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

  1. Bronner P Gonçalves  Is a corresponding author
  2. Matthew Hall
  3. Waasila Jassat
  4. Valeria Balan
  5. Srinivas Murthy
  6. Christiana Kartsonaki
  7. Malcolm G Semple
  8. Amanda Rojek
  9. Joaquín Baruch
  10. Luis Felipe Reyes
  11. Abhishek Dasgupta
  12. Jake Dunning
  13. Barbara Wanjiru Citarella
  14. Mark Pritchard
  15. Alejandro Martín-Quiros
  16. Uluhan Sili
  17. J Kenneth Baillie
  18. Diptesh Aryal
  19. Yaseen Arabi
  20. Aasiyah Rashan
  21. Andrea Angheben
  22. Janice Caoili
  23. François Martin Carrier
  24. Ewen M Harrison
  25. Joan Gómez-Junyent
  26. Claudia Figueiredo-Mello
  27. James Joshua Douglas
  28. Mohd Basri Mat Nor
  29. Yock Ping Chow
  30. Xin Ci Wong
  31. Silvia Bertagnolio
  32. Soe Soe Thwin
  33. Anca Streinu-Cercel
  34. Leonardo Salazar
  35. Asgar Rishu
  36. Rajavardhan Rangappa
  37. David SY Ong
  38. Madiha Hashmi
  39. Gail Carson
  40. Janet Diaz
  41. Rob Fowler
  42. Moritz UG Kraemer
  43. Evert-Jan Wils
  44. Peter Horby
  45. Laura Merson
  46. Piero L Olliaro
  47. ISARIC Clinical Characterisation Group
  1. ISARIC, Pandemic Sciences Institute, University of Oxford, United Kingdom
  2. Big Data Institute, Nuffield Department of Medicine, University of Oxford, United Kingdom
  3. National Institute for Communicable Diseases, South Africa; Right to Care, South Africa
  4. Faculty of Medicine, University of British Columbia, Canada
  5. MRC Population Health Research Unit, Clinical Trials Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, United Kingdom
  6. Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, United Kingdom
  7. Respiratory Medicine, Alder Hey Children's Hospital, University of Liverpool, United Kingdom
  8. Royal Melbourne Hospital, Australia
  9. Centre for Integrated Critical Care, University of Melbourne, Australia
  10. Universidad de La Sabana, Colombia
  11. Clinica Universidad de La Sabana, Colombia
  12. Department of Computer Science, University of Oxford, United Kingdom
  13. Department of Biology, University of Oxford, United Kingdom
  14. Emergency Department. Hospital Universitario La Paz – IdiPAZ, Spain
  15. Department of Infectious Diseases and Clinical Microbiology, School of Medicine, Marmara University, Turkey
  16. Roslin Institute, University of Edinburgh, United Kingdom
  17. Intensive Care Unit, Royal Infirmary of Edinburgh, United Kingdom
  18. Critical Care and Anesthesia, Nepal Mediciti Hospital, Nepal
  19. King Abdullah International Medical Research Center and King Saud Bin Abdulaziz University for Health Sciences, Saudi Arabia
  20. Network for Improving Critical care Systems and Training, Sri Lanka
  21. Department of Infectious, Tropical Diseases and Microbiology (DITM), IRCCS Sacro Cuore Don Calabria Hospital, Negrar di Valpolicella, Italy
  22. Makati Medical Center, Makati City, Philippines
  23. Department of Anesthesiology, Centre hospitalier de l'Université de Montréal, Canada
  24. Department of Medicine, Critical Care Division, Centre hospitalier de l'Université de Montréal, Canada
  25. Carrefour de l'innovation et santé des populations, Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Canada
  26. Department of Anesthesiology and Pain Medicine, Université de Montréal, Canada
  27. Centre for Medical Informatics, The University of Edinburgh, Usher Institute of Population Health Sciences and Informatics, United Kingdom
  28. Department of Infectious Diseases, Hospital del Mar, Infectious Pathology and Antimicrobial Research Group (IPAR), Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Universitat Autònoma de Barcelona (UAB), CEXS-Universitat Pompeu Fabra, Spain
  29. Instituto de Infectologia Emílio Ribas, Brazil
  30. Lions Gate Hospital, Canada
  31. International Islamic University Malaysia, Malaysia
  32. Clinical Research Centre, Sunway Medical Centre, Selangor Darul Ehsan, Malaysia
  33. Digital Health Research and Innovation Unit, Institute for Clinical Research, National Institutes of Health (NIH), Malaysia
  34. World Health Organization, Switzerland
  35. Carol Davila University of Medicine and Pharmacy, Romania
  36. National Institute for Infectious Diseases "Prof. Dr. Matei Bals", Romania
  37. Fundación Cardiovascular de Colombia, Colombia
  38. Department of Critical Care Medicine, Sunnybrook Health Sciences Centre, Canada
  39. Department of Critical Care Medicine, Manipal Hospital Whitefield, India
  40. Department of Medical Microbiology and Infection Control, Franciscus Gasthuis & Vlietland, Netherlands
  41. Critical Care Asia and Ziauddin University, Pakistan
  42. Pandemic Sciences Institute, University of Oxford, United Kingdom
  43. Department of Intensive Care, Franciscus Gasthuis & Vlietland, Netherlands
  44. Infectious Diseases Data Observatory, Centre for Tropical Medicine and Global Health, University of Oxford, United Kingdom

Abstract

Background:

Whilst 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.

Methods:

Here, 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.

Results:

Our 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.

Conclusions:

Although 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.

Funding:

Bronner P. Gonçalves, Peter Horby, Gail Carson, Piero L. Olliaro, Valeria Balan, Barbara Wanjiru Citarella, and research costs were supported by the UK Foreign, Commonwealth and Development Office (FCDO) and Wellcome [215091/Z/18/Z, 222410/Z/21/Z, 225288/Z/22/Z]; and Janice Caoili and Madiha Hashmi were supported by the UK FCDO and Wellcome [222048/Z/20/Z]. Peter Horby, Gail Carson, Piero L. Olliaro, Kalynn Kennon and Joaquin Baruch were supported by the Bill & Melinda Gates Foundation [OPP1209135]; Laura Merson was supported by University of Oxford’s COVID-19 Research Response Fund - with thanks to its donors for their philanthropic support. Matthew Hall was supported by a Li Ka Shing Foundation award to Christophe Fraser. Moritz U.G. Kraemer was supported by the Branco Weiss Fellowship, Google.org, the Oxford Martin School, the Rockefeller Foundation, and the European Union Horizon 2020 project MOOD (#874850). The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the European Commission. Contributions from Srinivas Murthy, Asgar Rishu, Rob Fowler, James Joshua Douglas, François Martin Carrier were supported by CIHR Coronavirus Rapid Research Funding Opportunity OV2170359 and coordinated out of Sunnybrook Research Institute. Contributions from Evert-Jan Wils and David S.Y. Ong were supported by a grant from foundation Bevordering Onderzoek Franciscus; and Andrea Angheben by the Italian Ministry of Health “Fondi Ricerca corrente–L1P6” to IRCCS Ospedale Sacro Cuore–Don Calabria. The data contributions of J.Kenneth Baillie, Malcolm G. Semple, and Ewen M. Harrison were supported by grants from the National Institute for Health Research (NIHR; award CO-CIN-01), the Medical Research Council (MRC; grant MC_PC_19059), and by the NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool in partnership with Public Health England (PHE) (award 200907), NIHR HPRU in Respiratory Infections at Imperial College London with PHE (award 200927), Liverpool Experimental Cancer Medicine Centre (grant C18616/A25153), NIHR Biomedical Research Centre at Imperial College London (award IS-BRC-1215-20013), and NIHR Clinical Research Network providing infrastructure support. All funders of the ISARIC Clinical Characterisation Group are listed in the appendix.

Editor's evaluation

This manuscript compares COVID-19 mortality during the pre-Omicron and Omicron emergence periods in several countries. It finds evidence suggesting the Omicron variant was associated with lower mortality than previous dominant variants in multiple countries, though other factors than changing variant virulence might explain these observations, as discussed by the authors. This paper will be of interest to infectious disease scientists both for its content and its methods, as it validates that population-level variant frequency can be a good proxy for individual-level variant data to derive insights on variant biology with population data.

https://doi.org/10.7554/eLife.80556.sa0

Introduction

The emergence of novel SARS-CoV-2 variants represents a threat to the long-term control of COVID-19 (Fontanet et al., 2021). Whilst efforts to develop vaccines that protect against severe disease have been successful (Polack et al., 2020; Voysey et al., 2021; Baden et al., 2021), mutations in the viral genome that lead to ability to escape immunity, and increased transmissibility and/or clinical severity, either via intrinsic virulence or reduced vaccine effectiveness (Lopez Bernal et al., 2021), have the potential to cause substantial disease burden despite high vaccine coverage in many countries (Our World in Data, 2021).

These concerns motivated the prompt reporting, initially from South Africa (Wolter et al., 2021; World Health Organization, 2022), of clinical characteristics of infection with the Omicron variant only weeks after its emergence (Wolter et al., 2022; Ulloa et al., 2022; Veneti et al., 2022), which provided key information for risk assessment and health policies worldwide. Early data from South Africa showed reduced severity of Omicron lineage BA.1 and similar results were reported in the United Kingdom and the United States (Wolter et al., 2022; Lewnard et al., 2022; Nyberg et al., 2022). However, the impact, in terms of clinical consequences (i.e. disease severity), of new variants has been shown to be context-specific, due to regional differences in disease epidemiology, including local circulation of previous variants and their cumulative incidences, variable vaccination coverages, and heterogeneity in population-level frequencies of risk factors (e.g. frequency of comorbidities) for severe disease and mortality. For this reason, international studies with standardised protocols are necessary to allow comparative assessments across different countries and epidemiological contexts.

To understand the impact of the emergence of the Omicron variant of SARS-CoV-2 on the clinical epidemiology of COVID-19 at the global level, in this study, we report multi-country data, from all six World Health Organization regions, on clinical characteristics and outcomes of Omicron variant infections in hospitalised patients and compare with infections in patients admitted with other SARS-CoV-2 variants. For that, we use publicly available population-level data on relative frequencies of the Omicron variant to determine periods when infections were likely to be caused by Omicron versus other variants/lineages and compare infections descriptively and using multivariable statistical models. In addition, we present an analysis that only includes patients with individual-level data on the infecting variant and paired clinical information.

Methods

ISARIC clinical characterisation protocol

Analyses presented in this manuscript use the ISARIC (International Severe Acute Respiratory and Emerging Infections Consortium) COVID-19 database, which includes prospectively collected data from countries where ISARIC partner institutions are located (see a global map of all ISARIC partner institutions here https://isaric.org/about-us/membership/). A full description of the data collection protocol and database can be found here https://isaric.org/research/covid-19-clinical-research-resources/. In short, data collection for this initiative was standardised, using the ISARIC case report forms, and pivoted into pandemic mode in January 2020 to enable rapid characterisation of the clinical presentation and severity of COVID-19. After the emergence of the Omicron variant, first reported in November 2021 (Viana et al., 2022), a call was launched to encourage international investigators partnering with ISARIC to rapidly share data on patients with confirmed or suspected COVID-19 to describe the clinical characteristics of Omicron variant infection in different settings; recommendations on possible hospitalised population sampling approaches were shared. Patients admitted to hospital from 1st October 2021 to 28th February 2022 were included in this analysis. More information on ISARIC can be found in ISARIC Clinical Characterisation Group, 2021; Hall et al., 2021; Reyes et al., 2022.

Population-level SARS-CoV-2 variant data

Two statistical analysis plans (SAPs) were developed in December 2021 with approaches to be used in the characterisation of Omicron variant infection. Analyses described in the first SAP required individual-level data on the clinical presentation and paired data on the variant causing the infection. In the second SAP, we used population-level frequencies of SARS-CoV-2 lineages to infer individual infecting variant during different time periods as Omicron or non-Omicron variants (Figure 1). Since individual-level data on the infecting variant were limited to a few countries, these data are presented for comparison with the analysis performed using population-level variant data.

Population-level relative frequency of Omicron variant infections by country and time.

