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Original research
Neighbourhood risk factors of recurrent tuberculosis in Cape Town: a cohort study using geocoded notification data
  1. Marjan Molemans1,2,3,4,
  2. Frank van Leth4,5,
  3. David Henry McKelly6,
  4. Robin Wood7,8,
  5. Sabine Hermans1,2,4,9
  1. 1 Amsterdam Institute for Global Health and Development, Amsterdam, Netherlands
  2. 2 Department of Global Health, Amsterdam UMC Locatie Meibergdreef, Amsterdam, Netherlands
  3. 3 Amsterdam Institute for Social Science Research, Amsterdam, Netherlands
  4. 4 Amsterdam Public Health Research Institute, Amsterdam, Netherlands
  5. 5 Department of Health Sciences, VU Amsterdam, Amsterdam, Netherlands
  6. 6 Smart Place, Council for Scientific and Industrial Research, Cape Town, South Africa
  7. 7 University of Cape Town Desmond Tutu HIV Centre, Cape Town, South Africa
  8. 8 Faculty of Health Sciences, University of Cape Town Institute of Infectious Disease and Molecular Medicine, Cape Town, South Africa
  9. 9 Department of Infectious Diseases, Amsterdam UMC Locatie Meibergdreef, Amsterdam, Netherlands
  1. Correspondence to Mrs Marjan Molemans, Amsterdam Institute for Global Health and Development, Amsterdam 1105, North Holland, The Netherlands; m.molemans{at}aighd.org

Abstract

Background Individuals with a history of tuberculosis (TB) disease are at higher risk of developing a subsequent episode than those without. Considering the role of social and environmental factors in tuberculosis, we assessed neighbourhood-level risk factors associated with recurrent tuberculosis in Cape Town, South Africa.

Methods This cohort consisted of patients who completed treatment for their first drug-sensitive TB episode between 2003 and 2015. Addresses were geocoded at neighbourhood level. Data on neighbourhood-level factors were obtained from the Census 2011 (household size, population density) and the City of Cape Town (Socio-Economic Index). Neighbourhood-level TB burden was calculated annually by dividing the number of notified TB episodes by the population in that neighbourhood. Multilevel survival analysis was performed with the outcome recurrent TB, defined as a second episode of TB, and controlling for individual-level risk factors (age, gender and time since first episode in years). Follow-up ended at the second episode, or on 31 December 2015, whichever came first.

Results The study included 173 421 patients from 700 neighbourhoods. Higher Socio-Economic Index was associated with a lower risk of recurrence compared with average Socio-Economic Index. An increased risk was found for higher household size and TB burden, with an increase of 20% for every additional person in mean household size and 10% for every additional TB episode/100 inhabitants. No association was found with population density.

Conclusion Recurrent TB was associated with increased household size and TB burden at neighbourhood level. These findings could be used to target TB screening activities.

  • GIS
  • SOCIAL CLASS
  • TUBERCULOSIS
  • Health inequalities

Data availability statement

Data may be obtained from a third party and are not publicly available. These data were obtained from the Cape Town Electronic TB register,after receiving permission from Cape Town City Health (Health.Services@capetown.gov.za). Access to the data was limited to the conduct of relevant analysis and publication of results. A request to access data can be made direct to the Cape Town City Health.

https://doi.org/10.1136/jech-2022-219622

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    Data availability statement

    Data may be obtained from a third party and are not publicly available. These data were obtained from the Cape Town Electronic TB register,after receiving permission from Cape Town City Health (Health.Services@capetown.gov.za). Access to the data was limited to the conduct of relevant analysis and publication of results. A request to access data can be made direct to the Cape Town City Health.

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    Footnotes

    • Twitter @FvLScience

    • Contributors MM: data management, geocoding, data analysis, interpretation of the data and drafting the manuscript. FvL: data analysis, interpretation of the data and critical review of the manuscript. DHM: data acquisition, geocoding, interpretation of the data and critical review of the manuscript. RW: study conceptualisation, data acquisition, interpretation of the data and critical review of the manuscript. SH: study conceptualisation, data acquisition, data analysis, interpretation of the data and critical review of the manuscript. All authors approved the final draft of the manuscript. MM is the guarantor.

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.