Using Generalized Structured Additive Regression Models to Determine Factors Associated with and Clusters for COVID-19 Hospital Deaths in South Africa

BackgroundThe first case of COVID-19 in South Africa was reported in March 2020 and the country has since recorded over 3.6 million laboratory-confirmed cases and 100 000 deaths as of March 2022. Transmission and infection of SARS-CoV-2 virus and deaths in general due to COVID-19 have been shown to be spatially associated but spatial patterns in in-hospital deaths have not fully been investigated in South Africa. This study uses national COVID-19 hospitalization data to investigate the spatial effects on hospital deaths after adjusting for known mortality risk factors. MethodsCOVID-19 hospitalization data and deaths were obtained from the National Institute for Communicable Diseases (NICD). Generalized structured additive logistic regression model was used to assess spatial effects on COVID-19 in-hospital deaths adjusting for demographic and clinical covariates. Continuous covariates were modelled by assuming second-order random walk priors, while spatial autocorrelation was specified with Markov random field prior and fixed effects with vague priors respectively. The inference was fully Bayesian. ResultsThe risk of COVID-19 in-hospital mortality increased with patient age, with admission to intensive care unit (ICU) (aOR=4.16; 95% Credible Interval: 4.05-4.27), being on oxygen (aOR=1.49; 95% Credible Interval: 1.46-1.51) and on invasive mechanical ventilation (aOR=3.74; 95% Credible Interval: 3.61-3.87). Being admitted in a public hospital (aOR= 3.16; 95% Credible Interval: 3.10-3.21) was also significantly associated with mortality. Risk of in-hospital deaths increased in months following a surge in infections and dropped after months of successive low infections highlighting crest and troughs lagging the epidemic curve. After controlling for these factors, districts such as Vhembe, Capricorn and Mopani in Limpopo province, and Buffalo City, O.R. Tambo, Joe Gqabi and Chris Hani in Eastern Cape province remained with significantly higher odds of COVID-19 hospital deaths suggesting possible health systems challenges in those districts. ConclusionThe results show substantial COVID-19 in-hospital mortality variation across the 52 districts. Our analysis provides information that can be important for strengthening health policies and the public health system for the benefit of the whole South African population. Understanding differences in in-hospital COVID-19 mortality across space could guide interventions to achieve better health outcomes in affected districts.

TRENDS IN CASES, HOSPITALISATION AND MORTALITY RELATED TO THE OMICRON BA.4/BA.5 SUB-VARIANTS IN SOUTH AFRICA

IntroductionThe Omicron BA.1/BA.2 wave in South Africa had lower hospitalisation and mortality than previous SARS-CoV-2 variants and was followed by an Omicron BA.4/BA.5 wave. This study compared admission incidence risk across waves, and the risk of mortality in the Omicron BA.4/BA.5 wave, to the Omicron BA.1/BA.2 and Delta waves. MethodsData from South Africas national hospital surveillance system, SARS-CoV-2 case linelist and Electronic Vaccine Data System were linked and analysed. Wave periods were defined when the country passed a weekly incidence of 30 cases/100,000 people. Mortality rates in the Delta, Omicron BA.1/BA.2 and Omicron BA.4/BA.5 wave periods were compared by post-imputation random effect multivariable logistic regression models. ResultsIn-hospital deaths declined 6-fold from 37,537 in the Delta wave to 6,074 in the Omicron BA.1/BA.2 wave and a further 7-fold to 837 in the Omicron BA.4/BA.5 wave. The case fatality ratio (CFR) was 25.9% (N=144,798), 10.9% (N=55,966) and 7.1% (N=11,860) in the Delta, Omicron BA.1/BA.2, and Omicron BA.4/BA.5 waves respectively. After adjusting for age, sex, race, comorbidities, health sector and province, compared to the Omicron BA.4/BA.5 wave, patients had higher risk of mortality in the Omicron BA.1/BA.2 wave (adjusted odds ratio [aOR] 1.43; 95% confidence interval [CI] 1.32-1.56) and Delta (aOR 3.22; 95% CI 2.98-3.49) wave. Being partially vaccinated (aOR 0.89, CI 0.86-0.93), fully vaccinated (aOR 0.63, CI 0.60-0.66) and boosted (aOR 0.31, CI 0.24-0.41); and prior laboratory-confirmed infection (aOR 0.38, CI 0.35-0.42) were associated with reduced risks of mortality. ConclusionOverall, admission incidence risk and in-hospital mortality, which had increased progressively in South Africas first three waves, decreased in the fourth Omicron BA.1/BA.2 wave and declined even further in the fifth Omicron BA.4/BA.5 wave. Mortality risk was lower in those with natural infection and vaccination, declining further as the number of vaccine doses increased.

Estimating the time-varying reproduction number for COVID-19 in South Africa during the first four waves using multiple measures of incidence for public and private sectors across four waves

ObjectivesWe aimed to quantify transmission trends in South Africa during the first four waves of the COVID-19 pandemic using estimates of the time-varying reproduction number (R) and to compare the robustness of R estimates based on three different data sources and using data from public and private sector service providers. MethodsWe estimated R from March 2020 through April 2022, nationally and by province, based on time series of rt-PCR-confirmed cases, hospitalizations, and hospital-associated deaths, using a method which models daily incidence as a weighted sum of past incidence. We also estimated R separately using public and private sector data. ResultsNationally, the maximum case-based R following the introduction of lockdown measures was 1.55 (CI: 1.43-1.66), 1.56 (CI: 1.47-1.64), 1.46 (CI: 1.38-1.53) and 3.33 (CI: 2.84-3.97) during the first (Wuhan-Hu), second (Beta), third (Delta), and fourth (Omicron) waves respectively. Estimates based on the three data sources (cases, hospitalisations, deaths) were generally similar during the first three waves but case-based estimates were higher during the fourth wave. Public and private sector R estimates were generally similar except during the initial lockdowns and in case-based estimates during the fourth wave. DiscussionAgreement between R estimates using different data sources during the first three waves suggests that data from any of these sources could be used in the early stages of a future pandemic. High R estimates for Omicron relative to earlier waves is interesting given a high level of exposure pre-Omicron. The agreement between public and private sector R estimates highlights the fact that clients of the public and private sectors did not experience two separate epidemics, except perhaps to a limited extent during the strictest lockdowns in the first wave.

