Estimates of Age-Specific Reductions in HIV Prevalence in Uganda: Bayesian Melding Estimation and Probabilistic Population Forecast with an HIV-enabled Cohort Component Projection Model

BACKGROUND Much of our knowledge of the epidemiology and demography of HIV epidemics in Africais derived from models fit to sparse, non-representative data. These often average over ageand other important dimensions, rarely quantify uncertainty, and typically do not imposeconsistency on the epidemiology and the demography of the population. OBJECTIVE This work conducts an empirical investigation of the history of the HIV epidemic inUganda and Tanzania through the late 1990s, focusing on sex-age-specific incidence,uses those results to produce probabilistic forecasts of HIV prevalence ten years later,and compares those to measures of HIV prevalence at the later time to describe the sexagepattern of changes in prevalence over the intervening period. METHODS We adapt an epidemographic model of a population affected by HIV so that its parameterscan be estimated using both the Bayesian melding with IMIS estimation methodand maximum likelihood methods. Using the Bayesian version of the model we produceprobabilistic forecasts of the population with HIV. RESULTS We produce estimates of sex-age-specific HIV incidence in Uganda and Tanzania in thelate 1990s, produce probabilistic forecasts of the HIV epidemics in Uganda and Tanzaniaduring the early 2000s, describe the sex-age pattern of changes in HIV prevalence inUganda during the early 2000s, and compare the performance and results of the Bayesianand maximum likelihood estimation procedures. CONCLUSIONS We demonstrate that: (1) it is possible to model HIV epidemics in Africa taking accountof sex and age, (2) there are important advantages to the Bayesian estimation method,including rigorous quantification of uncertainty and the ability to make probabilistic forecasts,and (3) that there were important age-specific changes in HIV incidence in Ugandaduring the early 2000s.