Post–Modern Epidemiology: When Methods Meet Matter

George Davey Smith*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

6 Citations (Scopus)
153 Downloads (Pure)


In the last third of the 20th century, etiological epidemiology within academia in high-income countries shifted its primary concern from attempting to tackle the apparent epidemic of noncommunicable diseases to an increasing focus on developing statistical and causal inference methodologies. This move was mutually constitutive with the failure of applied epidemiology to make major progress, with many of the advances in understanding the causes of noncommunicable diseases coming from outside the discipline, while ironically revealing the infectious origins of several major conditions. Conversely, there were many examples of epidemiologic studies promoting ineffective interventions and little evident attempt to account for such failure. Major advances in concrete understanding of disease etiology have been driven by a willingness to learn about and incorporate into epidemiology developments in biology and cognate data science disciplines. If fundamental epidemiologic principles regarding the rooting of disease risk within populations are retained, recent methodological developments combined with increased biological understanding and data sciences capability should herald a fruitful post-Modern Epidemiology world.

Original languageEnglish
Article numberkwz064
Pages (from-to)1410-1419
Number of pages10
JournalAmerican Journal of Epidemiology
Issue number8
Early online date16 Mar 2019
Publication statusPublished - 1 Aug 2019


  • Bradford Hill
  • causal inference
  • history of epidemiology
  • liability models
  • methodology
  • stochasticity

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