SARS-CoV-2 infections during Omicron (BA.1) dominant wave and subsequent population immunity in Gauteng, South Africa

BackgroundThe B.1.1.529 (Omicron BA.1) variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a global resurgence of coronavirus disease 2019 (Covid-19). The contribution of BA.1 infection to population immunity and its effect on subsequent resurgence of B.1.1.529 sub-lineages warrant investigation. MethodsWe conducted an epidemiologic survey to determine the sero-prevalence of SARS-CoV-2 IgG from March 1 to April 11, 2022, after the BA.1-dominant wave had subsided in Gauteng (South Africa), and prior to a resurgence of Covid-19 dominated by the BA.4 and BA.5 (BA.4/BA.5) sub-lineages. Population-based sampling included households in an earlier survey from October 22 to December 9, 2021 preceding the BA.1 dominant wave. Dried-blood-spot samples were quantitatively tested for IgG against SARS-CoV-2 spike protein and nucleocapsid protein. Epidemiologic trends in Gauteng for cases, hospitalizations, recorded deaths, and excess deaths were evaluated from the inception of the pandemic to the onset of the BA.1 dominant wave (pre-BA.1), during the BA.1 dominant wave, and for the BA.4/BA.5 dominant wave through June 6, 2022. ResultsThe 7510 participants included 2420 with paired samples from the earlier survey. Despite only 26.7% (1995/7470) of individuals having received a Covid-19 vaccine, the overall sero-prevalence was 90.9% (95% confidence interval [CI], 90.2 to 91.5), including 89.5% in Covid-19 unvaccinated individuals. Sixty-four percent (95%CI, 61.8-65.9) of individuals with paired samples had serological evidence of SARS-CoV-2 infection during the BA.1 dominant wave. Of all cumulative recorded hospitalisations and deaths, 14.1% and 5.9% were contributed by the BA.1 dominant wave, and 5.1% and 1.6% by the BA.4/BA.5 dominant wave. The SARS-CoV-2 infection fatality risk was lower in the BA.1 compared with pre-BA.1 waves for recorded deaths (0.02% vs. 0.33%) and Covid-19 attributable deaths based on excess mortality estimates (0.03% vs. 0.67%). ConclusionsGauteng province experienced high levels of infections in the BA.1 -dominant wave against a backdrop of high (73%) sero-prevalence. Covid-19 hospitalizations and deaths were further decoupled from infections during BA.4/BA.5 dominant wave than that observed during the BA.1 dominant wave. (Funded by the Bill and Melinda Gates Foundation.)

Outcomes of laboratory-confirmed SARS-CoV-2 infection during resurgence driven by Omicron lineages BA.4 and BA.5 compared with previous waves in the Western Cape Province, South Africa

ObjectiveWe aimed to compare clinical severity of Omicron BA.4/BA.5 infection with BA.1 and earlier variant infections among laboratory-confirmed SARS-CoV-2 cases in the Western Cape, South Africa, using timing of infection to infer the lineage/variant causing infection. MethodsWe included public sector patients aged [≥]20 years with laboratory-confirmed COVID-19 between 1-21 May 2022 (BA.4/BA.5 wave) and equivalent prior wave periods. We compared the risk between waves of (i) death and (ii) severe hospitalization/death (all within 21 days of diagnosis) using Cox regression adjusted for demographics, comorbidities, admission pressure, vaccination and prior infection. ResultsAmong 3,793 patients from the BA.4/BA.5 wave and 190,836 patients from previous waves the risk of severe hospitalization/death was similar in the BA.4/BA.5 and BA.1 waves (adjusted hazard ratio [aHR] 1.12; 95% confidence interval [CI] 0.93; 1.34). Both Omicron waves had lower risk of severe outcomes than previous waves. Prior infection (aHR 0.29, 95% CI 0.24; 0.36) and vaccination (aHR 0.17; 95% CI 0.07; 0.40 for boosted vs. no vaccine) were protective. ConclusionDisease severity was similar amongst diagnosed COVID-19 cases in the BA.4/BA.5 and BA.1 periods in the context of growing immunity against SARS-CoV-2 due to prior infection and vaccination, both of which were strongly protective.

