579) 1.278 (0.346) 1.287 (0.486) 1.274 (0.291) 1.294 (0.502) 1.274 (0.305) 1.280 (0.401) 1.264 (0.254)

�S 1.223 (0.569) 1.240 (0.346) 1.251 (0.476) 1.260 (0.290) 1.241 (0.492) 1.252 (0.304) 1.262 (0.398) 1.258 (0.253)

�R 1.212 (0.574) 1.234 (0.350) 1.236 (0.496) 1.253 (0.299) 1.223 (0.508) 1.242 (0.310) 1.251 (0.416) 1.256 (0.257)

�LR,.10 1.218 (0.571) 1.239 (0.347) 1.246 (0.485) 1.259 (0.295) 1.236 (0.498) 1.247 (0.306) 1.256 (0.411) 1.257 (0.255)

95% CI coverage (�OBS) 14.9% 0.2% 54.5% 19.2% 38.9% 5.7% 76.9% 48.8%

95% CI coverage (�G) 95.5% 95.1% 95.8% 95.4% 95.2% 95.2% 95.9% 94.4%

95% CI coverage (�S) 96.0% 95.5% 95.9% 95.2% 95.7% 95.8% 95.6% 94.7%

95% CI coverage (�LR,.10) 95.8% 95.3% 96.0% 95.0% 95.5% 95.5% 95.6% 94.5%

*nE � 200; Internal validation sampling rate � 25%; 2500 simulations in each case. Cells corresponding to estimators report empirical mean and standard deviation (inparentheses).

†In rare instances with n � 200 (�0.5% of runs), one or both ML estimates were unrealistically large in absolute value and were discarded.

TABLE 4. Simulation Results Under Transportability (True parameter values: �1 � 0.2, �0 � 0.3, � � �0.539)*


(n � 200) (n � 500) (n � 200) (n � 500) (n � 200) (n � 500) (n � 200) (n � 500)

�I,ML† �0.589 (0.695) �0.556 (0.396) �0.577 (0.577) �0.559 (0.328) �0.591 (0.638) �0.557 (0.350) �0.574 (0.508) �0.553 (0.284)

�G �0.545 (0.681) �0.521 (0.388) �0.503 (0.540) �0.526 (0.322) �0.513 (0.588) �0.514 (0.344) �0.516 (0.467) �0.535 (0.278)�C,ML

† �0.588 (0.684) �0.556 (0.392) �0.566 (0.542) �0.559 (0.325) �0.586 (0.613) �0.555 (0.346) �0.561 (0.470) �0.551 (0.279)�S �0.543 (0.668) �0.525 (0.383) �0.508 (0.505) �0.536 (0.317) �0.513 (0.573) �0.520 (0.337) �0.527 (0.439) �0.539 (0.273)�R �0.546 (0.673) �0.522 (0.385) �0.505 (0.524) �0.530 (0.320) �0.512 (0.577) �0.516 (0.340) �0.521 (0.455) �0.536 (0.276)�LR,.10 �0.544 (0.667) �0.524 (0.383) �0.509 (0.514) �0.535 (0.318) �0.511 (0.580) �0.519 (0.337) �0.523 (0.447) �0.538 (0.277)95% CI coverage (�G) 96.6% 96.2% 97.2% 96.7% 97.2% 96.3% 97.1% 96.7%95% CI coverage (�S) 96.7% 96.2% 97.5% 96.3% 97.4% 96.7% 96.9% 96.3%95% CI coverage (�LR,.10) 96.8% 96.1% 97.4% 96.3% 97.4% 96.6% 96.8% 96.2%

*nE � 200; Internal validation proportion sampling rate � 25%; 2500 simulations in each case. Cells corresponding to estimators report empirical mean and standard deviation(in parentheses).


TABLE 5. Simulation Results Under Transportability (True parameter values: �1 � 0.6, �0 � 0.3, � � 1.253)*


(n � 200) (n � 500) (n � 200) (n � 500) (n � 200) (n � 500) (n � 200) (n � 500)

�I,ML† 1.318 (0.594) 1.277 (0.364) 1.305 (0.518) 1.273 (0.293) 1.311 (0.527) 1.273 (0.314) 1.296 (0.425) 1.265 (0.256)

�G 1.237 (0.597) 1.227 (0.366) 1.220 (0.520) 1.246 (0.296) 1.222 (0.523) 1.240 (0.315) 1.251 (0.429) 1.256 (0.260)

�C,ML† 1.308 (0.561) 1.272 (0.355) 1.290 (0.469) 1.266 (0.280) 1.298 (0.487) 1.272 (0.304) 1.281 (0.393) 1.262 (0.243)

�S 1.248 (0.562) 1.245 (0.355) 1.257 (0.466) 1.257 (0.279) 1.254 (0.480) 1.260 (0.302) 1.269 (0.391) 1.259 (0.243)

�LR,.10 1.243 (0.569) 1.243 (0.537) 1.250 (0.483) 1.255 (0.283) 1.249 (0.491) 1.256 (0.305) 1.264 (0.406) 1.258 (0.246)

95% CI coverage (�G) 95.5% 94.9% 95.2% 96.2% 95.6% 95.1% 95.6% 96.3%

95% CI coverage (�S) 96.2% 94.8% 95.8% 95.8% 95.9% 95.0% 96.0% 95.6%

95% CI coverage (�LR,.10) 96.1% 94.7% 95.6% 95.8% 95.7% 94.8% 95.4% 95.7%

*nE � 1000; Internal validation sampling rate � 25%; 2500 simulations in each case. Cells corresponding to estimators report empirical mean and standard deviation (inparentheses).




and when one or both of the corresponding parameters in themain/internal study are lower, we see negative bias in both�C,ML and �S and subnominal CI coverage associated with �S

(particularly when SE�SP�0.6). On the other hand, withSEE and SPE both low (0.6) and with either SE or SPsubstantially higher, we find both �C,ML and �S to be inflatedaway from the null. In this case the bias associated with �C,ML

is markedly worse than that of �S, whereas the latter displaysreasonable CI coverage but substantially higher variabilitythan its competitors.

The proposed robust estimators (�R and �LR,.10) performcomparably in Table 6, and are nearly as efficient as�G , the estimator based solely on the main/internal validationdata. This indicates the discriminatory power of the likeli-hood ratio test (summarized in Table 6) and the resulting lackof weight placed upon the external validation data. Althoughnot summarized here, further simulations assuming largersample sizes (n and/or nE) or more modest differences be-tween the internal and external SE and SP parameters led tosimilar conclusions regarding the possible fallibilities of�C,ML and �S under nontransportability. With larger samples,�R and �LR,.10 tend to become completely equivalent to �G, asexpected.

ExampleTo illustrate, we return to a previously published example

involving a case-control study of the association between suddeninfant death syndrome (SIDS) and maternal antibiotic use duringpregnancy,5–7,16 which provides main and internal validationdata (Table 1) as follows: n11• � 122, n10• � 442, n111 � 29,n110 � 22, n101 � 17, n100 � 143, n01• � 101, n00• � 479, n011 �21, n010 � 12, n001 � 16, n000 � 168. The gold standard (E) ex-posure assessments were based on medical records and thesurrogate (X) assessments were based on self-reports of antibi-otic use. A likelihood ratio test of the hypothesis of nondiffer-ential exposure misclassification7,8 based on the Table 1 datawas not rejected (P � 0.14). For external validation data, we use322 maternal controls from the Fetal Growth and Development

Study,17–19 in which self-reported response to the samematernal antibiotic use question in a postpartum interviewwas cross-referenced by medical records. This study useda case-control design to estimate the prevalence of fetalalcohol syndrome in an urban teaching hospital and asuburban private hospital in metropolitan Atlanta in 1993and 1994. Cases were infants with birth weights less thanthe 10th percentile for gestational age, race, and sex.Controls, whose data are used in the current example, werea simple random sample of singleton infants with birthweights greater than the 10th percentile. These data pro-vide cell counts (Table 2) as follows: m11 � 69, m10 � 37,m01 � 69, and m00 � 147.

Table 7 summarizes the results of our analysis based onthese 2 sources of data, with estimated odds ratios andapproximate 95% CIs obtained by exponentiating the logodds ratio estimates and the bounds of the correspondinglog-scale intervals. First, note that estimated SE and SP wereboth noticeably higher based on the main/internal validationdata from the SIDS study as opposed to the external valida-tion data (0.60 and 0.90 versus 0.50 and 0.80, respectively).The likelihood ratio test for transportability (Appendix A.2)was soundly rejected (P � 0.002). This is not surprisinggiven the differences in populations, timing of the interviews(ie, within 48 hours of delivery versus 2 to 3 months later),

TABLE 6. Simulation Results Under Non-Transportability (True parameter values: �1 � 0.6, �0 � 0.3, � � 1.253)*

SEE � 0.9, SPE � 0.9 SEE � 0.6, SPE � 0.6

SE � 0.6,SP � 0.6

SE � 0.6,SP � 0.9

SE � 0.9,SP � 0.6

SE � 0.9,SP � 0.9

SE � 0.6,SP � 0.9

SE � 0.9,SP � 0.6

�G 1.241 (0.610) 1.226 (0.490) 1.205 (0.521) 1.257 (0.392) 1.232 (0.506) 1.219 (0.514)

�C,ML 0.751 (0.393) 1.085 (0.386) 1.012 (0.382) 1.748 (0.590) 1.509 (0.595) 1.546 (0.592)

�S 0.722 (0.381) 1.112 (0.407) 1.030 (0.391) 1.351 (0.642) 1.317 (0.646) 1.335 (0.636)

�R 1.241 (0.610) 1.224 (0.488) 1.202 (0.519) 1.257 (0.393) 1.233 (0.508) 1.221 (0.516)

�LR,.10 1.241 (0.610) 1.222 (0.488) 1.200 (0.518) 1.257 (0.392) 1.234 (0.513) 1.225 (0.527)

95% CI coverage (�G) 95.3% 96.0% 95.5% 95.8% 96.0% 96.0%

95% CI coverage (�S) 70.8% 93.8% 91.0% 95.6% 95.4% 95.8%

95% CI coverage (�LR,.10) 95.2% 95.9% 95.3% 95.8% 95.9% 95.7%

LR test rejection rate† 99.2% 79.8% 86.5% 98.8% 86.2% 77.4%

*n � 200; nE � 200; Internal validation sampling rate � 25%; 2500 simulations in each case. Cells corresponding to estimators report empirical mean and standard deviation(in parentheses).

†Empirical power of LR test for transportability (see Appendix A.2).

TABLE 7. Results of Example: Association Between SIDSand Maternal Antibiotic Use*16

EstimatorEstimate

(Standard Error)Odds Ratio Estimate

(95% CI)

�G 0.394 (0.195) 1.48 (1.01–2.17)

�C,ML 0.417 (0.209) 1.52 (1.01–2.29)

�S 0.396 (0.224) 1.49 (0.96–2.30)

�LR,.10 0.394 (0.195) 1.48 (1.01–2.17)

*LR statistic for test of transportability: tLR � 12.59 (P � 0.002).



etc. As a result, the example is qualitatively similar to thesimulation setting summarized in the fifth column of Table6 under nontransportability. Based on Table 6, we mightexpect a tendency for �C,ML (and less so, for �S) to beinflated away from the null, and a tendency for �S to beinefficient relative to �G and �LR,.10 despite the fact that itmakes use of more data. These tendencies appear to beborne out in Table 7. In particular, the standard errors of�C,ML and �S are larger than those of �G and �LR,.10, lead-ing to wider CIs. The latter 2 estimates are identical giventhe significance of the LR test for transportability. Thisexample highlights the importance of incorporating such aformal test before combining internal and external valida-tion data to correct for misclassification, with failure to doso in this case affecting the efficiency rather than thevalidity of the resulting estimate of effect.

DISCUSSIONWe have addressed 2 primary issues involving study

designs and statistical methods to deal with exposure mis-classification. First, we have evaluated the performance ofclosed-form inverse-variance weighted estimators (�G and �S)relative to numerically challenging MLEs, in study designsusing internal validation and study designs combining inter-nal and external validation data. Secondly, we have provideda statistical test to address the assumption of transportability ofexternal validation data,11 evaluated potential problems withexisting methods, and proposed estimators that remain robustwhen external data are not transportable.

When external validation data are transportable, themain conclusion of our empirical studies is that the closed-form estimators �G and �S are very competitive with thecorresponding MLEs (�I,ML and �C,ML), often even outper-forming them in realistic situations. This is a significantadvantage in terms of avoiding the need for an iterativecomputational routine to maximize the main/internal andmain/internal/external validation study likelihoods. Wealso found iterative ML routines to be unreliable in rareinstances (as shown in the footnotes of Tables 3–5).

While �S is the preferred estimator under transport-ability for the combined validation study design, Table 6demonstrates its potential invalidity or inefficiency whenexternal validation data are nontransportable. Thus, werecommend a formal test of the transportability assumptionbefore combining internal and external validation data, andwe prefer the proposed robust estimators (�R and �LR,.10) forgeneral use. Of these two, �LR,.10 is preferred due to its morestraightforward standard error calculation. Under transport-ability, �LR,.10 remained nearly as efficient as �S and wasalways more efficient than �G (Tables 3–5). Under nontrans-portability, �LR,.10 was essentially equivalent to �G and thusmore valid or efficient than �S (Table 6). We should note,however, that �S performed much better than the combinationstudy design MLE (�C,ML) under nontransportability. Also, �S

is often far less biased under nontransportability than onewould expect an estimator based on a main/external-onlyvalidation study design to be, because �s makes use of

internal validation data. An external-only design allows noassessment of transportability; the collection of at least someinternal validation data is always advisable if there is anydoubt about that assumption.

We have explored the setting of a case-control studywith binary exposure,5–8 and provided evidence in support ofinverse-variance weighted average estimators that have beenproposed for combination internal/external validation designsin more general contexts.9,10 Our results may motivate furtherresearch into the use of such estimators in studies subject tomisclassification or measurement error, including extensionsof the LR test and estimators robust to nontransportabilityproposed here to regression settings with covariates andmultiple error-prone exposure variables. In general, the over-all best estimator is likely to be a compromise between anefficient one under transportability using all of the data, andone that is efficient among those using only the main andinternal validation data.

ACKNOWLEDGMENTSThe authors thank Andrew Allen, Michael Haber, Sally

Thurston, and John Hanfelt for valuable discussions. Theauthors also appreciate insightful suggestions provided by ananonymous reviewer.

REFERENCES1. Bross IDJ. Misclassification in 2 � 2 tables. Biometrics. 1954;10:478–

486.2. Barron BA. The effects of misclassification on the estimation of relative

risk. Biometrics. 1977;33:414–418.3. Kleinbaum D, Kupper L, Morgenstern H. Epidemiologic Research:

Principles and Quantitative Methods. Belmont, CA: Lifetime Learning;1982.

4. Greenland S, Kleinbaum DG. Correcting for misclassification in two-way tables and matched-pair studies. Int J Epidemiol. 1983;12:93–97.

5. Greenland S. Variance estimation for epidemiologic effect estimatesunder misclassification. Stat Med. 1988;7:745–757.

6. Marshall RJ. Validation study methods for estimating proportions andodds ratios with misclassified data. J Clin Epidemiol. 1990;43:941–947.

7. Morrissey MJ, Spiegelman D. Matrix methods for estimating odds ratioswith misclassified exposure data: extensions and comparisons. Biomet-rics. 1999;55:338–344.

8. Lyles RH. A note on estimating crude odds ratios in case-control studieswith differentially misclassified exposure. Biometrics. 2002;58:1034–1037.

9. Spiegelman D, Carroll RJ, Kipnis V. Efficient regression calibration forlogistic regression in main study/internal validation study designs withan imperfect reference instrument. Stat Med. 2001;20:139–160.

10. Thurston SW, Williams PL, Hauser R, et al. A comparison of regressioncalibration approaches for designs with internal validation data. J StatPlann Inference. 2003;131:175–190.

11. Carroll RJ, Ruppert D, Stefanski LA. Measurement Error in NonlinearModels. London: Chapman and Hall; 1995.

12. Thomas D, Stram D, Dwyer J. Exposure measurement error: influenceon exposure-disease relationships and methods of correction. Ann RevPublic Health. 1993;14:69–93.

13. Lyles RH, Allen AS, Flanders WD, et al. Inference for case–controlstudies when exposure status is both informatively missing and misclas-sified. Stat Med. 2006;25:4065–4080.

14. Rosner B. Fundamentals of Biostatistics, 4th ed. Belmont, CA: Wads-worth; 1995.

15. SAS Institute, Inc. SAS/IML Software: Changes and EnhancementsThrough Release 6. 11. Cary, NC: SAS Institute; 1995.

16. Drews CD, Kraus JF, Greenland S. Recall bias in a case-control study ofsudden infant death syndrome. Int J Epidemiol. 1990;19:405–411.

17. Drews CD, Coles CD, Floyd RL, et al. Prevalence of prenatal drinking



assessed at an urban public hospital and a suburban private hospital.J Matern-Fetal Neonatal Med. 2003;13:85–93.

18. Sprauve ME, Lindsay MK, Drews-Botsch CD, et al. Racial patterns inthe effects of tobacco use on fetal growth. Am J Obstet Gynecol.1999;181:S22–S27.

19. Gauthier TW, Drews-Botsch CD, Falek A, et al. Maternal alcohol abuseand neonatal infection. Alcohol Clin Exp Res. 2005;29:1035–1043.

APPENDIX

A.1. Calculation of �E and Its VarianceThe log odds ratio estimator based on the main study datacombined with the internal validation data treated as if they

were external is given by �E � ln{�1 (1 � �0)

�0 (1 � �1)}, where

�d � (�d* � SPE � 1)(SEE � SPE � 1)�1, �d

* � nd1•/(nd1• � nd0•)via the main study data (d�0,1), and SEE � nI11/(nI11 � nI01)and SPE � nI00/(nI00 � nI10) based on the validation data withnI11 � n111 � n011, nI01 � n101 � n001, nI10 � n110 � n010, andnI00 � n100 � n000 (Table 1).

The delta method-based variance is as follows:

Var{ln (�E)} � �d�0

1

�{�d(1 � �d)}�2 Var(�d)�

� 2{�1(1 � �1)}�1{�0(1 � �0)}

�1 Cov(�1, �0),

where Var (�d) � Dd�Dd (d�0,1) and Cov(�1,�0) � D1�D0 .Here, � is the 4 � 4 diagonal matrix Diag �Var ��1

*�, Var ��0*�,

Var �SEE�, Var �SPE�� with Var (�d*) � �d

* (1��d*)/(nd1• � nd0•),

Var (SEE) � SEE(1� SEE)/(nI11 � nI01), and Var (SPE) � SPE (1�SPE)/(nI00�nI10). Also, Dd � (�d1, �d2, �d3, �d4) (d�0, 1), where�11 � �02 � (SEE�SPE�1)�1, �12 � �01�0, �d3 � �(�d

*�SPE�1)

(SEE � SPE � 1)�2, and �d4 � (SEE � �d*) (SEE � SPE � 1)�2

(d�0,1).

A.2. LR Test for Transportability Ignoring MainStudy Data

Up to a constant, we calculate the maximal unrestrictedlog-likelihood value as

l (�*,�*)��x�0

1

�e�0

1

nIxe ln (�Ixe) � �x�0

1

�e�0

1

mxe ln (�xe),

where the mxe are defined in Table 2. The nIxe are cell countsfrom the internal validation study under nondifferentialmisclassification, defined as in Appendix A.1. Letting

nI � �x�0

1

�e�0

1

nIxe and nE � �x�0

1

�e�0

1

mxe,

estimated cell probabilities are �Ixe � nIxe / nI and �xe �mxe / nE (x, e�0, 1).

The maximal restricted log-likelihood value l (�, �)takes the same form as l (�*, �*) above, except with theestimated cell probabilities defined as follows:

�I11

� SE�I, �I10

� (1 � SP) (1 � �I), �I01

� (1 � SE)�I, �00 � SP (1 � �I)

and

�11 � SE�E, �10 � (1 � SP) (1 � �E), �01 � (1 � SE)�E, �00 � SP (1 � �E),

where �I�(n111 � n101 � n011 � n001)/nI, �E � (m11� m01)/nE, SE � nI

*11/(nI

*11 � nI

*01), and SP � nI

*00/(nI

*00 � nI

*10), with

nI*11 � m11 � nI11, nI

*01 � m01 � nI01, nI

*00 � m00 � nI00, and

nI*10 � m10 � nI10. The LR test statistic follows as

�2�l(�,�) � l(�*,�*)�.



ORIGINAL ARTICLE

The Identification of Synergism in theSufficient-Component-Cause Framework

Tyler J. VanderWeele,* and James M. Robins†

Abstract: Various concepts of interaction are reconsidered in lightof a sufficient-component-cause framework. Conditions and statis-tical tests are derived for the presence of synergism within sufficientcauses. The conditions derived are sufficient but not necessary forthe presence of synergism. In the context of monotonic effects,the conditions derived are closely related to effect modification onthe risk difference scale; however, this is not the case without theassumption of monotonic effects.

(Epidemiology 2007;18: 329–339)

The distinction between a biologic interaction or synergismand a statistical interaction has frequently been noted.1–3

In the case of binary variables, concrete attempts have beenmade to articulate which types of counterfactual responsepatterns would constitute instances of interdependent ef-fects.4–6 In what follows we reconsider the definition ofcausal interdependence and its relation to that of synergism inlight of the sufficient-component-cause framework.7 Consid-eration of this framework gives rise to a definition of “definiteinterdependence,” which constitutes a sufficient but not nec-essary condition for the presence of synergism within thesufficient-component-cause framework. We then derive var-ious empirical conditions for the presence of synergism andprovide a number of observations that illustrate the differencebetween the concepts of definite interdependence and effectmodification on the risk difference scale. Although the ma-terial developed in this work arguably has implications forapplied data analysis, our principal aim here will be to extendtheory: to consider various conceptual and mathematicalrelations between different notions of interaction.

Synergism and Counterfactual Response TypesSuppose that D and 2 of its causes, E1 and E2, are

binary variables taking values 0 or 1. In the discussion that

follows, E1 and E2 are treated symmetrically so that E1 couldbe relabeled as E2 and E2 could be relabeled as E1. Weassume a deterministic counterfactual framework. Let Dij(�)be the counterfactual value of D for individual � if E1 wereset to i and E2 were set to j. Robins8 has shown that standardstatistical summaries of uncertainty due to sampling variabil-ity, such as P-values and confidence intervals for proportions,have meaning in a deterministic model if and only if weregard (i) the n study subjects as having been randomlysampled from a large, perhaps hypothetical, source popula-tion of size N, such that n/N is very small and (ii) probabilitystatements and expected values refer to proportions andaverages in the source population. Because we plan to discussstatistical tests, we adopt (i) and (ii). For event E we willdenote the complement of the event by E� . The probability ofan event E occurring, P(E � 1), we will frequently simplydenote by P(E). If there were some individual � for whomD10(�) � D01(�) � D00(�) � 0 but for whom D11(�) � 1 wemight say that there was synergism between the effect of E1 andE2 on D because in such a case there exists an individual forwhom E1 or E2 alone is insufficient for D but for whom E1 andE2 together yield D. There is thus joint action between E1 and E2

and so we might speak of synergism. Similarly if there wereindividuals for whom D11(�) � D01(�) � D00(�) � 0 andD10(�) � 1, or for whom D11(�) � D10(�) � D00(�) � 0 andD01(�) � 1, or for whom D11(�) � D01(�) � D10(�) � 0and D00(�) � 1 we might again say that some form ofsynergism was present. In the first of these 3 additional cases,we might say there is synergism because only E1 and E� 2

together imply D; in the second case, because only E� 1 and E2

together imply D; and in the third case, because onlyE� 1 and E� 2 together imply D. We have considered 4 differentresponse patterns which manifest some form of synergism.We will see below that these 4 response patterns and in fact2 others are closely related to synergism within the sufficient-component-cause framework.

Miettinen4,5 classified the various response patternswhich arise from 2 binary causes, E1 and E2, and a binaryoutcome D into 16 different response types according to theindividuals’ counterfactual outcomes as enumerated in Table 1.

Types 8, 10, 12, and 14 were classified by Miettinen asinstances of causal interdependence. Types 3, 5, 7, and 9 wereclassified as instances of preventive interdependence. Mietti-nen thus included types 3, 5, 7, 8, 9, 10, 12, and 14 as thosewhich constituted interdependent effects. Greenland andPoole6 criticized this classification because it was not invari-ant to interchanging the reference categories (ie, relabeling

Submitted 25 July 2006; accepted 22 December 2006.From the *Department of Health Studies, University of Chicago, Chicago,

Illinois; and the †Departments of Epidemiology and Biostatistics, Har-vard School of Public Health, Boston, Massachusetts.

Tyler VanderWeele was supported by a Predoctoral fellowship from theHoward Hughes Medical Institute.

Correspondence: Tyler J. VanderWeele, Department of Health Studies,University of Chicago, 5841 S. Maryland Avenue, MC 2007, Chicago, IL60637. E-mail: [email protected].



for E1 or for E2 the label “1” as “0” and “0” as “1”). Type 15,for instance, which is not classified as exhibiting causalinterdependence in Miettinen’s system, would become a type12 responder (which Miettinen did classify as exhibitingcausal interdependence) if E2 � 0 were relabeled E2 � 1 andvice versa. Greenland and Poole therefore partitioned thetypes into equivalence classes, which were invariant underthe recoding of exposure indicators. Under the classificationof Greenland and Poole, the equivalence class of types 7 and10 is invariant and is said to exhibit mutual antagonism; theclass composed of types 8, 12, 14, and 15 is invariant andconsists of those types in which disease occurs for only 1exposure combination; the class of types 2, 3, 5, and 9 isinvariant and consists of those types in which disease occursfor exactly 3 exposure combinations. Under the classificationof Greenland and Poole, these 3 classes constituting types 2,3, 5, 7, 8, 9, 10, 12, 14, and 15 are all said to represent causalinterdependence. Greenland and Poole noted that if none oftypes 2, 3, 5, 7, 8, 9, 10, 12, 14, or 15 are present then thecausal risk difference will be additive, so that ��D11� ��D00� � (��D10� � ��D00�) � (��D01� � ��D00�) where � de-notes the average in the study population. For any individualof one of these types it is also the case that the effect of E1 onD cannot be determined without knowledge of the value ofE2. Later we will show, however, that Greenland and Poole’sclassification is insufficiently stringent for associating causalinterdependence with synergism in the sufficient-component-cause framework to which we now turn.

In the analysis that follows we will frequently use thedisjunctive or OR operator, �, which is defined by a � b �a � b � ab and is such that a � b � 1 if and only if eithera � 1 or b � 1. A conjunction or product of the eventsX1, . . ., Xn will be written as X1 . . .Xn so that X1 . . . Xn � 1 ifand only if each of the events X1, . . ., Xn takes the value 1.

Under the sufficient-component-cause framework,7 if S1, . . ., Sn

are all the sufficient causes for D then D � S1 �. . .� Sn andeach Si is made up of some product of components, F1

i, . . ., Fmi

i ,which are binary so that Si � F1

i . . . Fmi

i . Following Roth-man,7 we will say that 2 causes, E1 and E2, for some outcomeD, exhibit synergism if E1 and E2 are ever present together inthe same sufficient cause.9 If E1 and E� 2 are present together inthe same sufficient cause then the 2 causes E1 and E2 are saidto exhibit antagonism; in this case it could also be said that E1

and E� 2 exhibit synergism. Note that E1 and E2 may exhibitboth antagonism and synergism if, for example, E1 and E2 arepresent together in one sufficient cause and if E1 and E� 2 arepresent together in another sufficient cause. In what followswe will not maintain the distinction between synergism andantagonism in so far as we will refer to a sufficient cause inwhich both E1 and E� 2 are present as synergism between E1

and E� 2 rather than as antagonism between E1 and E2. It hasbecome somewhat customary to refer to cases of synergismand antagonism in the sufficient-component-cause frameworkas “biologic interactions.”2,10 This nomenclature, however,may not always be appropriate. Consider a recessive diseasein which 2 mutant alleles convey the disease phenotype butone or zero copies of the mutant allele conveys the pheno-type complement. Let E1 � 1 if the allele inherited from themother is the mutant type and let E2 � 1 if the allele inheritedfrom the father is the mutant type then E1E2 is a sufficientcause for the disease, because if E1E2 � 1 then the diseasewill occur. Suppose that when both mutant types are present(E1E2 � 1) the disease occurs because neither allele allowsfor the production of an essential protein. Although there issynergism between E1 and E2 in the sufficient cause sense, asboth E1 � 1 and E2 � 1 are necessary for the disease, thereis no biologic sense in which the 2 alleles are interacting. Infact neither allele is involved in any activity at all, and it isprecisely this lack of action which brings about the disease.Thus, throughout this article we will refrain from the use ofthe term “biologic interaction” and will instead refer tosynergism within the sufficient-component-cause framework.In contrast with “biologic interaction” which suggests thatcauses biologically act upon each other in bringing about theoutcome, the term “synergism” suggests joint work on theoutcome regardless of whether or not the causes act on oneanother.

There are certain correspondences between responsetypes and sets of sufficient causes. Greenland and Poole,6 inthe case of 2 binary causes, enumerate 9 different sufficientcauses, each involving some combination of E1 and E2 andtheir complements along with certain binary backgroundcauses. We may label these background causes as A0, A1, A2,A3, A4, A5, A6, A7, A8. The 9 different sufficient causesGreenland and Poole give are then A0, A1E1, A2E� 1, A3E2,A4E� 2, A5E1E2, A6E� 1E2, A7E1E� 2 and A8E� 1E� 2. Note that in thecase of the Ai and Ei variables, the subscript denotes which ofthe causes or background factors is being indicated, whereasthe subscripts for Dij denote the counterfactual outcome withE1 set to i and E2 set to j. In many cases, not all of the 9sufficient causes will be present. Also, if one of the back-ground causes is unnecessary for the D (ie, some combination

TABLE 1. Enumeration of Response Patterns to 4 PossibleExposure Combinations

TypeE1 � 1,E2 � 1

E1 � 0,E2 � 1

E1 � 1,E2 � 0

E1 � 0,E2 � 0

1 1 1 1 1

2 1 1 1 0

3 1 1 0 1

4 1 1 0 0

5 1 0 1 1

6 1 0 1 0

7 1 0 0 1

8 1 0 0 0

9 0 1 1 1

10 0 1 1 0

11 0 1 0 1

12 0 1 0 0

13 0 0 1 1

14 0 0 1 0

15 0 0 0 1

16 0 0 0 0

VanderWeele and Robins Epidemiology • Volume 18, Number 3, May 2007


of E1, E2 and their complements are alone sufficient for D foreach member of the population) then the correspondingbackground cause Ai is equal to 1 for all subjects and we willin general suppress the Ai. Given the 9 background causes, wethus have that

D � A0 � A1E1 � A2E� 1 �

A3E2 � A4E� 2 � A5E1E2 �

A6E� 1E2 � A7E1E� 2 � A8E� 1E� 2. (1)

If one of A5, A6, A7, A8 were nonzero, then we would say thatE1 and E2 manifest a synergistic relationship. In what fol-lows, we will assume that interventions on E1 and E2 do notaffect any background causes Ai; if they did, then the Ai

variable would be an effect of E1 and E2 rather than abackground cause.11 Furthermore, if 1 of the Ai variable werean effect of E1 and E2, then this could obscure the identifi-cation of synergistic relationships between E1 and E2. Forexample, suppose that E1 and E2 together caused A0 so thatA0 � 1 whenever E1 � E2 � 1 and suppose that A0 was itselfa sufficient cause for D. Then the A0 variable would essen-tially serve as a proxy for the synergism between E1 and E2.We will thus require that none of the Ai variables are effectsof E1 and E2. It is in fact the case that it is always possible toconstruct the variables A0, A1, A2, A3, A4, A5, A6, A7, A8 sothat none of the Ai variables are effects of E1 and E2 and sothat equation (1) holds.12

Knowing whether there is a synergism between E1 andE2 will in general require having some knowledge of thecausal mechanisms for the outcome D. Although a particularset of sufficient causes, along with the presence or absence ofthe various background causes A0, A1, A2, A3, A4, A5, A6, A7,A8, for a particular individual suffices to fix a response type,the converse is not true.6,13 That is to say, knowledge of anindividual’s response type does not generally fully determinewhich background causes are present. As an example, anindividual who has A1(�) � A3(�) � 1 and Ai(�) � 0 for i �1, 3 has a sufficient cause completed if and only if E1 � 1 (inwhich case A1E1 is completed) or E2 � 1 (in which caseA3E2 is completed). For such a individual we could writeD � E1 � E2. Thus this individual would be of responsetype 2 because the individual will escape disease only ifexposed to neither E1 nor E2, so that no sufficient cause iscompleted. In contrast, knowledge of a individual’s responsetype does not generally fully determine which backgroundcauses are present. An individual who is of response type 2could have either A1(�) � A3(�) � 1 and Ai(�) � 0 for i �1, 3, in which case we could write D � E1 � E2, oralternatively such a individual may have A5(�) � A6(�) �A7(�) � 1 and Ai(�) � 0 for i � 5, 6, 7, in which case wecould write D � E1E2 � E� 1E2 � E1E� 2. As noted by Green-land and Brumback,13 it is thus impossible in this case todistinguish from the counterfactual response pattern alone theset of sufficient causes E1 � E2 from the set of sufficientcauses E1E2 � E� 1E2 � E1E� 2. With both sets of sufficientcauses, D will occur when either E1 or E2 is present. WhetherE1 � E2 or E1E2 � E� 1E2 � E1E� 2 represents the proper de-

scription of the causal mechanisms for D can only be resolvedwith knowledge of the subject matter in question.

Using the sufficient cause representation for D givenabove, we can see that the classification of Greenland and Poole6

of those types which represent causal interdependence is in-sufficiently stringent for associating causal interdependencewith synergism within the sufficient-component-cause frame-work. Greenland and Poole include types 2, 3, 5, and 9 amongthose types that are said to exhibit interdependent action.However, types 2, 3, 5, and 9 can in fact be observed evenwhen D can be represented as D � A0 � A1E1 � A2E� 1 �A3E2 � A4E� 2. For example, if A5 � A6 � A7 � A8 � 0 but iffor some individual �, A0(�) � A2(�) � A4(�) � 0 andA1(�) � A3(�) � 1 so that D�� E1 � E2, then, as seenabove, this would give rise to response type 2. Similarly ifA0(�) � A1(�) � A4(�) � 0 and A2(�) � A3(�) � 1 thenthis would give rise to response type 3; if A0(�) � A2(�) �A3(�) � 0 and A1(�) � A4(�) � 1 this would give rise toresponse type 5; if A0(�) � A1(�) � A3(�) � 0 and A2(�) �A4(�) � 1 this would give rise to response type 9. Responsetypes 2, 3, 5, and 9 might of course also arise from synergisticrelationships. As noted above, response type 2 would arise ifA0(�) � A1(�) � A2(�) � A3(�) � A4(�) � A5(�) � 0 andA5(�) � A6(�) � A7(�) � 1. Without further informationconcerning which causal mechanisms are present, we cannot,in the case of types 2, 3, 5, and 9, ascertain from thecounterfactual response patterns alone whether or not a syn-ergism is manifest. The types that Greenland and Pooleclassify as not representing causal interdependence (types 1, 4, 6,11, 13, 16) can, like types 2, 3, 5, and 9, also all be observedwhen D can be represented as D � A0 � A1E1 � A2E� 1 �A3E2 � A4E� 2. But all of these types, other than type 16, canalso be observed when one or more of A5, A6, A7, A8 arenonzero. In contrast, it can be shown that types 7, 8, 10, 12,14, and 15 cannot be present when A5 � A6 � A7 � A8 �0, ie, when D � A0 � A1E1 � A2E� 1 � A3E2 � A4E� 2.

12 These6 types thus clearly do constitute instances of synergismbecause 1 or more of A5, A6, A7, A8 must be nonzero for suchtypes to be present. Thus although synergistic relationshipswill sometimes be unidentified even when the counterfactualresponse patterns for all individuals are known, they will notalways be unidentified. We will use the term “definite inter-dependence,” which we make precise in Definition 1, to referto a counterfactual response pattern which necessarily entailsa synergistic relationship.

Note that D10(�) � D01(�) � 0 and D11(�) � 1 if andonly if individual � is of response type 7 or 8; also D11(�) �D00(�) � 0 and D01(�) � 1 if and only if individual � is ofresponse type 10 or 12; also D11(�) � D00(�) � 0 andD10(�) � 1 if and only if individual � is of response type 10or 14; and finally D01(�) � D10(�) � 0 and D00(�) � 1 if andonly if individual � is of response type 7 or 15. The presenceof 1 of the 6 types that necessarily entail the presence ofsynergism is thus equivalent to the presence of an individual� for whom one of the following 4 conditions hold: D10(�) �D01(�) � 0 and D11(�) � 1; or D11(�) � D00(�) � 0 andD01(�) � 1; or D11(�) � D00(�) � 0 and D10(�) � 1; or

Epidemiology • Volume 18, Number 3, May 2007 The Identification of Synergism


D01(�) � D10(�) � 0 and D00(�) � 1. Consequently, wedefine definite interdependence as follows.

Definition 1 (Definite Interdependence): Suppose that Dand 2 of its causes, E1 and E2, are binary. We say that there isdefinite interdependence between the effect of E1 and E2 on D ifthere exists an individual � for whom one of the following holds:D10(�) � D01(�) � 0 and D11(�) � 1; or D11(�) � D00(�) � 0and D01(�) � 1; or D11(�) � D00(�) � 0 and D10(�) � 1; orD01(�) � D10(�) � 0 and D00(�) � 1.

The definition of definite interdependence is equivalentto the presence within a population of an individual with acounterfactual response pattern of type 7, 8, 10, 12, 14, or 15.As defined above, if E1 and E2 exhibit definite interdepen-dence then there must be synergism between E1 and E2. IfD10(�) � D01(�) � 0 and D11(�) � 1, then A5 � 0 and therewill be synergism between E1 and E2. If D11(�) � D00(�) �0 and D01 � 1, then A6 � 0 and there will be synergismbetween E� 1 and E2. If D11(�) � D00(�) � 0 and D10(�) � 1,then A7 � 0 and there will be synergism between E1 and E� 2.If D01(�) � D10(�) � 0 and D00(�) � 1, then A8 � 0 andthere will be synergism between E� 1 and E� 2. As made clear inthe discussion above, however, although definite interdepen-dence is sufficient for a synergistic relationship, it is notnecessary. There may be synergism between E1 and E2 evenif they do not exhibit definite interdependence. Several re-lated concepts have been considered. In Figure 1 we give adiagram indicating the implications among these differentconcepts. First, as noted by Greenland and Poole,6 effectmodification on the risk difference scale implies causal inter-dependence. Second, definite interdependence implies bothcausal interdependence (because types 7, 8, 10, 12, 14, and 15are a subset of types 2, 3, 5, 7, 8, 9, 10, 12, 14, and 15) andthe presence of synergism.12 No other implications amongthese 4 concepts hold.

Two additional comments with regard to definite inter-dependence are worth noting. First, Greenland and Poole6

noted that there is a one-to-one correspondence betweenresponse types 8, 12, 14, and 15 and “cause types” corre-sponding to A5(�) � 1, A6(�) � 1, A7(�) � 1 and A8(�) �1, respectively, with all other Ai(�) � 0. They also noted thatresponse type 16 arises if and only if Ai(�) � 0 for all i.However, they claimed that there are no other one-to-onecorrespondences for the remaining 11 response types. Theyfailed to notice that response type 7 arises if and only ifA5(�) � 1 and A8(�) � 1 with Ai(�) � 0 for all i�{5,8} andthat response type 10 arises if and only if A6(�) � 1 andA7(�) � 1 with Ai(�) � 0 for all i�{6,7}. We will see belowthat this insight that response types 7 and 10 necessarily entaila synergistic relationship is important in constructing statis-tical tests for the presence of synergism.

Second, the definition of definite interdependence givenabove is invariant to the relabeling of the levels of E1 and E2,ie, relabeling for E1 and/or for E2 the level “1” as “0” and “0”as “1.” Definite interdependence as defined above is not,however, invariant to the relabeling of the levels of D. If D isrelabeled so that “1” is “0” and “0” is “1”, then types 8, 12,14, and 15 become types 9, 5, 3, and 2, respectively, and theselatter types do not exhibit definite interdependence. Thesufficient-component-cause framework (along with its philo-sophical counterpart)14 assumes that there is an asymmetrybetween the event and its complement in that there is aparticular event or state that needs explaining. It is the event,not its complement, which requires an explanation. For ex-ample, let D denote death, let E1 denote the presence of agene that gives rise to a peanut allergy, and let E2 denoteexposure to peanuts so that if both E1 and E2 are present theindividual will die. The event we seek to explain is death.Suppose that the lethal allergic reaction to peanuts is the onlycause of death in a particular time horizon. In this case, wewould represent the sufficient causes for death by D � E1E2and since E1 and E2 are present together in the same sufficientcause we would say that E1 and E2 manifest synergism. If,however, we were considering the outcome of survival,D� , then either E� 1 or E� 2 would be sufficient for averting deathand the sufficient causes for not dying would be represented byD� � E� 1 � E� 2 and no synergism between E� 1 and E� 2 would bethought to be present. We then see that the presence ofsynergism for an outcome does not imply the presence ofsynergism for the complement of that outcome. In the exam-ple just considered, however, it is death, not survival, thatrequires explanation, and so it is synergism for the event ofdeath that will be of interest.

TESTING FOR SYNERGISM IN THE SUFFICIENT-COMPONENT-CAUSE FRAMEWORK

When there is no confounding of the causal effects ofE1 and E2 on D, or if there exist a set of variables C such thatconditioning on C suffices to control for the confounding ofthe causal effects of E1 and E2 on D, then it is possible todevelop statistical tests for the presence of synergism. The-orem 1 gives a condition which is sufficient for the presenceof synergism and which can be statistically tested with data.The proofs of Theorems 1 and 2, below, are given in Appen-dix 1. We will say that C suffices to control for the confound-

FIGURE 1. Implications among different concepts of inter-action.

Effect modification of the risk difference: The expectedcausal risk difference of E1 on D varies within strata of E2.

Causal interdependence: The presence of a responsetype for whom the effect of E1 on D cannot be determinedwithout knowledge of E2.

Definite interdependence: Every sufficient cause repre-sentation for D must have a sufficient cause in which E1 and E2

are both present.Synergism: The sufficient cause representation for D that

corresponds to the actual causal mechanisms for D has asufficient cause in which E1 and E2 are both present.



ing of the causal effects of E1and E2 on D if the counterfac-tual variables Dij are conditionally independent of (E1, E2)given C. If this condition holds, then P(Dij � 1�C � c) � P(D �1�E1 � i, E2 � j, C � c).

Theorem 1: Suppose that D and 2 of its causes, E1 andE2, are binary. Let C be a set of variables that suffices tocontrol for the confounding of the causal effects of E1 andE2 on D. If for any value c of C we have that P(D � 1�E1 �1, E2 � 1, C � c)�P(D � 1�E1 � 0, E2 � 1, C � c)�P(D� 1�E1 � 1, E2 � 0, C � c) 0, then there is synergismbetween E1 and E2.

When the condition of Theorem 1 is met, an individualof either type 7 or type 8 must be present and from thediscussion above it follows that there must be synergismbetween E1 and E2. Theorem 1 has analogues for testing forsynergism between E1 and E� 2 or between E� 1 and E2 orbetween E� 1 and E� 2. If for some c, P(D � 1�E1 � 1, E2 � 0,C � c)� P(D � 1�E1 � 1, E2 � 1, C � c)� P(D � 1�E1 �0, E2 � 0, C � c) 0, then an individual of type 10 or type14 must be present and there will be synergism between E1 andE� 2. If P(D � 1�E1 � 0, E2 � 1, C � c)�P(D � 1�E1 � 1, E2 �1, C � c)�P(D � 1�E1 � 0, E2 � 0, C � c) 0, then anindividual of type 10 or type 12 must be present and there willbe synergism between E� 1 and E2. If P(D � 1�E1 � 0, E2 � 0,C � c)�P(D � 1�E1 � 0, E2 � 1, C � c)�P(D � 1�E1 � 1,E2 � 0, C � c) 0, then an individual of type 7 or type 15must be present and there will be synergism betweenE� 1 and E� 2. Theorem 1 and its analogues demonstrate that theclaim of Rothman and Greenland that “inferences about thepresence of particular response types or sufficient causesmust depend on very restrictive assumptions about absence ofother response types” is false.10(p. 339) Although their claimholds for inference about particular response types, Theorem1 demonstrates that it does not hold for inferences aboutsufficient causes. Theorem 1 makes no assumption about theabsence of any response type.

It is interesting to note that Theorem 1 does not makereference to probability of the outcome D when E1 and E2 areboth 0, ie, to P(D � 1�E1 � 0, E2 � 0, C � c). The conditionof Theorem 1 essentially ensures the presence of someindividual � for whom D11(�)�1 and for whom D10(�) �D01(�) � 0. For such an individual, if D00(�) � 0, thenindividual � is of type 8; if D00(�) � 1, then individual � isof type 7. Theorem 1 does not distinguish between types 7and 8; the conclusion of the theorem simply implies that anindividual of one of these 2 types must be present and thusthat there must be synergism between E1 and E2. Whetherindividual is � of type 7 or type 8 will affect the probabilityP(D � 1�E1 � 0, E2 � 0, C � c), but it will not affect theprobability involved in the condition given in Theorem 1,namely P(D � 1�E1 � 1, E2 � 1, C � c)�P(D � 1�E1 � 0,E2 � 1, C � c)�P(D � 1�E1 � 1, E2 � 0, C � c).

We consider an example concerning the effects ofsmoking and asbestos on lung cancer. For the purpose of thisexample we will ignore sampling variability. Suppose that therate ratios for lung cancer given smoking status S and asbes-tos exposure A are given in Table 2. Let Rij be the risk (ie,

cumulative incidence) of lung cancer before age 60 if smok-ing status S is i and asbestos exposure A is j , and let RRij bethe relative risk of lung cancer before age 60 for individualswith S � i and A � j compared with lung cancer risk forindividuals unexposed to smoking and asbestos. Since in allsmoking-asbestos categories the risk of lung cancer beforeage 60 is small, the risk ratio RRij closely approximates the rateratios in Table 2. Suppose that the data are unconfounded byother factors so that Rij � P(D � 1�S � i, A � j). The conditionof Theorem 1 may be written as R11�R01�R10 0. By divid-ing this condition by R00 the condition can be rewritten asRR11�RR01�RR10 0. In this case, RR11�RR01�RR10 �30�3�10 � 17 0. We could thus conclude that synergismbetween smoking and asbestos exposure was present in thesufficient cause sense. Note that the conclusion of the pres-ence of synergism in the sufficient cause sense holds in spiteof the fact that the multiplicative risk model holds, ie, RR11 �RR01RR10. Note further that the conclusion of the presence ofsynergism in the sufficient cause sense did not preclude casesin which one or both factors are sometimes preventive. Forexample, for certain individuals, exposure to asbestos mightprotect against lung cancer. This might be the case if (i) thereexist individuals who carry a very low risk of smoking-induced cancer due to a genetic polymorphism but who stillsuffer from smoking-induced chronic bronchitis and (ii) thenarrowed airways and increased mucous caused by theirbronchitis trap and eliminate asbestos fibers that would haveotherwise reached the lung parenchyma. Theorem 1 can stillbe applied to such cases. The example, of course, is rathersimplified in that smoking and asbestos exposure are bettercaptured by continuous rather than binary measures. Thedifficulties that continuous variables pose to the sufficient-component-cause framework are taken up in the Discussionsection. Also, in practice, with finite samples, one must usevarious statistical tests and methods of statistical inference todetermine whether the condition given in Theorem 1 holds.One such statistical test is given in Appendix 2. Such testscan be used empirically with epidemiologic data to test forsynergism in the sufficient-component-cause framework.Limitations of such tests are discussed at the end of the paper.

TESTING FOR SYNERGISM UNDER THEASSUMPTION OF MONOTONIC EFFECTS

We next consider a context in which the direction of theeffect (positive or negative) that E1 and E2 have on D isknown. We make these ideas precise by introducing theconcept of a monotonic effect. Considerable intuition regard-ing synergism can be garnered by the consideration of the

TABLE 2. Rate Ratios of Lung Cancer for Smoking andAsbestos Exposure

A � 0 A � 1

S � 0 1 3

S � 1 10 30

S indicates smoking status; A, asbestos exposure.



setting of monotonic effects. Furthermore, as will be seenshortly, the setting of monotonic effects also allows for theconstruction of more powerful tests for detecting synergismthan is possible without the assumption.

Definition 2 (Monotonic Effect): We will say that E1has a positive monotonic effect on D if for all individuals �we have Dij(�) � Dij(�) whenever i � i; we will say that E2has a positive monotonic effect on D if for all individuals �we have Dij(�) � Dij (�) whenever j � j. Similarly, we willsay that E1 has a negative monotonic effect on D if for allindividuals � we have Dij(�) � Dij(�) whenever i � i

and that E2 has a negative monotonic effect on D if for allindividuals � we have Dij(�) � Dij(�) whenever j � j.

The definition of a monotonic effect essentially requiresthat some intervention either increase or decrease some othervariable D—not merely on average over the entire popula-tion, but rather for every individual in that population, re-gardless of the other intervention. The requirements for theattribution of a monotonic effect are thus considerable. How-ever, whenever a particular intervention is always beneficialor neutral for all individuals, there is a positive monotoniceffect; whenever the intervention is always harmful or neutralfor all individuals, there is a negative monotonic effect. Theassumption of monotonic effects has been used elsewhere inthe context of concepts of interaction,9,10,11,15 and it is some-times referred to as an assumption of “no preventive effects”or purely “causative factors.” It can be shown that E1 has apositive monotonic effect on D if and only if E� 1 is not presentin any sufficient cause. Similarly, E1 has a negative mono-tonic effect on D if and only if E1 is not present in anysufficient cause (though E� 1 may still be present).

Theorem 2 gives a result similar to that of Theorem 1but under the assumption that both E1 and E2 have positivemonotonic effects on D.

Theorem 2: Suppose that D and 2 of its causes, E1 andE2, are binary and that E1 and E2 have a positive monotoniceffect on D. Let C be a set of variables that suffices to controlfor the confounding of the causal effects of E1 and E2 on D. Iffor any value c of C we have that P(D � 1�E1 � 1, E2 � 1, C� c)�P(D � 1�E1 � 0, E2 � 1, C � c) P(D � 1�E1 � 1, E2� 0, C � c)�P(D � 1�E1 � 0, E2 � 0, C � c), then there issynergism between E1 and E2.

The condition provided in Theorem 2 has obviousanalogues if one or both of E1 and E2 are replaced byE� 1 and E� 2, respectively, and if one or both of E1 and E2 havea negative monotonic effect rather than a positive monotoniceffect on D. If the condition of Theorem 2 is met, anindividual of type 8 must be present. Individuals of type 7, theother type that entails synergism between E1 and E2, areprecluded because E1 has a positive monotonic effect on D (andsimilarly because E2 has a positive monotonic effect on D).Rothman and Greenland10 noted the equivalent result in thesetting of no confounding factors. A statistical test for thecondition of Theorem 2 is given in Appendix 2. As noted above,such tests can be used empirically with epidemiologic data totest for synergism in the sufficient-component-cause framework.Note that the general condition of Theorem 1 for detecting the

presence of synergism between E1 and E2, P(D � 1�E1 � 1,E2 � 1, C � c)�P(D � 1�E1 � 0, E2 � 1, C � c)�P(D �1�E1 � 1, E2 � 0, C � c) 0, is stronger than the condition,P(D � 1�E1 � 1, E2 � 1, C � c)�P(D � 1�E1 � 0, E2 � 1,C � c)�P(D � 1�E1 � 1, E2 � 0, C � c) � P(D � 1�E1 �0, E2 � 0, C � c) 0, required in the setting of monotoniceffects. Indeed the former clearly implies the latter. Thestatistical tests based on this condition in the setting ofmonotonic effects will thus be more powerful than the equiv-alent tests in the general setting.

EFFECT MODIFICATION AND SYNERGISMTheorem 2 suggests the risk difference scale as the

means by which to test for synergism in the presence ofmonotonic effects. As will be seen below and as has beenpointed out before, effect modification on the risk differencescale need not imply any form of synergy. Furthermore, in thepresence of confounding, effect modification on the riskdifference scale need not even imply the modification of anactual causal effect. Nevertheless, Theorem 2 can be interpretedas stating that, conditional on confounding factors, if the riskdifference for E1 in the strata E2 � 1 is greater than the riskdifference for E1 in the strata E2 � 0, then E1 and E2 mustexhibit synergism. The condition can also be rewritten as P(D �1�E1 � 1, E2 � 1, C � c)�P(D � 1�E1 � 0, E2 � 0, C � c) {P(D � 1�E1 � 0, E2 � 1, C � c)�P(D � 1�E1 � 0, E2 � 0,C � c)} � {P(D � 1�E1 � 1, E2 � 0, C � c)�P(D � 1�E1 �0, E2 � 0, C � c)}, ie, the effect of E1 and E2 is greater thanthe sum of the effects of E1 and E2 separately. The result is inmany ways intuitive and not at all surprising. Nevertheless,several distinctions between the categories of effect modifi-cation on the risk difference scale and that of definite inter-dependence or synergism must be kept in mind. We givenumerical examples in Appendix 3 to demonstrate the fol-lowing observations. First, it is possible to have effect mod-ification on the risk difference scale without the presence ofsynergism in the sufficient-component-cause framework (seeAppendix 3, Numerical Example 1). This situation may arisewhen the effect modification is in the opposite direction ofthat required by Theorem 2.

Second, it is furthermore the case that the absence ofeffect modification on the risk difference scale does not implythe absence of synergism. In other words, if P(D � 1�E1 � 1,E2 � 1, C � c)�P(D � 1�E1 � 0, E2 � 1, C � c) P(D �1�E1 � 1, E2 � 0, C � c)�P(D � 1�E1 � 0, E2 � 0, C � c),then there must be synergism. Yet even if P(D � 1�E1 � 1,E2 � 1, C � c)�P(D � 1�E1 � 0, E2 � 1, C � c) � P(D �1�E1 � 1, E2 � 0, C � c)�P(D � 1�E1 � 0, E2 � 0, C � c),there may be a synergistic relationship (see Appendix 3,Numerical Example 2). Thus, Theorem 2 gives a condition(in terms of effect modification on the risk difference scale)that, in the setting of monotonic effects, is sufficient forsynergism but not necessary. Third, if it is not the case thatboth E1 and E2 have a monotonic effect on D, then we mayhave P(D � 1�E1 � 1, E2 � 1)�P(D � 1�E1 � 0, E2 �1) P(D � 1�E1 � 1, E2 � 0)�P(D � 1�E1 � 0, E2 � 0),even when there is no synergism. Furthermore in such caseswe can also have E2 acting as a qualitative effect modifier for



the risk difference of E1 on D without E1 and E2 manifestingsynergism (see Appendix 3, Numerical Example 3).

