Post-Modern Epidemiology: When Methods Meet Matter

Abstract

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.

Keywords: Bradford Hill; causal inference; history of epidemiology; liability models; methodology; stochasticity.

Figure 1.

“The chance events that contribute to disease aetiology can be analysed at many levels, from the social to the molecular. Consider Winnie (Figure 1); why has she managed to smoke for 93 years without developing lung cancer? Perhaps her genotype is particularly resilient in this regard? Or perhaps many years ago the postman called at one particular minute rather than another, and when she opened the door a blast of wind caused Winnie to cough, and through this dislodge a metaplastic cell from her alveoli? Individual biographies would involve a multitude of such events, and even the most enthusiastic lifecourse epidemiologist could not hope to capture them [54]. Perhaps chance is an under-appreciated contributor to the epidemiology of disease” (, p. 547). This photo of Winnie Langley, who smoked for 93 years, appeared in The Sun (138) and was reprinted in the International Journal of Epidemiology (45) Reprinted with permission.

Figure 2.

The major contribution of stochastic events and the bounds to personalized medicine is illustrated by cancers of bilateral organs.

Figure 3.

Number of Google Scholar citations from 1965 onward of Austin Bradford Hill’s seminal proto-triangulation paper “The Environment and Disease: Association or Causation?” (29) (A) and “causal inference” and “epidemiology” (B). Data from 2018 are preliminary and probably incomplete.

Figure 4.

An indicative powerpoint from a recent talk on causal inference (78).