Point: incident exposures, prevalent exposures, and causal inference: does limiting studies to persons who are followed from first exposure onward damage epidemiology?

Am J Epidemiol. 2015 Nov 15;182(10):826-33. doi: 10.1093/aje/kwv225. Epub 2015 Oct 26.


The idea that epidemiologic studies should start from first exposure onward has been advocated in the past few years. The study of incident exposures is contrasted with studies of prevalent exposures in which follow-up may commence after first exposure. The former approach is seen as a hallmark of a good study and necessary for causal inference. We argue that studying incident exposures may be necessary in some situations, but it is not always necessary and is not the preferred option in many instances. Conducting a study involves decisions as to which person-time experience should be included. Although studies of prevalent exposures involve left truncation (missingness on the left), studies of incident exposures may involve right censoring (missingness on the right) and therefore may not be able to assess the long-term effects of exposure. These considerations have consequences for studies of dynamic (open) populations that involve a mixture of prevalent and incident exposures. We argue that studies with prevalent exposures will remain a necessity for epidemiology. The purpose of this paper is to restore the balance between the emphasis on first exposure cohorts and the richness of epidemiologic information obtained when studying prevalent exposures.

Keywords: dynamic populations; epidemiologic methods; incidence rate; incident exposure; left truncation; prevalent exposure; right censoring; study design.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bias
  • Causality*
  • Confounding Factors, Epidemiologic
  • Drug Combinations
  • Epidemiologic Methods*
  • Estrogen Replacement Therapy / methods
  • Estrogens / administration & dosage
  • Female
  • Humans
  • Incidence
  • Life Tables
  • Myocardial Infarction / prevention & control
  • Prevalence
  • Progestins / administration & dosage
  • Proportional Hazards Models
  • Public Health Surveillance / methods
  • Research Design*
  • Time Factors


  • Drug Combinations
  • Estrogens
  • Progestins