Trends in Covid-19 Admissions and Deaths among People with Hiv in South Africa

Background: South Africa experienced five COVID-19 waves and over 90% of the population have developed immunity. HIV prevalence among adults is 19% and over 2 million people have uncontrolled viral loads, posing a risk for poor COVID-19 outcomes. Using national hospital surveillance data, we aimed to investigate trends in admission and factors associated with in-hospital COVID-19 mortality among people with HIV (PWH) in South Africa. Method(s): Data between March 5, 2020 and May 28, 2022 from the national COVID-19 hospital surveillance system, SARS-CoV-2 case linelist and Electronic Vaccine Data System were linked and analysed. A wave was defined as the period for which weekly incidence was >=30 cases/100,000 people. Descriptive statistics were employed for admissions and mortality trends. Postimputation random effect multivariable logistic regression models compared (a) characteristics of PWH and HIV-uninfected individuals, and (b) factors associated with mortality among PWH. Result(s): 68.7% (272,287/396,328) of COVID-19 admissions had a documented HIV status. PWH accounted for 8.4% (22,978/272,287) of total admissions, and 9.8%, 8.0%, 6.8%, 12.2% and 6.7% of admissions in the D614G, Beta, Delta, Omicron BA.1 and Omicron BA.4/BA.5 waves respectively. The case fatality ratio (CFR) among PWH and HIV-uninfected was 24.3% (5,584/22,978) vs 21.7% (54,110/249,309) overall, and in the respective waves was 23.7% vs 20.4% (D614G), 27.9% vs 26.6% (Beta), 26.2% vs 24.5% (Delta), 18.2% vs 9.1% (Omicron BA.1) and 16.8% vs 5.5% (Omicron BA.4/BA.5). Chronic renal disease, malignancy and past TB were more likely, and hypertension and diabetes were less likely in PWH compared to HIV-uninfected individuals. Among PWH, along with older age, male sex and presence of a comorbidity, there was a lower odds of mortality among individuals with prior SARS-CoV-2 infection (aOR 0.6;95% CI 0.4-0.8);>=1 dose vaccination (aOR 0.1;95% CI 0.1-0.1);and those admitted in the Delta (aOR 0.9;95% CI 0.8-0.9), Omicron BA.1 (aOR 0.5;95% CI 0.5-0.6) and Omicron BA.4/BA.5 (aOR 0.5;95% CI 0.4-0.7) waves compared to the D614G wave. PWH with CD4< 200 had higher odds of in-hospital mortality (aOR 1.9;95% CI 1.8-2.1). Conclusion(s): In South Africa, mortality among PWH was less likely in the Delta and Omicron waves but PWH had a disproportionate burden of mortality during the two Omicron waves. Prior immunity protected against mortality, emphasizing the importance of COVID-19 vaccination among PWH, particularly PWH with immunosuppression.

Pediatric Covid-19 Mortality: Twosided Challenges from Health Resource Disparity

BACKGROUND AND AIM: The COVID-19 pandemic impacted high (HICs) and low to high- middle income countries (LHMICs) disproportionately. We sought to investigate factors contributing to disparate pediatric COVID-19 mortality. METHOD(S): We used the International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) COVID-19 database, and stratified country group defined by World Bank criteria. All hospitalized patients aged less than 19 years with suspected or confirmed COVID-19 diagnosis from January 2020 through April 2021 were included. RESULT(S): A total of 12,860 patients with 3,819 cases from HICs and 9,041 cases from LHMICs were included in this study. Of these, 8,961 (73.8%) patiens were confirmed cases and 2444 (20.1%) were suspected COVID19. Overall in-hospital mortality was 425 (3.3%) patients, with 4.0% mortality in LHMICs (361/9041), which was higher than 1.7% mortality in HICs (64/3819);adjusted HR (aHR) 4.74, 95%CI 3.16-7.10, p<0.001. There were significant differences between country income groups in the use of interventions, with higher use of antibiotics, corticosteroid, prone position, high flow nasal cannula, and invasive mechanical ventilation in HICs, and higher use of anticoagulants and non-invasive ventilation in LHMICs. Infectious comorbidities such as tuberculosis and HIV/AIDS were shown to be more prevalent in LHMICs [2 (0.0%) vs 171 (1.9 %), 1 (0.0%) vs. 149 (1.6%) patients, respectively]. Mortality rates in children who received mechanical ventilation in LHMICs were higher compared with children in HICs [89 (43.6%) vs. 17 (7.2%) patients, aHR 12.0, CI95% 7.2-19.9, p<0.001]. CONCLUSION(S): Various contributing factors to COVID-19 mortality identified in this study may reflect management differences in HICs and LHMICs. (Figure Presented).

