Smith, CM;
Lessells, R;
Grant, AD;
Herbst, K;
Tanser, F;
(2018)
Spatial clustering of drug-resistant tuberculosis in Hlabisa subdistrict, KwaZulu-Natal, 2011–2015.
International Journal of Tuberculosis and Lung Disease
, 22
(3)
pp. 287-293.
10.5588/ijtld.17.0457.
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Abstract
SETTING: Incidencerates of tuberculosis (TB) in South Africa are among the highest in the world, and drug resistance is a major concern. Understanding geographic variations in disease may guide targeted interventions. OBJECTIVE: To characterise the spatial distribution of drug-resistant TB (DR-TB) in a rural area of KwaZulu-Natal, South Africa, and to test for clustering. DESIGN: This was a cross-sectional analysis of DR-TB patients managed at a rural district hospital from 2011 to 2015. We mapped all patients in hospital data to local areas, and then linked to a population-based demographic surveillance system to map the patients to individual homesteads. We used kernel density estimation to visualise the distribution of disease and tested for clustering using spatial scan statistics. RESULTS: There were 489 patients with DR-TB in the subdistrict; 111 lived in the smaller demographic surveillance area. Spatial clustering analysis identified a high-risk cluster (relative risk of DR-TB inside vs. outside cluster 3.0, P < 0.001) in the south-east, a region characterised by high population density and a high prevalence of human immunodeficiency virus infection. CONCLUSION: We have demonstrated evidence of a geographic high-risk cluster of DR-TB. This suggests that targeting interventions to spatial areas of highest risk, where transmission may be ongoing, could be effective.
Type: | Article |
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Title: | Spatial clustering of drug-resistant tuberculosis in Hlabisa subdistrict, KwaZulu-Natal, 2011–2015 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.5588/ijtld.17.0457 |
Publisher version: | https://doi.org/10.5588/ijtld.17.0457 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | HIV infection; disease clustering; geographic information systems |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics |
URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10037784 |
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