UC Berkeley

Background

The tight epidemiological coupling between HIV and its associated opportunistic infections leads to challenges and opportunities for disease surveillance.

Methodology/principal findings

We review efforts of WHO and collaborating agencies to track and fight the TB/HIV co-epidemic, and discuss modeling--via mathematical, statistical, and computational approaches--as a means to identify disease indicators designed to integrate data from linked diseases in order to characterize how co-epidemics change in time and space. We present R(TB/HIV), an index comparing changes in TB incidence relative to HIV prevalence, and use it to identify those sub-Saharan African countries with outlier TB/HIV dynamics. R(TB/HIV) can also be used to predict epidemiological trends, investigate the coherency of reported trends, and cross-check the anticipated impact of public health interventions. Identifying the cause(s) responsible for anomalous R(TB/HIV) values can reveal information crucial to the management of public health.

Conclusions/significance

We frame our suggestions for integrating and analyzing co-epidemic data within the context of global disease monitoring. Used routinely, joint disease indicators such as R(TB/HIV) could greatly enhance the monitoring and evaluation of public health programs.