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      Early assessment of the clinical severity of the SARS-CoV-2 omicron variant in South Africa: a data linkage study

      research-article
      , PhD a , f , * , , MMED b , , PhD a , g , , BSc b , , PhD c , f , , PhD b , , PhD a , i , , MSc a , , PhD d , h , , PhD d , h , , MSc a , , MSc a , , PhD a , f , , MPH b , , PhD e , , FCPath l , , Prof, PhD j , m , , Prof, FCPath f , m , , PhD f , m , , MSc f , m , , MMed m , n , , Prof, PhD f , k , m , , FCPath f , m , p , , MSc o , , Prof, PhD o , , Prof, PhD o , , Prof, PhD a , f , * , , Prof, PhD a , g , * , *
      Lancet (London, England)
      The Author(s). Published by Elsevier Ltd.

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          Abstract

          Background

          The SARS-CoV-2 omicron variant of concern was identified in South Africa in November, 2021, and was associated with an increase in COVID-19 cases. We aimed to assess the clinical severity of infections with the omicron variant using S gene target failure (SGTF) on the Thermo Fisher Scientific TaqPath COVID-19 PCR test as a proxy.

          Methods

          We did data linkages for national, South African COVID-19 case data, SARS-CoV-2 laboratory test data, SARS-CoV-2 genome data, and COVID-19 hospital admissions data. For individuals diagnosed with COVID-19 via TaqPath PCR tests, infections were designated as either SGTF or non-SGTF. The delta variant was identified by genome sequencing. Using multivariable logistic regression models, we assessed disease severity and hospitalisations by comparing individuals with SGTF versus non-SGTF infections diagnosed between Oct 1 and Nov 30, 2021, and we further assessed disease severity by comparing SGTF-infected individuals diagnosed between Oct 1 and Nov 30, 2021, with delta variant-infected individuals diagnosed between April 1 and Nov 9, 2021.

          Findings

          From Oct 1 (week 39), 2021, to Dec 6 (week 49), 2021, 161 328 cases of COVID-19 were reported in South Africa. 38 282 people were diagnosed via TaqPath PCR tests and 29 721 SGTF infections and 1412 non-SGTF infections were identified. The proportion of SGTF infections increased from two (3·2%) of 63 in week 39 to 21 978 (97·9%) of 22 455 in week 48. After controlling for factors associated with hospitalisation, individuals with SGTF infections had significantly lower odds of admission than did those with non-SGTF infections (256 [2·4%] of 10 547 vs 121 [12·8%] of 948; adjusted odds ratio [aOR] 0·2, 95% CI 0·1–0·3). After controlling for factors associated with disease severity, the odds of severe disease were similar between hospitalised individuals with SGTF versus non-SGTF infections (42 [21%] of 204 vs 45 [40%] of 113; aOR 0·7, 95% CI 0·3–1·4). Compared with individuals with earlier delta variant infections, SGTF-infected individuals had a significantly lower odds of severe disease (496 [62·5%] of 793 vs 57 [23·4%] of 244; aOR 0·3, 95% CI 0·2–0·5), after controlling for factors associated with disease severity.

          Interpretation

          Our early analyses suggest a significantly reduced odds of hospitalisation among individuals with SGTF versus non-SGTF infections diagnosed during the same time period. SGTF-infected individuals had a significantly reduced odds of severe disease compared with individuals infected earlier with the delta variant. Some of this reduced severity is probably a result of previous immunity.

          Funding

          The South African Medical Research Council, the South African National Department of Health, US Centers for Disease Control and Prevention, the African Society of Laboratory Medicine, Africa Centers for Disease Control and Prevention, the Bill & Melinda Gates Foundation, the Wellcome Trust, and the Fleming Fund.