An international observational study to assess the impact of the Omicron variant emergence on the clinical epidemiology of COVID-19 in hospitalised patients

BackgroundWhilst timely clinical characterisation of infections caused by novel SARS-CoV-2 variants is necessary for evidence-based policy response, individual-level data on infecting variants are typically only available for a minority of patients and settings. MethodsHere, we propose an innovative approach to study changes in COVID-19 hospital presentation and outcomes after the Omicron variant emergence using publicly available population-level data on variant relative frequency to infer SARS-CoV-2 variants likely responsible for clinical cases. We apply this method to data collected by a large international clinical consortium before and after the emergence of the Omicron variant in different countries. ResultsOur analysis, that includes more than 100,000 patients from 28 countries, suggests that in many settings patients hospitalised with Omicron variant infection less often presented with commonly reported symptoms compared to patients infected with pre-Omicron variants. Patients with COVID-19 admitted to hospital after Omicron variant emergence had lower mortality compared to patients admitted during the period when Omicron variant was responsible for only a minority of infections (odds ratio in a mixed-effects logistic regression adjusted for likely confounders, 0.67 [95% confidence interval 0.61 - 0.75]). Qualitatively similar findings were observed in sensitivity analyses with different assumptions on population-level Omicron variant relative frequencies, and in analyses using available individual-level data on infecting variant for a subset of the study population. ConclusionsAlthough clinical studies with matching viral genomic information should remain a priority, our approach combining publicly available data on variant frequency and a multi-country clinical characterisation dataset with more than 100,000 records allowed analysis of data from a wide range of settings and novel insights on real-world heterogeneity of COVID-19 presentation and clinical outcome.

A global systematic analysis of the occurrence, severity, and recovery pattern of long COVID in 2020 and 2021

ImportanceWhile much of the attention on the COVID-19 pandemic was directed at the daily counts of cases and those with serious disease overwhelming health services, increasingly, reports have appeared of people who experience debilitating symptoms after the initial infection. This is popularly known as long COVID. ObjectiveTo estimate by country and territory of the number of patients affected by long COVID in 2020 and 2021, the severity of their symptoms and expected pattern of recovery DesignWe jointly analyzed ten ongoing cohort studies in ten countries for the occurrence of three major symptom clusters of long COVID among representative COVID cases. The defining symptoms of the three clusters (fatigue, cognitive problems, and shortness of breath) are explicitly mentioned in the WHO clinical case definition. For incidence of long COVID, we adopted the minimum duration after infection of three months from the WHO case definition. We pooled data from the contributing studies, two large medical record databases in the United States, and findings from 44 published studies using a Bayesian meta-regression tool. We separately estimated occurrence and pattern of recovery in patients with milder acute infections and those hospitalized. We estimated the incidence and prevalence of long COVID globally and by country in 2020 and 2021 as well as the severity-weighted prevalence using disability weights from the Global Burden of Disease study. ResultsAnalyses are based on detailed information for 1906 community infections and 10526 hospitalized patients from the ten collaborating cohorts, three of which included children. We added published data on 37262 community infections and 9540 hospitalized patients as well as ICD-coded medical record data concerning 1.3 million infections. Globally, in 2020 and 2021, 144.7 million (95% uncertainty interval [UI] 54.8-312.9) people suffered from any of the three symptom clusters of long COVID. This corresponds to 3.69% (1.38-7.96) of all infections. The fatigue, respiratory, and cognitive clusters occurred in 51.0% (16.9-92.4), 60.4% (18.9-89.1), and 35.4% (9.4-75.1) of long COVID cases, respectively. Those with milder acute COVID-19 cases had a quicker estimated recovery (median duration 3.99 months [IQR 3.84-4.20]) than those admitted for the acute infection (median duration 8.84 months [IQR 8.10-9.78]). At twelve months, 15.1% (10.3-21.1) continued to experience long COVID symptoms. Conclusions and relevanceThe occurrence of debilitating ongoing symptoms of COVID-19 is common. Knowing how many people are affected, and for how long, is important to plan for rehabilitative services and support to return to social activities, places of learning, and the workplace when symptoms start to wane. Key PointsO_ST_ABSQuestionC_ST_ABSWhat are the extent and nature of the most common long COVID symptoms by country in 2020 and 2021? FindingsGlobally, 144.7 million people experienced one or more of three symptom clusters (fatigue; cognitive problems; and ongoing respiratory problems) of long COVID three months after infection, in 2020 and 2021. Most cases arose from milder infections. At 12 months after infection, 15.1% of these cases had not yet recovered. MeaningThe substantial number of people with long COVID are in need of rehabilitative care and support to transition back into the workplace or education when symptoms start to wane.

Post COVID-19 Condition in South Africa: 3-month follow-up after hospitalisation with SARS-CoV-2

BackgroundPost COVID-19 Condition (PCC) as defined by WHO refers to a wide range of new, returning, or ongoing health problems experienced by COVID-19 survivors, and represents a rapidly emerging public health priority. We aimed to establish how this developing condition has impacted patients in South Africa and which population groups are at risk. MethodsIn this prospective cohort study, participants [≥]18 years who had been hospitalised with laboratory-confirmed SARS-CoV-2 infection during the second and third wave between December 2020 and August 2021 underwent telephonic follow-up assessment up at one-month and three-months after hospital discharge. Participants were assessed using a standardised questionnaire for the evaluation of symptoms, functional status, health-related quality of life and occupational status. Multivariable logistic regression models were used to determine factors associated with PCC. FindingsIn total, 1,873 of 2,413 (78%) enrolled hospitalised COVID-19 participants were followed up at three-months after hospital discharge. Participants had a median age of 52 years (IQR 41-62) and 960 (51.3%) were women. At three-months follow-up, 1,249 (66.7%) participants reported one or more persistent COVID-related symptom(s), compared to 1,978/2,413 (82.1%) at one-month post-hospital discharge. The most common symptoms reported were fatigue (50.3%), shortness of breath (23.4%), confusion or lack of concentration (17.5%), headaches (13.8%) and problems seeing/blurred vision (10.1%). On multivariable analysis, factors associated with new or persistent symptoms following acute COVID-19 were age [≥]65 years [adjusted odds ratio (aOR) 1.62; 95%confidence interval (CI) 1.00-2.61]; female sex (aOR 2.00; 95% CI 1.51-2.65); mixed ethnicity (aOR 2.15; 95% CI 1.26-3.66) compared to black ethnicity; requiring supplemental oxygen during admission (aOR 1.44; 95% CI 1.06-1.97); ICU admission (aOR 1.87; 95% CI 1.36-2.57); pre-existing obesity (aOR 1.44; 95% CI 1.09-1.91); and the presence of [≥]4 acute symptoms (aOR 1.94; 95% CI 1.19-3.15) compared to no symptoms at onset. InterpretationThe majority of COVID-19 survivors in this cohort of previously hospitalised participants reported persistent symptoms at three-months from hospital discharge, as well as a significant impact of PCC on their functional and occupational status. The large burden of PCC symptoms identified in this study emphasises the need for a national health strategy. This should include the development of clinical guidelines and training of health care workers, in identifying, assessing and caring for patients affected by PCC, establishment of multidisciplinary national health services, and provision of information and support to people who suffer from PCC.

