Conditions for valid estimation of causal effects on prevalence in cross-sectional and other studies

• Type of Material: Article
• Authors: William Flanders, Emory University; Mitchel Klein, Emory University; Maria Mirabelli, Emory University
• Language: English
• Date: 2016-06-01
• Publisher: Elsevier
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2016 Elsevier Inc.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.annepidem.2016.04.010
• Title of Journal or Parent Work: Annals of Epidemiology
• ISSN: 1047-2797
• Volume: 26
• Issue: 6
• Start Page: 389
• End Page: 394
• Grant/Funding Information: This publication was supported by US EPA grant R834799.
• Keywords: ODDS RATIO; Public, Environmental & Occupational Health; INFERENCE; Cross-sectional studies; COMMUNITY; Causal effects; PRINCIPAL STRATIFICATION; HEALTH; Survey; DEFINITION; Prevalence; Validity; EPIDEMIOLOGIC RESEARCH; POTENTIAL OUTCOMES; CLINICAL-TRIAL; BIAS; Target population; Science & Technology; Life Sciences & Biomedicine
• Research Categories: Health Sciences, Epidemiology; Health Sciences, Public Health; Biology, Biostatistics

Abstract

Purpose: Causal effects in epidemiology are almost invariably studied by considering disease incidence even when prevalence data are used to estimate the causal effect. For example, if certain conditions are met, a prevalence odds ratio can provide a valid estimate of an incidence rate ratio. Our purpose and main result are conditions that assure causal effects on prevalence can be estimated in cross-sectional studies, even when the prevalence odds ratio does not estimate incidence. Methods: Using a general causal effect definition in a multivariate counterfactual framework, we define causal contrasts that compare prevalences among survivors from a target population had all been exposed at baseline with that prevalence had all been unexposed. Although prevalence is a measure reflecting a moment in time, we consider the time sequence to study causal effects. Results: Effects defined using a contrast of counterfactual prevalences can be estimated in an experiment and, with conditions provided, in cross-sectional studies. Proper interpretation of the effect includes recognition that the target is the baseline population, defined at the age or time of exposure. Conclusions: Prevalences are widely reported, readily available measures for assessing disabilities and disease burden. Effects on prevalence are estimable in cross-sectional studies but only if appropriate conditions hold.