Here, data aggregated by epidemiological week and country were used to calculate the proportions of infections caused by the Omicron variant. For analyses reported in the Results section, two epidemiological periods were defined: the first corresponds to the two months before the Omicron variant reaches a threshold frequency of 10% (blue area in each panel; the pre-Omicron period); the second period corresponds to the two months after Omicron variant frequency reaches 90% (red area in each panel; the Omicron period). Sensitivity analyses, using other relative frequencies for defining periods, are presented in the Appendix 1. Each panel presents data for a country (ISO3 code as title) contributing clinical data for this analysis; y-axes represent proportions in each epidemiological week (x-axes). Data for Laos are not shown as, at the time of the analysis, samples were not included in the database that informed population-level frequency of Omicron variant during the study period. In Pakistan, due to fluctuations in Omicron variant frequency in the dataset, study periods were not defined. More information on the spread of the Omicron variant in Laos and analysis of the clinical data from Pakistan are presented in the Appendix 1.

For the analysis that required information on population-level variant frequency, for countries contributing clinical data to this analysis, data from the Global Initiative on Sharing All Influenza Data (GISAID) on each of the main SARS-CoV-2 variants were collated. These data were aggregated by sample collection date and variant using a computational pipeline available here: https://github.com/globaldothealth/covid19-variants-summary, (Dasgupta and Kraemer, 2022, copy archived at swh:1:rev:8adf2f756b182711ad1d0b8707c44d3703786d23). The GISAID data were downloaded on 11 April 2022; Pango lineage designation v1.2.133 was used (pango-designation, 2022). We used these data to define calendar time periods when the Omicron variant represented the majority of infections in each country, and also periods during which the Omicron variant represented only a small (<10%) fraction of infections. For each country, the period during which infections were assumed to be caused by other variants ended in the epidemiological week before the Omicron variant relative frequency crossed a low threshold percentage (e.g. 10%) (see Figure 1). The first epidemiological week when Omicron variant frequency, as a proportion of all circulating variants, was higher than a given threshold percentage (90% in analyses presented in the Results section and 80% in sensitivity analyses) was used as the start date of the period during which all admissions were considered to be caused by the Omicron variant. Note (i) that amongst different countries these two study periods started in different calendar weeks, depending on when the Omicron variant was introduced to the location and on the rate of its local spread, and (ii) that in this analysis all Omicron sub-lineages are included (e.g. BA.1.1, BA.2). Only patients admitted to hospital in the two months before country-level Omicron variant frequency reached the lower threshold and those admitted in the first two months after Omicron variant relative frequency reached 90% were included in the primary analysis; the reason for restricting the study population to those admitted during these time windows was to reduce confounding by unmeasured factors whose frequencies in the hospitalised population also changed over time and which might be associated with clinical outcomes.

Statistical analysis

We report the frequencies of symptoms, comorbidities and vaccination status stratified by country and time periods (before and after Omicron emergence). We also assessed the case fatality risk and the frequency of a composite outcome that combined death and invasive mechanical ventilation use during the two study periods; in this analysis, patients who were discharged from hospital before the end of the follow-up period used in the definition of the outcome (14 or 28 days) were assumed to have been alive at the end of that period. When estimating risk of death by day 14 after admission or onset of symptoms, whichever happened later, numerators were numbers of patients who died before or on day 14 after admission; denominators in this calculation included those who died by day 14, those discharged at any time during follow-up, and those who were followed at least for 2 weeks, regardless of final outcome, including those who died after 14 days. The same approach was used to analyse the 28 day fatality risk. Note that for 35.5% of patients admitted to hospital during the two study periods defined by Omicron variant frequency, date of onset of symptoms was missing; for these patients we assumed onset of clinical disease happened before admission – that is that these were not hospital acquired infections. Furthermore, for 7.2% of patients, outcome date (date of death or discharge or latest date with follow-up information) was missing and 0.4% had an outcome date that was earlier than date of admission or of symptoms onset; except for those who were discharged and had missing outcome date, these two groups of patients were not included in analyses on the frequencies of clinical outcomes but were included in analyses describing distributions of symptoms and comorbidities. As described in the Results section, some patients included in this study were admitted for treatment of a medical condition other than COVID-19 but tested positive incidentally during hospitalisation.

We used mixed-effects logistic regression models to assess the association between study period, that is periods defined by the Omicron variant frequency at the population level, and 14-day death risk, adjusting for age, sex, and vaccination status. Age was included with the following categories: patients younger than 18 years, aged between 18 and 60 years, and older than 60 years. Random intercepts were used to account for potential variation in the risk of death between study sites in different countries. We also present models that adjust for the most commonly reported comorbidities; for each comorbidity included in the analysis, a binary variable was used to indicate presence or absence of the condition. Cox proportional hazards models on time to death, adjusted for age and sex and stratified by country and previous vaccination, were also fit; results of survival analyses are shown in the Appendix 1. Note that vaccination status was used as a binary variable in these models, without dose counts or timing of vaccination, and due to limited information on dates of doses we did not adjust for time since the most recent vaccination.

R and Python were used for data processing and descriptive analyses (R Development Core Team, 2022; The pandas development team, 2020). Code used for analyses and aggregated data used to generate figures are available (ISARIC Data Platform, 2022) (see also Data availability statement). Stata 17 was used to fit mixed-effects logistic models and perform survival analysis.

Results

Description of study population and study periods

Overall, 129,196 records from patients admitted to hospital between 1st October 2021 and 28th February 2022 were included in this analysis. Clinical centres in 30 countries contributed data (median 53 observations per country, interquartile range [IQR] 18–162); 11 countries contributed data on more than 100 hospitalised clinical cases (Appendix 1—table 1). A total of 54.0% and 42.6% of records were from South Africa and the United Kingdom, respectively. Appendix 1—table 2 and Appendix 1—table 3 show information on missing data for both symptoms and comorbidities.

In addition to the clinical data contributed by the collaborating centres, population-level variant frequency data were used to define time periods when most infections in a country were assumed to be caused by Omicron versus other lineages. As presented in Figure 1, different countries reached the threshold relative frequencies of 10 and 90% of infections being caused by the Omicron variant at different times. Similar plots are presented in Appendix 1—figure 1 for other threshold frequencies. In Appendix 1—table 4, we list limitations in the use of these data to define time periods when infections were more likely caused by Omicron versus previous variants.

Using information presented in Figure 1, 103,061 patients, from 28/30 countries, were admitted either in the two months before the Omicron variant represented 10% of infections at the country-level (N=22,921; henceforth, the pre-Omicron period) or in the two months after (N=80,140) the Omicron variant was responsible for at least 90% of the infections; for ease of reference, the latter period will be referred to as the Omicron period. Note that 12,085 patients were admitted during weeks between the end of the pre-Omicron period and the start of the Omicron period and are not included in analyses presented in the following subsections (Figure 2); and 12,560 records of patients admitted two months after Omicron variant represented 90% of infections were not analysed. All patients from South Africa, the United Kingdom and Malaysia were assumed to be SARS-CoV-2 positive, as this is one criterion for inclusion in their databases. Of the 2296 records from other countries, information on SARS-CoV-2 diagnostic testing was available for 1,999 observations; whilst patients with negative PCR test result (N=10) were excluded from the rest of the analysis, those with missing PCR data (N=297) were assumed positive (see Appendix 1—table 5 for distribution by country). Of note, clinical data from Laos were not included in comparative analyses as there was only limited evidence of increase in local Omicron variant relative frequency during the study period (additional information is provided in the Appendix 1). For Pakistan, population-level data available at the time of the analysis indicate increasing Omicron variant frequency during the study period, but the proportion of local infections caused by this variant fluctuated; analyses of clinical data from that country are described in the Appendix 1.

Study flowchart.

In this figure, we present the numbers of observations included in analyses in the different subsections of the Results section.

The median (IQR) ages of patients during the pre-Omicron and Omicron periods were 62 (43 – 76) and 50 (30 – 72) years, respectively; however, country-specific medians suggest that the younger age of patients after Omicron variant emergence in the combined dataset is at least partially explained by an increase in the proportion of data contributed by South Africa, relative to the proportion of data contributed by other countries (Appendix 1—table 6). A total of 48.3% and 54.8% of patients admitted during these periods, respectively, were female. 5.2% and 9.1% of patients in the pre-Omicron and Omicron periods, respectively, had the date of disease onset after admission date. In some countries, information on whether COVID-19 was the main reason for hospitalisation was also collected: 70.1% (N=2248) and 69.0% (N=27,804) of patients during the pre-Omicron and Omicron periods respectively were admitted to hospital due to COVID-19; patients for whom this information was available were primarily from South Africa (94.9%). There was no consistent pattern of within-country changes related to this variable (Appendix 1—table 7). Of note, 465/36,761 (1.3%) individuals reported a history of previous SARS-CoV-2 infection before the acute episode leading to hospitalisation included in this analysis (128/15,563 [0.8%] and 337/21,198 [1.6%] in the pre-Omicron and Omicron periods, respectively).

Temporal changes in frequencies of symptoms and comorbidities

Figure 3 shows age distributions of hospitalised patients before versus after Omicron variant emergence; only countries with at least 50 observations in each period are included. Despite similar medians of age in the two periods within countries, in some, but not all, country-specific datasets, an increase in the proportion of the study population from younger ages was observed, although the number of patients in some age categories is small. Furthermore, there were differences between countries with regard to age distribution of cases, which could reflect either epidemiological differences between settings or else differences in recruitment of patients for this analysis.

Age distributions by study period and country.

Age distributions (x-axes show proportions; y-axes, age groups) when Omicron variant relative frequency was below 10% (blue bars) and when the frequency was 90% or higher (red bars). Data from different countries are shown in different panels; only countries with 50 or more records in each period are presented. Numbers of observations with age information are shown for each study period next to country names.

The frequencies of the five most commonly reported symptoms and comorbidities in the combined (all countries) dataset during the two study periods are presented in Figure 4A and B, by country and study period. When analysing the combined dataset, there was a decrease in the percentage of patients with at least one of the comorbidities listed in Appendix 1—table 3 before versus during Omicron variant dominance (78.9% [N=15,574] and 59.6% [N=60,625], respectively); however, country-specific data show variable patterns (Appendix 1—table 8). With a total of 14 comorbidities being considered, median (IQR) numbers of comorbidity variables with non-missing information in the pre-Omicron and Omicron periods were 11 (0–12) and 9 (1 – 11), respectively. Whilst the directions of changes (increase or decrease) in frequencies of comorbidities were not consistent across countries, for many symptoms frequencies were lower during the Omicron period versus the pre-Omicron period. As can be seen in Appendix 1—figure 2, this pattern was consistent after stratifying frequencies of symptoms by age groups. The percentage of patients during the pre-Omicron period with at least one of the symptoms in Appendix 1—table 2 was 96.6% (N=11,683); this percentage was 88.6% (N=17,859) during the Omicron period (see Appendix 1—table 9 for country-specific numbers). These numbers refer to records from countries other than South Africa, where data on symptoms were not systematically available. The median (IQR) numbers of variables with non-missing data on symptoms were 14 (0–19) and 17 (0–19) for the pre-Omicron and Omicron periods, respectively.

Frequencies of symptoms and comorbidities by study period and country.

Frequencies of the five most common symptoms (A) and comorbidities (B) during the pre-Omicron (blue bars) and Omicron (red bars) periods. 95% confidence intervals are shown. Note that South Africa is included in panel B but not panel A. For panel (A), only data from the pre-Omicron period were used to identify the most frequent symptoms; for panel (B), as data on comorbidities were available in the two countries contributing most records, the United Kingdom and South Africa, and since their relative contributions to the study population changed in the two study periods, the dataset including both the pre-Omicron and Omicron periods was used to identify most common comorbidities. Only countries with at least 50 observations during each study period are included. For each symptom or comorbidity, whenever fewer than five observations without missing data were available, bars were not shown and the text ‘NS’ (not shown) was included.

Vaccination history in hospitalised patients

Data on vaccination status were available for 42,850/103,051 hospitalised patients (8,952 during the pre-Omicron period and 33,898 during the Omicron period). In Table 1, we present vaccination status for study participants in each of the two periods by country. As expected, there is considerable inter-country variation in the frequency of vaccination. Age-stratified vaccination frequencies are shown in Appendix 1—figure 3 and suggest increases in frequency of previous vaccination during the period after Omicron variant emergence. However, as shown in Appendix 1—figure 4, with population-level vaccination coverage from before Omicron variant emergence up to the end of February 2022, in many countries contributing data to this study there was an increase in vaccination coverage over time, including in the periods during and after the emergence of the Omicron variant. Note that 55.8% of vaccinated patients received two or more doses before hospital admission.