Undiagnosed comorbidities among individuals hospitalised with COVID-19 in South African public hospitals.

BACKGROUND: Previous studies have reported comorbid disease, including hypertension, diabetes mellitus, chronic cardiac and renal disease, malignancy, HIV, tuberculosis (TB) and obesity, to be associated with COVID­19 mortality. National demographic surveys have reported a high proportion of undiagnosed and untreated comorbid disease in South Africa (SA). OBJECTIVES: To determine the number of individuals with previously undiagnosed HIV, TB and non-communicable diseases (NCDs) among patients hospitalised with COVID­19, and the level of medical control of these chronic diseases. METHODS: We conducted a sentinel surveillance study to collect enhanced data on HIV, TB and NCDs among individuals with COVID­19 admitted to 16 secondary-level public hospitals in six of the nine provinces of SA. Trained surveillance officers approached all patients who met the surveillance case definition for inclusion in the study, and consenting patients were enrolled. The data collection instrument included questions on past medical history to determine the self-reported presence of comorbidities. The results of clinical and laboratory testing introduced as part of routine clinical care for hospitalised COVID­19 patients were collected for the study, to objectively determine the presence of hypertension, diabetes, HIV and TB and the levels of control of diabetes and HIV. RESULTS: On self-reported history, the most prevalent comorbidities were hypertension (n=1 658; 51.5%), diabetes (n=855; 26.6%) and HIV (n=603; 18.7%). The prevalence of self-reported active TB was 3.1%, and that of previous TB 5.5%. There were 1 254 patients admitted with COVID­19 (39.0%) who met the body mass index criteria for obesity. On clinical and laboratory testing, 87 patients were newly diagnosed with HIV, 29 with TB, 215 with diabetes and 40 with hypertension during their COVID­19 admission. There were 151/521 patients living with HIV (29.0%) with a viral load >1 000 copies/mL and 309/570 (54.2%) with a CD4 count <200 cells/µL. Among 901 patients classified as having diabetes, 777 (86.2%) had a glycated haemoglobin (HbA1c) level ≥6.5%. CONCLUSION: The study revealed a high prevalence of comorbid conditions among individuals with COVID­19 admitted to public hospitals in SA. In addition, a significant number of patients had previously undiagnosed hypertension, diabetes, HIV and active TB, and many and poorly controlled chronic disease, as evidenced by high HbA1c levels in patients with diabetes, and high viral loads and low CD4 levels in patients with HIV. The findings highlight the importance of strengthening health systems and care cascades for chronic disease management, which include prevention, screening for and effectively treating comorbidities, and ensuring secure and innovative supplies of medicines in primary healthcare during the COVID­19 pandemic.

Leveraging epidemiology as a decision support tool during the COVID-19 epidemic in South Africa.

By May 2021, South Africa (SA) had experienced two 'waves' of COVID-19 infections, with an initial peak of infections reached in July 2020, followed by a larger peak of infections in January 2021. Public health decisions rely on accurate and timely disease surveillance and epidemiological analyses, and accessibility of data at all levels of government is critical to inform stakeholders to respond effectively. In this paper, we describe the adaptation, development and operation of epidemiological surveillance and modelling systems in SA in response to the COVID-19 epidemic, including data systems for monitoring laboratory-confirmed COVID-19 cases, hospitalisations, mortality and recoveries at a national and provincial level, and how these systems were used to inform modelling projections and public health decisions. Detailed descriptions on the characteristics and completeness of individual datasets are not provided in this paper. Rapid development of robust data systems was necessary to support the response to the SA COVID-19 epidemic. These systems produced data streams that were used in decision-making at all levels of government. While much progress was made in producing epidemiological data, challenges remain to be overcome to address gaps to better prepare for future waves of COVID-19 and other health emergencies.