Clinical severity of COVID-19 patients admitted to hospitals during the Omicron wave in South Africa

BackgroundClinical severity of patients hospitalised with SARS-CoV-2 infection during the Omicron (fourth) wave was assessed and compared to trends in the D614G (first), Beta (second), and Delta (third) waves in South Africa. MethodsWeekly incidence of 30 laboratory-confirmed SARS-CoV-2 cases/100,000 population defined the start and end of each wave. Hospital admission data were collected through an active national COVID-19-specific surveillance programme. Disease severity was compared across waves by post-imputation random effect multivariable logistic regression models. Severe disease was defined as one or more of acute respiratory distress, supplemental oxygen, mechanical ventilation, intensive-care admission or death. Results335,219 laboratory-confirmed SARS-CoV-2 admissions were analysed, constituting 10.4% of 3,216,179 cases recorded during the 4 waves. In the Omicron wave, 8.3% of cases were admitted to hospital (52,038/629,617) compared to 12.9% (71,411/553,530) in the D614G, 12.6% (91,843/726,772) in the Beta and 10.0% (131,083/1,306,260) in the Delta waves (p<0.001). During the Omicron wave, 33.6% of admissions experienced severe disease compared to 52.3%, 63.4% and 63.0% in the D614G, Beta and Delta waves (p<0.001). The in-hospital case fatality ratio during the Omicron wave was 10.7%, compared to 21.5%, 28.8% and 26.4% in the D614G, Beta and Delta waves (p<0.001). Compared to the Omicron wave, patients had more severe clinical presentations in the D614G (adjusted odds ratio [aOR] 2.07; 95% confidence interval [CI] 2.01-2.13), Beta (aOR 3.59; CI: 3.49-3.70) and Delta (aOR 3.47: CI: 3.38-3.57) waves. ConclusionThe trend of increasing cases and admissions across South Africas first three waves shifted in Omicron fourth wave, with a higher and quicker peak but fewer admitted patients, who experienced less clinically severe illness and had a lower case-fatality ratio. Omicron marked a change in the SARS-CoV-2 epidemic curve, clinical profile and deaths in South Africa. Extrapolations to other populations should factor in differing vaccination and prior infection levels.

Clinical severity of Omicron sub-lineage BA.2 compared to BA.1 in South Africa

Early data indicated that infection with Omicron BA.1 sub-lineage was associated with a lower risk of hospitalisation and severe illness, compared to Delta infection. Recently, the BA.2 sub-lineage has increased in many areas globally. We aimed to assess the severity of BA.2 infections compared to BA.1 in South Africa. We performed data linkages for (i) national COVID-19 case data, (ii) SARS-CoV-2 laboratory test data, and (iii) COVID-19 hospitalisations data, nationally. For cases identified using TaqPath COVID-19 PCR, infections were designated as S-gene target failure (SGTF, proxy for BA.1) or S-gene positive (proxy for BA.2). Disease severity was assessed using multivariable logistic regression models comparing individuals with S-gene positive infection to SGTF-infected individuals diagnosed between 1 December 2021 to 20 January 2022. From week 49 (starting 5 December 2021) through week 4 (ending 29 January 2022), the proportion of S-gene positive infections increased from 3% (931/31,271) to 80% (2,425/3,031). The odds of being admitted to hospital did not differ between individuals with S-gene positive (BA.2 proxy) infection compared to SGTF (BA.1 proxy) infection (adjusted odds ratio (aOR) 0.96, 95% confidence interval (CI) 0.85-1.09). Among hospitalised individuals, after controlling for factors associated with severe disease, the odds of severe disease did not differ for individuals with S-gene positive infection compared to SGTF infection (aOR 0.91, 95%CI 0.68-1.22). These data suggest that while BA.2 may have a competitive advantage over BA.1 in some settings, the clinical profile of illness remains similar.

SARS-CoV-2 seroprevalence in a rural and urban community household cohort in South Africa, after the third wave, April-November 2021

By November 2021, after the third SARS-CoV-2 wave in South Africa, seroprevalence was 60% (95%CrI 56%-64%) in a rural and 70% (95%CrI 56%-64%) in an urban community; highest in individuals aged 13-18 years. High seroprevalence prior to Omicron emergence may have contributed to reduced severity observed in the 4th wave. Article Summary LineIn South Africa, after a third wave of SARS-CoV-2 infections, seroprevalence was 60% in a rural and 70% in an urban community, with case-to-infection, - hospitalization and -fatality ratios similar to the second wave.

