Background: Treatment outcomes are critical measures of TB treatment success, especially in resource-limited settings where tuberculosis remains a major public health issue. This study evaluated the treatment outcomes of patients with drug-resistant tuberculosis (DR-TB), co-infected with human immunodeficiency virus (HIV), and the impact of nutritional status, as measured by body mass index (BMI), on these outcomes in rural areas of the Olivier Reginald Tambo District Municipality, Eastern Cape, South Africa. Methods: A retrospective review of 360 patient files from four TB clinics and one referral hospital was conducted between January 2018 and December 2020. Data collected included patient demographics, clinical characteristics, BMI (categorized as underweight, normal, overweight, or obese), HIV status, DR-TB type, and treatment outcomes. Statistical analyses assessed the association between BMI categories, HIV status, and treatment outcomes. A scatter plot was used to illustrate BMI trends as a continuous variable in relation to age, enabling an analysis of BMI distribution across different age groups. Additionally, bar charts were utilized to explore categorical relationships and patterns in BMI across these groups. Results: The majority of patients were co-infected with HIV and had DR-TB, with rifampicin-resistant TB (RR-TB) and multidrug-resistant TB (MDR-TB) being the most prevalent forms. Treatment outcomes varied significantly by BMI category. Underweight patients had the lowest cure rates (23.2%), highlighting the adverse impact of malnutrition on DR-TB treatment success. Patients with normal BMI demonstrated higher cure rates (34.7%), while overweight and obese patients had moderate outcomes. HIV co-infection further reduced cure rates, with co-infected individuals showing poorer outcomes than HIV-negative patients. Gender disparities were also observed, with females achieving higher cure rates (39.1%) compared to males (31.4%). Weak trends linked BMI and DR-TB type, such as a higher prevalence of normal BMI among RR-TB cases. Conclusion: This study underscores the significant influence of nutritional status on DR-TB treatment outcomes, particularly among patients co-infected with HIV. Underweight patients face the greatest risk of poor outcomes, emphasizing the need for nutritional support as a critical component of DR-TB management. Comprehensive HIV care and gender-specific interventions are also essential to address disparities in treatment success. Tailored strategies focusing on these aspects can significantly enhance outcomes in high-burden, resource-limited settings.

Latent tuberculosis infection (LTBI) poses a significant public health challenge, especially in populations with high HIV prevalence and limited healthcare access. Early detection and targeted interventions are essential to prevent the progression of active tuberculosis. This study aimed to identify the key factors influencing LTBI outcomes through the application of predictive models, including logistic regression and machine learning techniques, while also evaluating strategies to enhance LTBI awareness and testing. Data from rural areas in the Eastern Cape, South Africa, were analyzed to identify key demographic, health, and knowledge-related factors influencing LTBI outcomes. Predictive models utilized, included logistic regression, decision trees, and random forests, to identify key determinants of LTBI positivity based on demographic, health, and knowledge-related factors in rural areas of the Eastern Cape, South Africa. The models evaluated factors such as age, HIV status, and LTBI awareness, with random forests demonstrating the best balance of accuracy and interpretability. Additionally, a knowledge diffusion model was employed to assess the effectiveness of educational strategies in increasing LTBI awareness and testing uptake. Logistic regression achieved an accuracy of 68% with high precision (70%) but low recall (33%) for LTBI-positive cases, identifying age, HIV status, and LTBI awareness as significant predictors. The random forest model outperformed logistic regression in accuracy (59.26%) and F1-score (0.63), providing a better balance between precision and recall. Feature importance analysis revealed that age, occupation, and knowledge of LTBI symptoms were the most critical factors across both models. The knowledge diffusion model demonstrated that targeted interventions significantly increased LTBI awareness and testing, particularly in high-risk groups. While logistic regression offers more interpretable results for public health interventions, machine learning models like random forests provide enhanced predictive power by capturing complex relationships between demographics and health factors. These findings highlight the need for targeted educational campaigns and increased LTBI testing in high-risk populations, particularly those with limited awareness of LTBI symptoms.

Tuberculosis (TB), a disease of poverty and inequality, is a leading cause of severe illness and death among people with human immunodeficiency virus (HIV). In South Africa, both TB and HIV epidemics have been closely related and persistent, posing a significant burden for healthcare provision. Studies have observed that TB-HIV integration reduces mortality. The operational implementation of integrated services is still challenging. This study aimed to describe patients’ perceptions on barriers to scaling up of TB-HIV integration services at selected health facilities (study sites) in Oliver Reginald (O.R) Tambo Municipality, Eastern Cape province, South Africa. We purposely recruited twenty-nine (29) patients accessing TB and HIV services at the study sites. Data were analyzed using qualitative content analysis and presented as emerging themes. Barriers identified included a lack of health education about TB and HIV; an inadequate counselling for HIV and the antiretroviral drugs (ARVs); and poor quality of services provided by the healthcare facilities. These findings suggest that the O.R. Tambo district needs to strengthen its TB-HIV integration immediately.

