A Bayesian multivariate approach to estimating the prevalence of a superordinate category of disorders

Objective: Epidemiological research plays an important role in public health, facilitated by the meta-analytic aggregation of epidemiological trials into a single, more powerful estimate. This form of aggregation is complicated when estimating the prevalence of a superordinate category of disorders (e.g., "any anxiety disorder," "any cardiac disorder") because epidemiological studies rarely include all of the disorders selected to define the superordinate category. In this paper, we suggest that estimating the prevalence of a superordinate category based on studies with differing operationalization of that category (in the form of different disorders measured) is both common and ill-advised. Our objective is to provide a better approach.

Methods: We propose a multivariate method using individual disorder prevalences to produce a fully Bayesian estimate of the probability of having one or more of those disorders. We validate this approach using a recent case study and parameter recovery simulations.

Results: Our approach produced less biased and more reliable estimates than other common approaches, which were at times highly biased.

Conclusion: Although our approach entails additional effort (e.g., contacting authors for individual participant data), the improved accuracy of the prevalence estimates obtained is significant and therefore recommended.