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          Most cited references15

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          Rapid epidemic expansion of the SARS-CoV-2 Omicron variant in southern Africa

          The SARS-CoV-2 epidemic in southern Africa has been characterized by three distinct waves. The first was associated with a mix of SARS-CoV-2 lineages, while the second and third waves were driven by the Beta (B.1.351) and Delta (B.1.617.2) variants, respectively 1–3 . In November 2021, genomic surveillance teams in South Africa and Botswana detected a new SARS-CoV-2 variant associated with a rapid resurgence of infections in Gauteng province, South Africa. Within three days of the first genome being uploaded, it was designated a variant of concern (Omicron, B.1.1.529) by the World Health Organization and, within three weeks, had been identified in 87 countries. The Omicron variant is exceptional for carrying over 30 mutations in the spike glycoprotein, which are predicted to influence antibody neutralization and spike function 4 . Here we describe the genomic profile and early transmission dynamics of Omicron, highlighting the rapid spread in regions with high levels of population immunity.
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            Increased risk of SARS-CoV-2 reinfection associated with emergence of the Omicron variant in South Africa

            Objective: To examine whether SARS-CoV-2 reinfection risk has changed through time in South Africa, in the context of the emergence of the Beta, Delta, and Omicron variants Design: Retrospective analysis of routine epidemiological surveillance data Setting: Line list data on SARS-CoV-2 with specimen receipt dates between 04 March 2020 and 27 November 2021, collected through South Africa's National Notifiable Medical Conditions Surveillance System Participants 2,796,982 individuals with laboratory-confirmed SARS-CoV-2 who had a positive test result at least 90 days prior to 27 November 2021. Individuals having sequential positive tests at least 90 days apart were considered to have suspected reinfections. Main outcome measures: Incidence of suspected reinfections through time; comparison of reinfection rates to the expectation under a null model (approach 1); empirical estimates of the time-varying hazards of infection and reinfection throughout the epidemic (approach 2) Results: 35,670 suspected reinfections were identified among 2,796,982 individuals with laboratory-confirmed SARS-CoV-2 who had a positive test result at least 90 days prior to 27 November 2021. The number of reinfections observed through the end of the third wave was consistent with the null model of no change in reinfection risk (approach 1). Although increases in the hazard of primary infection were observed following the introduction of both the Beta and Delta variants, no corresponding increase was observed in the reinfection hazard (approach 2). Contrary to expectation, the estimated hazard ratio for reinfection versus primary infection was lower during waves driven by the Beta and Delta variants than for the first wave (relative hazard ratio for wave 2 versus wave 1: 0.75 (CI95: 0.59-0.97); for wave 3 versus wave 1:0.71 (CI95: 0.56-0.92)). In contrast, the recent spread of the Omicron variant has been associated with a decrease in the hazard of primary infection and an increase in reinfection hazard. The estimated hazard ratio for reinfection versus primary infection for the period from 1 November 2021 to 27 November 2021 versus wave 1 was 2.39 (CI95: 1.88-3.11). Conclusion: Population-level evidence suggests that the Omicron variant is associated with substantial ability to evade immunity from prior infection. In contrast, there is no population-wide epidemiological evidence of immune escape associated with the Beta or Delta variants. This finding has important implications for public health planning, particularly in countries like South Africa with high rates of immunity from prior infection. Urgent questions remain regarding whether Omicron is also able to evade vaccine-induced immunity and the potential implications of reduced immunity to infection on protection against severe disease and death.
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              Risk factors for COVID-19-related in-hospital mortality in a high HIV and tuberculosis prevalence setting in South Africa: a cohort study

