estimating typhoid fever risk associated with lack of access to...

Research ArticleEstimating Typhoid Fever Risk Associated with Lack ofAccess to Safe Water: A Systematic Literature Review

Vijayalaxmi V. Mogasale,1 Enusa Ramani,2 Vittal Mogasale ,2

Ju Yeon Park,3 and Thomas F. Wierzba4,5

1Epidemiology Unit, International Vaccine Institute, Seoul, Republic of Korea2Policy and Economic Research Department, International Vaccine Institute, Seoul, Republic of Korea3Biostatistics and Data Management Department, International Vaccine Institute, Seoul, Republic of Korea4Development and Delivery Unit, International Vaccine Institute, Seoul, Republic of Korea5PATH, 455 Massachusetts Avenue NW, Suite 1000, Washington, DC, USA

Correspondence should be addressed to Vittal Mogasale; [email protected]

Received 28 December 2017; Accepted 28 May 2018; Published 4 July 2018

Academic Editor: Evelyn O. Talbott

Copyright © 2018 Vijayalaxmi V.Mogasale et al.This is an open access article distributed under the Creative Commons AttributionLicense, which permits unrestricted use, distribution, and reproduction in anymedium, provided the originalwork is properly cited.

Background. Unsafe water is a well-known risk for typhoid fever, but a pooled estimate of the population-level risk of typhoidfever resulting from exposure to unsafe water has not been quantified. An accurate estimation of the risk from unsafe water willbe useful in demarcating high-risk populations, modeling typhoid disease burden, and targeting prevention and control activities.Methods. We conducted a systematic literature review and meta-analysis of observational studies that measured the risk of typhoidfever associated with drinking unimproved water as per WHO-UNICEF’s definition or drinking microbiologically unsafe water.The mean value for the pooled odds ratio from case-control studies was calculated using a random effects model. In addition tounimproved water and unsafe water, we also listed categories of other risk factors from the selected studies. Results. The searchof published studies from January 1, 1990, to December 31, 2013 in PubMed, Embase, and World Health Organization databasesprovided 779 publications, of which 12 case-control studies presented the odds of having typhoid fever for those exposed tounimproved or unsafe versus improved drinking water sources. The odds of typhoid fever among those exposed to unimproved orunsafe water ranged from 1.06 to 9.26 with case weighted mean of 2.44 (95% CI: 1.65–3.59). Besides water-related risk, the studiesalso identified other risk factors related to socioeconomic aspects, type of food consumption, knowledge and awareness abouttyphoid fever, and hygiene practices. Conclusions. In this meta-analysis, we have quantified the pooled risk of typhoid fever amongpeople exposed to unimproved or unsafe water which is almost two and a half times more than people who were not exposed tounimproved or unsafe water. However, caution should be exercised in applying the findings from this study in modeling typhoidfever disease burden at country, regional, and global levels as improved water does not always equate to safe water.

1. Introduction

Typhoid fever is a systemic bacterial illness of public healthimportance. The disease is transmitted person to persondue to fecal contamination of food and water [1]. Thecausative agent, Salmonella enterica serovar Typhi (S. Typhi),is exclusive to humanswho are the natural host and reservoirs[2]. Humans can become chronic carriers and food handlingpractices among carriers can result in food contaminationand S. Typhi transmission [2]. However, use of sewage con-taminated water for irrigation and domestic use is considered

critical in maintaining typhoid endemicity in developingcountries as demonstrated in Santiago, Chile [2]. Since themajor routes of transmission of typhoid fever are throughdrinking water or eating food contaminated with Salmonellatyphi, the World Health Organization (WHO) recommendsprovision of safe water as one of the preventive measures fortyphoid fever [2].

Defining and monitoring quality and ensuring watersafety in low- and middle-income countries (LMICs) arechallenging. The WHO defines microbiologically safe waterbased on the amount of Escherichia coli which should be

HindawiJournal of Environmental and Public HealthVolume 2018, Article ID 9589208, 14 pageshttps://doi.org/10.1155/2018/9589208

http://orcid.org/0000-0003-0596-8072

https://doi.org/10.1155/2018/9589208

2 Journal of Environmental and Public Health

Table 1: Improved and unimproved drinking water sources based on WHO/UNICEF Joint Monitoring Programme for water supply andsanitation [4].

Improved drinking water source Unimproved drinking water sourcePiped water into dwelling, yard or plot Unprotected springPublic tap or standpipe Unprotected dug wellTubewell or borehole Cart with small tank/drumProtected dug well Tanker-truckProtected spring Surface waterRainwater collection Bottled water from unimproved water source∗∗Please refer to WHO/UNICEF Joint Monitoring Programme for water supply and sanitation [4] for details. Note that any microbiologically contaminatedwater source was considered unsafe water in the analysis.

Table 2: Selection criteria for systematic literature review.

Inclusion criteria(i) Publications listed from January 1, 1990 to December 31, 2013(ii) Studies in English language(iii) Research conducted in human subjects(iv) Studies listed in PubMed database or Embase database or WHO website or PAHO website(v) Study designs: case-control, cohort, randomized control trials(vi) At least one water related exposure variable that could be categorized either as improved or unimproved drinking water source [4](vii) Water is consumed by drinkingExclusion criteria(i) Descriptive cross sectional studies that did not present odds ratio, case reports and case series(ii) Studies that did not present water related risk-factors(iii) Studies conducted in typhoid non-endemic area are excluded in the estimation of pooled odds ratio

0 CFU/100 ml [3] suggesting there should not be any fecalcontamination. Continuous monitoring of the microbio-logically safe water requires periodic laboratory testing ofwater sources which is difficult in resource poor settingsof LMICs. To simplify the process WHO-UNICEF JointMonitoring Programme (JMP) has defined alternative indi-cators, “improved water” and “unimproved water” sources[4], which deemed to represent safe water and unsafe water,respectively (Table 1).

