Background The interaction between COVID-19, non-communicable diseases, and chronic infectious diseases such as HIV and tuberculosis is unclear, particularly in low-income and middle-income countries in Africa. South Africa has a national HIV prevalence of 19% among people aged 15-49 years and a tuberculosis prevalence of 0•7% in people of all ages. Using a nationally representative hospital surveillance system in South Africa, we aimed to investigate the factors associated with in-hospital mortality among patients with COVID-19. MethodsIn this cohort study, we used data submitted to DATCOV, a national active hospital surveillance system for COVID-19 hospital admissions, for patients admitted to hospital with laboratory-confirmed SARS-CoV-2 infection between March 5, 2020, and March 27, 2021. Age, sex, race or ethnicity, and comorbidities (hypertension, diabetes, chronic cardiac disease, chronic pulmonary disease and asthma, chronic renal disease, malignancy in the past 5 years, HIV, and past and current tuberculosis) were considered as risk factors for COVID-19-related in-hospital mortality. COVID-19 in-hospital mortality, the main outcome, was defined as a death related to COVID-19 that occurred during the hospital stay and excluded deaths that occurred because of other causes or after discharge from hospital; therefore, only patients with a known in-hospital outcome (died or discharged alive) were included. Chained equation multiple imputation was used to account for missing data and random-effects multivariable logistic regression models were used to assess the role of HIV status and underlying comorbidities on COVID-19 in-hospital mortality. FindingsAmong the 219 265 individuals admitted to hospital with laboratory-confirmed SARS-CoV-2 infection and known in-hospital outcome data, 51 037 (23•3%) died. Most commonly observed comorbidities among individuals with available data were hypertension in 61 098 (37•4%) of 163 350, diabetes in 43 885 (27•4%) of 159 932, and HIV in 13 793 (9•1%) of 151 779. Tuberculosis was reported in 5282 (3•6%) of 146 381 individuals. Increasing age was the strongest predictor of COVID-19 in-hospital mortality. Other factors associated were HIV infection (adjusted odds ratio 1•34, 95% CI 1•27-1•43), past tuberculosis (1•26, 1•15-1•38), current tuberculosis (1•42, 1•22-1•64), and both past and current tuberculosis (1•48, 1•32-1•67) compared with never tuberculosis, as well as other described risk factors for COVID-19, such as male sex; non-White race; underlying hypertension, diabetes, chronic cardiac disease, chronic renal disease, and malignancy in the past 5 years; and treatment in the public health sector. After adjusting for other factors, people with HIV not on antiretroviral therapy (ART; adjusted odds ratio 1•45, 95% CI 1•22-1•72) were more likely to die in hospital than were people with HIV on ART. Among people with HIV, the prevalence of other comorbidities was 29•2% compared with 30•8% among HIV-uninfected individuals. Increasing number of comorbidities was associated with...

