title: Estimating the Sizes of Populations At Risk of HIV Infection From Multiple Data Sources Using a Bayesian Hierarchical Model.
abstract: In most countries in the world outside of sub-Saharan Africa, HIV is largely concentrated in sub-populations whose behavior puts them at higher risk of contracting and transmitting HIV, such as people who inject drugs, sex workers and men who have sex with men. Estimating the size of these sub-populations is important for assessing overall HIV prevalence and designing effective interventions. We present a Bayesian hierarchical model for estimating the sizes of local and national HIV key affected populations. The model incorporates multiple commonly used data sources including mapping data, surveys, interventions, capture-recapture data, estimates or guesstimates from organizations, and expert opinion. The proposed model is used to estimate the numbers of people who inject drugs in Bangladesh.
authors.affiliation: Department of Statistics, The Pennsylvania State Univeristy.
authors.forename: Le
authors.initials: L
authors.lastname: Bao
authors.affiliation: Departments of Statistics and Sociology, University of Washington.
authors.forename: Adrian E
authors.initials: AE
authors.lastname: Raftery
authors.affiliation: UNAIDS Regional Support Team for Asia and the Pacific.
authors.forename: Amala
authors.initials: A
authors.lastname: Reddy
keywords: Capture-recapture; Expert opinion; HIV/AIDS epidemic; Heterogeneity; Injecting drug user; Key affected population; Mapping data; Markov chain Monte Carlo; Multiplier method