These comments and the 3 numerical examples in theAppendix thus help clarify the conceptual distinction betweeneffect modification on the risk difference scale and syner-gism, even in the presence of monotonic effects. There can beeffect modification on the risk difference scale without thepresence of synergism. There can be synergism without the riskdifference condition P(D � 1�E1 � 1, E2 � 1, C � c)�P(D �1�E1 � 0, E2 � 1, C � c) P(D � 1�E1 � 1, E2 � 0,C � c)�P(D � 1�E1 � 0, E2 � 0, C � c) holding. And,finally, outside the context of monotonic effects, we may haveP(D � 1�E1 � 1, E2 � 1, C � c)�P(D � 1�E1 � 0, E2 � 1, C �c) P(D � 1�E1 � 1, E2 � 0, C � c)�P(D � 1�E1 � 0, E2 �0, C � c) without the presence of synergism.

DISCUSSIONThe present work has extended the literature on the

relationship between counterfactual response types and theconcept of synergism in the sufficient-component-causeframework. The study contributes to the conceptual literatureon synergism and provides novel tests for detecting synergis-tic relationships. The specific contributions of the study are asfollows. First, we have provided a complete characterization,in the context of 2 binary causes, of those response types thatnecessarily entail synergism in the sufficient-component-cause framework; the collection of these response types wehave given the label “definite interdependence.” Second, thischaracterization has allowed for the derivation of empiricalconditions which, under the assumption of no unmeasuredconfounders, necessarily entail the presence of synergism(Theorems 1 and 2). Theorem 2, in the context of monotoniceffects, is a straightforward generalization of previous resultsin the literature. However, Theorem 1, which makes noassumptions about the absence of certain response types, isentirely novel. These results can be used to empirically testfor synergism in the sufficient-component-cause framework.Third, we have illustrated through a series of numerical exam-ples in Appendix 3 the distinction between the concept of effectmodification on the risk difference scale and the conditionswhich necessarily entail the presence of synergism.

Several issues merit further attention. First, it is to benoted that the sufficient-component-cause framework is lim-ited in an important respect: it is restricted to binary variables(or variables that can be recoded as binary variables). Thus, inbiologic systems governing continuous variables, the conceptof synergism arising from the sufficient-component-causeframework is not applicable. Note that it has also beenpointed out that with continuous variables it furthermorebecomes difficult to separate assumptions about interactionfrom those of induction time and dose-response.16,17 Second,the tests for synergism could be extended to the case of 3 ormore variables. We have addressed this extension in relatedresearch.12 Third, the discussion in the article has assumed adeterministic counterfactual and sufficient-component-causesetting. Relationships between a stochastic counterfactualsetting (wherein each individual has a certain probability ofdisease under each of the 4 exposure combinations) and

stochastic sufficient causes (wherein when a sufficient causeis completed, the individual has a certain probability of theoutcome) could also be considered. Finally, our focus hasbeen conceptual, with relatively little attention given to ap-plied data analysis. It has been noted elsewhere that thepower for tests of interaction are often low in many studysettings.18 Further work remains to be done in examiningwhether the statistical tests derived in this article could beusefully employed in actual studies. Recent studies in genet-ics with regard to gene-gene and gene-environment interac-tions might be a fruitful area in which to examine thepotential utility of these tests.

ACKNOWLEDGMENTSWe thank Sander Greenland for several helpful com-

ments on an earlier draft of this manuscript.

REFERENCES1. Blot WJ, Day NE. Synergism and interaction: are they equivalent? Am J

Epidemiol. 1979;100:99–100.2. Rothman KJ, Greenland S, Walker AM. Concepts of interaction. Am J

Epidemiol. 1980;112:467–470.3. Saracci R. Interaction and synergism. Am J Epidemiol. 1980;112:465–466.4. Miettinen OS. Causal and preventive interdependence: elementary prin-

ciples. Scand J Work Environ Health 1982;8:159–168.5. Miettinen OS. Modern Epidemiology. New York: Wiley; 1985.6. Greenland S, Poole C. Invariants and noninvariants in the concept of

interdependent effects. Scand J Work Environ Health 1988;14:125–129.7. Rothman KJ. Causes. Am J Epidemiol 1976;104:587–592.8. Robins JM. Confidence intervals for causal parameters. Stat Med.

1988;7:773–785.9. Koopman JS. Interaction between discrete causes. Am J Epidemiol.

1981;113:716–724.10. Rothman KJ, Greenland S. Modern Epidemiology. Philadelphia, PA:

Lippincott-Raven; 1998.11. Darroch J. Biologic synergism and parallelism. Am J Epidemiol. 1997;

145:661–668.12. VanderWeele TJ, Robins JM. A theory of sufficient cause interactions.

COBRA Preprint Series, 2006, Article 13. Available at: http://biostats.bepress.com/cobra/ps/art13.

13. Greenland S, Brumback B. An overview of relations among causalmodelling methods. Int J Epidemiol. 2002;31:1030–1037.

14. Mackie JL. Causes and conditions. Am Philos Q. 1965;2:245–255.15. Weinberg CR. Applicability of the simple independent action model to

epidemiologic studies involving two factors and a dichotomous out-come. Am J Epidemiol. 1986;123:162–173.

16. Thomas DC. Are dose-response, synergy, and latency confounded? In:Abstracts of the Joint Statistical Meetings. Alexandria, VA: AmericanStatistical Association; 1981.

17. Greenland S. Basic problems in interaction assessment. Enivon HealthPerspect. 1993;101(Suppl 4):59–66.

18. Greenland S. Tests for interaction in epidemiologic studies: a review andstudy of power. Stat Med. 1983;2:243–251.

19. Wacholder S. Binomial regression in GLIM: estimating risk ratios andrisk differences. Am J Epidemiol. 1986;123:174–184.

20. Greenland S. Estimating standardized parameters from generalized lin-ear models. Stat Med. 1991;10:1069–1074.

21. Zou G. A modified Poisson regression approach to prospective studieswith binary data. Am J Epidemiol. 2004;159:702–706.

22. Greenland S. Model-based estimation of relative risks and other epide-miologic measures in studies of common outcomes and in case-controlstudies. Am J Epidemiol. 2004;160:301–305.

23. Spiegelman D, Hertzmark E. Easy SAS calculations for risk or preva-lence ratios and differences. Am J Epidemiol. 2005;162:199–200.

24. Wild CJ. Fitting prospective regression models to case–control data.Biometrika. 1991;78:705–717.

25. Wacholder S. The case-control study as data missing by design: esti-



mating risk differences. Epidemiology. 1996;7:145–150.

APPENDIX

Appendix 1: ProofsProof of Theorem 1Suppose that for some set of variables V,

��D11 � D01 � D10�V � �� 0,

then there must be some individual � for whom V � v andD11(�) � 1 but D01(�) � D10(�) � 0; for if one ofD01(�), D10(�) were always 1 whenever D11(�) � 1 thenD11(�)�D01(�)�D10(�) would be less than or equal to zerofor all � and so we would have that

��D11 � D01 � D10�V � �� 0.

Let V � C. The condition ��D11 � D01 � D10�C � c� 0implies definite interdependence and thus the presence ofsynergism. Because C is a set of variables that suffices tocontrol for the confounding of the causal effects of E1 andE2 on D, we have that the counterfactual variables Dij areconditionally independent of (E1, E2) given C. Thus wehave, ��D11 � D01 � D10�C � c� � ��D11�E1 � 1, E2 �1, C � c� � ��D01�E1 � 0, E2 � 1, C � c� � ��D10�E1 �1, E2 � 0, C � c� � P�D � 1�E1 � 1, E2 � 1, C �c) � P�D � 1�E1 � 0, E2 � 1, C � c) � P�D � 1�E1 �1, E2 � 0, C � c). Consequently, if P�D � 1 � E1 �1, E2 � 1, C � c) � P�D � 1�E1 � 0, E2 � 1, C �c) � P�D � 1�E1 � 1, E2 � 0, C � c) 0 then E1 and E2must exhibit synergism.

Proof of Theorem 2We first show that if for some set of variables V,

��(D11 � D01) � (D10 � D00)�V � �� 0 for some �, thenthere must be synergism. For each individual � define B0(�),B1(�), B2(�) and B3(�) as follows: B0(�) � 1 if D00(�) � 1and 0 otherwise; B1(�) � 1 if D10(�) � 1 and 0 otherwise;B2(�) � 1 if D01(�) � 1 and 0 otherwise; and B3(�) � 1 ifD11(�) � 1 and D10(�) � D01(�) � 0 and 0 otherwise. ThenD00 � B0, D10 � B0 � B1, D01 � B0 � B2, D11 � B0 � B1 �B2 � B3. Suppose there is no synergism between E1 and E2;then B3(�) � 0 for all � � � so that D11 � B0 � B1 � B2. LetP�B0�V � �) � b0

�, P�B1�V � �) � b1�,

P�B2�V � �) � b2�, P�B0B1�V � �) � b01

� ,P�B0B2�V � �) � b02

� , P�B1B2�V � �) � b12�

and P�B0B1B2�V � �) � b012� .

Then P�B0�V � �) � b0�; P�B0 �

B1�V � �) � b0� � b1

� � b01� ; P�B0 �

B2�V � �) � b0� � b2

� � b02� ; P�B0 � B1 �

B2�V � �) � b0� � b1

� � b2� � (b01

� � b02� � b12

� ) � b012� .

��(D11 � D01) � (D10 � D00)�V � �� {P(B0 �B1 � B2�V � �) � P�B0 � B2�V � ��} � {P�B0 �B1�V � �) � P�B0�V � ��} � �{b0

� � b1� � b2

� � �b01� � b02

� �b12

� ) � b012� } � {b0

� � b2� � b02

� }� ��{b0� � b1

� � b01� }� b0

�� b012

� � b12� � b1

� � b01� ) � (b1

� � b01� ) � b012

� � b12� � 0.

Thus if ��(D11 � D01) � �D10 � D00)�V � �� 0 we cannot

have B3(�) � 0 for all � and so there must be synergismbetween E1 and E2. Now let V � C then we have thatsynergism is implied by the condition��(D11 � D01) � (D10 � D00)�C � c� 0. Because C is a setof variables that suffices to control for the confounding of thecausal effects of E1 and E2 on D we have that the counter-factual variables Dij are conditionally independent of (E1, E2)given C and so we have,��(D11 � D01) � (D10 � D00)�C � c� � {��D11�C � c� � ��D01�C �c�} � {��D10�C � c� � ��D00�C � c��} � {��D11�E1 � 1,E2 � 1,C � c��D01�E1 � 0, E2 � 1, C � c�} � {��D10�E1 � 1, E2 � 0,C � c� � ��D00�E1 � 0, E2 � 0, C � c�)} � {P�D � 1�E1 � 1,E2 � 1, C � c) � P(D � 1�E1 � 0, E2 � 1, C � c)} � {P�D �1�E1 � 1, E2 � 0, C � c) � P�D � 1�E1 � 0, E2 � 0, C � c�}.Thus if P(D � 1�E1 � 1, E2 � 1, C � c)�P(D � 1�E1 � 0, E2 �1, C � c) P(D � 1�E1 � 1, E2 � 0, C � c)�P(D � 1�E1 � 0,E2 � 0, C � c) then E1 and E2 must exhibit synergism.

Appendix 2: Statistical TestsIn this Appendix we develop statistical tests related to

Theorems 1 and 2. For Theorem 1, to test the null that P(D �1�E1 � 1, E2 � 1, C � c)�P(D � 1�E1 � 0, E2 � 1, C �c)�P(D � 1�E1 � 1, E2 � 0, C � c) � 0 for a particularsample one may let nij denote the number of individuals instratum C � c with E1 � i and E2 � j and let dij denote thenumber of individuals in stratum C � c with E1 � i, E2 � jand D � 1 then tests of the null hypothesis P(D � 1�E1 � 1,E2 � 1, C � c)�P(D � 1�E1 � 0, E2 � 1, C � c)�P(D �1�E1 � 1, E2 � 0, C � c) � 0 can be constructed usingcritical regions of the following form:

{ �d11

n11

�d01

n01

�d10

n10�

� d11 (n11�d11)

n113

�d01 (n01 � d01)

n013

�d10 (n10 � d10)

n103

Z1 � },

to carry out a one-sided (upper tail) test. This can be seen byletting pij denote the true probability of D � 1 conditional onE1 � i, E2 � j and C � c. The hypothesis P(D � 1�E1 � 1,E2 � 1, C � c)�P(D � 1�E1 � 0, E2 � 1, C � c)�P(D �1�E1 � 1, E2 � 0, C � c) � 0 is that (p11�p01�p10) � 0. We

have that dij Bin�nij, pij� with �[dij

nij

] � pij and

Var(dij

nij

) �pij(1 � pij)

nij

.

By the central limit central limit theorem

�d11

n11

�d01

n01

�d10

n10� � (p11 � p01 � p10)

�p11(1 � p11)

n11

�p01(1 � p01)

n01

�p10(1 � p10)

n10

N�0,1)



and by Slutsky’s theorem we have

�d11

n11

�d01

n01

�d10

n10� � (p11 � p01 � p10)

�d11(n11 � d11)

n113

�d01(n01 � d01)

n013

�d10(n10 � d10)

n103

N�0, 1).

To test the hypothesis H0: (p11 � p01 � p10) � 0 vs. HA: �p11 �p01 �p10) 0 one may thus use the test statistic:

�d11

n11

�d01

n01

�d10

n10�

�d11(n11 � d11)

n113

�d01(n01 � d01)

n013

�d10(n10 � d10)

n103

.

If C consists of a small number of binary or categoricalvariables then it may be possible to use the tests constructedabove to test all strata of C. When C includes a continuousvariable or many binary and categorical variables such testingbecomes difficult because the data in certain strata of C willbe sparse. One might then model the conditional probabilitiesP(D � 1�E1, E2, C) using a binomial or Poisson regressionmodel with a linear link.19–23 For case–control studies it willbe necessary to use an adapted set of modeling tech-niques.22,24,25

The condition of Theorem 2 can be tested in a manneranalogous to the condition of Theorem 1. In general to testthe null that P(D � 1�E1 � 1, E2 � 1, C � c)�P(D �1�E1 � 0, E2 � 1, C � c) � P(D � 1�E1 � 1, E2 � 0, C �c)�P(D � 1�E1 � 0, E2 � 0, C � c) for a particular sample,tests of the null hypothesis can be constructed using criticalregions of the following form:

{ �d11

n11

�d01

n01� � �d10

n10

�d00

n00�

�d11�n11 � d11�

n113

�d01(n01 � d01)

n013

�d10(n10 � d10)

n103

�d00(n00 � d00)

n003

Z1 � }.

Appendix 3: Numerical Examples for EffectModification, Definite Interdependence andthe Multiplicative Survival Model.

This appendix presents 3 computational examples illus-trating the difference between effect modification on the riskdifference scale and the concepts of definite interdependenceand synergism. Recall, effect modification on the risk differ-ence scale is said to be present if P(D � 1�E1 � 1, E2 �e2)�P(D � 1�E1 � 0, E2 � e2) varies with the value of e2. Inthe absence of confounding this is also equal to the causal riskdifference �[D1e2] � �[D0e2]. Definite interdependence be-tween E1 and E2 is said to be manifest if every sufficientcause representation for D must have a sufficient cause inwhich both E1 and E2 (or one or both their complements) arepresent. There is said to be synergism between E1 and E2 if

the sufficient cause representation that corresponds to theactual causal mechanisms for D has a sufficient cause inwhich both E1 and E2 are present.

Numerical Example 1We show that effect modification of the risk difference

may be present without definite interdependence or synergism.Suppose that D, E1, and E2 are binary and that D � A0 �A1E1 � A2E2. Then E1 and E2 have a positive monotoniceffect on D and E1 and E2 do not exhibit definite interdepen-dence. Suppose further that the causal effects of E1 and E2on D are unconfounded. LetP�A0) � a0, P�A1) � a1, P�A2) � a2, P�A0A1) �a01, P�A0A2) � a02, P�A1A2) � a12, P�A0A1A2) � a012.We then have P�D � 1�E1 � 0, E2 � 0) � P�A0) � a0;P(D � 1�E1 � 1, E2 � 0) � P�A0 � A1) � a0 � a1 � a01;P�D � 1�E1 � 1, E2 � 1) � P�A0 � A2) � a0 � a2 � a02; andP(D � 1�E1 � 1, E2 � 1) � P�A0 � A1 �A2) � a0 � a1 � a2 � a01 � a02 � a12 � a012.Conditional on E2 � 0, the risk difference for E1 is given by:P�D � 1�E1 � 1, E2 � 0) � P�D � 1�E1 � 0, E2 � 0) �a0 � a1 � a01 � a0 � a1 � a01.Conditional on E2 � 1, the risk difference for E1 is given by:P�D � 1�E1 � 1, E2 � 1) � P�D � 1�E1 � 0, E2 � 1) �a0 �a1 � a2 � a01 � a02 � a12 � a012 � (a0 � a2 � a02) �(a1 � a01) � (a12 � a012).In this example,P�D � 1�E1 � 1, E2 � 1) � P�D � 1�E1 � 0, E2 � 1) �(a1 � a01) � (a12 � a012)�a1 � a01 � P�D � 1�E1 � 1,E2 � 0) �P�D � 1�E1 � 0, E2 � 0).We see then from this example that we can have effect modifi-cation on the risk difference scale (“statistical interaction”) evenwhen no synergism (or antagonism) is present. This will occurwhenever (a12�a012) � 0, ie, when P(A1A2) � P(A0A1A2) orequivalently P(A0 � 1�A1 � 1, A2 � 1) � 1.

Numerical Example 1 also sheds light on the conditionsunder which a multiplicative survival model can be used to testfor synergism. The multiplicative survival model is said to holdwhen P(D � 0�E1 � 1, E2 � 1)P(D � 0�E1 � 0, E2 � 0) �P(D � 0�E1 � 1, E2 � 0)P(D � 0�E1 � 0, E2 � 1). InExample 1, the probabilities of survival are: P(D � 0�E1 � 0, E2 �0) � 1�a0; P(D � 0�E1 � 0, E2 � 1) � 1�a0�a1 � a01; P(D� 0�E1 � 0, E2 � 1) � 1�a0�a2 � a02; P(D � 0�E1 � 1, E2� 1) � 1�a0�a1�a2 � a01 � a02 � a12�a012 and thus: P(D� 0�E1 � 1, E2 � 1) P(D � 0�E1 � 0, E2 � 0) �(1�a0)(1�a0�a1�a2 � a01 � a02 � a12� a012) �1�a0�a1�a2 � a01 � a02 � a12�a012�a0(1�a0�a1�a2 �a01 � a02 � a12�a012); but P(D � 0�E1 � 1, E2 � 0) P(D �0�E1 � 0, E2 � 1) � (1�a0�a1 � a01)(1�a0�a2 � a02) �1�a0�a1 � a01�a0�a2 � a02 � a0

2 � a0a2�a0a02 � a0a1 �a1a2�a1a02�a0a01�a2a01 � a01a02. Thus, P(D � 0�E1 � 1,E2 � 1) P(D � 0�E1 � 0, E2 � 0)�P(D � 0�E1 � 1, E2 � 0)P(D � 0�E1 � 0, E2 � 1) � (a12�a1a2)�(a012�a1a02)�(a0a12�a2a01) � (a0a012�a01a02) � 0 which will generally benonzero so the multiplicative survival model will fail to hold inthis example. However, if A0, A1 and A2 were independentlydistributed, then the above expression is zero and the multipli-cative survival model holds. Somewhat more generally, if A1



and A2 were independent of one another and also either A1 or A2were independent of A0 then the expression would again bezero and the multiplicative survival model would hold.Greenland and Poole6 proposed the multiplicative survivalmodel as a means to assess the interdependence versus theindependence of causal effects under the setting that the“effects of exposures are probabilistically independent of anybackground causes, as well as of one another’s effect.”Example 1 underscores the necessity for the backgroundcauses to also be independent of one another when using themultiplicative survival model to detect the presence of syn-ergism. More precisely, we have shown that if E1 and E2 havea positive monotonic effect on D and if A1 and A2 areindependent of one another and either A1 or A2 is independentof A0, then the multiplicative survival model will hold whenthere is no synergism between E1 and E2. Therefore, if, underthese assumptions, the multiplicative survival model does nothold, then one could conclude that synergism between E1 andE2 was present. Consideration of the use of the multiplicativesurvival model to test for interactions regarding biologicmechanisms is also given elsewhere.11,15

Numerical Example 2We show that synergism may be present without

effect modification of the risk difference. Suppose that D,E1 and E2 are binary, that E1 and E2 are independent andthat D � A0 � A1E1 � A2E2 � A3E1E2. Then E1 and E2 havea positive monotonic effect on D and E1 and E2 do exhibitsynergism. Suppose further that the causal effects of E1 and E2on D are unconfounded. Let P(A0) � a0, P(A1) � a1, P(A2) �a2, P(A3) � a3, P(A0A1) � a01, P(A0A2) � a02, . . .,P(A0A1A2A3) � a0123. We then haveP�D � 1�E1 � 0, E2 � 0) � P�A0) � a0;P�D � 1�E1 � 1, E2 � 0) � P�A0 � A1) � a0 � a1 � a01;P�D � 1�E1 � 0, E2�1)�P�A0 � A2)�a0 � a2 � a02; andP�D � 1�E1 � 1, E2 � 1) � P�A0 � A1 �A2) � (a0 � a1 � a2 � a3) � (a01 � a02 � a03 � a12�a13�a23) � (a012 � a013 � a023 � a123) � a0123.

Thus P(D � 1�E1 � 1, E2 � 1)�P(D � 1�E1 � 0, E2 �1)�P(D � 1�E1 � 1, E2 � 0) � P(D � 1�E1 � 0, E2 � 0) �(a012�a12) � a3�(a03 � a13 � a23) � (a013 � a023 �a123)�a0123. Suppose now that with probability 0.5, A0 � 0,A1 � 0, A2 � 0, A3 � 1, and with probability 0.5, A0 � 0,A1 � 1, A2 � 1, A3 � 0, so that a3 � 0.5 and a12 � 0.5 anda012 � a03 � a13 � a23 � a013 � a023 � a123 � a0123 � 0.Then P(D � 1�E1 � 1, E2 � 1)�P(D � 1�E1 � 0, E2 �1)�P(D � 1�E1 � 1, E2 � 0) � P(D � 1�E1 � 0, E2 � 0) �a3�a12 � 0.5�0.5 � 0. Thus, although synergism is present,the inequality P(D � 1�E1 � 1, E2 � 1)�P(D � 1�E1 � 0, E2� 1) P(D � 1�E1 � 1, E2 � 0)�P(D � 1�E1 � 0, E2 � 0)fails to hold. The example demonstrates that, although theinequality is a sufficient condition for synergism under thesetting of monotonic effects, it is not necessary. It is alsointeresting to note that in this example P(D � 1�E1 � 1, E2 �1)�P(D � 1�E1 � 0, E2 � 1)�P(D � 1�E1 � 1, E2 � 0) �P(D � 1�E1 � 0, E2 � 0) � {a3�(a03 � a13 � a23) �(a013 � a023 � a123)�a0123}�(a12�a012) and this final ex-pression can be rewritten as P�A3A� 0A� 1A� 2) � P�A1A2A� 0) . This

equivalence suggests that the more likely that A3 occurs whenA0, A1, A2 are absent, the more power the test implied byTheorem 2 will have to detect the synergism; on the otherhand, the more likely that A1 and A2 occur together when A0

is absent, the less power the test implied by Theorem 2 willhave to detect the synergism.

The contrast between Examples 1 and 2 is interesting.Example 1 demonstrated that effect modification could bepresent without synergism. In Example 1, effect modificationon the risk difference scale would be present wheneverP(A1A2) � P(A0A1A2)-suggesting that, in general, effect mod-ification on the risk difference scale may be present withoutsynergism if the various background causes A0, A1 and A2 canoccur simultaneously, ie, when multiple causal mechanismsmay be simultaneously operative. It is, of course, also possi-ble to have effect modification that is attributable solely tosynergism rather than to the background causes. Example 2considered the general case of synergism between E1 and E2

under the setting of monotonic effects. The expression for{P(D � 1�E1 � 1, E2 � 1)�P(D � 1�E1 � 0, E2 �1)}�{P(D � 1�E1 � 1, E2 � 0)�P(D � 1�E1 � 0, E2 � 0)}could be rewritten as (a012�a12) � (a3�a03�a13�a23 �a013 � a023 � a123�a0123). For no effect modification on therisk difference scale to be present in Example 2, the sum ofthese 2 terms would have to be zero. Note that each part of thesecond term involves the subscript 3. The second term can thusbe seen as the synergistic component; it will be zero when A3 �0. We saw in Example 1 that the first term being zero,(a012�a12) � 0, was the condition for no effect modification inthe case of A3 � 0. Suppose that (a012�a12) � 0 but A3 � 0 and(a3�a03�a13�a23 � a013 � a023 � a123�a0123) � 0; then theeffect modification in Example 2 would be attributable solelyto synergism (ie, no effect modification would be present ifA3 � 0). Thus, in Example 1, the effect modification waswholly attributable to the possibility of the backgroundcauses A0, A1 and A2 occurring simultaneously, and in Ex-ample 2, if (a012�a12) � 0, the effect modification would bewholly attributable to the presence of synergism. In general,effect modification may arise due to background causes or thepresence of synergism or both.

Numerical Example 3We show that, without monotonic effects, one may

have “superadditive” effect modification of the risk differ-ence without definite interdependence or synergism. Supposethat D, E1 and E2 are binary, that E1 and E2 are independent,and that D � A1E1 � A2E� 1 � A3E� 2. Then E1 and E2 do notexhibit definite interdependence. Suppose further that the causaleffects of E1 and E2 on D are unconfounded. Finally, supposethat with probability 0.3, A1 � 1, A2 � 0, A3 � 1; with proba-bility 0.3, A1 � 1, A2 � 0, A3 � 0; and with probability 0.4,A1 � 0, A2 � 1, A3 � 0 so that a1 � 0.6, a2 � 0.4, a3 � 0.3,a13 � 0.3 and a23 � 0. We then haveP�D � 1�E1 � 0, E2 � 0) � P�A2 � A3) � a2 � a3 � a23;P�D � 1�E1 � 1, E2 � 0) � P�A1 � A3) � a1 � a3 � a13;P�D � 1�E1 � 0, E2 � 1) � P�A2) � a2; andP(D � 1�E1 � 1, E2 � 1) � P�A1) � a1.



Conditional on E2 � 0, the risk difference for E1 isgiven by: P(D � 1�E1 � 1, E2 � 0)�P(D � 1�E1 � 0,E2 � 0) � a1 � a3�a13� (a2 � a3�a23) � a1�a2�a13 �a23 � 0.6�0.4�0.3 � �0.1. Conditional on E2 � 1,the risk difference for E1 is given by: P(D � 1�E1 � 1,E2 � 1)�P(D � 1�E1 � 0, E2 � 1) � a1�a2 �

0.6�0.4 � 0.2. In this example, P(D � 1�E1 � 1, E2 �1)�P(D � 1�E1 � 0, E2 � 1) P(D � 1�E1 � 1, E2 �0)�P(D � 1�E1 � 0, E2 � 0) but no synergism waspresent. We see also from this example that we can havequalitative effect modification even when no synergism ispresent.



ORIGINAL ARTICLE

Risk Factors for Positive Tuberculin Skin Test inGuinea-Bissau

Per Gustafson,*†‡ Ida Lisse,†‡ Victor Gomes,†‡§ Cesaltina S. Vieira,§ Christian Lienhardt,¶Anders Naucler,*†‡ Henrik Jensen,†‡ and Peter Aaby†‡

Background: The tuberculin skin test is used for tracing of tuber-culosis transmission and identifying individuals in need of prophy-lactic treatment.Methods: Using a case-control study design, we recruited 220smear-positive tuberculosis cases and 223 randomly selected healthycommunity controls in Bissau, Guinea-Bissau, during 1999–2000.Tuberculin skin tests were performed on family members of casesand controls (n � 1059 and n � 921, respectively). Induration of 10mm or greater was considered positive. Risk factors were calculatedfor children (�15 years) and adults separately in multivariate logis-tic regression analysis.Results: The prevalence of positive tuberculin skin test was 41% incase-contacts compared with 22% in control-contacts, resulting in aprevalence ratio of 1.48 (95% confidence interval � 1.37–1.60).Positive skin tests among case-contacts increased with age forchildren, as well as with proximity to a case during the night, forboth children and adults. A Bacille Calmette Guerin scar increasedthe likelihood of having a positive tuberculin skin test for adults incase households, but not in other categories of contacts. Amongcontrol-contacts the prevalence of positive skin test was associatedwith older age in children, history of tuberculosis in the family, anda positive tuberculin skin test of the control person.Conclusions: Risk factors for a positive tuberculin skin test amongcase- and control-contacts are closely related to tuberculosis expo-sure. Having a BCG scar did not increase the risk of positive skin

test in unexposed individuals. Tuberculin skin testing remains auseful tool for diagnosing tuberculosis infection.

(Epidemiology 2007;18: 340–347)

The tuberculin skin test (TST) was introduced in the be-ginning of the 20th century and is one of the oldest and

most widely used immunologic tests.1 It is being used fordiagnosing active tuberculosis, for estimating the prevalenceof tuberculosis infection in populations, for identifying indi-viduals in need of prophylactic treatment, and for tracing oftuberculosis transmission to contacts of cases with active andpotentially infectious tuberculosis.

The present study was performed in Bissau, the capitalof Guinea-Bissau, a country on the African west coast withapproximately 1.3 million inhabitants. The country has a highincidence of smear-positive tuberculosis (over 200/100,000among adults).2 The Bacille Calmette Guerin (BCG) vacci-nation coverage in the area is high; more than 98% ofchildren are vaccinated in infancy.3 We evaluated potentialrisk factors for positive TST in children and adults who werein close contact with an infectious case. We also consideredthe factors associated with a positive skin test in communitycontrols, (ie, in persons without known recent contact withsmear-positive tuberculosis cases).

METHODSThe population of the study area has been followed

regarding tuberculosis since 1996, using active and passivecase finding.2 The present study was performed within alarger case-control study investigating the risk factors foractive tuberculosis in adults. The general design of the studyhas been described in detail.4 Adults aged 15 years and olderliving in Bissau, Guinea-Bissau, and with newly diagnosedpulmonary tuberculosis were recruited and investigated atHospital Raoul Follereau, the national referral hospital fortuberculosis, between May 1999 and November 2000. Directmicroscopy was performed on sputum smears from 3consecutive morning sputum samples. Two or more smearspositive for acid fast bacilli were required for inclusion inthe study. Sputum smears were graded according to theguidelines of the International Union Against Tuberculosisand Lung Disease5: “scanty” for 1–9 acid fast bacilli per100 oil immersion fields, “1�” for 10 –99 per 100 fields,“2�” for 1–10 per 1 field and “3�” for more than 10 per

Submitted 5 April 2006; accepted 1 December 2006.From the *Infectious Diseases Research Group, Department of Clinical

Sciences, Malmo, Lund University, Sweden; †Projecto de Saude deBandim, Bissau, Guinea-Bissau; ‡Danish Epidemiology Science Centre,Statens Serum Institut, Copenhagen, Denmark; §Hospital Raoul Folle-reau, Bissau, Guinea-Bissau; and ¶Institut de Recherche pour le Devel-oppement, Dakar, Senegal.

Supported by the European Union grant IC18CT980375, Agence Nationalede Recherche sur le SIDA/Projet SDAK in France, The Danish Interna-tional Development Assistance (DANIDA) and The Swedish Interna-tional Development Cooperation Agency (Sida).

Statens Serum Institut (SSI) is a producer of PPD used for tuberculin skintesting. Though the Projecto de Saude de Bandim is affiliated with theSSI, the work in Guinea-Bissau was independently funded.

Editors’ note: A commentary on this article appears on page 348.Supplemental material for this article is available with the online versionof the journal at www.epidem.com; click on “Article Plus.”

Correspondence: Per Gustafson, Department of Infectious Diseases, MalmoUniversity Hospital, SE-205 02 Malmo, Sweden. E-mail: [email protected].

Copyright © 2007 by Lippincott Williams & WilkinsISSN: 1044-3983/07/1803-0340DOI: 10.1097/01.ede.0000259987.46912.2b


1 field. These tuberculosis cases are denoted “index cases”in the present paper.

A field assistant visited the household of each indexcase, a census of the members was tabulated, and socioeco-nomic and demographic information was recorded. A nurseinterviewed each family member regarding disease historyand exposure to the index case, checked for presence of BCGscar, and performed a tuberculin skin test (TST) using theMantoux technique (2TU of RT23, Statens Serum Institut,Copenhagen, Denmark).1 The width and length of the indu-ration were measured 48–72 hours later. The mean of these 2diameters was used for analysis. An induration of 10 mm ormore was considered a positive skin reaction and possiblyreflecting tuberculosis infection.

For each index case, we recruited a community controlmatched within 10-year age bands and living in a randomlyselected household in the neighborhood of the index case.Family members of control households were investigated thesame way as family members of index case households. Thefamily members are denoted “case-contacts” and “control-contacts,” respectively.

For 244 cases we recruited 231 age-matched controls.A total of 24 cases and 8 controls lived alone and thereforewere not included in the analysis.

Houses in the study area are 1-storey, unattached,rectangular buildings, usually with 6–8 rooms and inhabitedby 2–4 families. The house is usually owned by 1 of thesefamilies. The majority of houses do not have an internalceiling; this leaves a gap between the internal walls and theroof allowing air to circulate freely among all the rooms.

Statistical AnalysesPotential risk factors for positive TST reaction among

case- and control-contacts were evaluated separately for chil-dren (�15 years) and adults in multivariable logistic regres-sion models using the method of Generalized EstimatingEquations.6 This analysis approach was adopted to take intoaccount the clustering of contacts within households. Acompound symmetry structure was used as the workingcorrelation structure, and results are expressed as odds ratios(ORs) with 95% confidence intervals (95% CIs) using em-pirical standard-error estimates. In a combined analysis ofcase-contacts and control-contacts, we assessed statisticalinteraction between type of contacts and risk factors. Thiswas done one-by-one and simultaneously controlling for allother risk factors. The cases and controls themselves wereexcluded from all analyses. Statistical analyses were per-formed using SAS for Windows, version 8.0 (SAS Institute,Cary, NC).

Ethical ConsiderationsInformed consent was acquired from all participants

before enrollment. Counseling was offered before and afterHIV testing. The study was approved by the Ministry ofHealth in Guinea-Bissau and by the Central Ethical Commit-tee of Denmark.

RESULTSAfter exclusion of single households, 220 index case

households and 223 community control households wereenrolled in the study. Fifty-eight percent (127/220) of thecases and 46% (103/223) of the controls were men. The mean(�SD) age of the cases was 36.7 years (�13.5) and 35.2years (�12.0) for the controls. There were a total of 1503members in the case households and 1331 in the controlhouseholds; tuberculin skin testing was performed in 1059(70%) case-contacts and 921 (69%) control-contacts. Forty-two percent (443/1059) of the tested case-contacts and 38%(353/921) of control-contacts were men, for a prevalenceratio (PR) of 1.07 (95% CI � 0.98–1.16). The mean age was19.8 (�16.1) for the case contacts and 15.8 (�14.8) for thecontrol contacts (P � 0.001). BCG scar prevalence wassimilar among case and control contacts (Table 1).

We assessed the risk factors for positive skin reaction incase households (in which exposure to pulmonary smear-positive tuberculosis was documented), and among control-contacts as a reflection of the general population. The prev-alence of positive reactions for various risk factors ispresented in Table 1 and the multivariable estimates areshown in Table 2.

The prevalence of positive skin reaction was higher incase contacts compared with control contacts, 41% (437/1059) versus 22% (201/921) (PR � 1.48; 95% CI � 1.37–1.60). Of the case contacts, 45% (480/1059) had no reactionto tuberculin at all, compared with 68% (623/921) of thecontrol contacts (PR � 0.66; 95% CI � 0.61–0.72). Thedistribution of skin test results in contacts in both case andcontrol households showed typical bimodal patterns (Figs.1–4 available with the online version of this article).

Both children and adults living in contact with a tuber-culosis case had higher risks of positive skin reactions thanthose living in contact with a community control (adjustedOR � 2.47 �95% CI � 1.78–3.42� for children and 1.89�1.31–2.72� for adults). The prevalence of positive skin reac-tion among case-contacts was similar in males and femalesamong both children and adults (Table 2, Fig. 5 availablewith the online version of this article). Among the control-contacts, males had a lower risk of positive skin tests thanfemales until the age of 14, after which they had higher risks(Table 2, Fig. 6 available with the online version of thisarticle). The risk of a positive skin test was higher amongolder children than among younger children, in both case andcontrol households (Table 2).

Compared with the control-contacts, female case-con-tacts had a higher risk of being skin-test-positive than malecase-contacts (adjusted OR � 2.35 �1.62–3.39� for womenand 0.96 �0.52–1.79� for men). For children there was, how-ever, no difference between males and females in this regard,(3.54 �2.01–6.25� for girls and 4.20 �2.03–8.68� for boys). Itthus appears as if women were more likely than men toexpress a positive skin reaction after exposure. As shown inTable 3, the prevalence of positive skin reaction was higherfor women who lived either with a tuberculosis case or witha skin-positive control, as compared with living with a skintest-negative control. This was not true for men.

Epidemiology • Volume 18, Number 3, May 2007 Risk Factors for Positive Tuberculin Reaction


TABLE 1. Prevalence of Positive Tuberculin Skin Test Reactions (�10 mm) for Various Risk Factors in Case- andControl-Contacts, for Children (Age �15 Years) and Adults

Case-Contacts Control-Contacts

Children Adults Children Adults

TotalNo.

Positive TotalNo.

Positive TotalNo.

Positive TotalNo.

PositiveNo. (%) No. (%) No. (%) No. (%)

Sex

Male 218 52 (24) 225 120 (53) 242 19 (8) 111 56 (51)

Female 264 82 (31) 352 183 (52) 299 40 (13) 269 86 (32)

Age (yrs)

0–4 146 31 (21) 202 7 (4)

5–14 336 103 (31) 339 52 (15)

15–24 269 143 (53) 207 75 (36)

25–34 138 58 (42) 73 22 (30)

35–49 99 69 (70) 60 29 (48)

50� 71 33 (47) 40 16 (40)

Ethnic group

Pepel 76 21 (28) 81 42 (52) 120 15 (13) 79 35 (44)

Manjaco 66 18 (27) 97 50 (52) 68 15 (22) 43 16 (37)

Mancanha 49 8 (16) 53 24 (45) 42 4 (10) 48 22 (46)

Balanta 93 32 (34) 90 56 (62) 127 12 (10) 72 19 (26)

Fula 42 9 (21) 51 25 (49) 46 5 (11) 39 9 (23)

Mandinga 37 8 (22) 56 26 (46) 38 1 (3) 25 8 (32)

Other 119 38 (32) 149 80 (54) 100 7 (7) 74 33 (45)

Presence of BCG scar

Yes 372 106 (29) 363 206 (57) 396 46 (12) 231 83 (36)

No 105 27 (26) 212 95 (45) 142 13 (9) 148 59 (40)

Missing 5 1 2 2 3 0 1 0

Personal history of tuberculosis

Yes 1 0 (0) 16 12 (75) 1 0 (0) 6 4 (67)

No 477 133 (28) 559 290 (52) 540 59 (11) 372 137 (37)

Missing 4 1 2 1 2 1

History of tuberculosis in household or family

Yes 5 1 (20) 18 9 (50)

No NA NA 535 58 (11) 360 132 (37)

Missing 1 0 2 1

Season for TST

June–Aug 163 35 (22) 196 96 (49) 178 16 (9) 118 46 (39)

Sept–May 319 99 (31) 381 207 (54) 363 43 (12) 262 96 (37)

Bacterial load in index case

Scanty, 1� 104 27 (26) 140 68 (49) NA NA

2� 156 44 (28) 167 92 (55)

3� 222 63 (28) 270 143 (53)

Proximity to case/control during day

Together major part of day 403 108 (27) 428 224 (52) 398 44 (11) 268 107 (40)

Together during some partof day

75 24 (32) 149 79 (53) 141 15 (11) 108 32 (30)

Missing 4 2 2 0 4 3

Proximity to case/control during night

Same house, different room 309 67 (22) 378 187 (50) 326 43 (13) 263 97 (37)

Same room, different bed 105 35 (33) 108 62 (57) 82 6 (7) 39 13 (33)

In same bed 63 31 (49) 88 54 (61) 132 10 (8) 75 29 (39)

Missing 5 1 3 0 1 0 3 3

(Continued)

Gustafson et al Epidemiology • Volume 18, Number 3, May 2007


Contacts in case and control households who were testedduring the early rainy season (from June to August) tended tohave a lower risk of being skin test positive compared with thosetested during the rest of the year (OR � 0.79 �CI � 0.57–1.10�).Consistent with this trend, the risk of having no skin test reactionduring June to August was higher than for those tested duringthe rest of the year (1.45 �1.03–2.29�).

In neither case- nor control-contacts could we establisha correlation between positive TST reaction and markers ofcrowding, such as the number of persons living in the house-holds or the size of the dwelling. Potential markers of eco-nomic situation, such as ownership of the house or presence

of animals in the house had no impact on skin test reactivity(Table 2).

Sleeping in the same bed or in the same room as theindex case increased the risk of positive skin test response incase-contacts. However, risk of positive skin test reaction wasnot increased by the proximity to the case during daytime, bythe bacterial load of the case’s sputum, or by the sex or ageof the index case, in either children or adults (Table 2).

Potential markers of exposure to tuberculosis wereassociated with skin test reaction also among the controlcontacts. A history of tuberculosis in the family was foundto increase the risk of a positive skin test in both children



Children Adults Children Adults

TotalNo.

Positive TotalNo.

Positive TotalNo.

Positive TotalNo.

PositiveNo. (%) No. (%) No. (%) No. (%)

Sex of case/control

Male 215 66 (31) 282 159 (56) 230 25 (11) 162 64 (40)

Female 267 68 (26) 295 144 (49) 311 34 (11) 218 78 (36)

Age of case/control (yrs)

15–24 103 26 (25) 126 68 (54) 116 15 (13) 87 36 (41)

25–34 99 29 (29) 131 73 (56) 158 13 (8) 92 26 (28)

35–49 188 61 (33) 195 108 (55) 194 26 (13) 130 51 (39)

50� 92 18 (20) 125 54 (43) 73 5 (7) 71 29 (41)

Ownership of house

Yes 365 103 (28) 418 210 (50) 368 37 (10) 280 109 (39)

No 105 27 (26) 135 74 (55) 160 19 (12) 91 30 (33)

Missing 12 4 24 19 13 3 9 3

Presence of ceiling

Yes 66 25 (38) 104 51 (49) 62 6 (10) 59 24 (41)

No 407 107 (26) 452 235 (52) 455 52 (11) 300 111 (37)

Missing 9 2 21 17 24 1 21 7

Physical size of dwelling

�2 rooms 178 49 (28) 243 139 (57) 267 32 (12) 155 56 (36)

�2 rooms 293 83 (28) 312 149 (48) 270 26 (10) 217 83 (38)

Missing 11 2 22 15 4 1 8 5

Number of persons living in household

�5 441 121 (27) 490 251 (51) 444 49 (11) 307 114 (37)

�5 41 13 (32) 87 52 (60) 97 10 (10) 73 28 (38)

Animals indoors during night

Yes 216 60 (28) 217 108 (50) 239 26 (11) 156 59 (38)

No 257 72 (28) 339 178 (53) 282 30 (11) 207 80 (39)

Missing 9 2 21 17 20 3 17 3

Positive TST reaction in control

Yes NA NA 224 29 (13) 130 61 (47)

No 220 22 (10) 191 58 (30)

Missing 97 8 59 23

HIV status of case/control

Positive (1 or 2) 123 30 (24) 162 82 (51) 78 8 (10) 62 16 (26)

Negative 341 101 (30) 397 213 (54) 379 46 (12) 244 99 (41)

Missing 18 3 18 8 84 5 74 27

NA indicates not applicable.



TABLE 2. Multivariable Analysis of Risk Factors for Tuberculin Skin Test Reaction �10 mm in Case and Control Households,Separated for Adults and Children (Age �15 Years) and Adults

Risk Factor


Children(Age <15 yrs)

(n � 458)OR (95% CI)*

Adults(n � 542)

OR (95% CI)*

Children(Age <15 yrs)

(n � 491)OR (95% CI)*

Adults(n � 336)

OR (95% CI)*

SexMale vs. female 0.74 (0.47–1.16) 0.98 (0.67–1.44) 0.54 (0.29–1.00) 2.10 (1.28–3.46)

Age group (yrs)5–14 vs. 0–4 1.75 (1.08–2.85) 5.26 (2.32–12.0)25–34 vs. 15–24 0.63 (0.41–0.99) 0.83 (0.41–1.71)35–49 vs. 15–24 2.01 (1.24–3.27) 1.74 (0.86–3.52)50� vs. 15–24 0.67 (0.39–1.14) 0.84 (0.39–1.80)

Ethnic groupManjaco vs. Pepel 0.84 (0.31–2.25) 0.94 (0.48–1.85) 1.63 (0.62–4.25) 0.91 (0.42–1.99)Mancanha vs. Pepel 0.55 (0.18–1.70) 0.87 (0.44–1.73) 0.81 (0.30–2.13) 0.97 (0.42–2.23)Balanta vs. Pepel 1.16 (0.45–2.96) 1.39 (0.67–2.85) 0.69 (0.31–1.55) 0.37 (0.18–0.79)Fula vs. Pepel 0.77 (0.21–2.87) 0.88 (0.41–1.90) 0.46 (0.16–1.33) 0.45 (0.23–0.88)Mandinga vs. Pepel 0.90 (0.27–2.97) 0.74 (0.37–1.49) 0.23 (0.02–2.34) 0.79 (0.30–2.07)Other vs. Pepel 1.34 (0.51–3.49) 0.89 (0.44–1.82) 0.34 (0.13–0.89) 1.13 (0.49–2.60)

Presence of BCG scarYes vs. no 1.26 (0.76–2.08) 1.45 (1.01–2.07) 1.60 (0.75–3.45) 0.76 (0.46–1.24)

Personal history of tuberculosisYes vs. no NA 1.83 (0.64–5.24) NA 4.46 (0.50–40.1)

History of tuberculosis in the household or familyYes vs. no NA NA 17.3 (3.32–90.4) 3.39 (1.10–10.5)

Season for TSTJune–Aug vs. Sept–May 0.81 (0.44–1.49) 0.83 (0.54–1.30) 0.68 (0.35–1.32) 1.32 (0.79–2.20)

Bacterial load in index case2� vs. 1�/scanty 0.93 (0.42–2.09) 1.22 (0.69–2.14) NA NA3� vs. 1�/scanty 0.97 (0.46–2.02) 1.07 (0.62–1.85)

Proximity to case/control during dayTogether major part vs. some part 0.93 (0.54–1.63) 1.06 (0.71–1.58) 0.91 (0.40–2.05) 1.52 (0.84–2.76)

Proximity to case/control during nightSame room, different bed vs. different room 1.99 (1.03–3.85) 1.54 (1.01–2.36) 0.41 (0.15–1.14) 1.52 (0.66–3.51)Same bed vs. different room 4.15 (2.09–8.25) 1.38 (0.81–2.34) 0.61 (0.29–1.26) 1.11 (0.55–2.24)

Sex of case/controlMale vs. female 1.33 (0.79–2.25) 1.15 (0.75–1.76) 0.93 (0.49–1.80) 1.67 (1.00–2.81)

Age of case/control (years)25–34vs. 15–24 1.37 (0.64–2.96) 1.26 (0.71–2.26) 0.71 (0.28–1.81) 0.75 (0.39–1.47)35–49 vs. 15–24 1.25 (0.64–2.44) 1.02 (0.55–1.88) 1.16 (0.53–2.53) 1.20 (0.61–2.36)50– vs. 15–24 0.58 (0.25–1.36) 0.74 (0.38–1.43) 0.73 (0.21–2.60) 1.26 (0.61–2.64)

Ownership of houseYes vs. no 1.36 (0.72–2.58) 0.92 (0.58–1.48) 0.99 (0.48–2.06) 1.24 (0.69–2.23)

Presence of ceilingYes vs. no 1.47 (0.64–3.41) 0.82 (0.48–1.41) 0.33 (0.10–1.12) 0.98 (0.45–2.15)

Physical size of dwelling�2 rooms vs. �2 rooms 0.73 (0.41–1.29) 1.25 (0.78–1.99) 1.28 (0.69–2.39) 0.86 (0.48–1.52)

Number of persons living in household�5 vs. �5 1.18 (0.47–3.02) 0.89 (0.49–1.60) 0.85 (0.30–2.40) 0.96 (0.46–2.03)

Animals indoors during nightYes vs. no 0.88 (0.49–1.58) 0.90 (0.58–1.41) 0.84 (0.43–1.65) 1.17 (0.72–1.91)

Positive TST reaction in control†

Yes vs. no NA NA 1.69 (0.76–3.78) 2.13 (1.17–3.86)HIV status of case/control†

Positive (1 or 2) vs. Negative 0.56 (0.29–1.08) 0.80 (0.51–1.26) 1.04 (0.39–2.78) 0.21 (0.09–0.50)

NA indicates not applicable.*Adjusted for all variables in table, except HIV and TST status of control. Owing to missing values of HIV status of the case/control and TST status of control these 2 factors

were not adjusted for in the multivariable model.†Analysis of HIV and TST status of control performed in separate analysis since many missing values, adjusting for all variables.



and adults, and a positive skin test reaction in the controlincreased the risk among the adult contacts (Table 2).Furthermore, if the control was HIV-positive then the adultcontact had a considerably reduced risk of being tubercu-lin-skin-test-positive. Contacts of HIV-positive tuberculo-sis cases were also somewhat less likely to have a positiveskin-test response. Presence of a BCG scar was found to bea risk factor for a positive skin test in adults in the casehouseholds, but this effect was not seen in any of the othergroups.

Among adult control contacts the risk of positive skinreaction appeared to be higher if the control person was a man(Table 2), but this association did not persist after adjustingfor HIV status (OR � 0.99; CI � 0.57–1.72).

DISCUSSIONWe estimated the prevalence of positive tuberculin

skin-test reactions among family members in close and recentcontact with a smear-positive tuberculosis case, and in familymembers without such recent contact, and we investigatedpotential risk factors for positive skin-test responses. Wehave presented data using a TST reaction of 10 mm or moreas positive in both case and control contacts. Performing theanalyses with 5 mm as the cut-off point for case contacts, asrecommended by some tuberculosis programs, had no impacton the results. Several studies have shown that contact withtuberculosis cases increases the risk of a positive skin testreaction.7–10 In our data, both children and adults with recentand close exposure to smear-positive tuberculosis cases weremore likely to have positive skin test than those who did nothave such contact. The prevalence was greater for case-contacts than for control-contacts of skin reactions (regard-less of size).

In persons without recent tuberculosis contact, theprevalence of positive tuberculin skin-test reaction after theage of 15 was higher among men than women. Overall, menin the community control households had twice the risk ofpositive skin tests compared with women. The finding thatmen in the general population had a higher prevalence ofpositive skin-test reactions than women has also been re-ported in several studies from both Africa10,11 and Europe.12

However, in our study, among persons in close contact witha tuberculosis case, there was no sex difference in the prev-alence of positive skin test reactions, suggesting that womenare more prone to develop a positive reaction once exposed.In contrast to men, women were more likely to be skin-test-positive when living in contact with a tuberculosis case or apositive control, compared with living with a skin-test-nega-tive control. However, given that the tuberculin test is mainlyan indicator of past exposure, there ought to be more tuber-culin positive women in the general population as well, ifwomen are truly more likely to react to exposure as indicatedabove. To reconcile these findings, we suggest that womenmight have a better primary or booster response to tubercu-losis than men; women may therefore become less severelyinfected and may be less likely to maintain the same level oftuberculin positivity over time, compared with men. What-ever the mechanism, these trends suggest that there are majordifferences in the way men and women respond to tubercu-losis exposure.

Crowding or economic status had no apparent associ-ation with skin test responses, either in adults or children, ineither case or control households. The intensity of expo-sure13–15 and bacterial load14–16 have previously been re-ported to be important risk factors for skin test positivity. Wecould not establish an effect of the infectivity of the tuber-culosis case as assessed through the bacterial load in sputum,similar to a study performed in The Gambia.10 However,according to previous studies,15–17 the major difference ininfectivity lies between those who are smear-positive andthose who are positive in culture only. It should be empha-sized that all tuberculosis cases in our study were positive indirect microscopy in at least 2 sputum samples. Our studyshows that close contact to the case during the night increasesthe risk in both children and adults. Among controls, areported history of tuberculosis in the family increased therisk of positive skin test reaction in both children and adults.Furthermore, adult contacts had an increased risk of skin testpositivity if the control person was positive as well, presum-ably due to similar exposure to stimuli that might renderpositive reactions.

Data regarding the influence of BCG vaccination onTSTs are conflicting. A meta-analysis showed that immuni-zation with BCG increased the risk of a positive skin test,18

although several studies have shown that the skin test reactionwanes with time after BCG vaccination.7,9,11,12,18,19 In ourstudy, we could not establish a correlation between thepresence of a BCG scar and a positive skin test reactionamong the family members in the control households or inchildren living with tuberculosis cases. However, in adultsliving in case households, an association between the pres-ence of a BCG scar and a positive skin-test reaction wasobserved (adjusted OR � 1.45; 95% CI � 1.01–2.07). Thisfinding may be due to a boosting phenomenon, as adults witha BCG scar had larger skin reactions compared with adults whodid not have a scar (mean � SD � 9.4 mm � 7.4 and 7.9 � 7.2,respectively; P � 0.02). We were not able to confirm vaccina-tion status with vaccination cards and had to use the presence ofa BCG scar instead. Because of potential problems with scar

TABLE 3. Effect of Exposure for Tuberculin Skin TestPositivity in Women and Men (Adults Only)

Adjusted OR (95%CI)*

Women

Living with a case 4.89 (2.88–8.30)

Living with a skin-test-positive control 3.39 (1.72–6.69)

Living with a skin-test-negative control† 1.00

Men

Living with a case 1.06 (0.56–1.98)

Living with a skin-test-positive control 1.07 (0.31–3.72)

Living with a skin-test-negative control† 1.00

*Adjusted for all variables from Table 2 (including HIV-status of the control).†Reference category.



reading, results should be interpreted with caution. Traumaticscars and scars from smallpox vaccination may be misclassifiedas BCG scars.20 We tried to minimize this problem by usingassistants who were trained in scar reading and had performedscar reading surveys in the area before. It is also well known thatsubjects with a recorded BCG vaccination do not all develop andretain a recognizable scar.21–23

Even though the TST is the most commonly used testto assess potential tuberculosis infection, several papershave pointed out that there are limitations to using this skintest for that purpose.1,19,24 False-positive reactions mayoccur due to cross-reaction with other mycobacteria shar-ing the same antigens,25 such as environmental nontuber-culous mycobacteria,26 or as a result of BCG vaccina-tion.27–29 Similarly, repeated tuberculin testing may resultin boosting phenomena.30 Conversely, a negative skin testresult cannot rule out tuberculosis infection in situations ofimmunosuppression, which may lead to a hampered reac-tion to tuberculin. HIV-infection,31 as well as other viral32

or bacterial33 infections, or even a recent vaccination withlive virus,34 –36 as well as malnutrition37,38 may reducesensitivity to tuberculin. Seasonality in active tuberculosishas previously been reported39,40 and we found that seasonmight be 1 of the immunosuppressive factors that affects theinterpretation of skin test responses. The risk of negative skintest response was increased during the beginning of the rainyseason, from June to August, compared with the rest of theyear. Such a pattern has been demonstrated in studies previ-ously performed in Bissau, where children who were tuber-culin-tested during the early rainy season had lower frequen-cies of positive reactions.3,41 This may be due to a reductionin the cell-mediated immunity during the rainy season,42 or tothe fact that other common infections during the rainy season,such as malaria43 and respiratory infections,44 may have anegative effects on the immune response. Shaheen et al41 andGarly et al3 have reported a seasonal effect when adjustingfor such infections. TST performed during the early rainyseason may cause false negative reactions; and extra careshould be taken when interpreting the result. Follow-uptesting may be required in negative case contacts.