People who drink safe water are likely to have lowerrisk of typhoid fever compared to people drink unsafewater which is one of the several risk factors for typhoidfever. However, typhoid fever global disease burden estimatesoften extrapolate the incidence rates obtained from high-riskpopulations to rest of the populations [5, 6] which is likelyto be an overestimation. Hence, it is necessary to correct theincidence rates while extrapolating the data collected frompopulations drinking unsafe water to population drinkingsafe water. But, there is no database that provides informationon drinking safe water or unsafe water that can be used inglobal disease burden estimation. Alternately, there is globaldatabase available on access to improvedwater to populations[7] which can be used as a proxy for safe water consump-tion. Therefore, it is necessary to link the risk of unsafewater to unimproved water. Although a systematic reviewpresented earlier has showed that the microbiological safetyof improved water is inconsistent [8] but provides a measureof sanitary protection and it is the only dataset that can beapplied at the global level for water-related risk correction.

While many studies have explored the risk of typhoidfever from unsafe water, there has not been a systematic

review that presented pooled estimate of the quantitative risk.We conducted a systematic literature review to quantify theprobability of symptomatic S. typhi infection among residentswho consumed unimproved or unsafe water compared to res-idents who did not consume unimproved or unsafewater.Theprimary purpose of this review was to derive a quantitativevalue on excess risk of typhoid fever due to the consummationof unimproved or unsafe water which can be used as acorrection factor in global disease burden estimates [6].

2. Materials and Methods

A systematic literature review was conducted independentlyby each of two researchers. Each researcher first identifiedstudies on risk factors for typhoid fever and then selectedfrom those publications, papers presenting water-relatedrisks.The search results from two researchers were comparedand any differences between them were resolved based ondiscussion and agreement. If unresolved, a third independentresearcher made the final decision. All selected papers werereviewed by a third researcher before data extraction toconfirm its adherence to inclusion and exclusion criteria.

To identify studies, in addition to searching primarydatabases, PubMed and Embase, searches were also madein WHO and Pan American Health Organization (PAHO)databases. The search was limited to studies published inEnglish language, from January 1, 1990, to December 31, 2013.The detailed inclusion and exclusion criteria are provided inTable 2. The search terms used were (“typhoid” OR “typhoidfever” OR “Salmonella Typhi” OR “S. Typhi” OR “Salmonellainfection” OR “enteric fever”) AND (“risk factors” OR

Journal of Environmental and Public Health 3

“predictive factors” OR “associated factors” OR “attributedfactors” OR “exposure factors” OR “related factors” OR“predisposing factors”). Search results are documented in aPRISMA diagram [31].

To quantify the risk, we selected one risk factor fromeach case-control study that best represented either improvedor unimproved water based on the definition providedby WHO/UNICEF-JMP (Table 1). If a water source in astudy was identified as “improved,” but was reported to be“microbiologically contaminated,” we considered the watersource as “unsafe.” We extracted the odds ratio of typhoidfever among those who got exposed to unimproved water orunsafe water compared to those who did not get exposed.A meta-analysis was conducted to pool the odds ratio ofmethodologically similar studies using a Metafor StatisticalPackages for R, version 1.9-8 [32, 33]. According to theheterogeneity test such as Q statistics and I2 [33], the meanvalue for pooled odds ratio from case-control studies werecalculated from a meta-analysis using random effects modelwith restricted maximum-likelihood estimator [33]. Cohortstudy findings were descriptively presented as they couldnot be combined with case-control study meta-analysis.We also descriptively summarized other risk factors thatshowed a statistically significant probability of symptomaticS. typhi infection from the selected studies for the betterunderstanding of overall risk factors.

3. Results and Discussion

Our review yielded a total of 779 publications from the searchdatabases (Figure 1). A total of 87 duplicates were removed,and 612 were excluded on title and abstract search becausethey lacked data on typhoid fever related risk factors. Fulltexts were accessed for remaining 80 papers. Of them, 58were excluded as they either (a) did not contain water-relatedrisk factors or (b) could not be classified into improved orunimproved water categories based on WHO-UNICEF-JMPdefinition or (c) descriptive cross- sectional studies that didnot present odd ratio. There were no randomized controltrials. Four cohort studies were presented descriptively [27–30] as the risk could not be merged and summarizedwith majority case-control studies. Finally, we could includeonly case-control studies in the estimation of pooled oddsratio. Sensitivity analysis was conducted for the decisionof study exclusion. At the beginning of the analysis, onestudy was automatically excluded due to zero-count cell[34] and another one was omitted by sensitivity analysisas an outlier [9]. Four studies had presented odds ratiofor improved water which could not be combined withodds ratio for unimproved water because inverse odds ofimproved water are technically not the same as unimprovedwater [23–26]. We cannot assume that people unexposed toimprovedwater are exposed to unimprovedwater.Thepooledodds ratio presented below include 12 case-control studiesfrom reported typhoid endemic regions and presented water-related risk factors.

