POINT-COUNTERPOINT

The triumph of the null hypothesis:epidemiology in an age of changeWasim Maziak

Accepted 8 May 2008

Summary The recent confusion concerning the relation between hormonereplacement therapy and cardiovascular disease has stirred a newwave of debate about the value and future of epidemiology.Opponents of epidemiology suggest an ever-diminishing role in anage of small risks and complex diseases, yet proponents are not inconsensus about how to adapt their discipline to the challengesassociated with ageing societies and changing disease patterns. Whileepidemiology is likely to be increasingly called upon to make sense ofthe risks involved with these changes, wading into this era with amindset and tools that were derived from epidemiologys golden eraof tackling major risk factors, has created more confusion thanunderstanding. Increasingly, we need to downsize epidemiology towhat is testable, measurable, and relevant, based on robustmethodology and public health rationale. Applying an evolutionaryperspective, that views health problems of modernity as a manifesta-tion of the mismatch between our ancient genes and hi-techlifestyles, can provide guidance for a 21st century research agenda.

Keywords Epidemiology, chronic disease, small risk, complex disease, evolu-tionary epidemiology

IntroductionThe recent uncertainty surrounding the relationbetween hormone replacement therapy and cardio-vascular disease (HRT-CVD) has again ignited thedebate about the value and future of epidemiology.1,2

The issue this time is more serious than the optimalamount of fruits and vegetables we need to eat daily,as it involves the devastating irony that millions ofhealthy women have been encouraged to take amedication that may put them at risk of the sameailment they were trying to ward off.3 Underlying thisdilemma is a credibility crisis brought about byinconsistencies in the results of various epidemiol-ogical studies.46 Increasingly, voices within andoutside the discipline of epidemiology are calling for

a total re-evaluation of its tools and paradigms, somegoing as far as to suggesting abandoning the fieldentirely.1,711 One can argue whether epidemiologyis to blame for this state of affairs by adopting theresults of cohort studies to formulate treatmentguidelines,12 or has been the voice of reason viaarguing caution about the protective relationbetween HRT-CVD,1315 or is an innocent bystanderor even pawn at the hands of mass media and cor-porate interests that manipulate public opinion aboutmedical treatments.1 Regardless, the unavoidableissue is the legitimate concern about the role of epi-demiology in an era of small effect, lifestyle-relatedrisks of chronic diseases. This concern has in recentyears stirred calls for major methodological andconceptual reevaluation of observational studies(e.g. case control and cohort),1,5,811 as their propen-sity for subtle forms of bias and confounding caninfluence their value for the study of small risks ofchronic disease. Yet a more suitable starting pointwould be to restore some of the fundamentals ofepidemiological practice based on strong theoretical

Corresponding author. University of Memphis, 633 NormalStreet, Memphis, TN 38152, USA.E-mail: wmaziak@memphis.edu

University of Memphis, School of Public Health, Memphis, TN38152, USA.

Published by Oxford University Press on behalf of the International Epidemiological Association

The Author 2008; all rights reserved. Advance Access publication 17 December 2008International Journal of Epidemiology 2009;38:393402

doi:10.1093/ije/dyn268

393

guidance, proper assessment tools and clear publichealth rationale. As these elements are usually withinresearchers control, addressing them in the context ofnew directions to improve the prospects of chronicdisease epidemiology is warranted.

A historical snapshotFor some time now, epidemiologists have been debat-ing the future ability of their discipline to accommodateemerging disease patterns resulting from the ageingand lifestyle changes of modern societies.79,1622

Mervyn and Ezra Susser identified three main historicalstages of epidemiology reflecting the main healththreats of the times and the level of knowledge aboutthem. Starting from the sanitary era with its Miasmaparadigm, to the infectious disease era accompanyingthe germ theory, to our chronic disease/risk factors erawith its so called black box paradigm, to quote PetrSkrabaneks famous metaphor.7,23 Perhaps, it is blackbox epidemiology, referring to the pursuit of exposure-outcome relations without much attention to biologicalunderstanding or inference, that has been mostproblematic.2224 The willingness of epidemiologists torun ahead of biology to influence the societal burden ofdisease is a longstanding tradition of the discipline withsome impressive successes.25,26 But while mechanisticassociations can lead to hypothesis formulation in thearea of major risk factors,26 they are unlikely to be assuccessful with small risks, given the complexity of thecausal grid. This inadequacy has paved the way for anew phase in epidemiology,9,27,28 called ecoepidemiol-ogy by the Sussers.27 The concept of ecoepidemiology isbased on a multilevel paradigm called the Chineseboxes to reinforce the importance of distal (societal),individual and microbiological interactions in diseasedevelopment.27 The ecoepidemiology concept also is anattempt to reclaim the public health edge of epidemiol-ogy, thought by many to have been lost amidst anoveremphasis on individual-level risk factors.1821,29