Newer serological tests, such as whole-blood interferon� (IFN-�) assays, may in the future play an important role indiagnosing tuberculosis infection. Studies from high-endemicpopulations have shown high agreement between TSTs andIFN-�-assay, both in children and adults, an advantage for thenewer tests being that they do not give false-positive reac-tions to prior BCG vaccination.45,46 The newer tests are,however, still hampered by the higher cost and the need forlaboratory infrastructure. In our study we found that a posi-tive TST is closely related to tuberculosis exposure, and thathaving a BCG scar had no impact on the skin-test result forunexposed persons. We believe that the inexpensive andeasily administered TST remains a useful tool for diagnosingtuberculosis infection in high-endemic populations.

REFERENCES1. Huebner RE, Schein MF, Bass JB Jr. The tuberculin skin test. Clin Infect

Dis. 1993;17:968–975.2. Gustafson P, Gomes VF, Vieira CS, et al. Tuberculosis in Bissau:

incidence and risk factors in an urban community in sub-Saharan Africa.Int J Epidemiol. 2004;33:163–172.

3. Garly ML, Bale C, Martins CL, et al. BCG vaccination among WestAfrican infants is associated with less anergy to tuberculin and diphthe-ria-tetanus antigens. Vaccine. 2001;20:468–474.

4. Lienhardt C, Bennett S, Del Prete G, et al. Investigation of environmen-tal and host-related risk factors for tuberculosis in Africa. I. Method-ological aspects of a combined design. Am J Epidemiol. 2002;155:1066–1073.

5. International Union Against Tuberculosis and Lung Disease. TechnicalGuide: Sputum Examination for Tuberculosis by Direct Microscopy inLow Income Countries. 5th ed. Paris: International Union AgainstTuberculosis and Lung Disease; 2000.

6. Diggle PJ, Liang K-Y, Zeger SL. Analysis of Longitudinal Data. NewYork: Oxford Science Publications; 1994.

7. Lockman S, Tappero JW, Kenyon TA, et al. Tuberculin reactivity in apediatric population with high BCG vaccination coverage. Int J TubercLung Dis. 1999;3:23–30.

8. Narain R, Nair SS, Rao GR, et al. Distribution of tuberculous infectionand disease among households in a rural community. Bull World HealthOrgan. 1966;34:639–654.

9. Saiman L, San Gabriel P, Schulte J, et al. Risk factors for latenttuberculosis infection among children in New York City. Pediatrics.2001;107:999–1003.

10. Lienhardt C, Fielding K, Sillah J, et al. Risk factors for tuberculosisinfection in sub-Saharan Africa: a contact study in The Gambia. Am JRespir Crit Care Med. 2003;168:448–455.

11. Fine PE, Bruce J, Ponnighaus JM, et al. Tuberculin sensitivity: conver-sions and reversions in a rural African population. Int J Tuberc LungDis. 1999;3:962–975.

12. Jentoft HF, Omenaas E, Eide GE, et al. Tuberculin reactivity: prevalenceand predictors in BCG-vaccinated young Norwegian adults. Respir Med.2002;96:1033–1039.

13. Lienhardt C, Sillah J, Fielding K, et al. Risk factors for tuberculosisinfection in children in contact with infectious tuberculosis cases in theGambia, West Africa. Pediatrics. 2003;111(5 Pt 1):e608–e614.

14. Almeida LM, Barbieri MA, Da Paixao AC, et al. Use of purified proteinderivative to assess the risk of infection in children in close contact withadults with tuberculosis in a population with high Calmette-Guerinbacillus coverage. Pediatr Infect Dis J. 2001;20:1061–1065.

15. Grzybowski S, Barnett GD, Styblo K. Contacts of cases of activepulmonary tuberculosis. Bull Int Union Tuberc. 1975;50:90–106.

16. Shaw JB, Wynn-Williams N. Infectivity of pulmonary tuberculosis inrelation to sputum status. Am Rev Tuberc. 1954;69:724–732.

17. van Geuns HA, Meijer J, Styblo K. Results of contact examination inRotterdam, 1967–1969. Bull Int Union Tuberc. 1975;50:107–121.

18. Wang L, Turner MO, Elwood RK, et al. A meta-analysis of the effect ofBacille Calmette Guerin vaccination on tuberculin skin test measure-ments. Thorax. 2002;57:804–809.

19. Snider DE Jr. Bacille Calmette-Guerin vaccinations and tuberculin skintests. JAMA. 1985;253:3438–3439.

20. Fine PE, Ponnighaus JM, Maine N. The distribution and implications ofBCG scars in northern Malawi. Bull World Health Organ. 1989;67:35–42.

21. Grindulis H, Baynham MI, Scott PH, et al. Tuberculin response twoyears after BCG vaccination at birth. Arch Dis Child. 1984;59:614–619.

22. Elliott A, Bradley AK, Tulloch S, et al. Tuberculin sensitivity in ruralGambian children. Ann Trop Paediatr. 1985;5:185–189.

23. Young TK, Mirdad S. Determinants of tuberculin sensitivity in a childpopulation covered by mass BCG vaccination. Tuber Lung Dis. 1992;73:94–100.

24. Rieder HL. Methodological issues in the estimation of the tuberculosisproblem from tuberculin surveys. Tuber Lung Dis. 1995;76:114–121.

25. Daniel TM, Janicki BW. Mycobacterial antigens: a review of theirisolation, chemistry, and immunological properties. Microbiol Rev.1978;42:84–113.

26. Edwards LB, Acquaviva FA, Livesay VT, et al. An atlas of sensitivity totuberculin, PPD-B, and histoplasmin in the United States. Am Rev RespirDis. 1969;99(Suppl):1–132.

27. Stewart CJ. Skin sensitivity to human, avian and BCG PPDs after BCGvaccination. Tubercle. 1968;49:84–91.

28. Sepulveda RL, Burr C, Ferrer X, et al. Booster effect of tuberculin



testing in healthy 6-year-old school children vaccinated with BacillusCalmette-Guerin at birth in Santiago, Chile. Pediatr Infect Dis J.1988;7:578–581.

29. Miret-Cuadras P, Pina-Gutierrez JM, Juncosa S. Tuberculin reactivity inBacillus Calmette-Guerin vaccinated subjects. Tuber Lung Dis. 1996;77:52–58.

30. Thompson NJ, Glassroth JL, Snider DEJr, et al. The booster phenome-non in serial tuberculin testing. Am Rev Respir Dis. 1979;119:587–597.

31. Garcia-Garcia ML, Valdespino-Gomez JL, Garcia-Sancho C, et al.Underestimation of Mycobacterium tuberculosis infection in HIV-infected subjects using reactivity to tuberculin and anergy panel. Int JEpidemiol. 2000;29:369–375.

32. Bloomfield AL, Mateer JG. Changes in skin sensitiveness to tuberculinduring epidemic influenza. Am Rev Tuberc. 1919;3:166–168.

33. Mitchell AG, Nelson WE, LeBlanc TJ. Studies in immunity. V. Effect ofacute diseases on the reaction of the skin to tuberculin. Am J Dis Child.1935;49:695–702.

34. Starr S, Berkovich S. Effects of measles, gamma-globulin-modifiedmeasles and vaccine measles on the tuberculin test. N Engl J Med.1964;270:386–391.

35. Brody JA, Overfield T, Hammes LM. Depression of the tuberculinreaction by viral vaccines. N Engl J Med. 1964;271:1294–1296.

36. Brody JA, McAlister R. Depression of tuberculin sensitivity followingmeasles vaccination. Am Rev Respir Dis. 1964;90:607–611.

37. Harland PS. Tuberculin reactions in malnourished children. Lancet.1965;2:719–721.

38. Sinha DP, Bang FB. Protein and calorie malnutrition, cell-mediated

immunity, and B.C.G. vaccination in children from rural West Bengal.Lancet. 1976;2:531–534.

39. Thorpe LE, Frieden TR, Laserson KF, et al. Seasonality of tubercu-losis in India: is it real and what does it tell us? Lancet. 2004;364:1613–1614.

40. Leung CC, Yew WW, Chan TY, et al. Seasonal pattern of tuberculosisin Hong Kong. Int J Epidemiol. 2005;34:924–930.

41. Shaheen SO, Aaby P, Hall AJ, et al. Cell mediated immunity aftermeasles in Guinea-Bissau: historical cohort study. Br Med J. 1996;313:969–974.

42. Lisse IM, Aaby P, Whittle H, et al. T-lymphocyte subsets in West Africanchildren: impact of age, sex, and season. J Pediatr. 1997;130:77–85.

43. Akinwolere OA, Williams AI, Akinkugbe FM, et al. Immunity inmalaria: depression of delayed hypersensitivity reaction in acute Plas-modium falciparum infection. Afr J Med Sci. 1988;17:47–52.

44. Kauffman CA, Linnemann CCJr, Schiff GM, et al. Effect of viral andbacterial pneumonias on cell-mediated immunity in humans. InfectImmun. 1976;13:78–83.

45. Pai M, Gokhale K, Joshi R, et al. Mycobacterium tuberculosis infectionin health care workers in rural India: comparison of a whole-bloodinterferon gamma assay with tuberculin skin testing. JAMA. 2005;293:2746–2755.

46. Dogra S, Narang P, Mendiratta K, et al. Comparison of a whole bloodinterferon-gamma assay with tuberculin skin testing for the detection oftuberculosis infection in hospitalized children in rural India. J Infect. Inpress.



COMMENTARY

Tuberculin Testing to Detect Latent Tuberculosis inDeveloping Countries

Kenrad Nelson

Abstract: Despite the multiple studies done over several decades thathave established the utility of the tuberculin skin test (TST) for thediagnosis of latent tuberculosis, the test is rarely used in developingcountries experiencing a resurgence of tuberculosis. Nevertheless, sev-eral clinical trials have found that treatment of HIV-positive or HIV-negative persons with latent tuberculosis is effective in the preventionof the clinical activation of tuberculosis.

Clinicians commonly justify their failure to diagnose and treatlatent tuberculosis with the belief that BCG vaccine, even when it isused in infancy, will cause false positive reactivity in the TST. Theimportant study by Gustafson and colleagues from Guinea-Bissau inthis issue of the journal refutes this belief. In this study only personswith a history of BCG who also had household contact with anactive case of tuberculosis had increased rates of TST positivity.

Although the current emphasis is on directly observed therapy,short course (DOTS) to control tuberculosis is necessary and criti-cally important, it is not always sufficient to control the tuberculosisepidemic in some countries with major epidemics of HIV. In manyof these countries, early diagnosis of active tuberculosis and pre-vention of activation of latent tuberculosis will also be needed. Theevidence from the Guinea-Bissau study suggests that a history ofBCG vaccination should not be an obstacle to the diagnosis andtreatment of latent tuberculosis.

(Epidemiology 2007;18: 348–349)

Tuberculin skin testing is a well-established tool to detectlatent infection with Mycobacterium tuberculosis. Al-

though it is an imperfect test, with both false positive andfalse negative results, it remains a very useful—indeed acritical—tool, both for epidemiologic research and the con-trol and prevention of clinical tuberculosis. Unfortunately,tuberculin skin testing is very much underutilized for tuber-culosis control in developing countries in Africa and Asiawhere there are serious HIV/AIDS epidemics. A major rea-son commonly given for not using the tuberculin skin test isthat prior BCG vaccination interferes with the interpretationof a positive skin test.

An important study of the utility of tuberculin skintesting among a population in Guinea-Bissau is described inthis issue of the journal,1 The authors studied 1059 familycontacts of 220 smear-positive tuberculosis cases, and 921contacts of 223 non-TB controls. They found that the tuber-culin skin test performed very well in the detection of latenttuberculosis. A BCG scar increased the likelihood of a pos-itive skin test only in persons who had household exposure toan active case of tuberculosis, not in the control households.The authors correctly interpret these data to indicate thattuberculin reactivity was due to latent M. tuberculosisinfection among those with household contact. In theabsence of such contact, BCG vaccination in infancy orchildhood rarely confounds the interpretation of a tuber-culin skin test several years later. Therefore, the widespreaduse of BCG among infants and children is not a generalcontraindication to the use of the tuberculin skin test.

A recently published study among medical house staffat 2 teaching hospitals in New York City found that graduatesfrom medical schools outside the United States were lesslikely than U.S. medical school graduates to offer treatment toprevent tuberculosis to persons with positive skin tests.2 Abouthalf of those educated elsewhere believed that a positive skin testwas usually due to a previous BCG vaccination.

We have recently published the results of a survey of anational sample of 300 physicians in Thailand, who wereselected by a multistage random cluster sampling of thosepracticing in public hospitals throughout the country.3 Thai-land has had a major outbreak of HIV since 1989, andtuberculosis is the most common opportunistic infectionamong patients with HIV in that country.4

It has been estimated that over 600,000 persons inThailand are currently infected with HIV.5 Over 25% ofadults have latent tuberculosis infections.6 Tuberculin skintesting of HIV-infected patients and prophylactic therapy isrecommended officially by the Ministry of Public Health toprevent reactivation tuberculosis in those with positive skintests.7 Despite these official recommendations, only 58 (19%)of the surveyed physicians reported that they screened HIV-infected patients for latent tuberculosis and provided therapyto skin-test reactors to prevent activation.

Many (40%) of these physicians stated that skin testingreagents were not available in their hospital. Also 25% wereconcerned that patients would not return to have their skintest read, and 33% felt that INH therapy for latent tubercu-losis infection would induce resistance or would be too toxic.One-third of the physicians who reported treating PPD-positive patients for latent tuberculosis infection did not do

From the Johns Hopkins School of Public Health Baltimore, Maryland.Correspondence: Kenrad Nelson, Johns Hopkins School of Public Health,

615 N. Wolfe Street, E7132, Baltimore, MD 21205. E-mail: [email protected].



chest radiographs to rule out active tuberculosis but relied onthe absence of clinical symptoms before initiating therapy.

The underutilization of skin testing and treatment ofthose with latent TB infection to prevent reactivation oftuberculosis for skin test positives is also a problem amongphysicians in the United States. A study among 630 physi-cians in San Francisco in 1995–1996 found that only 34%were aware of the US Public Health Service guidelines for theprevention of tuberculosis among HIV-positive patients andjust 39% did annual tuberculin skin testing and offeredtreatment of latent TB infection to skin test reactors.8

Clearly, tuberculin skin testing of HIV-positive sub-jects and those recently exposed to an active case of tuber-culosis are not as important as early detection and treatmentof active cases of tuberculosis (completion of a course ofeffective therapy, generally with directly observed therapy,short course, or DOTS). Nevertheless, latent tuberculosisinfection also deserves detection and treatment as part of anycomprehensive public health program to control tuberculosis.

It is of concern that skin testing and treatment of latenttuberculosis are rarely used in areas where they would bemost useful. In some countries with an active DOTS program,the tuberculosis epidemic has continued to expand,9,10 espe-cially in countries with a high prevalence of HIV infection.Additional strategies beyond DOTS are needed to controltuberculosis in these countries.9

There are, of course, some legitimate concerns aboutthe implementation of a widespread program of skin testingand treatment of latent tuberculosis infection. Inadequatescreening of skin-test-positives to rule out active tuberculosiscould lead to monotherapy of some patients with active TBand thus promote drug resistance. Some patients will notreturn to have their skin test read. However, only about 15%of HIV-positive patients who were enrolled in a prophylactictrial in Thailand failed to return.11 In addition, some patientswho are offered treatment of latent tuberculosis infection willfail to adhere to a course of drug therapy. Still, with adequatecounseling and frequent follow-up, over 80% of patientsadhered to 6–9 months of therapy for latent tuberculosis in 2reports from Thailand.11,12 In spite of these potential pitfallsof therapy for latent tuberculosis, a recent Cochrane review of11 trials (which included 8130 HIV-positive participants) foundan overall lower incidence of active tuberculosis in treatedpersons, (relative risk � 0.64; 95% confidence interval �0.51–0.81).13 The benefit was greater in persons with a positiveskin test, (0.38; 0.25–0.52) than in those who had a negativeskin test (0.83; 0.58–1.18). In 11 trials involving 73,375 HIV-uninfected patients the relative risk of tuberculosis was 0.40(0.31–0.52) after 6 or 12 months of isoniazid prophylaxis.14

Highly sensitive and specific in vitro assays for latentTB infection include new diagnostic tests that detect thesecretion of interferon gamma by peripheral mononuclearcells after exposure to RD1 antigens (ESAT6 and CFP10)from M. tuberculosis. These tests may be superior to tradi-tional tuberculin skin tests for the detection of latent tuber-culosis infection in some situations.15 However, these testsrequire a fairly sophisticated laboratory and little or no data

are available regarding their use in immunocompromisedpersons, especially those with HIV coinfection.

For the present, tuberculosis control programs in devel-oping countries with HIV epidemics should expand both theroutine use of the tuberculin skin test to screen high-riskpopulations, and treatment to prevent activation of latenttuberculosis. The evidence from Guinea-Bissau suggests thatprior BCG vaccination should not be an obstacle in theprovision of such services.

ABOUT THE AUTHORKENRAD E. NELSON is Professor of Epidemiology at

the Bloomberg School of Public Health, Johns HopkinsUniversity. His research interests are the epidemiologyand prevention of HIV infections and AIDS-related oppor-tunistic infections and viral hepatitis in the United States,Southeast Asia and the Republic of Georgia. He is theeditor of Infectious Diseases Epidemiology, Theory andPractice, 2nd edition.

REFERENCES1. Gustafson P, Lisse I, Gomes V, et al. Risk factors for positive tuberculin

skin test in Guinea-Bissau. Epidemiology. 2007;18:339–346.2. Hirsch-Moverman Y, Tsiouris S, Salazar-Schicchi J, et al. Physician

attitudes regarding latent tuberculosis infection, international vs. USmedical graduates. Int J Tuberc Lung Dis. 2006;10:1178–1180.

3. Hiransuthikul N, Hiransuthikul P, Nelson KE, et al. Physician adher-ence to isoniazid preventive therapy guidelines for HIV-infectedpatients in Thailand. Southeast Asian J Trop Med Public Health.2005;36:1208 –1215.

4. Chariyalertsak S, Sirisanthana T, Saengwonloey O, et al. Clinicalpresentation and risk behaviors of AIDS patients in Thailand 1994 –1998, regional variation and temporal trends. Clin Infect Dis. 2001;32:955–962.

5. UNAIDS. Report on the Global AIDS Epidemic. Geneva, Switzerland:WHO; 2005.

6. Siriarayapon P, Yanai H, Glynn JR, et al. The evolving epidemiology ofHIV infection and tuberculosis in Northern Thailand. J Acquir ImmuneDefic Dis. 2002;31:80–89.

7. Department of Communicable Disease Control, MOPH, Thailand andWorld Health organization. Second Review of the National TuberculosisProgram in Thailand. Bangkok, Thailand: Ministry of Public Health;1999.

8. DeRiemer K, Daley CL, Reingold AL. Preventing tuberculosis amongHIV-infected persons: a survey of physicians knowledge and practices.Prev Med. 1999;28:437–444.

9. DeCock KM, Chaisson RE. Will DOTS do it? A reappraisal of tuber-culosis control in countries with high rates of HIV infection. Int J TubercLung Dis. 1999;3:457–465.

10. Kenyon TA, Mwasekaga MJ, Huebner R, et al. Low levels of drugresistance amidst rapidly increasing tuberculosis and human immuno-deficiency virus co-epidemics in Botswana. Int J Tuberc Lung Dis.1999;3:4–11.

11. Hiransuthikul N, Nelson KE, Hiransuthikul P, et al. INH preventivetherapy among adult HIV-infected patients in Thailand. Int J TubercLung Dis. 2005;9:270–275.

12. Ngamvithayapong J, Uthaivoravit W, Yanai H, et al. Adherence totuberculosis preventive therapy among HIV-infected persons in ChiangRai, Thailand. AIDS. 2007;11:107–112.

13. Woldehanna S, Volmink J. Treatment of latent tuberculosis infection inHIV-infected persons. Cochrane Database of Syst Rev. 2006.

14. Smieja MJ, Marchetti CA, Cook DJ, et al. Isoniazid for preventingtuberculosis in non-HIV infected persons. Cochrane Database of SystRev. 1999:CD001363.

15. Pai M, Riley LW, Colford JM Jr. Interferon-gamma assays in theimmunodiagnosis of tuberculosis: a systematic review. Lancet InfectDis. 2004;4:761–776.

Epidemiology • Volume 18, Number 3, May 2007 Tuberculin Testing to Detect Latent Tuberculosis


ORIGINAL ARTICLE

Work Schedule During Pregnancy andSpontaneous Abortion

Elizabeth A. Whelan,* Christina C. Lawson,* Barbara Grajewski,* Eileen N. Hibert,†Donna Spiegelman,‡§ and Janet W. Rich-Edwards†‡¶

Background: There is inconsistent evidence as to whether workschedule (including rotating shifts and night work) can affect repro-ductive outcomes.Methods: We investigated the association between work scheduleand risk of spontaneous abortion in U.S. nurses. The Nurses’ HealthStudy II is a prospective cohort study established in 1989. In 2001,information about occupational activities and exposures duringpregnancy was collected from female nurses for the most recentpregnancy since 1993. Of 11,178 eligible respondents, 9547 (85%)indicated willingness to participate in the occupational study, and8461 of those (89%) returned the questionnaire, for an overallparticipation rate of 76%. Of these, 7688 women had pregnanciesthat were eligible for analysis.Results: Participants reported 6902 live births and 786 (10%)spontaneous abortions. Compared with women who reported usuallyworking “days only” during their first trimester, women who re-ported usually working “nights only” had a 60% increased risk ofspontaneous abortion (RR � 1.6; 95% confidence interval [CI] �1.3–1.9). A rotating schedule, with or without night shifts, was notassociated with an increase in risk (RR � 1.2 �CI � 0.9–1.5� and 1.0�CI � 0.8–1.2�, respectively). Women who reported working morethan 40 hours per week during the first trimester were also atincreased risk of spontaneous abortion (1.5; 1.3–1.7) compared withwomen working 21–40 hours, even after adjustment for workschedule.Conclusions: Nightwork and long work hours may be associatedwith an increased risk of spontaneous abortion. Further studies are

needed to determine whether hormonal disturbances attributed tonight work affect pregnancy outcome.

(Epidemiology 2007;18: 350–355)

Nearly 2.7 million nurses are employed in the UnitedStates; approximately half are women of reproductive

age.1 Many nurses maintain work schedules that includerotating shift work, night hours, and extended hours (morethan 40 hours per week). In the last decade, shift work,particularly rotating work and night work, has been reportedto increase the risk of certain adverse reproductive outcomessuch as spontaneous abortion.2,3,4 However, not all studieshave shown this association,5,6,7 and few studies have exam-ined the effect of shift work on the reproductive health ofU.S. health care workers.

The mechanisms by which shift work could affectpregnancy outcome are unclear. Hormonal disturbances, asan effect of sleep disturbance or circadian rhythm disruption,might possibly play a role. Nonstandard work hours disturbmany physiological functions and systems that are circadianin nature.8 We investigated the association between workschedule and risk of spontaneous abortion among participantsof the Nurses’ Health Study II.

METHODS

Study PopulationThe Nurses’ Health Study II is a prospective cohort

study of U.S. female nurses established in 1989. Approxi-mately 117,000 female nurses age 25 to 42 years and residingin 14 states responded to mailed questionnaires regardingtheir medical and reproductive histories and lifestyles.9 Fol-low-up questionnaires are mailed every 2 years to updateinformation on cardiovascular risk factors and the occurrenceof major illnesses. On the 2001 questionnaire, participantswere asked if they had experienced at least 1 pregnancy since1993, had worked as a nurse during the most recent of thesepregnancies, and would be willing to complete a supplemen-tal questionnaire concerning occupational activities and ex-posures. An occupational supplement was mailed to womenwho answered “yes” to all 3 questions. The questionnaireincluded detailed questions about specific exposures duringpregnancy. The survey was limited to events occurring duringthe most recent pregnancy to minimize recall error.

Submitted 1 May 2006; accepted 12 December 2006.From the *National Institute for Occupational Safety and Health, Centers for

Disease Control and Prevention, Cincinnati, OH; †Channing Laboratory,Department of Medicine, Brigham and Women’s Hospital, and HarvardMedical School; ‡Department of Epidemiology, Harvard School ofPublic Health; §Department of Biostatistics, Harvard School of PublicHealth; and ¶Connors Center for Women’s Health and Gender Biology,Brigham and Women’s Hospital, Boston, MA.

This work was supported by intramural funding from the National Institutefor Occupational Safety and Health and grant CA50385 from the Na-tional Cancer Institute.

The findings and conclusions in this report are those of the authors and do notnecessarily represent the views of the National Institute for OccupationalSafety and Health.

Correspondence: Christina C. Lawson, CDC/NIOSH, 4676 Columbia Park-way, R-15, Cincinnati, OH 45226. E-mail: [email protected].

Copyright © 2007 by Lippincott Williams & WilkinsISSN: 1044-3983/07/1803-0350DOI: 10.1097/01.ede.0000259988.77314.a4


Of 101,290 respondents to the main 2001 question-naire, 11,178 (11%) indicated that they had experienced apregnancy since 1993 during which they worked as a nurse.Of these women, 9547 (85%) indicated willingness to re-spond to a supplemental questionnaire; 645 (6%) declined;and 986 (9%) did not answer the question about the supple-mental survey. Of the 9547 women who were mailed thesupplemental questionnaire, 8461 responses were received(89%), with an overall response rate of 76%. Of those whoresponded, 262 pregnancies were ineligible for this analysis(31 women did not report the year that the pregnancy ended,130 pregnancies were not confirmed by a pregnancy test, 75respondents did not work as a nurse during the first trimesterand 26 respondents did not provide their work scheduleduring the first trimester). Of the 8199 eligible respondents,we excluded 511 (6%) pregnancies (228 twins or triplets, 144induced abortions, 56 tubal or ectopic pregnancies, 13 molarpregnancies, 41 stillbirths, and 29 who were missing infor-mation on length or outcome of pregnancy). This left 7688pregnancies for analysis.

Data CollectionTrimester-specific occupational exposures and activi-

ties assessed in the NHS-II supplement included work sched-ule (days only, evenings only, nights only, rotating withnights, rotating without nights, other/didn’t work); night work(none, 1–2 nights/month, 3–4 nights/month, 2–3 nights/week, 4� nights/week); and average hours worked per weekduring each trimester (none, 1–20 hours/week, 21–40 hours/week, 41–60 hours/week, 61� hours/week). Night shift wasdefined as a shift in which most work hours were betweenmidnight and 8:00 AM. Because only 38 women worked 61 ormore hours per week, we combined this group with thewomen who worked 41–60 hours per week. Other occupa-tional data included how often during the pregnancy therespondent lifted 25 pounds or more at work (never, 1–5times/day, 6–15 times/day, 16–30 times/day, 31� times/day); hours of standing or walking at work (�1 hour/day,1–4 hours/day, 5–8 hours/day, 9� hours/day); and hours perday of exposure to anesthetic gases, antineoplastic drugs,antiviral drugs, sterilizing agents, or x-ray radiation (0, 1–4,5–8, 9� hours). Data on potential confounders, such astrimester-specific smoking, caffeine, and alcohol consump-tion, were also collected. From the main cohort questionnaire,data were available on age, race/ethnicity, body mass index(BMI), prior spontaneous abortion, parity, and medicationuse. The questionnaire categories for self-reported race andethnicity included white, black or African American, Asian,American Indian/Alaska Native, Native Hawaiian or PacificIslander, and other.

We combined the work schedule data with informationabout night shifts to form the following mutually exclusivecategories: days only (reference), nights only, days/eveningswith no nights, and rotating shifts with nights.

We collected categorical information on pregnancyduration in weeks since last menstrual period (less than 8; 8to 11; 12–19; 20–23; 24–27; 28–31; 32–36; 37–41 (term);and 42 or more). Pregnancies that ended involuntarily before20 weeks gestation were considered spontaneous abortions.

We reclassified 14 spontaneous abortions that were reportedto occur at or after 20 weeks gestation as stillbirths, andrecoded 6 stillbirths that were reported to occur before 20weeks gestation as spontaneous abortions. Induced abortionswere excluded from the primary analyses, but were includedin a subanalysis to determine the effect of their exclusion onoverall results.

Statistical AnalysisAge-adjusted means and prevalence of selected cohort

characteristics were calculated. We examined the relationshipbetween work schedule and spontaneous abortion in bivariateand multivariate analyses. Indicator variables were createdfor shift work, age, hours worked, and parity. Variables wereretained in the model as confounders if they changed the riskestimate between work schedule and spontaneous abortion by10% or more.10 We used log binomial regression due to therelatively high prevalence (10%) of the outcome. Relativerisk (RR) estimates and their 95% confidence intervals (CIs)were computed using PROC GENMOD in SAS with thebinomial distribution and log link.11

The study was approved by the Institutional ReviewBoard of the Brigham and Women’s Hospital; completion ofthe self-administered questionnaire was considered to implyinformed consent.

RESULTSAmong 7688 confirmed pregnancies during which the

mother reported working as a nurse during the first trimester,786 (10%) ended in spontaneous abortion. Nearly 3-quarters(74%) of these ended before the 12th week of gestation. Themajority of women reported working a regular day scheduleduring their first trimester (68%), 16% reported rotatingbetween day and evening shifts, 9% reported working a nightschedule, and 7% reported working a rotating schedule thatincluded nights. Overall, 18% of women reported workingmore than 40 hours per week, on average, during their firsttrimester.

Table 1 shows the age-adjusted prevalence of selectedcharacteristics of the study population by category of shiftwork during the first trimester. Women who reported workingnights had a higher BMI and were somewhat less likely to bewhite compared with women working other shift schedules.Women who reported working nights or a rotating shift thatincluded nights reported consuming more servings of caffein-ated beverages per day during the first trimester comparedwith day and evening workers. Women who reported work-ing nights were somewhat more likely to smoke cigarettesduring the first trimester but were less likely to consumealcoholic beverages.

Some occupational risk factors varied by work shift.Women who worked nights and women who worked aday/evening shift were less likely than day workers androtating night shift workers to work more than 40 hours perweek during their first trimester (Table 1). Women whoworked nights and women who worked a rotating shift thatincluded nights reported a higher frequency of heavy lifting

Epidemiology • Volume 18, Number 3, May 2007 Work Schedule and Spontaneous Abortion


and prolonged standing or walking compared with day andevening workers.

Table 2 provides the estimated relative risks for spon-taneous abortion by work schedule, adjusting for age andparity. Compared with women who reported usually workingdays during their first trimester of pregnancy, women whoreported usually working nights were 60% more likely tohave a spontaneous abortion (RR � 1.6; 95% CI � 1.3–1.9).A rotating schedule, with or without nights, was not associ-ated with an increase in risk. Women who reported workingmore than 40 hours per week during the first trimester were50% more likely to have a spontaneous abortion (1.5;1.3–1.7), compared with women who reported working 21–40hours per week, even after adjustment for work shift. Otherwork-related factors, such as heavy lifting and prolongedstanding, were not associated with spontaneous abortion (datanot shown.) There were modest associations of spontaneousabortion with body mass index, regular use of medications,prior history of spontaneous abortion, caffeine consumption,alcohol consumption, cigarette smoking, and exposure toantineoplastic drugs or sterilizing agents at work; adjustingfor these variables did not materially change the findings.

To address concerns about the length of recall, westratified the analysis by year the pregnancy ended (1993–1996 versus 1997–2002), and the results were similar be-tween the 2 groups. We also stratified the analysis by reportedmedication use prior to the pregnancy, and both groups had

similar results. Including induced abortions in the final modeldid not change the results. Because parous women mightchoose to work different shifts than first-time pregnantwomen, we restricted the model to first pregnancies, and theresults showed a similar pattern (data not shown).

Table 3 provides results for analyses stratified by early(less than 12 weeks) versus late (12–20 weeks) spontaneousabortion; findings are similar to those in Table 2. Addition-ally, compared with women who reported usually working aday shift, women who worked a rotating day/evening shift(no nights) during their first trimester were at somewhathigher risk for late spontaneous abortion (RR � 1.5; 95%CI � 1.0–2.1). We observed the same pattern of results forthe effect of second-trimester exposures on late spontaneousabortion (not shown).

DISCUSSIONOur findings from this large cohort of nurses suggest

that consistent night work and extended hours of work duringthe first trimester of pregnancy may increase the risk ofspontaneous abortion. Our results for night work are consis-tent with 3 prior studies, which have reported relative risks of1.6 to 2.7 for regular night work.2,3,4 Three other prior studiesfound no associations for work shift with spontaneous abor-tion.5,6,7 Two of these studies combined night work withevening work,5,6 possibly explaining the inconsistency

TABLE 1. Age-Adjusted* Characteristics of Participants During Their First Trimester, by Categoryof Shift-Work

CharacteristicDays Only(n � 5242)

Nights Only(n � 680)

Rotating Shifts,With Nights

(n � 504)

Day/EveningRotating Shifts,Without Nights

(n � 1262)

Age at last menstrual period; mean � SD 36.9 � 3.5 36.3 � 3.6 36.4 � 3.5 36.2 � 3.4

BMI (kg/m2) before pregnancy; mean � SD 24.3 � 4.9 25.2 � 5.6 24.4 � 4.6 24.1 � 4.7

White; % 94.4 92.5 95.2 95.9

Parous; % 81.4 87.9 84.3 88.3

Prior spontaneous abortion; % 35.9 36.2 35.3 35.9

Beverage consumption

2� Caffeinated coffee per day; %† 10.2 13.4 15.5 9.8

2� Caffeinated soda or tea per day; %† 10.8 17.4 11.4 9.8

1� Alcoholic beverage per week; %‡ 5.5 3.7 4.5 4.9

Smokers; % 6.2 7.5 5.6 5.5

Hours worked per week; %

Percent distribution

�20 20.6 24.4 17.7 45.1

21–40 57.6 67.7 58.1 48.0

41� 21.8 7.9 24.2 6.9

Mean � SD 30.8 � 13.2 27.3 � 11.1 31.9 � 13.1 22.9 � 12.4

Lifting 6� times per day; %§ 16.1 40.6 36.4 30.5

Standing or walking at work 9� hours per day; % 16.4 42.0 39.8 20.3

*Directly standardized by year of age at pregnancy.†Servings of caffeinated beverages � 8 oz coffee, 12 oz soda, 8 oz hot tea, 16 oz iced tea.‡Servings of alcoholic beverages � 12 oz beer, 6 oz wine, 1 oz liquor.§Lifting refers to lifting or moving a physical load of 25 pounds or more, including repositioning or transferring patients.

Whelan et al Epidemiology • Volume 18, Number 3, May 2007


among findings. Differences in findings might also be ex-plained by the wide range in the number of women workingnights in each study; our study had the largest number ofnight workers (n � 680).

The mechanisms by which shift work could affectspontaneous abortion are unclear. However, hormonal distur-bances, as an effect of circadian rhythm disruption, sleepdisturbance, or psychosocial stress, might possibly play a rolein altering the balance of cellular immune response necessaryto maintain pregnancy. Nonstandard work hours disturbmany physiological functions and systems that are circadianin nature,8 including the normal nocturnal production of thehormone melatonin. In 1992, Sack et al12 reported that nightshift workers had more variability in the amount and timingof melatonin production, indicating incomplete shift adapta-tion by these workers. Many permanent night shift workersrevert to a daytime schedule on their days off and thereforehave a 180 degree reversal of their sleep-wake cycle everyweek. Further, exposure to light at night suppresses thenormal nocturnal release of melatonin, which in turn maytrigger an alteration in other hormone levels, including estro-gen. Recent research has found an elevated risk of breastcancer among women who work mainly at night.13,14 Anotherrecent report from the Nurses’ Health Study found increasedlevels of estradiol after longer duration of night work.15

Whether such alterations in hormones might affect the risk ofspontaneous abortion is not clear.

We saw little evidence for an association of sponta-neous abortion risk with a rotating work schedule thatinvolves nights, suggesting that it is the steady nightschedule that may affect pregnancy outcome. Women whowork a rotating schedule may not reach a particular thresh-old at which night work begins to have an effect onpregnancy. Other adverse health effects in rotating shiftworkers have been reported.16

Two population-based studies have addressed longhours of work and risk of spontaneous abortion.5,17 Ourfinding of an increased risk is consistent with one large priorstudy,17 but not with another prior study that had fewer nursesworking over 40 hours per week.5 Few studies have examinedthe reproductive effects of long working hours, and thepotential mechanism is unclear. Studies suggest that longwork hours may be associated with sleep disturbance, fatigue,stress, and decrements in physiologic functioning.18,19

Our study has several limitations. Because we relied onself-report, pregnancy-related exposures and outcomes mayhave been inadequately recalled. However, nurses are well-educated professionals who are presumably more sensitizedto health events than the general population. In addition,respondents reported events that occurred relatively recently(within the last 8 years). When we stratified our analysis bythe year the pregnancy ended (1993–1996 versus 1997–2002), the results were similar, suggesting that there was littleeffect of time between the event and the interview. A previ-ous study reported that among women who had a positivepregnancy test before the spontaneous abortion, 95% of thespontaneous abortions were validated by medical records.20

We restricted the main analysis to the 98% of participantswho reported that their pregnancies had been confirmed bypregnancy tests, reducing the potential problem of a latemenstrual period being misclassified as a miscarriage. How-ever, including the 130 women who did not confirm their

TABLE 2. Association Between Work Schedule During theFirst Trimester and Risk of Spontaneous Abortion

Adjusted*RR (95% CI)

Shift

Days only† 1.0

Rotating shifts, no nights 1.0 (0.8–1.2)

Rotating shifts, with nights 1.2 (0.9–1.5)

Nights only 1.6 (1.3–1.9)

Hours worked per week

�20 1.1 (0.9–1.3)

21–40† 1.0

41� 1.5 (1.3–1.7)

Age (yrs)

� 30 0.3 (0.2–0.7)

31–35 0.4 (0.4–0.5)

36–40† 1.0

41� 2.7 (2.3–3.0)

Parity

0† 1.0

1� 0.6 (0.5–0.7)

*Each variable in the model is adjusted for the remaining variables.†Reference category.

TABLE 3. Association Between Work Schedule During theFirst Trimester and Risk of Early Versus Late SpontaneousAbortion

Spontaneous Abortion

Early (<12 wk)Adjusted*

RR (95% CI)

Late (12–20 wk)Adjusted*

RR (95% CI)

Shift

Days only† 1.0 1.0

Rotating shifts, no nights 0.8 (0.7–1.1) 1.5 (1.0–2.1)

Rotating shifts, with nights 1.2 (0.9–1.6) 1.2 (0.7–2.0)

Nights only 1.6 (1.2–2.0) 1.8 (1.2–2.8)

Hours worked per week

�20 1.0 (0.8–1.3) 1.1 (0.8–1.6)

21–40† 1.0 1.0

41� 1.5 (1.3–1.8) 1.7 (1.2–2.3)

Age (yrs)

�30 0.2 (0.1–0.7) 0.5 (0.2–1.5)

31–35 0.5 (0.4–0.6) 0.3 (0.2–0.5)

36–40† 1.0 1.0

41� 3.0 (2.5–3.5) 2.5 (1.9–3.3)

Parity

0† 1.0 1.0

1� 0.5 (0.4–0.6) 0.9 (0.6–1.3)

*Each variable in the model is adjusted for the remaining variables.†Reference category.



pregnancies with a pregnancy test did not change the results.We do not know at what gestational age the pregnancy testswere taken. Although it is possible that women working nightshifts might take a pregnancy test later than women workingother shifts (thus failing to change lifestyle patterns as earlyas other workers), this does not seem a likely explanation ofour findings.

To investigate the potential for response bias, we com-pared demographic variables among eligible nonparticipantsand participants using data that were available from the mainNHS-II biennial questionnaires. Variables that we used forcomparison included age, BMI, household income, race,history of spontaneous abortion, and shift work (neverworked permanent night shifts and worked permanent nightshifts more than 6 months between 1995 and 2001). None ofthese variables differed materially between the 2 groups.

Relatively little information is available on the accu-racy of recall of occupational activities and exposures such aswork schedule, although 2 studies have shown good accuracyof job history data obtained from interview.21,22 Becausework schedule affects home and family, we expect that thenurses in our study recalled their work schedules duringpregnancy with a relatively good degree of accuracy. Thepossibility for recall bias also must be acknowledged in anystudy in which the information is based on self-report fol-lowing the occurrence of an adverse event. However, recallbias may have been minimized because there has not beenwidespread concern that night work may be associated withincreased risk of spontaneous abortion. In addition, otherwork-related factors, such as heavy lifting and prolongedstanding, were not associated with spontaneous abortion inthis study, which suggests that recall bias was unlikely.

We cannot rule out the possibility that women who chooseto work nights or longer hours have more health problems thatmay put them at higher risk of spontaneous abortion than thosewho choose a regular day schedule. We did observe that nightworkers had a higher body mass index, were more likely tosmoke, and consumed more caffeine than day workers, althoughadjustment for these factors did not change the increase in therisk estimates associated with night work and long hours ofwork. However, we did not assess general health status orpsychosocial stress during pregnancy. It is also possible thatmany nurses do not choose their schedules, but must work nightsor rotating shifts because of other factors, such as low seniorityor household income. Though there may be variabilities insocioeconomic status (SES) among nurses, the nurses in thisstudy comprise a fairly homogeneous group compared with thegeneral population. While we did not collect information aboutincome during each pregnancy, controlling for household in-come and husband’s or partner’s education level obtained fromthe 2001 biennial questionnaire did not change the associationbetween night work and spontaneous abortion. We also con-trolled for smoking and BMI, both commonly associated withSES, and they made little difference. However, we cannot ruleout the possibility that other unobserved markers of SES may, inpart, explain our results.

The U.S. Department of Labor estimates that approxi-mately 14% of the United States working population (about 15.5

million workers) work evening, night, irregular, or rotatingshifts, and over 30% of workers in health service occupationswork shifts other than a regular daytime schedule.23 In theNurses’ Worklife and Health study,24 more than a quarter of thesample reported that they typically worked 12 or more hours perday. A third worked more than 40 hours per week. Nurses areincreasingly working overtime as a way to reduce the impact ofcritical staffing shortages.25 Nearly half of the respondents to arecent American Nurses Association staffing survey reportedthat mandatory overtime was used to cover routine personnelshortages.26 The Association has put forward a position state-ment opposing mandatory overtime as a staffing tool.27

Our work suggests that night work and extended hoursof work are potential hazards to reproductive health. Al-though a causal relationship has not been firmly establishedand the biologic mechanism has not been clearly elucidated,alternative work patterns should be considered that may helpto counter the potential adverse effects.

ACKNOWLEDGMENTSWe are grateful for the valuable advice and guidance

from Joyce Clifford, Claire Caruso, Roger Rosa, and TeresaSchnorr.

REFERENCES1. United States Department of Health and Human Services, Health Re-

sources and Services Administration, Division of Nursing. The NationalSample of Registered Nurses, 2000. February, 2002.

2. Infante-Rivard C, David M, Gauthier R, et al. Pregnancy loss and workschedule during pregnancy. Epidemiology. 1993;4:73–75.

3. Axelsson G, Ahlborg G, Bodin L. Shift work, nitrous oxide exposure,and spontaneous abortion among Swedish midwives. Occup EnvironMed. 1996;53:374–378.

4. Zhu JL, Hjollund NH, Anderson AN, et al. Shift work, job stress, andlate fetal loss: the national birth cohort in Denmark. J Occup EnvironMed. 2004;46:1144–1149.

5. Eskenazi B, Fenster L, Wight S, et al. Physical exertion as a risk factorfor spontaneous abortion. Epidemiology. 1994;5:6–13.

6. Fenster L, Hubbard AE, Windham GC, et al. A prospective study ofwork-related physical exertion and spontaneous abortion. Epidemiology.1997;8:66–74.

7. Swan SH, Beaumont JJ, Hammon SK, et al. Historical cohort study ofspontaneous abortion among fabrication workers in the SemiconductorHealth Study: agent-level analysis. Am J Ind Med. 1995;28:751–769.

8. Akerstedt T. Psychological and psychophysiological effects of shiftwork. Scand J Work Environ Health. 1990;16:67–73.

9. The Nurses’ Health Study. Available at: http://www.channing.harvard.edu/nhs/. Accessed June 11, 2006.

10. Greenland S. Modeling and variable selection in epidemiologic analysis.Am J Public Health. 1989;8:669–673.

11. Spiegelman D, Hertzmark E. Easy SAS calculations for risk or preva-lence ratios and differences. Am J Epidemiol. 2005;162:199–200.

12. Sack RL, Blood ML, Lewy AJ. Melatonin rhythms in night shiftworkers. Sleep. 1992;15:434–441.

13. Hansen J. Increased breast cancer risk among women who work pre-dominantly at night. Epidemiology. 2001;12:74–77.

14. Schernhammer ES, Laden F, Speizer FE, et al. Rotating night shifts andrisk of breast cancer in women participating in the nurses’ health study.J Natl Cancer Inst. 2001;93:1563–1568.

15. Schernhammer ES, Rosner B, Willett WC, et al. Epidemiology ofurinary melatonin in women and its relation to other hormones and nightwork. Cancer Epidemiol Biomarkers Prev. 2004;13:936–943.

16. Gibbs M, Hampton S, Morgan L, et al. Effect of shift schedule onoffshore shiftworkers’ circadian rhythms and health. Health and SafetyExecutive Research Report 318. Her Majesty’s Stationery Office, Nor-wich, UK, 2005.

Whelan et al Epidemiology • Volume 18, Number 3, May 2007


17. McDonald AD, McDonald JC, Armstrong B, et al. Fetal death and workin pregnancy. Br J Indt Med. 1988;45:148–157.

18. Caruso CC, Bushnell T, Eggerth D, et al. Long working hours, safety,and health: toward a national research agenda. Am J Ind Med. 2006;49:930–42.

19. Caruso CC. Possible broad impacts of long working hours. Ind Health.2006;44:531–536.

20. Axelsson G. Use of questionnaires in a study of spontaneous abortion ina general population. J Epidemiol Community Health. 1990;44:202–204.

21. Baumgarten M, Siemiatycki J, Gibbs G. Validity of work historiesobtained by interview for epidemiologic purposes. Am J Epidemiol.1983;118:583–591.

22. Brisson C, Vezina M, Bernard PM, et al. Validity of occupationalhistories obtained by interview with female workers. Am J Ind Med.

1991;19:523–530.23. United States Department of Labor, Bureau of Labor Statistics. Current

Population Survey 2001. Washington, DC. Available at: http://www.bls.gov/news.release/flex.nr0. htm. Accessed January 11, 2006.

24. Trinkoff A, Geiger-Brown J, Brady B, et al. How long and how muchare nurses now working? Am J Nurs. 2006;106:60–71.

25. Trossman S. Fighting the clock: nurses take on mandatory overtime. AmNurse. 1998;30:1012

26. American Nurses Association. Analysis of American Nurses StaffingSurvey. Washington, DC: American Nurses Association, 2001.

27. American Nurses Association. Position Statement: Opposition to Man-datory Overtime. Washington, DC: American Nurses Association, 2001.Available at: http://www.nursingworld.org/readroom/position/workplac/revmot2. htm. Accessed January 11, 2006.



ORIGINAL ARTICLE

Maternal Stressful Life Events and Risks of Birth DefectsSuzan L. Carmichael,* Gary M. Shaw,* Wei Yang,* Barbara Abrams,† and Edward J. Lammer‡

Background: Several previous studies suggest that maternal stressmay be associated with increased risk of certain birth defects. Thisstudy examined the association of maternal stressful life events withrisks of several birth defects.Methods: The data are from a recent, population-based case-controlstudy. Telephone interviews were conducted with 1355 eligible casemothers and 700 control mothers. Maternal stress was measured byresponses to 18 yes/no questions about life events that occurred from2 months before through 2 months after conception.Results: An increase in the stressful life events index (ie, number of“yes” responses to the 18 life-events questions) was associated withincreased risk of cleft palate, cleft lip with or without cleft palate,d-transposition of the great arteries, and tetralogy of Fallot, afteradjustment for maternal race-ethnicity, education, obesity, age,smoking, drinking, intake of folic acid-containing supplements,neighborhood crime, and food insecurity. For example, the oddsratio for a 3-unit change in the stress index was 1.45 (95% confi-dence interval � 1.03–2.06) for cleft palate. Increased stress wasassociated with an increased risk of spina bifida and anencephalyparticularly among women who did not take folic acid supplements.A 3-unit change in stress was associated with a 2.35-fold increasedrisk of anencephaly among women who did not take supplements(CI �1.47–3.77) and a 1.42-fold increased risk among women whodid (CI � 0.89–2.25).Conclusion: The adverse health effects of stress may includeincreased risks of certain birth defects.

(Epidemiology 2007;18: 356–361)

Several observational studies have examined the associationof maternal stressful life events with risks of orofacial clefts

among offspring.1–9 All but 2 of these studies5,9 reported in-creased risk of clefts among offspring born to women withhigher stress. Only a few studies have examined birth defects

other than orofacial clefts; they have reported increased risks ofneural tube defects (NTDs)7,10 and conotruncal heart defects7,11

among women with higher stress. An important limitation ofprevious studies is that the measurement of stress has beennonstandardized or very limited in scope, for example collectingdata on only 2 or 3 life events.

One mechanism by which maternal stressors may causebirth defects is through increased production of corticosteroids.Corticosteroids are teratogenic in animal models, for variousorgan systems.1,12,13 Stressful life events have been shown to beassociated with elevated maternal corticotrophin-releasing hor-mone and corticosteroid levels during pregnancy.14,15 Furthersupport for an association between stress and birth defects riskscomes from the finding that infants born to women who tookcorticosteroid medications during the first trimester of pregnancyhad an increased risk of oral clefts.16–20 Another potentialmechanism by which stress may cause birth defects is negativecoping behaviors that lead to hazardous exposures such assmoking or alcohol intake or reduced nutrient intakes.

The evidence from animal models regarding corticoste-roids, combined with limited but suggestive findings fromhuman epidemiologic studies, establish the need to furtherexamine the association between maternal stress and birthdefects. This study examines the association of maternalstressful life events with risk of orofacial clefts, NTDs andconotruncal heart defects among offspring, using data from arecent, population-based case-control study.

METHODSThis case–control study included liveborn, stillborn

(fetal deaths at �20 weeks gestation), and prenatally diag-nosed, electively terminated cases that occurred to mothersresiding in Los Angeles, San Francisco, and Santa Claracounties. The study included data on deliveries that hadestimated due dates from July 1999 to June 2004. CaliforniaBirth Defects Monitoring Program staff abstracted case in-formation from medical records at hospitals and at geneticcounseling centers serving the study population, to find casesdiagnosed with birth defects before 1 year of age. Thisinformation was reviewed by a clinical geneticist (EJL).Infants diagnosed with single-gene disorders or chromosomalaneusomies were ineligible for this study. Each case wasclassified as isolated if there was no concurrent major mal-formation, or as nonisolated if there was at least 1 accompa-nying major malformation. Case groups included cleft palate,cleft lip with or without cleft palate, spina bifida, anenceph-aly, and the conotruncal heart defects (d-transposition of thegreat arteries and tetralogy of Fallot). Spina bifida includedcases of lipomeningocele, meningomyelocele, and myelocys-tocele. For each conotruncal heart defect case, anatomic and

Submitted 30 October 2006; accepted 29 December 2006.From the *March of Dimes Birth Defect Foundation/California Department

of Health Services, California Birth Defects Monitoring Program, Berke-ley, CA; †School of Public Health, University of California, Berkeley,CA; and ‡Children’s Hospital Research Institute, Oakland, CA.

Supported by NIH grant number R01 HD 42538-03; a cooperative agreementfrom the Centers for Disease Control and Prevention, Centers of Excel-lence Award No. U50/CCU913241; and the Cigarette and TobaccoSurtax Fund of the California Tobacco-Related Diseases Research Pro-gram, University of California, grant number 13RT-0109.

Correspondence: Suzan Carmichael, California Birth Defects Monitoring Pro-gram, 1917 Fifth Street, Berkeley, CA 94710. E-mail: [email protected].



physiologic features were confirmed by reviewing echocar-diography, cardiac catheterization, surgery, or autopsy re-ports (EJL). Infants with d-transposition of the great arteriesassociated with an endocardial cushion defect or with doubleoutlet right ventricle were excluded. Ascertainment of cleftsand NTDs ended with the estimated due date of June 30,2003; ascertainment of d-transposition of the great arteriesand tetralogy of Fallot ended with the estimated due date ofJune 30, 2004. Nonmalformed, liveborn controls were se-lected randomly from birth hospitals, to represent the popu-lation from which the cases were derived. Ascertainment ofcontrols ended with the estimated due date June 30, 2004.

Mothers were eligible for interview if 1) they were thebiologic mother and carried the pregnancy of the selectedstudy subject, 2) they were not incarcerated, and 3) theirprimary language was English or Spanish. Maternal inter-views were conducted using a standardized, computer-basedquestionnaire, primarily by telephone, in English or Spanish,no earlier than 6 weeks after the infant’s estimated due date.Numerous exposures were assessed, focusing on the pericon-ceptional time period, which was defined as the 2 monthsbefore through the 2 months after conception.

In total, 80% of eligible case mothers (n � 1355) and77% of control mothers (n � 700) were interviewed. Elevenpercent of eligible case mothers and 12% of control motherswere not locatable, and the remainder of the mothers declinedto participate. The median time between estimated due dateand interview completion was 10 months for cases and 8months for controls. We excluded from all analyses cases andcontrols with a family history of the selected defects in aparent or sibling, mothers who had type I or II diabetes, andmothers taking medications to prevent seizures (80 cases and5 controls). Nonisolated cleft cases were also excluded, giventhat a different etiology is suspected for nonisolated clefts (86cases). After these exclusions, we had available for analyses695 controls (analyses of clefts and NTDs were restricted tothe 623 controls with estimated due date, through June 30,2003) and 1189 cases—139 anencephaly, 186 spina bifida,145 isolated cleft palate, 419 isolated cleft lip with or withoutcleft palate, 165 tetralogy of Fallot, and 136 d-transpositionof the great arteries cases. (One case had 2 eligible diag-noses—anencephaly and tetralogy of Fallot).