Guiding equitable prioritisation of COVID-19 vaccine distribution and strategic deployment in South Africa to enhance effectiveness and access to vulnerable communities and prevent waste

Background. In South Africa (SA), >2.4 million cases of COVID-19 and >72 000 deaths were recorded between March 2020 and 1 August 2021, affecting the country's 52 districts to various extents. SA has committed to a COVID-19 vaccine roll-out in three phases, prioritising frontline workers, the elderly, people with comorbidities and essential workers. However, additional actions will be necessary to support efficient allocation and equitable access for vulnerable, access-constrained communities. Objectives. To explore various determinants of disease severity, resurgence risk and accessibility in order to aid an equitable, effective vaccine roll-out for SA that would maximise COVID-19 epidemic control by reducing the number of COVID-19 transmissions and resultant deaths, while at the same time reducing the risk of vaccine wastage. Methods. For the 52 districts of SA, 26 COVID-19 indicators such as hospital admissions, deaths in hospital and mobility were ranked and hierarchically clustered with cases to identify which indicators can be used as indicators for severity or resurgence risk. Districts were then ranked using the estimated COVID-19 severity and resurgence risk to assist with prioritisation of vaccine roll-out. Urban and rural accessibility were also explored as factors that could limit vaccine roll-out in hard-to-reach communities. Results. Highly populated urban districts showed the most cases. Districts such as Buffalo City, City of Cape Town and Nelson Mandela Bay experienced very severe first and second waves of the pandemic. Districts with high mobility, population size and density were found to be at highest risk of resurgence. In terms of accessibility, we found that 47.2% of the population are within 5 km of a hospital with 50 beds, and this percentage ranged from 87.0% in City of Cape Town to 0% in Namakwa district. Conclusions. The end goal is to provide equal distribution of vaccines proportional to district populations, which will provide fair protection. Districts with a high risk of resurgence and severity should be prioritised for vaccine roll-out, particularly the major metropolitan areas. We provide recommendations for allocations of different vaccine types for each district that consider levels of access, numbers of doses and cold-chain storage capability.

Guiding equitable prioritisation of COVID-19 vaccine distribution and strategic deployment in South Africa to enhance effectiveness and access to vulnerable communities and prevent waste.

BACKGROUND: In South Africa (SA), >2.4 million cases of COVID­19 and >72 000 deaths were recorded between March 2020 and 1 August 2021, affecting the country's 52 districts to various extents. SA has committed to a COVID­19 vaccine roll-out in three phases, prioritising frontline workers, the elderly, people with comorbidities and essential workers. However, additional actions will be necessary to support efficient allocation and equitable access for vulnerable, access-constrained communities. OBJECTIVES: To explore various determinants of disease severity, resurgence risk and accessibility in order to aid an equitable, effective vaccine roll-out for SA that would maximise COVID­19 epidemic control by reducing the number of COVID­19 transmissions and resultant deaths, while at the same time reducing the risk of vaccine wastage. METHODS: For the 52 districts of SA, 26 COVID­19 indicators such as hospital admissions, deaths in hospital and mobility were ranked and hierarchically clustered with cases to identify which indicators can be used as indicators for severity or resurgence risk. Districts were then ranked using the estimated COVID­19 severity and resurgence risk to assist with prioritisation of vaccine roll-out. Urban and rural accessibility were also explored as factors that could limit vaccine roll-out in hard-to-reach communities. RESULTS: Highly populated urban districts showed the most cases. Districts such as Buffalo City, City of Cape Town and Nelson Mandela Bay experienced very severe first and second waves of the pandemic. Districts with high mobility, population size and density were found to be at highest risk of resurgence. In terms of accessibility, we found that 47.2% of the population are within 5 km of a hospital with ≥50 beds, and this percentage ranged from 87.0% in City of Cape Town to 0% in Namakwa district. CONCLUSIONS: The end goal is to provide equal distribution of vaccines proportional to district populations, which will provide fair protection. Districts with a high risk of resurgence and severity should be prioritised for vaccine roll-out, particularly the major metropolitan areas. We provide recommendations for allocations of different vaccine types for each district that consider levels of access, numbers of doses and cold-chain storage capability.

