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...

HIV-related stigma may impede the successful implementation of HIV prevention and treatment programmes. Using a novel study-design nested within a large, community randomized trial we will evaluate the extent to which HIV-related stigma affects and is affected by the implementation of a comprehensive combination HIV prevention intervention including a universal test and treatment approach.

Objectives Describe the prevalence and determinants of HIV stigma in 21 communities in Zambia and South Africa. Design Analysis of baseline data from the HPTN 071 (PopART) cluster-randomised trial. HIV stigma data came from a random sample of 3859 people living with HIV. Community-level exposures reflecting HIV fears and judgements and perceptions of HIV stigma came from a random sample of community members not living with HIV (n=5088), and from health workers (n=851). Methods We calculated the prevalence of internalised stigma, and stigma experienced in the community or in a healthcare setting in the past year. We conducted risk-factor analyses using logistic regression, adjusting for clustering. Results Internalised stigma (868/3859, prevalence 22.5%) was not associated with sociodemographic characteristics but was less common among those with a longer period since diagnosis (p=0.043). Stigma experienced in the community (853/3859, 22.1%) was more common among women (p=0.016), older (p=0.011) and unmarried (p=0.009) individuals, those who had disclosed to others (p<0.001), and those with more lifetime sexual partners (p<0.001). Stigma experienced in a healthcare setting (280/3859, 7.3%) was more common among women (p=0.019) and those reporting more lifetime sexual partners (p=0.001) and higher wealth (p=0.003). Experienced stigma was more common in clusters where community members perceived higher levels of stigma, but was not associated with the beliefs of community members or health workers. Conclusions HIV stigma remains unacceptably high in South Africa and Zambia and may act as barrier to HIV prevention and treatment. Further research is needed to understand its determinants.

Introduction Integrating standardized measures of HIV stigma and discrimination into research studies of emerging HIV prevention approaches could enhance uptake and retention of these approaches, and care and treatment for people living with HIV (PLHIV), by informing stigma mitigation strategies. We sought to develop a succinct set of measures to capture key domains of stigma for use in research on HIV prevention technologies. Methods From 2013 to 2015, we collected baseline data on HIV stigma from three populations (PLHIV (N = 4053), community members (N = 5782) and health workers (N = 1560)) in 21 study communities in South Africa and Zambia participating in the HPTN 071 (PopART) cluster‐randomized trial. Forty questions were adapted from a harmonized set of measures developed in a consultative, global process. Informed by theory and factor analysis, we developed seven scales, with values ranging from 0 to 3, based on a 4‐point agreement Likert, and calculated means to assess different aspects of stigma. Higher means reflected more stigma. We developed two measures capturing percentages of PLHIV who reported experiencing any stigma in communities or healthcare settings in the past 12 months. We validated our measures by examining reliability using Cronbach's alpha and comparing the distribution of responses across characteristics previously associated with HIV stigma. Results Thirty‐five questions ultimately contributed to seven scales and two experience measures. All scales demonstrated acceptable to very good internal consistency. Among PLHIV, a scale captured internalized stigma, and experience measures demonstrated that 22.0% of PLHIV experienced stigma in the community and 7.1% in healthcare settings. Three scales for community members assessed fear and judgement, perceived stigma in the community and perceived stigma in healthcare settings. Similarly, health worker scales assessed fear and judgement, perceived stigma in the community and perceived co‐worker stigma in healthcare settings. A higher proportion of community members and health workers reported perceived stigma than the proportion of PLHIV who reported experiences of stigma. Conclusions We developed novel, valid measures that allowed for triangulation of HIV stigma across three populations in a large‐scale study. Such comparisons will illuminate how stigma influences and is influenced by programmatic changes to HIV service delivery over time.