3.1. Case-Control Studies. Of the 12 selected studies, five werefrom SouthAsia [12, 13, 19–21], four were from Southeast Asia

[15–17, 22], two were from Central Asia [11, 18], and one wasfrom South-Central Europe [14]. The studies included weremostly conducted in urban settings (75.00%) and only threewere outbreak investigations (25.00%, Table 3). There were915 typhoid fever cases and 1,609 nontyphoid fever controls.The exposure to unimproved water was higher among cases(62.95%; n = 576/915) compared to controls (46.30%; n =745/1609) (Figure 2). Half of the cases-controls studies havingimproved water source were microbiologically contaminatedand were considered unsafe water (Table 3). The odds oftyphoid fever among those who were exposed to unimprovedwater or unsafe water were ranged from 1.06 to 9.26 with caseweighted mean of 2.44 (95% CI: 1.65 – 3.59) (Figure 3).

Besideswater-related risk, the studies also listed other riskfactors related to socioeconomic aspects, living condition,food consumption, knowledge and awareness about typhoidfever, and hygiene practices (Table 4).

3.2. Cohort Studies. Four cohort studies presented relativerisk of typhoid fever attributable to exposure to unimprovedwater sources compared to improved water sources [27–30].The risk of contracting typhoid fever in groups exposed todrinking from a government water supply tank in Rajasthanwas 11.10 (95% CI: 3.70 – 33.00) times greater than those inthe nonexposed group [27].Thosewho drank from combinedsources of government tank, hand pump, and personal tubewell were 3.75 (95% CI: 1.02 – 13.80) times more likely at riskof typhoid than those not exposed to the three combinedsources indicating contamination of these sources. On afloating island restaurant in France, those who drank pipedwater onboard from untreated River Seine source had noexcess risk of typhoid fever compared to those unexposed tothose sources (RR = 1.40 95% CI: 0.60 – 3.00) [28].This studyconcluded that consumption of rice and chicken washed intap water resulted in outbreak and found fecal contaminationin the tap water which was untreated. In urban Karachi,univariate analysis showed that individuals who consumedtap or bottled water had same risk (RR = 0.70; 95% CI: 0.44– 1.11) of getting typhoid fever compared to those who didnot use tap or bottled water [30]. However, using regressionmodel, after adjusting for all covariates, the study found thatoverall risk of typhoid fever is lower among households usinga safe drinking water source (RR = 0.63; 95% CI: 0.41–0.99).In Eastern Kolkata, the study found that a significantly lowerproportion of households use tap water (RR = 0.07; p value =<0.001) in typhoid fever high-risk areas compared to typhoidfever low-risk areas [29].

3.3. Case-Control Studies Excluded from Meta-Analysis. Thefour excluded case-control studies that presented odds ofexposure to improved water among typhoid fever casescompared to controls [23–26] when combined together didnot show any significant association with water source (OR =0.70; 95% CI: 0.46 – 1.05) (Figure 4). The risk factors selectedfrom these four studies included utilization of municipaldrinking water (OR = 0.75; 95% CI: 0.31 – 1.84) in Diyarbakir,Turkey [23], utilization of piped water (OR = 1.00; 95% CI:0.37 – 2.72) inUjungPandang, Indonesia [24], drinking pipedwater (OR = 0.52; 95% CI: 0.23 – 1.16) in Jakarta, Indonesia


Full-text articles excluded (n=10) for: a)Record used in descriptive analysis (n=4) b)Presenting outlier data for meta-analysis (n=2)c)Presenting only improved water which could not be combined with odds ratio of unimproved water (n=4)

Studies included in final meta-analysis for water related risk factors

(n = 12)

PubMed search (n=292)Embase search (n=425)

Total hits (n= 717)

Extra searchWHO/PAHO

Websites (n=62)

Records excluded (n=699) due to: Being duplicates (n = 87)Irrelevance to study (n=612)

Full-text articles assessed for eligibility

(n = 22)

Records excluded based on inclusion criteria (n = 58)1) Did not contain water related risk-factors (n=31)2) Could not be classified into improved orunimproved water categories based on WHO-UNICEF JMP definition (n=20)3) Descriptive cross-sectional studies that did not present odd ratio (n=7)

Titles and abstractsscreened(n =80)

Scre

enin

gEl

igib

ility

Iden

tifica

tion

Incl

uded

Figure 1: PRISMA diagram representing search results of typhoid fever risk factors.

[25], and utilization of tap water (OR = 0.69; 95% CI: 0.34 –1.40) in Karachi, Pakistan [26].

Two case-control studies excluded from our investiga-tions at the time of sensitivity analysis were from Thailand[34] and Malaysia [9]. In Thailand study, drinking unboiledspring water had 37.80 (95%CI: 1.93 – 739.89) odds of typhoidfever compared to those who drank from either piped water,rain water, commercially bottled water, or water from wells.However, all typhoid fever cases were exposed to unboiledspring water. In Malaysian study, the accidental ingestion ofwater during swimming or bathing in a river had 32.78 (6.16

– 174.54) odds of getting typhoid fever compared to exposurefrom food items. In Figure 5 we have presented forest plotfor odds ratio without excluding this study to show how itsinclusion would have changed the results. Table 5 presentsPRISMA checklist.

3.4. Discussion. The systematic review of literature yielded 12case-control studies from 12 sites conducted in 10 differentcountries and presenting variables for water-related risk thatcould be categorized as unimproved water or unsafe waterand associated with typhoid fever. This review demonstrates


Table 3: Characteristics of case-control and cohort studies included in the systematic literature review.

Publication year Study year Study site Studysetting

Sitepopulation

Selectedunimproved/unsafe

water sourceReasons for typhoid fever Source

Case control studies

1999 1997 Dushanbe,Tajikistan

Outbreakin

endemicarea

Urban

Water for homeobtained from outside

tap [10](contaminated

improved source =unsafe)

Water source contaminationCessation of chlorination,intermittent water supply

creating negative pressure andcontaminating water supply with

surrounding contaminants.