Assessing the clinical severity of the Omicron variant in the Western Cape Province, South Africa, using the diagnostic PCR proxy marker of RdRp target delay to distinguish between Omicron and Delta infections: a survival analysis

BackgroundEmerging data suggest that SARS-CoV-2 Omicron variant of concern (VOC)is associated with reduced risk of severe disease. The extent to which this reflects a difference in the inherent virulence of Omicron, or just higher levels of population immunity, is currently not clear. MethodsRdRp target delay (RTD: a difference in cycle threshold value of RdRp - E > 3.5) in the Seegene Allplex 2019-nCoV PCR assay is a proxy marker for the Delta VOC. The absence of this proxy marker in the period of transition to Omicron was used to identify suspected Omicron VOC infections. Cox regression was performed for the outcome of hospital admission in those who tested positive for SARS-CoV-2 on the Seegene Allplex assay from 1 November to 14 December 2021 in the Western Cape Province, South Africa, public sector. Vaccination status at time of diagnosis, as well as prior diagnosed infection and comorbidities, were adjusted for. Results150 cases with RTD (proxy for Delta) and 1486 cases without RTD (proxy for Omicron) were included. Cases without RTD had a lower hazard of admission (adjusted Hazard Ratio [aHR] of 0.56, 95% confidence interval [CI] 0.34-0.91). Complete vaccination was protective of admission with an aHR of 0.45 (95%CI 0.26-0.77). ConclusionOmicron has resulted in a lower risk of hospital admission, compared to contemporaneous Delta infection in the Western Cape Province, when using the proxy marker of RTD. Under-ascertainment of reinfections with an immune escape variant like Omicron remains a challenge to accurately assessing variant virulence.

Outcomes of laboratory-confirmed SARS-CoV-2 infection in the Omicron-driven fourth wave compared with previous waves in the Western Cape Province, South Africa

ObjectivesWe aimed to compare COVID-19 outcomes in the Omicron-driven fourth wave with prior waves in the Western Cape, the contribution of undiagnosed prior infection to differences in outcomes in a context of high seroprevalence due to prior infection, and whether protection against severe disease conferred by prior infection and/or vaccination was maintained. MethodsIn this cohort study, we included public sector patients aged [≥]20 years with a laboratory confirmed COVID-19 diagnosis between 14 November-11 December 2021 (wave four) and equivalent prior wave periods. We compared the risk between waves of the following outcomes using Cox regression: death, severe hospitalization or death and any hospitalization or death (all [≤]14 days after diagnosis) adjusted for age, sex, comorbidities, geography, vaccination and prior infection. ResultsWe included 5,144 patients from wave four and 11,609 from prior waves. Risk of all outcomes was lower in wave four compared to the Delta-driven wave three (adjusted Hazard Ratio (aHR) [95% confidence interval (CI)] for death 0.27 [0.19; 0.38]. Risk reduction was lower when adjusting for vaccination and prior diagnosed infection (aHR:0.41, 95% CI: 0.29; 0.59) and reduced further when accounting for unascertained prior infections (aHR: 0.72). Vaccine protection was maintained in wave four (aHR for outcome of death: 0.24; 95% CI: 0.10; 0.58). ConclusionsIn the Omicron-driven wave, severe COVID-19 outcomes were reduced mostly due to protection conferred by prior infection and/or vaccination, but intrinsically reduced virulence may account for an approximately 25% reduced risk of severe hospitalization or death compared to Delta.

South African Population Immunity and Severe Covid-19 with Omicron Variant

BackgroundWe conducted a seroepidemiological survey from October 22 to December 9, 2021, in Gauteng Province, South Africa, to determine SARS-CoV-2 immunoglobulin G (IgG) seroprevalence primarily before the fourth wave of coronavirus disease 2019 (Covid-19), in which the B.1.1.529 (Omicron) variant was dominant. We evaluated epidemiological trends in case rates and rates of severe disease through to January 12, 2022, in Gauteng. MethodsWe contacted households from a previous seroepidemiological survey conducted from November 2020 to January 2021, plus an additional 10% of households using the same sampling framework. Dry blood spot samples were tested for anti-spike and anti-nucleocapsid protein IgG using quantitative assays on the Luminex platform. Daily case, hospital admission, and reported death data, and weekly excess deaths, were plotted over time. ResultsSamples were obtained from 7010 individuals, of whom 1319 (18.8%) had received a Covid-19 vaccine. Overall seroprevalence ranged from 56.2% (95% confidence interval [CI], 52.6 to 59.7) in children aged <12 years to 79.7% (95% CI, 77.6 to 81.5) in individuals aged >50 years. Seropositivity was more likely in vaccinated (93.1%) vs unvaccinated (68.4%) individuals. Epidemiological data showed SARS-CoV-2 infection rates increased and subsequently declined more rapidly than in previous waves. Infection rates were decoupled from Covid-19 hospitalizations, recorded deaths, and excess deaths relative to the previous three waves. ConclusionsWidespread underlying SARS-CoV-2 seropositivity was observed in Gauteng Province before the Omicron-dominant wave. Epidemiological data showed a decoupling of hospitalization and death rates from infection rate during Omicron circulation.