Risk factor epidemiology and theimportance of guiding hypothesesObservational studies have been instrumental for theidentification of major risk factors to health (e.g.smoking, hypertension, hypercholesterolemia, malnu-trition). Yet the HRT-CVD debate has drawn attentionto the potentially high price of making unwarrantedclaims about small and interconnected associations.Epidemiologys doubters argue that the success storiesof epidemiology were all easy hits; the magnitude ofthe association between cigarette smoking and lungcancer was so large that it could be reliably observedeven with flawed study designs.30 However, when wemove to the realm of complex diseases and smallereffect sizes, bias and confounding start to creep intocohort and case control studies in a variety of

unpredictable ways leading to their derailment inany direction.8,22,30,31 But, if we could establish majorrisk factors with crude tools, why cannot we be ableto assess small risks with better ones?

So far, the uncertainty about epidemiological evi-dence has led to the here-today-gone-tomorrownature of medical wisdom,30 and perhaps the confu-sion we ourselves have about how to lead a healthylifestyle.3032 Notwithstanding our position on theever-changing nature of scientific knowledge and theprocesses involved, epidemiology has been hurt mostby ill conceived and conducted studies with rushedconclusions. As we witnessed, an excess of suchstudies, combined with vested interests in certainresults and media hunger for the newsworthy, hasbecome a formula for trouble.

Epidemiologists have always been vigilant about thedanger of claiming associations that do not exist inreality by adopting the null/alternative hypothesisapproach, which emphasizes lower tolerance for sucherror (i.e. type I error) than for missing a real link(i.e. type II error).33 It is an approach similar to thejudicial system, which considers convicting an innocenta greater mistake than letting a criminal go free. Thisapproach emphasizes as well the need for research to bedriven at the outset by a sound and fully articulatedhypothesis. The wisdom of this safeguard seems tobe lost on many researchers nowadays, who like toformulate and interpret their studies by what comes outof the logistic regression grinder. Given researchersingeniousness in explaining exotic associations, and theever-expanding volume of knowledge, it is not hard tofind biological explanations for contradictory findings.For example, studies showing positive associationsbetween exposure to pets and childhood asthmaattribute this association to animal allergens (compat-ible with the allergy paradigm), while studies showingnegative associations attribute it to pet-related micro-bial products (compatible with the hygiene para-digm).34,35 There are ample examples of tailor-madepost hoc hypotheses, transforming epidemiology from arational to a ridiculous endeavour, and highlighting thegrowing importance of epidemiological studies beingguided by well-grounded a priori hypotheses.

The asthma epidemic and theimportance of measurementIn the current age of small risk factors, the need forepidemiology to be grounded in strong inference ismatched only by the need for sensitive tools to assessexposures and outcomes. As an example, childhoodasthma is a recent global epidemic, whose secularand spatial patterns favour environmental causes.36,37

Yet more than two decades of intensive research hasyet to yield a single target for intervention.8,38 To getout of this deadlock, researchers have been calling forlarge prospective studies, studies starting as early in

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life as possible and to differentiate between differentasthma phenotypes.3941 Others have argued, invokingGeoffrey Roses seminal work,42 that the etiologicalfactors involved in the asthma epidemic are likely tobe homogenously distributed within populations,indicating the need for international comparisons.43

These attempts to shore up asthma research reflectsound epidemiological reasoning, but have yet toadvance our understanding of the etiology of theasthma epidemic, despite a big price tag.4446