[11]

2013 2011 Kathmandu,Nepal Endemic Urban Use of stone spout

water

Multiple; drinking water spoutcontamination with sewage,

contamination of stored water,general sanitation issues such aslack of toilets or lack of water to

flush toilets

[12]

2009 2007 West Bengal,India

Outbreakin

endemicarea

Urbanslum

Drinking piped water[10] (contaminatedimproved source =

unsafe)

Unchlorinated water supplythrough pipes, drinking water

pipes close to open drainage andintermittent water supply

[13]

1992 1990 NeapolitanArea, Italy

Outbreakin

endemicarea

Urban Drinking non-potablewater

Multiple; foodborne, sanitation,and drinking water source

contamination due to sewageexposure to municipal water

supply

[14]

2004 2000MadayaTownship,Myanmar

Outbreakin

endemicarea

Rural Drinking untreatedriver water

Drinking water contaminationwith unchlorinated river water

which had direct sewagedrainage.

[15]

2005 2002Son La

Province, N.Vietnam

Endemic Urban Drinking untreatedwater

Drinking water contaminationand consumption of

unchlorinated water (dislike forchlorine smell)

[16]

2005 1996-1997

DongThapProvince,MekongDelta, S.Vietnam

Endemic Urban Drinking unboiledwater

Multiple; drinking river waterwhich had sewage (latrine)

drainage, drinking water sourcesfrom deep wells and ponds

contaminated with drainage fromlatrines situated in the proximity

[17]

2007 2002-2003SamarkandOblast,

UzbekistanEndemic Urban and

Rural

Consumption ofunboiled surfacewater outside the

home

Water source contamination. Thedrinking of un-boiled surfacewater outside home during thehot and dry summer months.

[18]

2009 2005-2006Darjeeling,West Bengal,

IndiaEndemic Rural Stream water

Multiple; foodborne, sanitationissues, and drinking water sourcecontamination. Untreated watersupply from unprotected springsand natural streams, untreatedwater supply from venders.

[19]

2007 2003-2004Dhaka slum,Kamalapur,Bangladesh

Endemic Urbanslum

Drinking unboiledwater at home

Multiple; sanitation issues, anddrinking water sourcecontamination. Partial

chlorination of municipal watersupply exposed to

contamination, drinking ofuntreated water

[20]

1998 1994 Karachi,Pakistan Endemic Urban

Drinking water atwork (improved or

unimprovedunknown)

Multiple; foodborne, sanitationissues, and drinking water source

contamination at workplace[21]


Table 3: Continued.

Publication year Study year Study site Studysetting

Sitepopulation

Selectedunimproved/unsafe

water sourceReasons for typhoid fever Source

2001 1992-1994 Samarang,Indonesia Endemic Urban

Drinkingnon-municipal water

source

Multiple; foodborne, sanitationissues, and drinking of

unchlorinated water fromvenders

[22]

2005 2001-2003 Diyarbakir,Turkey Endemic Urban &

rural

Municipality drinkingwater (contaminatedimproved watersource = unsafe)

Consumption of raw vegetablesirrigated with sewage water from

the city[23]

1997 1990-1991Ujung

Pandang,Indonesia

Endemic Urban

Piped water(contaminatedimproved watersource = unsafe)

Street food consumption [24]

2004 2001-2003 Jakarta,Indonesia Endemic Urban

Piped water(contaminatedimproved watersource == unsafe)

Hygienic practices such as no useof soap for handwashing, sharing

of food, and no toilet in thehousehold and household

crowding at home

[25]

2008 1999-2001 Karachi,Pakistan Endemic Urban

Piped water(contaminatedimproved watersource = unsafe)

Hygienic practices such as lack ofsoap availability at handwashingplace, frequently eating outsidehome and crowing at home

[26]

Cohort studies

2010 2007 Rajasthan,India

Outbreakin

endemicarea

Rural

Drinking water fromgovernment tank,hand pump andpersonal tube well

Contaminated sources due to anopen well supplying water to allthe three water supply facilities

[27]

2000 1998 River Seine,Paris, France Outbreak Urban Drinking untreated

river water Fecal contamination of tap water [28]

2007 2003 – 2004EasternKolkata,India

Endemic Urban Drinking unsafedrinking water NA [29]

2012 2003 – 2006 Karachi,Pakistan Endemic Urban Drinking tap water NA [30]

NA – Not provided.

that unimproved water and unsafe water are associatedwith quantifiable odds of having typhoid fever. The resultsummary has been used in estimation of typhoid fever diseaseburden in LMICs [6] which demarcates high-risk populationwho would benefit maximum from typhoid interventionssuch as improving water and sanitation or vaccination. Othersignificant risk factors associated with the occurrence oftyphoid fever were related to food consumption, socioeco-nomic status, hygiene and sanitary practices, living condition,and water storage and handling. These factors should bequantified in future analyses and should be included in futuretyphoid disease burden estimates. We have not accounted forenvironmental factors such as rain fall and temperatures, andanthropological measures such as age in this review whichshould be the other considerations in future disease burdenstudies.