Early assessment of the clinical severity of the SARS-CoV-2 Omicron variant in South Africa

BackgroundThe SARS-CoV-2 Omicron variant of concern (VOC) almost completely replaced other variants in South Africa during November 2021, and was associated with a rapid increase in COVID-19 cases. We aimed to assess clinical severity of individuals infected with Omicron, using S Gene Target Failure (SGTF) on the Thermo Fisher Scientific TaqPath COVID-19 PCR test as a proxy. MethodsWe performed data linkages for (i) SARS-CoV-2 laboratory tests, (ii) COVID-19 case data, (iii) genome data, and (iv) the DATCOV national hospital surveillance system for the whole of South Africa. For cases identified using Thermo Fisher TaqPath COVID-19 PCR, infections were designated as SGTF or non-SGTF. Disease severity was assessed using multivariable logistic regression models comparing SGTF-infected individuals diagnosed between 1 October to 30 November to (i) non-SGTF in the same period, and (ii) Delta infections diagnosed between April and November 2021. ResultsFrom 1 October through 6 December 2021, 161,328 COVID-19 cases were reported nationally; 38,282 were tested using TaqPath PCR and 29,721 SGTF infections were identified. The proportion of SGTF infections increased from 3% in early October (week 39) to 98% in early December (week 48). On multivariable analysis, after controlling for factors associated with hospitalisation, individuals with SGTF infection had lower odds of being admitted to hospital compared to non-SGTF infections (adjusted odds ratio (aOR) 0.2, 95% confidence interval (CI) 0.1-0.3). Among hospitalised individuals, after controlling for factors associated with severe disease, the odds of severe disease did not differ between SGTF-infected individuals compared to non-SGTF individuals diagnosed during the same time period (aOR 0.7, 95% CI 0.3-1.4). Compared to earlier Delta infections, after controlling for factors associated with severe disease, SGTF-infected individuals had a lower odds of severe disease (aOR 0.3, 95% CI 0.2-0.5). ConclusionEarly analyses suggest a reduced risk of hospitalisation among SGTF-infected individuals when compared to non-SGTF infected individuals in the same time period. Once hospitalised, risk of severe disease was similar for SGTF- and non-SGTF infected individuals, while SGTF-infected individuals had a reduced risk of severe disease when compared to earlier Delta-infected individuals. Some of this reducton is likely a result of high population immunity.

Rapid rise in paediatric COVID-19 hospitalisations during the early stages of the Omicron wave, Tshwane District, South Africa

BackgroundSouth Africa reported a notable increase in COVID-19 cases from mid-November 2021 onwards, starting in Tshwane District, linked to rapid community spread of the Omicron variant. This coincided with a rapid rise in paediatric COVID-19-associated hospitalisations. MethodsWe synthesized data from five sources to describe the impact of Omicron on clinical manifestations and outcomes of hospitalized children ([≤]19 years) with positive SARS-CoV-2 tests in Tshwane District from 31 October to 11 December 2021, including: 1) COVID-19 line lists; 2) collated SARS-CoV-2 testing data; 3) SARS-CoV-2 genomic sequencing data; 4) COVID-19 hospitalisation surveillance; and 5) clinical data of public sector paediatric ([≤]13 years) COVID-19 hospitalisations. FindingsDuring the six-week period 6,287 paediatric ([≤]19 years) COVID-19 cases were recorded in Tshwane District, of these 462 (7.2%) were hospitalized in 42 hospitals (18% of overall admissions). The number of paediatric cases was higher than in the prior 3 waves, uncharacteristically preceding adult hospitalisations. Of the 75 viral specimens sequenced from the district, 99% were Omicron. Detailed clinical information obtained from 139 of 183 (76%) admitted children ([≤]13 years; including all public sector hospitalisations) indicated that young children (0-4 years) were most affected (62%). Symptoms included fever (47%), cough (40%), vomiting (24%), difficulty breathing (23%), diarrhoea (20%) and convulsions (20%). Length of hospital stay was short (mean 3.2 days), and in 44% COVID-19 was the primary diagnosis. Most children received standard ward care (92%), with 31 (25%) receiving oxygen therapy. Seven children (6%) were ventilated; four children died, all related to complex underlying co-pathologies. All children and majority of parents for whom data were available were unvaccinated. InterpretationRapid increases in paediatric COVID-19 cases and hospitalisations mirror high community transmission of SARS-CoV-2 (Omicron variant) in Tshwane District, South Africa. Continued monitoring is needed to understand the long-term impact of the Omicron variant on children. Research in contextO_ST_ABSEvidence before the studyC_ST_ABSThe announcement of the new Omicron (B.1.1.529) variant of the SARS-CoV-2 virus was made on 24 November 2021. Clinical characteristics, and disease profiles of children with COVID-19 before the arrival of Omicron have been described in the literature. Added value of the studyThis study describes the rapid rise in paediatric COVID-19-associated hospitalisations in Tshwane District in the Gauteng Province of South Africa - one of the first known epicentres of the new Omicron variant of the SARS-CoV-2 virus. The clinical picture as well as the steep increase in paediatric positivity rates and hospitalizations are described in detail from the perspective of a large South African health district, providing a broad overview on how the Omicron variant affects the paediatric population. Implication of all available evidenceThis study describes the clinical picture and outcomes in children in the current wave of SARS-CoV-2 Omicron variant infections by incorporating data from 42 hospitals at all levels of care in a large district within the South African health system. This provides novel paediatric data to assist global preparation for the impact of the Omicron variant in the paediatric setting.

Higher mortality associated with the SARS-CoV-2 Delta variant in the Western Cape, South Africa, using RdRp target delay as a proxy

A novel proxy for the Delta variant, RNA-dependent RNA polymerase target delay in the Seegene Allplex 2019-nCoV PCR assay, was associated with higher mortality (adjusted Odds Ratio 1.45 [95%CI 1.13-1.86]), compared to presumptive Beta infection, in the Western Cape, South Africa (April-July 2021). Prior diagnosed infection and vaccination were protective.