Stigma and judgment by health workers toward people living with HIV (PLHIV) and key populations can undermine the uptake of HIV services. In 2014, we recruited health workers delivering HIV services from 21 urban communities in South Africa and Zambia participating in the first year of the HPTN 071 (PopART) cluster-randomized trial. We analyzed self-reported levels of stigma and judgment toward (1) PLHIV, (2) women who sell sex, (3) men who have sex with men (MSM), and (4) young women who become pregnant before marriage. Using logistic regression, we compared responses between three health worker cadres and explored risk factors for stigmatizing attitudes. Highest levels of stigma and judgment were in relation to women who sell sex and MSM, especially in Zambia. Heath workers did not generally think that clients should be denied services, although this was reported slightly more commonly by community health workers. Higher education levels were associated with lower judgmental beliefs, whereas higher perceptions of coworker stigmatizing behaviors toward PLHIV and each key population were associated with holding judgmental beliefs. Training experience was not associated with judgmental attitudes for any of the key populations. Our findings confirm a high prevalence of judgmental attitudes toward key population groups but lower levels in relation to PLHIV, among all cadres of health workers in both countries. Planning and implementing targeted stigma reduction interventions within health settings are critical to meet the needs of vulnerable populations that face more stigmatizing attitudes from health workers.

Background: The interaction between COVID-19, non-communicable diseases, and chronic infectious diseases such as HIV and tuberculosis (TB) are unclear, particularly in low- and middle-income countries (LMIC) in Africa. We investigated this interaction using a nationally representative hospital surveillance system in South Africa. Methods: A national surveillance system for laboratory-confirmed COVID-19 hospital admissions (DATCOV) was established. Using DATCOV data, we describe the demographic characteristics, clinical features, and in-hospital mortality among individuals admitted to public and private hospitals with COVID-19 during 5 March to 11 August 2020. Multivariable logistic regression models were used to compare individuals who were HIV-infected and HIV-uninfected and determine the factors associated with in-hospital mortality. Findings: Hospital admissions peaked at 1,560 admissions per day, in late July. Among the 41,877 individuals admitted with laboratory-confirmed COVID-19, 7,662 (18.3%) died. Comorbidities were documented in 27,555 (65.8%) individuals, most commonly observed were hypertension (36.8%), diabetes (29.6%), obesity (19.7%), and HIV (8.7%); TB was reported in 0.7% of individuals. Increased risk of in-hospital mortality was associated with HIV and TB, as well as other described risk factors for COVID-19, such as increasing age, male sex, non-White race (Black, mixed and Indian race), chronic underlying conditions particularly hypertension, diabetes and obesity. In addition, HIV-infected individuals with immunosuppression had increased risk of mortality (adjusted odds ratio 2.2; 95% confidence interval 1.6-3.1). Among HIV-infected individuals, the prevalence of other comorbidities associated with severe COVID-19 outcomes was 39.9%. The effect of multiple comorbidities on mortality was similar in HIV-infected and -uninfected individuals. Interpretation: These data provide a better understanding of the interaction of non-communicable diseases, chronic infectious diseases like HIV and TB and COVID-19. Increasing age and presence of chronic underlying comorbidities (particularly hypertension and diabetes) are important additional factors associated with COVID-19 mortality in a middle-income African setting and are common among HIV-infected individuals. HIV- and TB-infected individuals, particularly those with additional comorbidities, would benefit from COVID-19 prevention and treatment programmes.

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Introduction: We describe epidemiology and outcomes of confirmed SARS-CoV-2 infection and positive admissions among children <18 years in South Africa, an upper-middle income setting with high inequality. Methods: Laboratory and hospital COVID-19 surveillance data, 28 January -19 September 2020 was used. Testing rates were calculated as number of tested for SARS-CoV-2 divided by population at risk; test positivity rates were calculated as positive tests divided by total number of tests. In-hospital case fatality ratio (CFR) was calculated based on hospitalized positive admissions with outcome data who The findings and conclusions in this report are those of the author(s) and do not necessarily represent the official position of the U. S. Centers for Disease Control and Prevention (CDC).