So, is this impasse in asthma research due to aninadequate conceptual framework or faulty studydesigns and tools of epidemiology? Research into theasthma epidemic was guided for the most part by thehygiene hypothesis, which relied on early observationsof the protective effect of sibship size and birth orderto suggest that the asthma epidemic could be the sideeffect of our increased hygiene and success in curbinginfectious diseases.47 Biological underpinnings of thehygiene hypothesis were soon provided based onthe preferential development of immune-allergicresponses in the absence of infectious stimuli.48,49

Certainly, the hygiene hypothesis provided a timelymodel for an environmentally driven epidemic incor-porating changes in our lifestyles with a good dose ofevolutionary wisdom. While evidence for and againstthe hygiene model are accumulating, it has becomeapparent that it is an over-simplification of a morecomplex picture.45,5052

Part of the reason why we still do not have a verdicton the hygiene hypothesis despite countless studies,lies in the uncertainty surrounding the measurementtools we are applying in asthma research. For the mostpart, this research relied on self-reported or parental-reported questionnaires to assess asthma symptomsand exposures. As a result, asthma was transformedgradually into wheeze, as the symptom and thedisease became synonymous in epidemiologicalstudies. Even in English, this substitution results incrude assessment of a disease that has severalphenotypic and age-related manifestations53 but inother languages, translations of wheeze can relatepoorly to asthma.54,55 Video questionnaires or objectivemarkers (e.g. bronchial hyperresponsiveness, BHR)also have been used for outcome assessment, but nonehas come close to being a gold standard for theassessment of asthma in epidemiological studies.5664

Exposure assessment in asthma studies has not faredbetter, as questionnaires again have been the main toolto assess exposure to infection, animals and theirproducts, air pollutants, certain foods and medicines,and secondhand smoke.65,66 For example, exposure toair pollution in the International Study of Asthma andAllergies in Children (ISAAC) was assessed based onquestions asking about (i) the frequency of truckspassing on the residential street on weekdays; and(ii) the severity of traffic noise that forced participantsto close the window.67 One cannot expect suchassessment to provide credible information about this

exposure, let alone study its relative contributionamong a variety of other exposures measuredwith similarly crude instruments. In cases wheremore objective markers of exposure were sought(e.g. endotoxin, antibodies, skin tests) they wereeither non-specific/sensitive, were poor markers ofchronic exposure relevant to the development ofasthma, or were perhaps offset by the crude measure-ment of the outcome.6873 The resultant confusion waseven greater than researchers ingenuity to explain it,to the extent that it is not uncommon to see asthmastudies conclude that factor X was associated with pastyear wheeze, but not ever-wheeze or asthma diagnosis,or with sensitization but not asthma symptoms orBHR, or for that matter any combination of a myriadof outcomes that arguably represent the same clinicalentity.7486 Such inconsistencies have yet to invoke amajor re-examination of research paradigms and toolsused to study asthma, perhaps signifying black boxepidemiology at its darkest hour.

From static to dynamic andrelevant epidemiologyThe transformation needed in small risk epidemiol-ogy involves not only applying better guiding modelsand sensitive markers, but perhaps as importantly, adeparture from a static perception of the relationbetween exposure and disease, reflecting the era ofquestionnaires and categorical variables, to a moredynamic, context-relevant and life-course appro-ach.87,88 As such, snapshot exposure assessmentshould be replaced with modelling of exposures basedon information from a variety of sources/levels (GIS,mobility patterns, biosensors), the use of sensitivemarkers of long-term exposure (e.g. hair/nail nicotinefor exposure to ETS, or glycosolated haemoglobin forcontrol of blood sugar), and the incorporation of macro-level attributes (e.g. area level characteristics).8994 Thiscan potentially sharpen our ability to approximate thedynamic nature of our interaction with our ever-changing environments. Similar transformation isneeded in our perception of risk to reflect its dynamicnature across peoples life roles, past experiences andcurrent behaviors and contexts. For example, many ofus will fall under some risk category for several chronicdiseases only by virtue of our age, which may lead us toseek controversial treatments that can be themselvesrisky (e.g. vitamin/mineral supplements).9597

This inner conceptual dynamism should be supple-mented with an outer public health orientation.Without that, epidemiology may dwell in the realmof the ideal rather than deal with the constraints ofreality. For example, even if efficacy trials confirm thebenefits of a school-based programme or five portions/day of fruits in obesity prevention, this can be uselessif such results are not translatable into society-widesustainable interventions.98 Another form of thisreductionist approach is for epidemiological studies

