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Leveraging tuberculosis case relative locations to enhance case detection and linkage to care in Swaziland
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 p...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5798177/ https://www.ncbi.nlm.nih.gov/pubmed/29445773 http://dx.doi.org/10.1186/s41256-018-0058-y |
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author | Brunetti, Marie Rajasekharan, Sathyanath Ustero, Piluca Ngo, Katherine Sikhondze, Welile Mzileni, Buli Mandalakas, Anna Kay, Alexander W. |
author_facet | Brunetti, Marie Rajasekharan, Sathyanath Ustero, Piluca Ngo, Katherine Sikhondze, Welile Mzileni, Buli Mandalakas, Anna Kay, Alexander W. |
author_sort | Brunetti, Marie |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5798177 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-57981772018-02-14 Leveraging tuberculosis case relative locations to enhance case detection and linkage to care in Swaziland Brunetti, Marie Rajasekharan, Sathyanath Ustero, Piluca Ngo, Katherine Sikhondze, Welile Mzileni, Buli Mandalakas, Anna Kay, Alexander W. Glob Health Res Policy Short Report 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. BioMed Central 2018-02-05 /pmc/articles/PMC5798177/ /pubmed/29445773 http://dx.doi.org/10.1186/s41256-018-0058-y Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Short Report Brunetti, Marie Rajasekharan, Sathyanath Ustero, Piluca Ngo, Katherine Sikhondze, Welile Mzileni, Buli Mandalakas, Anna Kay, Alexander W. Leveraging tuberculosis case relative locations to enhance case detection and linkage to care in Swaziland |
title | Leveraging tuberculosis case relative locations to enhance case detection and linkage to care in Swaziland |
title_full | Leveraging tuberculosis case relative locations to enhance case detection and linkage to care in Swaziland |
title_fullStr | Leveraging tuberculosis case relative locations to enhance case detection and linkage to care in Swaziland |
title_full_unstemmed | Leveraging tuberculosis case relative locations to enhance case detection and linkage to care in Swaziland |
title_short | Leveraging tuberculosis case relative locations to enhance case detection and linkage to care in Swaziland |
title_sort | leveraging tuberculosis case relative locations to enhance case detection and linkage to care in swaziland |
topic | Short Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5798177/ https://www.ncbi.nlm.nih.gov/pubmed/29445773 http://dx.doi.org/10.1186/s41256-018-0058-y |
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