Modeling of the HIV infection epidemic in the Netherlands: A multi-parameter evidence synthesis approach

TL;DR

This paper demonstrates how multi-parameter evidence synthesis (MPES), a Bayesian statistical approach, effectively integrates diverse data sources to estimate HIV prevalence in the Netherlands, aiding public health decisions.

Contribution

It applies a Bayesian MPES method to model HIV prevalence using heterogeneous data, showcasing its ability to reconcile conflicting evidence and incorporate auxiliary information.

Findings

Reliable HIV prevalence estimates were produced for the Netherlands in 2007.

MPES effectively integrated diverse data sources and expert opinions.

The approach demonstrated robustness in handling biased and incomplete evidence.

Abstract

Multi-parameter evidence synthesis (MPES) is receiving growing attention from the epidemiological community as a coherent and flexible analytical framework to accommodate a disparate body of evidence available to inform disease incidence and prevalence estimation. MPES is the statistical methodology adopted by the Health Protection Agency in the UK for its annual national assessment of the HIV epidemic, and is acknowledged by the World Health Organization and UNAIDS as a valuable technique for the estimation of adult HIV prevalence from surveillance data. This paper describes the results of utilizing a Bayesian MPES approach to model HIV prevalence in the Netherlands at the end of 2007, using an array of field data from different study designs on various population risk subgroups and with a varying degree of regional coverage. Auxiliary data and expert opinion were additionally incorporated to resolve issues arising from biased, insufficient or inconsistent evidence. This case study offers a demonstration of the ability of MPES to naturally integrate and critically reconcile disparate and heterogeneous sources of evidence, while producing reliable estimates of HIV prevalence used to support public health decision-making.