The importance contaminatedwater as amajor risk factorfor typhoid fever is undisputable. During high-endemicperiod of typhoid fever in Santiago, Chile, the sewage con-tamination of food chain was demonstrated as the most

important factor contributing to typhoid fever transmission,more than the typhoid carrier state in the family members[2]. The past epidemiological studies have demonstrated theimportance of waterborne transmission, showing that onlysmall inocula is sufficient for waterborne typhoid transmis-sion, while foodborne transmission requires large inocula[2]. The key role of water and sanitation in typhoid fevertransmission is further validated by a correlation betweeninstallment of water and sanitation system and decline intyphoid fever cases in industrialized countries. The pro-gressive introduction of water filtration system in later 19thcenturywas correlatedwith decline in typhoid fevermortalityin United States of America (USA) [35]. A more decisivecorrelation was demonstrated in Philadelphia, USA, wherewater filtration system was serially introduced in six differentdistricts between 1902 and 1909 [36]. When the conditionof water supply and cause specific death rates for variousdiseases were examined, only typhoid fever deaths werefound declining significantly following the introduction ofwater filtration system.


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139

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150

200

250

Tota

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rovi

nce,

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, 200

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arka

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Cases exposedCases unexposed

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Figure 2: Exposure to unimproved water among typhoid fever cases and controls in selected studies.

Although poorwater and sanitation system is not the onlyrisk for typhoid transmission, its undisputable importancemakes it a key risk factor in defining high-risk groups.Demarcating the typhoid fever risk groups is especiallyimportant in effectively targeting control measures such asvaccination programs.TheWHOhas recommended targetedvaccination of high-risk population with existing typhoidpolysaccharide vaccine [1]. The significance of defining high-risk groups has increased with impending availability oftyphoid conjugate vaccine [37], which may necessitate revis-iting of WHO policies on vaccination strategies based onwell-delineated target population. Most surveillance studieswere conducted in known typhoid high-risk populations,which cannot be simply extrapolated to general populationbecause their risk of typhoid fever is lower [6, 10]. One ofthe several risk corrections that can be made in applying thetyphoid fever incidence from high-risk population to generalpopulation is correct for water-related risk. However, thereis no data available at global level of safe water drinking,but there is a database available on improved water andunimproved water [7]. Whereas improved water is represen-tative of safe water and unimproved water is representativeof unsafe water, the only available database can be appliedat the global level for water-related correction in diseaseburden estimate. Computing the excess risk associated withthe consumption of unsafe water or unimproved waterwill help in understanding the additional typhoid risk incertain populations and helps in measuring risk-differential

typhoid fever incidence in different communities [6]. Suchcharacterization of disease burden that can be linked toaccess to improved water can help in developing risk-basedvaccination strategies and forecasting vaccine demand [38],identifying high-risk populations within countries and tar-geting vaccination to specific population, estimate its impact,calculate cost-effectiveness, and compare the efficiency oftargeted vaccination versus vaccination of whole population.

3.5. Limitations. Our study has many limitations. First, weused a basic definition of improved water to represent safewater because this variable is officially reported by WHO-UNICEF-JMP and a global data base is available that can beapplied to LMICs in computing risk-differential typhoid feverdisease burden. However, improved water does not alwaysequate to safe water in many LMICs [8, 39] and in this paperhalf of the case-control studies reported microbiologicalcontamination of improved water sources. Although micro-biologically unsafe water sources were combined with unim-proved water sources to estimate the excess risk of typhoidfever associated with unsafe water, the results may not begeneralizable to country levels as this study representedonly small number of countries. Similarly, caution shouldbe applied in generalizing the finding to unimproved wateras we included both unimproved and unsafe water in onecategory. Second, evidence from randomized control trialsis valued the highest followed by longitudinal prospectivecohort studies and case-control studies based on hierarchy


0.69 [ 0.34 , 1.40 ]

0.52 [ 0.23 , 1.16 ]

1.00 [ 0.37 , 2.72 ]

0.75 [ 0.31 , 1.84 ]

0.70 [ 0.46 , 1.05 ]

Odds Ratio [95% CI]

RE Model

0.50 2.00 7.00Odds Ratio (log scale)

Karachi, Pakistan

Jakarta, Indonesia

Ujung Pandang, Indonesia

Diyarbakir, Turkey

72

7

11

55

16

62

39

9

143

119

9

114

22

548

32

14

Exposed Unexposed Exposed Unexposed

Typhoid Control

Place, Country

Figure 3: Forest plot showing odds ratio for typhoid fever for exposure and nonexposure to unimproved water.

of strength of evidence. We had to exclude four prospectivecohort studies from meta-analysis approach because it wasnot possible to integrate the findings from those studies intothe analysis. However, these cohort studies have suggestedunimproved water as an important risk factor for typhoidfever. Third, it is worth noting that we used only one variablefrom each case-control study that best matched “unimprovedwater” to keep the analysis simple. It was challenging tocategorize some water sources as improved or unimprovedas they did not fit into any category and we had to choose onefrom the remaining variables. Selection of any other variablesmay have presented different values or may have resultedin ambiguous findings. Fourth, some of the water sourcesmatched the definition of improved water but a statementfrom investigators revealed a case of clear contamination ofimproved water due to reasons such as proximity to sewagepipes and breakage in water supply systemsmade it necessaryto reconsider the improved water as unsafe. This actuallydeviated from the definition of unimproved water but repre-sented unsafe water whichwas criticalmeasure for risk differ-entials.Wehad club these two categories in our analysis. Fifth,the typhoid fever risk from unsafe water is represented onlyby 12 studies in our systematic review. Number of studies istoo small to generalize and mostly represent Asia. Caution isnecessary in the application of results to global disease burdenestimation. Sixth, we could have missed some vital papers on

water-related risk factors for typhoid fever published in otherlanguages besides English because of search criteria. Also, oursearch did not include unpublished literature such as confer-ence abstracts, doctoral thesis, or meeting presentations.Thismay have resulted in publication bias. Lastly, we have usedonly those papers containing water-related risk factors in ourreview and, hence, many other significant typhoid fever riskfactors outside selected papers may not have been capturedin this review. We could have missed some important otherrisk factors not presented in these studies.