Table 1
Vaccination status by country and study period.

Data for period-country combinations with less than 10 observations are not presented. Data on vaccination status were not available for patients from Saudi Arabia.

pre-Omicron periodOmicron period
Country% VaccinatedTotal N% VaccinatedTotal N
Brazil84.61387.933
Canada32.25957.3686
Colombia42.119-<10
Estonia-<10
Germany-<10-<10
India34.82384.833
Malaysia79.32980.010
Nepal25.319039.3183
Netherlands60.06051.051
New Zealand5.934-<10
Norway-<1082.245
Philippines78.614-<10
Portugal-<10-<10
Romania-<1078.698
South Africa15.1160527.924752
Spain45.02070.955
United Kingdom65.4686570.37846
United States of America-<10-<10
Argentina-<10
Australia-<10
Indonesia-<10
Israel54.511
Kuwait66.718
Turkey74.127

Clinical outcomes

Overall, 11,314 patients admitted during the two study periods died during hospitalisation: 8517/94,524 by day 14 after hospital admission or disease onset, whichever occurred latest, and 10,530/94,461 by day 28; 738 patients died after day 28 and 46 patients who died did not have an outcome date recorded. As explained in the Methods section, denominators for fatality risks included patients who were discharged or still in hospital by day 14 or 28. Median (IQR) times to death were 10 (5 – 17) and 6 (3 – 13) days for the periods before and after Omicron emergence, respectively; similar information, on time from admission or symptoms onset to death, stratified by country is shown in Appendix 1—table 10. In some countries (see Figure 5 for comparisons on 14-day fatality risk, and Appendix 1—figure 5 for comparisons using the 28-day period), during the Omicron period, a lower proportion of patients died during hospitalisation, compared to the period before Omicron emergence; in India, the opposite pattern was observed although numbers for that country were limited.

Risk of death (y-axes) in the first 14 days after hospital admission or disease onset, whichever occurred latest, during the pre-Omicron and Omicron periods.

In each panel, the x-axis shows countries (ISO3 codes are presented), with different periods represented by circles with different colours (blue circles for the pre-Omicron period; red circles, for the Omicron period). 95% confidence intervals are also presented. The top panel shows data for individuals of all ages; the bottom panels, data for patients aged less than 18 years, between 18 and 60 years, and older than 60 years. Only countries with at least 50 observations in both study periods are included in the figure; for panels presenting age-specific estimates (bottom row), a further requirement for inclusion was outcome data for at least 10 patients in the corresponding age range in both periods.

In a mixed-effects logistic model on 14-day fatality risk that adjusted for sex, age categories, and vaccination status, hospitalisations during the Omicron period were associated with lower risk of death (see Table 2). The inclusion of common comorbidities in the model did not change the estimated association. Similar results were obtained when using 28-day fatality risk as the outcome. We repeated the 14-day fatality risk analysis excluding patients who reported being admitted to hospital due to a medical condition other than COVID-19; the estimated odds ratio for the association between study period and the outcome was similar to those reported in Table 2. In an additional sensitivity analysis, estimates from a model that only included data from countries with at least 50 records per study period were also similar (OR 0.65, 95% CI 0.61–0.69, adjusted for covariates included in model I, Table 2). Survival analysis was also performed, and similar results were obtained (Appendix 1—table 11).

Table 2
Odds ratio for the association between study period and mortality outcome.

Results of multivariate logistic models, with random intercepts for countries, on 14-day fatality risk are presented. Different models were fit that included different variables. Model III adjusts for all variables in the table, however due to missing data in the vaccination and comorbidity variables, less than a third of the study population was included in the estimation of that model; models I and II were thus fit that did not adjust for these variables and included more individuals. In model IV, a category for missing data was created for the variable on previous vaccination; individuals in that category had an odds ratio of 0.74 (0.69–0.80; reference group in this comparison is the non-vaccinated group). Note that similar results were obtained when finer categorisation of the age variable, 10-year intervals, was used. As previous SARS-CoV-2 infection has been shown to reduce severity of COVID-19 (Altarawneh et al., 2022), a multivariable model that also adjusted for this variable was fit; in that model, the odds ratio for the association between study period and fatality risk was 0.70 (0.61–0.80). As in other epidemiological studies, estimates for covariates other than the primary exposure (study period) should be carefully interpreted (Westreich and Greenland, 2013).

ModelIIIIIIIV
Number of observations94,07739,95026,72856,329
Odds ratio (95% CI)Odds ratio (95% CI)Odds ratio (95% CI)Odds ratio (95% CI)
Variables
Omicron period*0.65 (0.62–0.69)0.67 (0.61–0.75)0.68 (0.60–0.77)0.64 (0.59–0.69)
Sex (male)1.32 (1.26–1.38)1.33 (1.23–1.43)1.36 (1.24–1.49)1.33 (1.25–1.42)
Age
Older than 60 yearsReferenceReferenceReferenceReference
Aged between 18 and 60 years0.26 (0.25–0.27)0.24 (0.22–0.26)0.27 (0.25–0.30)0.30 (0.27–0.32)
Younger than 18 years0.06 (0.05–0.07)0.06 (0.05–0.07)0.07 (0.05–0.09)0.06 (0.05–0.08)
Previous vaccination-0.60 (0.55–0.65)0.53 (0.48–0.59)0.59 (0.54–0.65)
Comorbidities
Hypertension--1.29 (1.16–1.42)1.26 (1.17–1.35)
Diabetes--1.22 (1.09–1.38)1.22 (1.12–1.32)
Chronic cardiac disease--1.50 (1.31–1.71)1.51 (1.39–1.65)
  1. *

    Odds ratio in univariate analysis 0.65 (0.61–0.69) (N=94,524).

In addition to using fatality risk in our analyses, we also considered the composite outcome of death or invasive mechanical ventilation (IMV). Data on IMV were available in 74,563 records. Of 74,563 patients, 3111 required IMV during hospitalisation; the date when IMV was initiated was reported for 1070/3111 patients. Of those patients with data on IMV, 10,049/67,383 patients either died or required IMV. Appendix 1—figure 6 shows proportions of patients with this outcome by country and study period. Since date of IMV initiation was only available for 1070/3111 records, we do not present graphs by time since admission date.

Comparison with individual-level variant data

Whilst our approach of using population-level variant composition information allowed inclusion in this analysis of data from settings where it was not feasible to systematically identify the infecting SARS-CoV-2 variant, the use of aggregated data to infer the infecting variant has limitations, including the possibility of misclassification (see Appendix 1—table 4 for a list of limitations of this approach). To assess whether patterns described in previous subsections are generally consistent with analyses using individual-level variant information, we repeated comparisons for countries where information on the infecting variant was collected; data on variant were available for 1275 records. Of these, 852 patients were admitted either during the pre-Omicron period or the Omicron period: whilst only 1.9% (16/827) of those admitted during the Omicron period were infected by a variant other than Omicron, 4.0% (1/25) of patients during the pre-Omicron period had Omicron as the causative virus variant; for the calculation of these percentages data from a participating institution that prioritised contributing Omicron variant cases were not included. Except for six clinical cases in South Africa and Saudi Arabia, all infections were caused either by Delta or Omicron variants, and for this reason only data on these two variants are presented (Appendix 1—table 12). Figures similar to Figures 35 but stratified by infecting variant, rather than study period, are shown in the Appendix 1 (Appendix 1—figures 710). The numbers of participants included in the latter comparisons are lower than the numbers included in the comparisons using population-level variant data; for countries with ten or more observations of both Omicron and Delta variants, the patterns observed are broadly consistent with results obtained using the population-level approach.

We also performed sensitivity analyses using different population-level threshold frequencies for the Omicron variant (10% and 80%, rather than 10% and 90%); these are shown in Appendix 1—figures 1114 and are consistent with findings described in the Results section.

Discussion

When new variants of SARS-CoV-2 emerge during the COVID-19 pandemic, several critical questions are asked by public health authorities as to differences in disease severity and risk factors, and vaccine protection. Here, we leveraged data from multiple sources, from population-level variant frequency information to individual-level data on the clinical journey of hospitalised patients with COVID-19, and from multiple countries, to compare characteristics of patients with infection during periods before Omicron emergence versus when this variant became locally dominant. We observed that when the relative frequency of the Omicron variant was high, the proportions of patients with some of the most common COVID-19 symptoms were lower compared to the pre-Omicron period. In most but not all countries, patients presenting to hospital during the Omicron period had better outcomes (lower fatality risk), compared to those hospitalised before Omicron emergence, which could be related to lower variant virulence, prior immunity or residual confounding. In summary, our approach, which was consistent with analyses that used individual-level variant data from a subset of the study population, suggests clinical differences in patients hospitalised with the Omicron variant versus those admitted before this variant spread, and these differences vary by country.

Our finding that mortality was generally lower during the period when the Omicron variant was dominant is consistent with data from South Africa reported earlier this year (Wolter et al., 2022). In that study, which included more than 30,000 patients with individual-level information on the infecting variant, individuals infected with the Omicron variant had a lower risk of disease progression that required hospital admission than individuals infected with other variants; amongst hospitalised patients, the odds ratio for the association between Omicron variant infection and severe disease was 0.7 (95% confidence interval [CI] 0.3–1.4), which is similar to that observed in this study using death as the outcome. A lower risk of death in Omicron variant-infected versus Delta variant-infected patients was also observed in a recent study in the United Kingdom, although that analysis did not assess risk of death conditional on hospitalisation but rather on infection (Nyberg et al., 2022). In our analyses, statistical models were adjusted for vaccination history, which is a potential confounder of the association between dominant variant period and risk of death. However, the simplistic approach of using vaccination as a binary variable may be subject to residual confounding by time since vaccination, number of doses, or vaccine type. Moreover, as part of the effort to characterise Omicron variant infection, information on whether COVID-19 was the main reason for hospitalisation was collected during the study period and suggests that for a non-negligible proportion of patients other clinical conditions might have prompted hospital admission. All these factors might have contributed to the observed association, possibly to different degrees in different countries, reason for which this result should not be assumed to necessarily relate to the differences in variant virulence previously suggested by mechanistic studies (Shuai et al., 2022; Halfmann et al., 2022). Of note, data from India (see Figure 5) suggest slightly higher fatality risk during the Omicron period compared to the pre-Omicron period for patients older than 60 years, which could be potentially explained by confounding unrelated to age, residual age-related confounding, not controlled by the categorisation used in our analysis, or alternatively by the limited sample size and consequent uncertainty.

During the period of Omicron variant dominance, fewer patients presented with the symptoms most commonly reported earlier. For example, we observed in the United Kingdom that shortness of breath was present in about three-quarters of patients before Omicron variant emergence and in about half of patients during the Omicron period. Notably, a similar pattern was observed in Nepal, where patients were more often recruited from critical care settings. One possible explanation for this finding would be if incidental SARS-CoV-2 infections, that is infections that were not the primary reason for hospitalisation, were more frequent during the Omicron period; the high transmissibility of this variant, and the consequent peaks in numbers of infections, together with its reported association with lower severity, provides support for this hypothesis. However, in the subset of patients with data on the reason for hospitalisation there was no increase in the proportion of admissions thought to be incidental infections and indeed proportions in both study periods were consistent with frequencies of incidental infections in recent studies in the United States (Klann et al., 2022) and the Netherlands (Voor In ’t Holt et al., 2022), although in the latter, non-incidental infections included patients for whom COVID-19 was a contributing but not the main cause of hospitalisation. An alternative and less plausible explanation for the lower frequency of symptoms during the Omicron period would be that some of these patients developed symptoms other than those presented here, and which are severe enough to prompt hospital admission. Finally, it is also possible that the question on the primary reason for hospitalisation might have been interpreted differently in different countries and even in different hospitals in the same country, which would complicate its use in identifying incidental infections.

We also observed that history of COVID-19 vaccination was more frequent during the Omicron period, although for most countries the number of patients with vaccination information was limited, especially after stratification by age. Whilst this pattern would be expected if current vaccines were less effective against the Omicron variant compared to previously circulating variants, as suggested by a recent study in England analysing symptomatic disease (Andrews et al., 2022a), there were changes in vaccination coverage in many settings during the second half of 2021 and early 2022, including in response to the reports of Omicron variant cases. Since non-COVID-19 patients (e.g., patients with respiratory infections caused by other pathogens) were not systematically recruited for this multi-country study, it is not possible to estimate vaccine effectiveness during the two study periods and assess its change (Andrews et al., 2022b).