THE NULL HYPOTHESIS AND FUTURE OF EPIDEMIOLOGY 395

to statistically control for socioeconomic deprivation,zooming in on more proximal behavioral determi-nants, while relegating an apparently major deter-minant of chronic disease risk to the status of anuisance variable.20 Both approaches shift focus fromthe society to the sufferers,17,20 who are arguablyblamed for failing to follow the health recommenda-tions we have imparted to them for years, and, in theprocess, stripping epidemiology of its public healthessence. This is not to say that public health cannot beadvanced by purely medical interventions (e.g. vacci-nation), or that we need to structure peoples livesaround some health ideals, but to desensitize epide-miology to a major determinant of chronic disease,such as socioeconomic deprivation has perhapscontributed to the widening of health inequalities,particularly in the USA.99 Classifications such associal epidemiology, clinical epidemiology, infectiousdisease or cardiovascular epidemiology, may havefacilitated this selectiveness in how we treat differentvariables, by creating a false perception of compart-mentalization of different disease processes withinindividuals and populations.

The opposite can be true as well, as some studiesof distal-level determinants lack clear publichealth orientation. For example, what are the publichealth implications of demonstrating that lowneighbourhood-level educational attainment is detri-mental to cognitive functions of elderly residents,100

as one can argue that intervening on older residentsto improve their cognitive ability is more practicalthan increasing the average education level of wholegeographic areas. Finally, many relations sought byepidemiologists are fly-by-night endeavours embody-ing no foreseeable public health rationale or follow-upplan, an example of which is the finding of a positiveassociation between husbands participation in house-work and wives psychosocial health.101 One canargue that such a study proves the obvious, canneither be free of residual confounding (husbandswho help with housework are likely to be a differentbreed from those who do not), nor has a clear publichealth message (who should use this information andhow?). For epidemiology to stay relevant, associationsmust not only be driven by statistical empiricism, butmust have a clear public health rationale.

Epidemiology in the age ofcomplex lifestyle-related diseasesIt has been hard for epidemiologists to adapt tochanging patterns of diseases and their risks inmodern societies, partly because of the pace of suchchanges. New epidemics, such as obesity, asthma anddepressive disorders are evolving rapidly and creatingpressure for evidence-based solutions. In response,inspired by past successes with major risk factors,epidemiologists rushed to the scene with their usualtools. As it turned out, the task this time was more

difficult, casting some serious doubt on epidemiol-ogys ability to respond to current and future threatsto health.8,9,30 While only time can tell the fate ofepidemiology, one major lesson to be learned from theHRT-CVD story is that observational epidemiologycan never be free of residual confounding whencomparisons involve health-oriented behaviours.102,103

Yet, observational studies can be valuable for thestudy of long-term side effects of drug treatments, asthese are mostly unintended and unpredictable,therefore are not confounded by indication; meaningthat they are usually not associated with the treat-ment decision.104106 A similar valid scenario forobservational epidemiology is the study of potentialrisk/protective factors that are unknown to the public,as this reduces the probability of associative-selectionbias based on differential health awareness betweenthe comparison groups.107

In reality, as we continue to monitor the health ofpopulations, epidemiology will likely be called uponincreasingly to make sense of the risks involved in thedramatic changes in our lifestyles and environments.But to continue doing epidemiological studies usingthe same tried and failed approaches is not an option;the asthma example is a clear indicator of howepidemiology can turn into an absurd exercise whenaims are put ahead of tools and concepts. Realizingthe current crisis of credibility, epidemiologists haveresponded by calling for larger and longer studies,coupling of epidemiology with molecular genetics(e.g. genetic epidemiology and biobanks), stronginference, incorporating multilevel attributes and forgreater attention to residual confounding.8,108114

They have also mobilized to improve the reportingof observational studies (STROBE guidelines) to alloweditors, reviewers and consumers of epidemiologicaldata to make informed judgement on the quality ofreported studies.115 All these approaches can poten-tially improve epidemiologys ability to zoom in onsmall risks, yet the defining feature of the small risk/chronic disease era has so far been the triumph of thenull hypothesis (i.e. no effect).8,9,116

