Drug-Resistant Tuberculosis Hotspots in Oliver Reginald Tambo District Municipality, Eastern Cape, South Africa

Abstract

Background: The 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. Methods: A 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 p 2 = 0.865, a mean squared error (MSE) of 507.175, and a mean absolute error (MAE) of 18.65. Conversely, the Random Forest model forecasts stabilization to around 30–50 cases annually after 2021, achieving an R2 = 0.882, an MSE of 443.226, and an MAE of 19.03. These models underscore the importance of adaptive strategies to sustain progress and avoid plateauing in DR-TB reduction efforts. Conclusions: This 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
• modeling
• linear regression