Bayesian melding for estimating uncertainty in national HIV prevalence estimates

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Abstract
Objective: To construct confidence intervals for HIV prevalence in countries with generalised epidemics. Methods: In the Bayesian melding approach, a sample of country-specific epidemic curves describing HIV prevalence over time is derived based on time series of antenatal clinic prevalence data and general information on the parameters that describe the HIV epidemic. The prevalence trends at antenatal clinics are calibrated to population-based HIV prevalence estimates from national surveys. For countries without population based estimates, a general calibration method is developed. Based on the sample of calibrated epidemic curves, we derive annual 95% confidence intervals for HIV prevalence. The curve that best represents the data at antenatal clinics and population-based surveys, as well as general information about the epidemic, is chosen to represent the best estimates and predictions. Results: We present results for urban areas in Haiti and Namibia to illustrate the estimates and confidence intervals that are derived with the methodology.