Background: Tuberculosis (TB) remains a leading cause of morbidity and mortality, with South Africa among the highest-burden countries. The Eastern Cape is particularly affected due to poverty, HIV co-infection, and weak health systems. Understanding treatment outcomes and their determinants is required to achieve the WHO End TB Strategy targets. The objective of this study was to examine treatment outcomes for tuberculosis (TB) in both rural and urban clinics within the Eastern Cape Province. We aimed to identify the socio-demographic, clinical, and geographic factors that influence treatment success or failure. We included simple geographic visualisations comparing treatment outcomes between the two participating clinics to inform the development of targeted interventions aimed at enhancing TB control efforts. Methods: A retrospective cohort study of 385 TB patients treated at two public clinics in the Eastern Cape (2020–2024) was conducted. Socio-demographic, clinical, and geographical data were extracted from records. Outcomes were classified using WHO and South African National TB Programme guidelines. Logistic regression identified predictors of success, and spatial analysis mapped treatment outcomes. Results: The mean patient age was 40.6 years; 69.1% were HIV-positive, and 89.9% had pulmonary TB. The overall treatment success rate was 63.8%, below the WHO target of ≥85%. Pulmonary TB was independently associated with greater odds of success (aOR = 2.86, 95% CI: 1.23–6.65), while older age predicted poorer outcomes (aOR = 0.98, 95% CI: 0.963–0.998). HIV status and socioeconomic variables were not independently associated after adjustment, although poverty and unemployment were widespread. Spatial mapping showed clustering of poor outcomes in specific clinics, highlighting geographic and health system disparities. Conclusions: TB treatment outcomes in the Eastern Cape remain unsatisfactory. Older patients and those with extrapulmonary TB face higher risks of unfavourable outcomes, underscoring the need for closer monitoring and adherence support. Integrated TB/HIV care, social protection, and geographically targeted interventions are essential to strengthen health systems and reduce inequalities.

Background/Objectives: Drug-resistant tuberculosis (DR-TB) presents significant challenges to public health, particularly in rural South Africa, where limited infrastructure, high HIV co-infection rates, and weak clinical governance contribute to poor treatment outcomes. This study evaluates treatment trajectories and the impact of clinical governance and public health interventions on DR-TB outcomes in the rural Eastern Cape. Methods: A retrospective cohort study was conducted among 323 laboratory-confirmed DR-TB patients treated between 2018 and 2021. Kaplan–Meier curves and Cox proportional hazards analysis identified predictors of unfavorable outcomes. Logistic regression analysis simulated the impact of enhanced clinical governance scenarios on treatment success. Results: Treatment outcomes included cure (36.2%), completion (26.0%), loss to follow up (LTFU) (9.0%), death (9.3%), failure (2.2%), and transfer (9.3%). The median treatment duration was 10 months (IQR: 9–11). Survival analysis indicates the highest risk of death and LTFU occurred in the first 6–8 months of treatment. Multivariate Cox regression revealed that primary (HR = 0.39; 95% CI: 0.23–0.68; p = 0.0017) and secondary education (HR = 0.50; 95% CI: 0.31–0.85; p = 0.0103) were significantly protective. Paradoxically, patients with pre-XDR (HR = 0.13; p = 0.034) and XDR TB (HR = 0.16; p = 0.043) showed lower hazard of poor outcomes, likely due to early mortality or referral. HIV-negative status was associated with higher risk of poor outcomes (HR = 1.74; p = 0.010). Simulations suggested that improved clinical governance via better follow-up, TB/HIV integration, and adherence support could improve treatment success by up to 20 percentage points in high-impact scenarios. Conclusions: Strengthening clinical governance through targeted interventions could substantially reduce LTFU and mortality, especially in vulnerable subgroups. A coordinated, patient-centered approach is critical for improving DR-TB outcomes in rural, high-burden settings.

Despite the policy, frameworks for integration exist; integration of TB and HIV services is far from ideal in many resource-limited countries, including South Africa. Few studies have examined the advantages and disadvantages of integrated TB and HIV care in public health facilities, and even fewer have proposed conceptual models for proven integration. This study aims to fill this vacuum by describing the development of a paradigm for integrating TB, HIV, and patient services in a single facility and highlights the importance of TB-HIV services for greater accessibility under one roof. Development of the proposed model occurred in several phases that included assessment of the existing integration model for TB-HIV and synthesis of quantitative and qualitative data from the study sites, which were selected public health facilities in rural and peri-urban areas in the Oliver Reginald (O.R.) Tambo District Municipality in the Eastern Cape, South Africa. Secondary data on clinical outcomes from 2009–2013 TB–HIV were obtained from various sources for the quantitative analysis of Part 1. Qualitative data included focus group discussions with patients and healthcare workers, which were analyzed thematically in Parts 2 and 3. The development of a potentially better model and the validation of this model shows that the district health system was strengthened by the guiding principles of the model, which placed a strong emphasis on inputs, processes, outcomes, and integration effects. The model is adaptable to different healthcare delivery systems but requires the support of patients, providers (professionals and institutions), payers, and policymakers to be successful.