Stressful Life EventsMothers answered an 18-item inventory of stressful life

events to assess the occurrence of specific events during thepericonceptional period. The questions were taken from theKaiser Permanente/ California Department of Health Study ofPregnancy and Stress, and largely parallel many of the ques-tions in existing, validated stressful life events assessmenttools.21–23 Questions included only potentially major events,and responses were yes/no, to maximize ability to recall theevents objectively. Each woman was asked whether she orher husband (or partner) had started a new job or lost a job;whether she or anyone close to her had had a serious illnessor injury, serious legal or financial problems, problems withdrinking or drugs, or had problems with immigration;whether she or anyone close to her had been a victim ofviolence or crime; whether anyone close to her had died;

whether she was separated or divorced or had had seriousdifficulties with her husband or partner; whether she hadmoved; and whether she had had serious problems or dis-agreements with relatives, neighbors, or in-laws. For legalreasons, mothers who were less than 18-year-old at the timeof conception were not asked the questions about violence orcrime. These young women (60 case mothers and 45 controlmothers) were assigned as “No” for both of these questions sothat they could be retained in the analyses. A stressful-life-events index was formed by summing the number of “yes”responses to these questions, giving equal weight to each.This assumes that the effects of the stressful life events arecumulative and additive across the various events.24

CovariatesSeveral known risk factors for the selected birth defects

were included as covariates: maternal race–ethnicity (US-born Hispanic, foreign-born Hispanic, non-Hispanic white,other); education (did not complete high school, high schoolgraduate, some college, 4-year college degree or more);prepregnancy obesity (body mass index �29 versus �29kg/m2); age at delivery; the following exposures during thefirst 2 months after conception: cigarette smoking (any versusnone), and alcohol intake (any versus none), and intake offolic acid-containing multivitamin/mineral supplement (anyversus none) during the periconceptional period. Mothersalso answered a series of 6 questions related to neighborhoodcrime25 and 5 questions related to food insecurity26 (ie, a lackof access to food to meet basic needs)26 during the pericon-ceptional period; these 2 factors served as measures ofchronic stress in the living environment. Previous studiessuggest that chronic stressors may exacerbate the effects ofacute stressors24 and may be important to reproductive out-comes.24,25,27 A summary index was formed for each of theseseries of questions, by summing the number of “yes” re-sponses to the questions.

AnalysesThe unadjusted association of the sum of stressful life

events with each outcome was evaluated using logistic re-gression to estimate odds ratios (ORs) and 95% confidenceintervals (CIs). We specified the stress index as continuousfor most analyses, but we also examined the index as cate-gorical to ensure that a continuous specification seemedappropriate. Second, we evaluated whether the addition of aquadratic term improved the fit of the models, to determinewhether the association of stress with the outcomes wasnonlinear or suggested a threshold effect. Third, potentialeffect modification by covariates was assessed by includingall 2-way interactions of stressful life events with eachcovariate in a single model for each outcome. The finalmultivariable models contained all covariates, and any inter-action terms that had a P value �0.10 in the model thatincluded all 2-way interaction terms.

RESULTSMost of the mothers of case and controls were His-

panic, many had less than a high school education, a majoritytook a folic acid-containing supplement during the pericon-

Epidemiology • Volume 18, Number 3, May 2007 Maternal Stressful Life Events and Risks of Birth Defects


ceptional period, a majority did not report any positiveresponses to the questions about neighborhood crime, andmost did not report any positive responses to the questionsabout food insecurity (Table 1). The percentage of mothers ofcontrols reporting “yes” to each individual stressful life eventquestion ranged from 1% to 21%. Correlations among thestressful life events questions ranged from 0.003 to 0.43, withmost ranging from 0.1 to 0.2; results were similar for themothers of cases (data not shown). A total of 56% of themothers of controls and 64% of the mothers of cases reportedat least 1 stressful life event (Table 2).

Table 2 shows the frequency distribution of the stress-ful life events index. When birth defects are examined as asingle group, the ORs tend to increase with increasing stress-ful life events. A similar pattern of results was observed foreach phenotype when analyzed separately (data not shown).We therefore specified the stressful life events index as a

continuous variable in further analyses. Table 3 shows theunadjusted odds ratios reflecting the association of the con-tinuous stressful life events index with each outcome. Anincrease in the stressful life events index was associated withincreased risk of all types of birth defects, with the strongestassociation for isolated cleft lip with or without cleft palateand anencephaly. Addition of a quadratic term for the stress-ful life events index did not substantially improve models(P-values for the quadratic terms were all �0.05). We re-stricted the unadjusted analyses to women with no missingdata on any covariates (1036 cases and 622 controls); resultswere similar when they included all women with data onstress (1174 cases and 688 controls) (data not shown).

Results adjusted for all covariates are also shown inTable 3. A 3-unit change in the stressful life events index wasassociated with an approximately 30–80% increased risk.The adjusted ORs tended to be slightly larger than theunadjusted ORs.

In the multivariable models that contained all 2-wayinteractions of the stress index with the covariates, there were2 interaction terms with P-values �0.10: stress by folic acidsupplement intake for anencephaly (P value 0.078) and stressby folic acid for spina bifida (P � 0.098). We simplified themodels to explore these 2 interactions: these models includedall covariates and the interaction terms for the stress indexwith folic acid intake but excluded the other 2-way interac-tion terms. The P-values for the folic acid interaction terms inthese simplified models were 0.108 for anencephaly and0.109 for spina bifida. The association of stressful life eventswith anencephaly and spina bifida was stronger amongwomen who did not take folic acid supplements than amongwomen who did take supplements (Table 4). For example, a

TABLE 1. Characteristics of Mothers of Cases (n � 1189)and Controls (n � 695)

Cases* Controls*

Race-ethnicity; no. (%)

US-born Hispanic 194 (17) 153 (22)

Foreign-born Hispanic 501 (43) 263 (38)

Non-Hispanic White 273 (23) 142 (21)

Other 208 (18) 127 (18)

Education; no. (%)

�High school graduation 381 (32) 200 (29)

High school graduation 240 (20) 166 (24)

1–3 yr of college 276 (24) 150 (22)

4 or more yr of college 277 (24) 166 (24)

Prepregnancy obesity

BMI �29 kg/m2 881 (82) 532 (82)

BMI �29 kg/m2 197 (18) 118 (18)

Age at delivery (yrs); mean � SD† 28.74 � 6.36 28.31 � 6.42

Smoking‡

No 1108 (94) 633 (92)

Yes 72 (6) 54 (8)

Alcohol intake‡

No 941 (80) 563 (82)

Yes 235 (20) 122 (18)

Intake of folic acid-containing supplements§

No 515 (43) 270 (39)

Yes 672 (57) 423 (61)

Neighborhood crime�

None 640 (55) 374 (55)

Any 516 (45) 303 (45)

Food insecurity�

None 978 (83) 597 (87)

Any 201 (17) 91 (13)

*Numbers of cases and controls are less than the total due to missing data.†Based on 1187 cases and 692 controls.‡During the first 2 months after conception.§During the 2 months before or 2 months after conception.�The percentage of women with any versus no “yes” responses to the series of

questions, during the 2 months before through 2 months after conception.

TABLE 2. Frequency of Number of Stressful Life EventsReported by Mothers of Cases and Controls, From 2 MonthsBefore Through 2 Months After Conception, and AssociationWith Risk of Birth Defects

No. ofStressful Life Events

Cases(n � 1174)*

No. (%)

Controls(n � 688)*

No. (%) OR (95% CI)

0† 424 (36) 300 (44) 1.0

1 248 (21) 146 (21) 1.20 (0.93–1.55)

2 195 (17) 100 (15) 1.38 (1.04–1.83)

3 111 (9) 56 (8) 1.40 (0.99–2.00)

4 86 (7) 38 (6) 1.60 (1.06–2.41)

5 41 (3) 19 (3) 1.53 (0.87–2.68)

6 39 (3) 11 (2) 2.51 (1.26–4.98)

7 9 (1) 8 (1) 1.18 (0.65–2.16)‡

8 9 (1) 5 (1) —

9 8 (1) 5 (1) —

10 2 (�1) 0 —

11 2 (�1) 0 —

*Numbers of cases and controls are less than the total due to missing data.†Reference category.‡Seven or more stressful life events were collapsed into a single category for

estimation of the odds ratio.

Carmichael et al Epidemiology • Volume 18, Number 3, May 2007


3-unit change in the stressful life events index was associatedwith a 2.4-fold increased risk of anencephaly among womenwho did not take folic acid supplements, and a 1.4-foldincreased risk among women who did take supplements.

DISCUSSIONIn this population-based case-control study, more

stressful life events experienced by the mother around thetime of conception were associated with increased risks oforofacial clefts, NTDs, and conotruncal heart defects amongoffspring. Increased risks were not explained by potentialcovariates—maternal race–ethnicity, education, obesity, age,smoking, drinking, intake of folic acid-containing supple-ments, neighborhood crime, and food insecurity. Observa-tions are consistent with previous studies,1–4,6–8,10,11 with amuch more detailed assessment of stress and a larger set ofpotential covariates than in previous studies.

As noted above, previous studies of these birth defectsand stress also suggested increased risks. Whether this con-sistency of findings is due to a true association or pervasive

bias across studies is uncertain, given the retrospective natureof most designs and the extremely limited exposure assess-ment by previous studies. Most studies of clefts includedmeasures of stress that were vague or assigned by the inter-viewer rather than the woman.2–6,9 Studies that includedneural tube defects and conotruncal defects were limited toonly a few events.7,8,10,11

Several lines of evidence support the plausibility of ourfindings. Stress results in increased catecholamine produc-tion, which in turn leads to decreased uterine blood flow andincreased fetal hypoxia.28 Animal studies indicate that hyp-oxia affects a variety of developmental processes (eg, celldeath)29 and organ systems, which could result in varioustypes of birth defects.12,30 Increased glucocorticoid levels arealso associated with hyperinsulinemia and insulin resis-tance,31 which in turn may be associated with increased risksof the studied birth defects.11,32

In an evaluation of effect modification by potentialcovariates, the association of anencephaly and spina bifidawith maternal stressful life events was stronger amongwomen who did not take folic acid-containing supplements.Experimental evidence suggests that vitamin B6, which is acomponent of most folic acid-containing multivitamin/min-eral supplements, may prevent glucocorticoid-mediated cleftpalate.33–35 A proposed mechanism for this observation isthat increased vitamin B6 results in reduced tissue respon-siveness to glucocorticoids via suppression of glucocorticoidreceptor activity.36 Most experimental studies of the terato-genic effects of glucocorticoids have investigated orofacialclefting in the offspring; the explanation for our finding withNTDs, but not with clefts or conotruncal heart defects, is notapparent.

Strengths of this study include its comprehensive caseascertainment, detailed phenotypic review, population-basedcontrol selection, and satisfactory level of participation inmaternal interviews. Given the relatively low frequency ofthe individual birth defects, we were limited to a retrospectivestudy design, which does not allow measurement of potentialphysiologic correlates of stress during organogenesis. Evenwith improvements in the assessment of stress relative to

TABLE 4. Association of a 3-Unit Change in theStressful-Life-Events Index With Risk of Neural Tube Defects,Stratified by Folic Acid Supplement Intake During the First 2Months After Conception

Folic AcidNo.

CasesNo.

Controls OR (95% CI)

Anencephaly

No 55 210 2.35 (1.47, 3.77)

Yes 70 346 1.42 (0.89, 2.25)

Spina Bifida

No 70 210 1.79 (1.14, 2.81)

Yes 86 346 1.09 (0.69, 1.72)

*Reflects the OR for a 3-unit change in the stressful life events index; the OR foran n-unit change equals (OR for a 3-unit change)n/3; eg the OR for a 6-unit changeequals (OR for a 3-unit change)6/3; ORs are adjusted for maternal race-ethnicity,education, obesity, age, smoking, alcohol intake, neighborhood crime and foodinsecurity.

TABLE 3. Unadjusted and Adjusted Association of a 3-Unit Change in the Stressful-Life-EventsIndex With Risk of Selected Birth Defects

No.Cases

No.Controls

OR (95% Confidence Interval)*

UnadjustedOR (95% CI)

Adjusted†

OR (95% CI)

Isolated cleft lip with or without cleft palate 364 556 1.40 (1.13–1.72) 1.34 (1.06–1.71)

Isolated cleft palate 122 556 1.26 (0.93–1.70) 1.45 (1.03–2.06)

Anencephaly 125 556 1.56 (1.18–2.07) 1.81 (1.28–2.56)

Spina Bifida 156 556 1.29 (0.98–1.70) 1.39 (1.00–1.94)

d-Transposition of the great arteries 124 622 1.15 (0.85–1.55) 1.27 (0.89–1.81)

Tetralogy of Fallot 145 622 1.26 (0.96–1.66) 1.38 (1.00–1.91)

*Reflects the OR for a 3-unit change in the stressful life events index; the OR for an n-unit change equals (OR for a 3-unit change)n/3;eg the OR for a 6-unit change equals (OR for a 3-unit change)6/3.

†Adjusted for maternal race-ethnicity, education, obesity, age, smoking, alcohol intake, folic acid supplement intake, neighborhood crime,and food insecurity.



previous studies of birth defects, our assessment of stress hadlimitations. The time-window of stressful events (2 monthsbefore through 2 months after conception) was somewhatbroad. We cannot be sure whether we were truly studying theeffects of chronic or of acute stress. Furthermore, the expo-sure window was not completely inclusive of development ofall organs being studied (eg, closure of the palate maycontinue up to 12 weeks after conception). We limited theassessment of stress to the relatively objective report ofwhether certain events had occurred around the time ofconception, because we felt women would be less able toobjectively recall their perceived level of stress around thetime of conception, after having delivered offspring withmajor malformations. However, recall bias still could haveoccurred, and the interpretation of the questions could havediffered for mothers of cases and controls (eg, in how theydefined “anyone close” to them). There is concern that moth-ers of malformed infants will overreport or more thoroughlyreport exposures than controls,37–39 but several studies sug-gest that for many exposures recall bias is likely to be mini-mal38–41; however, none of these studies considered stress perse. We lacked information regarding whether the events oc-curred to the woman herself or the relatively subjective categoryof “anyone close” to her, and we gave equal weighting to allevents. We also did not assess social support, which may be animportant buffer of the stress response. One previous studyobserved that emotional support was associated with reducedrisks of NTDs, but it did not modify or “buffer” their associationwith stressful life events.10 Another study observed that socialnetworks were protective against NTDs.42 We are unaware ofother studies of social support and birth defects risks.

Increasing evidence suggests that stress during preg-nancy is associated with adverse health effects among off-spring.43,44 The present study indicates that these adverseeffects may include increased risks of certain birth defects.

REFERENCES1. Montenegro MA, Palomino H, Palomino HM. The influence of earth-

quake-induced stress on human facial clefting and its simulation in mice.Archs Oral Biol. 1995;40:33–37.

2. Laumon B, Martin JL, Bertucat I, et al. Exposure to organic solventsduring pregnancy and oral clefts: a case–control study. Reprod Toxicol.1996;10:15–19.

3. Czeizel A, Nagy E. A recent aetiological study on facial clefting inHungary. Acta Paediatr Hung. 1986;27:145–166.

4. Saxen I. Cleft lip and palate in Finland: parental histories, course ofpregnancy and selected environmental factors. Int J Epidemiol. 1974;3:263–270.

5. Fraser FC, Warburton D. No association of emotional stress or vitaminsupplement during pregnancy to cleft lip or palate in man. Plast ReconstrSurg. 1964;33:395–399.

6. Strean LP, Peer LA. Stress as an etiologic factor in the development ofcleft palate. Plast Reconstr Surg. 1956;18:1–8.

7. Carmichael SL, Shaw GM. Maternal life event stress and congenitalanomalies. Epidemiology. 2000;11:30–35.

8. Hansen D, Lou HC, Olsen J. Serious life events and congenital malfor-mations: a national study with complete follow-up. Lancet. 2000;356:875–880.

9. Rajabian MH, Sherkat M. An epidemiologic study of oral clefts in Iran:analysis of 1,669 cases. Cleft Palate Craniofac J. 2000;37:191–196.

10. Suarez L, Cardarelli K, Hendricks K. Maternal stress, social support, and

risk of neural tube defects among Mexican Americans. Epidemiology.2003;14:612–616.

11. Adams MM, Mulinare J, Dooley K. Risk factors for conotruncal cardiacdefects in Atlanta. J Am Coll Cardiol. 1989;14:432–442.

12. Rowland J, Hendrickx A. Corticosteroid teratogenicity. Adv Vet SciComp Med. 1983;27:99–128.

13. Fraser F, Fainstat T. The production of congenital defects in theoffspring of pregnant mice treated with cortisone: a progress report.Pediatrics. 1951;8:527–533.

14. Hobel CJ, Dunkel-Schetter C, Roesch SC, et al. Maternal plasmacorticotropin-releasing hormone associated with stress at 20 weeks’gestation in pregnancies ending in preterm delivery. Am J ObstetGynecol. 1999;180(1):S257–S263.

15. Wadhwa PD, Dunkel-Schetter C, Chicz-DeMet A, et al. Prenatal psy-chosocial factors and the neuroendocrine axis in human pregnancy.Psychosom Med. 1996;58:432–446.

16. Carmichael SL, Shaw GM. Maternal corticosteroid use and orofacialclefts. Am J Med Genet. 1999;86:242–244.

17. Czeizel AE, Rockenbauer M. Population-based case–control study ofteratogenic potential of corticosteroids. Teratology. 1997;56:335–340.

18. Robert E, Vollset S, Botto L, et al. Malformation surveillance andmaternal drug exposure: the MADRE project. Risk Saf Med. 1994;6:78–118.

19. Rodriguez-Pinilla E, Martinez-Frias M. Corticosteroids during preg-nancy and oral clefts: a case-control study. Teratology. 1998;58:2–5.

20. Kallen B. Maternal drug use and infant cleft lip/palate with specialreference to corticoids. Cleft Palate Craniofac J. 2003;40:624–628.

21. Holmes TH, Rahe RH. The social readjustment rating scale. J Psycho-som Res. 1967;11:213–218.

22. Sarason IG, Johnson JH, Siegel JM. Assessing the impact of lifechanges: development of the life experiences survey. J Consult ClinPsychol. 1978;46:932–946.

23. Newton RW, Hunt LP. Psychosocial stress in pregnancy and its relationto low birth weight. Br Med J. 1984;288:1191–1194.

24. McLean D, Hatfield-Timajchy K, Wingo P, et al. Psychosocial measure-ment: implications for the study of preterm delivery in black women.Am J Prev Med. 1993;9(Suppl):39–81.

25. Collins JW, David RJ, Symons R, et al. African American mothers’perception of their residential environment, stressful life events, andvery low birthweight. Epidemiology. 1998;9:286–289.

26. Blumberg SJ, Bialostosky K, Hamilton WL, et al. The effectiveness ofa short form of the household food security scale. Am J Public Health.1999;89:1231–1234.

27. Hobel C, Culhane J. Role of psychosocial and nutritional stress on poorpregnancy outcome. J Nutr. 2003;133:1709S–1717S.

28. Istvan J. Stress, anxiety, and birth outcomes: a critical review of theevidence. Psychol Bull. 1986;100:331–348.

29. Sulik KK, Cook CS, Webster WS. Teratogens and craniofacial malfor-mations: relationships to cell death. Craniofac Dev. 1988;103:213–232.

30. Meyer R, Aldrich T, Easterly C. Effects of noise and electromagneticfields on reproductive outcomes. Environ Health Perspect. 1989;81:193–200.

31. Andrews RC, Walker BR. Glucocorticoids and insulin resistance: oldhormones, new targets. Clin Sci (Lond). 1999;96:513–523.

32. Aberg A, Westbom L, Kallen B. Congenital malformations amonginfants whose mothers had gestational diabetes or preexisting diabetes.Early Hum Dev. 2001;61:85–95.

33. McCarty MF. Prenatal high-dose pyridoxine may prevent hypertensionand syndrome X in-utero by protecting the fetus from excess glucocor-ticoid activity. Med Hypotheses. 2000;54:808–813.

34. Yoneda T, Pratt RM. Vitamin B6 reduces cortisone-induced cleft palatein the mouse. Teratology. 1982;26:255–258.

35. Peer LA, Bryan WH, Strean LP, et al. Induction of cleft palate in miceby cortisone and its reduction by vitamins. J Int Coll Surg. 1958;30:249–254.

36. McCarty MF. High-dose pyridoxine as an ‘anti-stress’ strategy. MedHypotheses. 2000;54:803–807.

37. Lippman A, Mackenzie SG. What is “recall bias” and does it exist? In:Marois M, ed. Prevention of Physical and Mental Congenital Defects.New York: Alan R. Liss; 1985:205–209.

38. Swan SH, Shaw GM, Shulman J. Reporting and selection bias in

Carmichael et al Epidemiology • Volume 18, Number 3, May 2007


case-control studies of congenital malformations. Epidemiology. 1992;3:356–363.

39. Khoury MJ, James LM, Erickson JD. On the use of affected controls toaddress recall bias in case–control studies of birth defects. Teratology.1994;49:273–281.

40. Klemetti A, Saxen L. Prospective versus retrospective approach in thesearch for environmental causes of malformations. Am J Public Health.1967;57:2071–2075.

41. Werler MM, Pober BR, Nelson K, et al. Reporting accuracy among

mothers of malformed and nonmalformed infants. Am J Epidemiol.1989;129:415–421.

42. Carmichael SL, Shaw GM, Neri E, et al. Social networks and risk ofneural tube defects. Eur J Epidemiol. 2003;18:129–133.

43. Hobel CJ. Stress and preterm birth. Clin Obstet Gynecol. 2004;47:856–880.

44. Weinstock M. The potential influence of maternal stress hormones ondevelopment and mental health of the offspring. Brain Behav Immun.2005;19:296–308.



ORIGINAL ARTICLE

Vitamin B12 and the Risk of Neural Tube Defects in aFolic-Acid-Fortified Population

Joel G. Ray,* Philip R. Wyatt,† Miles D. Thompson,‡ Marian J. Vermeulen,§ Chris Meier,¶Pui-Yuen Wong,‡ Sandra A. Farrell,� and David E. C. Cole**

Background: Low maternal vitamin B12 status may be a risk factorfor neural tube defects (NTDs). Prior studies used relatively insen-sitive measures of B12, did not adjust for folate levels, and wereconducted in countries without folic acid food fortification. InCanada, flour has been fortified with folic acid since mid-1997.Methods: We completed a population-based case–control study inOntario. We measured serum holotranscobalamin (holoTC), a sen-sitive indicator of B12 status, at 15 to 20 weeks’ gestation. Therewere 89 women with an NTD and 422 unaffected pregnant controls.A low serum holoTC was defined as less than 55.3 pmol/L, thebottom quartile value in the controls.Results: The geometric mean serum holoTC levels were 67.8pmol/L in cases and 81.2 pmol/L in controls. There was a trend ofincreasing risk with lower levels of holoTC, reaching an adjustedodds ratio of 2.9 (95% confidence interval � 1.2–6.9) when com-paring the lowest versus highest quartile.Conclusions: There was almost a tripling in the risk for NTD in thepresence of low maternal B12 status, measured by holoTC. Thebenefits of adding synthetic B12 to current recommendations forpericonceptional folic acid tablet supplements or folic-acid-fortifiedfoods need to be considered. It remains to be determined whatfraction of NTD cases in a universally folate-fortified environmentmight be prevented by higher periconceptional intake of B12.

(Epidemiology 2007;18: 362–366)

Neural tube defects (NTD), manifesting as anencephaly orspina bifida, are to some degree preventable develop-

mental anomalies. The risk of NTD can be reduced nearly50% with periconceptional maternal exposure to folic acidtablet supplements1 or fortified flour, as we have seen inCanada.2 Despite these remarkable accomplishments, about 6to 12 in every 10,000 fetuses in Canada have NTD.2,3

A systematic overview of previously published datasuggested that a deficiency of vitamin B12 (B12, cobalamin)might also be associated with a higher risk of NTD.4 How-ever, these studies are characterized by relatively few partic-ipants, lack of adjustment for maternal folate status, variabil-ity in the time of specimen collection during pregnancy, andrelatively inaccurate measurement of bioavailable maternalB12.4 As a consequence, there is ongoing debate aboutwhether B12 should be added to folic acid tablet supplementsor fortified foods.5 We evaluated serum holotranscobalamin(holoTC)—the fraction of total circulating B12 bound totranscobalamin II. Since holoTC is the only form of B12 takenup by tissues, it should be a sensitive measure of maternal B12

status, as it may be related to NTD risk.6 B12 also appears tobe the leading nutritional determinant of hyperhomocysteine-mia within folic acid-fortified populations.7 We thereforestudied whether this associated risk changed after the fortifi-cation of Canadian flour by folic acid.

METHODSWe performed a population-based case–control study.2,8

Since 1993, under the universal Ontario Health Insurance Planall pregnant women are offered standardized maternal serumscreening at no financial cost. Maternal screening is madeavailable at 15 to 20 weeks’ gestation through a physician ormidwife; about 60% of pregnant women are screened. Self-reported maternal date of birth, gravidity, ethnicity, weight atthe time of screening, and pregestational diabetes mellituswere recorded on the screening requisition sheet.

Women with a positive screen are referred for counsel-ing at one of 17 genetics centers in Ontario. Each centersupplies follow-up data to the Ontario Maternal SerumScreening Database. Open NTD cases are detected antena-tally by ultrasonography or fetal autopsy, or postnatallythrough data linkage of the mother’s insurance plan numberwith that of her infant during the delivery hospitalization,through the Canadian Institute for Health Information Dis-charge Abstract Database.2,8

Submitted 13 September 2006; accepted 4 December 2006; posted 28February 2007.

From the *Departments of Medicine, Obstetrics and Gynecology, and HealthPolicy Management and Evaluation, St. Michael’s Hospital, Universityof Toronto, Toronto, Ontario; †Department of Genetics, York CentralHospital, Richmond Hill, Ontario; ‡Department of Clinical Biochemis-try, University of Toronto, Toronto, Ontario; §Institute for ClinicalEvaluative Sciences, University of Toronto, Toronto, Ontario; ¶St. Mi-chael’s Hospital, University of Toronto, Toronto, Ontario; �RegionalGenetics Program, The Credit Valley Hospital, Mississauga, Ontario; and**Department of Clinical Pathology, Sunnybrook Health Sciences Cen-tre, University of Toronto, Toronto, Ontario.

Supported by the Spina Bifida and Hydrocephalus Association of Canada,and the physicians of Ontario, through the Physicians’ Services Incor-porated Foundation. Dr. Ray is supported by the Canadian Institutes forHealth Research New Investigator Award.

Editors’ note: A commentary on this article appers on page 367.Correspondence: Joel G. Ray, Department of Medicine, St. Michael’s Hos-

pital, 30 Bond Street, Toronto, Ontario M5B 1W8, Canada. E-mail:[email protected].

Copyright © 2007 by Lippincott Williams & WilkinsISSN: 1044-3983/07/1803-0362DOI: 10.1097/01.ede.0000257063.77411.e9


Two large metropolitan genetics centers in SouthernOntario—the North York General Hospital (Toronto) andThe Credit Valley Hospital (Mississauga)—handle about halfof all serum specimens acquired at the time of screening andstored frozen. Blood samples were collected in a red topvacutainer or serum separator tube, and the serum transferredinto a plastic transport tube, without a preservative, and thenfrozen at 70°C.

Cases were all women who had a pregnancy affected bya myelomeningocele or anencephaly, and whose screeningdatabase record corresponded to one of the 2 genetics centers.For each case, 5 maternal controls with a healthy pregnancywere randomly selected from these 2 centers within a plus-or-minus 12 months of the case specimen. Serum HoloTCwas determined using a radioimmunoassay, according to themanufacturer’s protocol (Axis-Shield, Ireland).9 Analyticsensitivity is about 5 pmol/L, with a between-assay impreci-sion of 8.5% at 32 pmol/L and 7.6% at 94 pmol/L. Serumfolate was analyzed using the Bayer Centaur immunoassayanalyzer method (Bayer Diagnostics Division, Toronto, ON).The distributions of the holoTC and folate values werepositively skewed, so we log-transformed these measures andused inverse transformations to generate geometric meansand standard deviations.

Statistical AnalysisWe compared the year of screening for cases and

controls using a �2 test for trend. The mean serum folate andmean holoTC concentrations among cases and controls werecompared using an unpaired t test. An abnormally low ho-loTC was defined, a priori, as a concentration less than orequal to the bottom quartile value in the control group. Forthe main analysis, we calculated a crude odds ratio (OR) and95% confidence interval (CI). We used logistic regression toestimate the adjusted OR, with the highest quartile concen-tration of holoTC as the referent. We adjusted for maternalage (1-year increments), gravidity, race (white versus non-white), weight, low-income status, presence of pregestationaldiabetes mellitus and serum folate concentration (as a con-tinuous variable) at the time of screening. Low-income statuswas based on data from the 2001 Canadian Census.10 Thismethod uses the first 3 digits of each woman’s 6-digit postalcode to designate a geographical region, and then determinesthe prevalence of households in that same region who spend55% or more of their income on food, shelter, and clothing.10

A secondary analysis, limited to the period after March 1,1997, when folic acid food fortification was begun,11 used thesame multivariate model as above. Finally, we estimated apopulation attributable risk percent of NTD in relation to lowholoTC, using the adjusted OR.12,13

All variables were included in the model a priori. Theresearch protocol was approved by the Ministry of Health andLongterm Care in Ontario, as well as the Research EthicsBoards of Sunnybrook Health Sciences Centre, St. Michael’sHospital and The North York General Hospital, with partic-ipant identifiers removed from the data set prior to analysis.

RESULTSThere were 317 cases of NTD in Ontario during the

period of study. Of these, 89 cases originated from the NorthYork General and Credit Valley Hospitals. Of the 434 unaf-fected pregnant controls selected from these 2 centers, 12were excluded due to an insufficient volume of serum, leav-ing 422 controls (Table 1). About half (n � 248) of allparticipants were enrolled after fortification of flour hadbegun. Of 89 NTD cases, 67 (75%) were diagnosed antena-tally. No difference was observed between cases and controlsin the year of specimen collection (�2 test for trend: P � 0.99)(Table 1). The mean concentration of serum folate did notdiffer appreciably between cases and controls across theentire study period (13.3 versus 13.9 nmol/L; P � 0.07).

The overall geometric mean serum holoTC level wassubstantially lower among cases (67.8 pmol/L) than con-trols (81.2 pmol/L), a mean of difference of 13.4 pmol/L(95% CI � 13.0–13.8) (Table 1). Comparing the lowest withthe highest quartile of maternal holoTC concentration, thecrude OR for NTD was 2.0 (95% CI � 1.1–3.9), increasingto 2.9 (1.2–6.9) in the adjusted model (Table 2). Thecorresponding population-attributable risk for NTD in re-lation to low holoTC was 34%.

TABLE 1. Characteristics of Selected Women in OntarioWith (Cases) and Without (Controls) a Pregnancy Affectedby a Neural Tube Defect (NTD), 1993–2004

Maternal CharacteristicCases

(n � 89)Controls(n � 422)

Age (yrs); mean � SD 28.6 � 4.8 29.8 � 5.1

Gravidity; median 2.0 2.0

Weight (kg); mean � SD 68.6 � 15.5 67.3 � 13.6

Prepregnancy diabetes mellitus; no. (%) 3 (3) 5 (1)

Nonwhite ethnicity; no. (%) 23 (29) 60 (14)

Postfortification of flour; no. (%) 44 (49) 204 (48)

Percent low income status; mean � SD 13.0 � 6.7 13.2 � 7.5

Year of enrollment; no. (%)

1993 5 (6) 23 (5)

1994 24 (27) 120 (28)

1995 16 (18) 75 (18)

1996–1997* 7 (8) 29 (7)

1998 12 (14) 59 (14)

1999 6 (7) 26 (6)

2000 5 (6) 25 (6)

2001 3 (3) 21 (5)

2002 6 (7) 20 (5)

2003–2004* 5 (6) 24 (6)

Serum folate (nmol/L); geometricmean � SD

13.3 � 3.0 13.9 � 2.8

Serum holotranscobalamin concentration(pmol/L)

Geometric mean � SD 67.8 � 2.0 81.2 � 1.8

Lowest quartile — 55.3

*Few cases and controls were recorded, and are therefore combined over thetwo-year period.

Epidemiology • Volume 18, Number 3, May 2007 Low Holotranscobalamin and NTD Risk


In the period before fortification, the mean � SD serumfolate concentrations were 8.8 � 3.5 nmol/L among the casesand 10.3 � 3. nmol/L in the controls. After fortification,serum folate concentrations rose to 20.9 � 2.0 nmol/L forcases and 19.4 � 2.2 nmol/L for controls. Similarly, thelevels of holoTC were 58.5 � 1.9 pmol/L in cases and 72.7 �1.8 pmol/L among the controls and after fortification 78.8 �2.0 pmol/L in cases and 91.3 � 1.8 pmol/L in controls.Restricting the analyses to the time after food fortificationwith folic acid, the association of low holoTC with risk ofNTD remained strong (adjusted OR � 3.2; CI � 0.94–11.0).

DISCUSSIONUsing a sensitive and specific measure of bioavailable

cobalamin, we observed nearly a tripling in the risk of NTDin the presence of low maternal B12 status. In a Canadiansetting of moderate folic acid tablet supplement use14 anduniversal folic acid flour fortification,2 these data suggest thatabout 34% of NTD may be due to low B12.

Our study has some limitations. The collection ofmaternal specimens about 15 weeks after conception wouldbe expected to dilute any true relationship between B12insufficiency and NTD risk.15 We do not know how manycases or controls were taking folic acid or B12-containingtablet supplements periconceptionally (ie, before and afterconception), nor could we assess a “contamination” effect onmeasured holoTC concentration from B12 supplement usestarted after conception. We adjusted for serum folate con-centration at the time of screening—which differed littlebetween cases and controls—as well as maternal age andsocioeconomic status, each predictors of early access toantenatal care and periconceptional vitamin supplementuse.14 There were more nonwhite women among cases thancontrols, and we adjusted for nonwhite ethnicity. This heter-ogeneous group of women (mainly Asian and black) may differfrom whites in terms of dietary intake and metabolism of B12.16

Surveys suggest that the majority of women in Ontariotake a periconceptional tablet supplement containing folic

TABLE 3. Published Case–Control Studies Evaluating the Risk of Neural Tube Defects in Association With Indicators ofMaternal B12 Status

First AuthorNo. NTD

Cases

No.Non-NTD

ControlsSerum/Plasma

Analyte

Comparison ofAbnormal vs.

Normal Cut-PointsOddsRatio (95% CI)

Odds RatioAdjusted for

MaternalFolate

Concentration?

Kirke22 81 247 B12 and folate �lower quartile vs. �upper quartile,both analytes

5.4 (1.2–25.2) No

Molloy23 32 384 B12 �185 pmol/L vs. �185 pmol/L 0.9 (0.4–1.9) No

van der Put24 60 94 B12 �5th centile vs. �95 centile 3.9 (1.3–11.9) No

Groenen25 44 83 B12 �10th centile vs. �10th centile 3.5 (1.3–8.9) No

Suarez26 157 186 B12 �lower quintile vs. �upper quintile 2.6 (1.2–5.4) Yes

Wilson27 58 89 B12 �lower quartile vs. �second quartile 2.1 (0.9–5.2) No

B12 and MTRR B12 as above, combined with MTRRhomozygosity

4.8 (1.5–15.8) No

Adams28 33 132 MMA �90th centile vs. �10th centile 13.3 (2.7–65.5) Yes

Afman19 46 73 B12 �lower quartile vs. �upper quartile 1.8 (0.6–5.2) No

HoloTC �lower quartile vs. �upper quartile 2.9 (0.9–9.2) No

Current 89 422 HoloTC �lower quartile vs. �upper quartile 2.9 (1.2–6.9) Yes

MMA indicates methylmalonic acid.

TABLE 2. Risk of an Open Neural Tube Defect in Relation to Low HolotranscobalaminConcentrations

Serum holoTCQuartile (pmol/L)

Cases ControlsCrude

Odds Ratio (95% CI)Adjusted

Odds Ratio* (95% CI)No. % No. %

�55.3 35 39 106 25 2.0 (1.1–3.9) 2.9 (1.2–6.9)

�55.3–84.0 19 21 105 25 1.1 (0.55–2.3) 2.0 (0.75–5.1)

�84.0–121.0 18 20 106 25 1.0 (0.51–2.1) 1.1 (0.40–2.9)

�121.0† 17 19 105 25 1.0 1.0

*Adjusted for maternal age, gravidity, weight, ethnicity, low-income status, presence of pregestational diabetes mellitus andserum folate concentration.

†Reference category.

Ray et al Epidemiology • Volume 18, Number 3, May 2007


acid,14,17 in keeping with Canadian guidelines.18 The smallquantity of B12 contained in each prenatal multivitamin tabletin Canada (2.5 �g) is unlikely to explain the observeddifference in holoTC levels (for example, if a greater numberof controls than cases had initiated multivitamin tablets soonafter conception).

The concentration of holoTC defining the bottom quar-tile among our pregnant controls (55 pmol/L) was compara-ble to the lower-quartile values described in other studies ofpregnant women19 and healthy adults.9,20 A lower referencelimit of 50 pmol/L is recommended for a diagnosis of B12

deficiency in young and middle-aged adults.21 Together, thisreinforces the robustness of the holoTC threshold that weused to define “abnormal,” and the comparability of ourcontrols to other study populations of young adults.

Though not a central focus of our study, we weresurprised that the concentration of serum folate was onlyslightly lower among the cases than controls. This may havebeen due to the dilutional effect of pregnancy on folate levels,a similar prevalence of use of folic acid tablet supplementsamong cases and controls around the time of maternal screen-ing, or perhaps the fact that nearly half of all study partici-pants were screened after universal folic acid flour fortifica-tion. Other Canadian studies have found that serum folatelevels have increased by more than 60% after the introductionof flour fortification, with 99% of women now replete.11

Previous studies have directly or indirectly assessed anassociation between low maternal B12 and NTD risk (Table3). One small case–control study has observed an associationof low holoTC with risk of NTD (crude OR � 2.9; 95% CI �0.9–9.2).19 Another study,28 comprising 33 cases and 132controls, found an adjusted OR for NTD of 13 (95% CI �2.7–66) using another sensitive functional indicator of B12deficiency (maternal serum methylmalonic acid concentrationabove the 90th centile).29 The combination of a low B12 anda genetic polymorphism MTRR, which makes of methioninesynthase reductase, (an enzyme that activates cobalamin-dependent methionine synthase), was associated with an ORfor NTD of 4.8 (95% CI � 1.5–16).27 In general, while thereappears to be a consistent association between maternal B12insufficiency and NTD risk, few studies have examinedmaternal B12 status using methods of sufficient sensitivity, orcontrolled for influential maternal risk factors such as folatestatus (Table 3).

Our findings suggest that as much as 34% of all NTDsin Canada may be attributable to low maternal B12 status, aconclusion which could have profound public health impli-cations, if generalizable. However, only about 60% of preg-nant women in Ontario have maternal screening, which limitsthe generalizability of our data. Women who do not accessprenatal screening may differ from those who do,30 withlower rates of pregnancy planning and periconceptional folicacid tablet supplement use.14

B12 and folic acid are the main nutritional determinantsof homocysteine metabolism.7 While folic acid supplemen-tation may lower both plasma homocysteine and the risk ofNTD, the impact of higher B12 intake on NTD risk is notknown. Given our results and those of other studies, a

randomized clinical trial of periconceptional B12 may beindicated. Adding B12 to folic acid in food fortification mayhelp prevent NTD, while at the same time reducing concernabout masking B12-related neurologic disease, which canoccur when fortifying with folic acid alone.31,32

REFERENCES1. Lumley J, Watson L, Watson M, et al. Periconceptional supplementation

with folate and/or multivitamins for preventing neural tube defects.Cochrane Database Syst Rev 2001; CD001056.

2. Ray JG, Meier C, Vermeulen MJ, et al. Association of neural tubedefects and folic acid food fortification in Canada. Lancet. 2002;360:2047–2048.

3. De Wals P, Rusen ID, Lee NS, et al. Trend in prevalence of neural tubedefects in Quebec. Birth Defects Res A Clin Mol Teratol. 2003;67:919–923.

4. Ray JG, Blom HJ. Vitamin B12 insufficiency and the risk of fetal neuraltube defects. QJM 2003;96:289–295.

5. Finglas PM, de Meer K, Molloy A, et al. Research goals for folate andrelated B vitamin in Europe. Eur J Clin Nutr. 2006;60:287–294.

6. Hvas AM, Nexo E. Holotranscobalamin—a first choice assay for diag-nosing early vitamin B deficiency? J Intern Med. 2005;257:289–298.

7. Green R, Miller JW. Vitamin B12 deficiency is the dominant nutritionalcause of hyperhomocysteinemia in a folic acid-fortified population. ClinChem Lab Med. 2005;43:1048–1051.

8. Ray JG, Wyatt PR, Vermeulen MJ, et al. Greater maternal weight andthe ongoing risk of neural tube defects after folic acid flour fortification.Obstet Gynecol. 2005;105:261–265.

9. Refsum H, Johnston C, Guttormsen AB, et al. Holotranscobalamin andtotal transcobalamin in human plasma: determination, determinants, andreference values in healthy adults. Clin Chem. 2006;52:129–137.

10. Statistics Canada. Income in Canada 2000. Ottawa: Statistics Canada;Catalogue No. 75–202 XIE, November 2002.

11. Ray JG, Vermeulen MJ, Langman LJ, et al. Persistence of vitamin B12

insufficiency among elderly women after folic acid food fortification.Clin Biochem. 2003;36:387–391.

12. Rockhill B, Newman B, Weinberg C. Use and misuse of populationattributable fractions. Am J Public Health. 1998;88:15–19.

13. Heller RF, Buchan I, Edwards R, et al. Communicating risks at thepopulation level: application of population impact numbers. BMJ. 2003;327:1162–1165.

14. Ray JG, Singh G, Burrows RF. Evidence for suboptimal use of pericon-ceptional folic acid supplements globally. BJOG. 2004;111:399–408.

15. Metz J, McGrath K, Bennett M, et al. Biochemical indices of vitaminB12 nutrition in pregnant patients with subnormal serum vitamin B12

levels. Am J Hematol. 1995;48:251–255.16. Saxena S, Carmel R. Racial differences in vitamin B12 levels in the

United States. Am J Clin Pathol 1987;88:95–97.17. Tam LE, McDonald SD, Wen SW, et al. A survey of preconceptional

folic acid use in a group of Canadian women. J Obstet Gynaecol Can.2005;27:232–236.

18. Van Allen MI, Fraser FC, Dallaire L, et al. Recommendations on the useof folic acid supplementation to prevent the recurrence of neural tubedefects. Clinical Teratology Committee, Canadian College of MedicalGeneticists. CMAJ. 1993;149:1239–1243.

19. Afman LA, Van Der Put NM, Thomas CM, et al. Reduced vitamin B12

binding by transcobalamin II increases the risk of neural tube defects.QJM. 2001;94:159–166.

20. Loikas S, Lopponen M, Suominen P, et al. RIA for serum holo-transcobalamin: method evaluation in the clinical laboratory and refer-ence interval. Clin Chem. 2003;49:455–462.

21. Lloyd-Wright Z, Hvas AM, Moller J, et al. Holotranscobalamin as anindicator of dietary vitamin B12 deficiency. Clin Chem. 2003;49:2076–208.

22. Kirke PN, Molloy AM, Daly LE, et al. Maternal plasma folate andvitamin B12 are independent risk factors for neural tube defects. QJM.1993;86:703–708.

23. Molloy AM, Kirke P, Hillary I, et al. Maternal serum folate and vitaminB12 concentrations in pregnancies associated with neural tube defects.Arch Dis Child. 1985;60:660–665.

24. van der Put NM, Thomas CM, Eskes TK, et al. Altered folate and

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vitamin B12 metabolism in families with spina bifida offspring. QJM.1997;90:505–510.

25. Groenen PM, van Rooij IA, Peer PG, et al. Marginal maternal vitaminB12 status increases the risk of offspring with spina bifida. Am J ObstetGynecol. 2004;191:11–17.

26. Suarez L, Hendricks K, Felkner M, et al. Maternal serum B12 levels andrisk for neural tube defects in a Texas-Mexico border population. AnnEpidemiol. 2003;13:81–88.

27. Wilson A, Platt R, Wu Q, et al. A common variant in methioninesynthase reductase combined with low cobalamin (vitamin B12) in-creases risk for spina bifida. Mol Genet Metab. 1999;67:317–323.

28. Adams MJ Jr, Khoury MJ, Scanlon KS, et al. Elevated midtrimester

serum methylmalonic acid levels as a risk factor for neural tube defects.Teratology. 1995;51:311–317.

29. McMullin MF, Young PB, Bailie KE, et al. Homocysteine and methyl-malonic acid as indicators of folate and vitamin B12 deficiency inpregnancy. Clin Lab Haematol. 2001;23:161–165.

30. Kuppermann M, Learman LA, Gates E, et al. Beyond race or ethnicityand socioeconomic status: predictors of prenatal testing for Downsyndrome. Obstet Gynecol. 2006;107:1087–1097.

31. Czernichow S, Noisette N, Blacher J, et al. Case for folic acid andvitamin B12 fortification in Europe. Semin Vasc Med. 2005;5:156–162.

32. Eichholzer M, Tonz O, Zimmermann R. Folic acid: a public-healthchallenge. Lancet. 2006;367:1352–1361.



COMMENTARY

When Will We Eliminate Folic Acid-PreventableSpina Bifida?Godfrey P. Oakley, Jr

Abstract: Mandatory vitamin B12 fortification of enriched grainproducts is long overdue in the United States and Canada. Fortifi-cation would help provide the 2.4 �g of synthetic vitamin B12 thatthe US Institute of Medicine recommends for all persons 50 yearsand older. The findings of Ray and colleagues1 in this issue suggestthat B12 may also help to prevent neural tube defects. If recommen-dations for B12 fortification were followed, it is possible that casesof spina bifida and anencephaly would be prevented. Two hundredtwenty thousand children each year acquire folic acid-preventablespina bifida because many governments, including all in Europe,have yet to implement mandatory folic acid fortification. Fortifica-tion with folic acid and vitamin B12 is safe and should be imple-mented in all countries.

(Epidemiology 2007;18: 367–368)

Ray et al1 link indicators of vitamin B12 deficiency withneural tube defects, and suggest that consideration should

be given to adding vitamin B12 to enriched grains. Suchfortification with Vitamin B12 should in fact be required. TheInstitute of Medicine/Food Nutrition Board recommended in1998 that persons 50 years and older should consume at least2.4 �g of synthetic B12 daily.2 I suggested in 1997, and morerecently with Brent,3 that there is ample justification to fortifyflour with vitamin B12. Neither the FDA4 nor Canadianauthorities have required vitamin B12, even though manda-tory fortification of flour would likely be as successful as folicacid fortification. If B12 also helps to prevent birth defects,there is even more reason for B12 fortification.

The elephant in the living room, however, is not theabsence of B12 in flour. It is rather that about 220,000children around the globe each year continue to be born withfolate-deficiency spina bifida and anencephaly. These babiesare unnecessarily paralyzed or die prematurely because mostgovernments have failed to require folic acid in centrallyprocessed flours, or have failed to require enough (ie, theUnited States and Canada).5 We have known since the sum-

mer of 1991 that folic acid will prevent most cases of spinabifdia and anencephaly.6 No country in Europe has yet torequire folic acid fortification. The behavior of the Dutch isthe most difficult to understand. That government has refusedto require folic acid fortification of flour. One consequence isthat spina bifida is the main reason for childhood euthanasiain the Netherlands. The behavior of the English regulators isequally problematic. Their refusal to require mandatory folicacid fortification means that the taxpayers who paid for thestudy showing that folic acid will prevent birth defects haveyet to reap the rewards of fortification.

Although an effective prevention for pediatric AIDS israther rapidly implemented in developed countries, manda-tory folic acid fortification has required years of discussion.The United States Public Health Service (PHS) took a year torecommend that all women capable of pregnancy shouldconsume 400 �g of synthetic folic acid a day to prevent birthdefects.7 It took the FDA (part of the PHS) more than 3 yearsto decide in 1996 to require folic acid fortification by January1, 1998 —7 1/2 years after the publication of the rele-vant study.8

The FDA reviewed much data, published proposedregulations for public review, and then issued the regulations.Does every country have to go through the same process?Much of the data are the same. It remains urgent for nutri-tional regulatory bodies to find a way to expedite regulationsthat will prevent spina bifida through folic acid fortification.Every day’s delay results in children being born with unnec-essary serious birth defects.

Procedural process is not the only problem with nutri-tional regulations. With folic acid, there has been unreason-able concern about safety. In making a decision to require asubstance in the food, one should make a reasonable reviewof the safety issues. Unreasonable delays cause disability andearly death.

Folic acid is not a new drug. It is a chemical that hasbeen available since the 1950s, and has been used widely andsafely to treat folate-deficiency diseases. Even 50,000 �g/dayproduces no signs of risk or toxicity. Before folic acid wasadded to flour, at least 20% of the US population had chronicexposures of at least 400 �g/day from multivitamins andbreakfast cereals. The Harvard Nurses’ Study has providedsubstantial evidence of safety and even the possibility thatfolic acid prevents cardiovascular disease and colon cancer.9

Even so, regulators continue to raise new and old issues ofsafety that delay fortification.

It is unfortunate that the IOM report included a “toler-able upper level” for folic acid.2 Although folic acid is not

From the Department of Epidemiology, Rollins School of Public Health ofEmory University, Altanta, GA 30322.

Correspondence: Godfrey P. Oakley, Jr, Research Professor, Department ofEpidemiology, Rollins School of Public Health of Emory University, 1518Clifton Road, NE, Altanta, GA 30322. E-mail: [email protected].



toxic like vitamins A and D, toxicologists on the IOMcommittee unfortunately treated folic acid as being toxic.Even the IOM report noted that, if there were a risk from folicacid, it would be only among people consuming at least 5000�g/day. The “evidence” for risk from 5000 �g/day derivesfrom studies in the 1950s when persons who had perniciousanemia in remission from getting liver extract were deliber-ately removed from this successful therapy and placed on atherapy for at least 5000 �g of folic acid a day (which wenow know is an ineffective therapy for pernicious anemia).10

When people are taken off vitamin B12, their perniciousanemia is now known to recur. In the studies in the 1950s, thepatients without liver extract on folic acid became worse. Itseems much more reasonable to conclude that the withdrawalof effective treatment caused the signs and symptoms ofpernicious anemia rather than anything to do with folic acid.It is time to remove the tolerable upper level for folic acid.

If all this were not enough, there are new confusionsarising. Recent discussion has centered on observations of“free” folic acid in humans, and to the supposed desirabilityof substituting synthetic 5 methyl tetrahydrofolate (5MTHF)for folic acid. The tone of current writing about “free” folicacid is that it is a new “risk” that regulators should discuss.Human exposure to free folic acid is not new. It is present inhuman serum when a bolus of 300 or more micrograms offolic acid is consumed. Most prenatal vitamins in the UnitedStates have had 800 or 1000 �g for at least 20 years, meaningthat most fetuses and pregnant women have been exposed tofree folic acid. Multivitamins in the United States also havehad 400 �g for more than 20 years.

Merck is marketing 5 MTHF as “better” than folic acidbecause it is the “natural” form of folate. Putting aside thefact that we have little experience with human exposure tothis synthetic folate, there are other reasons that it is inferiorto folic acid. It is heat labile. It cannot be used to fortify flour,as its activity would be lost in cooking, although it could beused in supplements. As long as there is a tolerable upperlevel to worry regulators, putting 5 methyl in supplementswould relieve worry as it cannot treat the anemia of B12deficiency—the rationale for the tolerable upper limit. On theother hand, women of reproductive age with the B12 defi-ciencies described by Ray and colleagues may be harmed. Inthe presence of B12 deficiency, 5 MTHF will not producetetrahydrofolate (THF), but folic acid will. Thus, women withB12 deficiency who consume folic acid supplements mayhave fewer babies with spina bifida than if they consumedsupplements with 5 MTHF.

Finally, a new WHO manual on micronutrient fortifi-cation has issued recommendations that may be harmful. TheUS Institute of Medicine/Food Nutrition Board issued arecommendation for folate consumption at the populationlevel that is lower than the recommendation for women of

reproductive age. To prevent birth defects, it also recom-mended that all women of reproductive age consume 400 �gof synthetic folic acid in addition to eating a diet rich innatural folates. I learned in December at a meeting in Ukrainethat WHO had decided not to consider the prevention of birthdefects in its recommendation for consumption of folic acidfrom fortified foods. Rather, it would make recommendationsbased on the low level that is recommended for the generalpopulation. This is an error. Any country considering folicacid fortification should set concentrations that will result inas many women as possible consuming 400 �g of syntheticfolic acid from fortified foods.

ABOUT THE AUTHORGODFREY OAKLEY is a research professor of epide-

miology at the Rollins School of Public Health of EmoryUniversity, and the former Director of the Division of BirthDefects and Developmental Disabilities at CDC. He and hisgroup at CDC were critical to marshalling the argumentsthat led FDA to require folic acid fortification in the UnitedStates. He continues to work to prevent spina bifida.

REFERENCES1. Ray JG, Wyatt P, Thompson M, et al. Vitamin B12 and the risk of neural

tube defects in a folic-acid-fortified population. Epidemiology. 2007;18:361–365.

2. Institute of Medicine. Dietary reference intakes for thiamin, riboflavin,niacin, vitamin B6, folate, vitamin B12, pantothenic acid, biotin, andcholine. In: Food and Nutrition Board, Institute of Medicine, ed. Stand-ing Committee on the Scientific Evaluation of Dietary Reference Intakesand Its Panel on Folate Obv, and Choline, and Subcommittee on UpperReference Levels of Nutrients. Washington, DC: Institute of Medicine,National Academy Press, 1998.

3. Brent RL, Oakley GP Jr. The Food and Drug Administration mustrequire the addition of more folic acid in “enriched ” flour and othergrains. Pediatrics . 2005;116:753–755.

4. Rader JI, Schneeman BO. Prevalence of neural tube defects, folatestatus, and folate fortification of enriched cereal-grain products in theUnited States. Pediatrics. 2006;117:1394–1399.

5. Bell KN, Oakley GP Jr. Tracking the Prevention of Folic Acid-Prevent-able Spina Bifida. Birth Defects Res, Part A: Clin Mol Teratol. 2006;76:654–657.

6. MRC Vitamin Study Research Group.Prevention of neural tube defects:results of the Medical Research Council Vitamin Study. Lancet. 1991;338:131–137.

7. Centers for Disease Control and Prevention. Recommendations for theuse of folic acid to reduce the number of cases of spina bifida and otherneural tube defects. MMWR. 1992;41:1–7.

8. Food and Drug Administration. Food standards: amendment of standardsof indentity for enriched grain products to require addition of folic acid.Federal Registry. 1996;61:8781–8797.

9. Giovannucci E, Stampfer MJ, Colditz GA, et al. Multivitamin use,folate, and colon cancer in women in the Nurses’ Health Study. AnnIntern Med. 1998;129:517–524.

10. Vilter CF, Vilter RW, Spies TD. The treatment of pernicious andrelated anemias with synthetic folic acid: observations on mainte-nance of normal hematologic status and on occurrence of combinedsystem disease at the end of one year. J Lab Clin Med. 1947;32:262–273.

Oakley Epidemiology • Volume 18, Number 3, May 2007


ORIGINAL ARTICLE

Temperature and Cardiovascular Deaths in theUS Elderly

Changes Over Time

Adrian Gerard Barnett

Background: Short-term changes in temperature have been associ-ated with cardiovascular deaths. This study examines changes in thisassociation over time among the US elderly.Methods: Daily cardiovascular mortality counts from 107 citiesin the US National Morbidity and Mortality Air Pollution Studywere regressed against daily temperature using the case-crossovermethod. Estimates were averaged by time and season using ameta-analysis.Results: In summer 1987 the average increase in cardiovasculardeaths due to a 10°F increase in temperature was 4.7%. By summer2000, the risk with higher temperature had disappeared (�0.4%). Incontrast, an increase in temperature in fall, winter and spring wasassociated with a decrease in deaths, and this decrease remainedconstant over time.Conclusions: Heat-related cardiovascular deaths in the elderly havedeclined over time, probably due to increased use of air condition-ing, while increased risks with cold-related temperature persist.