Such responses reflect healthy self-criticism on thepart of epidemiologists, but a clearer articulation ofpriorities is needed in the face of mounting criticism.Bigger samples and longer studies, for example, canlead to the magnification of errors, loss to follow up,and increase in costs, while applying multilevelapproaches without a sound theoretical frameworkruns the risk of becoming another form of black boxepidemiology.117 Genetic epidemiology on the otherhand, is not free of the problems of observationalepidemiology,8 yet recent years have witnessedsome promising advances in this field, especially theapplication of Mendelian randomization to controlfor environmental confounders in observationalstudies.118 Mendelian randomization utilizes therandom distribution of genetic alleles at the time ofgamete formation to identify genetic variants that

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robustly predict environmentally modifiable exposuresand uses them as un-confounded proxies (instru-ments) for those exposures. While the promise of thisapproach is still unfolding,119 there are currently notmany conditions that can be studied this way, i.e. inwhich we have a clear understanding of the geneticbasis of suspected etiological exposures.

As epidemiologists relish the promise of ecoepide-miology,28,111 this concept can come with its owncaveats. Namely, a comprehensive hypothesis basedon this approach can involve many-to-many factorsto account for,8,120 as the range and possibilitiesof geneenvironment interactions and pathwaysinvolved in chronic disease form a vast causaluniverse. As such, a vision that lies between blackbox and Chinese boxes may be needed, wherebycertain causal pathways can be identified and workedupon without the need to unravel the whole complex-ity of the relation between exposures, genes, andchronic diseases. For example, if we can establish aneffective way to influence obesity in the society (e.g.through policy that rewards/supports active transpor-tation), we can potentially influence a good deal ofCVD, and perhaps a variety of other illnesses, withoutneeding to have all the information about the factorsand mechanisms involved. Ecological models of majorhealth risks are emerging, which can harness candi-date pathways for study and intervention (Figure 1).In parallel, methodological paradigms and analyticaltools are being developed to accommodate this newdirection (e.g. complexity theory, multilevel model-ling, pathway analysis).98,115,121124 For example, theuse of multilevel analysis for the study of obesity andCVD risk has helped to shift the focus from personalbehaviours to encompass wider environmental influ-ences (e.g. neighbourhood adversities) that maybe more amenable to sustainable interventionsthrough policy.125129 The widening of health inequal-ities in the US, driven mainly by non-linear socio-economic and political dynamics,99 underscores theimportance of these new trends in epidemiology.

An evolutionary perspectivefor epidemiologyGiven the methodological constraints imposed onepidemiological research by the complex nature ofproblems we are increasingly facing, epidemiology canbenefit from some guidance as to what risk factorsrepresent good targets for exploration, and how tointerpret inconsistent epidemiological data. An evolu-tionary perspective can help provide a guiding frame-work for the epidemiology of chronic diseases, beingviewed as a result of the immense adaptive pressuresbrought about by the mismatch between our genes stilllingering at the hunter-gatherer era and our hi-techlifestyles.130 Accordingly, exposures relevant to ourhealth are those that either underwent a rapid changewithin a short time, or represent an obvious diversionfrom the environments that prevailed during most ofour evolution. In this sense, our eating, mobility,recreation, socialization and communication patterns,as well as our increasingly indoor existence should berelevant to the development of chronic diseases suchas obesity, asthma, CVD and depression.131

As broad as this perspective can be, its applicationcan help to sift through the tides of confusing healthinformation we are bombarded with each day. Forexample, examining the HRT-CVD relation under theevolutionary lens would have raised a big red flagabout a potential for harm; women did not evolve tohave lifelong active ovaries, despite the clear repro-ductive advantage such a trait would have conferred.The same would apply to the long-held protectiverelation between low fat diet and CVD risk132136,which was recently refuted in a randomized clinicaltrial.137 As huntergatherers, humans until 500 gen-erations ago consumed mainly wild and unprocessedfood foraged and hunted from their environment andrich in fats, fibre, vitamins and minerals.138140 So,the low-fat mania perhaps does not tell the wholestory about the dietCVD risk relation, especiallywhen corporate voices join the choir by promoting

Influences Mediators Behaviours Outcomes

Increased sedentary pastime; TV/PC/games

Over consumption ofjunk food-soft drinks

Individual-FamilyStructure (parenthood, siblings, pets), SES,

perceptions/attitudes (safety, healthy nutrition & behaviour), time structure

SchoolFood/PA policy, PA equipment, food sales

and sponsorships

Area Walkability, safety (crime-traffic), social

networks, deprivation, access to fast food orquality foods, recreational facilities

