Modeling the relative influence of socio-demographic variables on post-acute COVID-19 quality of life: an application to settings in Europe, Asia, Africa, and South America (preprint)

Long-term COVID-19 complications are a globally pervasive threat, but their plausible social drivers are often not prioritized. Here, we use data from a multinational consortium to quantify the relative contributions of social and clinical factors to differences in quality of life among participants experiencing long COVID and measure the extent to which social variables impacts can be attributed to clinical intermediates, across diverse contexts. In addition to age, neuropsychological and rheumatological comorbidities, educational attainment, employment status, and female sex were identified as important predictors of long COVID-associated quality of life days (long COVID QALDs). Furthermore, a great majority of their impacts on long COVID QALDs could not be tied to key long COVID-predicting comorbidities, such as asthma, diabetes, hypertension, psychological disorder, and obesity. In Norway, 90% (95% CI: 77%, 100%) of the effect of belonging to the highest versus lowest educational attainment quintile was not attributed to intermediate comorbidity impacts. The same was true for 86% (73%, 100%) of the protective effects of full-time employment versus all other employment status categories (excluding retirement) in the UK and 74% (46%,100%) of the protective effects of full-time employment versus all other employment status categories in a cohort of four middle-income countries (MIC). Of the effects of female sex on long COVID QALDs in Norway, UK, and the MIC cohort, 77% (46%,100%), 73% (52%, 94%), and 84% (62%, 100%) were unexplained by the clinical mediators, respectively. Our findings highlight that socio-economic proxies and sex may be as predictive of long COVID QALDs as commonly emphasized comorbidities and that broader structural determinants likely drive their impacts. Importantly, we outline a multi-method, adaptable causal machine learning approach for evaluating the isolated contributions of social disparities to long COVID quality of life experiences.

COVID-19 vaccine uptake and effectiveness by time since vaccination in the Western Cape province, South Africa: An observational cohort study during 2020-2022 (preprint)

Background: There are few data on the real-world effectiveness of COVID-19 vaccines and boosting in Africa, which experienced high levels of SARS-CoV-2 infection in a mostly vaccine-naive population, and has limited vaccine coverage and competing health service priorities. We assessed the association between vaccination and severe COVID-19 in the Western Cape, South Africa. Methods We performed an observational cohort study of >2 million adults during 2020-2022. We described SARS-CoV-2 testing, COVID-19 outcomes, and vaccine uptake over time. We used multivariable cox models to estimate the association of BNT162b2 and Ad26.COV2.S vaccination with COVID-19-related hospitalisation and death, adjusting for demographic characteristics, underlying health conditions, socioeconomic status proxies and healthcare utilisation. Results By end 2022, only 41% of surviving adults had completed vaccination and 8% a booster dose, despite several waves of severe COVID-19. Recent vaccination was associated with notable reductions in severe COVID-19 during distinct analysis periods dominated by Delta, Omicron BA.1/2 and BA.4/5 (sub)lineages: within 6 months of completing vaccination or boosting, vaccine effectiveness was 46-92% for death (range across periods), 45-92% for admission with severe disease or death, and 25-90% for any admission or death. During the Omicron BA.4/5 wave, within 3 months of vaccination or boosting, BNT162b2 and Ad26.COV2.S were each 84% effective against death (95% CIs: 57-94 and 49-95, respectively). However, there were distinct reductions of VE at larger times post completing or boosting vaccination. Conclusions Continued emphasis on regular COVID-19 vaccination including boosting is important for those at high risk of severe COVID-19 even in settings with widespread infection-induced immunity.

Using Generalized Structured Additive Regression Models to determine factors associated with and clusters for COVID-19 hospital deaths in South Africa (preprint)

Background The 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.Methods COVID-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.Results The 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.Conclusion The 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.

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

Background: The 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 as well as deaths in general due to COVID-19 have been shown to be spatially associated but spatial patterns 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. Methods: COVID-19 hospitalization data and deaths were obtained from the National Institute for Communicable Diseases (NICD), who together with the South African National Department of Health (SANDoH) collected hospital admissions data through DATCOV, an active electronic hospital surveillance system for COVID-19. We used the generalized structured additive logistic regression model that allows for modelling spatial correlation to realistically estimate risk factors for hospital COVID-19 deaths. The model included patient demographic and clinical factors as well as time in months which accounted for different waves. 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. Results: The risk of COVID-19 in-hospital mortality increased with patient age as well as 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 a significant risk factor for mortality. Risk of deaths also 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. Conclusion: The results show substantial COVID-19 in-hospital mortality variation across the 52 districts. This highlights the importance of modelling spatial patterns simultaneously with fixed and nonlinear effects of continuous covariates to identify clusters at high risk of health outcome. The flexible approach to modelling data that has spatial patterns helps to account for possible loss of efficiency due to spatial correlation that spatial patterns can induce in data. Our analysis suggests notable COVID-19 hospital deaths clustering in some districts in Limpopo and Eastern Cape provinces and this information 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.

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

Introduction: The 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. Methods: Data from South Africa's 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. Results: In-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. Conclusion: Overall, admission incidence risk and in-hospital mortality, which had increased progressively in South Africa's 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 (preprint)

Objectives We 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. Methods We 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. Results Nationally, 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 cases-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. Discussion Agreement between R estimates using different data sources during the first three waves suggests data from any of these sources could be used in 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 during the first wave.

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

ABSTRACT Background The 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. Methods We 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. Results The 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%). Conclusions Gauteng 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.)

Clinical severity of SARS-CoV-2 Omicron BA.4 and BA.5 lineages in South Africa (preprint)

Omicron lineages BA.4 and BA.5 drove a fifth wave of COVID-19 cases in South Africa. We assessed the severity of BA.4/BA.5 infections using the presence/absence of the S-gene target for infections diagnosed using the TaqPath PCR assay between 1 October 2021 and 26 April 2022. We linked national COVID-19 individual-level data including case, laboratory test and hospitalisation data. We assessed severity using multivariable logistic regression comparing the risk of hospitalisation and risk of severe disease, once hospitalised, for Delta, BA.1, BA.2 and BA.4/BA.5 infections. After controlling for factors associated with hospitalisation and severe outcome respectively, BA.4/BA.5-infected individuals had a similar odds of hospitalisation (aOR1.24, 95% CI 0.98–1.55) and severe outcome (aOR 0.71, 95%CI 0.41–1.25) compared to BA.1-infected individuals. Newly emerged Omicron lineages BA.4/BA.5 continue to show reduced clinical severity compared to previous variants, as observed for Omicron BA.1.

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 (preprint)

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 (preprint)

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 (preprint)

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 (preprint)

Background Post 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. Methods In 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. Findings In 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. Interpretation The 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 (preprint)

Background: Clinical 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. Methods: Weekly 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. Results: 335,219 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. Conclusion: The trend of increasing cases and admissions across South Africa's 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 (preprint)

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 (preprint)

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.

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 (preprint)

Background Emerging 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. Methods RdRp 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. Results 150 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). Conclusion Omicron 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 (preprint)

Objectives: We 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. Methods: In 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. Results: We 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). Conclusions: In 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.