Decreased severity of disease during the first global omicron variant covid-19 outbreak in a large hospital in tshwane, south africa.

INTRODUCTION: The coronavirus disease 2019 (COVID-19) first reported in Wuhan, China in December 2019 is a global pandemic that is threatening the health and wellbeing of people worldwide. To date there have been more than 274 million reported cases and 5.3 million deaths. The Omicron variant first documented in the City of Tshwane, Gauteng Province, South Africa on 9 November 2021 led to exponential increases in cases and a sharp rise in hospital admissions. The clinical profile of patients admitted at a large hospital in Tshwane is compared with previous waves. METHODS: 466 hospital COVID-19 admissions since 14 November 2021 were compared to 3962 admissions since 4 May 2020, prior to the Omicron outbreak. Ninety-eight patient records at peak bed occupancy during the outbreak were reviewed for primary indication for admission, clinical severity, oxygen supplementation level, vaccination and prior COVID-19 infection. Provincial and city-wide daily cases and reported deaths, hospital admissions and excess deaths data were sourced from the National Institute for Communicable Diseases, the National Department of Health and the South African Medical Research Council. RESULTS: For the Omicron and previous waves, deaths and ICU admissions were 4.5% vs 21.3% (p<0.00001), and 1% vs 4.3% (p<0.00001) respectively; length of stay was 4.0 days vs 8.8 days; and mean age was 39 years vs 49,8 years. Admissions in the Omicron wave peaked and declined rapidly with peak bed occupancy at 51% of the highest previous peak during the Delta wave. Sixty two (63%) patients in COVID-19 wards had incidental COVID-19 following a positive SARS-CoV-2 PCR test . Only one third (36) had COVID-19 pneumonia, of which 72% had mild to moderate disease. The remaining 28% required high care or ICU admission. Fewer than half (45%) of patients in COVID-19 wards required oxygen supplementation compared to 99.5% in the first wave. The death rate in the face of an exponential increase in cases during the Omicron wave at the city and provincial levels shows a decoupling of cases and deaths compared to previous waves, corroborating the clinical findings of decreased severity of disease seen in patients admitted to the Steve Biko Academic Hospital. CONCLUSION: There was decreased severity of COVID-19 disease in the Omicron-driven fourth wave in the City of Tshwane, its first global epicentre.

Understanding the differential impacts of COVID-19 among hospitalised patients in South Africa for equitable response.