- Increased time-indoors- Increased access to food - Decreased opportunity of PA - Increased TV/screen time- Increased exposure to food advertisement

Decreased unstructured PA En

ergy

imba

lanc

e/at

risk

of

obe

sity/

obes

ity

Society Food policies and regulations, urban

planning, energy policies

Figure 1 Simplified multilevel approach to the study of environmental influences on obesity98

THE NULL HYPOTHESIS AND FUTURE OF EPIDEMIOLOGY 397

low-fat options, potentially creating a complacencythat can even lead to increased consumption.98

Another benefit of the evolutionary perspective is tohelp understand the difficulties facing genetic epide-miology, based on the fact that the direction ofevolution of traits is from phenotype to genotype andnot the opposite.141 This can underlie the existence ofmultiple genetic pathways to each outcome compli-cating the genetic study of chronic diseases. To addanother layer of complexity is the fact that phenotypefitness is not a simple measure, but represents acomplex fitness landscape involving the trade-off ofnumerous traits, each of which is the result of evengreater genetic diversity.142 For complex traits there-fore, considerable genetic variance can be maintainedwithin the population even in the face of strongselection forces.143 Such complexity undermines thehopes that genetic epidemiology will provide readyanswers to the puzzles of chronic diseases.

Finally, an evolutionary perspective can guideintervention research. Specifically, rather than basingnutritional and physical activity recommendations onobservational data that may well reflect a systematicdifference in the way we assemble our comparisongroups (e.g. those who eat healthy are likely to beengaged in other less-measurable health behaviours),we may need to inform such recommendations withknowledge of our ancestral dietary and activitypatterns.144,145 In addition, the appreciation that wehave a genetically hard-wired taste for energy-densefood can help us understand why most dietary-basedobesity interventions fail in the long run.98 So insteadof trying to work more forcefully against our instinctsby increasing the intensity and length of interven-tions, we may be better off trying to manipulate foodtaste and energy content,146 to imitate the Paleolithicdiet but without the excess calories.

Concluding remarksApparently, the golden era of major risk factor epidemi-ology is giving way to a less glorious, but certainly morecomplex and perhaps more important one of studyingsmall and interconnected risk factors related to ourever-changing lifestyles and environments. We perhapshave just scratched the surface of what epidemiologycan achieve and how it can help us understand howunfavourable environments are shaping our behaviourand health. Wading into this era with a major riskfactor mentality and instruments has created confu-sion. While endorsing new developments in epidemiol-ogical research, the era of chronic disease epidemiologymandates more than ever the need to rely on soundtheoretical models as well as accurately measurableoutcomes and exposures. So in contrast to the calls forlarger, longer and wider-reach epidemiology, what isadvocated here is to downsize epidemiological researchto what is testable, measurable and relevant. Anevolutionary perspective of the dynamic interaction ofhumans with their environments can help guide such aresearch agenda. In the age of publish or perish, vestedinterests, publication bias, scarce funding, mediashunger for hit news and web publishing, epidemiologycan best navigate these rough waters by being anchoredin a clear sense of its inner methodological constraintsand outer public health thrust.

FundingNIDA (R01DA024876-01 to W.M.).

AcknowledgementsWasim Maziak thankfully acknowledges Dr KennethWard for critically reading and editing this article.

Conflict of interest: None declared.

KEY MESSAGES

There is a crisis of credibility facing epidemiology today, brought about by the barrage of studies withless than optimal methods and conflicting results.

As epidemiology enters the era of chronic disease and small risk, it becomes more critical forepidemiological studies to be guided at the inception by well-grounded hypotheses, a dynamicperception of the relation between exposure-outcome and to utilize accurate assessment tools.

Novelty or methodological precision should not substitute for public health relevance when evaluatingepidemiological studies.

New conceptual (e.g. multilevel ecoepidemiology) and methodological (e.g. Mendelian randomization)advances should be embraced in light of the need to downsize epidemiology to what is testable,measurable and relevant.

An evolutionary and dynamic understanding of our interactions with our changing environments canprovide a guiding context for epidemiological research.

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