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

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  • Zoë M McLaren
  • Kathryn Schnippel
  • Alana Sharp

Abstract

Objective: Identifying 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. Methods: We 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. Findings: Higher TB prevalence is statistically significantly associated with more urban municipalities (slope coefficient β1 = 0.129, p

Suggested Citation

  • Zoë M McLaren & Kathryn Schnippel & Alana Sharp, 2016. "A Data-Driven Evaluation of the Stop TB Global Partnership Strategy of Targeting Key Populations at Greater Risk for Tuberculosis," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-12, October.
    • Handle: RePEc:plo:pone00:0163083
    DOI: 10.1371/journal.pone.0163083
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    References listed on IDEAS

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    1. Juliet N Sekandi & Kevin Dobbin & James Oloya & Alphonse Okwera & Christopher C Whalen & Phaedra S Corso, 2015. "Cost-Effectiveness Analysis of Community Active Case Finding and Household Contact Investigation for Tuberculosis Case Detection in Urban Africa," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-18, February.
    2. Lönnroth, Knut & Jaramillo, Ernesto & Williams, Brian G. & Dye, Christopher & Raviglione, Mario, 2009. "Drivers of tuberculosis epidemics: The role of risk factors and social determinants," Social Science & Medicine, Elsevier, vol. 68(12), pages 2240-2246, June.
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    1. John Friesen & Victoria Friesen & Ingo Dietrich & Peter F. Pelz, 2020. "Slums, Space, and State of Health—A Link between Settlement Morphology and Health Data," IJERPH, MDPI, vol. 17(6), pages 1-28, March.

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