3.6. Conclusions. In conclusion, based on literature reviewwe demonstrated that the exposure to unimproved water orunsafe water is significantly associated with typhoid fever.Our findings suggest that the population without access tosafe water may be considered as one indicator to delineatehigh-risk population for typhoid related interventions. Thehigh-risk population decided based on lack of access to safewater can be targeted for typhoid vaccination in additionto ongoing effort to improve water and sanitation infras-tructure. Future research should focus on demarcating andquantifying other factors associated with typhoid fever inaddition to water-related one, so that more comprehensiverisk-association mapping based on geographical informationsystem could be developed and used for targeting typhoidinterventions.


Table4:Other

significantrisk

factorsfor

typh

oidfeveridentified

inreview

edpapers.

Stud

ySite

Risk

Category

Expo

sure

factor

AOR(95%

CI;p-value

ifany)

Source

Dushanb

e,Tajik

istan

Water

storage

andhand

ling

Unb

oiledwater

6.5(3.0–24.0;p

<0.05)∗

[11]

Food

hand

lingandconsum

ption

Eatin

gfood

obtained

from

streetvendo

r2.9(1.4–7.2

;p<0.05)∗

Kathmandu

,Nepal

Socio-econ

omic

Hou

seho

ldmon

thlyincome<

US$125

2.55

(1.1–6.0;p<0.05)

[12]

Hygiene

&sanitary

Hou

seho

ldlatrine

8.52

(1.8–40.1;

p<0.05)

Water

storage

&hand

ling

Water

storedaft

ercollection

3.09

(1.2–8.1;p<0.05)

Eatenstreetfood

<2weeks

2.34

(1.4–4.0;p<0.05)

WestB

engal,India

Food

hand

lingandconsum

ption

Food

from

sweetsho

p6.2(2.4–2.2)∗∗

[19]

Socio-econ

omic

Mon

thlyfamily

income<

1500

6(1.3–26.8)

INR(eqvt.US$

34)

Living

cond

ition

Hou

seho

ld>4mem

bers

4.2(1.7–11.1)

Living

cond

ition

Anyon

eillin

neighb

orho

od2.5(1.2–5.2)

Food

hand

lingandconsum

ption

Eatin

gof

Paratha(

flatbread

inlayers)

2.1(0.87–5.3)

Neapo

litan

Area,Ita

ly

Food

hand

lingandconsum

ption

Any

rawShellfish

13.3(5.5–32.8;p

<0.05)∗∗

[14]

Food

hand

lingandconsum

ption

RawOysters

9.3(1.7–67.3;p

<0.05)∗∗

Food

hand

lingandconsum

ption

RawMussels

8.9(3.9–21.1;

p<0.05)∗∗

Food

hand

lingandconsum

ption

RawHen

Clam

s8.1(2.8–23.7;p

<0.05)∗∗

Food

hand

lingandconsum

ption

RawSeaT

ruffles

6.4(1.4–32.9;p

<0.05)∗∗

MadayaT

ownship,Myanm

arLiving

cond

ition

Con

tactwith

typh

oidpatie

nt10.9(2.0-79.7

)[15]

SonLa

Province,N

orthernVietnam

Socio-econ

omic

Beingun

educated

2.0(1.0–3.7;p=0.03)

[16]

Living

cond

ition

Con

tactwith

typh

oidpatie

nt3.3(1.7–6.2;p<0.05)

Samarkand

Oblast,Uzbekistan

Socio-econ

omic

Stud

entasp

rimaryoccupatio

n4.0(1.4–11.3;p

<0.05)

[18]

Takenantim

icrobialsin2weeks

before

illnesson

set

12.2(4.0–37.0;p

<0.05)

Darjeeling,WestB

engal,India

Food

hand

lingandconsum

ption

Eatin

grawcabb

age

2.8(1.7-4.8)∗

[19]

Water

storage

&hand

ling

Scoo

ping

water

from

acon

tainer

with

acup

2.5(1.3-4.7)∗

Food

hand

lingandconsum

ption

Con

sumptionof

butte

r2.3(1.3-4.1)∗

Food

hand

lingandconsum

ption

Con

sumptionof

Yoghurt

2.3(1.4-3.7)∗

Food

hand

lingandconsum

ption

Eatin

gun

washedgrapes

2.2(1.3-4.0)∗

Food

hand

lingandconsum

ption

Eatin

grawon

ion

2.1(1.2

-3.9)∗

Food

hand

lingandconsum

ption

Eatin

grawcarrot

2.1(1.2

-3.9)∗

Dhaka

slum,K

amalapur,B

angladesh

Water

storage

&hand

ling

Con

sumptionof

foul-smellin

gwater

7.4(2.1–25.4;p

=0.002)

[20]

Food

hand

lingandconsum

ption

Con

sumptionof

unwashedPapaya

5.2(1.2–22.2;p

=0.03)

Karachi,Pakista

n

Takenantim

icrobialsin2weeks

priortoillness

3.0(1.4–6.5)

[21]