The major strength of our study relates to inclusion of data from all WHO geographic regions, collected with standardised forms, with over 100,000 records. However, we note that 96.6% of patients were from two countries - South Africa and the United Kingdom - and that the relative contributions of these countries to the study data were different in the two study periods (Appendix 1—table 5); to avoid misinterpretations linked to changes in country-specific contributions to data in the pre-Omicron and Omicron periods, we present descriptive analyses by country and use statistical models that adjust for country-level variation. It is also important to consider the relative contributions of these countries when interpreting descriptive analyses that refer to the combined dataset. Other limitations of our study relate, as mentioned in Appendix 1—table 4, to the use of population-level variant data to define periods when infections were likely caused by Omicron variant. For example, if infection by Omicron variant is associated with lower severity and if samples used to inform population-level frequency were often from community cases, then these aggregated data might not represent variant frequency in the hospitalised population. Another weakness of our study is that recruitment procedure was not standardised and was defined locally. Whilst this likely affected the generalisability of our descriptive estimates (fatality risk and frequencies of symptoms and comorbidities) to local populations of hospitalised COVID-19 cases (Lash et al., 2021; Rothman et al., 2013), it might not have affected the association between study period and fatality risk, at least not beyond the well-described potential for collider bias in hospital-based studies on COVID-19 outcomes (Griffith et al., 2020). Finally, missing information on symptoms for patients from South Africa prevented our descriptive analysis of changes in clinical presentation in an African setting. However, despite potential weaknesses in this approach, our results are consistent with reports from South Africa and elsewhere (Wolter et al., 2022), and individual-level variant data available for this study population often matched the two study periods defined by Omicron variant frequency.

In conclusion, we believe our approach of comparing changes in clinical characteristics of COVID-19 using multi-country standardised data, especially when combined with smaller scale studies that collect individual-level data on infecting variants for validation, will be useful in understanding the impact of new variants in the future. Another application will be in using routinely collected health data for cross-country comparisons of variant characteristics. Equally importantly, the successful conduct of this study, and the lessons learned, including the potential weaknesses discussed above, shows that multi-country efforts to study emerging SARS-CoV-2 variants are feasible, improvable and can generate insights to inform policy decision making.

Appendix 1

Acknowledgements (partner institutions)

The contribution of those listed in the ISARIC Clinical Characterisation Group was supported by a grant from the Oxford University COVID-19 Research Response fund (grant 0009109); endorsement of the Irish Critical Care- Clinical Trials Group, co-ordinated in Ireland by the Irish Critical Care- Clinical Trials Network at University College Dublin and funded by the Health Research Board of Ireland [CTN-2014–12]; grants from Rapid European COVID-19 Emergency Response research (RECOVER) [H2020 project 101003589] and European Clinical Research Alliance on Infectious Diseases (ECRAID) [965313]; Wellcome Trust [Turtle, Lance-fellowship 205228/Z/16/Z]; Research Council of Norway grant no 312780, and a philanthropic donation from Vivaldi Invest A/S owned by Jon Stephenson von Tetzchner; PJMO is supported by the UK’s National Institute for Health Research (NIHR) via Imperial’s Biomedical Research Centre (NIHR Imperial BRC), Imperial’s Health Protection Research Unit in Respiratory Infections (NIHR HPRU RI), the Comprehensive Local Research Networks (CLRNs) and is an NIHR Senior Investigator (NIHR201385); Cambridge NIHR Biomedical Research Centre; Institute for Clinical Research (ICR), National Institutes of Health (NIH) supported by the Ministry of Health Malaysia; Gender Equity Strategic Fund at University of Queensland, Artificial Intelligence for Pandemics (A14PAN) at University of Queensland, The Australian Research Council Centre of Excellence for Engineered Quantum Systems (EQUS, CE170100009), The Prince Charles Hospital Foundation, Australia; the South Eastern Norway Health Authority and the Research Council of Norway; the U.S. DoD Armed Forces Health Surveillance Division, Global Emerging Infectious Diseases Branch to the U.S Naval Medical Research Unit No. TWO (NAMRU-2) (Work Unit #: P0153_21_N2). These authors would like to thank Vysnova Partners, Inc for the management of this research project. The Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit is funded by the Wellcome Trust.

The investigators acknowledge the philanthropic support of the COVID clinical management team, AIIMS, Rishikesh, India; COVID-19 Clinical Management team, Manipal Hospital Whitefield, Bengaluru, India; the dedication and hard work of the Groote Schuur Hospital Covid ICU Team, supported by the Groote Schuur nursing and University of Cape Town registrar bodies coordinated by the Division of Critical Care at the University of Cape Town; the dedication and hard work of the Norwegian SARS-CoV-2 study team; and Preparedness work conducted by the Short Period Incidence Study of Severe Acute Respiratory Infection.

This work uses data provided by patients and collected by the NHS as part of their care and support #DataSavesLives. The data used for this research were obtained from ISARIC4C. The authors are extremely grateful to the 2648 frontline NHS clinical and research staff and volunteer medical students who collected these data in challenging circumstances; and the generosity of the patients and their families for their individual contributions in these difficult times. The COVID-19 Clinical Information Network (CO-CIN) data was collated by ISARIC4C Investigators. The authors also acknowledge the support of Jeremy J Farrar and Nahoko Shindo.

Conflict of interest declarations for the ISARIC Clinical Characterisation Group

A Angheben declares support from Italian Ministry of Health - “Fondi Ricerca Corrente” Line1 Project 5 to IRCCS Sacro Cuore – Don Calabria Hospital.

FM Carrier declares a grant from the Canadian Institute of Health Research.

H Dalton declares personal fees for medical director of Innovative ECMO Concepts and honorarium from Abiomed/BREETHE Oxi-1 and Instrumentation Labs. Consultant fee, Entegrion Inc, Medtronic and Hemocue.

AM Dyrhol-Riise declares grants from Gilead outside this work.

CA Donnelly declares research funding from the UK Medical Research Council and the UK National Institute for Health Research.

JJ Douglas declares personal fees from lectures from Sunovion and Merck; consulting fees from Pfizer.

R Fowler declares a peer reviewed research grant from the Canadian Institutes of Health Research.

J Gómez-Junyent declares support by Pfizer, Angelini and MSD to attend meetings (registration to meetings only).

AM Guerguerian participated as site investigator for the Hospital For Sick Children, Toronto, Canada as a site through SPRINT-SARI Study via the Canadian Critical Care Trials Group sponsored in part by the Canadian Institutes of Health Research.

A Ho declares grant funding from Medical Research Council UK, Scottish Funding Council - Grand Challenges Research Fund, and the Wellcome Trust, outside this submitted work.

JC Holter reports grants from Research Council of Norway grant no 312780, and from Vivaldi Invest A/S owned by Jon Stephenson von Tetzchner, during the conduct of the study.

Kumar, D. declares grants and personal fees from Roche, GSK and Merck; and personal fees from Pfizer and Sanofi.

DJ Kutsogiannis declares personal fees for a lecture from Tabuk Pharmaceuticals and the Saudi Critical Care Society

J Lee reports grants from European Commission PREPARE grant agreement No 602525, European Commission RECOVER Grant Agreement No 101003589 and European Commission ECRAID Grant Agreement 965313 supporting the conduct, coordination and management of the work.

WS Lim declares his institution has received unrestricted investigator-initiated research funding from Pfizer for an unrelated multicentre cohort study in which he is the Chief Investigator, and research funding from the National Institute for Health Research, UK for various clinical trials outside the submitted work.

I Martin-Loeches declared lectures for Gilead, Thermofisher, MSD; advisory board participation for Fresenius Kabi, Advanz Pharma, Gilead, Accelerate, Merck; and consulting fees for Gilead outside of the submitted work.

A Martín-Quiros declares consulting fees for Gilead and MSD, presentation fees for GILEAD, Pfizer and MSD, support for attending ECCMID from Gilead, and advisory board fees for MSD and Gilead.

S Murthy declares receiving salary support from the Health Research Foundation and Innovative Medicines Canada Chair in Pandemic Preparedness Research.

A Nichol declares a grant from the Health Research Board of Ireland to support data collection in Ireland (CTN-2014–012), an unrestricted grant from BAXTER for the TAME trial kidney substudy and consultancy fees paid to his institution from AM-PHARMA.

P Openshaw has served on scientific advisory boards for Janssen/J&J, Oxford Immunotech Ltd, GSK, Nestle and Pfizer (fees to Imperial College). He is Imperial College lead investigator on EMINENT, a consortium funded by the MRC and GSK. He is a member of the RSV Consortium in Europe (RESCEU) and Inno4Vac, Innovative Medicines Initiatives (IMI) from the European Union.

R Parke declares that the Cardiothoracic and Vascular Intensive Care Unit, Auckland City Hospital, receives support by way of an unrestricted grant from Fisher and Paykel Healthcare New Zealand Ltd.

O Rewa declares honoraria from Baxter Healthcare Inc and Leading Biosciences Inc

O Săndulescu has been an investigator in COVID-19 clinical trials by Algernon Pharmaceuticals, Atea Pharmaceuticals, Regeneron Pharmaceuticals, Diffusion Pharmaceuticals, Celltrion, Inc and Atriva Therapeutics, outside the scope of the submitted work.

MG Semple reports grants from DHSC National Institute of Health Research UK, from the Medical Research Council UK, and from the Health Protection Research Unit in Emerging & Zoonotic Infections, University of Liverpool, supporting the conduct of the study; other interest in Integrum Scientific LLC, Greensboro, NC, USA, outside the submitted work.

S Shrapnel participated as an investigator for an observational study analysing ICU patients with COVID-19 (for the Critical Care Consortium including ECMOCARD) funded by The Prince Charles Hospital Foundation during the conduct of this study.

Adrian Streinu-Cercel has been an investigator in COVID-19 clinical trials by Algernon Pharmaceuticals, Atea Pharmaceuticals, Regeneron Pharmaceuticals, Diffusion Pharmaceuticals, and Celltrion, Inc, outside the scope of the submitted work.

Anca Streinu-Cercel has been an investigator in COVID-19 clinical trials by Algernon Pharmaceuticals, Atea Pharmaceuticals, Regeneron Pharmaceuticals, Diffusion Pharmaceuticals, Celltrion, Inc and Atriva Therapeutics, outside the scope of the submitted work.

C Summers reports that she has received fees for consultancy for Abbvie and Roche relating to COVID-19 therapeutics. She was also the UK Chief Investigator of a GlaxoSmithKline plc sponsored study of a therapy for COVID, and is a member of the UK COVID Therapeutic Advisory Panel (UK-CTAP). Outside the scope of this work, Dr Summers’ institution receives research grants from the Wellcome Trust, UKRI/MRC, National Institute for Health Research (NIHR), GlaxoSmithKline and AstraZeneca to support research in her laboratory.

S Dudman reports grants from Research Council of Norway grant no 312780.

R Tedder reports grants from MRC/UKRI during the conduct of the study. In addition, R Tedder has a patent United Kingdom Patent Application No. 2014047.1 “SARS-CoV-2 antibody detection assay” issued.

L Turtle reports grants from MRC/UKRI during the conduct of the study and fees from Eisai for delivering a lecture related to COVID-19 and cancer, paid to the University of Liverpool.

Supplementary results

Frequency of symptoms outside the United Kingdom and South Africa

Most, 82.5% (N=579), patients admitted to hospital during the pre-Omicron period outside the United Kingdom and South Africa had at least one symptom; this percentage is lower than the frequency estimated including the United Kingdom data (96.6%), possibly due to the low frequency of symptoms in India (Appendix 1—table 9). The corresponding frequency during the Omicron period was 81.5% (N=1,702).

Epidemiology of Omicron variant in Laos

Population-level variant data from Laos were not available in the Global Initiative on Sharing All Influenza Data (GISAID) platform that covered the period between October 2021 and February 2022, and for this reason clinical data from this country were not included in analyses presented in the Results section of the manuscript. Local data suggest that Omicron variant spread in the country only after this period. Indeed, unpublished data from the Lao-Oxford-Mahosot Hospital-Wellcome Trust Research Unit indicate that Omicron variant was responsible for a large proportion of infections in March but not February 2022, although the numbers of infections genotyped were limited (Elizabeth Ashley, personal communication).