Background The first wave of COVID-19 in South Africa peaked in July, 2020, and a larger second wave peaked in January, 2021, in which the SARS-CoV-2 501Y.V2 (Beta) lineage predominated. We aimed to compare in-hospital mortality and other patient characteristics between the first and second waves.Methods In this prospective cohort study, we analysed data from the DATCOV national active surveillance system for COVID-19 admissions to hospital from March 5, 2020, to March 27, 2021. The system contained data from all hospitals in South Africa that have admitted a patient with COVID-19. We used incidence risk for admission to hospital and determined cutoff dates to define five wave periods: pre-wave 1, wave 1, post-wave 1, wave 2, and post-wave 2. We compared the characteristics of patients with COVID-19 who were admitted to hospital in wave 1 and wave 2, and risk factors for in-hospital mortality accounting for wave period using random-effect multivariable logistic regression.Findings Peak rates of COVID-19 cases, admissions, and in-hospital deaths in the second wave exceeded rates in the first wave: COVID-19 cases, 240•4 cases per 100 000 people vs 136•0 cases per 100 000 people; admissions, 27•9 admissions per 100 000 people vs 16•1 admissions per 100 000 people; deaths, 8•3 deaths per 100 000 people vs 3•6 deaths per 100 000 people. The weekly average growth rate in hospital admissions was 20% in wave 1 and 43% in wave 2 (ratio of growth rate in wave 2 compared with wave 1 was 1•19, 95% CI 1•18-1•20). Compared with the first wave, individuals admitted to hospital in the second wave were more likely to be age 40-64 years (adjusted odds ratio [aOR] 1•22, 95% CI 1•14-1•31), and older than 65 years (aOR 1•38, 1•25-1•52), compared with younger than 40 years; of Mixed race (aOR 1•21, 1•06-1•38) compared with White race; and admitted in the public sector (aOR 1•65, 1•41-1•92); and less likely to be Black (aOR 0•53, 0•47-0•60) and Indian (aOR 0•77, 0•66-0•91), compared with White; and have a comorbid condition (aOR 0•60, 0•55-0•67).For multivariable analysis, after adjusting for weekly COVID-19 hospital admissions, there was a 31% increased risk of in-hospital mortality in the second wave (aOR 1•31, 95% CI 1•28-1•35). In-hospital case-fatality risk increased from 17•7% in weeks of low admission (<3500 admissions) to 26•9% in weeks of very high admission (>8000 admissions; aOR 1•24, 1•17-1•32).Interpretation In South Africa, the second wave was associated with higher incidence of COVID-19, more rapid increase in admissions to hospital, and increased in-hospital mortality. Although some of the increased mortality can be explained by admissions in the second wave being more likely in older individuals, in the public sector, and by the increased health system pressure, a residual increase in mortality of patients admitted to hospital could be related to the new Beta lineage.

Background: An adequate health workforce is an essential building block of effective health systems. In South Africa, medical officers (MOs) are a key component of service delivery in district health services. The Stellenbosch University Family Physician Research Network in the Western Cape identified that retention of MOs was a key issue. The aim of this study was to explore the factors that influence the retention of MOs in public sector district health services in the Western Cape, South Africa.Methods: This is a descriptive exploratory qualitative study. Medical officers were purposefully selected in terms of districts, facility types, gender, seniority and perceived likelihood of leaving in the next four years. Semi-structured interviews were performed by family physicians, and the qualitative data were analysed using the framework method.Results: Fourteen MOs were interviewed, and four major themes were identified: career intentions; experience of clinical work; experience of the organisation; and personal, family and community issues. Key issues that influenced retention were: ensure that the foundational elements are in place (e.g. adequate salary and good infrastructure), nurture cohesive team dynamics and relationships, have a family physician, continue the shift towards more collaborative and appreciative management styles, create stronger career pathways and opportunities for professional development in the district health services, be open to flexible working hours and overtime, and ensure workload is manageable.Conclusion: A number of important factors influencing retention were identified. Leaders and managers of the healthcare services could intervene across these multiple factors to enhance the conditions needed to retain MOs.

BackgroundThe health workforce is critical to strengthening district health services (DHS). In the public sector of South Africa medical officers (MO) are essential. Family physicians, responsible for clinical governance, identified their retention as a key issue.AimTo evaluate factors that influence retention of MOs in public sector DHS.Design & settingA descriptive survey of MOs working in DHS, Western Cape, South Africa.MethodAll 125 MOs working in facilities associated with the Stellenbosch University Family Physician Research Network were included in the survey. A questionnaire measured the prevalence of key factors that might be associated with retention (staying >4 years) and included the Satisfaction of Employees in Health Care tool and Short Warwick-Edinburgh Mental Well-being Scale. Data was collected in REDCap and analysed in the Statistical Package for Social Sciences.ResultsNinety-five MOs completed the survey. The overall rating of the facility (P=0.001), age (P=0.004), seniority (P=0.015), career plans (<0.001) and intention to stay in the public sector (P<0.001) were associated with retention. More personal factors such as social support (P=0.007), educational opportunities for children (P=0.002) and staying with one’s partner (P=0.036) were also associated. Gender, rural vs urban location, district hospital vs primary care facility, overtime, remuneration and additional rural allowance, were not associated.ConclusionsThe overall rating of the facility was important and subsequent qualitative work has explored the underlying issues. These findings can guide strategies in the Western Cape and similar settings to retain MOs in the DHS.

To cite this article: JJ Wessels & W Viljoen (2009) Letter: Why do patients leave our practice? A qualitative investigation into the reasons contributing to patients' decisions to leave a multi-partner general practice,