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

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 < 0.05.

RESULTS

A 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 = 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 R = 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.