A Data-Driven Evaluation of the Stop TB Global Partnership Strategy of Targeting Key Populations at Greater Risk for Tuberculosis.

Abstract

ObjectiveIdentifying those infected with tuberculosis (TB) is an important component of any strategy for reducing TB transmission and population prevalence. The Stop TB Global Partnership recently launched an initiative with a focus on key populations at greater risk for TB infection or poor clinical outcomes, due to housing and working conditions, incarceration, low household income, malnutrition, co-morbidities, exposure to tobacco and silica dust, or barriers to accessing medical care. To achieve operational targets, the global health community needs effective, low cost, and large-scale strategies for identifying key populations. Using South Africa as a test case, we assess the feasibility and effectiveness of targeting active case finding to populations with TB risk factors identified from regularly collected sources of data. Our approach is applicable to all countries with TB testing and census data. It allows countries to tailor their outreach activities to the particular risk factors of greatest significance in their national context.MethodsWe use a national database of TB test results to estimate municipality-level TB infection prevalence, and link it to Census data to measure population risk factors for TB including rates of urban households, informal settlements, household income, unemployment, and mobile phone ownership. To examine the relationship between TB prevalence and risk factors, we perform linear regression analysis and plot the set of population characteristics against TB prevalence and TB testing rate by municipality. We overlay lines of best fit and smoothed curves of best fit from locally weighted scatter plot smoothing.FindingsHigher TB prevalence is statistically significantly associated with more urban municipalities (slope coefficient β1 = 0.129, p < 0.0001, R2 = 0.133), lower mobile phone access (β1 = -0.053, p < 0.001, R2 = 0.089), lower unemployment rates (β1 = -0.020, p = 0.003, R2 = 0.048), and a lower proportion of low-income households (β1 = -0.048, p < 0.0001, R2 = 0.084). Municipalities with more low-income households also have marginally higher TB testing rates, however, this association is not statistically significant (β1 = -0.025, p = 0.676, R2 = 0.001). There is no relationship between TB prevalence and the proportion of informal settlement households (β1 = 0.021, p = 0.136, R2 = 0.014).ConclusionsThese analyses reveal that the set of characteristics identified by the Global Plan as defining key populations do not adequately predict populations with high TB burden. For example, we find that higher TB prevalence is correlated with more urbanized municipalities but not with informal settlements. We highlight several factors that are counter-intuitively those most associated with high TB burdens and which should therefore play a large role in any effective targeting strategy. Targeting active case finding to key populations at higher risk of infection or poor clinical outcomes may prove more cost effective than broad efforts. However, these results should increase caution in current targeting of active case finding interventions.