Version 1
: Received: 19 September 2024 / Approved: 20 September 2024 / Online: 20 September 2024 (11:49:09 CEST)
How to cite:
Faye, L. M.; Hosu, M. C.; Apalata, T. Drug-Resistant Tuberculosis Hotspots in Oliver Reginald Tambo (O.R.) District Municipality, Eastern Cape, South Africa. Preprints2024, 2024091578. https://doi.org/10.20944/preprints202409.1578.v1
Faye, L. M.; Hosu, M. C.; Apalata, T. Drug-Resistant Tuberculosis Hotspots in Oliver Reginald Tambo (O.R.) District Municipality, Eastern Cape, South Africa. Preprints 2024, 2024091578. https://doi.org/10.20944/preprints202409.1578.v1
Faye, L. M.; Hosu, M. C.; Apalata, T. Drug-Resistant Tuberculosis Hotspots in Oliver Reginald Tambo (O.R.) District Municipality, Eastern Cape, South Africa. Preprints2024, 2024091578. https://doi.org/10.20944/preprints202409.1578.v1
APA Style
Faye, L. M., Hosu, M. C., & Apalata, T. (2024). Drug-Resistant Tuberculosis Hotspots in Oliver Reginald Tambo (O.R.) District Municipality, Eastern Cape, South Africa. Preprints. https://doi.org/10.20944/preprints202409.1578.v1
Chicago/Turabian Style
Faye, L. M., Mojisola Clara Hosu and Teke Apalata. 2024 "Drug-Resistant Tuberculosis Hotspots in Oliver Reginald Tambo (O.R.) District Municipality, Eastern Cape, South Africa" Preprints. https://doi.org/10.20944/preprints202409.1578.v1
Abstract
Background/Objectives: The global push to eliminate tuberculosis (TB) as a public health threat is in-creasingly urgent, particularly in high-burden areas like O.R. 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. Methods: A retrospective cohort study was conducted across five decentralized DR-TB facilities in O.R. Tambo District Municipality from January 2018 to De-cember 2020. Data were obtained from Statistics South Africa, and patient GPS coordinates were used to identify clusters of DR-TB cases via DBSCAN clustering. Hot Spot 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 p < 0.05. Results: A total of 456 DR-TB patients 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 dispropor-tionately affected. Key risk factors included poverty, lack of education, and occupational exposure. 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 socioeconomically disadvantaged areas. A major cluster was identified, along with a distinct outlier. Predictive models indicated differing trends, with the Random Forest model predicting stabilization at 30 cases per year by 2022, while the Linear Regression model projected a decline to zero by 2026.
Conclusions: The study highlights the need for targeted interventions in vulnerable populations to curb DR-TB transmission and improve treatment outcomes.
Keywords
DR-TB; O.R. Tambo District Municipality; TB hotspots; Sociodemographic factors
Subject
Public Health and Healthcare, Public Health and Health Services
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
