Abstract
BackgroundThe tight epidemiological coupling between HIV and its associated opportunistic infections leads to challenges and opportunities for disease surveillance.Methodology/Principal FindingsWe 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/SignificanceWe 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.
Highlights
Epidemiological monitoring presents serious challenges in both developing and developed nations
We present a TB/HIV indicator, RTB/HIV, which permits us to compare the rate of change of TB incidence relative to that of HIV prevalence, and conduct a comparative study of TB/HIV codynamics across sub-Saharan Africa
The tightly knit co-dynamics of HIV and its opportunistic infections have led public health officials to promote and intensify collaborative activities among programs directed toward HIV/AIDS care and control with those focusing on HIV-associated diseases
Summary
Epidemiological monitoring presents serious challenges in both developing and developed nations. These challenges can stem from a lack of data, and from a wealth of data that can be disconnected and analyzed in non-integrative ways. Tuberculosis (TB) ranks among the most deadly and prevalent re-emerging infections of persons living with HIV/AIDS (PLWHA). In the last 20 years the number of new TB cases has tripled in high HIV prevalence countries, and at least 33% of the world’s 33.2 million PLWHA are infected with Mycobacterium tuberculosis [7]. The tight epidemiological coupling between HIV and its associated opportunistic infections leads to challenges and opportunities for disease surveillance
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