Estimation of HIV Burden through Bayesian Evidence Synthesis

TL;DR

This paper introduces a Bayesian evidence synthesis method for estimating HIV burden, integrating diverse indirect data sources to improve accuracy and has been adopted as the official UK estimation approach since 2005.

Contribution

The paper presents a novel Bayesian evidence synthesis framework that combines multiple data sources for HIV burden estimation, surpassing traditional methods in flexibility and comprehensiveness.

Findings

Successfully estimates HIV prevalence using indirect data sources.

Adopted as the official HIV estimation method in the UK since 2005.

Enhances accuracy of HIV burden estimates through Bayesian integration.

Abstract

Planning, implementation and evaluation of public health policies to control the human immunodeficiency virus (HIV) epidemic require regular monitoring of disease burden. This includes the proportion living with HIV, whether diagnosed or not, and the rate of new infections in the general population and in specific risk groups and regions. Estimation of these quantities is not straightforward: data informing them directly are not typically available, but a wealth of indirect information from surveillance systems and ad hoc studies can inform functions of these quantities. In this paper we show how the estimation problem can be successfully solved through a Bayesian evidence synthesis approach, relaxing the focus on "best available" data to which classical methods are typically restricted. This more comprehensive and flexible use of evidence has led to the adoption of our proposed approach as the official method to estimate HIV prevalence in the United Kingdom since 2005.