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



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.



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.



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.
Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA; Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA. Electronic address: wflande@sph.emory.edu.
W Dana
WD
Flanders
Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA; Department of Environmental and Occupational Health, Rollins School of Public Health, Emory University, Atlanta, GA.
Mitchel
M
Klein
Air Pollution and Respiratory Health Branch, Division of Environmental Hazards and Health Effects, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, GA.
Maria C
MC
Mirabelli
D015984:Causality; D003430:Cross-Sectional Studies; D006233:Persons with Disabilities / Q000706:statistics & numerical data*; D004812:Epidemiologic Methods; D005260:Female; D006301:Health Services Needs and Demand; D006801:Humans; D008297:Male; D015233:Models, Statistical; D015999:Multivariate Analysis; D015995:Prevalence; D012680:Sensitivity and Specificity; D018434:Sickness Impact Profile; D017741:Survivors / Q000706:statistics & numerical data
Causal effects; Cross-sectional studies; Prevalence; Survey; Target population; Validity