              Background The interaction between COVID-19, non-communicable diseases, and chronic infectious diseases such as HIV and tuberculosis is unclear, particularly in low-income and middle-income countries in Africa. South Africa has a national HIV prevalence of 19% among people aged 15–49 years and a tuberculosis prevalence of 0·7% in people of all ages. Using a nationally representative hospital surveillance system in South Africa, we aimed to investigate the factors associated with in-hospital mortality among patients with COVID-19. Methods In this cohort study, we used data submitted to DATCOV, a national active hospital surveillance system for COVID-19 hospital admissions, for patients admitted to hospital with laboratory-confirmed SARS-CoV-2 infection between March 5, 2020, and March 27, 2021. Age, sex, race or ethnicity, and comorbidities (hypertension, diabetes, chronic cardiac disease, chronic pulmonary disease and asthma, chronic renal disease, malignancy in the past 5 years, HIV, and past and current tuberculosis) were considered as risk factors for COVID-19-related in-hospital mortality. COVID-19 in-hospital mortality, the main outcome, was defined as a death related to COVID-19 that occurred during the hospital stay and excluded deaths that occurred because of other causes or after discharge from hospital; therefore, only patients with a known in-hospital outcome (died or discharged alive) were included. Chained equation multiple imputation was used to account for missing data and random-effects multivariable logistic regression models were used to assess the role of HIV status and underlying comorbidities on COVID-19 in-hospital mortality. Findings Among the 219 265 individuals admitted to hospital with laboratory-confirmed SARS-CoV-2 infection and known in-hospital outcome data, 51 037 (23·3%) died. Most commonly observed comorbidities among individuals with available data were hypertension in 61 098 (37·4%) of 163 350, diabetes in 43 885 (27·4%) of 159 932, and HIV in 13 793 (9·1%) of 151 779. Tuberculosis was reported in 5282 (3·6%) of 146 381 individuals. Increasing age was the strongest predictor of COVID-19 in-hospital mortality. Other factors associated were HIV infection (adjusted odds ratio 1·34, 95% CI 1·27–1·43), past tuberculosis (1·26, 1·15–1·38), current tuberculosis (1·42, 1·22–1·64), and both past and current tuberculosis (1·48, 1·32–1·67) compared with never tuberculosis, as well as other described risk factors for COVID-19, such as male sex; non-White race; underlying hypertension, diabetes, chronic cardiac disease, chronic renal disease, and malignancy in the past 5 years; and treatment in the public health sector. After adjusting for other factors, people with HIV not on antiretroviral therapy (ART; adjusted odds ratio 1·45, 95% CI 1·22–1·72) were more likely to die in hospital than were people with HIV on ART. Among people with HIV, the prevalence of other comorbidities was 29·2% compared with 30·8% among HIV-uninfected individuals. Increasing number of comorbidities was associated with increased COVID-19 in-hospital mortality risk in both people with HIV and HIV-uninfected individuals. Interpretation Individuals identified as being at high risk of COVID-19 in-hospital mortality (older individuals and those with chronic comorbidities and people with HIV, particularly those not on ART) would benefit from COVID-19 prevention programmes such as vaccine prioritisation as well as early referral and treatment. Funding South African National Government.
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                Author and article information

                Journal
                Lancet
                Lancet
                Lancet (London, England)
                The Author(s). Published by Elsevier Ltd.
                0140-6736
                1474-547X
                19 January 2022
                19 January 2022
                Affiliations
                [a ]Centre for Respiratory Diseases and Meningitis, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
                [b ]Division of Public Health Surveillance and Response, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
                [c ]Centre for Tuberculosis, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
                [d ]Centre for HIV and STIs, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
                [e ]Sequencing Core Facility, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg, South Africa
                [f ]School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
                [g ]School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
                [h ]SA MRC Antibody Immunity Research Unit, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
                [i ]School of Health Sciences, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
                [j ]School of Laboratory Medicine and Medical Sciences, University of KwaZulu-Natal, Durban, South Africa
                [k ]Department of Virology, University of KwaZulu-Natal, Durban, South Africa
                [l ]Lancet Laboratories, Johannesburg, South Africa
                [m ]National Health Laboratory Service, Johannesburg, South Africa
                [n ]Division of Medical Virology, University of Cape Town, Cape Town, South Africa
                [o ]Western Cape Government Health and School of Public Health and Family Medicine, University of Cape Town, Cape Town, South Africa
                [p ]Department of Clinical Microbiology and Infectious Diseases, CH Baragwanath Academic Hospital, Johannesburg, South Africa
                Author notes
                [* ]Correspondence to: Dr Nicole Wolter, Centre for Respiratory Diseases and Meningitis, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg 2131, South Africa
                [* ]Correspondence to: Prof Cheryl Cohen, Centre for Respiratory Diseases and Meningitis, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg 2131, South Africa
                [*]

                Contributed equally

                Article
                S0140-6736(22)00017-4
                10.1016/S0140-6736(22)00017-4
                8769664
                35065011
                f58a27f3-ce3e-488d-abaf-71af39dbb84a
                © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

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