Food

hand

lingandconsum

ption

Eatin

gatrestaurant

betweenJulyandAu

gust

2.7(1.1-6.6;p=0.01)∗

Food

hand

lingandconsum

ption

Eatin

gatroad

sidec

abin

betweenJulyandAu

gust

2.4(1.0-5.6;p

=0.03)∗

Food

hand

lingandconsum

ption

Eatin

gou

t>1p

erweekbetweenJulyandAu

gust

2.3(1.0-5.2;p

=0.02)∗

Food

hand

lingandconsum

ption

Eatin

gicec

ream

1.7(1.0-3.1;

p=0.03)∗

Food

hand

lingandconsum

ption

Eatin

gac

ommercialbrandof

icec

ream

1.6(1.0-2.9;p

=0.04)∗

Semarang,Indo

nesia

Hygiene

&sanitatio

nNever

orsometim

eswashing

hand

sbeforee

ating

3.97

1.22-12.93;p=0.022)

[22]

Hygiene

&sanitatio

nOpensewages

ystem

orno

drainage

syste

min

house

7.19(1.33

-38.82;p

=0.022)

Socio-econ

omic

Beingun

employed

orpart-timejob

ber

31.3(3.08-317.4

;p=0.036)

Mekon

gDelta,Vietnam

Living

cond

ition

Recent

contactw

ithtyph

oidfeverc

ase

4.3(1.4-13.4;p=0.04

)[17]

Socio-econ

omic

Lowecon

omiclevel

2.5(1.3-5.1;

p=0.01)

RiverS

eine,Paris,

France

Eatin

gchickenon

boat

2.0(0.3–13.7)#

[28]

Eatin

gric

eonbo

at2.9(0.4–19.8)#

Easte

rnKo

lkata,India

Usin

glatrinein-ho

uselatrin

efor

defecatio

n5.32

(p=0.9)

[29]

Karachi,Pakistan

Living

indensely

popu

lated

area

2.43

(1.27–4.64

;p=0.01)

[30]

∧Frozen

mam

eypu

lpim

ported

from

Guatemalau

sedin

fruitshake

∧∧CigKo

fteisatraditio

nalraw

food

madefrom

rawmeatrolledin

aballform

∗MOR–Matched

odds

ratio

∗∗OR–Odd

sratio

# RR–Re

lativ

erisk

ratio

.

10 Journal of Environmental and Public HealthTa

ble5:PR

ISMA2009

checklist.

Section/topic

#Ch

ecklist

item

Repo

rted

onpage

#TI

TLE

Title

1Identifyther

eportasa

syste

maticreview

,meta-analysis,

orbo

th.

Cover

page

Abstr

act

2,3

ABS

TRAC

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Structured

summary

2

Providea

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andinterventio

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s;syste

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Abstr

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INTR

OD

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Ratio

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3Describethe

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es,and

study

desig

n(PIC

OS).

1

MET

HODS

Protocoland

registr

ation

5Indicateifar

eviewprotocolexists,ifandwhere

itcanbe

accessed

(e.g.,Web

address),and

,ifavailable,provider

egistratio

ninform

ationinclu

ding

registratio

nnu

mber.

NA

Eligibilitycriteria

6Specify

study

characteris

tics(e.g

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S,leng

thof

follow-up)

andrepo

rtcharacteris

tics(e.g

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onsid

ered,langu

age,pu

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ligibility,givingratio

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2,16

Inform

ationsources

7Describea

llinform

ationsources(e.g

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dateso

fcoverage,contactw

ithstu

dyauthorstoidentifyadditio

nalstudies)inthes

earchanddatelastsearched.

2

Search

8Presentfullelectronics

earchstrategy

fora

tleaston

edatabase,inclu

ding

anylim

itsused,suchthatitcouldbe

repeated.

2,Table2

Stud

yselection

9Statethe

processfor

selectingstu

dies

(i.e.,

screening,eligibility,includedin

syste

maticreview

,and

,ifapp

licable,

inclu

dedin

them

eta-analysis).

2,Table2

Datac

ollection

process

10Describem

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s,independ

ently,indu

plicate)andanyprocessesfor

obtainingandconfi

rmingdata

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2,Figure

1

Dataitems

11Listanddefin

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S,fund

ing

sources)andanyassumptions

andsim

plificatio

nsmade.

9,Table3

Risk

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alstu

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specificatio

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whether

thiswas

done

atthes

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orou

tcom

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howthis

inform

ationisto

beused

inanydatasynthesis.

NA

Summarymeasures

13Statethe

principalsum

marymeasures(e.g

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kratio

,difference

inmeans).

3,16

Synthesis

ofresults

14Describethe

metho

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one,

inclu

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measureso

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Figure

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15Specify

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frisk

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NA

Additio

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16Describem

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additio

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NA


Table5:Con

tinued.

Section/topic

#Ch

ecklist

item

Repo

rted

onpage

#RE

SULT

S

Stud

yselection

17Given

umbersof

studies

screened,assessedfore

ligibility,and

inclu

dedin

the

review

,with

reason

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sions

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aflow

diagram.

Figure

1

Stud

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(e.g.,stu

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atao

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kof

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study

and,ifavailable,anyou

tcom

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NA

Results

ofindividu

alstu

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rallou

tcom

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(benefitsor

harm

s),present,for

each

study

:(a)

simple

summarydatafore

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ates

andconfi

dence

intervals,ideally

with

aforestp

lot.

Figures3

and4

Synthesis

ofresults

21Presentresultsof

each

meta-analysisdo

ne,including

confi

denceintervalsand

measureso

fcon

sistency.

Figures3

and4

Risk

ofbias

across

studies

22Presentresultsof

anyassessmento

frisk

ofbias

acrossstu

dies

(see

Item

15).