(Epidemiology 2007;18: 369–372)

Cardiovascular disease rates change with temperature,and the direction of this change depends on location

and season.1–3 In the winter increases in cardiovasculardeaths are associated with decreases in temperature,3 whilein the summer increases in deaths are associated withincreases in temperature. These increases appear sharplyduring heat waves.4 This J-shaped risk suggests a comfort-zone outside of which physiological stresses produce cir-culatory problems. These temperature-related stresses aremore dangerous in the elderly because the elderly are morelikely to have pre-existing cardiovascular disease, includ-ing hypertension.1,5

The effects of temperature on health have been evidentsince at least the 1960s.6 An increased public awareness ofthe problem and improvements in the standard of living andhealth care should have led to a decrease in deaths over time.7

This study explored the evidence for such a decrease in theUnited States.

METHODSThe analysis is based on data from the publicly avail-

able National Morbidity and Mortality Air Pollution Study(NMMAPS) study.8,9 This study monitored daily climaticconditions, air pollution levels, morbidity and mortality in108 cities in the United States. Our analysis is restricted to theelderly because they are more sensitive to changes in tem-perature.1,5,10 Arkansas was excluded because of some miss-ing data for dew-point temperature, leaving 107 cities withcomplete mortality and temperature data for 14 years (Janu-ary 1987 to December 2000). Temperature data came fromthe National Climatic Data Center, and daily mortality countscame from the National Center for Health Statistics. Moreinformation on the cities used is available from the NMMAPSweb site.9

The analysis had 2 stages: the association betweentemperature and counts of cardiovascular deaths was mea-sured in each city, and then these estimates were combined togive an average effect. (A similar 2-stage design has beenused in other analysis of the NMMAPS data.11) The analysisin each city used the case-crossover method.12 This method issimilar to a case–control design because it compares theassociation between temperature and deaths in case (or index)days to nearby control (or referent) days. As each individualacts as their own control, time-independent confounders (eg,age) are controlled for by design. By choosing referent daysthat are close to the index day, the method also controls fortrends and seasonality in cardiovascular deaths, and for trendsin population size.12

Data AnalysisA short delay between temperature exposure and car-

diovascular disease onset was modeled using the mean tem-perature on the current day and the 6 preceding days. Theoptimal number of previous days was chosen using the meanAkaike Information Criteria (AIC) from all 107 cities.13

Results are shown for a 10°F increase in temperature.

Submitted 7 June 2006; accepted 7 November 2006; posted 26 February2007.

From the School of Population Health, University of Queensland, Herston,Australia.

Supported by the National Health and Medical Research Council of Australia(grant number 252834).

Correspondence: Adrian Gerard Barnett, School of Population Health, Uni-versity of Queensland, Herston, QLD 4006, Australia. E-mail: [email protected].

Copyright © 2007 by Lippincott Williams & WilkinsISSN: 1044-3983/07/1803-0369DOI: 10.1097/01.ede.0000257515.34445.a0


Separate analyses were made in each season. Thisallowed a positive relation between increasing temperatureand death in summer, and a negative relation in winter.1 Tocontrol for the effects of humidity, the same-day dew-pointtemperature was included. Day of the week was also includedas an indicator variable to control for any weekly pattern incardiovascular deaths.

A time-stratified case-crossover design was used,12

with months as the strata. Index days were every day in amonth. The referents were the other days in the month,excluding the index day and 2 days either side of the indexday (to reduce the correlation in exposure between the indexand referent days). For example, an index day of January 9had referent days of January 1 to January 6, and January 12to January 31. Seasons were defined as: winter, December toFebruary; spring, March to May; summer, June to August;and fall, September to November.

Heterogeneity among cities in the case-crossover esti-mates was calculated using the I-squared statistic.14 Thisstatistic is on a scale of 0% (completely homogeneous) to100% (extreme heterogeneity).

The case-crossover estimates across cities, years and sea-sons were combined using a Bayesian hierarchical model.15

Each city was given a random intercept and a random lineareffect of time. A sensitivity analysis examined the randomnonlinear (quadratic) effect of time. This change to the modelwas assessed using the Deviance Information Criteria(DIC).16 To examine differences in the effect of temperatureby average climate, a hierarchy of geographical region wasadded to the model. The results from Anchorage and Hono-lulu were excluded from this sensitivity analysis, as they werenot clearly a member of any region.

The case-crossover analysis was conducted using SAS(SAS Institute Inc., Cary, NC) and the Bayesian hierarchicalmodel using WinBUGS (MRC Biostatistics Unit, Cambridge,UK). The Markov chain Monte Carlo (MCMC) analysis useda burn-in of 10,000 samples followed by a run of 50,000thinned by 5. Convergence of the Markov chains was as-sessed using the Gelman-Rubin statistic.17

RESULTSThe highest risk of death after temperature exposure

(according to the AIC) came at 0 to 4 days. The hierarchicalmodel fit (according to the DIC) was not improved by usinga random quadratic term for time, and so the results are basedon modeling a linear change over time. The Markov chainMonte Carlo estimates showed good convergence in all 4seasons.

Table 1 presents the estimated mean changes in cardio-vascular deaths due to a 10°F increase in temperature (overthe previous 0–4 days) at the start and end of the study (basedon the hierarchical model). The largest change over time wasin the effect of hot temperatures in summer. In summer 1987cardiovascular deaths increased by 4.7% with a 10°F increasein temperature (95% posterior interval (PI) � 3.0% to 6.5%).By summer 2000 the risk was virtually zero (�0.4%; �3.2%to 2.5%). There was very little change over time during theother seasons, as shown by Figure 1. In all seasons the

posterior intervals for the mean effect of temperature widenedover time, partly due to a reduction in cardiovascular deathsover time. The annual percentage change in deaths (and 95%PI) were: for winter �0.06 (�0.23 to 0.10); for spring �0.06(�0.24 to 0.11); for summer �0.39 (�0.65 to �0.13); andfor fall �0.04 (�0.22 to 0.14).

Figure 2 shows that the decline in summer deaths overtime varied by geographical region. The biggest declineswere in the Northeast, Northwest, Industrial Midwest andSouthern California; these were also the regions with thehighest levels of temperature-related mortality in 1987. In theSoutheast and Southwest regions, the higher mortality withsummer heat changed little over time. In the Upper Midwestregion, there was little evidence for temperature-relateddeaths in summer, and little change in this pattern over time.There was relatively little change over time in the effects ofcold in winter, except that the mortality risk with coldtemperatures got somewhat worse in the Industrial Midwestregion and somewhat better in Southern California.

The I-squared values of between 21% and 31% insummer and winter indicate mild heterogeneity in the effectsof temperature between the cities (Table 1). This heteroge-neity is apparent in Figure 2, which shows variations inaverage temperature effects by region. This finding of mildheterogeneity is in contrast to a similar study that found alarge disagreement in heat-related mortality among 12 UScities.18

DISCUSSIONThe was a clear decrease in heat-related mortality in the

elderly between 1987 and 2000. Related studies in the UnitedStates have found similar declines in heat-related all-causemortality (all ages) from the 1960s to the 1990s,19 and from1971 to 1997 in North Carolina.20 The authors of both studiessuggested that the decline was due to increases in air condi-tioning use. Two studies of all-cause mortality in the UnitedStates have found that the effect of hot temperatures wasassociated with the level of air conditioning use in a city.18,21

Figures from the US Energy Information Administration for

TABLE 1. Mean Change in Daily Cardiovascular Deaths (%)Over 107 US Cities due to a 10°F Increase in Temperatureby Season for the Years 1987 and 2000

Season Year Change (%) (95% PI)I-squared*

%

Spring 1987 �2.4 (�3.3 to �1.4) 20.9

2000 �3.2 (�5.2 to �1.2) 27.4

Summer 1987 4.7 (3.0 to 6.5) 16.0

2000 �0.4 (�3.2 to 2.5) 19.5

Fall 1987 �3.7 (�4.7 to �2.7) 13.2

2000 �4.2 (�6.1 to �2.2) 3.7

Winter 1987 �4.2 (�5.1 to �3.2) 25.7

2000 �4.9 (�6.8 to �3.1) 30.6

*I-squared is a measure of the heterogeneity among cities.PI indicates posterior interval.

Barnett Epidemiology • Volume 18, Number 3, May 2007


1980 to 2001 show that air conditioning use has been steadilyincreasing in all areas of the United States.22 Although anincrease in air conditioning is a plausible explanation of thedecrease in heat-related cardiovascular deaths, it is con-founded with other changes over time, such as improvedhealth care.

Cold-Related MortalityWhile an increase in air conditioning over time may

have affected heat-related cardiovascular deaths, nothing haschanged the impact of cold temperatures on mortality. Themechanism by which cold temperatures lead to increasedcardiovascular deaths is most likely via blood pressure.23

Susceptibility to cold-related mortality has been associated

with race,24,25 education24 and female sex.3,25 The sex differ-ence suggests either that clothing is an important modifier26 orthat there is a biologic difference between the ability tothermoregulate. Body temperature is regulated by the hypo-thalamus neurons, which are directly influenced by estrogenthrough estrogen receptors. The associations with race andeducation suggest a socioeconomic effect, although results onthe socioeconomic effect on cold-related deaths have beenmixed. Of 2 large UK studies, one found an associationbetween cold homes and increased risk of death,10 but an-other found that deprivation in an area was not related to riskof excess winter all-cause mortality.27

It is plausible that improvements over time in thestandard of living (specifically housing quality and heating)would reduce the number of cold-related deaths. The resultsfrom this study suggest either that improvements in the USstandard of living were insufficient or that such improve-ments are in fact not protective. Another possible pathway toprotection of the elderly from low temperatures is more andbetter clothes in cold weather.26 The results shown heresuggest that protective measures need to be taken not just inwinter, but also in relatively cold days spring and fall.However, there is no direct evidence in the literature tosupport an intervention of increased clothing. Basu andSamet1 have provided guidelines for the future research intoheat-related mortality. The results from the present studyindicate that new studies of cold-related cardiovasculardeaths are also needed. To date most research has analyzedtemperature at a population level, using temperature measure-ments obtained from outdoor monitors. Although logisticallymore difficult, a cohort study that monitored temperature insubjects’ homes and collected details on subjects’ clothingwould have much greater power to detect differences in riskrelated to individual and socioeconomic factors.

A successful intervention for cold-related mortalitycould have a substantial public health impact. Using the data

FIGURE 2. Mean changes in daily cardiovascular deaths (%)due to a 10°F increase temperature in summer and winter byregion.

FIGURE 1. Mean changes in daily cardiovasculardeaths (%) and 95% posterior intervals due to a10°F increase in temperature by year and season.

Epidemiology • Volume 18, Number 3, May 2007 Temperature and Cardiovascular Deaths in the US Elderly


analyzed here, the average difference in the number of car-diovascular deaths between winter and summer was 28 per1000 of the elderly population, although some of this differ-ence is due to other seasonal risk factors such as influenza.28

Related StudiesThe results from Figure 2 are similar to a related study,

which found the strongest association for heat-related cardio-respiratory mortality in the Southwest, and a negligible effectin the Upper Midwest.29 That study examined temperatureand cardiorespiratory mortality in the elderly in the 20 largestmetropolitan areas in the United States in 1992. The UpperMidwest had the best result of any region for heat-relatedmortality despite the study period including the 1995 Chicagoheat wave.1 This study examined trends in temperature-related mortality, and thus extreme events were averaged out.

A related study in the United States showed a declineover time in the seasonality of cardiovascular disease.30 Thestudy covered the years 1937 to 1991 and showed an approx-imate 2% per annum decline in the winter-summer ratio of allcoronary heart disease deaths. The authors suggested thedecline may be due to improvements in heating (indoor andvehicular) and air-conditioning. Declines in seasonality mor-tality, however, are not the same as declines due to temper-ature-related mortality. Seasonal estimates include the effectsof other seasonal risk factors such as diet, physical activityand influenza.28 This study specifically examined the effectsof temperature, and showed a substantial decline in summer-heat-related mortality after controlling for seasonality.

The strengths of the present study are that it covered alarge number of cities in the United States, and so had powerto look at changes over time in temperature-related deaths byseason and geographical region. It used the case-crossovermethod to control for the large decrease over time in cardio-vascular disease death rates. One limitation is that the delaybetween temperature exposure and death was fixed to 4 days,when it is possible that the delays varied by city and season.Also the study was able to examine only total cardiovasculardeaths, while specific cardiovascular outcomes (eg, myocar-dial infarction) may be more sensitive to temperature.

ACKNOWLEDGMENTSThe author thanks the Department of Biostatistics at the

Johns Hopkins Bloomberg School of Public Health and theHealth Effects Institute for making the National Morbidityand Mortality Air Pollution Study data publicly available.

REFERENCES1. Basu R, Samet JM. Relation between elevated ambient temperature and

mortality: a review of the epidemiologic evidence. Epidemiol Rev.2002;24:190–202.

2. Braga ALF, Zanobetti A, Schwartz J. The effect of weather on respira-tory and cardiovascular deaths in 12 U.S. cities. Environ Health Per-spect. 2002;110:859–863.

3. Barnett AG, Dobson AJ, McElduff P, et al. Cold periods and coronaryevents: an analysis of populations worldwide. J Epidemiol CommunityHealth. 2005;59:551–557.

4. Keatinge WR. Death in heat waves. BMJ. 2003;327:512–513.5. Keatinge WR, Donaldson GC. The impact of global warming on health

and mortality. South Med J. 2004;97:1093–1099.

6. Pell JP, Cobbe SM. Seasonal variations in coronary heart disease.Q J Med. 1999;92:689 – 696.

7. Patz JA, McGeehin MA, Bernard SM, et al. The potential health impactsof climate variability and change for the United States: executivesummary of the report of the health sector of the U.S. national assess-ment. Environ Health Perspect. 2000;108:367–376.

8. Samet JM, Dominici F, Zeger SL, Schwartz J, Dockery DW. TheNational Morbidity, Mortality, and Air Pollution Study. I. Methods andMethodologic Issues. Health Effects Institute; 2000.

9. Johns Hopkins University Biostatistics Department, Bloomberg SchoolOf Public Health. iHAPPS home page. Available at: http://www.ihapss.jhsph.edu/data/data.htm. Accessed August 7, 2006.

10. Wilkinson P, Armstrong B, Landon M, et al. Cold comfort: The socialand environmental determinants of excess winter deaths in England,1986–1996. Joseph Rowntree Foundation, York; 2001:N11.

11. Bell M, McDermott A, Zeger S, Samet J, Dominici F. Ozone andShort-term Mortality in 95 US Urban Communities. JAMA. 2005;292:2372–2378.

12. Janes H, Sheppard L, Lumley T. Case-crossover analyses of air pollutionexposure data: referent selection strategies and their implications forbias. Epidemiology. 2005;16:717–726.

13. Dobson AJ. An Introduction to Generalized Linear Models. 2nd ed.Boca Raton, FL: Chapman & Hall/CRC; 2002.

14. Higgins JPT, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21:1539–1558.

15. Gelman A, Carlin JB, Stern HS, Rubin DB. Bayesian Data Analysis. 2nded. Boca Raton, FL: Chapman & Hall/CRC; 2003.

16. Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A. Bayesianmeasures of model complexity and fit (with discussion). J R Stat Soc SerB Methodol. 2002;64:583–640.

17. Brooks SP, Gelman A. General methods for monitoring convergence ofiterative simulations. J Computat Graph Stat. 1998;7:434–455.

18. Curriero F, Heiner K, Samet J, Zeger S, Strug L, Patz J. Temperature andmortality in 11 cities of the eastern United States. Am J Epidemiol.2002;155:80–87.

19. Davis RE, Knappenberger PC, Michaels PJ, Novicoff WM. Changingheat-related mortality in the United States. Environ Health Perspect.2003;111:1712–1718.

20. Donaldson GC, Keatinge WR, Nayha S. Changes in summer tempera-ture and heat-related mortality since 1971 in North Carolina, SouthFinland, and Southeast England. Environ Res. 2003;91:1–7.

21. Braga ALF, Zanobetti A, Schwartz J. The time course of weather-relateddeaths. Epidemiology. 2001;12:662–667.

22. Energy Information Administration. Appliance market-share trends,U.S. households. Available at: http://www.eia.doe.gov/emeu/reps/appli/all_tables.html. Accessed March 2, 2006.

23. Donaldson G, Robinson D, Allaway S. An analysis of arterial diseasemortality and BUPA health screening data in men, in relation to outdoortemperature. Clin Sci. 1997;92:261–268.

24. O’Neill MS, Zanobetti A, Schwartz J. Modifiers of the temperature andmortality association in seven US cities. Am J Epidemiol. 2003;157:1074–1082.

25. Schwartz J. Who is sensitive to extremes of temperature? A case-onlyanalysis. Epidemiology. 2005;16:67–72.

26. The Eurowinter Group. Cold exposure and winter mortality from isch-aemic heart disease, cerebrovascular disease, respiratory disease, and allcauses in warm and cold regions of Europe. Lancet. 1997;349:1341–1346.

27. Lawlor DA, Maxwell R, Wheeler BW. Rurality, deprivation, and excesswinter mortality: an ecological study. J Epidemiol Community Health.2002;56:373–374.

28. Reichert TA, Simonsen L, Sharma A, Pardo SA, Fedson DS, Miller MA.Influenza and the winter increase in mortality in the United States,1959–1999. Am J Epidemiol. 2004;160:492–502.

29. Basu R, Dominici F, Samet JM. Temperature and mortality among theelderly in the United States. Epidemiology. 2005;16:58–66.

30. Seretakis D, Lagiou P, Lipworth L, Signorello LB, Rothman KJ,Trichopoulos D. Changing seasonality of mortality from coronary heartdisease. JAMA. 1997;278:1012–1014.

Barnett Epidemiology • Volume 18, Number 3, May 2007


ORIGINAL ARTICLE

Cooked Meat and Risk of Breast Cancer—Lifetime Versus Recent Dietary Intake

Susan E. Steck,* Mia M. Gaudet,† Sybil M. Eng,‡ Julie A. Britton,§ Susan L. Teitelbaum,§Alfred I. Neugut,� Regina M. Santella,** and Marilie D. Gammon†

Background: Polycyclic aromatic hydrocarbons (PAHs) and het-erocyclic amines (HCAs) are carcinogens formed in or on thesurface of well-done meat, cooked at high temperature.Methods: We estimated breast cancer risk in relation to intake ofcooked meat in a population-based, case-control study (1508 casesand 1556 controls) conducted in Long Island, NY from 1996 to1997. Lifetime intakes of grilled or barbecued and smoked meatswere derived from the interviewer-administered questionnaire data.Dietary intakes of PAH and HCA were derived from the self-administered modified Block food frequency questionnaire of intake1 year before reference date. Unconditional logistic regression wasused to estimate adjusted odds ratios (ORs) and 95% confidenceintervals (CIs).Results: Modest increased risk was observed among postmeno-pausal, but not premenopausal, women consuming the most grilledor barbecued and smoked meats over the life course (OR � 1.47;CI � 1.12–1.92 for highest vs. lowest tertile of intake). Postmeno-pausal women with low fruit and vegetable intake, but high lifetimeintake of grilled or barbecued and smoked meats, had a higher ORof 1.74 (CI � 1.20–2.50). No associations were observed with thefood frequency questionnaire-derived intake measures of PAHs andHCAs, with the possible exception of benzo(�)pyrene from meatamong postmenopausal women whose tumors were positive for bothestrogen receptors and progesterone receptors (OR � 1.47; CI �0.99–2.19).

Conclusions: These results support the accumulating evidence thatconsumption of meats cooked by methods that promote carcinogenformation may increase risk of postmenopausal breast cancer.

(Epidemiology 2007;18: 373–382)

Polycyclic aromatic hydrocarbons (PAHs) and heterocyclicamines (HCAs) are 2 classes of carcinogens that are found

in the human diet. PAHs can appear on or near the surface offoods from the smoke created by incomplete combustion ofcarbon and hydrogen in fat that has fallen onto hot coals (suchas in grilling or barbecuing), or by contamination from air orwater pollutants.1 PAHs are found in a variety of foodproducts, including grilled or barbecued meat, smoked meat,vegetables, fruits, yogurt, margarine, grains, and cereals.1

HCAs are formed when amino acids pyrolyze in meat juice,and are particularly high in pan-fried, grilled and, to a lesserextent, broiled meat.2 The method, temperature, and durationof cooking greatly affect the amount of HCAs found in meatproducts, with greater doneness associated with higher con-centrations of HCAs.3,4 HCAs and PAHs are known carcin-ogens in animals, and are involved in the development ofmammary tumors.5,6 Dietary intake of these compounds hasbeen consistently linked to colorectal cancer,7 and has beenlinked to breast cancer in a few epidemiologic studies,8,9 butnot all.10

Several epidemiologic studies have observed positiveassociations between recent intake of well-done cooked meatand breast cancer,11–13 whereas other studies have not.14–16

None of the previous studies assessed lifetime intake ofcooked meat, which may be the more relevant time frame ofexposure in carcinogenesis. Additionally, many of the studieswere limited in their ability to estimate dietary intake ofspecific carcinogens from cooked meat because the exposureassessment method relied on food frequency questionnaires(FFQs) without specific questions related to meat preparationtechniques or doneness preference.

This large population-based study was undertaken toaddress the hypothesis that breast cancer risk may be associ-ated with dietary intake of PAHs, HCAs, and meat, accordingto cooking methods and level of doneness. We assessedintake both recently and throughout the lifetime. Further, welooked for possible interactions with fruit and vegetableintake, based on the possibility that these foods may mitigate

Submitted 8 February 2006; accepted 1 December 2006.From the *Departments of Nutrition and †Epidemiology, School of Public

Health, University of North Carolina, Chapel Hill, NC; ‡Global Epide-miology, Worldwide Safety and Risk Management, Pfizer, Inc., NewYork, NY; §Department of Community and Preventive Medicine, MountSinai School of Medicine, New York, NY; ¶Department of Epidemiol-ogy, Mailman School of Public Health; �Department of Medicine, Col-lege of Physicians and Surgeons; and **Department of EnvironmentalHealth Sciences, Mailman School of Public Health, Columbia Univer-sity, New York, NY.

This work was supported in part by grants from the National CancerInstitute, the National Institutes of Environmental Health and Sciences,and the American Institute for Cancer Research (Grant nos. CA/ES66572, P30ES10126, P30ES09089, CA58233, 1K07CA102640-01,AICR-03B091).

Correspondence: Susan E. Steck, Department of Epidemiology and Biostatistics,Arnold School of Public Health, University of South Carolina, 2221 DevineStreet, Room 231, Columbia, SC 29208. E-mail: [email protected].



the carcinogenic effects of PAHs and HCAs, as shown inanimal models.17,18

METHODSInstitutional Review Board approval was obtained by

all collaborating institutions. The study was conducted inaccordance with national and institutional guidelines for theprotection of all study participants.

Study Design and PopulationThe Long Island Breast Cancer Study Project is a

population-based case-control study funded by the NationalCancer Institute and the National Institute of EnvironmentalHealth Sciences in response to a federal mandate (Public Law103-43, June 10, 1993) that a case-control study be conductedon Long Island, New York to examine the relationshipbetween environmental exposures and breast cancer. Meth-ods have been described in detail previously.19 Cases (n �1508; 82% of eligible cases) were identified through pathol-ogy/cytology records of 33 institutions; they included resi-dents of Nassau and Suffolk counties, newly diagnosed withbreast cancer between 1 August 1996 and 31 July 1997.Population-based controls (n � 1556; 63% of eligible con-trols) were identified using random digit dialing for womenunder the age of 65 years and by Center for Medicare andMedicaid Services (formerly known as Health Care Financ-ing Administration) rosters for women 65 years and older.Controls were frequency-matched to the expected age distri-bution of cases by 5-year age group. All participants signedinformed consent forms before enrolling in the study.

Exposure AssessmentAn in-home questionnaire that lasted on average 101

minutes was administered to each subject by a trained inter-viewer. The questionnaire elicited information on demo-graphic factors, current and past residences, occupationalhistory, environmental exposures, reproductive history, men-struation and menopause history, contraceptive and hormoneuse, medical history, body size, physical activity, familyhistory of disease, alcohol consumption, and smoking history(questionnaire available at http://epi.grants.cancer.gov/LIBCSP/projects/Questionnaire.html). This main questionnaire alsoincluded assessment of intake of 4 categories of grilled/barbecued and smoked meats over each decade of life sincethe teenage years (Section C. Residential History, pages 19–20of the questionnaire on the Web site). Among the respondentswho completed the main questionnaire, 98% of breast cancercases and 98% of controls also completed a self-administeredBlock FFQ (detailed below), including approximately 100food items that assessed diet in the previous year.

Exposure Indices CalculatedLifetime Intake of Grilled or Barbecued andSmoked Foods

As previously described,20 variables quantifying life-time consumption of grilled/barbecued and smoked foodswere constructed based on 1) data collected in the mainquestionnaire that focused on past lifetime consumption and

2) a checklist that focused on consumption in the 4 weeksbefore the interview. In the main questionnaire, women werequeried about their consumption patterns over 6 decades oflife (�20 years, 20–29 years, 30–39 years, 40–49 years,50–59 years, 60� years) for 4 different groups of PAH-containing foods: smoked beef, lamb, and pork; grilled/barbequed beef, lamb, and pork; smoked poultry or fish; and,grilled/barbequed poultry or fish. The average of the 6 (orfewer) decades was calculated to derive an average lifetimeconsumption of these 4 PAH-containing food groups.

Values were missing for �2% of respondents for eachof the 24 groups (6 decades of consumption � 4 foodgroups), which translated to 9%–10% of respondents missingthe values for the 4 derived averages. Most of missing valueswere restricted to a single decade of reported consumption.Imputations for the missing values were derived by multipleregression21 using data from women with complete data topredict the missing variables in each of the 24 groups. Inaddition, regression analyses were performed separately bythe decade of age at interview because of concerns aboutpossible cohort effects. For example, to predict averageconsumption of smoked beef, lamb, and pork between theages of 20 and 29 years, for women in their 30s at reference,multiple regression was conducted using subjects in their 30sat reference to construct a model:

Beef consumption during ages 20–29 years

� � � �1 (beef consumption under age 20 years)

� �2 (beef consumption during ages 30–39 years) � �

These regression coefficients, �1 and �2, were thenused to impute beef consumption for all women in their 30swho were missing only beef consumption during the decade20 to 29 years of age. These steps were repeated for otherwomen missing only 1 interval of consumption within eachfood category. To correct for artificially minimized standarderrors for the odds ratios (ORs) produced when using impu-tations, the standard errors obtained using imputed data wereinflated back to the lower sample-size level. This imputationstrategy reduced the amount of missing intake data in thefollowing manner: lifetime grilled/barbecued beef, lamb, orpork consumption reduced from 9% to 3%; lifetime intake ofgrilled/barbecued poultry or fish from 10% to 3%; lifetimesmoked beef, lamb, or pork consumption from 9% to 3%;lifetime intake of smoked poultry or fish from 10% to 5%;and lifetime consumption of all 4 types of food combinedfrom 15% to 5%.

The results obtained from this imputed data set werenot materially different from those obtained from the data setin which missing consumption values were simply dropped,although, as expected, confidence intervals were wider for thelatter data set (data not shown). Sensitivity analyses revealedthat substitution of more crudely derived imputations (forexample the highest or lowest observed values) did notsubstantially affect the observed ORs (data not shown). Thus,the results based on models with the more precisely estimatedregression coefficients imputed for the missing values areshown.

Steck et al Epidemiology • Volume 18, Number 3, May 2007


HCA and Benzo(�)pyrene IndicesCalculation of these exposure indices was based on data

collected as part of the Block FFQ,22 which was modified toinclude a detailed assessment of frequency, preparation tech-niques, and doneness levels for 5 items (hamburgers, steak,pork, poultry, and fish).23 Frequency of intake was catego-rized as never, �1 per month, 1 per month, 2–3 per month,1–2 per week, 3–4 per week, 5–6 per week, and at least 1time per day. For each meat and fish item, participants wereasked, “how often during the last 12 months, did you eatmeat . . . grilled or barbequed, pan-fried, (not deep-fat-fried),oven-broiled, oven-baked, microwaved, and other (specify:

).” Levels of doneness were categorized as rare/medium, well-done, or very well-done. Frequency of cookingmethods for each food item was converted to the commondenominator of times per week. Responses to the frequencyand portion sizes of food items were translated into dailyintakes (in grams) for each food item using the NationalCancer Institute DietSys program, version 3.24

We examined 1 PAH �benzo(�)pyrene� and 3 HCAs�2-amino-1-methyl-6-phenyl-imidazo�4,5-b�pyridine (PhIP),2-amino-3,8-dimethylimidazo�4,5-f�quinoxaline (MeIQx), and2-amino-3,4,8-trimethylimidazo�4,5-f�quinoxaline (DiMeIQX,)�.The values per gram of food item were each applied toconsumption (in grams) of hamburger, steak, pork, bacon,sausage, fried chicken, and other types of poultry using amodification of the method developed by Sinha.3,4,25,26

PAH and HCA values for meats vary with cookingmethod, level of doneness, and consumption of the skin forpoultry items.1 To translate participants’ responses into dailyintake in grams for each meat item, cooking method weightswere created based on the percent of time a women used eachspecific cooking method. These weights were then multipliedto the HCA and benzo(�)pyrene values unique to that cook-ing method and doneness level. Weighted HCA andbenzo(�)pyrene levels for each cooking method were thensummed across cooking methods for each meat item. Theresulting number of nanograms of HCA or benzo(�)pyreneper 1 g for each meat item was multiplied by the woman’sintake (in grams) of that meat item.

Women who did not respond to a meat item’s donenesslevel and frequency of intake were assumed to be noncon-sumers of that item (assigned to 0). Among the minimalnumber of remaining women with missing data for thecooking method section, HCA and benzo(�)pyrene values forthe missing meat item were set to missing; this was done for 28hamburger responses �14 cases (0.9%), 14 controls (0.9%)�, 28steak responses �14 cases (0.9%), 14 controls (0.9%)�, 44 porkresponses �24 cases (1.6%), 20 controls (1.3%)�, and 36chicken responses �15 cases (1.0%), 21 controls (1.4%)�. TheFFQ did not specifically ask cooking methods for steaks orroasts, so the frequency of cooking methods for “beef, in-cluding hamburger” was assigned to steaks or roasts. Cook-ing methods for bacon and sausage were also not specificallyqueried, so cooking methods reported for pork were assignedto bacon and sausage. In the control population, the catego-ries “Don’t know” and missing values for doneness of meat

items were assumed to be the most common doneness re-sponse. For hamburger �22 (1.6%) cases, 35 (2.4%) controls�and steak �44 (1.7%) cases, 29 (2.0%) controls], missingvalues were assigned the medium doneness level that wasselected by greater than 50% of controls. For pork, 41 (3.3%)cases and 51 (2.9%) controls, and for poultry, 28 (1.9%) casesand 40 (2.7%) controls were assigned the “well” donenessselected by greater than 50% of controls.

Covariate AssessmentThe following variables were studied with regard to

confounding or effect modification: 1) from the main ques-tionnaire, reference age (defined as date of diagnosis for casesand date of identification for controls), menopausal status,race, education, age at first birth, parity, age at menarche,history of breast-feeding, use of oral contraceptives, use ofhormone replacement therapy, family history of breast can-cer, smoking status, physical activity, body mass index,alcohol intake; and 2) from the FFQ, consumption of energy,fruits and vegetables, and single and multiple vitamin use.Stratified analyses were performed to test for heterogeneityby stage of disease and estrogen receptor/progesterone recep-tor (ER/PR) status as previously described.19

Statistical MethodsThe main exposure variables were total and average

lifetime intake of grilled/barbecued and smoked meats esti-mated from the main questionnaire, and estimated intake oftotal benzo(�)pyrene, benzo(�)pyrene from meat only,benzo(�)pyrene from sources other than meat, and each ofthe 3 HCAs, all based on FFQ responses as described above.These variables were categorized into quantiles (deciles,quintiles, quartiles, tertiles, or dichotomous) based on meno-pause-specific distributions among the controls to assess thebest representation of the data. Because the findings weresimilar regardless of the quantile chosen, we used tertiles toimprove power during stratified analyses. Unconditional lo-gistic regression was used to calculate ORs and 95% confi-dence intervals (CIs) of breast cancer in relation to tertiles ofthe main exposure variables. Crude ORs and corresponding95% CIs adjusting only for age were calculated, as well asadjusted ORs controlling for potential confounders.

Effect modification on the multiplicative scale wasevaluated by comparing the ORs for meat intake variablesacross strata of covariates and testing for heterogeneity. Wecreated interaction terms between meat intake variables andcovariates, and conducted a likelihood ratio test to comparethe model with the interaction terms to the reduced modelcontaining only the individual variables. We assessed additiveinteraction by computing interaction contrast ratios using thehypothesized lowest risk category of low meat intake and highfruit and vegetable intake as the common referent and compar-ing all other joint categories of meat and fruit and vegetableintake to this common referent. In these data, menopausalstatus was not an effect modifier on a multiplicative scale.Nonetheless, because we27 and others28 have found breastcancer risk in relation to diet to vary with menopause, andbecause there was some indication that the lifetime cooked

Epidemiology • Volume 18, Number 3, May 2007 Lifetime Versus Recent Intake of Cooked Meat and Breast Cancer


meat results varied with menopausal status, we present allanalyses stratified by menopausal status.

We assessed confounding with backward elimination,beginning with a full model. A covariate remained in themodel if the OR of the reduced model (without the covariate)changed by more than 10% when compared with the OR ofthe full model. Tests for trend were calculated by setting each

tertile equal to the median value and treating these 3 mediantertile values as a continuous variable.

RESULTSBreast cancer risk among postmenopausal women was

elevated in relation to measures of both total and average

TABLE 1. Adjusted* ORs and 95% CIs for Breast Cancer by Intake of Grilled/Barbecued and Smoked Meats Over theWoman’s Lifetime (Assessed From Main Questionnaire) in the Long Island Breast Cancer Study Project, 1996–1997

Intake† Premenopausal Postmenopausal

Range MedianCases

No. (%)ControlsNo. (%) OR (95% CI)

CasesNo. (%)

ControlsNo. (%) OR (95% CI)

Grilled/barbecued beef, pork, and lambAverage over lifetime

0–13‡ 3 119 (25) 134 (27) 1.00 391 (40) 415 (43) 1.00

14–48 28 145 (31) 145 (29) 1.09 (0.72–1.63) 327 (33) 302 (32) 1.21 (0.93–1.57)

49–364 79 203 (43) 215 (44) 0.96 (0.66–1.40) 271 (27) 244 (25) 1.28 (0.96–1.71)

P for trend P � 0.47 P � 0.18

Total over lifetime

0–630‡ 219 124 (27) 137 (29) 1.00 289 (32) 316 (35) 1.00

631–2162 1358 175 (38) 186 (39) 0.98 (0.67–1.42) 261 (28) 266 (30) 1.18 (0.89–1.57)

2163–17,217 3640 158 (35) 155 (32) 0.85 (0.57–1.26) 366 (40) 310 (35) 1.32 (1.01–1.72)

P for trend P � 0.24 P � 0.10

Smoked ham, pork, and lambAverage over lifetime

0–21‡ 1 229 (49) 240 (48) 1.00 469 (48) 499 (51) 1.00

22–52 38 140 (30) 160 (32) 1.06 (0.75–1.52) 281 (29) 270 (28) 1.25 (0.96–1.63)

53–364 97 98 (21) 99 (20) 0.96 (0.63–1.47) 233 (24) 208 (21) 1.13 (0.84–1.51)

P for trend P � 0.88 P � 0.35

Total over lifetime

0–810‡ 323 163 (43) 155 (40) 1.00 187 (25) 215 (30) 1.00

811–2277 1490 132 (35) 153 (39) 0.97 (0.68–1.39) 240 (31) 215 (30) 1.45 (1.09–1.93)

2278–24,253 3750 82 (22) 81 (21) 0.94 (0.60–1.47) 332 (44) 298 (40) 1.30 (0.99–1.69)

P for trend P � 0.29 P � 0.22

Total grilled/barbecued and smoked meatsAverage over lifetime

0–54‡ 24 91 (20) 111 (23) 1.00 324 (35) 373 (41) 1.00

55–137 89 169 (38) 170 (36) 1.24 (0.83–1.86) 320 (34) 274 (30) 1.48 (1.13–1.93)

138–1092 208 191 (42) 197 (41) 1.13 (0.76–1.68) 295 (31) 259 (29) 1.35 (1.02–1.79)

P for trend P � 0.89 P � 0.12

Total over lifetime

0–2562‡ 1164 153 (34) 166 (34) 1.00 280 (29) 330 (35) 1.00

2565–6081 4264 161 (35) 181 (38) 0.98 (0.68–1.40) 287 (30) 272 (30) 1.47 (1.11–1.95)

6085–51,652 9448 143 (31) 136 (28) 1.03 (0.68–1.54) 390 (41) 330 (35) 1.47 (1.12–1.92)

P for trend P � 0.98 P � 0.02

Average over previousdecade of life

0–48‡ 12 109 (24) 126 (26) 1.00 395 (41) 418 (45) 1.00

49–140 96 161 (35) 146 (30) 1.05 (0.70–1.56) 312 (32) 278 (30) 1.31 (1.00–1.70)

142–1092 220 185 (41) 216 (44) 0.88 (0.60–1.30) 254 (26) 234 (25) 1.20 (0.90–1.60)

P for trend P � 0.52 P � 0.27

*Adjusted for age, energy intake, fruit and vegetable intake, and multivitamin supplement use.†Average indicates the number of times consumed per year; total indicates the total number of times consumed.‡Reference category.



lifetime intake of grilled/barbecued and smoked meats (asassessed in the main questionnaire; Table 1). Compared withwomen in the lowest tertile, ORs for total lifetime intakeamong women in the second and third tertile were both 1.47(95% CI � 1.11–1.95 and 1.12–1.92, respectively); the cor-responding results for average lifetime intake were similar(OR � 1.48 �CI � 1.13–1.93� and 1.35 �CI � 1.02–1.79�,respectively). Modest increased risks among postmenopausalwomen were also observed in relation to average and totallifetime intake of grilled/barbecued beef, pork, and lamb, andin relation to total lifetime intake of smoked ham, pork, andlamb. No associations were observed for any of the lifetimecooked meat intake variables among premenopausal women(Table 1). Further, no associations were observed for intakesof grilled/barbecued poultry or smoked fish among pre- orpostmenopausal women (data not shown).

We also examined the association between breast can-cer and intake of cooked meat by decade of life (data notshown). For postmenopausal women, although some individ-ual associations were noted, no clear patterns emerged tosuggest differential risk by intake in specific decades of life.In general, associations between the various cooked meatvariables and breast cancer risk were spread throughout thelifetime. When examining associations by decade of life inpremenopausal women, a few associations were observed inthe second but not the third tertiles of intake.

As shown in Table 2, intakes of benzo(�)pyrene andHCAs in the previous year (as assessed by the FFQ) were notassociated with increased risk of breast cancer. Contrary toour hypothesis, for premenopausal women, a reduced risk ofbreast cancer was observed in women in the third tertile ofintake of 2 HCAs (MeIQx and DiMeIQx) compared with

TABLE 2. Adjusted* ORs and 95% CIs for Breast Cancer by Intake of Polycyclic Aromatic Hydrocarbons and HeterocyclicAmines in the Previous Year (Assessed From FFQ)

Intake† Premenopausal Postmenopausal

Range MedianCases


CasesNo. (%)


Total BaPs from food0–56‡ 42 142 (32) 159 (33) 1.00 336 (35) 318 (34) 1.00

57–84 70 156 (35) 160 (34) 1.20 (0.79–1.81) 347 (36) 296 (32) 1.22 (0.89–1.67)

85–411 107 150 (33) 156 (33) 1.15 (0.68–1.94) 281 (29) 324 (34) 1.01 (0.68–1.50)

P for trend P � 0.52 P � 0.92

BaPs from meat0‡ 0 208 (46) 219 (46) 1.00 568 (59) 552 (59) 1.00

1–3 1 112 (25) 124 (26) 0.75 (0.51–1.11) 226 (23) 224 (24) 0.91 (0.70–1.20)

4–189 11 128 (29) 132 (28) 0.91 (0.62–1.32) 170 (18) 162 (17) 1.07 (0.79–1.46)

P for trend P � 0.71 P � 0.51

BaPs from foods other than meat0–54‡ 41 153 (33) 182 (37) 1.00 357 (36) 316 (33) 1.00

55–80 67 165 (36) 158 (32) 1.27 (0.82–1.97) 342 (34) 317 (33) 0.95 (0.69–1.32)

81–406 100 139 (31) 152 (31) 1.04 (0.56–1.92) 294 (30) 335 (34) 0.88 (0.58–1.34)

P for trend P � 0.99 P � 0.37

PhIP0‡ 0 152 (34) 139 (29) 1.00 385 (40) 377 (40) 1.00

1–14 4 115 (26) 153 (32) 0.58 (0.39–0.87) 290 (30) 299 (32) 0.75 (0.57–0.99)

15–942 58 181 (40) 183 (39) 0.83 (0.57–1.21) 289 (30) 262 (28) 0.92 (0.70–1.22)

P for trend P � 0.97 P � 0.76

MeIQx0‡ 0 135 (30) 117 (25) 1.00 334 (35) 334 (36) 1.00

1–11 4 159 (36) 170 (36) 0.78 (0.52–1.15) 335 (35) 324 (34) 0.90 (0.69–1.19)

12–323 25 154 (34) 188 (39) 0.60 (0.40–0.91) 295 (30) 280 (30) 0.94 (0.71–1.25)

P for trend P � 0.007 P � 0.96

Di MeIQx0‡ 0 208 (46) 212 (45) 1.00 534 (55) 512 (55) 1.00

1–2 1 152 (34) 141 (30) 1.02 (0.71–1.46) 275 (29) 274 (29) 0.86 (0.66–1.12)

3–40 5 88 (20) 122 (25) 0.59 (0.38–0.91) 155 (16) 152 (16) 0.91 (0.66–1.26)

P for trend P � 0.003 P � 0.70

*Adjusted for age, energy intake, fruit and vegetable intake, and multivitamin supplement use.†ng per day.‡Reference category.



women in the first tertiles. Among postmenopausal women,no associations were observed with regard to benzo(�)pyreneand HCA indices.

As shown in Table 3, when the results among post-menopausal women were further stratified by fruit and veg-etable intake (as assessed in the FFQ), the increased risk ofbreast cancer associated with the highest tertile of lifetimeconsumption of grilled/barbecued and smoked meats was

limited to women in the lowest category of fruit and vegeta-ble intake (P value for multiplicative interaction � 0.25). Theincreased risk associated with intake of smoked ham, pork,and lamb was also limited to consumers of low fruit andvegetable diets. No associations between benzo(�)pyrene orHCA intake and breast cancer were observed in either high orlow consumers of fruits and vegetables for postmenopausalwomen (Table 4). There was no evidence for additive inter-

TABLE 3. Adjusted* ORs and 95% CIs for Postmenopausal Breast Cancer by Intake of Grilled/Barbecued and Smoked MeatsOver the Women’s Lifetime (Assessed From Main Questionnaire) Stratified by Fruit and Vegetable Intake

Intake† Low Fruit and Vegetable Intake High Fruit and Vegetable Intake

Range MedianCases


CasesNo. (%)


Grilled/barbecued beef, pork and lambAverage over lifetime

0–10‡ 2 211 (35) 197 (38) 1.00 136 (37) 173 (41) 1.00

11–40 24 198 (32) 159 (31) 1.42 (0.98–2.05) 115 (32) 117 (28) 1.37 (0.90–2.08)

41–364 67 202 (33) 159 (31) 1.37 (0.95–1.98) 113 (31) 133 (31) 1.14 (0.75–1.73)

P for trend P � 0.24 P � 0.66

Total over lifetime

8–537‡ 181 160 (28) 154 (32) 1.00 101 (30) 135 (34) 1.00

540–2197 1343 190 (33) 163 (34) 1.37 (0.95–1.97) 99 (30) 125 (32) 1.05 (0.68–1.60)

2201–17,217 3683 223 (39) 163 (34) 1.48 (1.03–2.13) 133 (40) 134 (34) 1.29 (0.86–1.54)

P for trend P � 0.23 P � 0.35

Smoked ham, pork and lambAverage over lifetime

0–20‡ 1 268 (44) 250 (47) 1.00 184 (51) 233 (54) 1.00

21–52 38 185 (30) 157 (30) 1.54 (1.08–2.18) 102 (28) 109 (26) 1.07 (0.72–1.60)

53–364 98 155 (26) 120 (23) 1.28 (0.87–1.87) 75 (21) 86 (20) 1.04 (0.66–1.64)

P for trend P � 0.27 P � 0.60

Total over lifetime

10–1010‡ 391 138 (29) 125 (31) 1.00 86 (32) 111 (36) 1.00

1013–2632 1784 160 (33) 145 (35) 1.56 (1.08–2.25) 89 (33) 91 (30) 1.19 (0.77–1.82)

2637–24,253 4400 186 (38) 137 (34) 1.31 (0.91–1.89) 92 (34) 106 (34) 1.04 (0.68–1.60)

P for trend P � 0.47 P � 0.95

Total grilled/barbecued and smoked meatsAverage over lifetime

0–46‡ 19 177 (30) 172 (35) 1.00 101 (29) 145 (36) 1.00

47–122 80 188 (32) 154 (32) 1.67 (1.15–2.42) 126 (37) 125 (31) 1.27 (0.85–1.92)

123–1092 189 219 (38) 159 (33) 1.56 (1.08–2.26) 116 (34) 132 (33) 1.14 (0.75–1.73)

P for trend P � 0.19 P � 0.26

Total over lifetime

0–2553‡ 1025 170 (29) 178 (36) 1.00 104 (30) 142 (35) 1.00

2574–6514 4456 192 (32) 162 (32) 1.84 (1.27–2.67) 118 (34) 129 (31) 1.15 (0.76–1.73)

6533–51,652 10,094 232 (39) 159 (32) 1.74 (1.20–2.50) 127 (36) 140 (34) 1.15 (0.76–1.74)

P for trend P � 0.07 P � 0.23

Average over previousdecade of life

0–29‡ 8 197 (33) 187 (37) 1.00 120 (34) 159 (39) 1.00

30–108 64 195 (33) 160 (32) 1.47 (1.02–2.12) 118 (33) 120 (29) 1.44 (0.96–2.18)

109–1092 208 202 (34) 155 (31) 1.35 (0.93–1.97) 117 (33) 130 (32) 1.32 (0.87–2.01)

P for trend P � 0.34 P � 0.28

*Adjusted for age, energy intake, and multivitamin supplement use.†Average indicates the number of times consumed per year; total indicates the total number of times consumed.‡Reference category.



action for any of the meat variables with fruit and vegetableintake. Additionally, we examined the interactions shown inTables 3 and 4 for premenopausal women separately, andfound no joint effects between any of the meat intake vari-ables and fruit and vegetable intake (data not shown).

Our large sample size allowed for examination ofeffects within joint categories of estrogen- and progesterone-receptor status in postmenopausal women. The risk of breastcancer among postmenopausal women consuming highamounts of grilled/barbecued meats over their lifetime wassimilar for ER�/PR� and ER�/PR� tumors (data notshown). We found no strong associations for any of thebenzo(�)pyrene or HCA intake variables assessed from theFFQ across categories of ER/PR status (data not shown), withthe exception of a positive association observed in ER�/PR�cases (OR � 1.47; CI � 0.99–2.19; P for trend � 0.07) in the

highest tertile of benzo(�)pyrene from meats intake whencompared with those in the lowest tertile. No consistentassociations were observed for meat intake variables withinER�/PR� and ER�/PR� tumors, though the small numbersof cases within these categories limited our power to detectassociations within these groups (data not shown).

Analyses stratified by stage of diagnosis (in situ versusinvasive) revealed slight differences in effect estimates forlifetime intake variables, although these were inconsistentacross different intake variables (data not shown). For exam-ple, the increased risk associated with total grilled/barbecuedbeef, lamb, or pork was slightly higher for in situ cases,compared with invasive cancers, in postmenopausal women(for third tertile compared with the first tertile, the OR for insitu cancer was 1.41 �95% CI � 0.79–2.52� and for invasivecancers it was 1.23 �0.92–1.66�). In contrast, for total smoked

TABLE 4. Adjusted* ORs and 95% CIs for Postmenopausal Breast Cancer by Intake of Polycyclic Aromatic Hydrocarbons andHeterocyclic Amines in the Previous Year (Assessed From FFQ) Stratified by Fruit and Vegetable Intake

Intake† Low Fruit and Vegetable Intake High Fruit and Vegetable Intake

Range MedianCases


CasesNo. (%)


Total BaPs from food0–56‡ 41 316 (53) 279 (53) 1.00 19 (5) 39 (9) 1.00

57–85 70 222 (37) 184 (35) 1.10 (0.78–1.55) 134 (37) 124 (30) 1.47 (0.73–2.98)

86–309 107 61 (10) 57 (11) 1.10 (0.63–1.93) 211 (58) 255 (61) 1.09 (0.52–2.26)

P for trend P � 0.32 P � 0.29

BaPs from meat0‡ 0 353 (59) 305 (59) 1.00 214 (59) 247 (59) 1.00

1–2 1 119 (20) 117 (22) 0.77 (0.53–1.12) 78 (21) 78 (19) 1.30 (0.83–2.03)

3–126 8 127 (21) 98 (19) 1.03 (0.70–1.52) 72 (20) 93 (22) 0.93 (0.60–1.44)

P for trend P � 0.59 P � 0.55

BaPs from foods other than meat0–55‡ 41 350 (57) 289 (54) 1.00 21 (6) 39 (9) 1.00

56–81 67 206 (33) 186 (35) 0.88 (0.62–1.25) 132 (35) 126 (29) 1.35 (0.62–2.95)

82–280 101 63 (10) 59 (11) 0.89 (0.50–1.58) 220 (59) 268 (62) 1.03 (0.45–2.31)

P for trend P � 0.56 P � 0.35

PhIP0‡ 0 231 (39) 192 (37) 1.00 153 (42) 185 (44) 1.00

1–13 3 181 (30) 181 (35) 0.72 (0.50–1.03) 99 (27) 109 (26) 0.77 (0.50–1.17)

14–839 51 187 (31) 147 (28) 0.93 (0.64–1.34) 112 (31) 124 (30) 0.91 (0.60–1.38)

P for trend P � 0.60 P � 0.94

MeIQx0‡ 0 201 (34) 166 (32) 1.00 132 (36) 168 (40) 1.00

1–10 4 204 (34) 201 (39) 0.69 (0.48–1.00) 118 (33) 111 (27) 1.29 (0.84–1.99)

11–247 24 194 (32) 153 (29) 0.90 (0.61–1.33) 114 (31) 139 (33) 0.98 (0.64–1.50)

P for trend P � 0.63 P � 0.57

Di MeIQx0‡ 0 327 (55) 270 (52) 1.00 206 (56) 242 (58) 1.00

1–2 1 179 (30) 175 (34) 0.80 (0.57–1.13) 96 (26) 99 (24) 0.94 (0.62–1.42)

3–34 5 93 (15) 75 (14) 0.99 (0.64–1.54) 62 (17) 77 (18) 0.81 (0.50–1.30)

P for trend P � 0.81 P � 0.36

*Adjusted for age, energy intake, and multivitamin supplement use.†ng per day.‡Reference category.



ham, lamb, or pork, the increased risk was higher for invasivecancer (OR � 1.43; CI � 1.01–2.03) than for in situ cases(1.04; 0.53–2.05). No differences in effect estimates betweenin situ and invasive cancer were observed in the levels ofbenzo(�)pyrene and HCA intake in the year before interview(data not shown).

DISCUSSIONUsing data from a large population-based case-control

study, we examined associations of breast cancer risk withlifetime intake of grilled/barbecued and smoked meats, andalso with intake of benzo(�)pyrene and HCAs in the previousyear. Modest positive associations were found for high life-time intake of grilled/barbecued and smoked meats in post-menopausal women. These associations were strongestamong women with low consumption of fruits and vegeta-bles. The associations did not appear to differ substantially bytumor stage or hormone receptor status.

We found no consistent associations between breastcancer and high dietary intake of HCAs or benzo(�)pyrene inthe previous year among pre- or postmenopausal women.Two other studies of recent diet and breast cancer risk foundno associations with cooked or processed meat,14,29 and 2large prospective studies of up to 18 years of follow-up foundno associations between meat intake and breast cancer, al-though detailed information related to cooking methods anddoneness preferences were not collected.15,16 In contrast,intake of very well-done cooked red meat was associated withincreased risk of breast cancer in 1 case-control study,12 andintake of well-done deep-fried red meat in the previous 5years was highly associated with increased risk of breastcancer, particularly in women at high body mass indexes inanother study.11

We found high lifetime intake of grilled/barbecued andsmoked meats was associated with increased risk of breastcancer among women consuming few fruits and vegetables.We previously reported an inverse association between fruitand vegetable intake and postmenopausal breast cancer in thispopulation.27 Experimental studies suggest that chemopre-ventive constituents of fruits and vegetables, such as isothio-cyanates and chlorophyllin, may protect against PAH- andHCA-induced genotoxicity.17,18 Our results support labora-tory findings in animal models that fruits and vegetables mayconfer some protection against the harmful effects of cookedmeat intake.

In animal models, benzo(�)pyrene and HCAs are po-tent mammary carcinogens. Their mechanism of action isbelieved to be through direct damage to DNA (formation ofDNA adducts).5 However, more recently, there is evidencefrom cell culture experiments that 1 HCA (PhIP, but notMeIQx) may be estrogenic.30 The majority of epidemiologicand animal model evidence supports a causal relationshipbetween estrogen levels and breast cancer risk. Thus, it issurprising that we did not observe increased risk for breastcancer with increasing intake of HCAs, and in particular,PhIP. One possible explanation may be that intake in the yearbefore diagnosis is not representative of a woman’s lifetimeintake of HCA-related foods. Most cancers are believed to be

slow-growing, developing over a lifetime with initiatingevents occurring possibly as early as adolescence. A fewstudies have attempted to retrospectively assess adolescentdiet and examine associations with breast cancer,31–34 withfew significant findings. One study by Baer et al35 reportedpositive associations of intake of total meat, red meat, and hotdogs with risk of proliferative benign breast disease. Thenumbers of questions relating to diet are usually limited inthese studies, and none has attempted to assess meat cookingmethods in adolescence or over the lifetime.

