Global Health Research and Policy (Feb 2018)

Leveraging tuberculosis case relative locations to enhance case detection and linkage to care in Swaziland

  • Marie Brunetti,
  • Sathyanath Rajasekharan,
  • Piluca Ustero,
  • Katherine Ngo,
  • Welile Sikhondze,
  • Buli Mzileni,
  • Anna Mandalakas,
  • Alexander W. Kay

DOI
https://doi.org/10.1186/s41256-018-0058-y
Journal volume & issue
Vol. 3, no. 1
pp. 1 – 6

Abstract

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Abstract Background In Swaziland, as in many high HIV/TB burden settings, there is not information available regarding the household location of TB cases for identifying areas of increased TB incidence, limiting the development of targeted interventions. Data from “Butimba”, a TB REACH active case finding project, was re-analyzed to provide insight into the location of TB cases surrounding Mbabane, Swaziland. Objective The project aimed to identify geographical areas with high TB burdens to inform active case finding efforts. Methods Butimba implemented household contact tracing; obtaining landmark based, informal directions, to index case homes, defined here as relative locations. The relative locations were matched to census enumeration areas (known location reference areas) using the Microsoft Excel Fuzzy Lookup function. Of 403 relative locations, an enumeration area reference was detected in 388 (96%). TB cases in each census enumeration area and the active case finders in each Tinkhundla, a local governmental region, were mapped using the geographic information system, QGIS 2.16. Results Urban Tinkhundla predictably accounted for most cases; however, after adjusting for population, the highest density of cases was found in rural Tinkhundla. There was no correlation between the number of active case finders currently assigned to the 7 Tinkhundla surrounding Mbabane and the total number of TB cases (Spearman rho = −0.57, p = 0.17) or the population adjusted TB cases (Spearman rho = 0.14, p = 0.75) per Tinkhundla. Discussion Reducing TB incidence in high-burden settings demands novel analytic approaches to study TB case locations. We demonstrated the feasibility of linking relative locations to more precise geographical areas, enabling data-driven guidance for National Tuberculosis Programs’ resource allocation. In collaboration with the Swazi National Tuberculosis Control Program, this analysis highlighted opportunities to better align the active case finding national strategy with the TB disease burden.

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