NA

Additio

nalanalysis

23Giver

esultsof

additio

nalanalyses,ifdo

ne(e.g.,sensitivityor

subgroup

analyses,

meta-regressio

n[see

Item

16]).

Figure

4

DIS

CUSS

ION

Summaryof

evidence

24Summarizethe

mainfin

ding

sincluding

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treng

thof

evidence

fore

achmain

outcom

e;consider

theirrele

vancetokeygrou

ps(e.g.,healthcare

providers,users,

andpo

licymakers).

8

Limitatio

ns25

Disc

usslim

itatio

nsatstu

dyandou

tcom

elevel(e.g.,ris

kof

bias),andatreview

-level

(e.g.,incompleter

etrie

valofidentified

research,reportin

gbias).

7,8

Con

clusio

ns26

Providea

generalinterpretationof

ther

esultsin

thec

ontext

ofothere

vidence,and

implications

forfuturer

esearch.

8,Figure

2

FUND

ING

Fund

ing

27Describes

ources

offund

ingforthe

syste

maticreview

andothersup

port(e.g.,

supp

lyof

data);roleof

fund

ersfor

thes

ystematicreview

.9

From

[31].


RE Model

0.50 2.00 7.00

Odds Ratio (log scale)

Karachi, Pakistan

Jakarta, Indonesia

Ujung Pandang, Indonesia

Diyarbakir, Turkey

72

7

11

55

16

62

39

9

143

119

9

114

22

548

32

14

0.69 [ 0.34 , 1.40 ]

0.52 [ 0.23 , 1.16 ]

1.00 [ 0.37 , 2.72 ]

0.75 [ 0.31 , 1.84 ]

0.70 [ 0.46 , 1.05 ]


Typhoid Control

Place, Country Odds Ratio [95% CI]

Figure 4: Forest plot showing odds ratio for typhoid fever for exposure and nonexposure to improved water.

RE Model

0.50 2.00 7.00

Odds Ratio (log scale)

Anita S, MJM, 2012

Semarang, Indonesia; 1992−94

Karachi, Pakistan; 1994

Dhaka slum, Bangladesh; 2003−04

Darjeeling, West Bengal, India;2005−06

Samarkand Oblast, Uzbekistan; 2002−03

Dong Thap Province; Vietnam; 1996−97

Son La Province, Vietnam; 2002

Madaya Township, Myanmar; 2000

Neapolitan area, Italy; 1990

West Bengal, India; 2007

Kathmandu, Nepal; 2011

Dushanbe, Tajikistan; 1997

10

67

50

37

81

35

124

70

30

6

39

27

10

2

8

50

4

42

62

25

20

3

45

26

22

32

9

57

97

48

70

51

231

79

27

5

14

56

10

59

18

103

34

53

139

57

101

25

97

51

80

106


Typhoid Control

Place, Country, Data collection year

32.78 [ 6.16 , 174.53 ]

2.64 [ 1.07 , 6.54 ]

1.06 [ 0.66 , 1.72 ]

6.55 [ 2.14 , 20.11 ]

1.46 [ 0.87 , 2.45 ]

1.54 [ 0.91 , 2.60 ]

1.22 [ 0.73 , 2.06 ]

4.47 [ 2.51 , 7.97 ]

9.26 [ 2.51 , 34.16 ]

2.59 [ 0.75 , 8.92 ]

5.46 [ 2.53 , 11.83 ]

1.75 [ 0.91 , 3.39 ]

3.31 [ 1.27 , 8.66 ]

2.78 [ 1.80 , 4.32 ]

Odds Ratio [95% CI]

Figure 5: Forest plot showing odds ratio for typhoid fever for exposure and nonexposure to unimproved water after including one outlierstudy [9].


Abbreviations

S. Typhi: Salmonella enterica serovar TyphiWHO: World Health OrganizationLMICs: Low- and middle-income countriesCFU: Colony forming unitsUNICEF: United Nations International Children

Emergency Relief FundJMP: Joint Monitoring ProgrammePAHO: Pan American Health OrganizationRR: Relative riskOR: Odds ratioUSA: United States of America.

Data Availability

All data related to the research is available in the manuscript.

Ethical Approval

Research involved analysis of secondary data available inpublic domain. No human subjects are involved in this study.No ethical approval was sought.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors’ Contributions

Vittal Mogasale has conceptualized the study, advised ondeveloping search terms, served as the third reviewer inconducting the search, guided the analysis, interpreted theresults, and rewrote the final manuscript. Vijayalaxmi V.Mogasale has served as first reviewer in conducting thesearch, developed search terms, extracted data, conductedthe analysis, and wrote the first draft of manuscript. EnusaRamani has served as second reviewer in conducting thesearch, assisted in data extraction and analysis, and reviewedand put together the final manuscript. Ju Yeon Park esti-mated weightedmean sensitivity using random effects modeland drew forest plots. Thomas F. Wierzba provided overalltechnical advice for conceptualization and data analysisand reviewed and edited the manuscript. All authors haveapproved the final manuscript.

Funding

This work was supported the Vi-based Vaccines for Asia(VIVA) Initiative, which is funded by the Bill and MelindaGates Foundation (Grant no. 417.01). International VaccineInstitute receives core funding by the Governments of Koreaand Sweden.

Acknowledgments

The authors thank Dr. Jin Kyung Park for statistical support.This work was supported by the Vi-based Vaccines for Asia(VIVA) Initiative, which is funded by the Bill and Melinda

Gates Foundation (Grant no. 417.01). International VaccineInstitute received core funding from the Governments ofKorea and Sweden.

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http://mdgs.un.org/unsd/mdg/data.aspx


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