Clinical data from Pakistan

In Pakistan, there was an increase in the relative frequency of Omicron variant during the period from October 2021 to February 2022. However, despite causing 96.1% of infections in the GISAID data from the country in mid-January 2022, throughout February this percentage fluctuated. Data from Pakistan were thus not included in the Results section. Here, we discuss clinical data from this country; for that, we used as the start of the Omicron period the first week when this variant was responsible for more than 90% of infections, regardless of whether this percentage was lower in the following weeks.

Data from 929 patients from Pakistan were contributed to the study; 249 records were from the pre-Omicron period, and 478, from the Omicron period. The percentage of patients with at least one symptom was 83.9% in the pre-Omicron period, and 57.9%, in the Omicron period. 52.2% and 59.2% had at least one comorbidity during these two periods, respectively. Vaccination data were available for 474 patients admitted during the study periods: 37.7% and 62.9% had history of COVID-19 vaccination during the pre-Omicron and Omicron periods. The 14-day fatality risk for hospitalised patients during the pre-Omicron period was 52.5%, and during the Omicron period, 45.4%.

Sensitivity analysis that excludes patients with other primary reason for hospitalisation

For 30,052 patients admitted during the two study periods, information was available on whether COVID-19 was the primary medical reason for hospitalisation; most of these patients were from South Africa. As a sensitivity analysis, we fit a mixed-effects logistic regression model on the 14-day fatality risk excluding patients who had reported that COVID-19 was not the reason for hospitalisation; patients for whom this information was missing were included. The odds ratio for the association between study period and 14-day fatality risk was 0.68 (95% confidence interval 0.61–0.75).

Appendix 1—table 1
Numbers of records contributed by partner institutions in different countries between 01/10/2021 and 28/02/2022.
CountryNumber of records
South Africa69766
United Kingdom55049
Pakistan929
Canada919
Nepal504
Laos456
India409
Romania166
Saudi Arabia151
Spain151
Netherlands134
Malaysia90
Norway67
Turkey57
Brazil54
Colombia52
New Zealand46
Kuwait35
United States32
Philippines26
Ghana21
Ireland20
Israel17
Italy12
Estonia7
Australia7
Indonesia6
Portugal5
Germany4
Argentina4
Appendix 1—table 2
Missing data on symptoms.

Note that this information was not systematically recorded in South Africa, and for this reason data from that country are not included in this table.

SymptomsYesNoMissing data
Any cough204311372625273
Fever160451946523920
Headache38962839827136
Confusion59602854824922
Seizures5703342425436
Sore throat23942935327683
Runny nose16393027927512
Vomiting69562773424740
Wheezing20423119126197
Diarrhoea44182998925023
Chest pain54882873225210
Conjunctivitis1063260626718
Myalgia36862819527549
Rash4763287726077
Fatigue113392215025941
Ageusia16822834129407
Inability to walk252379755381
Anosmia13932904028997
Shortness of breath204901403024910
Lymphadenopathy1453279526490
Appendix 1—table 3
Missing data on comorbidities.

In this table, data from all countries are included.

ComorbiditiesYesNoMissing data
Liver disease17864099286418
Diabetes129566874347497
Chronic cardiac disease135467342342227
Hypertension320525740139743
Current smoking50902667497432
COPD93047779442098
Active TB15794573181886
Asthma87207917541301
Chronic kidney disease84417845342302
Malignant neoplasm50628146542669
Dementia46463853086020
HIV59257912144150
Chronic neurological disorder56153774085841
Obesity57234536778106
Appendix 1—table 4
Potential limitations of population-level variant data used to determine time periods when Omicron variant was dominant.
Potential limitationLikely impact on analyses
Population-level data come from a range of sources in each country, and for most samples it is not possible to determine whether patient was hospitalised or was a community (mild) caseIf different variants are associated with different severities upon infection and if a large fraction of samples used in the estimation of population-level frequency of variants are from community cases, then it is possible that this frequency does not fully represent the frequency in the hospitalised population. In particular, if Omicron variant infection is linked to lower risk of hospitalisation, as previous studies suggest, it is possible that even during periods when community-level frequency of Omicron variant was high, the frequency of Omicron variant in the hospitalised population might have been relatively low.
Use of country-level data, rather than data on variant frequency in the catchment areas of clinical centres contributing dataIf Omicron variant spreads asynchronously in a country, with some regions reaching high relative frequency faster than others, it is possible that country-level data, rather than data at a finer geographical level, might not reflect Omicron variant frequency in the population from which patients were recruited.
Delay between infection, onset of symptoms and hospitalisationDepending on the data source used to define population-level frequency of variants, if clinical samples were obtained early during the infection, hospitalised cases might only have the same variant composition after a time lag, corresponding to average time from infection, or onset of symptoms, to hospital admission.
Appendix 1—table 5
Numbers of records in the pre-Omicron and Omicron periods by country.
Omicron emergence
CountryBefore 10%After 90%Total
South Africa41805192956109
United Kingdom181242647944603
Canada61763824
Nepal197204401
India89212301
Netherlands6065125
Saudi Arabia2121123
Romania1100101
Spain215677
Malaysia421153
Norway54550
Brazil153348
New Zealand34640
Colombia26531
Turkey02727
Philippines16521
United States of America14721
Kuwait01919
Ghana41519
Ireland14317
Israel01414
Australia066
Portugal325
Indonesia145
Germany224
Italy303
Argentina033
Estonia101
Appendix 1—table 6
Medians (interquartile ranges [Q1 - Q3]) of age by study period and country.

Only countries with 10 or more observations in both study periods are shown.

Before 10%After 90%
CountryMedianQ1Q3MedianQ1Q3
Brazil595070554870
Canada635071623576
Spain686375765984
United Kingdom664878673881
India634772706076
Malaysia635268595563
Netherlands746480705577
Nepal634277644275
South Africa453062412763
Appendix 1—table 7
Numbers of hospitalised patients admitted due to COVID-19.

For country-time period combinations with less than 10 observations, numbers are not presented.

Before 10%After 90%
CountryCOVID-19 as reason (N)COVID-19 as reason (%)TotalCOVID-19 as reason (N)COVID-19 as reason (%)Total
Australia--<10
Argentina--<10
Brazil14100143296.733
Canada1252.22351467.5761
Colombia27.726--<10
Germany--<10--<10
Ghana--<10
India001228.324
Indonesia--<10
Israel866.712
Kuwait0018
Malaysia--<10--<10
Nepal--<100015
Netherlands4981.7603961.963
New Zealand3090.933--<10
Norway--<103475.545
Philippines1610016--<10
Romania--<10100100100
Saudi Arabia--<106868.799
South Africa143371.120151830669.026512
Spain1164.7173766.156
Turkey2710027
USA0011--<10
Appendix 1—table 8
Percentages of patients with at least one comorbidity by country and study period.

Only countries with at least 10 patients in each study period are included.

Before 10%After 90%
Country% with one or more comorbiditiesTotal% with one or more comorbiditiesTotal
Brazil78.61481.833
Canada76.76074.2760
India44.98956.9209
Malaysia64.34272.711
Nepal46.219755.4204
Netherlands86.76078.565
South Africa53.9317044.737412
Spain76.22176.856
United Kingdom86.51182084.621501
Appendix 1—table 9
Percentages of patients with at least one symptom by country and study period.

Only countries with at least 10 patients in each study period are included.

Before 10%After 90%
Country% with one or more symptomsTotal% with one or more symptomsTotal
Brazil100.014100.032
Canada91.76091.6754
India28.18918.6210
Malaysia64.34290.911
Nepal97.019786.3204
Netherlands96.76096.965
Spain100.02187.556
United Kingdom97.41110489.316157
Appendix 1—table 10
Medians (interquartile ranges [Q1 - Q3]) of time from admission or disease onset to death by study period and country.

Only countries with 10 or more observations in both study periods are presented.

pre-Omicron periodOmicron period
CountryMedianQ1Q3MedianQ1Q3
Canada1062110518
United Kingdom1161911619
India6387312
Nepal6512428
South Africa62125210
Appendix 1—table 11
Survival models.

Results of a Cox proportional hazards model, stratified by country, on time to death in the first 28 days since hospital admission or onset of symptoms, which happened latest, are shown in the Hazard ratio column. For this analysis, if follow-up duration was longer than 28 days, it was set to 28 days, and patients who were discharged were censored on the day of discharge. The assumption of proportional hazards was violated for the variable on previous vaccination; for this reason, the model was also stratified by this variable. An alternative analysis assumed that patients discharged from hospital were censored on day 28; in this analysis, the hazard ratio for the variable corresponding to study period was 0.68 (0.63–0.74); for this model, the proportional hazards assumption did not hold for the study period variable. We also fit a competing risk model, with hospital discharge as competing event; estimates from this model are presented in the Subhazard ratio column. In this model, previous COVID-19 vaccination was included as a covariate (subhazard ratio 0.55, 95% CI 0.52–0.59). We also fit a competing risk model using only data from the six countries included in Figures 35 and that included country as a dummy variable; in this model, the subhazard ratio for the Omicron period variable was 0.68 95% CI (0.63–0.74).

Hazard ratioSubhazard ratio
Variables
Omicron period0.77 (0.71–0.84)0.79 (0.73–0.84)
Sex (male)1.24 (1.17–1.32)1.32 (1.24–1.40)
Age
Older than 60 yearsReference
Aged between 18 and 60 years0.41 (0.38–0.44)0.26 (0.24–0.28)
Younger than 18 years0.13 (0.11–0.17)0.06 (0.04–0.07)
Appendix 1—table 12
Distribution of infections with individual-level variant information by country and variant.

Only countries with at least 10 observations for Delta and Omicron variants are listed. Note that other countries had limited numbers for both or one of the two variants.

CountryDeltaOmicron
Canada26303
Netherlands1252
Norway1522
South Africa17720
Spain1016
Appendix 1—figure 1
In this figure, population-level variant data are presented for countries with clinical data included in our analysis.

The same structure of Figure 1 was used but different cut-off frequencies for Omicron variant were applied: in (A), the lower and upper threshold frequencies were 10% and 80%; in (B), these frequencies were 5% and 90%.

Appendix 1—figure 2
Frequencies of the five most common symptoms during the period before (blue bars) and after (red bars) Omicron variant frequency reached 10% and 90%, respectively.

95% confidence intervals are also shown. In (A), data from individuals aged between 18 and 60 years are shown; and (B) shows the same information for individuals older than 60 years. Data from children are not presented.

Appendix 1—figure 3
Frequency of previous vaccination by study period, age category and country.

Only data from countries with at least 50 observations with information on previous vaccination during both study periods defined by Omicron variant frequency are shown. In each panel, the x-axis shows different age categories, with blue bars corresponding to the pre-Omicron period and red bars, to the period after Omicron variant frequency, relative to other variants, reaches 90%. Above each bar, the total number of records included in the calculation of the proportions (y-axes) are presented.

Appendix 1—figure 4
Population-level vaccination coverage.

Data from different countries are presented in different panels; x-axes show epidemiological weeks since the first epidemiological week of 2020. As in Figure 1, continuous black lines represent frequency of Omicron variant relative to the other variants. In addition to information on Omicron variant frequency, each panel also shows data on vaccination: the dashed line shows the proportion of population vaccinated with at least one dose relative to the maximum number vaccinated in each country at the time of the analysis (March 2022). Data used to generate this figure were downloaded from https://ourworldindata.org/.

Appendix 1—figure 5
Risk of death in the first 28 days after hospital admission or disease onset, whichever occurred latest, during pre-Omicron and Omicron periods.

In each panel, the x-axis shows countries, with different periods represented by circles with different colours (blue circles for the pre-Omicron period; red circles, for period after Omicron variant frequency reaches 90%). 95% confidence intervals are presented. The top panel shows data for individuals of all ages; the bottom panels, data for patients aged less than 18 years, between 18 and 60 years, and older than 60 years. Only countries with at least 50 observations in both study periods are included in the figure; for panels presenting age-specific estimates (bottom row), a further requirement for inclusion was outcome data for at least 10 patients in the corresponding age range in both periods.

