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: Drug-resistant tuberculosis (DR-TB) remains a major public health challenge in South Africa, particularly in rural settings with high HIV co-infection rates. Understanding predictors of treatment response among people living with HIV is essential for improving clinical management and programmatic outcomes. This study aimed to identify socio-demographic and clinical predictors of treatment outcomes among HIV-positive individuals diagnosed with multidrug-resistant (MDR) and extensively drug-resistant tuberculosis (XDR-TB) in rural Eastern Cape Province, South Africa. Methods: A retrospective cohort study was conducted using routinely collected clinical records of DR-TB patients initiated on treatment between January 2020 and December 2024 at two public healthcare facilities. A total of 239 patients with complete treatment outcome data were included. Treatment outcomes were classified as favourable (cured or treatment completed) or unfavourable (death, treatment failure, or loss to follow-up). Descriptive statistics were used to summarise patient characteristics, while univariate and multivariable logistic regression analyses were performed to identify factors associated with treatment outcomes. Results: Most participants were aged ≤ 39 years (58%), male (60%), unemployed (90%), and without income (80%). MDR-TB accounted for 40% of cases, rifampicin-resistant-TB (RR-TB) for 53%, and XDR-TB for 7.1%. Multivariable analysis showed that XDR-TB was the strongest independent predictor of unfavourable treatment outcome (AOR = 0.18; 95% CI: 0.06–0.58; p = 0.004). Income status was also significantly associated with outcome, with participants reporting some incomes having lower odds of favourable outcomes (AOR = 0.46; 95% CI: 0.23–0.92; p = 0.036). The model demonstrated modest predictive performance (AUC = 0.67). Conclusions: These findings highlight the dominant influence of resistance phenotype, particularly XDR-TB, on treatment prognosis among HIV-positive DR-TB patients in rural Eastern Cape. Integrating early resistance profiling, intensified clinical management of XDR-TB, and socioeconomic support mechanisms may improve treatment outcomes in high-burden rural settings.

Background: Drug-resistant tuberculosis remains a major challenge in resource-limited settings, particularly in rural regions of the Eastern Cape Province, where limited laboratory infrastructure, constrained access to advanced molecular diagnostics, shortages of specialized healthcare personnel, and prolonged diagnostic turnaround times can delay appropriate treatment initiation. This study examined whether routinely detectable genomic resistance markers could be integrated with parsimonious machine learning approaches to support early risk stratification for isoniazid (INH) and/or rifampicin (RIF) resistance and multidrug-resistant tuberculosis (MDR-TB). Methods: We conducted a retrospective analysis of clinical, demographic, and genomic data from 207 Mycobacterium tuberculosis isolates representing 207 unique patients. Resistance was classified as INH and/or RIF resistance or MDR-TB (concurrent resistance to both drugs). Predictors included age, sex, and canonical resistance-associated mutations (katG S315T, inhA −15C>T, and rpoB codon substitutions). Logistic regression was used to estimate adjusted odds ratios (aORs), while Random Forest models were applied to assess non-linear feature importance. Internal validation was performed using 10-fold cross-validation. A systems network analysis mapped the integration of model-derived risk bands into Clinical Governance structures and Community-Engaged Education pathways, including interventions delivered by Community Health Workers (CHWs). Results: INH and/or RIF resistance was identified in 58.9% of isolates, with 21.7% classified as MDR-TB. The most frequently detected mutations were katG S315T (29.0%) and rpoB S450L (26.6%). Logistic regression identified rpoB S450L (aOR 4.20; 95% CI: 2.10–8.45) and katG S315T (aOR 2.85; 95% CI: 1.40–5.80) as the strongest independent predictors, while age and sex were not statistically significant. Models demonstrated strong internal discrimination (AUCs of 0.96 for INH and/or RIF resistance and 0.99 for MDR-TB). Risk stratification categorized 18% of patients as high risk. Scenario-based modelling suggested that prioritizing high-risk patients for reflex Line Probe Assay testing could reduce the median time to appropriate treatment from 14 to 3 days and may reduce progression from isoniazid-resistant TB to MDR-TB under specified operational assumptions. Conclusions: Mutation-informed predictive modelling demonstrates strong internally validated discrimination and provides a structured framework for risk-stratified intervention. Integrating probability-based risk thresholds within Clinical Governance systems and community-level support structures, including CHW-led adherence and education strategies, may support earlier treatment optimization in high-burden rural settings. External validation and prospective implementation studies are required before broader programmatic adoption.