BACKGROUND: There are limited in-depth analyses of COVID-19 differential impacts, especially in resource-limited settings such as South Africa (SA). OBJECTIVES: To explore context-specific sociodemographic heterogeneities in order to understand the differential impacts of COVID-19. METHODS: Descriptive epidemiological COVID-19 hospitalisation and mortality data were drawn from daily hospital surveillance data, National Institute for Communicable Diseases (NICD) update reports (6 March 2020 - 24 January 2021) and the Eastern Cape Daily Epidemiological Report (as of 24 March 2021). We examined hospitalisations and mortality by sociodemographics (age using 10-year age bands, sex and race) using absolute numbers, proportions and ratios. The data are presented using tables received from the NICD, and charts were created to show trends and patterns. Mortality rates (per 100 000 population) were calculated using population estimates as a denominator for standardisation. Associations were determined through relative risks (RRs), 95% confidence intervals (CIs) and p-values <0.001. RESULTS: Black African females had a significantly higher rate of hospitalisation (8.7% (95% CI 8.5 - 8.9)) compared with coloureds, Indians and whites (6.7% (95% CI 6.0 - 7.4), 6.3% (95% CI 5.5 - 7.2) and 4% (95% CI 3.5 - 4.5), respectively). Similarly, black African females had the highest hospitalisation rates at a younger age category of 30 - 39 years (16.1%) compared with other race groups. Whites were hospitalised at older ages than other races, with a median age of 63 years. Black Africans were hospitalised at younger ages than other race groups, with a median age of 52 years. Whites were significantly more likely to die at older ages compared with black Africans (RR 1.07; 95% CI 1.06 - 1.08) or coloureds (RR 1.44; 95% CI 1.33 - 1.54); a similar pattern was found between Indians and whites (RR 1.59; 95% CI 1.47 - 1.73). Women died at older ages than men, although they were admitted to hospital at younger ages. Among black Africans and coloureds, females (50.9 deaths per 100 000 and 37 per 100 000, respectively) had a higher COVID-19 death rate than males (41.2 per 100 000 and 41.5 per 100 000, respectively). However, among Indians and whites, males had higher rates of deaths than females. The ratio of deaths to hospitalisations by race and gender increased with increasing age. In each age group, this ratio was highest among black Africans and lowest among whites. CONCLUSIONS: The study revealed the heterogeneous nature of COVID-19 impacts in SA. Existing socioeconomic inequalities appear to shape COVID-19 impacts, with a disproportionate effect on black Africans and marginalised and low socioeconomic groups. These differential impacts call for considered attention to mitigating the health disparities among black Africans.

Sentinel seroprevalence of SARS-CoV-2 in Gauteng Province, South Africa, August - October 2020.

BACKGROUND: Estimates of prevalence of anti-SARS-CoV-2 antibody positivity (seroprevalence) for tracking the COVID-19 epidemic are lacking for most African countries. OBJECTIVES: To determine the prevalence of antibodies against SARS-CoV-2 in a sentinel cohort of patient samples received for routine testing at tertiary laboratories in Johannesburg, South Africa. METHODS: This sentinel study was conducted using remnant serum samples received at three National Health Laboratory Service laboratories in the City of Johannesburg (CoJ) district. Collection was from 1 August to 31 October 2020. We extracted accompanying laboratory results for glycated haemoglobin (HbA1c), creatinine, HIV, viral load and CD4 T-cell count. An anti-SARS-CoV-2 targeting the nucleocapsid (N) protein of the coronavirus with higher affinity for IgM and IgG antibodies was used. We reported crude as well as population-weighted and test-adjusted seroprevalence. Multivariate logistic regression analysis was used to determine whether age, sex, HIV and diabetic status were associated with increased risk for seropositivity. RESULTS: A total of 6 477 samples were analysed, the majority (n=5 290) from the CoJ region. After excluding samples with no age or sex stated, the model population-weighted and test-adjusted seroprevalence for the CoJ (n=4 393) was 27.0% (95% confidence interval (CI) 25.4 - 28.6). Seroprevalence was highest in those aged 45 - 49 years (29.8%; 95% CI 25.5 - 35.0) and in those from the most densely populated areas of the CoJ. Risk for seropositivity was highest in those aged 18 - 49 years (adjusted odds ratio (aOR) 1.52; 95% CI 1.13 - 2.13; p=0.0005) and in samples from diabetics (aOR 1.36; 95% CI 1.13 - 1.63; p=0.001). CONCLUSIONS: Our study conducted between the first and second waves of the pandemic shows high levels of current infection among patients attending public health facilities in Gauteng Province.

A call to action: Temporal trends of COVID-19 deaths in the South African Muslim community.

Letter by Omar on letter by Jassat et al. (Jassat W, Brey Z, Parker S, et al. A call to action: Temporal trends of COVID-19 deaths in the South African Muslim community. S Afr Med J 2021;111(8):692-694. https://doi.org/10.7196/SAMJ.2021.v111i8.15878); and response by Jassat et al.