A Bayesian hierarchical modeling approach to combining multiple data sources: A case study in size estimation

To combat the HIV/AIDS pandemic effectively, certain key populations play a critical role. Examples of such key populations include sex workers, injection drug users, and men who have sex with men. While having accurate estimates for the size of these key populations is important, any attempt to directly contact or count members of these populations is difficult. As a result, indirect methods are used to produce size estimates. Multiple approaches for estimating the size of such populations have been suggested but often give conflicting results. It is therefore necessary to have a principled way to combine and reconcile these results. To this end, we present a Bayesian hierarchical model for estimating the size of key populations that combines multiple estimates and sources of information. The proposed model can make use of multiple years of data and explicitly models the systematic error in the data sources used. We use the model to estimate the size of injection drug users in Ukraine. We evaluate the appropriateness of the model and compare the contribution of each data source to the final estimates.