There are significant delays in initiation of multidrug-resistant tuberculosis (MDR –TB) treatment. The Xpert MTB/RIF test has been shown to reduce the time to diagnosis and treatment of MDR-TB predominantly in urban centres. This study describes the time to treatment of MDR-TB and the effect of Xpert MTB/RIF on time to treatment in a deprived rural area in South Africa. This was a retrospective cohort study analysing the medical records of patients diagnosed with MDR-TB in King Sabata Dalindyebo Sub-District between 2009 and 2014. Numerical data were reported using the Kruskal-Wallis and Wilcoxon sum rank tests and categorical data compared using the two-sample test of proportions. Of the 342 patients with MDR-TB identified, 285 were eligible for analysis, of whom 145 (61.4%) were HIV positive. The median time from sputum collection to MDR-TB diagnosis was 27 days (IQR: 2–45) and differed significantly between diagnostic modalities: Xpert MTB/RIF, 1 day (IQR: 1–4; n = 114: p < 0.0001); Line Probe Assay 12 days (IQR: 8–21; n = 28; p < 0.0001); and culture/phenotypic drug sensitivity testing 45 days (IQR: 39–59; n = 143: p < 0.0001). The time from diagnosis to treatment initiation was 14 days (IQR: 8–27) and did not differ significantly between diagnostic modality. The median time from sputum collection to treatment initiation was 49 days (IQR: 20–69) but differed significantly between diagnostic modalities: Xpert MTB/RIF, 18 days (IQR: 11–27; n = 114; p < 0.0001); Line Probe Assay 29 days (IQR: 14.5–53; n = 28; p < 0.0001); and culture/phenotypic drug sensitivity, 64 days (IQR: 50–103; n = 143: P < 0.0001). Age, sex and HIV status did not influence the time intervals. Xpert MTB/RIF significantly reduced the time to MDR-TB treatment in a deprived rural setting as a result of a reduced time to diagnosis. However, the national target of five days was not achieved. Further research is needed to explore and address programmatic and patient-related challenges contributing to delayed treatment initiation.

Background: Anaemia affects one in four adults in South Africa, with a higher prevalence in persons with HIV and tuberculosis. The aim of this study is to characterise the causes of anaemia in primary care and a district hospital setting. Methods: A cross-sectional study design investigated a purposive sample of adult males and non-pregnant females at two community health centres and a hospital casualty and outpatients. Fingerpick blood haemoglobin was measured with HemoCueHb201+. Those with moderate and severe anaemia underwent clinical examination and laboratory tests. Results: Of 1327 patients screened, median age was 48 years, and 63.5% were female. Of 471 (35.5%) with moderate and severe anaemia on HemoCue, 55.2% had HIV, 16.6% tuberculosis, 5.9% chronic kidney disease, 2.6% cancer, and 1.3% heart failure. Laboratory testing confirmed 227 (48.2%) with moderate and 111 (23.6%) with severe anaemia, of whom 72.3% had anaemia of inflammation, 26.5% iron-deficiency anaemia, 6.1% folate deficiency, and 2.5% vitamin B12 deficiency. Overall, 57.5% had two or more causes of anaemia. Multivariate modelling showed that patients with severe anaemia were three times more likely to have tuberculosis (OR = 3.1, 95% CI = 1.5–6.5; p-value = 0.002). Microcytosis was present in 40.5% with iron deficiency, macrocytosis in 22.2% with folate deficiency, and 33.3% with vitamin B12 deficiency. The sensitivities of the reticulocyte haemoglobin content and % hypochromic red blood cells in diagnosing iron deficiency were 34.7% and 29.7%, respectively. Conclusions: HIV, iron deficiency, and tuberculosis were the most prevalent causes of moderate and severe anaemia. The majority had multiple causes. Iron, folate, and vitamin B12 deficiencies should be identified by biochemical testing rather than by red cell volume.

Background: Drug-resistant tuberculosis (DR-TB) continues to challenge global efforts toward eradicating and having a tuberculosis-free world. Considering the high early mortality, especially among HIV-infected individuals, early diagnosis and prompt initiation of effective treatment are needed to significantly reduce mortality and halt transmission of DR-TB in the community. Aim: This study aims to assess the effectiveness of a community DR-TB care model with the specific objective of determining the Time-to-treatment initiation of DR-TB among patients in the OR Tambo district municipality. Methods: A prospective cohort study of patients with DR-TB was conducted in the OR Tambo district municipality of Eastern Cape Province, South Africa. Patients were enrolled as they presented for treatment initiation at the decentralised facilities following a diagnosis of DR-TB and compared with a centralised site. Results: A total of 454 DR-TB patients from six facilities between 2018 and 2020 were included in the analysis. The mean age was 37.54 (SD = 14.94) years. There were slightly more males (56.2%) than females (43.8%). Most of the patients were aged 18–44 years (67.5%), without income (82.3%). Results showed that slightly over thirteen percent (13.4%) of patients initiated treatment the same day they were diagnosed with DR-TB, while 36.3% were on the time-to-treatment target of being initiated within 5 days. However, about a quarter (25.8%) of patients failed to initiate treatment two weeks after diagnosis. Time-to-treatment initiation (TTTI) varied according to the decentralised sites, with progressive improvement with each successive year between 2018 and 2021. No demographic factor was significantly associated with TTTI. Conclusion: Despite rapid diagnosis, only 36% of patients were initiated on treatment promptly. Operational challenges remained, and services needed to be reorganised to maximise the exceptional potentials that a decentralised community DR-TB care model brings.

The African Journal of Primary Health & Family Medicine welcomes submissions that encourage scholarly exchange between family medicine and primary health care researchers and practitioners across Africa and the developing world, whilst providing a contextual and holistic view of family medicine as practiced across the continent.

Background: Drug-resistant tuberculosis (DR-TB) is a formidable challenge to global health. Patients are compelled to adhere to intricate medication regimens over extended periods, and any failure to comply with these treatment protocols can lead to treatment failure, increased mortality rates, and a heightened risk of developing further drug resistance. This study identifies the key factors that influence treatment adherence among patients with DR-TB. Furthermore, it rigorously evaluates the predictive accuracy of machine learning models in assessing treatment adherence, with a strong focus on socioeconomic, demographic, and clinical factors. Methods: A retrospective analysis was conducted on patients with DR-TB in rural Eastern Cape. Data were collected from medical records. Four different models were developed and tested to evaluate their effectiveness in predicting treatment adherence: Random Forest, Logistic regression, Support Vector Machine (SVM), and Gradient Boosting. Results: The Random Forest model achieved an accuracy of 53.3% in predicting treatment adherence. An analysis of feature importance indicated that age, income, education, social history, patient category, and comorbidities were the most significant factors influencing adherence. Patients with higher incomes, higher levels of education, and fewer comorbidities were more likely to follow their treatment plans. Conclusion: Socioeconomic and clinical factors, such as income, education level, and the presence of comorbidities, significantly influence adherence to DR-TB treatment. These findings indicate that machine-learning models, particularly Random Forest algorithms, can effectively assist in clinical decision-making by identifying patients who may be at risk of not adhering to their treatment.