Also surprising were the inverse associations betweenthe third tertile of MeIQx and DiMeIQX intake as comparedwith the first tertiles and breast cancer among premenopausalwomen. Another study found decreased risk of breast cancerfor the highest quartile of PhIP intake as compared with thelowest quartile (which was attenuated after adjustment forchicken intake), and nonsignificant decreased risk for thehighest quartiles of MeIQx and DiMeIQx as compared withthe lowest quartile in premenopausal and postmenopausalwomen combined.10 Chicken is a major contributor to PhIPintake. The authors suggest this may partially explain theinverse associations for PhIP because white meat may helpsupport proper immune function, or may be a surrogate forother healthy lifestyle factors. However, 2 other studies foundeither no effect of DiMeIQX and MeIQX and an increasedrisk with PhIP intake in women aged 55–69 years,8 orincreased risks in postmenopausal women with high intakesof MeIQx and PhIP and no associations in premenopausalwomen.9 The ranges of estimated HCA intake in these studieswere similar to ours. Foods high in MeIQx are pan-friedhamburger, sausage, and steak cooked well-done or verywell-done, whereas DiMeIQx has been detected in smallquantities in pan-fried very well-done steak and in pan-friedand grilled/barbecued chicken.4,36 Thus, the inverse associa-tions in premenopausal women in our study are unexplainedand may be due to chance.

Using an in-person, interviewer-administered question-naire, we indirectly assessed dietary intake of PAHs byasking about intake of 4 types of meat prepared either asgrilled/barbecued or smoked during each decade of life. Ourresults based on these measures must be interpreted with carebecause the lifetime meat intake questionnaire has not beenvalidated. Although the validity of recall of lifetime intake ofcooked meats has not been directly addressed in the literature,we can extrapolate from other studies that have attempted tovalidate long-term recall of dietary intake. These studies havereported modest correlations between dietary assessments atone point in time, and recall of the same diet 11–24 yearslater (average correlations for food and nutrient intakesranged from 0.23 to 0.59).26,37–41 Recall of adolescent diet inadulthood as compared with maternal reporting has also beenexamined in the Nurses’ Health Study II, and moderatecorrelations were observed (correlations ranged from 0.13 to0.59).42 Misclassification of exposure, when the variable ismultilevel, may result in bias toward or away from the null.An additional concern is that the accuracy of long-term recallmay have differed by case or control status; however, this isunlikely for the following reasons. First, previous validation



studies of long-term recall of diet have not found differentialrecall between cancer cases and controls.40,41,43 Second, toassess whether preconceived notions of breast cancer etiologywould bias a women’s recall of meat intake, we examined theparticipants’ responses to what they believed caused breastcancer. Few women listed meat intake as a causal factor, andamong these women there was no case-control difference.Therefore, recall bias is unlikely to have a strong influence onour results.

Given the known measurement error in FFQs, nondif-ferential misclassification of cooked meat intake variablesmay be another explanation for our findings of no associationbetween recent intake of benzo(�)pyrene and HCAs andbreast cancer. Unlike some studies showing positive associ-ations,8,12 we did not use color photographs of meat cookedto varying levels of doneness, but instead relied on writtendescriptions of doneness preference, which may be a lessaccurate means of collecting these data. Additionally, somemeat items that do not have similar HCA or PAH values weregrouped together in the food questionnaire. For example,steaks and roasts were grouped in “beef (steaks, roasts, etc.,including on sandwiches).” However, the content of HCAsand PAHs in meats is highly dependent on cooking methods,so the fact that we used reported frequency of the cookingmethods to assign HCA and PAH values to the meat itemsshould have improved our estimation of intake of thesecarcinogens over the basic FFQ. We also did not specificallyask cooking methods for bacon and sausage, but insteadassigned the reported cooking methods for pork to that ofbacon and sausage. As the true cooking methods for thesedifferent meat items may vary considerably (eg, pan-fryingfor bacon and sausage vs. oven-broiling or baking for pork)and the corresponding HCA and benzo(�)pyrene content ofbacon is different depending on whether the meat is oven-broiled or pan-fried,1,3 this assumption may have led tomisclassification of exposure.

Due to the lack of validation of methods to assesslong-term dietary recall, it is possible that current diet, ifcorrelated with recall of long-term diet, could confound therelationship between lifetime intake and breast cancer.35 Weexamined the correlation between intake of grilled/barbecuedbeef, pork, and lamb from the lifetime intake questionnairewith intake of grilled and barbecued beef and pork on theFFQ. The Spearman correlation coefficient between intakebefore age 20 and intake in the year before interview wasweak (r � 0.25), whereas the correlation between intake inthe previous decade and intake in the year before interviewwas stronger (r � 0.53). Similar results were found forpoultry and fish, and the results did not differ greatly betweencases and controls. These correlations support the findingsfrom Baer et al35 that current diet is not strongly correlatedwith long-term recall of diet.

Given that we did not collect data on overall lifetimedietary intake (especially lifetime total meat intake irrespec-tive of the cooking methods), we cannot exclude the possi-bility that our observed results may be due to other potentiallycorrelated dietary factors, such as lifetime total meat intake orlifetime total fat intake, rather than the specific cooking

methods of meat that were queried. We examined confound-ing by all known risk factors for breast cancer. However,there may have been residual confounding by exposuresmeasured with error, such as other dietary intake variables,lifetime physical activity, or alcohol use. It is unlikely,though, that residual confounding could fully account for ourfindings.

Our study supports a role in postmenopausal breastcancer etiology for lifetime intake, but not more recent intake,of meats cooked in ways that enhance PAH formation,particularly among women consuming few fruits and vege-tables. Future investigation into this area in larger studiesmay provide useful information on the contribution of thesedietary exposures to breast cancer etiology.

ACKNOWLEDGMENTSFor their valuable contributions to the Long Island

Breast Cancer Study Project, the authors thank: members ofthe Long Island Breast Cancer Network; the 31 participatinginstitutions on Long Island and in New York City, NY; ourNational Institutes of Health collaborators, Gwen Collman,National Institutes of Environmental Health Sciences; G. IrisObrams, formerly of the National Cancer Institute; membersof the External Advisory Committee to the population-basedcase-control study: Leslie Bernstein, (Committee chair) Ger-ald Akland, Barbara Balaban, Blake Cady, Dale Sandler,Roy Shore, and Gerald Wogan, as well as other collaboratorswho assisted with various aspects of our data collectionefforts including Steve Stellman, Maureen Hatch, MaryWolff, Geoff Kabat, Gail Garbowski, H. Leon Bradlow, MartinTrent, Ruby Senie, Carla Maffeo, Pat Montalvan, GertrudBerkowitz, Margaret Kemeny, Mark Citron, Freya Schnabel,Allen Schuss, Steven Hajdu, and Vincent Vinciguerra.

REFERENCES1. Kazerouni N, Sinha R, Hsu CH, et al. Analysis of 200 food items for

benzo�a�pyrene and estimation of its intake in an epidemiologic study.Food Chem Toxicol. 2001;39:423–436.

2. Knize MG, Salmon CP, Pais P, et al. Food heating and the formation ofheterocyclic aromatic amine and polycyclic aromatic hydrocarbon mu-tagens/carcinogens. Adv Exp Med Biol. 1999;459:179–193.

3. Sinha R, Knize MG, Salmon CP, et al. Heterocyclic amine content ofpork products cooked by different methods and to varying degrees ofdoneness. Food Chem Toxicol. 1998;36:289–297.

4. Sinha R, Rothman N, Salmon CP, et al. Heterocyclic amine content inbeef cooked by different methods to varying degrees of doneness and gravymade from meat drippings. Food Chem Toxicol. 1998;36:279–287.

5. Snyderwine EG, Venugopal M, Yu M. Mammary gland carcinogenesisby food-derived heterocyclic amines and studies on the mechanisms ofcarcinogenesis of 2-amino-1-methyl-6-phenylimidazo�4,5-b�pyridine(PhIP). Mutat Res. 2002;506/507:145–152.

6. el-Bayoumy K, Chae YH, Upadhyaya P, et al. Comparative tumorige-nicity of benzo�a�pyrene, 1-nitropyrene and 2-amino-1-methyl-6-pheny-limidazo�4,5-b�pyridine administered by gavage to female CD rats.Carcinogenesis. 1995;16:431–434.

7. Cross AJ, Sinha R. Meat-related mutagens/carcinogens in the etiology ofcolorectal cancer. Environ Mol Mutagen. 2004;44:44–55.

8. Sinha R, Gustafson DR, Kulldorff M, et al. 2-amino-1-methyl-6-phenylimidazo�4,5-b�pyridine, a carcinogen in high-temperature-cookedmeat, and breast cancer risk. J Natl Cancer Inst. 2000;92:1352–1354.

9. De Stefani E, Ronco A, Mendilaharsu M, et al. Meat intake, heterocyclicamines, and risk of breast cancer: a case-control study in Uruguay.Cancer Epidemiol Biomarkers Prev. 1997;6:573–581.



10. Delfino RJ, Sinha R, Smith C, et al. Breast cancer, heterocyclic aromaticamines from meat and N-acetyltransferase 2 genotype. Carcinogenesis.2000;21:607–615.

11. Dai Q, Shu XO, Jin F, et al. Consumption of animal foods, cookingmethods, and risk of breast cancer. Cancer Epidemiol Biomarkers Prev.2002;11:801–808.

12. Zheng W, Gustafson DR, Sinha R, et al. Well-done meat intake and therisk of breast cancer. J Natl Cancer Inst. 1998;90:1724–1729.

13. Hermann S, Linseisen J, Chang-Claude J. Nutrition and breast cancerrisk by age 50: a population-based case-control study in Germany. NutrCancer. 2002;44:23–34.

14. Gertig DM, Hankinson SE, Hough H, et al. N-acetyl transferase 2genotypes, meat intake and breast cancer risk. Int J Cancer. 1999;80:13–17.

15. Missmer SA, Smith-Warner SA, Spiegelman D, et al. Meat and dairyfood consumption and breast cancer: a pooled analysis of cohort studies.Int J Epidemiol. 2002;31:78–85.

16. Holmes MD, Colditz GA, Hunter DJ, et al. Meat, fish and egg intake andrisk of breast cancer. Int J Cancer. 2003;104:221–227.

17. Dingley KH, Ubick EA, Chiarappa-Zucca ML, et al. Effect of dietaryconstituents with chemopreventive potential on adduct formation of alow dose of the heterocyclic amines PhIP and IQ and phase II hepaticenzymes. Nutr Cancer. 2003;46:212–221.

18. Conaway CC, Wang CX, Pittman B, et al. Phenethyl isothiocyanate andsulforaphane and their N-acetylcysteine conjugates inhibit malignantprogression of lung adenomas induced by tobacco carcinogens in A/Jmice. Cancer Res. 2005;65:8548–8557.

19. Gammon MD, Neugut AI, Santella RM, et al. The Long Island BreastCancer Study Project: description of a multi-institutional collaborationto identify environmental risk factors for breast cancer. Breast CancerRes Treat. 2002;74:235–254.

20. Gammon MD, Santella RM, Neugut AI, et al. Environmental toxins andbreast cancer on Long Island. I. Polycyclic aromatic hydrocarbon DNAadducts. Cancer Epidemiol Biomarkers Prev. 2002;11:677–685.

21. Selvin S. Statistical Analysis of Epidemiologic Data. New York: OxfordUniversity Press; 1996.

22. Block G, Hartman AM, Dresser CM, et al. A data-based approach to dietquestionnaire design and testing. Am J Epidemiol. 1986;124:453–469.

23. Potischman N, Swanson CA, Coates RJ, et al. Intake of food groups andassociated micronutrients in relation to risk of early-stage breast cancer.Int J Cancer. 1999;82:315–321.

24. HHHQ-DietSys Analysis Software, version. 3. 0. Bethesda, MD: Na-tional Cancer Institute; 1999.

25. Sinha R, Rothman N. Exposure assessment of heterocyclic amines(HCAs) in epidemiologic studies. Mutat Res. 1997;376:195–202.

26. Bakkum A, Bloemberg B, van Staveren WA, et al. The relative validityof a retrospective estimate of food consumption based on a currentdietary history and a food frequency list. Nutr Cancer. 1988;11:41–53.

27. Gaudet MM, Britton JA, Kabat GC, et al. Fruits, vegetables, and

micronutrients in relation to breast cancer modified by menopause andhormone receptor status. Cancer Epidemiol Biomarkers Prev. 2004;13:1485–1494.

28. Ambrosone CB, McCann SE, Freudenheim JL, et al. Breast cancer riskin premenopausal women is inversely associated with consumption ofbroccoli, a source of isothiocyanates, but is not modified by GSTgenotype. J Nutr. 2004;134:1134–1138.

29. Ambrosone CB, Freudenheim JL, Sinha R, et al. Breast cancer risk, meatconsumption and N-acetyltransferase (NAT2) genetic polymorphisms.Int J Cancer. 1998;75:825–830.

30. Lauber SN, Ali S, Gooderham NJ. The cooked food derived carcinogen2-amino-1-methyl-6-phenylimidazo�4,5-b� pyridine is a potent oestro-gen: a mechanistic basis for its tissue-specific carcinogenicity. Carcino-genesis. 2004;25:2509–2517.

31. Hislop TG, Coldman AJ, Elwood JM, et al. Childhood and recent eatingpatterns and risk of breast cancer. Cancer Detect Prev. 1986;9:47–58.

32. Pryor M, Slattery ML, Robison LM, et al. Adolescent diet and breastcancer in Utah. Cancer Res. 1989;49:2161–2167.

33. Potischman N, Weiss HA, Swanson CA, et al. Diet during adolescenceand risk of breast cancer among young women. J Natl Cancer Inst.1998;90:226–233.

34. Frazier AL, Ryan CT, Rockett H, et al. Adolescent diet and risk of breastcancer. Breast Cancer Res. 2003;5:R59–R64.

35. Baer HJ, Schnitt SJ, Connolly JL, et al. Adolescent diet and incidence ofproliferative benign breast disease. Cancer Epidemiol Biomarkers Prev.2003;12:1159–1167.

36. Sinha R, Rothman N, Brown ED, et al. High concentrations of thecarcinogen 2-amino-1-methyl-6-phenylimidazo- �4,5-b�pyridine (PhIP)occur in chicken but are dependent on the cooking method. Cancer Res.1995;55:4516–4519.

37. Wu ML, Whittemore AS, Jung DL. Errors in reported dietary intakes. II.Long-term recall. Am J Epidemiol. 1988;128:1137–1145.

38. Lindsted KD, Kuzma JW. Long-term (24-year) recall reliability incancer cases and controls using a 21-item food frequency questionnaire.Nutr Cancer. 1989;12:135–149.

39. Sobell J, Block G, Koslowe P, et al. Validation of a retrospectivequestionnaire assessing diet 10–15 years ago. Am J Epidemiol. 1989;130:173–187.

40. Byers TE, Rosenthal RI, Marshall JR, et al. Dietary history from thedistant past: a methodological study. Nutr Cancer. 1983;5:69–77.

41. Jensen OM, Wahrendorf J, Rosenqvist A, et al. The reliability ofquestionnaire-derived historical dietary information and temporal stabil-ity of food habits in individuals. Am J Epidemiol. 1984;120:281–290.

42. Maruti SS, Feskanich D, Colditz GA, et al. Adult recall of adolescentdiet: reproducibility and comparison with maternal reporting. Am JEpidemiol. 2005;161:89–97.

43. Friedenreich CM, Slimani N, Riboli E. Measurement of past diet: reviewof previous and proposed methods. Epidemiol Rev. 1992;14:177–196.



ORIGINAL ARTICLE

Occupational Exposures and Breast Cancer Among WomenTextile Workers in Shanghai

Roberta M. Ray,* Dao Li Gao,† Wenjin Li,* Karen J. Wernli,*‡ George Astrakianakis,§Noah S. Seixas,§ Janice E. Camp,§ E. Dawn Fitzgibbons,* Ziding Feng,* David B. Thomas,*‡

and Harvey Checkoway*†§

Background: Breast cancer incidence rates have been increasing inChina over the past 2 decades. Most studies have focused onreproductive, dietary, and genetic risk factors. Little is known aboutthe contribution of occupational exposures.Methods: We conducted a case-cohort study within a cohort offemale textile workers who had participated in a randomized trial ofbreast self-examination in Shanghai, China. We compared 1709incident breast cancer cases with an age-stratified reference subco-hort (n � 3155 noncases). Cox proportional hazards modeling,adapted for the case-cohort design, was used to estimate hazardratios for breast cancer in relation to duration of employment invarious job processes and duration of exposure to several agents. Wealso evaluated the associations of cotton dust and endotoxin withbreast cancer.Results: Cumulative exposures to cotton dust and endotoxin dem-onstrated strong inverse gradients with breast cancer risk whenexposures were lagged by 20 years (trend P-values �0.001). We didnot observe consistent associations with exposures to electromag-netic fields, solvents, or other chemicals.Conclusion: Endotoxin or other components of cotton dust expo-sures may have reduced risks for breast cancer in this cohort,perhaps acting at early stages of carcinogenesis. Replication of thesefindings in other occupational settings with similar exposures will beneeded to confirm or refute any hypothesis regarding protectionagainst breast cancer.

(Epidemiology 2007;18: 383–392)

Breast cancer rates in China are considerably lower than inthe United States and other developed countries, although

incidence rates have increased markedly in recent years.1

Menstrual and reproductive factors are estimated to explain40 to 50% of breast cancer risk in Shanghai.2 Changingdietary and other lifestyle practices may be responsible forsome of the increased incidence.3 Occupational and otherenvironmental risk exposures may also have contributed tothe rise in breast cancer occurrence in China, but have beenless thoroughly investigated than other factors.

Although women comprise a substantial and increasingproportion of the workforce worldwide, there are very fewspecific established occupational risk factors for femalebreast cancer. A noteworthy exception is ionizing radiation,for which there is strong evidence of associations in certainoccupational groups.4 There are varying levels of support forcausal associations with occupations and specific workplaceexposures. Among the more consistent findings are elevatedrisks among teachers, librarians, and administrative workers,which may reflect the influence of factors associated withhigh socioeconomic status.5 There has been considerableinterest in possible associations of breast cancer with occu-pational and nonoccupational (residential) exposures to elec-tromagnetic field (EMF) exposures, although the evidence isinconsistent.6 Nonetheless, some recent studies suggest mod-estly elevated risks.7–9 Suppression of melatonin by EMF isthe presumed mechanism of mammary carcinogenesis for thisexposure. Somewhat stronger associations for breast cancerrisk have emerged from investigations of night shift work,ostensibly acting by a similar mechanism of melatonin sup-pression.10,11 Results from studies of breast cancer and per-sistent organochlorine environmental chemicals, such asPCBs and DDT, have been mixed.12,13 Some studies indicateelevated risks related to occupational exposures to solvents,and polycyclic aromatic hydrocarbons,14,15 but these associ-ations have not been observed consistently. There is verylittle evidence that machining fluids are related to breastcancer risk.16 Relatively sedentary jobs have been linked tomoderate risk excesses.17

The textile industry is one of the world’s largest em-ployers of women, and this industry entails exposures tonumerous potential carcinogens, including solvents, dyes,EMF, formaldehyde, machining fluids, cotton, wool, syn-thetic fibers, and silk dusts, and silica.18,19 Neither consis-tently elevated overall breast cancer risks nor associations

Submitted 13 July 2006; accepted 19 December 2006.From the *Program in Epidemiology, Division of Public Health Sciences,

Fred Hutchinson Cancer Research Center, Seattle, WA; †Department ofEpidemiology, Zhong Shan Hospital Cancer Center, Shanghai, China;‡Department of Epidemiology, University of Washington, Seattle, WA;and §Department of Environmental and Occupational Health SciencesUniversity of Washington, Seattle, WA.

Supported by grant R01CA80180 from the US National Cancer Institute,National Institutes of Health, and US National Institute of EnvironmentalHealth Sciences Training Grant ES 07262.

Correspondence: Roberta M. Ray, Fred Hutchinson Cancer Research Center,M4-A402, 1100 Fairview Avenue North, PO Box 19024, Seattle, WA98109-1024. E-mail: [email protected].

Copyright © 2007 by Lippincott Williams & WilkinsISSN: 1044-3983/07/1803-0383DOI: 10.1097/01.ede.0000259984.40934.ae


with specific jobs or exposures have been detected in the fewoccupational cohort studies of women textile workers con-ducted to date.20,21 There was no overall increase in risk ofbreast cancer in the cohort on which this study was based.22

Results from a census linkage in Shanghai23 indicated verysmall excesses among women employed in textile weaving,knitting, bleaching, and dyeing jobs. Population-based case–control studies in the United States5,24,25 and China26 havenot consistently identified occupational breast cancer riskfactors, although these studies have generally been limited bylow prevalence of exposure and nonspecific exposure assess-ments.

We undertook the current study to investigate associa-tions between specific textile industry exposures and breastcancer risks in a large, well-characterized cohort. In spite ofthe seemingly weak evidence for causal relations betweentextile industry exposure and breast cancer, the rationale forthis investigation was the opportunity to improve on the detailand specificity of exposure assessment over what has beenpossible in previous research.

METHODS

Study SubjectsThe cohort in which this study was conducted consisted

of 267,400 women textile workers from 526 factories of theShanghai Textile Industry Bureau who had been enrolledfrom 1989 to 1991 in a randomized trial of breast self-examination.27,28 The cohort included active and retired em-ployees who were permanent residents of Shanghai bornbetween January 1, 1925 and December 31, 1958. At enroll-ment into the trial, women were administered a baselinequestionnaire that elicited information on demographic vari-ables, lifestyle habits (including cigarette smoking and alco-hol use), and reproductive history.

Follow-up of the cohort has been described previ-ously.22,27,28 Cancer incidence was ascertained through July2000 from annual medical reports submitted by the factoryclinics to a cancer and death registry maintained by theShanghai Textile Industry Bureau Station for the Preventionand Treatment of Cancer. Manual reviews of records from theShanghai Cancer Registry29 and a computerized matching ofthe trial cohort to the Registry data for 1989 through 1998supplemented the active case finding procedures and identi-fied breast cancer in women who had permanently left theShanghai Textile Industry Bureau prior to diagnosis.

We compared the number of observed breast cancercases (ICD9 code 174) diagnosed in the study between 1989and 1998 (n � 1369) with the expected number of cases,calculated by applying age-specific rates available from theRegistry to the appropriate groupings of women in the co-hort.22 This comparison indicated that the case-finding for thetrial was at least as complete as for the Registry.

Women received all medical care through their affili-ated factory, even after retirement. Participants in the trialwho reported a suspicious breast lump through July 2000were evaluated by medical workers at the factories, whoreferred the woman for subsequent evaluation by a surgeon if

her symptoms suggested that she might have breast cancer.Referrals were made to 1 of 3 hospitals operated by the textileindustry Bureau, or to other hospitals contracted by individ-ual factories for the care of their workers. For each womanhaving breast surgery, study personnel abstracted the histo-logic diagnosis and information on tumor size and stage fromthe woman’s medical record. In all, 1763 breast cancer caseswere identified during the follow-up period, among which thediagnoses of 99% were based on histology. The majority ofthese (84%) were ductal carcinoma; the next largest diagnos-tic subgroups were mucous carcinoma (n � 50), medullarycarcinoma (n � 48), and lobular carcinoma (n � 40).

A comparison subcohort of 3199 women was randomlyselected from the entire cohort, stratified on year of birth,using 5-year interval groupings. Because the subcohort wasto be used in analyses of other incident cancers, the samplingwas designed such that the distribution of birth year in thesubcohort corresponded to that for all cancers. Sixteenwomen with breast cancer were included in the subcohort.

Experienced field-workers abstracted information fromfactory personnel records on all jobs that each woman heldsince the beginning of her employment in the textile industryBureau. If personnel records were not available, informationwas collected by interview of supervisors, coworkers, orstudy subjects. Data were collected on the textile process inwhich the woman was employed, a description of her maintask, and the dates she was employed in each job.

Exposure AssessmentThe Shanghai textile industry includes the manufacture

of fibers, cloth, and ancillary products (eg, raincoats), gar-ment assembly, and textile machine manufacture and repair.With the assistance of a team of occupational hygienistsemployed by the textile industry Bureau, we developed ajob-exposure matrix that permitted classification of work-shops, processes, and component jobs according to exposuresto dusts, chemicals, and physical agents potentially associatedwith cancer risk.30 Agents of interest included the following:cotton, wool, silk, and synthetic fiber dusts; endotoxin; sol-vents; metals; dyes; inks; lubricants (including machiningfluids); bleaches; acids, bases, and caustics; resins, mono-mers, and coatings; formaldehyde; silica; and EMF. Exposureclassification was based on factory sector (eg, cotton, wool,cotton/synthetic fibers), workshop (textile process), and job-or task-related information obtained from government andfactory inspection reports describing basic processes andmaterials used since each factory was opened. For mostagents, the historical exposure data permitted dichotomous(exposed versus not exposed) classifications, but not classi-fication as to intensity of exposure.

In addition, we reconstructed quantitative exposures tocotton dust and endotoxin. Endotoxin is a component ofgram-negative bacteria that contaminates cotton dust. Detailsof this assessment are provided elsewhere.31 Briefly, quanti-tative assessment of the cotton dust exposure was estimatedfor each specific textile process based on historical measure-ments collected by textile industry hygienists from 56 facto-ries between 1975 and 1999. Endotoxin concentrations wereestimated using the predicted cotton dust estimates (mg/m3)



and average concentrations of endotoxin per unit dust mass(EU/mg dust) in each major process from independent studiesof respiratory symptoms and lung function conducted byChristiani et al32–34 and from additional cotton and endotoxinmeasurement data collected for this study.31

Complete textile industry work history information wasobtained for 1709 breast cancer cases and 3188 subcohortmembers. The majority of work history information wascollected from factory personnel records (77% for cases and79% for subcohort members). Six percent of the cases(n � 109) and 4% of the subcohort subjects (n � 142) wereapproached and consented to an in-person interview about thejobs they held in the textile industry. We could not locatework history information for 54 breast cancer cases and 11subcohort noncases. We derived cumulative exposure dura-tions by linking the job exposure matrix with study subjects’work history data. Nineteen subcohort women who gave ahistory of prior breast cancer or mastectomy on the baselinequestionnaire were excluded, leaving 1709 breast cancercases and 3155 noncase subcohort women in the analyses.

Data AnalysisWe conducted Cox proportional hazards modeling,

adapted for the stratified case-cohort design,35 to estimaterelative risks (hazard ratios �HRs�) and associated 95% con-fidence intervals associated with duration of employment invarious jobs and processes, and duration of exposures tospecific agents. Robust variance estimates were used to com-pute standard errors (and confidence intervals) of hazardratios.36 The period of risk was defined as time since entryinto the cohort until the date of breast cancer diagnosis, death,or end of follow-up on July 31, 2000. The 16 breast cancercases who overlapped with the subcohort contributed person-time from entry in the cohort until date of diagnosis. Womenwho were diagnosed with other diseases during the follow-upperiod continued to be observed for breast cancer outcomesand were censored at date of death or end of the study. Asampling probability weight of 1 was assigned to all cases,and weights equal to the reciprocal of the sampling fractionswere applied to the noncase subcohort members. The naturallogarithm of the weight was included as an offset term in eachCox regression model statement to reflect the sampling planof the study design.

We categorized duration of employment in each ofseventeen mutually exclusive job process groupings as 0(reference), 1–9, 10–19, and �20 years. We defined durationof exposure to agents in 5-year strata as 0 (reference), 1–4,5–9, 10–19, and �20 years. In instances of small numbers,we defined strata as 0, 1–9, and �10 years. Cumulativeexposures to cotton dust (mg/m3 � years) and endotoxin(EU/m3 � years) were categorized into strata defined asnonexposed and quartiles according to the exposure distribu-tions of the subcohort members. We conducted trend tests forduration of exposure or cumulative exposure, using as thescore variable the median values of the subcohort’s distribu-tion within exposure strata. We excluded from the analyses ofquantitative endotoxin levels those women who ever heldjobs with potential endotoxin exposures from noncottonsources (machining fluids, wool, sanitation workers) because

their levels of endotoxin exposure could not be estimated.P-values for trend were based on the Wald test for inclusionof the score variable in the Cox regression model. Additionalanalyses with exposures lagged by 10 and 20 years wereconducted to take into account possible latency periods be-tween exposure and disease occurrence.

All effect estimates were adjusted for age at baseline.We examined breast cancer risk in relation to age at men-arche, total number of live births, age at first live birth,duration of breast-feeding, contraceptive use, cigarette smok-ing, alcohol use, history of prior breast lumps, menopausalstatus at baseline, and history of breast cancer in mother orsister. These nonoccupational factors were also evaluated aspossible confounders of the effect estimates for the occupa-tional exposures. Variables were considered to be confoundersif their inclusion in a model resulted in a change in the HRestimate for the main factor of at least 10%. Only total numberof live births and age at first live birth were included asconfounders in the final models. Data analyses were performedwith Stata v.8.0 (Stata Corp., College Station, TX).

This study was approved by the Institutional ReviewBoards at Fred Hutchinson Cancer Research Center in Seat-tle, WA, and the Station for the Prevention and Treatment ofCancer of the STIB in Shanghai, China. Women whose workhistories were obtained by interview provided informed oralconsent to participate.

RESULTSBreast cancer case women tended to be younger than

the reference-subcohort women at baseline (mean ages of48 years and 53 years, respectively). As shown in Table 1,after adjusting for age, risk of breast cancer decreased withincreasing age at first menstrual period and was greater inwomen with no live births. In a model that included boththe number of live births and age at first birth, breastcancer risk decreased with increasing numbers of livebirths and increased in women whose first live birth was atage 30 or later. Breast cancer risk was reduced in womenwho breast-fed their children for 36 months or longer, afteradjusting for number of children and age at first birth.Breast cancer risk was not related to types of contracep-tives used, cigarette smoking, or alcohol consumption.Risk was increased in women who had a history of a priorbreast lump, in women who were menopausal at entry intothe cohort, and in women having a mother or sister withhistory of breast cancer.

The majority of women (78%) held 1 or 2 jobs duringtheir lifetime employment in the textile industry. Casestended to hold more jobs than noncases (28% of cases had 3or more jobs compared with 20% of noncases). There was asubstantial inverse trend in breast cancer risk with duration ofemployment in cotton handling, fiber processing, and spin-ning jobs (Table 2). There were no other consistent patternsof elevated or reduced risk associated with employment inother job categories.

As shown in Table 3, duration of exposures to solventsand EMFs, agents of a priori interest, were unrelated to breastcancer risk. We observed a modest risk reduction among

Epidemiology • Volume 18, Number 3, May 2007 Breast Cancer Risk Among Women Textile Workers


women exposed to cotton dust for 20 or more years. Therewas also a modest reduction in risk in women exposed to silk,and a slight excess risk in women exposed 20 or more yearsto inks. No other agent-specific associations were observed.

Breast cancer risk was inversely related to cumulativeexposure to cotton dust and endotoxin (Table 4). The inverserisk trends, which were quantitatively very similar for these 2exposures, were especially pronounced when exposures werelagged 20 years (trend P-values �0.001).

We also examined breast cancer risk in relation tocotton dust and endotoxin exposure separately for womenwho were pre- and postmenopausal at baseline, and sepa-rately for those under 52 and 52 or older at baseline. Riskestimates and risk trends in all subgroups were similar tothose observed in the overall analyses (data not shown).

Age at employment in the cotton sector ranged between5 and 52 years of age for women ever employed in cottonworkshops, with 60% beginning their work in the cottonsector between the ages of 15 and 24. Only 15% were firstemployed in this sector at age 30 or older. There was noimportant difference between cases and noncases in the age atfirst employment in the cotton sector.

DISCUSSIONOur most noteworthy findings were seemingly protec-

tive effects of cotton dust and endotoxin exposures on the riskof breast cancer. Collinearity between cotton dust and itscontaminant, endotoxin, precluded analyses that would dis-

TABLE 1. Risk of Breast Cancer in Relation to Characteristicsat Baseline Interview

CharacteristicCases

(n � 1709)Noncases

(n � 3155) HR* (95% CI)

Age at first menstrual period (yrs)

�13† 179 269 1.00

14 315 497 0.96 (0.75–1.23)

15 415 635 0.97 (0.76–1.23)

16 360 687 0.88 (0.69–1.12)

�17 439 1066 0.75 (0.59–0.95)

P for trend 0.002

Live births

Yes† 1591 3034 1.00

None 118 121 1.99 (1.30–3.03)

Number of live births‡

1† 695 743 1.00

2 367 567 1.10 (0.89–1.37)

3 244 632 0.75 (0.57–0.97)

4 150 512 0.60 (0.45–0.82)

�5 135 580 0.51 (0.36–0.70)

P for trend �0.001

Age at first live birth (yrs)§

�19† 72 266 1.00

20–24 462 1,290 1.14 (0.86–1.53)

25–29 721 1,144 1.23 (0.91–1.67)

30 or older 336 334 1.79 (1.28–2.51)

P for trend �0.001

Duration of breast-feeding (mos)¶

0† 240 321 1.00

�6 205 261 0.98 (0.75–1.27)

7–12 456 591 0.99 (0.79–1.23)

13–24 298 551 0.93 (0.72–1.20)

25–36 185 432 1.03 (0.77–1.38)

37–48 102 350 0.78 (0.56–1.10)

49� 105 528 0.59 (0.41–0.83)

P for trend 0.06

Oral contraceptives§

Never† 1460 2737 1.00

Yes, currently 17 23 0.99 (0.51–1.90)

Yes, formerly 232 395 1.01 (0.84–1.22)

Injectable contraceptives§

Never† 1631 3030 1.00

Yes, currently 5 8 0.68 (0.22–2.12)

Yes, formerly 73 117 1.00 (0.73–1.37)

IUD§

Never† 1031 2259 1.00

Yes, currently 537 588 0.88 (0.72–1.09)

Yes, formerly 141 308 0.80 (0.64–1.01)

Tubal ligation§

No† 1370 2231 1.00

Yes 339 924 1.04 (0.89–1.23)

(Continued)


CharacteristicCases


(n � 3155) HR* (95% CI)

Ever smoked regularly

Never† 1663 3011 1.00

Yes, but quit 8 26 0.85 (0.38–1.90)

Yes, currently 37 118 0.81 (0.55–1.19)

Ever drink alcohol

No† 1368 2583 1.00

Yes 341 572 0.98 (0.84–1.15)

Ever had breast lump evaluatedby medical worker

No† 1579 3043 1.00

Yes 130 112 1.77 (1.34–2.34)

Menstrual period in past 6 mos

No† 801 2174 1.00

Yes 908 977 1.63 (1.23–2.16)

Mother or sister with breast cancer

No† 1650 3083 1.00

Yes 59 67 1.53 (1.05–2.22)

*Adjusted for age.†Reference category.‡HR estimates also adjusted for age at first live birth.§HR estimates also adjusted for number of live births.¶Among women with a live birth; HR estimates also adjusted for number of live

births and age at first live birth.



TABLE 2. Risk of Breast Cancer in Relation to Duration ofEmployment in Various Job Processes in the Textile Industry

Duration of Work (yrs)Cases


(n � 3155) HR* (95%CI)

Warehouse

0† 1411 2642 1.00

�10 116 178 1.04 (0.80–1.35)

10 to �20 94 143 1.07 (0.80–1.42)

20� 88 192 0.86 (0.65–1.13)

P for trend 0.48

Cotton fibers‡

0† 1463 2563 1.00

�10 62 120 1.01 (0.72–1.41)

10 to �20 78 152 0.86 (0.64–1.15)

20� 106 320 0.70 (0.55–0.89)

P for trend 0.002

Wool fibers‡

0† 1660 3053 1.00

�10 22 20 1.97 (1.04–3.76)

10 to �20 6 27 0.46 (0.18–1.16)

20� 21 55 0.98 (0.57–1.68)

P for trend 0.60

Silk fibers‡

0† 1700 3124 1.00

�10 2 8 0.68 (0.13–3.49)

10 to �20 1 9 0.30 (0.04–2.45)

20� 6 14 1.10 (0.40–3.05)

P for trend 0.81

Synthetic fibers‡

0† 1666 3067 1.00

�10 12 31 0.86 (0.43–1.73)

10 to �20 15 24 0.95 (0.48–1.88)

20� 16 33 1.03 (0.54–1.99)

P for trend 0.98

Mixed fibers‡

0† 1480 2716 1.00

�10 66 125 1.10 (0.79–1.52)

10 to �20 85 113 1.43 (1.05–1.96)

20� 78 201 0.90 (0.68–1.19)

P for trend 0.70

Mineral fibers‡

0† 1697 3131 1.00

�10 5 16 0.61 (0.22–1.72)

10 to �20 5 5 2.71 (0.77–9.58)

20� 2 3 1.40 (0.20–9.85)

P for trend 0.42

Synthetics manufacture

0† 1705 3153 1.00

�0 4 2 4.49 (0.83–24.2)

Scour/Bleach

0† 1688 3128 1.00

�10 11 12 1.24 (0.53–2.92)

10 to �20 9 10 1.37 (0.52–3.60)

20� 1 5 0.29 (0.03–2.61)

P for trend 0.98

(Continued)


Duration of Work (yrs)Cases


(n � 3155) HR* (95%CI)

Dyeing

0† 1670 3099 1.00

�10 18 19 1.42 (0.72–2.80)

10 to �20 13 19 1.28 (0.61–2.70)

20� 8 18 0.73 (0.31–1.75)

P for trend 0.99

Finishing

0† 1682 3115 1.00

�10 7 13 0.80 (0.30–2.14)

10 to �20 13 17 1.38 (0.63–3.02)

20� 7 10 1.39 (0.44–4.41)

P for trend 0.41

Weaving

0† 1119 2047 1.00

�10 133 229 1.01 (0.79–1.29)

10 to �20 193 292 1.15 (0.93–1.43)

20� 264 587 1.00 (0.84–1.19)

P for trend 0.69

Printing

0† 1690 3131 1.00

�10 2 8 0.36 (0.07–1.73)

10 to �20 7 9 1.09 (0.37–3.27)

20� 10 7 2.07 (0.75–5.72)

P for trend 0.25

Cutting/sewing

0† 1531 2863 1.00

�10 47 86 0.83 (0.56–1.22)

10 to �20 84 95 1.61 (1.16–2.25)

20� 47 111 0.80 (0.56–1.16)

P for trend 0.56

Maintenance

0† 1640 3049 1.00

�10 31 48 1.17 (0.72–1.93)

10 to �20 26 25 1.83 (1.01–3.32)

20� 12 33 0.67 (0.34–1.33)

P for trend 0.82

Administration/nonproduction

0† 1100 2257 1.00

�10 240 335 1.23 (1.01–1.49)

10 to �20 190 264 1.15 (0.93–1.43)

20� 179 299 1.06 (0.86–1.31)

P for trend 0.30

Other manufacturing

0† 1602 2990 1.00

�10 26 59 0.67 (0.41–1.09)

10 to �20 37 46 1.30 (0.81–2.09)

20� y 44 60 1.27 (0.84–1.91)

P for trend 0.23

*Adjusted for age at baseline, number of live births, and age at first live birth.†Reference category.‡Material handling, fiber processing, spinning.



TABLE 3. Risk of Breast Cancer in Relation to EstimatedDuration of Exposure to Various Agents

Agents (yrs)Cases


(n � 3155) HR* (95%CI)

Cotton dust

0† 723 1220 1.00

�5 92 145 1.10 (0.82–1.47)

5 to �10 98 142 1.14 (0.84–1.53)

10 to �20 324 474 1.15 (0.96–1.37)

20� 472 1174 0.84 (0.72–0.98)

P for trend 0.05

Wool dust

0† 1456 2707 1.00

�5 35 56 1.44 (0.92–2.26)

5 to �10 24 38 0.94 (0.55–1.63)

10 to �20 74 104 1.36 (0.97–1.89)

20� 120 250 1.04 (0.81–1.32)

P for trend 0.36

Silk

0† 1639 2995 1.00

�10 14 35 0.65 (0.34–1.26)

10� 56 125 0.87 (0.62–1.22)

P for trend 0.36

Synthetic fiber dust

0† 995 1900 1.00

�5 86 128 1.20 (0.89–1.62)

5 to �10 66 110 1.00 (0.71–1.40)

10 to �20 242 355 1.17 (0.96–1.42)

20� 320 662 1.07 (0.91–1.26)

P for trend 0.26

Mixed fiber dust, NOC (synthetic/natural)

0† 1651 3018 1.00

�5 13 27 1.08 (0.51–2.27)

5 to �10 6 16 0.65 (0.24–1.74)

10 to �20 17 49 0.78 (0.43–1.40)

20� 22 45 1.22 (0.71–2.11)

P for trend 0.95

Mineral dust, mineral fiber, dust (NOC), and nontextile dust

0† 1443 2634 1.00

�5 66 98 1.17 (0.84–1.64)

5 to �10 44 99 0.79 (0.54–1.15)

10 to �20 92 163 0.99 (0.75–1.32)

20� 64 161 0.77 (0.56–1.05)

P for trend 0.12

Solvents

0† 1402 2701 1.00

�5 57 83 1.05 (0.73–1.53)

5 to �10 51 84 0.94 (0.64–1.36)

10 to �20 108 138 1.27 (0.96–1.68)

20� 91 149 1.04 (0.79–1.39)

P for trend 0.33

Benzene

0† 1703 3140 1.00

�10 2 7 0.52 (0.10–2.58)

10� 4 8 0.80 (0.23–2.83)

P for trend 0.55

(Continued)


Agents (yrs)Cases


(n � 3155) HR* (95%CI)

Bleaching agents

Never 1667 3109 1.00

�10 17 16 1.33 (0.66–2.70)

10� 25 30 1.43 (0.81–2.52)

P for trend 0.16

Acids, bases, and caustics

0† 1542 2893 1.00

�5 28 43 0.95 (0.57–1.59)

5 to �10 22 47 0.65 (0.38–1.11)

10 to �20 54 85 1.07 (0.74–1.55)

20� 63 87 1.28 (0.91–1.81)

P for trend 0.29

Dyes

0† 1642 3059 1.00

�10 25 30 1.12 (0.64–1.97)

10� 42 66 1.14 (0.75–1.72)

P for trend 0.49

Inks

0† 1684 3127 1.00

�10 4 7 0.93 (0.26–3.25)

10� 21 21 1.43 (0.73–2.77)

P for trend 0.30

Resins, monomers, coatings

0† 1668 3067 1.00

�10 14 31 0.71 (0.37–1.39)

10� 27 57 0.75 (0.46–1.22)

P for trend 0.18

Metals

0† 1564 2928 1.00

�5 24 46 0.92 (0.55–1.55)

5 to �10 25 41 0.95 (0.55–1.62)

10 to �20 47 58 1.38 (0.91–2.10)

20� 49 82 1.07 (0.73–1.55)

P for trend 0.15

EMFs or nonionizing radiation

0† 608 1006 1.00

�5 103 175 0.97 (0.73–1.28)

5 to �10 111 166 1.02 (0.77–1.34)

10 to �20 377 567 1.09 (0.91–1.30)

20� 510 1241 0.86 (0.74–1.01)

P for trend 0.10

Lubricants

0† 701 1198 1.00

�5 101 172 1.09 (0.83–1.45)

5 to �10 107 156 1.04 (0.79–1.38)

10 to �20 312 488 1.10 (0.91–1.32)

20� 488 1141 0.93 (0.80–1.08)

P for trend 0.37

(Continued)



criminate between the possible effects of these 2 exposureson breast cancer risk.37 Endotoxin is generally considered themost biologically active component of cotton dust exposure,in terms of capacity for inducing acute respiratory and sys-temic inflammatory responses.38 That the inverse relative riskgradients became increasingly strong as exposure lag inter-vals increased suggests that endotoxin, or perhaps othercomponents of cotton dust, may exert early-stage anticarci-nogenic activity. Endotoxin causes acute respiratory symp-toms (byssanosis) and lung function decrements that mighthave caused a “healthy worker survivor bias” due to affectedworkers selectively transferring from or leaving heavily ex-posed jobs. To address this possibility, we performed separateanalyses among cases (n � 578) and subcohort members(n � 1398) who only held 1 job, and the results were notmaterially different. The HR at the highest endotoxin expo-sure level, lagged 20 years, was 0.66 (95% CI � 0.46–0.95).Thus, the healthy worker survivor effect was an unlikelyexplanation for our findings.

The results for cumulative exposure to cotton dust andendotoxin are unlikely to be confounded by age at firstexposure. The majority of women who worked in cottonworkshops began working at a young age, and age at firstemployment was not different between cases and noncases.

Confounding by exposures to other agents is also un-likely. Correlation of cotton dust or endotoxin exposure withother exposures was low, as not many other exposures ofinterest occurred in the cotton processing jobs. Furthermore,movement in the textile industry was limited prior to 1994and most women held few jobs during their lifetime employ-ment in the textile industry.

We had not anticipated that either cotton dust or endo-toxin would be associated with breast cancer. In fact, to ourknowledge, this is the first study to report an inverse relationof breast cancer with cotton dust or endotoxin. Previousepidemiologic literature indicates that workers in the textileindustry39,40 and in other occupations with endotoxin expo-sure41,42 have reduced risks for lung cancer. We also ob-served a similar inverse exposure-response gradient for en-dotoxin and lung cancer in this cohort.37 Specific mechanisms


Agents (yrs)Cases


(n � 3155) HR* (95%CI)

Pesticides

0† 1688 3107 1.00

�10 10 19 0.77 (0.34–1.77)

10� 11 29 0.70 (0.33–1.47)

P for trend 0.32

Formaldehyde

0† 1707 3144 1.00

�10 0 6 NE

10� 2 5 0.85 (0.14–5.23)

*Adjusted for age at baseline, number of live births, and age at first live birth.†Reference category.NE indicates not estimated.

TABLE 4. Risk of Breast Cancer in Relation to EstimatedCumulative Cotton Dust and Endotoxin Exposures, With 3Lag Times

Exposure VariableCases


(n � 3155) HR* 95% CI

Cotton dust (mg/m3 � yr)

No lag

0† 591 1,008 1.00

�0–54.94 384 538 1.09 (0.91–1.30)

54.95–95.81 280 538 0.91 (0.76–1.10)

95.82–142.00 210 537 0.81 (0.67–1.00)

�142.00 244 534 1.01 (0.83–1.23)

P for trend‡ 0.56

10 yr lag

0† 614 1,010 1.00

�0–54.94 465 592 1.10 (0.93–1.31)

54.95–95.81 233 523 0.77 (0.64–0.94)

95.82–142.00 193 518 0.79 (0.64–0.96)

�142.00 204 512 0.87 (0.71–1.07)

P for trend‡ 0.03

20 yr lag

0† 782 1,071 1.00

�0–54.94 468 699 0.81 (0.69–0.96)

54.95–95.81 198 480 0.70 (0.57–0.85)

95.82–142.00 144 452 0.62 (0.49–0.77)

�142.00 117 453 0.49 (0.39–0.62)

P for trend‡ �0.001

Endotoxin (EU§/m3 � yr)

No lag

0† 518 908 1.00

�0–1509.19 366 527 1.07 (0.89–1.29)

1509.20–2418.29 270 526 0.96 (0.79–1.17)

2418.30–3516.98 217 523 0.86 (0.70–1.05)

�3516.98 231 523 0.95 (0.78–1.16)

P for trend‡ 0.29

10 yr lag

0† 541 910 1.00

�0–1509.19 462 588 1.10 (0.93–1.31)

1509.20–2418.29 228 518 0.82 (0.67–1.00)

2418.30–3516.98 173 489 0.76 (0.61–0.94)

�3516.98 198 502 0.82 (0.67–1.01)

P for trend‡ 0.009

20 yr lag

0† 700 969 1.00

�0–1509.19 476 718 0.80 (0.68–0.95)

1509.20–2418.29 174 481 0.68 (0.55–0.83)

2418.30–3516.98 114 418 0.52 (0.41–0.66)

�3516.98 138 421 0.62 (0.49–0.77)

P for trend‡ �0.001

*Adjusted for age at baseline, number of live births, and age at first live birth.†Reference category.‡Trend tests across median of fourths, among exposed only.§EU, endotoxin units; women who were ever employed as machinists, in sanitation,

or in wool production were excluded from quantitative analyses of endotoxin.



by which endotoxin may reduce breast cancer risk are notknown, although there is experimental evidence for general-ized anticarcinogenic effects that involve immune systemactivation. In particular, endotoxin induces up-regulation ofproinflammatory mediators that can inhibit tumor growth.43

The role that Toll-like receptors (TLRs) play in immuneresponse is of particular interest; TLRs are binding proteinslocated on antigen-presenting cells such as macrophages anddendritic cells. TLR4 is the primary receptor for lipopolysac-charides and is necessary for endotoxin binding.44 In thepresence of a lipopolysaccharide-binding protein, the im-mune response triggers the release of cytokines such asinterleukin 1 (IL1), IL6, IL8, and tumor necrosis factor-�(TNF).45 The interleukins have been shown to promote breastcancer proliferation or invasion or to be indicators of poorerprognosis,46 while TNF-� has been shown to act on tumorcell mechanisms, directly inducing apoptosis.47 The protec-tive effect of endotoxin seen in this study may be due to a netpolarization of the adaptive immune response towards anti-tumor activity, mediated by TLRs.48 However, data fromclinical trials or other observational studies of breast cancerand endotoxin are lacking. Functional research is also neededto provide insight into the role of TLRs and how the innateand adaptive immune responses to endotoxin exposure couldresult in the protective associations that we observed.

We did not observe an association with duration ofexposure to EMF, which has been a controversial topic. It ispossible that duration of employment in EMF-exposed jobswas too crude a dose metric to detect a weak to modestassociation, as has been observed in some other occupationalstudies.6 Ongoing research on this cohort will assess EMFexposures quantitatively, and should provide much clearerindications of whether EMF influences breast cancer risk intextile workers. The absence of consistent associations withsolvents, pesticides, and other textile industry chemicals—none of which is as widespread as cotton dust or endotoxin—was not surprising, given the limited and inconsistent evi-dence from prior studies.

Our study has some notable strengths, including thelarge size of the breast cancer case group derived from awell-characterized source cohort, the availability of relativelycomplete work history data, a thorough exposure assessmentbased on a job-exposure matrix developed specifically for theShanghai textile industry, and quantitative data for cottondust and endotoxin exposures. Moreover, data were availablefor some of the most important established nonoccupationalrisk factors for breast cancers, which permitted control forpotential confounding of associations with workplace expo-sures. Our findings of reduced risks associated with older ageat menarche, number of live births, early age at first live birth,and duration of breast-feeding are consistent with resultsreported previously for the complete cohort49 and by others inShanghai,2 and thus support the validity of these data forcontrol as potential confounders.

We also acknowledge some limitations of our study.The exposure assessment for agents other than cotton dustand endotoxin relied on a dichotomous classification of jobsas exposed or not exposed. Consequently, duration of expo-

sure was the only available dose metric for most agents. It ispossible that exposure misclassification, presumably nondif-ferential with respect to breast cancer status, may haveresulted in some missed or under-estimated associations. Asmentioned previously, future work on this cohort will remedythis situation for EMF exposure, but historical quantitativedata were not available for a more thorough reconstruction ofexposures to chemicals. We have greatest confidence in therisk estimates based on the most frequent exposures. Womenwho work in the textile industry are likely to incur greaterlevels of exposures to the agents under study than women inthe general population; absence of associations with theseagents thus provide some reassurance that the lower levels ofexposure in the general population are not carcinogenic forthe breast.

Misclassification of cotton dust and endotoxin expo-sures could also have occurred.37 Historical measurements ofcotton dust were available only for a subset of the factories,and were made between 1975 and 1999. Exposures occurringprior to 1975 were based on levels estimated for 1975 andmight have generated underestimates of exposure for thoseyears. In addition, we did not have historical information onvariables (such as source of cotton) that might affect endo-toxin concentrations, and estimates of endotoxin exposurerelied on empirical correlations between cotton dust andendotoxin derived from studies by other investigators as wellas data from this study. However, any misclassification ofboth the qualitative and quantitative exposures would benondifferential with respect to case status and thus would biasrelative risk estimates toward the null. Changes in assump-tions about the pre-1975 exposures did not in result indifferent dose-response relationships for endotoxin and lungcancer37 and would not be expected to alter our findings.Although data on reproductive factors and breast cancerhistory in first-degree relatives permitted control for somepotential confounders, the absence of data on other breastcancer risk factors, such as diet and body mass index, is apossible limitation. However, the cohort was economicallyhomogeneous, and thus diet and body mass index probablydid not vary systematically by occupational exposure, andconfounding by these factors is unlikely to have appreciablyaffected our results. The small number of case women withdiagnoses other than ductal carcinoma precluded investiga-tion of occupational exposures in relation to specific histo-logic types of breast cancer.

Our study is the most comprehensive evaluation ofbreast cancer among women textile workers undertaken todate. We conclude that there were no strong or consistentassociations with chemical exposures or EMF and breastcancer in this cohort of textile workers. Our findings ofreduced risks related to the most intense cotton dust andendotoxin exposures are suggestive of protective effects act-ing at early stages of breast carcinogenesis. Endotoxin is themost plausible candidate protective component of cottondust. Insofar as endotoxin is a widespread exposure in manyoccupational settings throughout the world, efforts to repli-cate our findings should yield valuable insights into breastcancer etiology.



ACKNOWLEDGMENTSWe thank Wen Wan Wang, the Shanghai study man-

ager, and 6 industrial hygienists (He Lian Dai, Zhu MingWang, A-Zhen Chi, Xia Ming Wang, Wei Ping Xiang, and YuFang Li) for their effort in collecting and coding data; FanLiang Chen, Yong Wei Hu, Guan Lin Zhao, and Lei Da Panfor their support of this cohort; Yu-Tang Gao, Fan Jin, andYong-Bing Xiang for access to the data from the SCR; andShirley Zhang and Ted Grichuhin for technical support. Wealso thank David Christiani for providing cotton dust andendotoxin measurement data that were used in our exposureassessment.

REFERENCES1. Yang L, Parkin DM, Ferlay J, et al. Estimates of cancer incidence in

China for 2000 and projections for 2005. Cancer Epidemiol BiomarkersPrev. 2005;14:243–250.

2. Gao YT, Shu XO, Dai Q, et al. Association of menstrual and reproduc-tive factors with breast cancer risk: results from the Shanghai breastcancer study. Int J Cancer. 2000;87:295–300.

3. Popkin BM, Du S. Dynamics of the nutrition transition toward theanimal foods sector in China and its implications: a worried perspective.J Nutr. 2003;133:3898S–3906S.

4. Yoshinaga S, Mabuchi K, Sigurdson AJ, et al. Cancer risks amongradiologists and radiologic technologists: review of epidemiologic stud-ies. Radiology. 2004;233:313–321.

5. Teitelbaum SL, Britton JA, Gammon MD, et al. Occupation and breastcancer in women 20–44 years of age (United States). Cancer CausesControl. 2003;14:627–637.

6. Feychting M, Forssen U. Electromagnetic fields and female breastcancer. Cancer Causes Control. 2006;17:553–558.

7. Forssen UM, Rutqvist LE, Ahlbom A, et al. Occupational magneticfields and female breast cancer: a case–control study using Swedishpopulation registers and new exposure data. Am J Epidemiol. 2005;161:250–259.

8. Kliukiene J, Tynes T, Andersen A. Residential and occupational expo-sures to 50-Hz magnetic fields and breast cancer in women: a popula-tion-based study. Am J Epidemiol. 2004;159:852–861.

9. Labreche F, Goldberg MS, Valois MF, et al. Occupational exposures toextremely low frequency magnetic fields and postmenopausal breastcancer. Am J Ind Med. 2003;44:643–652.

10. Davis S, Mirick DK, Stevens RG. Night shift work, light at night, andrisk of breast cancer. J Natl Cancer Inst. 2001;93:1557–1562.