Appendix 1—figure 6
Risk of death or invasive mechanical ventilation by study period.

In each panel, the x-axis shows countries, with different periods represented by circles with different colours (blue circles for the pre-Omicron period; red circles, for the Omicron period). 95% confidence intervals are presented. The top panel shows data for individuals of all ages; the bottom panels, data for patients aged less than 18 years, between 18 and 60 years, and older than 60 years. Only countries with at least 50 observations in both study periods are included in the figure; for panels presenting age-specific estimates (bottom row), a further requirement for inclusion was outcome data for at least 10 patients in the corresponding age range in both periods. Different from Figure 5 and Appendix 1—figure 5, time since hospital admission or onset of symptoms was not used since for most patients who required invasive mechanical ventilation the start date of the therapeutic approach was not available. Only patients with information on invasive mechanical ventilation use and who were either discharged or died were included.

Appendix 1—figure 7
This figure shows similar information to that presented in Figure 3.

The legend of that figure applies to this figure, except that instead of referring to time periods, the figure shows data for Delta and Omicron variants. Only countries with at least 10 observations for Delta and Omicron variants are included.

Appendix 1—figure 8
This figure shows similar information to that presented in Figure 4.

The legend of that figure applies to this figure, except that instead of referring to time periods, the figure shows data for Delta and Omicron variants. Only countries with at least 10 observations for Delta and Omicron variants are included.

Appendix 1—figure 9
This figure shows similar information to that presented in Appendix 1—figure 3.

The legend of that figure applies to this figure, except that instead of referring to time periods, the figure shows data for Delta and Omicron variants. Only countries with at least 10 observations for Delta and Omicron variants are included; note that, different from Appendix 1—figure 3, the criterion did not consider missingness of vaccination data.

Appendix 1—figure 10
This figure shows similar information to that presented in Figure 5.

The legend of that figure applies to this figure, except that instead of referring to time periods, the figure shows data for Delta and Omicron variants. Only countries with at least 10 observations for Delta and Omicron variants are included. Age-stratified panels are not shown due to the limited number of observations with individual-level variant data.

Appendix 1—figure 11
This figure shows similar information to that presented in Figure 3.

The legend of that figure applies to this figure. Here, the upper threshold frequency used to define Omicron variant dominance was 80% rather than 90%.

Appendix 1—figure 12
This figure shows similar information to that presented in Figure 4.

The legend of that figure applies to this figure. Here, the upper threshold frequency used to define Omicron variant dominance was 80% rather than 90%.

Appendix 1—figure 13
This figure shows similar information to that presented in Appendix 1—figure 3.

The legend of that figure applies to this figure. Here, the upper threshold frequency used to define Omicron variant dominance was 80% rather than 90%.

Appendix 1—figure 14
This figure shows similar information to that presented in Figure 5.

The legend of that figure applies to this figure. Here, the upper threshold frequency used to define Omicron variant dominance was 80% rather than 90%.

Data availability

The data that underpin this analysis are highly detailed clinical data on individuals hospitalised with COVID-19. Due to the sensitive nature of these data and the associated privacy concerns, they are available via a governed data access mechanism following review of a data access committee. Data can be requested via the IDDO COVID-19 Data Sharing Platform (http://www.iddo.org/covid-19). The Data Access Application, Terms of Access and details of the Data Access Committee are available on the website. Briefly, the requirements for access are a request from a qualified researcher working with a legal entity who have a health and/or research remit; a scientifically valid reason for data access which adheres to appropriate ethical principles. The full terms are at https://www.iddo.org/document/covid-19-data-access-guidelines. A small subset of sites who contributed data to this analysis have not agreed to pooled data sharing as above. In the case of requiring access to these data, please contact the corresponding author in the first instance who will look to facilitate access. Code used for statistical analysis and aggregated data used to generate figures are available https://github.com/ISARICDataPlatform/Omicron, (copy archived at swh:1:rev:7ae11685df40e08dc1295bc07658ca5b1cae3265).

References

  1. Book
    1. Lash TL
    2. VanderWeele TJ
    3. Haneause S
    4. Rothman KJ
    (2021)
    Modern Epidemiology
    Lippincott Williams & Wilkins.
  2. Software
    1. R Development Core Team
    (2022) R: a language and environment for statistical computing
    R Foundation for Statistical Computing, Vienna, Austria.

Decision letter

  1. Talía Malagón
    Reviewing Editor; McGill University, Canada
  2. Eduardo Franco
    Senior Editor; McGill University, Canada
  3. Matthew Whitaker
    Reviewer; Imperial College London, United Kingdom

Our editorial process produces two outputs: (i) public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "An international observational study to assess the impact of the Omicron variant emergence on the clinical epidemiology of COVID-19 in hospitalised patients" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by a Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Matthew Whitaker (Reviewer #3).

As is customary in eLife, the reviewers have discussed their critiques with one another. What follows below is the Reviewing Editor's edited compilation of the essential and ancillary points provided by reviewers in their critiques and in their interaction post-review. Please submit a revised version that addresses these concerns directly. Although we expect that you will address these comments in your response letter, we also need to see the corresponding revision clearly marked in the text of the manuscript. Some of the reviewers' comments may seem to be simple queries or challenges that do not prompt revisions to the text. Please keep in mind, however, that readers may have the same perspective as the reviewers. Therefore, it is essential that you attempt to amend or expand the text to clarify the narrative accordingly.

Essential revisions:

1. Please address the various requests for clarifications brought up by reviewers #1 and #2, which will help readers better understand the methods of the paper, especially in regard to inclusion/exclusion criteria and recruitment of patients.

2. Please consider performing a sensitivity analysis restricted to countries with large numbers of cases as suggested by reviewer #2 to assess whether results were impacted by the inclusion of countries with few cases; it is likely that the cases from these countries are highly selected and therefore that their inclusion may have led to a selection bias.

Reviewer #1 (Recommendations for the authors):

• Figure 2: it is not clear to me why there were 12,665 records excluded from before or after the study periods, as to my mind these would have fit the definition of pre- and post- Omicron. Please explain.

• Table S7: please add percentages of patients hospitalized for COVID as well as numbers as these are easier to compare between countries and periods.

• Table S11: This analysis shows the results from a standard Cox model where discharge is treated as a censored observation. However, discharge is informative censoring, as presumably, the risk of death is much lower in a discharged patient. The authors may want to consider performing a competing risk analysis (Fine & Grey model) instead of a Cox model for this sensitivity analysis. The results would be more comparable to the logistic regressions, where discharges are included in the denominators but not the numerators.

• Figure S4: it is not clear how vaccination coverage is defined here (1 dose? 2 doses? 3 doses?)

• The authors may want to comment on the results from individual countries where mortality increased in the Omicron era (ex. India, Brazil, Spain), and the factors that might explain this. Presumably, these results may be due to some confounding by age and clinical profile of patients in both eras.

Reviewer #2 (Recommendations for the authors):

This is a great attempt for collaborative work, and we do know and appreciate how difficult it is to set up such a framework.

However, when reading the paper, it looks as if the project is not mature yet. Patients' enrolment per country is extremely disproportional and discredits the project. Can you just include the countries that sent at least 100 patients's data and name all countries as part of the consortium? countries that have sent very few forms should not be mentioned.

Patients' data sources seem to differ a lot per country, at the end it looks like a patchwork of random data/patients that is being analyzed to see if it could be of any use.

To make the paper clearer I would suggest that you:

1. Clarify the goals:

If the goal is to assist policy makers when a new variant emerges it will always be too late as variants are not introduced in different countries on the same calendar week. For Omicron (a bad example) policy makers reacted within 24h or less after the first announcement of this new variant.

Such collaborative programme could be very useful to identify unique variant characteristics compared to previous variants and identify specific country patterns linked to different variant exposure, different vaccination level, comorbidity rates, access to care etc…such global initiative doesn't exist yet.

2. Clarify which patients are included in the data base. All COVID-19 patients hospitalized, only the ones with COVID-19 as main reason for hospitalization, critical care patients, all patients with respiratory symptoms? How are patients with COVID-19 as the main reason for hospitalization?

3. Standardize the data so that the analysis is not done on different populations for each criteria. Currently symptoms analysis seems to mostly reflect UK patients, vaccination status mostly South Africa patients.

4. Again I would keep the top 8 or 10 countries, the small number of data provided by the other countries introduces confusion and alter the impact of your paper.

Other questions brought up by the reviewer to be addressed by authors (editor's note: originally these questions were in the public review but have been moved to the comments to authors to be addressed):

• The study is presented as a multi-center international study that includes more than 100,000 patients from 30 countries, however, 96.6% of the study patients originated from 2 countries, South Africa (54%) and the United Kingdom (42.6%). Can this still qualify as a multicenter study?

• Country specific medians suggest that the younger age of patients after the Omicron variant experience in the combined dataset is at least partially explained by an increase of data contributed by South Africa. Could the high proportion of South African patients' data have impacted on other measures too?

• Are the patients recruited COVID-19 proven patients? Are incidental cases included or excluded? How was the primary reason for hospitalization identified? Interpretation of mortality rate could be influenced by the recruitment of patients.

Reviewer #3 (Recommendations for the authors):

I only have one specific suggestion for further analysis:

In the Results section on vaccination history, it is implied that more granular detail on vaccination status of patients (ie number of vaccines received) is available but is not used in the models. If this is the case, it might be interesting to see a version of the main model adjusted on number of vaccine doses, even if this is restricted to countries where this data is available.

https://doi.org/10.7554/eLife.80556.sa1

Author response

Essential revisions:

1. Please address the various requests for clarifications brought up by reviewers #1 and #2, which will help readers better understand the methods of the paper, especially in regard to inclusion/exclusion criteria and recruitment of patients.

To address comments from reviewers, we made several changes in the manuscript that clarified methods used and explained implications for the interpretation of results (see answers to comments from reviewers #1 and #2).

2. Please consider performing a sensitivity analysis restricted to countries with large numbers of cases as suggested by reviewer #2 to assess whether results were impacted by the inclusion of countries with few cases; it is likely that the cases from these countries are highly selected and therefore that their inclusion may have led to a selection bias.

In our response to comments from reviewer #2, we have modified the main figures of the manuscript, and only present data from countries with at least 50 observations in each study period. Furthermore, we now report in the Results section a sensitivity analysis using mixed-effects logistic regression that only included data from countries meeting this criterion (see answer to reviewer #2 for estimates from this analysis).

Reviewer #1 (Recommendations for the authors):

• Figure 2: it is not clear to me why there were 12,665 records excluded from before or after the study periods, as to my mind these would have fit the definition of pre- and post- Omicron. Please explain.

The reason why records of patients admitted to hospital more than two months before or after country-level frequencies of the Omicron variant reached the thresholds used in the definition of the study periods were excluded was to improve comparability between patients who were admitted during the pre-Omicron period versus those admitted during the Omicron period. For example, patients admitted to hospital several months before the emergence of the Omicron variant most likely differed from patients hospitalised with the Omicron variant not only with regard to the infecting variant but also unmeasured (potential) confounders (e.g. time since last vaccination dose or since previous infection). In epidemiological terms, our objective in restricting the analytical population to relatively narrow time windows was to increase the plausibility of the exchangeability assumption (Greenland and Robins, Identifiability, Exchangeability, and Epidemiological Confounding. International Journal of Epidemiology 1986; VanderWeele, Rothman, Lash, Confounding and Confounders. in Modern Epidemiology. 4th Edition, 2021). We have now included the following statement in the Methods section to explain why data from these patients were not analysed (here and throughout this document, changes in the manuscript are underlined):

“For each country, the period during which infections were assumed to be caused by other variants ended in the epidemiological week before the Omicron variant relative frequency crossed a low threshold percentage (e.g., 10%) (see Figure 1). The first epidemiological week when Omicron variant frequency, as a proportion of all circulating variants, was higher than a given threshold percentage (90% in analyses presented in the Results section and 80% in sensitivity analyses) was used as the start date of the period during which all admissions were considered to be caused by the Omicron variant. Note (i) that amongst different countries these two study periods started in different calendar weeks, depending on when the Omicron variant was introduced to the location and on the rate of its local spread, and (ii) that in this analysis all Omicron sub-lineages are included (e.g., BA.1.1, BA.2). Only patients admitted to hospital in the two months before country-level Omicron variant frequency reached the lower threshold and those admitted in the first two months after Omicron variant relative frequency reached 90% were included in the primary analysis; the reason for restricting the study population to those admitted during these time windows was to reduce confounding by unmeasured factors whose frequencies in the hospitalised population also changed over time and which might be associated with clinical outcomes.

