Extensively drug-resistant tuberculosis 'hotspots' and sociodemographic associations in Durban, South Africa

M. L. Peterson, N. R. Gandhi, J. Clennon, K. N. Nelson, N. Morris, N. Ismail, S. Allana, A. Campbell, James C.M. Brust, S. C. Auld, B. Mathema, K. Mlisana, P. Moodley, N. S. Shah

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

BACKGROUND: In KwaZulu-Natal, South Africa, the incidence of extensively drug-resistant tuberculosis (XDR-TB) is driven by the transmission of resistant strains. As data suggest that cases may be spatially clustered, we sought to identify 'hotspots' and describe these communities. METHODS : We enrolled XDR-TB patients diagnosed from 2011 to 2014 in eThekwini. Global positioning system (GPS) coordinates for participant homes were collected and hotspots were identified based on population- adjusted XDR-TB incidence. The sociodemographic features of hotspots were characterised using census data. For a subset of participants, we mapped non-home XDR-TB congregate locations and compared these with results including only homes. RESULT S : Among 132 participants, 75 (57%) were female and 87 (66%) lived in urban or suburban locations. Fifteen of 197 census tracts were identified as XDR-TB hotspots with 95% confidence. Four spatial mapping methods identified one large hotspot in northeastern eThekwini. Hotspot communities had higher proportions of low educational attainment (12% vs. 9%) and unemployment (29.3% vs. 20.4%), and lower proportion of homes with flush toilets (36.4% vs. 68.9%). The case density shifted towards downtown Durban when congregate locations (e.g., workplaces) for 43 (33%) participants were mapped. CONCLUS IONS : In eThekwini, XDR-TB case homes were clustered into hotspots with more poverty indicators than non-hotspots. Prevention efforts targeting hotspot communities and congregate settings may be effective in reducing community transmission.

Original languageEnglish (US)
Pages (from-to)720-727
Number of pages8
JournalInternational Journal of Tuberculosis and Lung Disease
Volume23
Issue number6
DOIs
StatePublished - Jun 1 2019

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Extensively Drug-Resistant Tuberculosis
South Africa
Censuses
Geographic Information Systems
Unemployment
Incidence
Poverty
Workplace

Keywords

  • Activity space
  • Hotspot
  • Sociodemographic
  • TB
  • XDR-TB

ASJC Scopus subject areas

  • Pulmonary and Respiratory Medicine
  • Infectious Diseases

Cite this

Extensively drug-resistant tuberculosis 'hotspots' and sociodemographic associations in Durban, South Africa. / Peterson, M. L.; Gandhi, N. R.; Clennon, J.; Nelson, K. N.; Morris, N.; Ismail, N.; Allana, S.; Campbell, A.; Brust, James C.M.; Auld, S. C.; Mathema, B.; Mlisana, K.; Moodley, P.; Shah, N. S.

In: International Journal of Tuberculosis and Lung Disease, Vol. 23, No. 6, 01.06.2019, p. 720-727.

Research output: Contribution to journalArticle

Peterson, ML, Gandhi, NR, Clennon, J, Nelson, KN, Morris, N, Ismail, N, Allana, S, Campbell, A, Brust, JCM, Auld, SC, Mathema, B, Mlisana, K, Moodley, P & Shah, NS 2019, 'Extensively drug-resistant tuberculosis 'hotspots' and sociodemographic associations in Durban, South Africa', International Journal of Tuberculosis and Lung Disease, vol. 23, no. 6, pp. 720-727. https://doi.org/10.5588/ijtld.18.0575
Peterson, M. L. ; Gandhi, N. R. ; Clennon, J. ; Nelson, K. N. ; Morris, N. ; Ismail, N. ; Allana, S. ; Campbell, A. ; Brust, James C.M. ; Auld, S. C. ; Mathema, B. ; Mlisana, K. ; Moodley, P. ; Shah, N. S. / Extensively drug-resistant tuberculosis 'hotspots' and sociodemographic associations in Durban, South Africa. In: International Journal of Tuberculosis and Lung Disease. 2019 ; Vol. 23, No. 6. pp. 720-727.
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T1 - Extensively drug-resistant tuberculosis 'hotspots' and sociodemographic associations in Durban, South Africa

AU - Peterson, M. L.

AU - Gandhi, N. R.

AU - Clennon, J.

AU - Nelson, K. N.

AU - Morris, N.

AU - Ismail, N.

AU - Allana, S.

AU - Campbell, A.

AU - Brust, James C.M.

AU - Auld, S. C.

AU - Mathema, B.

AU - Mlisana, K.

AU - Moodley, P.

AU - Shah, N. S.

PY - 2019/6/1

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N2 - BACKGROUND: In KwaZulu-Natal, South Africa, the incidence of extensively drug-resistant tuberculosis (XDR-TB) is driven by the transmission of resistant strains. As data suggest that cases may be spatially clustered, we sought to identify 'hotspots' and describe these communities. METHODS : We enrolled XDR-TB patients diagnosed from 2011 to 2014 in eThekwini. Global positioning system (GPS) coordinates for participant homes were collected and hotspots were identified based on population- adjusted XDR-TB incidence. The sociodemographic features of hotspots were characterised using census data. For a subset of participants, we mapped non-home XDR-TB congregate locations and compared these with results including only homes. RESULT S : Among 132 participants, 75 (57%) were female and 87 (66%) lived in urban or suburban locations. Fifteen of 197 census tracts were identified as XDR-TB hotspots with 95% confidence. Four spatial mapping methods identified one large hotspot in northeastern eThekwini. Hotspot communities had higher proportions of low educational attainment (12% vs. 9%) and unemployment (29.3% vs. 20.4%), and lower proportion of homes with flush toilets (36.4% vs. 68.9%). The case density shifted towards downtown Durban when congregate locations (e.g., workplaces) for 43 (33%) participants were mapped. CONCLUS IONS : In eThekwini, XDR-TB case homes were clustered into hotspots with more poverty indicators than non-hotspots. Prevention efforts targeting hotspot communities and congregate settings may be effective in reducing community transmission.

AB - BACKGROUND: In KwaZulu-Natal, South Africa, the incidence of extensively drug-resistant tuberculosis (XDR-TB) is driven by the transmission of resistant strains. As data suggest that cases may be spatially clustered, we sought to identify 'hotspots' and describe these communities. METHODS : We enrolled XDR-TB patients diagnosed from 2011 to 2014 in eThekwini. Global positioning system (GPS) coordinates for participant homes were collected and hotspots were identified based on population- adjusted XDR-TB incidence. The sociodemographic features of hotspots were characterised using census data. For a subset of participants, we mapped non-home XDR-TB congregate locations and compared these with results including only homes. RESULT S : Among 132 participants, 75 (57%) were female and 87 (66%) lived in urban or suburban locations. Fifteen of 197 census tracts were identified as XDR-TB hotspots with 95% confidence. Four spatial mapping methods identified one large hotspot in northeastern eThekwini. Hotspot communities had higher proportions of low educational attainment (12% vs. 9%) and unemployment (29.3% vs. 20.4%), and lower proportion of homes with flush toilets (36.4% vs. 68.9%). The case density shifted towards downtown Durban when congregate locations (e.g., workplaces) for 43 (33%) participants were mapped. CONCLUS IONS : In eThekwini, XDR-TB case homes were clustered into hotspots with more poverty indicators than non-hotspots. Prevention efforts targeting hotspot communities and congregate settings may be effective in reducing community transmission.

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