11. Schernhammer ES, Laden F, Speizer FE, et al. Rotating night shifts andrisk of breast cancer in women participating in the nurses’ health study.J Natl Cancer Inst. 2001;93:1563–1568.

12. Millikan R, DeVoto E, Duell EJ, et al. Dichlorodiphenyldichloroethene,polychlorinated biphenyls, and breast cancer among African-Americanand white women in North Carolina. Cancer Epidemiol BiomarkersPrev. 2000;9:1233–1240.

13. O’Leary ES, Vena JE, Freudenheim JL, et al. Pesticide exposure and riskof breast cancer: a nested case–control study of residentially stablewomen living on Long Island. Environ Res. 2004;94:134–144.

14. Petralia SA, Vena JE, Freudenheim JL, et al. Risk of premenopausalbreast cancer in association with occupational exposure to polycyclicaromatic hydrocarbons and benzene. Scand J Work Environ Health.1999;25:215–221.

15. Rennix CP, Quinn MM, Amoroso PJ, et al. Risk of breast cancer amongenlisted army women occupationally exposed to volatile organic com-pounds. Am J Ind Med. 2005;48:157–167.

16. Thompson D, Kriebel D, Quinn MM, et al. Occupational exposure tometalworking fluids and risk of breast cancer among female autowork-ers. Am J Ind Med. 2005;47:153–160.

17. Moradi T, Nyren O, Zack M, et al. Breast cancer risk and lifetimeleisure-time and occupational physical activity (Sweden). CancerCauses Control. 2000;11:523–531.

18. International Agency for Research on Cancer. some flame retardants andtextile chemicals and exposures in the textile industry. IARC Mono-

graphs on the Evaluation of Carcinogenic Risks to Humans. Vol. 48.Lyon: IARC; 1990.

19. Hansen NH, Sobel E, Davanipour Z, et al. EMF exposure assessment inthe Finnish garment industry: evaluation of proposed EMF exposuremetrics. Bioelectromagnetics. 2000;21:57–67.

20. Pinkerton LE, Hein MJ, Stayner LT. Mortality among a cohort ofgarment workers exposed to formaldehyde: an update. Occup EnvironMed. 2004;61:193–200.

21. Kuzmickiene I, Didziapetris R, Stukonis M. Cancer incidence in theworkers cohort of textile manufacturing factory in Alytus, Lithuania.J Occup Environ Med. 2004;46:147–153.

22. Wernli KJ, Ray RM, Gao DL, et al. Cancer among women textileworkers in Shanghai, China: overall incidence patterns, 1989–1998.Am J Ind Med. 2003;44:595–599.

23. Petralia SA, Chow WH, McLaughlin J, et al. Occupational risk factorsfor breast cancer among women in Shanghai. Am J Ind Med. 1998;34:477–483.

24. Habel LA, Stanford JL, Vaughan TL, et al. Occupation and breast cancerrisk in middle-aged women. J Occup Environ Med. 1995;37:349–356.

25. Coogan PF, Clapp RW, Newcomb PA, et al. Variation in female breastcancer risk by occupation. Am J Ind Med. 1996;30:430–437.

26. Gardner KM, Shu XO, Jin F, et al. Occupations and breast cancer riskamong Chinese women in urban Shanghai. Am J Ind Med. 2002;42:296–308.

27. Thomas DB, Gao DL, Self SG, et al. Randomized trial of breastself-examination in Shanghai: methodology and preliminary results.J Natl Cancer Inst. 1997;89:355–365.

28. Thomas DB, Gao DL, Ray RM, et al. Randomized trial of breastself-examination in Shanghai: final results. J Natl Cancer Inst. 2002;94:1445–1457.

29. Parkin DM, Whelan S, Ferlay J, et al, eds. Cancer Incidence in FiveContinents. Vol. VIII. No. 155. Lyon: International Agency for Researchon Cancer; 2002.

30. Wernli KJ, Astrakianakis G, Camp JE, et al. Development of a jobexposure matrix (JEM) for the textile industry in Shanghai, China.J Occup Environ Hyg. 2006;3:521–529.

31. Astrakianakis G, Seixas NS, Camp JE, et al. Modeling, estimation andvalidation of cotton dust and endotoxin exposures in Chinese textileoperations. Ann Occup Hyg. 2006;50:573–582.

32. Christiani DC, Wegman DH, Eisen EA, et al. Cotton dust and gram-negative bacterial endotoxin correlations in two cotton textile mills.Am J Ind Med. 1993;23:333–342.

33. Christiani DC, Ye TT, Zhang S, et al. Cotton dust and endotoxinexposure and long-term decline in lung function: results of a longitudinalstudy. Am J Ind Med. 1999;35:321–331.

34. Wang XR, Zhang HX, Sun BX, et al. A 20-year follow-up study onchronic respiratory effects of exposure to cotton dust. Eur Respir J.2005;26:881–886.

35. Borgan O, Langholz B, Samuelsen SO, et al. Exposure stratified case-cohort designs. Lifetime Data Anal. 2000;6:39–58.

36. Barlow WE. Robust variance estimation for the case-cohort design.Biometrics. 1994;50:1064–1072.

37. Astrakianakis G, Seixas NS, Ray RM, et al. Reduced lung cancer riskamong female textile workers exposed to endotoxin. J Natl Cancer Inst.2007;99:357–364.

38. Rylander R. Endotoxin in the environment—exposure and effects.J Endotoxin Res. 2002;8:241–252.

39. Merchant JA, Ortmeyer C. Mortality of employees of two cotton mills inNorth Carolina. Chest. 1981;79(4 Suppl):6S–11S.

40. Hodgson JT, Jones RD. Mortality of workers in the British cottonindustry in 1968–1984. Scand J Work Environ Health. 1990;16:113–120.

41. Schroeder JC, Tolbert PE, Eisen EA, et al. Mortality studies of machin-ing fluid exposure in the automobile industry. IV: a case–control studyof lung cancer. Am J Ind Med. 1997;31:525–533.

42. Mastrangelo G, Grange JM, Fadda E, et al. Lung cancer risk: effect ofdairy farming and the consequence of removing that occupationalexposure. Am J Epidemiol. 2005;161:1037–1046.

43. Dranoff G. Cytokines in cancer pathogenesis and cancer therapy. NatRev Cancer. 2004;4:11–22.

44. Poltorak A, He X, Smirnova I, et al. Defective LPS signaling in



C3H/HeJ and C57BL/10ScCr mice: mutations in Tlr4 gene. Science.1998;282:2085–2088.

45. Heine H, Rietschel ET, Ulmer AJ. The biology of endotoxin. MolBiotechnol. 2001;19:279–296.

46. Nicolini A, Carpi A, Rossi G. Cytokines in breast cancer. CytokineGrowth Factor Rev. 2006;17:325–337.

47. Kim MH, Billiar TR, Seol DW. The secretable form of trimeric TRAIL,

a potent inducer of apoptosis. Biochem Biophys Res Commun. 2004;321:930–935.

48. Schmidt C. Immune system’s Toll-like receptors have good opp-ortunity for cancer treatment. J Natl Cancer Inst. 2006;98:574 –575.

49. Ye Z, Gao DL, Qin Q, et al. Breast cancer in relation to induced abortionsin a cohort of Chinese women. Br J Cancer. 2002;87:977–981.



ORIGINAL ARTICLE

A Prospective Study of Dietary Patterns and Mortality inChinese Women

Hui Cai,* Xiao Ou Shu,* Yu-Tang Gao,† Honglan Li,† Gong Yang,* and Wei Zheng*

Background: Many foods and nutrients have been suggested toinfluence life expectancy. However, previous studies have not ex-amined the relationship between dietary patterns and cause-specificmortality. Our study prospectively examines the relationship ofdietary patterns with total mortality and cause-specific mortality in apopulation-based cohort study of Chinese women.Methods: The Shanghai Women’s Health Study is a population-based cohort study of 74,942 women age 40 to 70 years at the timeof recruitment (September 1996 to May 2000). Detailed dietaryinformation was collected using a validated, quantitative food fre-quency questionnaire. The cohort has been followed using a com-bination of in-person interviews and record linkage with variousregistries. Dietary patterns, derived from principal component anal-ysis, were examined for their relation to total mortality and cause-specific mortality using Cox regression models.Results: After an average of 5.7 years of follow-up (423,717person-years of observation), there were 1565 deaths. We derived 3major dietary patterns (vegetable-rich, fruit-rich, and meat-rich).The adjusted hazard ratios for the fruit-rich diet were 0.94 (95%CI � 0.89–0.98) for all causes of death and 0.89 (0.81–0.99), 0.79(0.69–0.91), and 0.51 (0.39–0.65) for death caused by cardiovas-cular disease, stroke, and diabetes, respectively. The meat-rich dietwas associated with increased risk of diabetes (HR � 1.18; 95%CI � 0.98–1.42) and a slightly elevated risk of total mortality.Conclusion: In general, a fruit-rich diet was related to lowermortality, whereas a meat-rich diet appeared to increase the proba-bility of death.

(Epidemiology 2007;18: 393–401)

Certain dietary patterns, such as the so-called Mediterra-nean diet characterized by high intake of fruits and

vegetables, moderate to high intake of fish, low intake ofsaturated fat, and low intake of meat and poultry,1 can help tomaintain health and prolong life. Traditionally, investigationsof associations between diet and health have focused on

single nutrients or foods, such as fruits, vegetables, or redmeat.2,3 Even though these studies provide valuable informa-tion for specific food items related to the risk of disease andmortality, such one-to-one relationships have limitations. Forexample, foods are combined in complex ways. In a singlemeal, people generally consume a combination of meats,vegetables, and drinks. Also, diets may vary day-to-day eventhough most individuals consume certain dishes or foodsmore regularly than others. Because dietary variables areoften highly intercorrelated, it can be difficult to determinethe effects of single dietary components.4 Therefore, researchmethods that examine the effects of overall diet on humanhealth are important epidemiologic tools.

Dietary pattern analysis has been used to overcome themethodologic limitations of previous dietary studies.5–7 Thisapproach has proved informative and is used increasingly instudies of Western populations,8–13 However, relatively littlework has been done on the investigation of dietary patternsand their relationships with health outcomes in non-Westernpopulations. Such investigations may prove important, be-cause dietary traditions and cultural and social norms indifferent populations lead to distinct dietary patterns.14 TheShanghai Women’s Health Study is a large population-basedprospective cohort study of Chinese women. In this study, wederived dietary patterns from a baseline food frequencyquestionnaire (FFQ) using principal component analysis. Wethen investigated the prospective relationship of these pat-terns with total mortality and cause-specific mortality.

METHODS

SubjectsThe Shanghai Women’s Health Study is an ongoing,

population-based, prospective study conducted in 7 commu-nities in urban Shanghai. All permanent female residentsbetween 40 and 70 years of age in the study communities(n � 81,170) were recruited between September 1996 andMay 2000. In-person interviews were conducted for 74,942(92%) women during this period. This baseline survey in-cluded information on socioeconomic status, living habits,history of chronic disease, physical activity, and dietaryhabits. A FFQ was administered to assess usual dietary intakeover the 12 months before the interview. The major reasonsfor nonparticipation were refusal (3%), absence during theenrollment period (3%), and other miscellaneous reason suchas health, hearing, or speaking problems (2%). Details of thebaseline survey have been reported elsewhere.15

Submitted 10 July 2006; accepted 1 January 2007.From the *Department of Medicine, Vanderbilt Epidemiology Center,

Vanderbilt University Medical Center and Vanderbilt-Ingram CancerCenter, Nashville, TN; and †Department of Epidemiology, ShanghaiCancer Institute, Shanghai, People’s Republic of China.

Supported by the US National Institutes of Health (RO1 CA070867).Correspondence: Hui Cai, Vanderbilt Epidemiology Center, 6000 MCE,

Vanderbilt University, 1215 21st Avenue South, Nashville, TN 37232-8300. E-mail: [email protected].



Data CollectionThe FFQ was based on a similar dietary questionnaire

used in previous epidemiologic studies of cancer in Shanghai.A total of 71 foods and food groups were included in thequestionnaire, which covered about 86% of commonly con-sumed foods in urban Shanghai. For each food or food group,subjects were asked how frequently (daily, weekly, monthly,yearly, or never) they consumed the food or food groups overthe preceding year, followed by a question on the amountconsumed in lians (a unit of weight equal to 50 g) per unit oftime. During the baseline survey, approximately 1000 partic-ipants in each season were asked for information on thenumber of months per year each seasonal food was con-sumed. The seasonal consumption information was then re-gressed on the age, education, and income of the subgroup ofstudy participants, and the regression coefficients were usedto weight the derived consumption of seasonal foods for thewhole cohort of women. The validation study indicated thatthe FFQ can reliably and accurately measure usual intake of

major nutrients and food groups among women in Shang-hai.16

Since baseline recruitment, the cohort has been fol-lowed using a combination of biannual in-person interviewsand record linkage with the tumor and death registries main-tained by the Shanghai Center for Disease Control andPrevention. The first in-person follow-up for all living cohortmembers was conducted from 2000 to 2002. Follow-up ofdisease or death outcomes was completed for 74,764 cohortmembers, a response rate of 99.8%. The second follow-upsurvey was launched in May 2002 and completed in Decem-ber 2004, with a response rate of 99.1%. Only 10 womenwere lost to follow-up.

Dietary Pattern DerivationTo perform a dietary pattern analysis using principal

component analysis,17 we included 71 individual foods orfood groups from the FFQ in the analysis as absolute weight

TABLE 1. Description of Demographic Factors

Survived(n � 73,367)

Deceased(n � 1565) OR (95% CI)*

Age (yrs); mean � SE 52.0 � 0.03 60.5 � 0.205

BMI* (kg/m2); mean � SE 24.0 � 0.01 23.8 � 0.09

Waist-hip ratio*; mean � SE 0.81 � 0.0002 0.82 � 0.001

Physical activity energy expenditure(MET-hrs/wk)*†; mean � SE

107 � 0.2 94 � 1.2

Education; no. (%)

Primary or lower‡ 15,343 (21) 837 (54) 1.0

Middle and high 47,971 (65) 598 (38) 0.72 (0.63–0.82)

College and higher 10,041 (14) 129 (8) 0.55 (0.46–0.67)

Married; no. (%)

Yes‡ 65,251 (89) 1242 (79) 1.0

No 8116 (11) 323 (21) 1.16 (1.02–1.33)

Income/person§; no. (%)

Low‡ 20,133 (27) 675 (43) 1.0

Middle 28,530 (39) 600 (38) 0.85 (0.76–0.95)

High 24,688 (34) 290 (19) 0.69 (0.59–0.79)

Smoking; no. (%)

Never‡ 71,354 (97) 1466 (94) 1.0

Ever 2013 (3) 99 (6) 1.35 (1.09–1.67)

Alcohol consumption; no. (%)

Never‡ 71,720 (98) 1534 (98) 1.0

Ever 1647 (2) 31 (2) 0.75 (0.53–1.08)

Tea consumption; no. (%)

Never‡ 51,388 (70) 1220 (78) 1.0

Ever 21,979 (30) 345 (22) 0.89 (0.79–1.01)

Ginseng intake; no. (%)

Never‡ 51,814 (71) 905 (58) 1.0

Ever 21,553 (29) 660 (42) 1.19 (1.08–1.33)

*Adjusted for age.†Physical activity energy expenditure (MET-hrs/wk): metabolic equivalent was calculated using the Compen-

dium of Physical Activities18 and was expressed in terms of activity intensity (METs) and duration (hrs/wk).‡Reference category.§Low: �5000 yuan; middle: 5000–9999 yuan; high: �10,000 yuan (1US$ � 8.3 yuan).

Cai et al Epidemiology • Volume 18, Number 3, May 2007


in grams per day. Before analysis, all dietary variables wereadjusted for energy intake using the residual approach.

We used the PROC FACTOR procedure in SAS (ver-sion 9.1; SAS Institute, Cary, NC) to perform the analysis.The procedure uses principal component analysis and orthog-onal rotation (the varimax option in SAS) to derive noncor-related factors and to render results more easily interpretable.To determine the number of factors to retain, we examinedboth the scree plots and the factors themselves to see whichsets of factors most meaningfully described distinct foodpatterns. From these analyses, 3 main factors were identified.Factor loadings were calculated for each food or food groupacross the 3 factors. Factors were thereby interpreted asdietary patterns and each pattern was named after the foodgroup with the highest loading (absolute value of loading�0.30). These loadings can be considered correlation coef-ficients between food groups and dietary patterns; they takevalues between �1 and �1. We then calculated for eachstudy participant a factor score for each of the 3 factors; thestandardized intake of each of the 71 foods or food groupswas weighted by its factor loading and summed. The sumswere then standardized (mean � SD � 0 � 1).

Risk AnalysisFactor scores were used for comparison with other

lifestyle factors and for estimating associations with total

mortality and cause-specific mortality. These scores werecategorized into quartiles based on their distribution in thestudy population. To determine the association between di-etary patterns and main causes of death, we estimated theadjusted hazard ratios (HRs) and 95% confidence intervals(CIs) for each quartile compared with the lowest quartile ofeach dietary pattern, using Cox proportional hazard models.We adjusted for the following potential confounders in themultivariable models: age (4 categories); education (primaryschool or lower, middle and high school, college and above);marriage status (yes, no); income per person (low, middle,high); smoking status (never, ever); alcohol consumption(never, ever); tea consumption (never, ever); ginseng use(never, ever); physical activity energy expenditure18 includ-ing leisure-time physical activity, house work, walking, cy-cling, etc. (MET-hours/wk, quartile); and body mass index(BMI, kg/m2; 4 categories). We tested linear trends acrosscategories of dietary patterns by modeling the category valueof each participant as a continuous variable. These hazardratios were then used to determine risk in the correspondingdietary patterns. The interaction effect between food patternsand the presence of chronic disease at baseline was alsocalculated for cause-specific mortality using cross-productterms in the model. In our study, women who died of causesother than the one under study were censored from theanalysis at the time of death.

TABLE 2. Factor Loading for 3 Food Patterns at Baseline for 74,942 Female Adults Participating in the Shanghai Women’sHealth Study*

No.

Factor 1 (Vegetable-Rich Diet) Factor 2 (Fruit-Rich Diet) Factor 3 (Meat-Rich Diet)

Food(Group)

FactorLoading

Food(Group)

FactorLoading Food (Group)

FactorLoading

1 Green beans 54 Oranges, grapefruit 67 Chicken 47

2 Yard long beans 52 Apples 66 Animal parts (heart, brain, tongue, tripe, intestines) 42

3 Wild rice stems 49 Pears 63 Liver (pig, cow, sheep) 36

4 Eggplant 48 Bananas 54 Rice field eel or river eel 36

5 Celery 47 Watermelon 52 Pig’s feet 34

6 Cucumber, luffa 46 Peaches 50 Pork chops 33

7 Cauliflower 43 Other fruits 50 Pork ribs 33

8 Green cabbage 42 Grapes 49 Beef, lamb 32

9 Chinese cabbage 42 Tomatoes (29) Duck, goose 32

10 Wax gourd 41 Shrimp, crab, etc (28) Fresh pork (lean) 31

11 Asparagus, lettuce 40 Bamboo shoots (26) Fresh pork (mixture) 30

12 Potatoes 40 Rice �40 Shrimp, crab, etc (29)

13 Chinese greens 38 Rice �43

14 Spinach 37

15 Fresh peppers 37

16 Hyacinth beans/snow peas (Dutch peas) 37

17 Tomatoes 37

18 White turnips 33

19 Fresh mushrooms 32

20 Lotus roots 32

21 Bamboo shoots 31

*Factor loadings are multiplied by 100 and rounded to the nearest integer. Only absolute values of factor loadings �30 are listed in the table.

Epidemiology • Volume 18, Number 3, May 2007 Association of Dietary Patterns and Mortality


RESULTSThe study included 74,942 women aged 40 to 70 years.

During average of 5.7 years of follow-up, there were 1565deaths. The baseline characteristics of both surviving anddeceased women are shown in Table 1. As expected, de-ceased women were older, less likely to have been married,more likely to have lower levels of education, income andenergy expenditure, and a higher rate of ginseng use. Also,they were more likely to be smokers than surviving women,although their levels of BMI and waist-hip ratio were almostthe same as surviving women.

The scree plot of eigenvalues depicted 3 major dietarypatterns; the factor-loading matrices for those dietary patternsare listed in Table 2. The larger the loading of a given food itemto the factor, the greater the contribution of that food item to thespecific factor. Negative loading indicates a negative associationwith the factor. The first dietary pattern was heavily loaded with

vegetables such as green beans and yard long beans; it wasnamed the “vegetable-rich” diet. The second dietary pattern wasloaded mainly with fruits; it was named the “fruit-rich” diet. Thethird dietary pattern was loaded with meat, poultry, and animalorgans; it was named the “meat-rich” diet.

Table 3 shows the covariate-adjusted hazard ratios bycategory of dietary patterns for mortality from all causes andselected causes. A strong inverse association between thefruit-rich diet and all causes of mortality was observed (HR �0.94 by modeling categories of fruit-rich diet as a continuousvariable; 95% CI � 0.89–0.98), with the highest quartileshowing a risk reduction of 20% relative to the lowestquartile. An inverse association was also observed for car-diovascular disease, stroke, and diabetes (HR � 0.89, 0.79and 0.51, respectively). There was a modest positive associ-ation between the meat-rich diet and all causes of mortality.A positive association was also found between the meat-rich

TABLE 3. Mortality Due to Various Chronic Diseases by Quartile of Dietary Patterns*

Quartile

Vegetable-Rich Diet Fruit-Rich Diet Meat-Rich Diet

Deaths No./Total HR (95% CI) Deaths No./Total HR (95% CI) Deaths No./Total HR (95% CI)

Total deaths (n � 1565)

Q1† 426/18,766 1.0 552/18,895 1.0 511/18,854 1.0

Q2 391/18,736 0.90 (0.79–1.03) 426/18,766 0.96 (0.84–1.09) 431/18,769 1.03 (0.90–1.17)

Q3 382/18,721 0.93 (0.81–1.07) 341/18,683 0.91 (0.79–1.04) 368/18,710 1.11 (0.97–1.27)

Q4 366/18,707 0.97 (0.85–1.12) 246/18,588 0.80 (0.69–0.94) 255/18,599 1.04 (0.89–1.22)

P for trend 0.9220 0.0090 0.4516

Cardiovascular disease (n � 395)

Q1† 101/18,766 1.0 161/18,895 1.0 135/18,854 1.0

Q2 96/18,738 0.92 (0.70–1.22) 104/18,766 0.86 (0.67–1.11) 114/18,769 1.07 (0.83–1.37)

Q3 94/18,721 0.96 (0.73–1.28) 77/18,683 0.79 (0.60–1.05) 89/18,710 1.12 (0.85–1.47)

Q4 104/18,707 1.18 (0.90–1.56) 53/18,588 0.71 (0.51–0.98) 57/18,599 1.04 (0.75–1.44)

P for trend 0.2157 0.0309 0.6019

Stroke (n � 224)

Q1† 50/18,766 1.0 105/18,895 1.0 82/18,854 1.0

Q2 62/18,738 1.20 (0.83–1.75) 54/18,766 0.67 (0.48–0.93) 68/18,769 1.03 (0.75–1.43)

Q3 53/18,721 1.09 (0.74–1.61) 37/18,683 0.56 (0.38–0.82) 47/18,710 0.94 (0.65–1.35)

Q4 59/18,707 1.35 (0.92–1.97) 28/18,588 0.53 (0.34–0.82) 27/18,599 0.76 (0.48–1.19)

P for trend 0.2375 0.0006 0.3710

Coronary heart disease (n � 77)

Q1† 23/18,766 1.0 30/18,895 1.0 27/18,854 1.0

Q2 12/18,738 0.51 (0.25–1.02) 20/18,766 0.92 (0.52–1.62) 20/18,769 0.99 (0.56–1.78)

Q3 20/18,721 0.91 (0.50–1.65) 18/18,683 1.05 (0.57–1.90) 16/18,710 1.14 (0.61–2.14)

Q4 22/18,707 1.10 (0.61–1.99) 9/18,588 0.71 (0.33–1.53) 14/18,599 1.58 (0.81–3.08)

P for trend 0.5035 0.5496 0.1760

Diabetes (n � 107)

Q1† 28/18,766 1.0 68/18,895 1.0 32/18,854 1.0

Q2 31/18,738 1.06 (0.64–1.76) 28/18,766 0.56 (0.36–0.88) 31/18,769 1.24 (0.76–2.03)

Q3 22/18,721 0.82 (0.47–1.43) 6/18,683 0.16 (0.07–0.37) 26/18,710 1.51 (0.89–2.55)

Q4 26/18,707 1.16 (0.68–1.99) 5/18,588 0.19 (0.08–0.48) 18/18,599 1.72 (0.95–3.12)

P for trend 0.7784 �0.0001 0.0738

*Adjusted for age, BMI, education, marriage, income per person, smoking, alcohol consumption, tea consumption, ginseng intake, and physical activity energy expenditure.†Reference category.



diet and diabetes (HR � 1.18 by modeling categories ofmeat-rich diet as a continuous variable; 95% CI � 0.98–1.42), with the highest quartile of meat consumption associ-ated with a more than 72% increase in risk compared with thelowest quartile. No overall association of dietary pattern andcancer mortality was observed (Table 4), although the meat-rich diet was related to an elevated risk of colorectal cancer.

To evaluate the influence of chronic disease at baseline onthese associations, we conducted further analyses stratified bythe presence of diabetes, hypertension, coronary heart disease,stroke, and cancer at baseline (Table 5). Generally, the HRs of

mortality for women with a chronic disease at baseline wereconsistent with that of all women in the study, whereas the HRsin the healthy women at baseline were attenuated. There were nointeraction effects between the presence of chronic disease atbaseline and dietary patterns for most cause-specific mortalities,with the exception of breast cancer.

To eliminate the possibility that the observed associa-tions were skewed by dietary change resulting from recentdiagnosis of chronic disease, we repeated the risk analysisusing the Cox proportional hazard model and excludingparticipants who died within 1 year of the baseline survey

TABLE 4. Mortality Due to Various Cancers by Quartile of Dietary Patterns*

Quartile

Vegetable-Rich Diet Fruit-Rich Diet Meat-Rich Diet

Deaths No./Total HR (95% CI) Deaths No./Total HR (95% CI) Deaths No./Total HR (95% CI)

Cancer (n � 770)

Q1† 210/18,766 1.0 222/18,895 1.0 252/18,854 1.0

Q2 195/18,738 0.92 (0.76–1.12) 217/18,766 1.13 (0.94–1.37) 208/18,769 0.97 (0.81–1.17)

Q3 192/18,721 0.95 (0.78–1.15) 191/18,683 1.12 (0.92–1.37) 177/18,710 1.00 (0.82–1.22)

Q4 173/18,707 0.91 (0.74–1.11) 140/18,588 0.96 (0.77–1.20) 133/18,599 0.95 (0.76–1.19)

P for trend 0.4268 0.9503 0.7462

Lung cancer (n � 140)

Q1† 40/18,766 1.0 30/18,895 1.0 42/18,854 1.0

Q2 31/18,738 0.77 (0.48–1.23) 48/18,766 1.95 (1.23–3.09) 32/18,769 0.91 (0.57–1.44)

Q3 32/18,721 0.83 (0.52–1.33) 31/18,683 1.45 (0.87–2.43) 38/18,710 1.32 (0.84–2.06)

Q4 37/18,707 1.03 (0.66–1.61) 31/18,588 1.74 (1.03–2.93) 28/18,599 1.26 (0.77–2.09)

P for trend 0.7880 0.1063 0.2389

Stomach cancer (n � 88)

Q1† 22/18,766 1.0 28/18,895 1.0 27/18,854 1.0

Q2 26/18,738 1.17 (0.66–2.06) 24/18,766 0.97 (0.56–1.68) 21/18,769 0.94 (0.53–1.67)

Q3 17/18,721 0.79 (0.42–1.50) 22/18,683 0.99 (0.56–1.75) 21/18,710 1.16 (0.65–2.08)

Q4 23/18,707 1.13 (0.63–2.04) 14/18,588 0.72 (0.37–1.40) 19/18,599 1.36 (0.73–2.52)

P for trend 0.8853 0.4096 0.3859

Liver cancer (n � 76)

Q1† 20/18,766 1.0 21/18,895 1.0 29/18,854 1.0

Q2 19/18,738 0.94 (0.50–1.76) 20/18,766 1.07 (0.58–1.99) 23/18,769 0.91 (0.53–1.58)

Q3 17/18,721 0.88 (0.46–1.68) 20/18,683 1.20 (0.64–2.25) 15/18,710 0.71 (0.37–1.34)

Q4 20/18,707 1.12 (0.60–2.09) 15/18,588 1.07 (0.54–2.13) 9/18,599 0.53 (0.25–1.16)

P for trend 0.8237 0.7474 0.0975

Colorectal cancer (n � 94)

Q1† 27/18,766 1.0 30/18,895 1.0 28/18,854 1.0

Q2 20/18,738 0.74 (0.41–1.32) 24/18,766 0.93 (0.54–1.60) 22/18,769 0.98 (0.56–1.72)

Q3 27/18,721 1.04 (0.61–1.77) 25/18,683 1.08 (0.62–1.85) 22/18,710 1.25 (0.71–2.22)

Q4 20/18,707 0.79 (0.44–1.41) 15/18,588 0.75 (0.39–1.42) 22/18,599 1.68 (0.93–3.01)

P for trend 0.6220 0.5498 0.0651

Breast cancer (n � 66)

Q1† 23/18,766 1.0 17/18,895 1.0 22/18,854 1.0

Q2 13/18,738 0.57 (0.29–1.13) 15/18,766 0.93 (0.46–1.87) 13/18,769 0.62 (0.31–1.23)

Q3 20/18,721 0.91 (0.50–1.65) 17/18,683 1.10 (0.55–2.19) 19/18,710 0.97 (0.52–1.82)

Q4 10/18,707 0.47 (0.22–0.98) 17/18,588 1.23 (0.61–2.48) 12/18,599 0.68 (0.33–1.42)

P for trend 0.1593 0.3143 0.8017

*Adjusted for age, BMI, education, marriage, income per person, smoking, alcohol consumption, tea consumption, ginseng intake, and physical activity energy expenditure.†Reference category.



TABLE 5. Mortality Due to Chronic Diseases by Quartile of Dietary Patterns for Healthy Women and for Women WithChronic Disease at Baseline*

Quartile

Women With Chronic Disease at Baseline† (n � 24,598) Women Without Chronic Disease at Baseline (n � 52,698)

Vegetable-Rich DietHR (95% CI)

Fruit-Rich DietHR (95% CI)

Meat-Rich DietHR (95% CI)




All causes of death (n � 877) All causes of death (n � 688)Q1‡ 1.0 1.0 1.0 1.0 1.0 1.0

Q2 0.88 (0.73–1.06) 0.95 (0.81–1.13) 1.04 (0.87–1.23) 0.89 (0.72–1.09) 1.03 (0.84–1.26) 1.03 (0.85–1.26)

Q3 0.84 (0.70–1.02) 0.86 (0.71–1.04) 1.24 (1.04–1.49) 0.97 (0.79–1.19) 1.04 (0.84–1.28) 1.02 (0.83–1.26)

Q4 0.90 (0.75–1.09) 0.83 (0.67–1.03) 1.20 (0.97–1.50) 0.96 (0.77–1.18) 0.84 (0.66–1.06) 0.99 (0.79–1.25)

P for trend 0.3688 0.0804 0.0698 0.9011 0.2479 0.9309

P for interaction 0.8228 0.8838 0.3280

Cardiovascular disease (n � 274) Cardiovascular disease (n � 121)Q1‡ 1.0 1.0 1.0 1.0 1.0 1.0

Q2 0.79 (0.56–1.10) 0.90 (0.67–1.21) 1.13 (0.84–1.51) 1.19 (0.72–1.97) 0.91 (0.57–1.45) 1.02 (0.63–1.67)

Q3 0.79 (0.56–1.10) 0.78 (0.56–1.10) 1.10 (0.79–1.52) 1.21 (0.72–2.04) 0.97 (0.59–1.59) 1.36 (0.84–2.22)

Q4 0.95 (0.69–1.32) 0.71 (0.47–1.05) 1.11 (0.74–1.67) 1.49 (0.89–2.47) 0.85 (0.49–1.49) 1.28 (0.75–2.20)

P for trend 0.8657 0.0737 0.6590 0.1296 0.6548 0.2456


Stroke (n � 167) Stroke (n � 57)Q1‡ 1.0 1.0 1.0 1.0 1.0 1.0

Q2 1.04 (0.68–1.58) 0.75 (0.52–1.10) 1.00 (0.69–1.44) 1.66 (0.76–3.67) 0.59 (0.29–1.18) 1.38 (0.68–2.76)

Q3 0.86 (0.55–1.33) 0.59 (0.37–0.92) 0.87 (0.56–1.33) 1.64 (0.73–3.69) 0.63 (0.30–1.30) 1.54 (0.74–3.19)

Q4 1.01 (0.65–1.55) 0.55 (0.33–0.92) 0.76 (0.44–1.32) 2.15 (0.98–4.70) 0.61 (0.28–1.37) 1.27 (0.55–2.90)

P for trend 0.8272 0.0060 0.2844 0.0814 0.1784 0.3786


Coronary heart disease (n � 59) Coronary heart disease (n � 18)Q1‡ 1.0 1.0 1.0 1.0 1.0 1.0

Q2 0.52 (0.24–1.14) 1.01 (0.54–1.92) 1.52 (0.79–2.89) 0.35 (0.07–1.72) 0.78 (0.22–2.81) 0.13 (0.02–1.06)

Q3 0.74 (0.36–1.49) 0.99 (0.49–2.02) 1.74 (0.86–3.52) 1.16 (0.38–3.61) 1.39 (0.43–4.47) 0.32 (0.07–1.54)

Q4 1.02 (0.53–1.99) 0.83 (0.35–1.98) 1.98 (0.84–4.66) 0.85 (0.24–3.01) 0.55 (0.11–2.88) 1.17 (0.38–3.58)

P for trend 0.7317 0.7528 0.0693 0.9019 0.8030 0.9056


Diabetes (n � 97) Diabetes (n � 10)Q1‡ 1.0 1.0 1.0 1.0 1.0 1.0

Q2 0.99 (0.58–1.70) 0.67 (0.42–1.05) 1.24 (0.73–2.09) 1.04 (0.21–5.13) 0.18 (0.02–1.45) 1.48 (0.33–6.71)

Q3 0.71 (0.40–1.29) 0.21 (0.09–0.48) 1.72 (1.00–2.97) 0.77 (0.13–4.63) — 0.85 (0.14–5.30)

Q4 1.02 (0.58–1.80) 0.15 (0.05–0.50) 2.44 (1.32–4.51) 0.89 (0.15–5.35) 0.52 (0.10–2.75) 0.48 (0.05–5.97)

P for trend 0.8837 �0.0001 0.0115 0.7909 0.1471 0.5662


All cancer (n � 381) All cancer (n � 389)Q1‡ 1.0 1.0 1.0 1.0 1.0 1.0

Q2 0.92 (0.70–1.23) 1.05 (0.80–1.37) 0.97 (0.74–1.26) 0.89 (0.68–1.17) 1.26 (0.96–1.65) 0.98 (0.76–1.27)

Q3 0.88 (0.66–1.17) 1.10 (0.83–1.45) 1.30 (1.00–1.70) 0.97 (0.74–1.27) 1.19 (0.90–1.58) 0.78 (0.58–1.05)

Q4 0.85 (0.64–1.14) 1.06 (0.78–1.45) 1.11 (0.80–1.54) 0.90 (0.67–1.19) 0.92 (0.67–1.26) 0.88 (0.65–1.19)

P for trend 0.3329 0.4604 0.2352 0.5386 0.6378 0.2185


Lung cancer (n � 69) Lung cancer (n � 71)Q1‡ 1.0 1.0 1.0 1.0 1.0 1.0

Q2 0.66 (0.32–1.35) 1.67 (0.89–3.13) 1.01 (0.51–1.98) 0.87 (0.47–1.62) 2.42 (1.21–4.83) 0.82 (0.44–1.54)

Q3 0.88 (0.46–1.70) 1.27 (0.62–2.60) 1.96 (1.05–3.67) 0.74 (0.38–1.45) 1.78 (0.84–3.80) 0.88 (0.46–1.69)

Q4 1.02 (0.54–1.94) 1.73 (0.85–3.55) 1.83 (0.88–3.81) 0.98 (0.52–1.86) 1.92 (0.88–4.19) 0.94 (0.48–1.87)

P for trend 0.6751 0.1982 0.0412 0.8370 0.2294 0.8281


(Continued)



(196 deaths) or whose disease was diagnosed within 2 yearsof the baseline survey. The associations remained unchangedby these exclusions (data not shown).

DISCUSSIONThere has been increasing interest in the identification

of dietary patterns as an alternative or complementary ap-proach to single-nutrient analysis in relation to risk of diseaseor death. Dietary patterns are characterized on the basis ofhabitual food consumption, represent a combination of nutri-ents and foods, and may be a better predictor of healthoutcomes than any single nutrient.5

The number and description of dietary patterns identi-fied to date has varied widely. One in particular, the Medi-terranean diet, has been evaluated in many studies for itsbenefits to health.1 Examples of other dietary patterns that areoften reported include the animal fat pattern identified byAkin et al19 and the “healthy” pattern identified by Pryer etal20 In our large, population-based, cohort study we identified

3 common dietary patterns found in Chinese women between40 and 70 years of age, based on a comprehensive andvalidated FFQ. Our results were similar to those found in theMultiethnic Cohort Study.21 The fruit- and vegetable-richdiets in our study population were similar to the “prudent”patterns in the Nurses’ Health Study,22 and the meat-rich dietwas similar to their “western” pattern.

Many epidemiologic studies have examined the asso-ciation of dietary patterns with demographic or anthropomet-ric characteristics23,24 or with chronic disease.25–27 Somestudies have investigated the relationship of the Mediterra-nean dietary pattern and longevity in Greek, Danish, Austra-lian, and Spanish populations. These studies have all beenrelatively small scale (including fewer than 400 sub-jects),28–30 or were conducted exclusively among elderlypersons in Western populations.31 Few studies have shown anassociation of dietary patterns with cause-specific mortality.32

Our study found that a fruit-rich diet was associated witha reduction in all-cause mortality. This reduction in mortality


Quartile

Women With Chronic Disease at Baseline (n � 24,598) Women Without Chronic Disease at Baseline (n � 52,698)







Stomach cancer (n � 38) Stomach cancer (n � 50)Q1‡ 1.0 1.0 1.0 1.0 1.0 1.0

Q2 1.10 (0.46–2.66) 1.10 (0.50–2.42) 0.96 (0.39–2.40) 1.20 (0.57–2.52) 0.86 (0.40–1.86) 0.91 (0.44–1.90)

Q3 0.77 (0.30–2.00) 0.91 (0.38–2.16) 2.00 (0.87–4.60) 0.79 (0.34–1.85) 1.05 (0.49–2.26) 0.72 (0.31–1.65)

Q4 0.96 (0.39–2.36) 0.47 (0.15–1.48) 1.81 (0.68–4.85) 1.24 (0.57–2.69) 0.92 (0.40–2.13) 1.09 (0.50–2.41)

P for trend 0.7766 0.2434 0.1127 0.7604 0.9774 0.8590


Liver cancer (n � 31) Liver cancer (n � 45)Q1‡ 1.0 1.0 1.0 1.0 1.0 1.0

Q2 1.15 (0.43–3.08) 0.65 (0.24–1.77) 0.97 (0.42–2.23) 0.81 (0.35–1.84) 1.49 (0.65–3.37) 0.87 (0.42–1.82)

Q3 0.87 (0.31–2.49) 0.97 (0.38–2.45) 0.77 (0.29–2.06) 0.90 (0.40–2.06) 1.42 (0.60–3.36) 0.66 (0.29–1.53)

Q4 0.99 (0.36–2.75) 0.88 (0.31–2.47) 0.39 (0.09–1.78) 1.23 (0.56–2.69) 1.25 (0.49–3.20) 0.58 (0.23–1.47)

P for trend 0.8335 0.9160 0.2461 0.6393 0.6601 0.2168


Colorectal cancer (n � 46) Colorectal cancer (n � 48)Q1‡ 1.0 1.0 1.0 1.0 1.0 1.0

Q2 0.22 (0.07–0.64) 0.70 (0.33–1.52) 1.05 (0.51–2.17) 1.68 (0.76–3.70) 1.28 (0.58–2.83) 0.94 (0.39–2.28)

Q3 0.63 (0.30–1.33) 0.98 (0.47–2.06) 0.90 (0.39–2.12) 1.73 (0.78–3.85) 1.28 (0.56–2.92) 1.72 (0.77–3.85)

Q4 0.67 (0.33–1.39) 0.50 (0.18–1.38) 1.46 (0.61–3.50) 0.83 (0.32–2.19) 1.09 (0.45–2.64) 2.04 (0.90–4.64)

P for trend 0.5820 0.3229 0.6198 0.7726 0.8530 0.0303


Breast cancer (n � 40) Breast cancer (n � 26)Q1‡ 1.0 1.0 1.0 1.0 1.0 1.0

Q2 1.14 (0.45–2.88) 0.76 (0.29–1.99) 0.72 (0.28–1.88) 0.21 (0.06–0.73) 1.28 (0.44–3.71) 0.52 (0.19–1.41)

Q3 1.65 (0.70–3.88) 1.17 (0.49–2.81) 1.85 (0.83–4.10) 0.38 (0.14–1.05) 1.13 (0.37–3.46) 0.35 (0.11–1.12)

Q4 0.75 (0.27–2.06) 1.65 (0.70–3.88) 1.15 (0.43–3.07) 0.24 (0.07–0.83) 0.88 (0.26–3.00) 0.44 (0.14–1.32)

P for trend 0.9368 0.0896 0.1605 0.0108 0.8046 0.0872


*Adjusted for age, BMI, education, marriage, income per person, smoking, alcohol consumption, tea consumption, ginseng intake, and physical activity energy expenditure.†Women with diabetes, hypertension, coronary heart disease, stroke, or cancer at the baseline survey.‡Reference categogy.



was also evident with respect to death due to both cardiovasculardisease and diabetes, although it was slightly more pronouncedwith respect to the latter. The meat-rich diet in our study showeda weak association with an increase in risk for all-cause mortal-ity and mortality due to diabetes and colorectal cancer. Takingour fruit-rich and vegetable-rich diets together yields a dietarypattern similar to the Mediterranean diet, decreasing the risk ofmortality by 6% with each quartile increase. This magnitude ofrisk reduction or increase associated with the dietary patternsagrees, in general, with findings from other reports on theMediterranean-type diet, even reports that used different meth-ods for deriving the dietary scores. Studies in Greece andDenmark found a 17%–20% reduction in smoking-adjusted riskof mortality with a 1-unit increase in a Mediterranean dietscore.11–20 Similarly, a 13% reduction in age-, smoking-, andalcohol-adjusted risk of mortality in men (n � 3045) in thehighest third of a Healthy Diet Index was noted in the SevenCountries Study.12 Conversely, Osler et al13 reported no asso-ciation of their 4-point Healthy Food Index with all-causemortality. In addition, several studies in European populationshave produced conflicting results regarding the association be-tween mortality and the Mediterranean diet.28

In our study, we found an inverse association between thefruit-rich diet and risk of mortality caused by type 2 diabetes(HR � 0.51) and a positive association between the meat-richdiet and mortality due to this condition (HR � 1.18). Althoughdiet is widely believed to play an important role in the develop-ment of type 2 diabetes, its specific mechanisms have not beenclearly defined. One possibility is that higher vitamin C intakeresulting from high intake of fruits may play a role in themodulation of insulin action. Paolisso et al reported that plasmavitamin C level was associated with higher insulin action in bothhealthy and diabetic people.33 Another possible protective effectof a high-fruit diet is through the combined action of antioxi-dants in fruits.34 This combined action has been suggested as apossible reason for the controversial inconsistencies betweensupplementation trials and observational studies on the healtheffects of antioxidants. Factors other than dietary antioxidantsmay also explain the findings. It is possible that individuals withdiets high in antioxidants have healthier lifestyles, in general,than other people.35 Hu et al reported a low-glycaemic index dietwith a higher amount of fiber products reduces glycaemic andinsulinaemic responses and lowers the risk of diabetes.36

The meat-rich diet in our study was associated withmortality from colorectal cancer, especially in women who havebeen healthy at the baseline survey. This finding is generally inagreement with findings from other studies of dietary patternsand cancer incidence, or of diet index and cancer mortality.Slattery et al37 identified a “Western” pattern (including redmeats, processed meats, and fast food) associated with an in-creased risk of colon cancer in women (OR � 1.49) and a“prudent” pattern (including fruits and vegetables) associatedwith a reduced risk of colon cancer in women (OR � 0.73).Fung et al38 identified a similar “Western” pattern that wasassociated with an increased risk of colorectal cancer (RR �1.46) and a “prudent” pattern of fruits, vegetables, legumes, fish,and poultry that was inversely associated with colon cancer (RR� 0.71) in a large prospective study of nurses in the United

States. However, in a prospective study conducted by Terry etal,39 the “Western” dietary pattern was not associated with anincreased risk of colorectal cancer. Another multicenter cohortstudy, the Dietary Patterns and Cancer project, found that thevegetable pattern was generally not associated with colorectalcancer in any cohort. However, a pattern consisting mostly ofpork, processed meats, and potatoes was associated with anincreased risk of colorectal cancer in a study of Swedish wom-en.25 In our study, the meat-rich diet was associated with anincreased risk of mortality from colorectal cancer, and thevegetable-rich diet was associated with a modestly-decreasedrisk of death from colorectal cancer.

Because mortality reflects both incidence and survivalof chronic diseases, the question remains as to whetherdietary patterns are important in the etiology or in the prog-nosis of these diseases. We examined dietary patterns inrelation to mortality of specific chronic diseases stratified bythe presence of selected chronic diseases at baseline. Exceptfor colorectal cancer, all inverse associations for the fruit-richdiet and positive associations for the meat-rich diet held forall-cause deaths and several cause-specific deaths (such ascardiovascular disease, stroke, coronary heart disease, anddiabetes) for women with those chronic diseases at baseline.An interaction between the presence of chronic disease atbaseline and the meat-rich diet was found for breast cancermortality as well, suggesting that fruit- and meat-rich dietsare associated with life expectancy or prognosis in womenwith those diseases. The meat-rich diet may be associatedwith both the etiology and prognosis of colorectal cancer,because death due to this disease was highly associated withthe meat-rich diet in healthy women at baseline survey.

In nutritional epidemiology, dietary patterns may be de-fined theoretically, in which case foods are grouped according tosome a priori criteria of nutritional health. They may also bedefined empirically, in which case foods are reduced to a fewdietary patterns through statistical manipulation and then eval-uated a posteriori. Theoretically derived dietary patterns gener-ally use a dietary index to rank more or less healthy dietarybehaviors.11–12 Such structures are built upon current nutritionalknowledge or theory, include variables from current nutritionguidelines, recommendations, and specific dietary components,and provide an overall measure of dietary quality. Conflicts canarise, however, when guidelines or recommendations do nothave scientific consensus. Also, these patterns often includedifferent foods or different weightings of foods, resulting inindices that measure different definitions of “healthy” behavior.Principal component analysis and cluster analysis are 2 com-monly used empirical methods for deriving dietary patterns.Principal component analysis reduces data into patterns based onintercorrelations between dietary items, whereas cluster analysisreduces data into patterns based on individual differences inmean intake. Patterns derived from both factor analysis24 andcluster analysis40 are comparable and are similarly associatedwith plasma lipids.41 Compared with cluster analysis, the majordietary patterns derived using principal component have reason-able reproducibility and validity.4 The challenge in principalcomponent analysis is that the relationships do not hold true forindividuals but rather for a dietary pattern, because factor scores



are continuous variables and individuals have scores for eachfactor. Clusters are arguably easier to handle and interpret in theanalysis, because they are mutually exclusive and continuous.However, their reproducibility and validity are not clear.5

Strengths of our study include its prospective nature, itslarge size, its reliance on a population sample framework, andits use of a validated, comprehensive FFQ. There are alsosome limitations for this study. Mortality is a complex endpoint and is strongly influenced by factors such as treatment,screening practices, and severity of disease. Unmeasuredvariables associated with diet may have confounded ourobservations or resulted in suboptimal measurement. Theaverage follow-up time was 5.7 years, which limited thestatistical power to detect associations because of smallnumbers for some cancer endpoints. A longer follow-upperiod would result in a more stable and powerful analysis.

In conclusion, we identified 3 main dietary patterns ina population of Chinese women and found that a fruit-richdiet reduced total mortality and mortality caused by cardio-vascular disease and diabetes. Conversely, the meat-rich dietincreased the risk of all-cause mortality, and especially mor-tality caused by diabetes and colorectal cancer.

ACKNOWLEDGMENTSWe thank the participants and staff members of the

Shanghai Women’s Health Study for their important contri-butions. We also thank Bethanie Hull for her assistance inmanuscript preparation.

REFERENCES1. Willett WC, Sacks F, Trichopoulou A, et al. Mediterranean diet pyra-

mid: a cultural model for healthy eating. Am J Clin Nutr. 1995;61(suppl6):S1402–A1406.

2. Vecchia CL, Franceschi S. Nutrition and gastric cancer. Can J Gastro-enterol. 2000;14(suppl D):51D–54D.

3. Riboli E, Norat T. Cancer prevention and diet: opportunities in Europe.Public Health Nutr. 2001;4:475–484.

4. Ledikwe JH, Wright HS, Mitchell DC, et al. Dietary pattern of ruralolder adults are associated with weight and nutritional status. J AmGeriatr Soc. 2004;52:589–595.

5. Hu FB. Dietary pattern analysis: a new direction in nutritional epidemi-ology. Curr Opin Lipidol. 2002;13:3–9.

6. Quatromoni PA, Copenhafer DL, Demissie S, et al. The internal validityof a dietary pattern analysis. The Framingham Nutrition Studies.J Epidemiol Community Health. 2002;56:381–388.

7. Balder HF. Common and country-specific dietary patterns in four Eu-ropean cohort studies. J Nutr. 2003;133:4246–4251.

8. Kant AK, Schatzkin A, Graubard BI, et al. A prospective study of dietquality and mortality in women. JAMA. 2000;283:2109–2115.

9. Kumagai S, Shibata H, Watanabe S, et al. Effect of food intake patternon all-cause mortality in the community elderly: a 7-year longitudinalstudy. J Nutr Health Aging. 1999;3:57–61.

10. Michels KB, Wolk A. A prospective study of variety of healthy foodsand mortality in women. Int J Epidemiol. 2002;31:847–854.

11. Osler M, Schroll M. Diet and mortality in a cohort of elderly people inNorth European community. Int J Epidemiol. 1997;26:155–159.

12. Huijbregts P, Feskens E, Raasnen L, et al. Dietary pattern and 20-ymortality in elderly men in Finland, Italy, and the Netherlands: longi-tudinal cohort study. Br Med J. 1997;81:13–17.

13. Osler M, Heitmann BL, Gerdes LU, et al. Dietary patterns and mortalityin Danish men and women: a prospective observational study. Br J Nutr.2001;85:219–225.

14. Willett WC. Diet and health: what should we eat? Science. 1994;264:532–537.

15. Zheng W, Chow WH, Yang G, et al. The Shanghai Women’s HealthStudy: rationale, study design, and baseline characteristics. Am J Epi-demiol. 2005;162:1123–1131.

16. Shu XO, Yang G, Jin F, et al. Validity and reproducibility of the foodfrequency questionnaire used in the Shanghai Women’s Health Study.Eur J Clin Nutr. 2004;58:17–23.

17. Kleinbaum DG, Kupper LL, Muller KE. Variable reduction and factoranalysis. In: Applicated Regression Analysis and Other MultivariableMethods. Boston, MA: PWS-Kent publishing company; 1988:595:–640.

18. Ainsworth BE, Haskell WL, Whitt MC, et al. Compendium of physicalactivities: an update of activity codes and MET intensities. Med SciSports Exerc. 2000;32(suppl):S498–S504.

19. Akin JS, Guilkey DK, Popkin BM, et al. Cluster analysis of food consumptionpatterns of older Americans. J Am Diet Assoc. 1986;86:616–624.

20. Pryer JA, Cook A, Shetty P. Identification of groups who report similarpatterns of diet among a representative national sample of British adultsaged 65 years of age or more. Public Health Nutr. 2001;4:787–795.

21. Park SY, Murphy SP, Wilkens LR, et al. Dietary patterns using the FoodGuide Pyramid groups are associated with sociodemographic and life-style factors: the multiethnic cohort study. J Nutr. 2005;135:843–849.

22. Trichopoulou A, for members of the EPIC-Elderly Prospective StudyGroup. Modified Mediterranean diet and survival: EPIC-elderly prospec-tive cohort study. BMJ. 2005;330:991–995.

23. Schroder H, Marrugat J, Vila J, et al. Adherence to the traditionalMediterranean diet is inversely associated with body mass index andobesity in a Spanish population. J Nutr. 2004;134:3355–3361.

24. Newby PK, Muller D, Hallfrisch J, et al. Food patterns measured byfactor analysis and anthropometric changes in adults. Am J Clin Nutr.2004;80:504–513.

25. Dixon LB, Balder HF, Virtamen MJ, et al. Dietary patterns associatedwith colon and rectal cancer: results from the dietary patterns and cancer(DIETSCAN) project. Am J Clin Nutr. 2004;80:1003–1011.

26. Lopez-Garci E, Schulze MB, Fung TT, et al. Major dietary patterns arerelated to plasma concentrations of markers of inflammation and endo-thelial dysfunction. Am J Clin Nutr. 2004;80:1029–1035.

27. Kim MK, Sasaki S, Sasazuki S, et al. Prospective study of three majordietary patterns and risk of gastric cancer in Japan. Int J Cancer.2004;110:435–442.

28. Kouris-Blazos A, Gnardellis C, Wahlqvist ML, et al. Are the advantagesof the Mediterranean diet transferable to other population? A cohortstudy in Melbourne, Australia. Br J Nutr. 1999;82:57–61.

29. Lasheras C, Fernandez S, Patterson AM. Mediterranean diet and agewith respect to overall survival in institutionalized, nonsmoking elderlypeople. Am J Clin Nutr. 2000;71:987–992.

30. Trichopoulou A, Critselis E. Mediterranean diet and longevity. Eur JCancer Prev. 2004;13:453–456.

31. Trichopoulou A, Kouris-Blazos A, Wahlqvist ML, et al. Diet and overallsurvival in the elderly. Br Med J. 1995;311:1457–1460.

32. Trichopoulos D, Lagiou P. Dietary patterns and mortality. Br J Nutr.2001;85:133–134.

33. Paolisso G, D’Amore A, Balbi V, et al. Plasma vitamin C affects glucosehomeostasis in healthy subjects and in non-insulin-dependent diabetics.Am J Physiol. 1994;266:E261–E268.

34. Ford ES, Mokdad AH. Fruit and vegetable consumption and diabetesmellitus incidence among U.S. adults. Prev Med. 2001;32:33–39.

35. Montonen J, Knekt P, Jarvinen R, et al. Dietary antioxidant intake andrisk of type 2 diabetes. Diabetes Care. 2004;27:362–366.

36. Hu FB, Van Dam RM, Liu S. Diet and risk of type II diabetes: the roleof types of fat and carbohydrate. Diabetologia. 2001;44:805–817.

