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.