Neighbourhood factors and tuberculosis incidence in Cape Town: A negative binomial regression and spatial analysis

M Molemans; L Kayaert; Q Olislagers; S Abrahams; N Berkowitz; E Mohr-Holland; D McKelly; R Wood; F van Leth; S Hermans

doi:10.1111/tmi.14001

Neighbourhood factors and tuberculosis incidence in Cape Town: A negative binomial regression and spatial analysis

Trop Med Int Health. 2024 May 16. doi: 10.1111/tmi.14001. Online ahead of print.

Authors

M Molemans^{1

2

3}, L Kayaert¹, Q Olislagers⁴, S Abrahams⁵, N Berkowitz^{5

6}, E Mohr-Holland⁶, D McKelly⁷, R Wood^{8

9}, F van Leth^{3

4}, S Hermans^{1

3

10

11}

Affiliations

¹ Amsterdam UMC, Location University of Amsterdam, Department of Global Health, Amsterdam Institute for Global Health and Development, Amsterdam, The Netherlands.
² Amsterdam Institute for Social Science Research, University of Amsterdam, Amsterdam, The Netherlands.
³ Amsterdam Public Health Research Institute, Amsterdam, The Netherlands.
⁴ Department of Health Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
⁵ City Health Department, City of Cape Town, South Africa.
⁶ School of Public Health and Family Medicine, University of Cape Town, Cape Town, South Africa.
⁷ Council for Scientific and Industrial Research, Smart Places, Cape Town, South Africa.
⁸ Desmond Tutu Health Foundation, Cape Town, South Africa.
⁹ Institute of Infectious Disease and Molecular Medicine and Department of Medicine, University of Cape Town, Cape Town, South Africa.
¹⁰ Center for Tropical and Travel Medicine, Department of Infectious Diseases, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands.
¹¹ Amsterdam Institute for Immunology and Infectious Diseases, Amsterdam, The Netherlands.

PMID: 38757387
DOI: 10.1111/tmi.14001

Abstract

Objectives: Although the link between poverty and tuberculosis (TB) is widely recognised, limited studies have investigated the association between neighbourhood factors and TB incidence. Since the factors influencing different episodes of TB might be different, this study focused on the first episode of TB disease (first-episode TB).

Methods: All first episodes in previously linked and geocoded TB notification data from 2007 to 2015 in Cape Town, South Africa, were aggregated at the neighbourhood level and merged with the 2011 census data. We conducted an ecological study to assess the association between neighbourhood incidence of first-episode TB and neighbourhood factors (total TB burden [all episodes] in the previous year, socioeconomic index, mean household size, mean age, and percentage males) using a negative binomial regression. We also examined the presence of hotspots in neighbourhood TB incidence with the Global Moran's I statistic and assessed spatial dependency in the association between neighbourhood factors and TB incidence using a spatial lag model.

Results: The study included 684 neighbourhoods with a median first-episode TB incidence rate of 114 (IQR: 0-345) per 100,000 people. We found lower neighbourhood socioeconomic index (SEI), higher neighbourhood total TB burden, lower neighbourhood mean household size, and lower neighbourhood mean age were associated with increased neighbourhood first-episode TB incidence. Our findings revealed a hotspot of first-episode TB incidence in Cape Town and evidence of spatial dependency in the association between neighbourhood factors and TB incidence.

Conclusion: Neighbourhood TB burden and SEI were associated with first-episode TB incidence, and there was spatial dependency in this association. Our findings can inform targeted interventions to reduce TB in high-risk neighbourhoods, thereby reducing health disparities and promoting health equity.

Keywords: neighbourhood; social determinants; spatial analysis; tuberculosis.