Inconsistent directions of change in case severity across successive SARS-CoV-2 variant waves suggests an unpredictable future

Abstract

Objective To determine how the severity of successively dominant SARS-CoV-2 variants has changed over the course of the COVID-19 pandemic. Design Prospective cohort analysis. Setting Community- and hospital- sequenced COVID-19 cases in the NHS Greater Glasgow and Clyde (NHS GG&C) Health Board (1.2 million people). Participants All sequenced non-nosocomial adult COVID-19 cases in NHS GG&C identified to be infected with the relevant SARS-CoV-2 lineage during the following analysis periods. B.1.177/Alpha analysis: 1st November 2020 - 30th January 2021 (n = 1640). Alpha/Delta analysis: 1st April - 30th June 2021 (n = 5552). AY.4.2 Delta/non-AY.4.2 Delta analysis: 1st July - 31st October 2021 (n = 9613). Non-AY.4.2 Delta/Omicron analysis: 1st - 31st December 2021 (n = 3858). Main outcome measures Admission to hospital, admission to ICU, or death within 28 days of first positive COVID-19 test Results In the B.1.177/Alpha analysis, 300 of 807 (37.2%) B.1.177 cases were recorded as hospitalised or having a more severe outcome, compared to 232 of 833 (27.9%) Alpha cases. After adjusting for the following covariates: age, sex, time of positive test, comorbidities and partial postcode, the cumulative odds ratio was 1.51 (95% central credible interval 1.08-2.11) for Alpha versus B.1.177. In the Alpha/Delta analysis, 113 of 2104 (5.4%) Alpha cases were recorded as hospitalised or having a more severe outcome, compared to 230 of 3448 (6.7%) Delta cases. After adjusting for the above covariates plus number of vaccine doses and reinfection, the cumulative odds ratio was 2.09 (95% central credible interval 1.42-3.08) for Delta versus Alpha. In the non-AY.4.2 Delta/AY.4.2 Delta analysis, 845 of 8644 (9.8%) non-AY.4.2 Delta cases were recorded as hospitalised or having a more severe outcome, compared to 101 of 969 (10.4%) AY.4.2 Delta cases. After adjusting for the previously stated covariates, the cumulative odds ratio was 0.99 (95% central credible interval 0.76-1.27) for AY.4.2 Delta versus non-AY.4.2 Delta. In the non-AY.4.2 Delta/Omicron analysis, 30 of 1164 (2.6%) non-AY.4.2 Delta cases were recorded as hospitalised or having a more severe outcome, compared to 26 of 2694 (1.0%) Omicron cases. After adjusting for the previously listed covariates, the median cumulative odds ratio was 0.49 (95% central credible interval 0.22-1.06) for Omicron versus non-AY.4.2 Delta. Conclusions The direction of change in disease severity between successively emerging SARS-CoV-2 variants of concern was inconsistent. This heterogeneity in virulence between variants, coupled with independent evolutionary emergence, demonstrates that severity associated with future SARS-CoV-2 variants is inherently unpredictable.

Severity and outcomes of Omicron variant of SARS-CoV-2 compared to Delta variant and severity of Omicron sublineages: a systematic review and metanalysis

“…The PRISMA flow diagram (online supplemental material 1) provides the detailed reasons for exclusions. From the 57 full-text reports, 43 were included in the review9–11 24–63 and analysis. One additional study60 was excluded as the data reported in the paper was unclear and the study authors did not respond to emails, leaving us with 42 studies.…”

“…Table 1 describes the characteristics of the included studies. Five of six WHO regions were represented (Africa, Americas, Eastern Mediterranean, Europe and South-East Asia), and most studies were from high-income or upper-middle-income settings: 12 from the USA,9 25 26 29 30 32 36 40 44 53 54 57 7 from the UK,27 31 37 42 46 50 59 7 from South Africa,33 51 52 55 56 58 61 5 from France24 28 34 38 62 and two from Norway 35 39. There was one study each from Denmark,47 Germany,41 India,45 Indonesia,49 Italy,63 Portugal,10Turkey46 and Qatar 43.…”

Elder and booster vaccination associates with decreased risk of serious clinical outcomes in comparison of Omicron and Delta variant: A meta-analysis of SARS-CoV-2 infection

“…Of the 5,720 articles identified, 273 were eligible for full-text screening; 36 cohort and 7 registry studies (Adjei et al, 2022;Auvigne et al, 2022;Bager et al, 2022;Beraud et al, 2022;Bonsignore et al, 2022;Bouzid et al, 2022;Butt et al, 2022a,b,c;Català et al, 2022;Chanda et al, 2022;Davies et al, 2022;DeSilva et al, 2022;Fall et al, 2022;Goga et al, 2022;Jassat et al, 2022;Kahn et al, 2022;Krutikov et al, 2022;Lewnard et al, 2022;Mayr et al, 2022;Menni et al, 2022;Nevejan et al, 2022;Nyberg et al, 2022;Pascall et al, 2022;Sacco et al, 2022;Shi et al, 2022;Sievers et al, 2022;Skarbinski et al, 2022;Stålcrantz et al, 2022;Stepanova et al, 2022;Strasser et al, 2022;Ulloa et al, 2022;Van Goethem et al, 2022;Vieillard-Baron et al, 2022;Wang L. et al, 2022;Wang X. et al, 2022;Whittaker et al, 2022;Wolter et al, 2022;Wrenn et al, 2022;Esper et al, 2023;Greene et al, 2023;Intawong et al, 2023;Trobajo-Sanmartín et al, 2023) were included finally (Figure 1). Altog...…”

Multimodal Biomedical Data Fusion Using Sparse Canonical Correlation Analysis and Cooperative Learning: A Cohort Study on COVID-19

“…Factors like male sex, age, and comorbidities have been tied to disease severity 9,10 . Anomalies in laboratory results, radiological abnormalities, and the presence of specific viral mutations can also influence the clinical course of the disease [11][12][13][14][15][16][17][18] . A plethora of clinical, laboratory, imaging, and viral genome sequencing data is available for research, and COVID-19 offers a promising avenue for applying multi-modal data fusion.…”