Based on this comment, we also fit, as a sensitivity analysis, a logistic regression model that included the 12,665 records that were excluded from the primary analysis and obtained similar estimates of the association between study period and fatality risk.

• Table S7: please add percentages of patients hospitalized for COVID as well as numbers as these are easier to compare between countries and periods.

We have now included two columns that show percentages for the two study periods.

• Table S11: This analysis shows the results from a standard Cox model where discharge is treated as a censored observation. However, discharge is informative censoring, as presumably, the risk of death is much lower in a discharged patient. The authors may want to consider performing a competing risk analysis (Fine & Grey model) instead of a Cox model for this sensitivity analysis. The results would be more comparable to the logistic regressions, where discharges are included in the denominators but not the numerators.

We would like to thank the reviewer for this suggestion. We agree that a competing risk analysis might be appropriate in this context. We now present results of a competing risk model, fit using the stcrreg command in Stata (https://www.stata.com/manuals/ststcrreg.pdf) (see below). Results of the two different approaches are presented (Latouche et al. A competing risks analysis should report results on all cause-specific hazards and cumulative incidence functions. Journal of Clinical Epidemiology 2013).

“Table S11. Survival models. Results of a Cox proportional hazards model, stratified by country, on time to death in the first 28 days since hospital admission or onset of symptoms, which happened latest, are shown in the Hazard ratio column. For this analysis, if follow-up duration was longer than 28 days, it was set to 28 days, and patients who were discharged were censored on the day of discharge. The assumption of proportional hazards was violated for the variable on previous vaccination; for this reason, the model was also stratified by this variable. An alternative analysis assumed that patients discharged from hospital were censored on day 28; in this analysis, the hazard ratio for the variable corresponding to study period was 0.68 (0.63 – 0.74); for this model, the proportional hazards assumption did not hold for the study period variable. We also fit a competing risk model, with hospital discharge as competing event; estimates from this model are presented in the Subhazard ratio column. In this model, previous COVID-19 vaccination was included as a covariate (subhazard ratio 0.55, 95% CI 0.52 – 0.59). We also fit a competing risk model using only data from the six countries included in Figures 3 – 5 and that included country as a dummy variable; in this model, the subhazard ratio for the Omicron period variable was 0.68 95% CI (0.63 – 0.74).

• Figure S4: it is not clear how vaccination coverage is defined here (1 dose? 2 doses? 3 doses?)

In Figure S4, vaccination coverage refers to any vaccination, i.e. at least one dose. Below is the updated legend of the figure:

“In addition to information on Omicron variant frequency, each panel also shows data on vaccination: the dashed line shows the proportion of population vaccinated with at least one dose relative to the maximum number vaccinated in each country at the time of the analysis (March 2022).”

• The authors may want to comment on the results from individual countries where mortality increased in the Omicron era (ex. India, Brazil, Spain), and the factors that might explain this. Presumably, these results may be due to some confounding by age and clinical profile of patients in both eras.

We have now modified the following paragraph in the Discussion section to comment on these results:

“… All these factors might have contributed to the observed association, possibly to different degrees in different countries, reason for which this result should not be assumed to necessarily relate to differences in variant virulence. Of note, data from India (see Figure 5) suggest slightly higher fatality risk during the Omicron period compared to the pre-Omicron period for patients older than 60 years, which could be potentially explained by confounding unrelated to age, residual age-related confounding, not controlled by the categorisation used in our analysis, or alternatively by the limited sample size and consequent uncertainty.”

Note that to address a comment from reviewer #2, regarding presenting data from countries with relatively small study sample sizes, estimates for the Brazilian and Spanish study populations were removed from Figure 5.

Reviewer #2 (Recommendations for the authors):

This is a great attempt for collaborative work, and we do know and appreciate how difficult it is to set up such a framework.

However, when reading the paper, it looks as if the project is not mature yet. Patients' enrolment per country is extremely disproportional and discredits the project. Can you just include the countries that sent at least 100 patient's data and name all countries as part of the consortium? countries that have sent very few forms should not be mentioned.

Patients' data sources seem to differ a lot per country, at the end it looks like a patchwork of random data/patients that is being analyzed to see if it could be of any use.

The reason why numbers of records contributed by different countries are so variable is that datasets from both the United Kingdom and South Africa are part of country-wide, geographically representative epidemiological efforts, whilst in some of the other participating countries, a limited number of ISARIC partner centres recruited patients. As mentioned in another comment by this reviewer (see below), a major strength of this research project is its international and collaborative character, and for this reason, it is important to describe the data contribution of all participating countries. Note that the number of records contributed by each partner institution could be influenced by a multitude of factors: local incidence of SARS-CoV-2 infection during the study period, logistical constraints (e.g. number of staff collecting data), and spread of the Omicron variant locally; the latter determined the distribution of records in the two study periods.

To address this comment, we modified Figures 3, 4 and 5 to include only countries with at least 50 observations in each study period (see updated figures below); figures in the Supplementary Appendix presenting country-level data were also updated. Furthermore, in our multivariate analysis of the association between study period and fatality risk, we performed a sensitivity analysis that included this same set of countries; the modified paragraph in the Results section is also presented below.

“In a mixed-effects logistic model on 14-day fatality risk that adjusted for sex, age categories, and vaccination status, hospitalisations during the Omicron period were associated with lower risk of death (see Table 2). The inclusion of common comorbidities in the model did not change the estimated association. Similar results were obtained when using 28-day fatality risk as the outcome. We repeated the 14-day fatality risk analysis excluding patients who reported being admitted to hospital due to a medical condition other than COVID-19; the estimated odds ratio for the association between study period and the outcome was similar to those reported in Table 2. In an additional sensitivity analysis, estimates from a model that only included data from countries with at least 50 records per study period were also similar (OR 0.65, 95% CI 0.61 – 0.69, adjusted for covariates included in model I, Table 2). Survival analysis was also performed, and similar results were obtained (Table S11).”

To make the paper clearer I would suggest that you:

1. Clarify the goals:

If the goal is to assist policy makers when a new variant emerges it will always be too late as variants are not introduced in different countries on the same calendar week. For Omicron (a bad example) policy makers reacted within 24h or less after the first announcement of this new variant.

Such collaborative programme could be very useful to identify unique variant characteristics compared to previous variants and identify specific country patterns linked to different variant exposure, different vaccination level, comorbidity rates, access to care etc…such global initiative doesn't exist yet.

We would like to thank this reviewer for the valuable comments and for highlighting the public health value of our work. Global collaborations are needed when responding to international public health threats, and we believe the work reported in this manuscript will motivate research groups to establish similar initiatives. We modified the final paragraph of the manuscript based on this comment:

“In conclusion, we believe our approach of comparing changes in clinical characteristics of COVID-19 using multi-country standardised data, especially when combined with smaller scale studies that collect individual-level data on infecting variants for validation, will be useful in understanding the impact of new variants in the future. Another application will be in using routinely collected health data for cross-country comparisons of variant characteristics. Equally importantly, the successful conduct of this study, and the lessons learned, including the potential weaknesses discussed above, shows that multi-country efforts to study emerging SARS-CoV-2 variants are feasible, improvable and can generate insights to inform policy decision making.

2. Clarify which patients are included in the data base. All COVID-19 patients hospitalized, only the ones with COVID-19 as main reason for hospitalization, critical care patients, all patients with respiratory symptoms? How are patients with COVID-19 as the main reason for hospitalization?

These questions were addressed in the responses to other comments. Only hospitalised patients with SARS-CoV-2 infection were recruited, and local investigators were responsible for the recruitment procedure. We included the following sentences in the Discussion section:

Another weakness of our study is that recruitment procedure was not standardised and was defined locally. Whilst this likely affected the generalisability of our descriptive estimates (fatality risk and frequencies of symptoms and comorbidities) to local populations of hospitalised COVID-19 cases (Lash and Rothman, Selection Bias and Generalizability. in Modern Epidemiology 4th Edition 2021; Rothman et al. Why representativeness should be avoided. International Journal of Epidemiology 2013), it might not have affected the association between study period and fatality risk, at least not beyond the well-described potential for collider bias in hospital-based studies on COVID-19 outcomes (Griffith et al. Collider bias undermines our understanding of COVID-19 disease risk and severity. Nature Communications 2020).”

3. Standardize the data so that the analysis is not done on different populations for each criteria. Currently symptoms analysis seems to mostly reflect UK patients, vaccination status mostly South Africa patients.

As mentioned in the Results and Discussion sections, most patients in this study were from the United Kingdom and South Africa. In our analyses, we accounted for this by presenting data stratified by country, including in the main figures of the manuscript. Our regression analysis does include data from all study countries, but variation in risk is accounted for by the statistical method used. We included a statement about this:

“The major strength of our study relates to inclusion of data from all WHO geographic regions, collected with standardised forms, with over 100,000 records. However we note that 96.6% of patients were from two countries – South Africa and the United Kingdom – and that the relative contributions of these countries to the study data were different in the two study periods (Table S5); to avoid misinterpretations linked to changes in country-specific contributions to data in the pre-Omicron and Omicron periods we present descriptive analyses by country and use statistical models that adjust for country-level variation. It is also important to consider the relative contributions of these countries when interpreting descriptive analyses that refer to the combined dataset.”

In addition to including this statement in the manuscript, we have also included a short sub-section in the Supplementary Appendix that discusses the frequency of symptoms after excluding data from the United Kingdom:

“Frequency of symptoms outside the United Kingdom and South Africa

Most, 82.5% (N = 579), patients admitted to hospital during the pre-Omicron period outside the United Kingdom and South Africa had at least one symptom; this percentage is lower than the frequency estimated including the United Kingdom data (96.6%), possibly due to the low frequency of symptoms in India (Table S9). The corresponding frequency during the Omicron period was 81.5% (N = 1702).”

4. Again I would keep the top 8 or 10 countries, the small number of data provided by the other countries introduces confusion and alter the impact of your paper.

To address this and another comment from the same reviewer, we have now modified the main figures of the manuscript to include only countries with at least 50 hospitalised patients in each study period. We have also included a sensitivity analysis that estimates odds ratio after excluding countries with limited study data (see above).

Other questions brought up by the reviewer to be addressed by authors (editor's note: originally these questions were in the public review but have been moved to the comments to authors to be addressed):

• The study is presented as a multi-center international study that includes more than 100,000 patients from 30 countries, however, 96.6% of the study patients originated from 2 countries, South Africa (54%) and the United Kingdom (42.6%). Can this still qualify as a multicenter study?

Response to comment

Eight countries contributed at least 100 records during the two study periods, and three additional countries recruited more than 100 patients but due to the timing of the local spread of the Omicron variant some of these patients were not included in the analysis. We believe this justifies referring to this study as an international, or multi-country, epidemiological study. It is also important to note that data from multiple countries, including from the United Kingdom and South Africa, were collected in multiple centres.

• Country specific medians suggest that the younger age of patients after the Omicron variant experience in the combined dataset is at least partially explained by an increase of data contributed by South Africa. Could the high proportion of South African patients' data have impacted on other measures too?

As there is considerable between-country variation in vaccination coverage, we did not report aggregated frequencies for the history of vaccination, and most of the statements in the Results sub-section Vaccination history in hospitalised patients refer to Figure S3 and Table 1, which present data stratified by country. For this reason, we believe that data from South Africa did not impact reporting of the vaccination outcome. Regarding the fatality risk, our multivariate analysis uses random intercepts that account for differences in risk between countries; furthermore, our analysis is adjusted for factors that might vary in distribution between countries, such as age and history of vaccination.

• Are the patients recruited COVID-19 proven patients? Are incidental cases included or excluded? How was the primary reason for hospitalization identified? Interpretation of mortality rate could be influenced by the recruitment of patients.

As described in the Results section, patients with negative PCR test result for SARS-CoV-2 detection were excluded from the analysis. The statement is the following:

“All patients from South Africa, the United Kingdom and Malaysia were assumed to be SARS-CoV-2 positive, as this is one criterion for inclusion in their databases. Of the 2,296 records from other countries, information on SARS-CoV-2 diagnostic testing was available for 1,999 observations; whilst patients with negative PCR test result (N=10) were excluded from the rest of the analysis, those with missing PCR data (N=297) were assumed positive (see Table S5 for distribution by country).”

