International Journal of Epidemiology, Volume 33, Number 3, pp. 441-442
IJE vol.33 no.3 © International Epidemiological Association 2004; all rights reserved.
Editor's Choice |
Classics in epidemiology: should they get it right?
One of the best ways to learn epidemiology is through reading the classic texts. Excellent anthologies of such classics have been published, and Snow on cholera, Goldberger on pellagra, Doll and Hill on smoking and lung cancer, Morris on exercise and coronary heart disease, and Keys and Stamler on cholesterol and heart disease are tremendous models for how to get things right. In the International Journal of Epidemiology we have reprinted several early (and often under-appreciated) papers in which novel empirical or methodological advances, that now would be considered central to epidemiological thinking and methodology, were advanced.1–14
There is a natural tendency to apply asymmetrical criteria to papers that reached what, with hindsight, were correct or incorrect conclusions. In the former case we look for the exemplary design, analysis and interpretation, and in the latter case for the flaws that should have been detected at the time. There is, however, at least as much to be learnt from studies that have reached what now appear to be the wrong conclusion as those that got it right. In many cases these will have been carried out to the highest contemporary standards, and yet somewhere along the line the authors were misled. In this issue of the IJE we reprint a highly cited and influential meta-analysis and systematic review of observational studies on hormone replacement therapy (HRT) and coronary heart disease risk, first published in 1991.15 This paper is a model of clarity, yet with the findings of randomized controlled trials (RCT) of HRT the conclusion it reached—that the apparent protective effect of HRT was ‘unlikely to be explained by confounding factors’—appears wrong. A series of commentaries16–21 debate why this situation arose. While the commentators are not unanimous in their opinions it is, at the very least, clear that observational epidemiology may be more fallible than some have suggested. Other examples of apparently convincing findings from observational studies failing to be confirmed though RCTs exist with respect to associations between several dietary factors, in particular anti-oxidant vitamins, and disease.22 However, in only a small minority of cases can the findings of observational epidemiological studies be compared to those from randomized trials. Surely assuming that the various factors that mitigated against a correct interpretation of the effect of HRT or anti-oxidants on disease do not apply in these cases is not a sensible way to proceed?
One appropriate response to demonstrations of such problems in observational epidemiology is through improved methodology, and several papers in this issue of the IJE address such issues.23–26 An important clue as to whether the findings of individual-level associations in observational epidemiological studies are likely to be causal can come from time-trend or ecological data. For example it has been suggested that cannabis use has a major influence on the risk of schizophrenia, yet large increases in cannabis use in a population that are not accompanied by any increase in schizophrenia suggest that such associations are non-causal.27 Similarly in this issue of the IJE Foliaki et al. test the proposition that antibiotic use early in life may increase the subsequent risk of asthma, as observed in several observational studies.28 They demonstrate that, ecologically, there is no robust association between antibiotic use and the prevalence of asthma or other such allergic diseases, which throws some doubt on the causal nature of the previously observed associations. In the case of changes in practices considered to be related to human immunodeficiency virus (HIV) risk, ecological data reported in this issue of the IJE support an important causal role, as trends in HIV infection in Uganda—where extensive interventions have been put in place—are considerably more favourable than in other sub-Saran African countries, including the closely geographically proximal districts of Kenya.29
Journal editors spend much of their time writing rejection letters to authors who have submitted papers. A consideration of the ‘classic’ papers that got things right but failed to have much impact at the time, perhaps because they appeared in lower impact journals, following rejection from more prestigious journals, emphasizes this. Authors are, understandably, often severely displeased, and the letters we sometimes receive in response to our rejection letters generally do not make pleasant reading. In lieu of sharing any real letters from authors whose papers have been rejected we reprint Fleur Adcock's exemplary response to a rejection slip.30 We look forward to receiving similar reactions from disappointed authors in future.
References
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15 Stampfer MJ, Colditz GA. Estrogen replacement therapy and coronary heart disease: a quantitative assessment of the epidemiologic evidence. Int J Epidemiol 2004;33:445–53.
16 Stampfer M. Commentary: Hormones and heart disease: do trials and observational studies address different questions? Int J Epidemiol 2004;33:454–55.
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25 Leung MW, Yen IH, Minkler M. Community based participatory research: a promising approach for increasing epidemiology's relevance in the 21st century. Int J Epidemiol 2004;33:499–506.
26 Cologne JB, Sharp GB, Neriishi K, Verkasalo PK, Land CE, Nakachi K. Improving the efficiency of nested case-control studies of interaction by selecting controls using counter matching on exposure. Int J Epidemiol 2004;33:485–92.
27 Degenhardt L, Hall W, Lynskey N. Testing hypotheses about the relationship between cannabis use and psychosis. Drug Alcohol Depend 2003;71:37–48.[CrossRef][Web of Science][Medline]
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29 Moore DM, Hogg RS. Trends in antenatal human immuno-deficiency virus prevalence in Western Kenya and Eastern Uganda: evidence of differences in health policies? Int J Epidemiol 2004;33:554–60.
30 Adcock F. Future Work. Int J Epidemiol 2004;33:468.
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