Background: Tuberculosis (TB) remains a major public health challenge globally, particularly in high-burden countries such as South Africa. Treatment interruption is a critical barrier to effective TB control, contributing to poor treatment outcomes, increased risk of multidrug-resistant tuberculosis (MDR-TB), and continued community transmission. Understanding the determinants of treatment interruption in rural healthcare settings is essential for strengthening TB programme implementation. Methods: This qualitative study explored the factors influencing TB treatment interruption from the perspectives of professional nurses working in primary healthcare facilities in the Nyandeni Subdistrict, Eastern Cape, South Africa. Semi-structured interviews were conducted with nurses involved in TB programme implementation. Data were analysed using thematic analysis following the six-phase approach described by Braun and Clarke. Descriptive statistical analyses were also used to summarize participant characteristics, including age and years of nursing experience. Conceptual frameworks were developed to illustrate the multilevel determinants of TB treatment interruption. Results: Participants had a mean age of 40.6 years and an average of 14.2 years of nursing experience, reflecting a workforce with substantial clinical exposure to TB management. Thematic analysis identified multiple interconnected determinants of treatment interruption. Key barriers included poverty, food insecurity, transport costs, long distances to healthcare facilities, limited family support, and challenges related to patient tracing. These factors interact across structural, community, health system, and interpersonal levels to influence patient adherence behaviour. Conceptual models developed from the findings illustrate the complex pathways through which these determinants contribute to treatment interruption and programme-level consequences such as reduced treatment success and increased risk of MDR-TB. Conclusions: TB treatment interruption in rural settings is driven by multilevel socioeconomic and health system determinants rather than individual patient behaviour alone. Strengthening community health worker programmes, improving patient tracing systems, addressing socioeconomic barriers, and enhancing community-based support mechanisms are essential for improving treatment adherence. Integrated, multisectoral interventions are required to strengthen TB programme outcomes in rural high-burden settings.

Background: Hypertension is a major contributor to cardiovascular morbidity and mortality globally, with particularly poor control in disadvantaged communities. Although awareness is generally high, sustaining lifestyle changes and treatment adherence remains difficult. This study examined socioeconomic, behavioural, and health system factors associated with hypertension management and explored the role of community-engaged health education in strengthening primary health care. Methods: A cross-sectional descriptive study was conducted among 107 adults with hypertension attending public primary health care facilities. Data were collected using structured questionnaires covering socioeconomic status, health system access, medication adherence, lifestyle practices, and knowledge and attitudes. Descriptive statistics summarized participant characteristics, while chi-square tests and multivariable logistic regression identified factors associated with clinic attendance, dietary adherence, lifestyle practices, and self-management. Results: Awareness of hypertension (86.9%) and trust in clinic care (91.6%) were high; however, a clear knowledge practice gap was evident. Although 79.4% acknowledged the importance of lifestyle changes, only 45.8% followed a hypertension-specific diet. Structural barriers included low income (39.3% earning <R3 000/month) and long distances to clinics (46.7% living ≥5 km away). Health worker advice strongly predicted dietary adherence (OR = 6.36, p = 0.001), lifestyle adherence (OR = 4.24, p = 0.006), and overall self-management (OR = 12.46, p = 0.024), whereas knowledge alone did not. Conclusions: Hypertension management is constrained more by structural and health system factors than by lack of awareness. Strengthening primary health care counselling and community-based health education may improve sustained lifestyle change and outcomes.

Background: Drug-resistant tuberculosis (DR-TB) remains a critical challenge in high-burden rural settings. This study aims to bridge genomic resistance markers with machine learning to create a predictive model for timely diagnosis and the prevention of multidrug-resistant tuberculosis (MDR-TB). Methods: We conducted a retrospective analysis of clinical, demographic, and genomic data from 207 Mycobacterium tuberculosis isolates (representing 207 unique patients). Resistance was categorized as any resistance or MDR-TB.” Predictors included age, sex, and key mutations (S315T, -15C>T, and substitutions). Logistic regression (LR) was used to calculate adjusted odds ratios (aORs), while Random Forest (RF) assessed non-linear feature importance. Model validity was confirmed via 10-fold cross-validation. A Systems Network Analysis mapped the integration of model outputs into Clinical Governance (SOPs/KPIs) and Community-Engaged Education. Results: Resistance to at least one drug was found in 58.9% of isolates, with 21.7% classified as MDR-TB. Predominant mutations included S315T (29.0%) and S450L (26.6%). LR identified S450L (aOR 4.20, 95% CI: 2.10–8.45) and S315T (aOR 2.85, 95% CI: 1.40–5.80) as the strongest predictors; demographic variables (age, sex) were not statistically significant. Models achieved high discriminative power (AUC: 0.96 for any resistance; 0.99 for MDR-TB). Risk stratification categorized 18% of patients as high risk. Simulations showed that prioritizing high-risk patients for reflex LPA testing could reduce the median time to appropriate treatment from 14 to 3 days, potentially preventing 12–15% of Isoniazid-resistant TB (Hr-TB) cases from progressing to MDR-TB. Conclusion: Integrating molecular markers with machine learning enables highly accurate, risk-stratified clinical interventions. By embedding these predictive models into governance frameworks and utilizing Community Health Workers (CHWs) for targeted education, rural health systems can significantly reduce diagnostic delays and interrupt the progression of MDR-TB.