Furthermore, information on the primary reason for hospitalisation was available for nearly 30,000 patients; ~70% reported that the reason for hospitalisation was COVID-19. In the Discussion section, we mention that incidental infections might have affected our findings, although the inclusion of patients with incidental infections in hospital-based COVID-19 epidemiological studies is common and not specific to our design. The following paragraph was modified:

“One possible explanation for this finding would be if incidental SARS-CoV-2 infections, i.e. infections that were not the primary reason for hospitalisation, were more frequent during the Omicron period; the high transmissibility of this variant, and the consequent peaks in numbers of infections, together with its reported association with lower severity, provides support for this hypothesis. However, in the subset of patients with data on the reason for hospitalisation there was no increase in the proportion of admissions thought to be incidental infections and indeed proportions in both study periods were consistent frequencies of incidental infections in recent studies in the United States (Klann et al. Distinguishing Admissions Specifically for COVID-19 From Incidental SARS-CoV-2 Admissions: National Retrospective Electronic Health Record Study. J Med Internet Res) and the Netherlands (Voor in ’t holt et al. Admissions to a large tertiary care hospital and Omicron BA.1 and BA.2 SARS-CoV-2 polymerase chain reaction positivity: primary, contributing, or incidental COVID-19. International Journal of Infectious Diseases 2022), although in the latter, non-incidental infections included patients for whom COVID-19 was a contributing but not the main cause of hospitalisation.”

Reviewer #3 (Recommendations for the authors):

I only have one specific suggestion for further analysis:

In the Results section on vaccination history, it is implied that more granular detail on vaccination status of patients (ie number of vaccines received) is available but is not used in the models. If this is the case, it might be interesting to see a version of the main model adjusted on number of vaccine doses, even if this is restricted to countries where this data is available.

We performed an analysis that adjusted for the number of vaccine doses received; 19,360 patients, 18.8% of the study population, were included. In the model, also adjusted for sex and age, the odds ratio quantifying the association between study period and fatality risk is 0.72 95% confidence interval (0.63 – 0.82).

https://doi.org/10.7554/eLife.80556.sa2

Article and author information

Author details

  1. Bronner P Gonçalves

    ISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, United Kingdom
    Contribution
    Conceptualization, Software, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing
    For correspondence
    bronner.goncalves@ndm.ox.ac.uk
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3329-6050
  2. Matthew Hall

    Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    Contribution
    Conceptualization, Data curation, Software, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2671-3864
  3. Waasila Jassat

    National Institute for Communicable Diseases, South Africa; Right to Care, Johannesburg, South Africa
    Contribution
    Conceptualization, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  4. Valeria Balan

    ISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, United Kingdom
    Contribution
    Data curation, Investigation, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
  5. Srinivas Murthy

    Faculty of Medicine, University of British Columbia, Vancouver, Canada
    Contribution
    Conceptualization, Investigation, Writing – review and editing
    Competing interests
    declares receiving salary support from the Health Research Foundation and Innovative Medicines Canada Chair in Pandemic Preparedness Research
  6. Christiana Kartsonaki

    MRC Population Health Research Unit, Clinical Trials Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
    Contribution
    Conceptualization, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  7. Malcolm G Semple

    1. Institute of Infection, Veterinary and Ecological Sciences, Faculty of Health and Life Sciences, University of Liverpool, Liverpool, United Kingdom
    2. Respiratory Medicine, Alder Hey Children's Hospital, University of Liverpool, Liverpool, United Kingdom
    Contribution
    Conceptualization, Investigation, Writing – review and editing
    Competing interests
    reports grants from DHSC National Institute of Health Research UK, from the Medical Research Council UK, and from the Health Protection Research Unit in Emerging & Zoonotic Infections, University of Liverpool, supporting the conduct of the study; other interest in Integrum Scientific LLC, Greensboro, NC, USA, outside the submitted work
  8. Amanda Rojek

    1. ISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, United Kingdom
    2. Royal Melbourne Hospital, Melbourne, Australia
    3. Centre for Integrated Critical Care, University of Melbourne, Melbourne, Australia
    Contribution
    Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
  9. Joaquín Baruch

    ISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, United Kingdom
    Contribution
    Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
  10. Luis Felipe Reyes

    1. ISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, United Kingdom
    2. Universidad de La Sabana, Chia, Colombia
    3. Clinica Universidad de La Sabana, Chia, Colombia
    Contribution
    Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  11. Abhishek Dasgupta

    1. Department of Computer Science, University of Oxford, Oxford, United Kingdom
    2. Department of Biology, University of Oxford, Oxford, United Kingdom
    Contribution
    Data curation, Software, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4420-0656
  12. Jake Dunning

    ISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, United Kingdom
    Contribution
    Conceptualization, Investigation, Methodology
    Competing interests
    No competing interests declared
  13. Barbara Wanjiru Citarella

    ISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, United Kingdom
    Contribution
    Data curation, Investigation, Methodology, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
  14. Mark Pritchard

    ISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, United Kingdom
    Contribution
    Conceptualization, Investigation, Methodology
    Competing interests
    No competing interests declared
  15. Alejandro Martín-Quiros

    Emergency Department. Hospital Universitario La Paz – IdiPAZ, Madrid, Spain
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    declares consulting fees for Gilead and MSD, presentation fees for GILEAD, Pfizer and MSD, support for attending ECCMID from Gilead, and advisory board fees for MSD and Gilead
  16. Uluhan Sili

    Department of Infectious Diseases and Clinical Microbiology, School of Medicine, Marmara University, Istanbul, Turkey
    Contribution
    Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9939-9298
  17. J Kenneth Baillie

    1. Roslin Institute, University of Edinburgh, Edinburgh, United Kingdom
    2. Intensive Care Unit, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom
    Contribution
    Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5258-793X
  18. Diptesh Aryal

    Critical Care and Anesthesia, Nepal Mediciti Hospital, Lalitpur, Nepal
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  19. Yaseen Arabi

    King Abdullah International Medical Research Center and King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
    Contribution
    Conceptualization, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  20. Aasiyah Rashan

    Network for Improving Critical care Systems and Training, Colombo, Sri Lanka
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  21. Andrea Angheben

    Department of Infectious, Tropical Diseases and Microbiology (DITM), IRCCS Sacro Cuore Don Calabria Hospital, Negrar di Valpolicella, Verona, Italy
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    declares support from Italian Ministry of Health - "Fondi Ricerca Corrente" Line1 Project 5 to IRCCS Sacro Cuore - Don Calabria Hospital
  22. Janice Caoili

    Makati Medical Center, Makati City, Makati, Philippines
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  23. François Martin Carrier

    1. Department of Anesthesiology, Centre hospitalier de l'Université de Montréal, Montréal, Canada
    2. Department of Medicine, Critical Care Division, Centre hospitalier de l'Université de Montréal, Montréal, Canada
    3. Carrefour de l'innovation et santé des populations, Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada
    4. Department of Anesthesiology and Pain Medicine, Université de Montréal, Montréal, Canada
    Contribution
    Investigation
    Competing interests
    declares a grant from the Canadian Institute of Health Research
  24. Ewen M Harrison

    Centre for Medical Informatics, The University of Edinburgh, Usher Institute of Population Health Sciences and Informatics, Edinburgh, United Kingdom
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  25. Joan Gómez-Junyent

    Department of Infectious Diseases, Hospital del Mar, Infectious Pathology and Antimicrobial Research Group (IPAR), Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Universitat Autònoma de Barcelona (UAB), CEXS-Universitat Pompeu Fabra, Barcelona, Spain
    Contribution
    Investigation
    Competing interests
    declares support by Pfizer, Angelini and MSD to attend meetings (registration to meetings only)
  26. Claudia Figueiredo-Mello

    Instituto de Infectologia Emílio Ribas, São Paulo, Brazil
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  27. James Joshua Douglas

    Lions Gate Hospital, North Vancouver, Canada
    Contribution
    Investigation
    Competing interests
    declares personal fees from lectures from Sunovion and Merck and consulting fees from Pfizer
  28. Mohd Basri Mat Nor

    International Islamic University Malaysia, Selangor, Malaysia
    Contribution
    Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5433-6357
  29. Yock Ping Chow

    Clinical Research Centre, Sunway Medical Centre, Selangor Darul Ehsan, Selangor, Malaysia
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  30. Xin Ci Wong

    Digital Health Research and Innovation Unit, Institute for Clinical Research, National Institutes of Health (NIH), Selangor, Malaysia
    Contribution
    Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1036-8023
  31. Silvia Bertagnolio

    World Health Organization, Genève, Switzerland
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  32. Soe Soe Thwin

    World Health Organization, Genève, Switzerland
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  33. Anca Streinu-Cercel

    1. Carol Davila University of Medicine and Pharmacy, Bucharest, Romania
    2. National Institute for Infectious Diseases "Prof. Dr. Matei Bals", Bucharest, Romania
    Contribution
    Investigation
    Competing interests
    has been an investigator in COVID-19 clinical trials by Algernon Pharmaceuticals, Atea Pharmaceuticals, Regeneron Pharmaceuticals, Diffusion Pharmaceuticals, Celltrion, Inc and Atriva Therapeutics, outside the scope of the submitted work
  34. Leonardo Salazar

    Fundación Cardiovascular de Colombia, Santander, Colombia
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  35. Asgar Rishu

    Department of Critical Care Medicine, Sunnybrook Health Sciences Centre, Toronto, Canada
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  36. Rajavardhan Rangappa

    Department of Critical Care Medicine, Manipal Hospital Whitefield, Bengaluru, India
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  37. David SY Ong

    Department of Medical Microbiology and Infection Control, Franciscus Gasthuis & Vlietland, Rotterdam, Netherlands
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  38. Madiha Hashmi

    Critical Care Asia and Ziauddin University, Karachi, Pakistan
    Contribution
    Conceptualization, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  39. Gail Carson

    ISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, United Kingdom
    Contribution
    Conceptualization, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
  40. Janet Diaz

    World Health Organization, Genève, Switzerland
    Contribution
    Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  41. Rob Fowler

    Department of Critical Care Medicine, Sunnybrook Health Sciences Centre, Toronto, Canada
    Contribution
    Conceptualization, Investigation, Methodology, Writing – review and editing
    Competing interests
    declares a peer reviewed research grant from the Canadian Institutes of Health Research
  42. Moritz UG Kraemer

    1. Department of Biology, University of Oxford, Oxford, United Kingdom
    2. Pandemic Sciences Institute, University of Oxford, Oxford, United Kingdom
    Contribution
    Data curation, Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  43. Evert-Jan Wils

    Department of Intensive Care, Franciscus Gasthuis & Vlietland, Rotterdam, Netherlands
    Contribution
    Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2868-0920
  44. Peter Horby

    ISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, United Kingdom
    Contribution
    Conceptualization, Funding acquisition, Investigation, Methodology, Writing – original draft
    Competing interests
    No competing interests declared
  45. Laura Merson

    1. ISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, United Kingdom
    2. Infectious Diseases Data Observatory, Centre for Tropical Medicine and Global Health, University of Oxford, Oxford, United Kingdom
    Contribution
    Conceptualization, Funding acquisition, Investigation, Writing – original draft
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4168-1960
  46. Piero L Olliaro

    ISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, United Kingdom
    Contribution
    Conceptualization, Funding acquisition, Investigation, Methodology, Writing – original draft
    Competing interests
    No competing interests declared
  47. ISARIC Clinical Characterisation Group

    Contribution
    Funding acquisition
    Competing interests
    See Appendix 1 for competing interests for group author members
    1. Sheryl Ann Abdukahil
    2. Audrey Dubot-Pérès
    3. José Antonio Lepe
    4. Ali Abbas
    5. Kamal Abu Jabal
    6. Nashat Abu Salah
    7. Francisca Adewhajah
    8. Enrico Adriano
    9. Marina Aiello
    10. Kate Ainscough
    11. Eman Al Qasim
    12. Angela Alberti
    13. Beatrice Alex
    14. Abdulrahman Al-Fares
    15. Phoebe Ampaw
    16. Sophia Ankrah
    17. Ardiyan Apriyana
    18. Yaseen Arabi
    19. