ArticleInfectious disease reports2024
Drug-Resistant Tuberculosis Hotspots in Oliver Reginald Tambo District Municipality, Eastern Cape, South Africa.
Article in Infectious disease reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Routine Molecular Surveillance of Drug-Resistant Tuberculosis: Translating Mutation Proxies into Clinical Governance Intelligence in Rural Eastern Cape.Healthcare (Basel, Switzerland) · 2026Article
- Spatial Analysis of Drug-Resistant Tuberculosis in Colombia (2020-2023): Departmental Rates, Clusters, and Associated Factors.Tropical medicine and infectious disease · 2025Article
- Hearing Impairment Among Drug-Resistant Tuberculosis Patients in Rural Eastern Cape: A Retrospective Analysis of Audiometric Findings.International journal of environmental research and public health · 2025Article
- Community-engaged clinical governance and machine learning for optimizing tuberculosis management in rural Eastern Cape.Frontiers in public health · 2025Article
- Assessing awareness of latent tuberculosis infection: the urgent need for clinical governance-driven education in rural Eastern Cape - insights from a community-based study.Frontiers in public health · 2025Article
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3 authors.
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Abstract
backgroundThe global push to eliminate tuberculosis (TB) as a public health threat is increasingly urgent, particularly in high-burden areas like the Oliver Reginald Tambo District Municipality, South Africa. Drug-resistant TB (DR-TB) poses a significant challenge to TB control efforts and is a leading cause of TB-related deaths. This study aimed to assess DR-TB transmission patterns and predict future cases using geospatial and predictive modeling techniques.
methodsA retrospective cross-sectional study was conducted across five decentralized DR-TB facilities in the O.R. Tambo District Municipality from January 2018 to December 2020. Data were obtained from Statistics South Africa, and patient GPS coordinates were used to identify clusters of DR-TB cases via DBSCAN clustering. Hotspot analysis (Getis-Ord Gi) was performed, and two predictive models (Linear Regression and Random Forest) were developed to estimate future DR-TB cases. Analyses were conducted using Python 3.8 and R 4.1.1, with significance set at
resultsA total of 456 patients with DR-TB were enrolled, with 56.1% males and 43.9% females. The mean age was 37.5 (±14.9) years. The incidence of DR-TB was 11.89 cases per 100,000 population, with males being disproportionately affected. Key risk factors included poverty, lack of education, and occupational exposure. The DR-TB types included RR-TB (60%), MDR-TB (30%), Pre-XDR-TB (5%), XDR-TB (3%), and INHR-TB (2%). Spatial analysis revealed significant clustering in socio-economically disadvantaged areas. A major cluster was identified, along with a distinct outlier. The analyses of DR-TB case trends using historical data (2018-2021) and projections (2022-2026) from Linear Regression and Random Forest models reveal historical data with a sharp decline in DR-TB case, from 186 in 2018 to 15 in 2021, highlighting substantial progress. The Linear Regression model predicts a continued decline to zero cases by 2026, with an R
conclusionsThis study highlights the need for targeted interventions in vulnerable populations to curb DR-TB transmission and improve treatment outcomes.
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