Longitudinal SARS-CoV-2 seroprevalence in a rural and urban community household cohort in South Africa, during the first and second waves July 2020-March 2021

BackgroundSARS-CoV-2 infections may be underestimated due to limited testing access, particularly in sub-Saharan Africa. South Africa experienced two SARS-CoV-2 waves, the second associated with emergence of variant 501Y.V2. In this study, we report longitudinal SARS-CoV-2 seroprevalence in cohorts in two communities in South Africa. MethodsWe measured SARS-CoV-2 seroprevalence two monthly in randomly selected household cohorts in a rural and an urban community (July 2020-March 2021). We compared seroprevalence to laboratory-confirmed infections, hospitalisations and deaths reported in the districts to calculate infection-case (ICR), infection-hospitalisation (IHR) and infection-fatality ratio (IFR) in the two waves of infection. FindingsSeroprevalence after the second wave ranged from 18% (95%CrI 10-26%) and 28% (95%CrI 17-41%) in children <5 years to 37% (95%CrI 28-47%) in adults aged 19-34 years and 59% (95%CrI 49-68%) in adults aged 35-59 years in the rural and urban community respectively. Individuals infected in the second wave were more likely to be from the rural site (aOR 4.7, 95%CI 2.9-7.6), and 5-12 years (aOR 2.1, 95%CI 1.1-4.2) or [≥]60 years (aOR 2.8, 95%CI 1.1-7.0), compared to 35-59 years. The in-hospital IFR in the urban site was significantly increased in the second wave 0.36% (95%CI 0.28-0.57%) compared to the first wave 0.17% (95%CI 0.15-0.20%). ICR ranged from 3.69% (95%CI 2.59-6.40%) in second wave at urban community, to 5.55% (95%CI 3.40-11.23%) in first wave in rural community. InterpretationThe second wave was associated with a shift in age distribution of cases from individuals aged to 35-59 to individuals at the extremes of age, higher attack rates in the rural community and a higher IFR in the urban community. Approximately 95% of SARS-CoV-2 infections in these two communities were not reported to the national surveillance system, which has implications for contact tracing and infection containment. FundingUS Centers for Disease Control and Prevention Research in contextO_ST_ABSEvidence before this studyC_ST_ABSSeroprevalence studies provide better estimates of SARS-CoV-2 burden than laboratory-confirmed cases because many infections may be missed due to restricted access to care and testing, or differences in disease severity and health-care seeking behaviour. This underestimation may be amplified in African countries, where testing access may be limited. Seroprevalence data from sub-Saharan Africa are limited, and comparing seroprevalence estimates between countries can be challenging because populations studied and timing of the study relative to country-specific epidemics differs. During the first wave of infections in each country, seroprevalence was estimated at 4% in Kenya and 11% in Zambia. Seroprevalence estimates in South African blood donors is estimated to range between 32% to 63%. South Africa has experienced two waves of infection, with the emergence of the B.1.351/501Y.V2 variant of concern after the first wave. Reported SARS-CoV-2 cases may not be a true reflection of SARS-CoV-2 burden and specifically the differential impact of the first and second waves of infection. Added value of this studyWe collected longitudinal blood samples from prospectively followed rural and urban communities, randomly selected, household cohorts in South Africa between July 2020 and March 2021. From 668 and 598 individuals included from the rural and urban communities, respectively, seroprevalence was found to be 7% (95%CrI 5-9%) and 27% (95%CrI 23-31%), after the first wave of infection, and 26% (95%CrI 22-29%) and 41% (95%CrI 37-45%) after the second wave, in rural and urban study districts, respectively. After standardising for age, we estimated that only 5% of SARS-CoV-2 infections were laboratory-confirmed and reported. Infection-hospitalisation ratios in the urban community were higher in the first (2.01%, 95%CI 1.57-2.57%) and second (2.29%, 95%CI 1.63-3.94%) wave than the rural community where there was a 0.75% (95%CI 0.49-1.41%) and 0.66% (95%CI 0.50-0.98%) infection-hospitalisation ratio in the first and second wave, respectively. When comparing the infection fatality ratios for the first and second SARS-CoV-2 waves, at the urban site, the ratios for both in-hospital and excess deaths to cases were significantly higher in the second wave (0.36%, 95%CI 0.28-0.57% in-hospital and 0.51%, 95%CI 0.34-0.93% excess deaths), compared to the first wave in-hospital (0.17%, 95%CI 0.15-0.20%) and excess (0.13%, 95%CI 0.10-0.17%) fatality ratios, p<0.001 and p<0.001, respectively). In the rural community, the point estimates for infection-fatality ratios also increased in the second wave compared to the first wave for in-hospital deaths, 0.13% (95%CI 0.10-0.23%) first wave vs 0.20% (95%CI 0.13%-0.28%) second wave, and excess deaths (0.51%, 95%CI 0.30-1.06% vs 0.70%, 95%CI 0.49-1.12%), although neither change was statistically significant. Implications of all the available evidenceIn South Africa, the overall prevalence of SARS-CoV-2 infections is substantially underestimated, resulting in many cases being undiagnosed and without the necessary public health action to isolate and trace contacts to prevent further transmission. There were more infections during the first wave in the urban community, and the second wave in the rural community. Although there were less infections during the second wave in the urban community, the infection-fatality ratios were significantly higher compared to the first wave. The lower infection-hospitalisation ratio and higher excess infection-fatality ratio in the rural community likely reflect differences in access to care or prevalence of risk factors for progression to severe disease in these two communities. In-hospital infection-fatality ratios for both communities during the first wave were comparable with what was experienced during the first wave in India (0.15%) for SARS-CoV-2 confirmed deaths. To our knowledge, these are the first longitudinal seroprevalence data from a sub-Saharan Africa cohort, and provide a more accurate understanding of the pandemic, allowing for serial comparisons of antibody responses in relation to reported laboratory-confirmed SARS-CoV-2 infections within diverse communities.