37. Slattery ML, Boucher KM, Caan BJ, et al. Eating patterns and risk ofcolon cancer. Am J Epidemiol. 1998;148:4–16.

38. Fung T, Hu FB, Fuchs C, et al. Major dietary patterns and the risk ofcolorectal cancer in women. Arch Intern Med. 2003;163:309–314.

39. Terry P, Hu FB, Hansen J, et al. Prospective study of major dietarypatterns and colorectal cancer risk in women. Am J Epidemiol. 2001;154:1143–1149.

40. Newby PK, Muller D, Hallfrisch J, et al. Dietary patterns and changes inbody mass index and waist circumference in adults. Am J Clin Nutr.2003;77:1417–1425.

41. Newby PK, Tucker KL. Empirically derived eating patterns using factoror cluster analysis: a review. Nutr Rev. 2004;62:177–203.



ORIGINAL ARTICLE

Effect of Soy Isoflavones on EndometriosisInteraction With Estrogen Receptor 2 Gene Polymorphism

Masaki Tsuchiya,*§ Tsutomu Miura,* Tomoyuki Hanaoka,* Motoki Iwasaki,* Hiroshi Sasaki,†Tadao Tanaka,† Hiroyuki Nakao,‡ Takahiko Katoh,‡ Tsuyomu Ikenoue,§ Michinori Kabuto,¶

and Shoichiro Tsugane*

Background: Progression of endometriosis is considered estrogen-dependent. Dietary soy isoflavones may affect the risk of endome-triosis, and polymorphisms in estrogen receptor genes may modifythis association. We examined associations among soy isoflavoneintake, estrogen receptor 2 (ESR2) gene polymorphisms and risk ofendometriosis.Methods: We recruited women age 20–45 years old who hadconsulted a university hospital for infertility in Tokyo, Japan in 1999or 2000. A total of 138 eligible women were diagnosed laparoscopi-cally and classified into 3 subgroups: control (no endometriosis),early endometriosis (stage I–II) and advanced endometriosis (stageIII–IV). We measured urinary levels of genistein and daidzein asmarkers for dietary intake of soy isoflavones, and genotyped ESR2gene RsaI polymorphisms.Results: Higher levels of urinary genistein and daidzein wereassociated with decreased risk of advanced endometriosis (P fortrend � 0.01 and 0.06, respectively) but not early endometriosis. Foradvanced endometriosis, the adjusted odds ratio for the highestquartile group was 0.21 (95% confidence interval � 0.06–0.76) forgenistein and 0.29 (0.08–1.03) for daidzein, when compared withthe lowest group. Inverse associations were also noted betweenurinary isoflavones and the severity of endometriosis (P for trend �0.01 for genistein and 0.07 for daidzein). For advanced endometri-osis, ESR2 gene RsaI polymorphism appeared to modify the effectsof genistein (P for interaction � 0.03).

Conclusions: Dietary isoflavones may reduce the risk of endome-triosis among Japanese women.

(Epidemiology 2007;18: 402–408)

Soy isoflavones are phytoestrogens found in soybeans.Phytoestrogens are plant-derived nonsteroidal compounds

that possess estrogen-like biologic activities. These com-pounds reportedly display weak estrogenic and antiestrogenicproperties.1–3 The 2 primary isoflavones found in soy aregenistein and daidzein. Structural similarities allow isofla-vones to bind to estrogen receptors.4

It has been hypothesized that soy isoflavones may playa role in the etiology of estrogen-related diseases and severalepidemiologic studies have been conducted; however, find-ings have been complicated and inconsistent.5–7 A prospec-tive study in Japan, where isoflavone intake is known to berelatively high, showed a protective effect on postmenopausalbreast cancer.5 On the other hand, a nested case–controlstudy in the United Kingdom, where intake is relatively low,showed that serum and urinary isoflavone levels were asso-ciated with increased breast cancer risk.6 A recent meta-analysis found a small reduction in breast cancer risk asso-ciated with soy intake.7 However, the authors suggested thatthe results should be interpreted cautiously due to potentialexposure misclassification, confounding, lack of a dose-re-sponse pattern and the possibility of adverse effects of soyconstituents.

Endometriosis is a benign, proliferative disease inwhich tissue similar to endometrial tissue is found outside theuterus—usually in the pelvic cavity, but sometimes in distantorgans. Endometriosis is commonly accompanied by pelvicpain and infertility. Both genetic and environmental factorsmay contribute.8 The reported prevalence of largely asymp-tomatic endometriosis found in women undergoing tuballigation is about 4%, ranging from 1% to 7%.9 Progression ofendometriosis is considered estrogen-dependent.10 Soy isofla-vones might thus be expected to affect the risk and severity ofendometriosis. However, few studies have investigated theeffects of soy isoflavones on endometriosis.

Several studies have recently described associationsbetween estrogen receptor (ESR) gene polymorphisms andendometriosis.11–13 Genistein and daidzein reportedly displaymuch greater affinity for ESR2 than for ESR1,14 suggesting

Submitted 3 March 2006; accepted 7 November 2006; posted 6 March 2007.From the *Epidemiology and Prevention Division, Research Center for

Cancer Prevention and Screening, National Cancer Center, Tokyo, Japan;†Department of Obstetrics and Gynecology, Jikei University School ofMedicine, Tokyo, Japan; ‡Department of Public Health, University ofMiyazaki, Miyazaki, Japan; §Department of Obstetrics and Gynecology,University of Miyazaki, Miyazaki, Japan; and ¶National Institute forEnvironmental Studies, Ibaraki, Japan.

Supported by Grants-in-Aid for Research on Risk of Chemical Substancesand for the 3rd term Comprehensive 10-Year Strategy for Cancer Controlfrom the Ministry of Health, Labour and Welfare of Japan. MasakiTsuchiya and Tsutomu Miura are awardees of a research resident fel-lowship from the Foundation for Promotion of Cancer Research, for the2nd and 3rd term Comprehensive 10-Year Strategy for Cancer Control.

Correspondence: Motoki Iwasaki, Epidemiology and Prevention Division,Research Center for Cancer Prevention and Screening, National CancerCenter, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan. E-mail:[email protected]

Copyright © 2007 by Lippincott Williams & WilkinsISSN: 1044-3983/07/1803-0402DOI: 10.1097/01.ede.0000257571.01358.f9


that the estrogenic or antiestrogenic properties of soy isofla-vones may occur preferentially through ESR2. Althoughfunctional variability of ESR2 gene polymorphisms couldfeasibly be associated with response to soy isoflavones, whetherESR2 gene polymorphisms exert altered phenotypic effects onendometriosis through interactions with soy isoflavones is notknown.

The present study investigated whether urinary genisteinand daidzein are associated with risk and severity of endo-metriosis, and whether polymorphisms in the ESR2 gene areassociated with response to soy isoflavones.

METHODS

Study Protocol and EthicsThis study was part of a case–control study conducted

on a Japanese population to investigate associations betweengenetic and environmental factors in endometriosis.15 Werecruited consecutive female patients age 20 to 45-year-oldwho attended the Department of Obstetrics and Gynecologyat Jikei University School of Medicine Hospital for infertilityin 1999 or 2000. Since pregnancy commonly results incomplete resolution of minimal or mild endometriosis,women who had given birth or lactated were ineligible,leaving a total of 159 women who met the criteria. Afterexcluding 15 women who did not give consent, 5 who did notundergo blood screening or laparoscopic examination, and 1whose DNA sample was not available, a total of 138 womenwere available for the study (participation rate � 87%). Noparticipants had undergone therapy before laparoscopic ex-amination.

All study protocols were approved by the InstitutionalReview Boards of Jikei University, National Cancer Centerand National Institute for Environmental Studies. All partic-ipants provided written informed consent before laparoscopicexamination.

Before the laparoscopic examination, participants wereinterviewed by a single trained interviewer using a structuredquestionnaire to collect information on demographic factors,age, height, weight, medical history for themselves and theirfamilies, reproductive and menstrual history, oral contracep-tive use, food- and alcohol-consumption frequency, andsmoking history.

Participants collected first morning urine sample usinga paper cup and plastic tube, and gave a fasting blood samplebefore the laparoscopic examination. Blood samples weredivided into plasma and buffy layers. All biologic sampleswere stored at �80°C until analysis.

Diagnosis of EndometriosisLaparoscopy is necessary for definitive diagnosis of

endometriosis. In the present study, all participants under-went diagnostic laparoscopy, and stage of endometriosis wasdetermined by trained gynecologists in accordance with therevised classifications of the American Fertility Society.16

Endometriosis was absent in 59 women (43%), Stage I in 21women (15%), Stage II in 10 women (7%), Stage III in 23women (17%) and Stage IV in 25 women (18%). Currenttheories of endometriosis suggest that what is defined as

minimal/mild endometriosis may actually represent a normalphysiologic process. Furthermore, a lack of consistency be-tween laparoscopic and histologic diagnosis has been re-ported, particularly for minimal/mild endometriosis.17 Con-sidering the more severe stages as a separate category thusappears logical.18 Based on surgically or pathologically con-firmed disease status, we classified cases into 2 subgroups:early (Stage I–II) or advanced endometriosis (stage III–IV).Women without endometriosis were defined as controls.

Determination of Soy Isoflavone LevelsUrinary levels of soy isoflavones offer a useful biomar-

ker for dietary intake and plasma concentration of isofla-vones.19–21 The present study measured urinary levels ofgenistein and daidzein as markers for dietary intake of soyisoflavones. A total of 30 mL of first-morning urine wascollected before laparoscopic examination. Genistein anddaidzein levels were analyzed using high-performance liquidchromatography with a coulometric array detector in accor-dance with the modified methods of Gamache and Acworth.22

Concentrations of genistein and daidzein were deter-mined by linear regression of peak height for each standard,and were adjusted according to recovery rate of the internalstandard. The regression coefficient of peak height and con-centration calculated for soy isoflavones revealed a linearityrange of 0–8.0 �g/mL, with correlation coefficient values�0.995. Voltametric response for the standard solution dis-played coefficients of variation of 2.7%–8.4% for intradayvariation and 11.1%–12.2% for interday variation. Recoveryrates of soy isoflavones in urine samples ranged betweenapproximately 85% and 100%. Detection limits were 3.22ng/mL for genistein and 4.14 ng/mL for daidzein.

Concentrations of urinary genistein and daidzein wereadjusted by urinary creatinine concentration to correct forvariability in urine dilution (�mol/g Cre). All measurementswere performed by investigators blinded to case–controlstatus.

Genotyping of ESR2 Gene PolymorphismThe ESR2 RsaI polymorphism, comprising a G-to-A

change at nucleotide 1082 in exon 5, was genotyped usingpolymerase chain reaction (PCR) restriction fragment lengthpolymorphism methods.23 Blood samples were obtained be-fore laparoscopic examination. Genomic DNA samples wereextracted from peripheral white blood cells using a standardprotease K method. PCR products were digested using 5 U ofRsaI restriction enzyme at 37°C for 8 hours, then electropho-resed on a 3% agarose gel containing ethidium bromide.

In this study, ESR2 RsaI polymorphism is representedby the r and R alleles, with R indicating the presence ofcorresponding restriction sites, and r indicating the absenceof restriction sites. For quality control, blinded controlsamples were inserted to validate genotyping identificationprocedures. Concordance for blinded samples was 100%.Genotyping was conducted by investigators blinded tocase– control status.

Epidemiology • Volume 18, Number 3, May 2007 Soy Isoflavones, Endometriosis and ESR2 Polymorphism


Statistical AnalysisTo assess differences between cases and controls, basic

characteristics and possible risk factors for endometriosiswere compared using Student t test and the �2 test. Spearmancorrelation coefficients between urinary level of genistein anddaidzein were calculated. We calculated odds ratios (ORs)and 95% confidence intervals (CIs) for urinary levels by anunconditional logistic regression model following classifica-tion into medians or quartiles based on control distribution.Linear trends for ORs were tested in the unconditional logis-tic regression model by treating the categories as ordinalvariables. We evaluated trends for median values accordingto disease stage to assess associations between urinary levelsof genistein and daidzein and disease stage.

To compare observed and expected genotype frequen-cies, we tested for Hardy-Weinberg equilibrium by using anexact test. ESR2 RsaI polymorphism was classified into 2subgroups according to the presence of corresponding restric-tion sites: r/r genotype; and R/r � R/R genotype. ORs and95% CIs were calculated for associations between ESR2 RsaIpolymorphism and endometriosis using the unconditionallogistic regression model.

To investigate whether the ESR2 RsaI genotype modi-fied the effect of urinary levels of genistein or daidzein, wecalculated ORs and 95% CIs of endometriosis according to acombination of subgroups for the ESR2 RsaI genotype andurinary isoflavones, using the unconditional logistic regres-sion model. A low level of urinary isoflavones in combinationwith R/r � R/R genotype was considered as the referencegroup. Interactions between ESR2 RsaI polymorphism andurinary isoflavones in the risk of endometriosis were testedwith the Wald test using product terms between urinarygenistein or daidzein and genotypes.

The present study was designed to have 80% power todetect a decrease in risk of two-thirds at the 5% level ofsignificance. All statistical tests were based on 2-tailed prob-abilities. We adjusted ORs and 95% CIs for possible con-founding factors of endometriosis, namely age (continuous),menstrual cycle (continuous), and duration of menstrualbleeding (less than 7 days or 7 days or more).9,10 We usedSPSS for Windows software version 11.0 (SPSS JAPAN,Tokyo, Japan) for statistical analyses.

RESULTS

Baseline Characteristics and Possible RiskFactors for Endometriosis

Table 1 shows baseline characteristics and possible riskfactors for endometriosis in controls and cases. No importantdifferences in mean age or body mass index were identifiedbetween groups. Distribution of menstrual bleeding, hyper-menorrhea, and smoking also did not differ substantially. Theadvanced endometriosis group had a shorter mean menstrualcycle length than controls (controls, 30.7 � 6.1 days; ad-vanced endometriosis, 28.3 � 3.0 days) and was more likelyto have menstrual cramps and dyspareunia.

Effect of Urinary Isoflavones on EndometriosisTable 2 shows risk of endometriosis according to me-

dian or quartile levels of urinary isoflavones. In controls,median isoflavone level was 3.24 �mol/g Cre for genisteinand 4.01 �mol/g Cre for daidzein. The Spearman correlationcoefficient between genistein and daidzein was 0.84. Urinarygenistein and daidzein levels were inversely associatedwith advanced endometriosis (P for trend � 0.01 and 0.06,respectively) but not with early endometriosis. For ad-vanced endometriosis, the adjusted odds ratio for thehighest quartile group was 0.21 (95% CI � 0.06 – 0.76) forgenistein and 0.29 (0.08 –1.03) for daidzein when com-pared with the lowest group.

Table 3 shows the trends of median values for urinaryisoflavones according to disease stage. An inverse relation-ship with stage of endometriosis was observed for bothgenistein levels (P for trend � 0.01) and daidzein levels (Pfor trend � 0.07).

Associations Between ESR2 RsaI Polymorphismand Endometriosis

Table 4 shows the genotypic distribution of ESR2 RsaIpolymorphism and associations with risk of endometriosis.The ESR2 RsaI r/r genotype was predominant. Allele fre-

TABLE 1. Baseline Characteristics and Possible Risk Factorsfor Endometriosis

Baseline CharacteristicsControls(n � 59)

Endometriosis

Early(Stage I–II)

(n � 31)

Advanced(Stage III–IV)

(n � 48)

Age (yrs); mean � SD 33.1 � 4.1 32.3 � 3.2 32.6 � 3.7

Body mass index (kg/m2);mean � SD

21.0 � 3.4 20.6 � 2.1 20.2 � 2.1

Menstrual cycle (d);mean � SD

30.7 � 6.1 29.6 � 3.6 28.3 � 3.0

Menstrual bleeding; no. (%)

�7 days 42 (71) 21 (68) 35 (73)

�7 days 15 (25) 6 (19) 10 (21)

Missing 2 (3) 4 (13) 3 (6)

Hypermenorrhea; no. (%)

No 39 (66) 20 (65) 29 (60)

Yes 18 (31) 7 (23) 15 (31)

Missing 2 (3) 4 (13) 4 (8)

Menstrual cramps; no. (%)

No 10 (17) 2 (6) 1 (2)

Yes 47 (80) 25 (81) 44 (92)

Missing 2 (3) 4 (13) 3 (6)

Dyspareunia; no. (%)

No 31 (53) 12 (39) 10 (21)

Yes 25 (42) 14 (45) 34 (71)

Missing 3 (5) 5 (16) 4 (8)

Smoking status; no. (%)

Never 38 (64) 19 (61) 29 (60)

Current or ever-smoker 19 (32) 8 (26) 15 (31)

Missing 2 (3) 4 (13) 4 (8)

Tsuchiya et al Epidemiology • Volume 18, Number 3, May 2007


quencies of ESR2 RsaI polymorphism were 0.77 for the rallele and 0.23 for the R allele. In addition, the distribution ofESR2 RsaI polymorphism was in Hardy–Weinberg equilib-rium (P � 0.26). The ESR2 RsaI r/r genotype was associated

with reduced risk of early endometriosis compared with theR/r � R/R genotype (OR � 0.30; CI � 0.11–0.85). Theassociation was weaker for advanced endometriosis (0.67;0.29–1.55).

Interactions Between ESR2 RsaI Polymorphismand Urinary Isoflavones in the Risk ofEndometriosis

Table 5 shows ORs and 95% CIs of endometriosis forcombinations of ESR2 RsaI genotype and urinary isoflavonelevels. Compared with subjects with the ESR2 RsaI R/r �R/R genotype and a low genistein level, ORs of advancedendometriosis were lower among the 3 other groups. Theadjusted OR was 0.10 (95% CI � 0.02–0.48) for subjectswith ESR2 RsaI R/r � R/R genotype with high genisteinlevel; 0.32 (0.10–1.04) for subjects with ESR2 RsaI r/rgenotype with low genistein level; 0.27 (0.08–0.92) forsubjects with ESR2 RsaI r/r genotype with high genistein

TABLE 4. Association Between ESR2 RsaI Polymorphismand Risk of Endometriosis

Genotype*No.

Controls

Early(Stage I–II)


No. OR (95% CI)† No. OR (95% CI)†

R/r � R/R‡ 26 21 1.00 26 1.00

r/r 33 10 0.30 (0.11–0.85) 22 0.67 (0.29–1.55)

*Exact test for Hardy–Weinberg equilibrium: P � 0.26.†Adjusted for age (continuous); menstrual cycle (continuous); and duration of

menstrual bleeding (less than 7 days or 7 days or more).‡Reference category.

TABLE 2. Association between Urinary Isoflavone Level and Risk of Endometriosis

Urinary IsoflavoneNo.

Controls

Endometriosis

Early(Stage I–II)


No. OR (95% CI)* No. ORs (95% CI)*

Genistein (�mol/g creatinine)

�1.60† 14 7 1.00 22 1.00

1.60–3.23 15 12 1.86 (0.49–7.09) 13 0.65 (0.21–2.01)

3.24–6.49 15 7 0.82 (0.18–3.80) 7 0.40 (0.12–1.34)

�6.50 15 5 0.63 (0.14–2.89) 6 0.21 (0.06–0.76)

P for trend 0.34 0.01

Low level (�3.24)† 29 19 1.00 35 1.00

High level (�3.24) 30 12 0.50 (0.18–1.39) 13 0.35 (0.14–0.87)

Daidzein (�mol/g creatinine)

�1.94† 14 5 1.00 16 1.00

1.94–4.00 15 7 1.87 (0.41–8.57) 15 0.84 (0.26–2.73)

4.01–7.94 15 12 2.16 (0.49–9.41) 10 0.65 (0.20–2.09)

�7.95 15 7 1.33 (0.30–5.97) 7 0.29 (0.08–1.03)

P for trend 0.73 0.06

Low level (�4.01)† 29 12 1.00 31 1.00

High level (�4.01) 30 19 1.21 (0.45–3.27) 17 0.49 (0.21–1.15)

*Adjusted for age (continuous); menstrual cycle (continuous); and duration of menstrual bleeding (less than 7 days or 7 days ormore).

†Reference category.

TABLE 3. Median Values† of Urinary Isoflavone Level and Stage of Endometriosis

Urinary IsoflavoneControls(n � 59)

Early(Stage I–II)

(n � 31)


(n � 48) P for Trend*

Genistein (�mol/g creatinine) 3.2 (1.6–6.5) 2.6 (1.7–5.2) 1.7 (0.6–4.1) 0.01

Daidzein (�mol/g creatinine) 4.0 (1.9–8.0) 4.9 (2.6–7.6) 2.6 (1.0–5.0) 0.07

*Jonckheere-Terpstra test.†Median (25th–75th percentile).



level. A significant interaction was noted between ESR2 RsaIpolymorphism and genistein levels in risk of advanced endo-metriosis (P for interaction � 0.03). Interactions betweenESR2 RsaI polymorphism and genistein level were not ob-served in early endometriosis. Although a similar pattern wasobserved for ORs of both early and advanced endometriosisfor the combinations of ESR2 RsaI genotype and urinarydaidzein level, these may have been due to chance.

DISCUSSIONThe present study showed an inverse association be-

tween urinary isoflavones and the risk of advanced endome-triosis. This association was stronger for genistein than daid-zein. In addition, there was statistical evidence for interactionbetween urinary genistein and ESR2 gene polymorphisms.

The reduced risk of endometriosis following ingestionof soy isoflavones may be attributable to antiestrogenic prop-erties of these compounds. A previous study showed thatprolonged exposure to genistein results in decreased levels ofestrogen receptor mRNA in addition to decreased response toestradiol stimulation.24 Plasma levels of isoflavones can be10,000- to 100,000-fold higher than those of estradiol.25

When the relative binding affinity of 17�-estradiol was set at100 in solid-phase competition experiments, relative bindingaffinity for ESR2 was 87 for genistein and 0.5 for daidzein.14

Although the elimination half-life from blood and urine isreportedly 7–8 hours for both genistein and daidzein,26 long-term soy diets may modify the physiologic effects of estro-gens. Given these facts, a lower prevalence of endometriosismight be expected in Japanese populations compared withWestern countries, as with breast cancer. Nevertheless, theprevalence of endometriosis in the Japanese general popula-tion remains unclear due to the need for surgical diagnosis.

Our finding showed that the strength of association wasstronger for genistein than for daidzein. One possible expla-nation is the difference in their binding affinities to ESR2. Asecond possibility is based on the difference in metabolismbetween genistein and daidzein. Daidzein can be metabolizedto equol and O-desmethylangolites by intestinal bacteria, andthese metabolites are absorbed, enter the circulation, and areexcreted in urine. Although equol has been suggested topossess stronger estrogenic properties than genistein, someindividuals are capable of equol production whereas othersare not, probably because of differences in gut microflora.This difference might play a role in the weaker associationsfor daidzein than genistein.27

ESR2 plays important roles in endometrial function, inaddition to the well-known role of ESR1 in endometrialproliferation and differentiation.28 The ESR2 RsaI polymor-phism does not cause amino acid changes, but may well beassociated with altered ligand-binding affinity or transcrip-tional activity. Genes containing single nucleotide polymor-phisms (SNPs) can cause different structural folds inmRNA,29 and these mRNA variants may possess differentbiologic functions during interactions with other cellularcomponents. Altered estrogen or soy isoflavone signal transduc-tion thanks to ESR2 gene polymorphisms may be directlyresponsible for interindividual susceptibility to and severity ofendometriosis.

The present study found evidence of an interactionbetween urinary genistein and ESR2 gene polymorphisms.Isoflavones may play a more effective role among the ESR2RsaI R/r � R/R genotype than the r/r genotype, although thelatter itself is likely to be protective for endometriosis. Thisresult should be interpreted cautiously, however, because ofthe relatively small number of subjects—a major limitation of

TABLE 5. Interactions Between ESR2 RsaI Polymorphism and Urinary Isoflavone in the Riskof Endometriosis

GenotypeUrinary Isoflavone(�mol/g creatinine)

No.Controls

Early(Stage I–II)


No. OR (95%CI)* No. OR (95%CI)*

Genistein

R/r � R/R Low† 11 11 1.00 22 1.00

R/r � R/R High 15 10 0.88 (0.23–3.38) 4 0.10 (0.02–0.48)

r/r Low 18 8 0.54 (0.15–1.93) 3 0.32 (0.10–1.04)

r/r High 15 2 NC 9 0.27 (0.08–0.92)

P for interaction NC 0.03

Daidzein

R/r � R/R Low† 15 10 1.00 19 1.00

R/r � R/R High 11 11 1.28 (0.33–4.96) 7 0.35 (0.09–1.34)

r/r Low 14 2 0.18 (0.03–1.09) 12 0.56 (0.18–1.78)

r/r High 19 8 0.44 (0.12–1.61) 10 0.39 (0.13–1.20)

P for interaction 0.58 0.45

*Adjusted for age (continuous); menstrual cycle (continuous); and duration of menstrual bleeding (less than 7 days or 7 days or more).†Reference category.NC, estimates were not calculated due to missing data.



this study. When the number of subjects studied is not largeand the expected difference is small, actual differences arequite likely to pass undetected. Inconsistent results betweenearly and advanced endometriosis might be attributable to thelack of sufficient numbers and possible misclassification inthe early endometriosis group. Alternatively, the observedinteractions may have occurred merely by chance.

A second issue is our definition of cases and controls.In accordance with the revised classifications of the AmericanFertility Society, we defined women without endometriosis ascontrols and women with early (Stage I–II) and advancedendometriosis (Stage III–IV) as cases,16 although there is noclear criterion for dichotomizing cases. The present study didnot show a persuasive inverse association between urinaryisoflavones and the risk of early endometriosis, although astrong protective effect was found for advanced endometrio-sis. Further analysis, however, did show an inverse associationbetween urinary isoflavones and the severity of endometriosis.This finding may be reasonable given that endometriosis occursin a continuum of severity.

A third issue is measurement of urinary levels ofisoflavones. The present study measured urinary excretion ofgenistein and daidzein as markers of soy isoflavone consump-tion. Urinary excretion of soy isoflavones is reportedly re-lated to annual dietary intake of soy isoflavones.19 Since wecollected spot urine samples, intraindividual variation in urinaryisoflavones cannot be ignored. Such misclassification, however,is probably nondifferential and would lead to a null result.

Participants in the present study were infertile. Theymight therefore have changed their diet due to their symp-toms or in attempt to become pregnant. If a change in dietwas more likely among patients with advanced endometriosisthan the controls, our findings might have been the result ofthe change in diet. In addition, given reports that factorsassociated with endometriosis differ between parous women(who experienced neither primary nor secondary infertility)and nulliparous infertile women,30,31 the influence of urinaryisoflavone levels on endometriosis risk between the 2 groupsmay have differed. Therefore, our present findings may belimited to infertile women.

In conclusion, in a case–control study in infertile Jap-anese women, we found that higher urinary level of isofla-vones was associated with a reduced risk of advanced endo-metriosis. Although the interaction between urinary genisteinand ESR2 gene polymorphisms supported the mechanism fora role of isoflavones in the etiology of endometriosis, furtherstudies with a large number of subjects are needed to confirmthese findings.

ACKNOWLEDGMENTSThe authors are grateful for the collaboration of

Amanda Sue Niskar (Israel Center for Disease Control,Gertner Institute) in designing the study protocol. In addition,the authors wish to thank Hiroaki Itoh (Epidemiology andPrevention Division, Research Center for Cancer Preventionand Screening, National Cancer Center, Tokyo, Japan) forhis helpful comments.

REFERENCES1. Tham DM, Gardner CD, Haskell WL. Clinical review 97: potential

health benefits of dietary phytoestrogens: a review of the clinical,epidemiological, and mechanistic evidence. J Clin Endocrinol Metab.1998;83:2223–2235.

2. Wagner JD, Anthony MS, Cline JM. Soy phytoestrogens: research onbenefits and risks. Clin Obstet Gynecol. 2001;44:843–852.

3. Gruber CJ, Tschugguel W, Schneeberger C, et al. Production and actionsof estrogens. N Engl J Med. 2002;346:340–352.

4. Limer JL, Speirs V. Phyto-oestrogens and breast cancer chemopreven-tion. Breast Cancer Res. 2004;6:119–127.

5. Yamamoto S, Sobue T, Kobayashi M, et al. Soy, isoflavones, and breastcancer risk in Japan. J Natl Cancer Inst. 2003;95:906–913.

6. Grace PB, Taylor JI, Low YL, et al. Phytoestrogen concentrations inserum and spot urine as biomarkers for dietary phytoestrogen intake andtheir relation to breast cancer risk in European prospective investigationof cancer and nutrition-norfolk. Cancer Epidemiol Biomarkers Prev.2004;13:698–708.

7. Trock BJ, Hilakivi-Clarke L, Clarke R. Meta-analysis of soy intake andbreast cancer risk. J Natl Cancer Inst. 2006;98:459–471.

8. Kennedy S. The genetics of endometriosis. J Reprod Med. 1998;43:263–268.

9. Cramer DW, Missmer SA. The epidemiology of endometriosis. Ann NYAcad Sci. 2002;955:11–22.

10. Vigano P, Parazzini F, Somigliana E, et al. Endometriosis: epidemiologyand aetiological factors. Best Pract Res Clin Obstet Gynaecol. 2004;18:177–200.

11. Georgiou I, Syrrou M, Bouba I, et al. Association of estrogen receptor genepolymorphisms with endometriosis. Fertil Steril. 1999;72:164–166.

12. Kitawaki J, Obayashi H, Ishihara H, et al. Oestrogen receptor-alpha genepolymorphism is associated with endometriosis, adenomyosis andleiomyomata. Hum Reprod. 2001;16:51–55.

13. Wang Z, Yoshida S, Negoro K, et al. Polymorphisms in the estrogenreceptor beta gene but not estrogen receptor alpha gene affect the risk ofdeveloping endometriosis in a Japanese population. Fertil Steril. 2004;81:1650–1656.

14. Kuiper GG, Lemmen JG, Carlsson B, et al. Interaction of estrogenicchemicals and phytoestrogens with estrogen receptor beta. Endocrinol-ogy. 1998;139:4252–4263.

15. Tsukino H, Hanaoka T, Sasaki H, et al. Associations between serumlevels of selected organochlorine compounds and endometriosis ininfertile Japanese women. Environ Res. 2005;99:118–125.

16. American Fertility Society. Revised American Fertility Society classi-fication of endometriosis. Fertil Steril. 1985;43:351–352.

17. Marchino GL, Gennarelli G, Enria R, et al. Diagnosis of pelvic endo-metriosis with use of macroscopic versus histologic findings. FertilSteril. 2005;84:12–15.

18. Zondervan KT, Cardon LR, Kennedy SH. What makes a good case-control study? Design issues for complex traits such as endometriosis.Hum Reprod. 2002;17:1415–1423.

19. Maskarinec G, Singh S, Meng L, et al. Dietary soy intake and urinaryisoflavone excretion among women from a multiethnic population.Cancer Epidemiol Biomarkers Prev. 1998;7:613–619.

20. Arai Y, Uehara M, Sato Y, et al. Comparison of isoflavones amongdietary intake, plasma concentration and urinary excretion for accurateestimation of phytoestrogen intake. J Epidemiol. 2000;10:127–135.

21. Yamamoto S, Sobue T, Sasaki S, et al. Validity and reproducibility of aself-administered food-frequency questionnaire to assess isoflavone in-take in a japanese population in comparison with dietary records andblood and urine isoflavones. J Nutr. 2001;131:2741–2747.

22. Gamache PH, Acworth IN. Analysis of phytoestrogens and polyphenolsin plasma, tissue, and urine using HPLC with coulometric array detec-tion. Proc Soc Exp Biol Med. 1998;217:274–280.

23. Sundarrajan C, Liao WX, Roy AC, et al. Association between estrogenreceptor-beta gene polymorphisms and ovulatory dysfunctions in patientswith menstrual disorders. J Clin Endocrinol Metab. 2001;86:135–139.

24. Wang TT, Sathyamoorthy N, Phang JM. Molecular effects of genistein onestrogen receptor mediated pathways. Carcinogenesis. 1996;17:271–275.

25. Manach C, Williamson G, Morand C, et al. Bioavailability and bioeffi-cacy of polyphenols in humans. I. Review of 97 bioavailability studies.Am J Clin Nutr. 2005;81(1 Suppl):230S–242S.



26. King RA, Bursill DB. Plasma and urinary kinetics of the isoflavonesdaidzein and genistein after a single soy meal in humans. Am J ClinNutr. 1998;67:867–872.

27. Atkinson C, Frankenfeld CL, Lampe JW. Gut bacterial metabolism ofthe soy isoflavone daidzein: exploring the relevance to human health.Exp Biol Med (Maywood). 2005;230:155–170.

28. Lecce G, Meduri G, Ancelin M, et al. Presence of estrogen receptor beta inthe human endometrium through the cycle: expression in glandular, stromal,and vascular cells. J Clin Endocrinol Metab. 2001;86:1379–1386.

29. Shen LX, Basilion JP, Stanton VP Jr. Single-nucleotide polymorphismscan cause different structural folds of mRNA. Proc Natl Acad Sci USA.1999;96:7871–876.

30. Missmer SA, Hankinson SE, Spiegelman D, et al. Reproductive historyand endometriosis among premenopausal women. Obstet Gynecol.2004;104:965–974.

31. Missmer SA, Hankinson SE, Spiegelman D, et al. Incidence of laparo-scopically confirmed endometriosis by demographic, anthropometric,and lifestyle factors. Am J Epidemiol. 2004;160:784–796.



VIGNETTE

The Halifax ExplosionWarren Winkelstein, Jr.

An explosion occurred in the harbor of Halifax, NovaScotia on 6 December 1917, killing more than 2000

persons and injuring another 10,000. The explosion leveled alarge part of the city and left at least 20,000 people homeless.It was the largest man-made explosion in history to that date,not surpassed until an atomic bomb was dropped on Hiro-shima 3 decades later. The cause of the explosion was thecollision of a Belgian ship, the Imo, carrying relief supplies toBelgium, and a French ship, the Mont Blanc, carrying 2600tons of high explosives bound for France.

The harbor at Nova Scotia comprises an inner basinable to accommodate more than 100 ships, and invisible fromthe open ocean (and from enemy submarines). The narrowentry passage is flanked by industrial and cargo handlingfacilities. In the First World War (as well as the Second),Halifax Harbor was a major venue for assembling convoysfrom America to Europe.

On the morning of the collision, the Imo was steamingout of the inner basin as the Mont Blanc was bound inward.The Imo was off-course, forcing the Mont Blanc towardshallow waters at the edge of the narrows. Caught betweenimminent collision with the Imo and running aground, theMont Blanc tried to escape by crossing the bow of the Imo.The maneuver failed and the collision set the Mont Blanc onfire. The ship drifted across the narrows toward the center ofthe city. Twenty minutes later, the Mont Blanc exploded.

The blast was powerful enough to transport a 1000-pound section of the Mont Blanc’s anchor 2 miles away.Within and around the epicenter, the death rate was estimatedto be 29 per 1000. Sixty percent of the deaths were male and

40% were female. Forty percent of deaths were under 20years of age, 50% between 20 and 60, and 10% over 60. Dataregarding injuries are limited; an estimated 20% of thosepermanently disabled were blinded by broken glass—theyapparently had been behind windows watching the MontBlanc in flames, when it exploded.

Halifax, Nova Scotia, 6 December 1917.


LETTERS TO THE EDITOR

Lower Limb CellulitisAfter a Typhoon and

Flood

To the Editor:

Outbreaks of gastrointestinal (GI)and respiratory diseases are gener-

ally among the major concerns of publichealth in the floods that follow a ty-phoon or hurricane.1–2 Although contactwith polluted water may also lead toother types of infection,3 few investiga-tions have been conducted to studythose outcomes. We evaluated the im-pact of flooding on the incidence ofcellulitis in the lower limbs.

Around 2:50 PM on July 18, 2005,Typhoon Haitang landed on Taiwan,and soon engulfed the island. TyphoonHaitang was a supertyphoon, which isdefined as having the maximum sus-tained winds over 200 km/h. Althoughthe typhoon left Taiwan just 7 hourslater, huge rainfalls led to flooding inmany areas, including Tainan. We iden-tified patients treated for cellulitis oflower limbs at the largest hospital inthe Tainan area (with 1335 beds, serv-ing about 4000 out-patient visits on atypical weekday). We estimated therelative risk (RR) of disease associatedwith the event by dividing the numberof cases in the 2 weeks after the ty-phoon by the number of cases in the 2weeks before.4

There were 43 patients treated forcellulitis of the lower limbs in the 2weeks after the typhoon, compared with22 in the 2-week period before (RR �2.0; 95% confidence interval �CI� �1.4–2.6). Among patients treated afterthe typhoon, 12 (28%) reported that theyhad immersed the affected limbs inflood water, while none treated beforethe typhoon had immersed the affectedlimbs in water other than tap water (Ta-ble 1). The 2 groups of patients had

similar mean ages (56 and 57-year-old),with relatively more men affected afterthe typhoon (OR � 2.2; 95% CI �0.7–6.3). The 2 groups of patients weresimilar in the prevalence of diseases thatcan compromise the immune system,such as diabetes mellitus, liver cirrhosis,and cancer. While all patients were ad-mitted to the hospital for treatment,those who arrived after the typhoonwere less likely to be admitted to theintensive care unit. In addition, therewere 5 patients with cellulitis in theirupper limbs, of whom 4 reported contactof the affected limbs with flood waterafter the typhoon.

Health practitioners are often mostconcerned about GI and respiratory in-fections following floods. Cellulitis isgenerally more life-threatening andmore often requires hospitalization. Ourobservations suggest that some attentionto the prevention of cellulitis after floodsmay be useful.

Hung-Jung LinChien-Chin Hsu

Department of Emergency MedicineChi-Mei Medical Center

Tainan, Taiwan

How-Ran GuoDepartment of Environmental and

Occupational HealthCollege of Medicine

National Cheng Kung UniversityTainan, Taiwan

[email protected]

REFERENCES1. Lee LE, Fonseca V, Brett KM, et al. Active

morbidity surveillance after Hurricane Andrew—Florida, 1992. JAMA. 1993;270:591–594.

2. Setzer C, Dpmino ME. Medicaid outpatientutilization for waterborne pathogenic illnessfollowing Hurricane Floyd. Public Health Rep.2004;119:472–478.

3. Centers for Disease Control and Prevention.Vibrio illnesses after Hurricane Katrina—mul-tiple states, August–September 2005. MMWR.2005;54:928–931.

4. Hendrickson LA, Vogt RL, Goebert D, et al.Morbidity on Kauai before and after HurricaneIniki. Prev Med. 1997;26:711–716.

SSRIs and Birth Defects

To the Editor:

We are writing to express our con-cerns about some of the conclu-

sions made by Wogelius1 and colleaguesin their recent paper. Currently, there isa considerable amount of conflicting in-formation about the safety of antidepres-sants in pregnancy, causing women andtheir health-care providers difficulty inmaking an evidence-based decision as towhether or not to treat depression withpharmacotherapy. Since studies finding

This study was jointly supported in part by theNational Science Council and EnvironmentalProtection Agency of Taiwan, ROC, throughGrants NSC93-EPA-Z-006-003 and NSC95-95-EPA-Z-006-001.

Copyright © 2007 by Lippincott Williams &WilkinsISSN: 1044-3983/07/1803-0410DOI: 10.1097/01.ede.0000259989.77630.77

TABLE 1. Characteristics of Patients With Lower Limb Cellulitis Before and Afterthe Landing of Typhoon Haitang on Taiwan in July 2005

Characteristics

Before Typhoon(n � 22)No. (%)

After Typhoon(n � 43)No. (%) OR (95% CI)

Men 12 (55) 31 (72) 2.2 (0.7–6.3)

Diabetes mellitus 4 (18) 10 (23) 1.4 (0.4–5.0)

Liver cirrhosis 2 (9) 3 (7) 0.8 (0.1–4.9)

Cancer 1 (5) 0 (0) 0.2† (0.0–4.2)

Treatment with surgery 5 (23) 10 (23) 1.0 (0.3–3.5)

Admission to the intensive care unit 3 (14) 3 (7) 0.5 (0.1–2.6)

Affected limb immersed in water* 0 (0) 12 (28) — —

*Except for water from the tap water system.†Logit estimator obtained by using a correction of 0.5 in every cell of the table.

Photo credit: Pin-Chao Fang, courtesy ofThe Liberty Times, Taiwan.


increased risk tend to receive more me-dia attention,2 it is particularly importantthat they be rigorously conducted andthe findings and conclusions carefullyanalyzed and presented.

We feel that the authors over-stepped the limitations of the studymethodology with some of their conclu-sions for the following reasons: 1) Therewas no pattern of specific defects, andthis lack of specificity is generally con-sidered an indication that teratogenicityis an unlikely explanation. 2) There wasno separation of major versus minorcongenital anomalies, and minor anom-alies, by definition, cause no functionalimpairment.3 This distinction is of theutmost clinical importance, and omittingit from the analyses markedly limits theclinical usefulness of the study’s data. 3)As this was a prescription-events moni-toring study, it was not known whetherthe medications were actually taken bythe women. 4) Psychiatrically-ill patientsfrequently use other psychotropic medi-cations, alcohol and illicit drugs, andthese potential confounders were not ad-dressed in this study. 5) Finally, therewas no control for maternal illness. Thisis of great importance because exposureof a fetus to antenatal maternal depres-sion, stress or anxiety may have directadverse effects on the offspring.4 Majordepression when untreated may result indangerous self-neglect, with disorganizedthoughts and behavior. This is particularlyworrying during pregnancy, for it mayresult in smaller babies, preterm labor,and other obstetrical difficulties.5

Vivien BurtThe Women’s Life Center of The Semel

Institute for Neuroscience and HumanBehaviour

Geffen School of Medicine at UCLALos Angeles, CA

Laura MillerDepartment of Psychiatry

University of IllinoisChicago, IL

Adrienne EinarsonThe Motherisk Program

The Hospital for Sick ChildrenToronto, Canada

[email protected]

REFERENCES1. Wogelius P, Nørgaard M, Gislum M, et al.

Maternal use of selective serotonin reuptakeinhibitors and risk of congenital malformations.

Epidemiology. 2006;17:701–704.2. Einarson A, Schachtschneider A, Halil R, et al.

SSRI’S and other antidepressant use duringpregnancy and potential neonatal adverse ef-fects: Impact of a public health advisory andsubsequent reports in the news media. BMCPregnancy Childbirth. 2005;5:11.

3. Holmes LB. Need for inclusion and exclusioncriteria for the structural abnormalities re-corded in children born from exposed pregnan-cies. Teratology. 1999;59:1–2.

4. Rubinow DR. Antidepressant treatment duringpregnancy: between Scylla and Charybdis. AmJ Psychiatry. 2006;163:954–956.

5. Jablensky AV, Morgan V, Zubrick SR, et al.Pregnancy, delivery, and neonatal complica-tions in a population cohort of women withschizophrenia and major affective disorders.Am J Psychiatry. 2005;162:79–91.

SSRIs and Birth Defects

To the Editor:

Treatment with selective serotonin re-uptake inhibitors (SSRIs) during

pregnancy requires additional data fromwell-designed studies. In the article,“Maternal use of selective serotonin re-uptake inhibitors and risk of congenitalmalformations” by Wogelius et al,1 sev-eral positive methodologic choices de-serve comment. These include the com-prehensiveness of the linked prescriptiondata and the Danish Medical Birth andhospital discharge registries, control forspecific medications associated with con-genital malformations, similar distributionof congenital malformation in this studywith published data,2 and the large popu-lation-based sample.

However, several aspects of themethodology diminish the validity ofthe authors’ conclusion that SSRI expo-sure is associated with congenital mal-formations, much less causally related asclaimed by their statement: the “observedassociation . . . was stronger �among pa-tients with SSRI exposure during thesecond or third month after conceptioncompared with exposure within the firsttrimester or 30 days before�, which isconsistent with a causal effect.” Becausemedication compliance and prescriptionlength were not addressed, measurementof exposure was inexact—especially sinceSSRI treatment preconception and duringthe first month of gestation may affect theembryonic cell lines involved in organo-genesis. Lacking an assessment of ma-ternal depression severity biases their

analyses since depression can directlyimpact fetal outcomes,3 and severity ofdepression may have even greater im-pact than drug exposure. Other research-ers have studied similar relationshipswhile more adequately controlling forimportant non-SSRI-related maternal fac-tors.4,5 Controlled factors in these otherstudies included maternal demograph-ics, prepregnancy body mass index orpregnancy weight gain, gravidity, medi-cal histories, socioeconomic status, phys-iological and psychiatric comorbidities,smoking, alcohol, other substance use,concurrent medication and vitamin use.While Wogelius and colleagues con-sider some factors, other covariates ofdepression are excluded, which necessi-tates a more cautious interpretation oftheir results. Of note, the authors foundthat the rate of smoking was nearlytwice as high among the SSRI group ascompared with the non-SSRI group.6

Comorbid alcohol use, although directlyassociated with depression7 and congen-ital malformations in offspring,8 is notassessed. Additionally, the overlappingepochs used to define “early pregnancy”complicate the definition of exposuresince the group with second or thirdmonth SSRI use appears to be a sub-group of patients using SSRI from pre-conception through the first trimester.

SSRI use during pregnancy re-mains a controversial public health is-sue. Publications on this topic must besubjected to careful scrutiny. Despite itsstrengths, the methodologic shortcom-ings of the Wogelius et al paper do notmove the existing scientific literatureforward.

Monique B. KellyKatherine L. Wisner

Marie D. CorneliusDepartment of Psychiatry

School of MedicineUniversity of Pittsburgh

Pittsburgh, [email protected]

REFERENCES1. Wogelius P, Norgaard M, Gislum M, et al.

Maternal use of selective serotonin reuptakeinhibitors and risk of congenital malformations.Epidemiology. 2006;17:701–704.

2. Zhu J, Basso O, Obel C, et al. Infertility,infertility treatment, and congenital malforma-tions: Danish national birth cohort. BMJ. 2006;333:679–683.

Epidemiology • Volume 18, Number 3, May 2007 Letters to the Editor


3. Bonari, L Pinto N, Ahn E, et al. Perinatal risksof untreated depression during pregnancy. CanJ Psychiatry. 2004;49:726–735.

4. Chambers C, Hernandez-Diaz S, Van MarterLJ, et al. Selective serotonin-reuptake inhibi-tors and risk of persistent pulmonary hyperten-sion of the newborn. N Eng J Med. 2006;354:579–587.

5. Oberlander T, Warburton W, Misri S, et al.Neonatal outcomes after prenatal exposure toselective serotonin reuptake inhibitor antide-pressants and maternal depression using popu-lation-based linked health data. Arch Gen Psy-chiatry. 2006;63:898–906.

6. van Rooij IA, Groenen PM, van DrongelenM, et al. Orofacial clefts and spina bifida:N-acetyltransferase phenotype, maternal smok-ing, and medication use. Teratology. 2002;66:260–266.

7. Kessler RC. Lifetime co-occurrence of DSM-IIIR alcohol abuse and dependence with otherpsychiatric disorders in the national comorbid-ity survey. Arch Gen Psychiatry. 1997;54:313–321.

8. Sokol RJ, Delany-Black V, Nordstrom B. Fetalalcohol spectrum disorder. JAMA. 2003;290:2996–2999.

The authors respond:

We thank Burt et al1 and Kelly et al2

for their interest in our study.Burt and colleagues1 argue that

“positive” studies on the safety of anti-depressants need special care in con-duct and presentation due to potentialhigh media attention. In line with thatopinion, Kelly et al2 argue that SSRIuse in pregnancy is a “controversialpublic health issue” and accordinglythese studies should be presented withscrutiny. We disagree in that all studies,and not positive studies in particular,should be conducted with the highestpossible quality. The awareness of pos-sible media interest or controversy asbenchmarks for how to conduct andpresent studies may, in the long run,threaten the credibility of research.

We are aware that we did not finda pattern of specific defects as men-tioned by Burt et al1 (the only exceptionwas the rate of malformations of thedigestive organs, which seem to be in-creased among the offspring of SSRIusers.) However, the finding of in-creased risk of malformations in chil-dren born to mothers with drug prescrip-tions during 2nd and 3rd pregnancymonth compared with women with pre-scriptions during 1st trimester and onemonth before the conception date pro-vides support for a causal effect, be-cause the 2nd and 3rd pregnancy months

constitute the critical period for mostbirth defects. This may represent ageneralized serotonin-mediated effectrather than a specific effect from dif-ferent SSRIs that do not share chemi-cal structure.3

The lack of separation betweenmajor and minor malformations wasquestioned by Burt et al.1 We want tounderscore the fact that we excludedcongenital dislocations of the hip andundescended testes. Furthermore, weidentified the 3 most prevalent congeni-tal malformations in the offspring ofwomen with a SSRI prescription in earlypregnancy and did not find that the in-creased risk could be caused by minormalformations.

Another point of criticism fromboth groups was the uncertainty of com-pliance. Our analyses showed that manyof the exposed women filled more thanone prescription. Furthermore, drugs arepaid for in part by the women and there-fore there is likely to be a considerabledegree of compliance in taking the pillsthat were purchased. Finally, noncom-pliance would likely bias the results byattenuating an effect.4

Burt et al1 and Kelly et al2 men-tioned failure to control for maternalillness and other possible confoundersas a limitation in our study. This is awell-established problem in nonrandom-ized observational studies of drug ef-fects5—for example, the finding of anincreased mortality among patients withcystic fibrosis treated with tobramycinfor inhalation.6 To explore the influenceof depression-related factors, we ana-lyzed the risk of malformations amonganother group of women with depres-sion—women exposed to non-SSRI an-tidepressants in early pregnancy. Find-ing no increased risk of malformationsamong these women suggests that theunderlying disease is of less importancethan the SSRI medications. Bonari et al7

reviewed the literature on perinatal risksafter untreated depression during preg-nancy. While there were suggestions ofan increased risk of adverse pregnancyoutcomes such as preterm delivery; nostudies demonstrated increased risk ofmalformations.

Kelly et al2 noted that the rate ofsmoking in our study was nearly twiceas high among the SSRI group as com-

pared with the non-SSRI group. Al-though the evidence for smoking as ageneral teratogen is weak, we agree thatsmoking may be indicative of an un-healthy lifestyle. Our best options forexamining the influence of the underly-ing disease in our setting were 1) tocontrol for smoking and 2) to examinethe risk among non-SSRI antidepres-sant users. The results of these analy-ses did not allow us to exclude a causalassociation.

Our findings should be discussedin a context of adverse effects of depres-sion itself. However, we actually out-lined the possibility of the underlyingdisease as a confounding factor, and al-ready stressed in the conclusion, as well asin the abstract, that the observed associa-tion may not be causal. We believe thatclinicians with the final responsibility forcounseling individual patients have thefull ability to combine their knowledgefrom research, clinical experience and un-derstanding of the patient’s situationinto the best treatment. Thus in eachcounseling situation the absolute risk ofmalformations associated with and with-out drug use in pregnancy should berelated to the benefits of drug use.

Pia WogeliusMette Nørgaard

Mette GislumLars Pedersen

Estrid MunkDepartment of Clinical Epidemiology

Aarhus University HospitalAarhus, Denmark

[email protected]

Preben Bo MortensenNational Centre for Register-based

ResearchAarhus UniversityAarhus, Denmark

Loren LipworthDepartment of Preventive Medicine

Vanderbilt University Medical CenterNashville, TN

Henrik Toft SørensenDepartment of Clinical Epidemiology

Aarhus University HospitalAarhus, Denmark

REFERENCES1. Burt V, Miller L, Einarson A. SSRIs and birth

defects �letter�. Epidemiology. 2007;18:409–410.2. Kelly MB, Wisner KL, Cornelius MD. SSRIs

and birth defects �letter�. Epidemiology. 2007;18:410–411.

Letters to the Editor Epidemiology • Volume 18, Number 3, May 2007


3. Briggs GG, Freeman RK, Yaffe SJ, eds. Drugsin Pregnancy and Lactation. 7th ed. Philadel-phia: Lippincott Williams & Wilkins; 2005.

4. Strom BL, ed. Pharmacoepidemiology. 2nd ed.Chichester: Wiley; 1994.

5. Sørensen HT, Lash TL, Rothman KJ. Beyondrandomized controlled trials: a critical compar-ison of trials with nonrandomized studies �re-view�. Hepatology. 2006;44:1075–1082.

6. Rothman KJ, Wentworth CE 3rd. Mortality ofcystic fibrosis patients treated with tobramycinsolution for inhalation. Epidemiology. 2003;14:55–59.

7. Bonari L, Pinto N, Ahn E, et al. Perinatal risksof untreated depression during pregnancy �re-view�. Can J Psychiatry. 2004;49:726–735.

Janet Lane-Claypon

To the Editor:

We ought to be grateful to WarrenWinkelstein, Jr. for bringing to

our attention the life and work of JanetLane-Claypon.1,2 It can be added thatJanet Lane-Claypon’s contribution toepidemiology appeared when it was his-torically expected.

It is indeed tempting to relate thework of Janet Lane-Claypon, born in1877, to drastic changes in the wom-en’s role in society that occurred dur-

ing the Age of Empire (1880 –1914).3

Women such as Marie Curie, SelmaLagerlof, and Rosa Luxemburg, just toname a few with prestigious careers,became key theoreticians in science,literature, and politics. Marie Curie(1867–1934) is the only person whoreceived the Nobel Prize in 2 fields ofscience (physics in 1903 and chemistryin 1911). In 1909, Selma Lagerlof(1858–1940) was the first woman towin the Nobel Prize in Literature. ThePolish-born Rosa Luxemburg (1870 –1919), was leader of the German Social-Democratic Party. She debated and, in myopinion, intellectually dominated EdouardBernstein and Vladimir Illich Lenin onstrategy, economy, and nationalism.

Marie Curie, Selma Lagerlof, andRosa Luxemburg were contemporariesof Janet Lane-Claypon. Whether Flo-rence Nightingale (1820–1910) was anepidemiologist is a matter of debate, butJanet Lane-Claypon is the first womanwho enters epidemiology’s portraitgallery for her innovations in epidemi-ologic methods, in that area precedingmany better-known men (Hill, Gold-berger, Greenwood, and Frost). Theoriginality of her contribution would be

impressive if only for her pioneeringapplications of retrospective cohortstudies1 and case–control studies.4,5

While epidemiology may some-times be considered an arcane scientificdiscipline, Lane-Claypon’s contributionsuggests that our field is not alien to thegreat currents of world history.

Alfredo MorabiaCenter for the Biology of Natural Systems

Queens College - CUNYFlushing, NY

[email protected]

REFERENCES1. Winkelstein W Jr. Vignettes of the history of

epidemiology: Three firsts by Janet ElizabethLane-Claypon. Am J Epidemiol. 2004;160:97–101.

2. Winkelstein W Jr. Janet Elizabeth Lane-Claypon: a forgotten epidemiologic pioneer.Epidemiology. 2006;17:705.

3. Hobsbawm E. The Age of Empire: 1875–1914.New York: Vintage Books; 1989.

4. Lane-Claypon J. A further report on cancer ofthe breast: reports on public health and medicalsubjects. London: Ministry of Health; 1926.

5. Paneth N, Susser E, Susser M. Origin and earlydevelopment of the case-control study. In:Morabia A, ed. History of Epidemiologic Meth-ods and Concepts. Basel: Birkhauser, 2004:291–312.

Epidemiology • Volume 18, Number 3, May 2007 Letters to the Editor
