Tuberculosis (TB) is the leading cause of infectious disease deaths globally. With TB incidence currently decreasing by 1.9% annually, achieving the World Health Organization's ENDTB goal of TB elimination by 2050 will require a substantial reworking of our TB control approach. Drug-resistant TB (DR-TB) data are particularly lacking in spatial granularity as surveillance for DR-TB can be especially resource intensive. Previous elimination strategies for other diseases have only been successful once spatial variation in disease incidence was identified and then locally relevant interventions implemented. TB and DR-TB elimination strategies require improved real-time spatial surveillance tools that incorporate individual movement over time. South Africa is an ideal setting for our work with both the second highest TB incidence globally and a central National Health Laboratory Service (NHLS) database of routinely-collected laboratory results. We propose to develop a framework to track rifampicin-resistant (RR) TB patients in time and space, using data from the Western Cape Province. We will focus on RR-TB patients, in this proof of principle study, due to their increased monitoring during treatment. We hypothesize that NHLS data can be used to identify facility transfers of RR-TB patients during treatment and facilities that have high rates of patients lost from care.
In aim 1 we will use probabilistic record linkage to create unique patient identifiers in the NHLS data for all RR-TB patients, allowing us to link an individual's test results and track those confirmed cases spatially and temporally.
In aim 2 we will quantify the movement patterns of RR-TB patients during treatment, mapping these movements to identify common travel patterns.
In aim 3 we will identify locations with high attrition of RR-TB patients, allowing for the design of interventions to target those locations and improve retention in care. This contribution is significant because it will develop a framework to use currently existing, routinely-collected data to monitor RR-TB patients during treatment spatially and temporally, enabling local public health professionals to develop locally appropriate interventions to improve retention and accelerating TB elimination. The proposed work is innovative because it will capture longitudinal RR-TB data, allowing us to track retention in care and patient movements. Thus, it will provide a method to monitor, in near-real time, RR-TB patients during treatment. This will allow policy makers to design locally-relevant interventions targeting high attrition areas and highly mobile populations, preventing further spread and ultimately reducing disease and mortality due to RR-TB. By using routinely collected laboratory data, our model adds minimal financial costs, can be implemented prospectively, and can be adapted for similar TB high-burden, middle-income settings. These results will lay the groundwork for nationwide roll-out and future R01 studies that incorporate genetic data, HIV information, and develop spatial methods to refine our understanding of the impact of population movement on TB dynamics.
The proposed research is relevant to public health because it will provide a framework to use routinely collected South African laboratory data to track rifampicin-resistant tuberculosis patients through time and space. In turn this will allow us to identify common transfer patterns and facilities with high ?loss from care? rates and enable the targeting of these locations with locally appropriate interventions. This is relevant to the NIH's mission because identifying and targeting these areas will ultimately reduce morbidity and mortality due to tuberculosis.