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

BackgroundEstimates of prevalence of anti-SARS-CoV-2 antibody positivity (seroprevalence) are for tracking the Covid-19 epidemic and are lacking for most African countries. ObjectivesTo determine the prevalence of antibodies against SARS-CoV2 in a sentinel cohort of patient samples received for routine testing at tertiary laboratories in Johannesburg, South Africa MethodsThis sentinel study was conducted using remnant serum samples received at three National Health Laboratory Services laboratories situated in the City of Johannesburg (COJ) district, South Africa. Collection was from 1 August until the 31 October 2020. We extracted accompanying laboratory results for haemoglobin A1c, 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 method was used to determine if age, sex, HIV and diabetic status were associated with increased risk for seropositivity. ResultsA total of 6477 samples were analysed; the majority (5290) from the COJ region. After excluding samples with no age or sex stated, the model population weighted and test adjusted seroprevalence for COJ (N=4393) was 27.0 % (95% CI: 25.4-28.6%). Seroprevalence was highest in those aged 45-49 [29.8% (95% CI: 25.5-35.0 %)] and in those from the most densely populated areas of COJ. Risk for seropositivity was highest in those aged 18-49 as well as samples from diabetics (aOR =1.52; 95% CI: 1.13-2.13; p=0.0005) and (aOR=1.36; 95% CI: 1.13-1.63; p=0.001) respectively. ConclusionOur study conducted during the first wave of the pandemic shows high levels of infection among patients attending public health facilities in Gauteng.

Increased mortality among individuals hospitalised with COVID-19 during the second wave in South Africa

IntroductionSouth Africa experienced its first wave of COVID-19 peaking in mid-July 2020 and a larger second wave peaking in January 2021, in which the SARS-CoV-2 501Y.V2 lineage predominated. We aimed to compare in-hospital mortality and other patient characteristics between the first and second waves of COVID-19. MethodsWe analysed data from the DATCOV national active surveillance system for COVID-19 hospitalisations. We defined four wave periods using incidence risk for hospitalisation, pre-wave 1, wave 1, pre-wave 2 and wave 2. We compared the characteristics of hospitalised COVID-19 cases in wave 1 and wave 2, and risk factors for in-hospital mortality accounting for wave period using multivariable logistic regression. ResultsPeak rates of COVID-19 cases, admissions and in-hospital deaths in the second wave exceeded the rates in the first wave (138.1 versus 240.1; 16.7 versus 28.9; and 3.3 versus 7.1 respectively per 100,000 persons). The weekly average incidence risk increase in hospitalisation was 22% in wave 1 and 28% in wave 2 [ratio of growth rate in wave two compared to wave one: 1.04, 95% CI 1.04-1.05]. On multivariable analysis, after adjusting for weekly COVID-19 hospital admissions, there was a 20% increased risk of in-hospital mortality in the second wave (adjusted OR 1.2, 95% CI 1.2-1.3). In-hospital case fatality-risk (CFR) increased in weeks of peak hospital occupancy, from 17.9% in weeks of low occupancy (<3,500 admissions) to 29.6% in weeks of very high occupancy (>12,500 admissions) (adjusted OR 1.5, 95% CI 1.4-1.5). Compared to the first wave, individuals hospitalised in the second wave, were more likely to be older, 40-64 years [OR 1.1, 95% CI 1.0-1.1] and [≥]65 years [OR 1.1, 95% CI 1.1-1.1] compared to <40 years; and admitted in the public sector [OR 2.2, 95% CI 1.7-2.8]; and less likely to have comorbidities [OR 0.5, 95% CI 0.5-0.5]. ConclusionsIn South Africa, the second wave was associated with higher incidence and more rapid increase in hospitalisations, and increased in-hospital mortality. While some of this is explained by increasing pressure on the health system, a residual increase in mortality of hospitalised patients beyond this, could be related to the new lineage 501Y.V2. RESEARCH IN CONTEXT O_TEXTBOXEvidence before this studyMost countries have reported higher numbers of COVID-19 cases in the second wave but lower case-fatality risk (CFR), in part due to new therapeutic interventions, increased testing and better prepared health systems. South Africa experienced its second wave which peaked in January 2021, in which the variant of concern, SARS-CoV-2 501Y.V2 predominated. New variants have been shown to be more transmissible and in the United Kingdom, to be associated with increased hospitalisation and mortality rates in people infected with variant B.1.1.7 compared to infection with non-B.1.1.7 viruses. There are currently limited data on the severity of lineage 501Y.V2. Added value of this studyWe analysed data from the DATCOV national active surveillance system for COVID-19 hospitalisations, comparing in-hospital mortality and other patient characteristics between the first and second waves of COVID-19. The study revealed that after adjusting for weekly COVID-19 hospital admissions, there was a 20% increased risk of in-hospital mortality in the second wave. Our study also describes the demographic shift from the first to the second wave of COVID-19 in South Africa, and quantifies the impact of overwhelmed hospital capacity on in-hospital mortality. Implications of all the available evidenceOur data suggest that the new lineage (501Y.V2) in South Africa may be associated with increased in-hospital mortality during the second wave. Our data should be interpreted with caution however as our analysis is based on a comparison of mortality in the first and second wave as a proxy for dominant lineage and we did not have individual-level data on lineage. Individual level studies comparing outcomes of people with and without the new lineage based on sequencing data are needed. To prevent high mortality in a potential third wave, we require a combination of strategies to slow the transmission of SARS-CoV-2, to spread out the peak of the epidemic, which would prevent hospital capacity from being breached. C_TEXTBOX