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.

Since its first appearance in Wuhan (China), countries have been employing, to varying degrees of success, a series of non-pharmaceutical interventions aimed at limiting the spread of SARS-CoV-2 within their populations. In this article, we build on scientific work that demonstrates that culture is part of the explanation for the observed variability between countries in their ability to effectively control the transmission of SARS-CoV-2. We present a theoretical framework of how culture influences decision-making at the level of the individual. This conceptualization is formalized in an agent-based model that simulates how cultural factors can combine to produce differences across populations in terms of the behavioral responses of individuals to the COVID-19 crisis. We illustrate that, within our simulated environment, the culturally-dependent willingness of people to comply with public health related measures might constitute an important determinant of differences in infection dynamics across populations. Our model generates the highest rates of non-compliance within cultures marked as individualist, progressive and egalitarian. Our model illustrates the potential role of culture as a population-level predictor of infections associated with COVID-19. In doing so, the model, and theoretical framework on which it is based, may inform future studies aimed at incorporating the effect of culture on individual decision-making processes during a pandemic within social simulation models.

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