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Topics in Antiviral Medicine ; 30(1 SUPPL):300-301, 2022.
Article in English | EMBASE | ID: covidwho-1880872


Background: South Africa is one of the African countries most affected by the COVID-19 pandemic. SARS-CoV-2 seroprevalence surveys provide valuable epidemiological information given the existence of asymptomatic cases. We report the findings of the first nationwide household-based population estimates of SARS-CoV-2 seroprevalence among people aged 12 years and older in South Africa. Methods: The survey used a cross-sectional multi-stage stratified cluster design undertaken over two separate time periods (November 2020-February 2021 and April-June 2021) which coincided with the second and third waves of the pandemic in South Africa. The Abbott® and Euroimmun® ani-SARS CoV-2 antibody assays were used to test for SARS-CoV-2 antibodies, the latter being the final result. The survey data was weighted with final individual weights benchmarked against 2020 mid-year population estimates by age, race, sex, and province. Frequencies were used to describe characteristics of the study population and SARS-CoV-2 seroprevalence. Bivariate and multivariate logistics regression analysis were used to identify factors associated with SARS-CoV-2 seropositivity. Results: 13640 participants gave a blood sample. The SARS-CoV-2 seroprevalence using the Euroimmun assay was 19.6% (95% CI 17.9-21.3) over the study period, translating to an estimated 8 675 265 (95% CI 7 508 393-9 842 137) estimated infections among people aged 12 years and older across South Africa by June 2021. Seroprevalence was higher in the Free State (26.8%), and Eastern Cape (26.0%) provinces (Figure). Increased odds of seropositivity were associated with prior PCR testing [aOR=1.29 (95% CI: 0.99-1.66)], being female [aOR=1.28 (95% CI 1.00-1.64), p=0.048] and hypertension, [aOR=1.28 (95% CI 1.00-1.640, p=0.048]. Conclusion: These findings highlight the burden of infection in South Africa by June 2021, and support testing strategies that focus on individuals with known exposure or symptoms since universal testing is not feasible. Females and younger people were more likely to be infected suggesting need for additional strategies targeting these populations. The estimated number of infections was 6.5 times higher than the number of SARS-CoV-2 cases reported nationally, suggesting that the country's testing strategy and capacity partly explain the dynamics of the pandemic. It is therefore essential to bolster testing capacity and to rapidly scale up vaccinations in order to contain the spread of the virus in the country.

Topics in Antiviral Medicine ; 30(1 SUPPL):332, 2022.
Article in English | EMBASE | ID: covidwho-1880610


Background: Accurate and reliable serological assays are essential for epidemiological surveillance of SARS-CoV-2. Several commercial anti-SARS assays are available and use cases for serological testing includes surveillance. However, there is growing evidence of varying performance of SARS-CoV-2 assays dependent of their format. We compare the performance of 3 different assays used in a national serosurvey undertaken between April and June 2021, in South Africa before widescale vaccination roll out. Methods: Venous blood samples from participants ≥12 years were transported under cold chain to a central testing laboratory within 24 hours of collection. Samples were tested for SARS CoV-2 antibodies with the Abbott nucleocapsid (NC)-based Architect anti-SARS CoV-2 chemiluminescent microparticle immunoassay (CMIA), the EuroImmun Spike (S)-based assay and the Roche total IgG NC-based Elecsys Anti-SARS-CoV-2 electrochemiluminescence immunoassay (ECLIA) on the Cobas e411 platform. We compared antibody detection proportions. Results: 8146 participants (median age 40 years, IQR 26-55) 5.6% of whom reported ≥1 SARS-CoV-2 symptom in the preceding 3 months gave a blood sample. Samples were tested on the Abbott assay with different cut-offs:-15.5% tested positive at the 1.40 cut-off and 26.8% at the 0.49 lower cut-off. 21.6% of the samples tested positive on the Euroimmun and 39.0% tested positive on the Roche assay (Table). 286 samples were from respondents self-reporting a prior positive PCR test, and among them 149(52.1%), 156(54.6%), and 206(72.3%) were positive on the Abbott (1.40 cut-off), Euroimmun and Roche assays respectively. 116/286(40.6%) of these were positive on all three assays and with 21(7.3%) positive on Roche only. 224/286(78.3%) of those reporting prior PCR test positivity were positive at the lower Abbott cut-off, with 47(16.4%) positive on Abbott only. Conclusion: These samples collected before wide scale vaccination roll out in South Africa show variable performance of these assays with the Roche NC assay detecting more infections that both the Abbott NC assay(0.40 cut-off) and the Euroimmun S assay.This could be reflective of seroreversion previously reported with Abbott and Euroimmun, and the greater sensitivity of Roche assay targeting the more abundant NC as an epitope. Use of direct, double Antigen-sandwich-based assays that are stable and have increased sensitivity over time may be optimal to detect both natural and vaccine-induced immunity in serosurveys.

S Afr Med J ; 111(11): 1084-1091, 2021 11 05.
Article in English | MEDLINE | ID: covidwho-1534500


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.

COVID-19/epidemiology , Health Status Disparities , Hospitalization/statistics & numerical data , /statistics & numerical data , Adolescent , Adult , Age Distribution , Aged , Aged, 80 and over , COVID-19/mortality , Child , Child, Preschool , Female , Humans , Infant , Infant, Newborn , Male , Middle Aged , Risk Factors , Sex Distribution , Socioeconomic Factors , South Africa/epidemiology , Young Adult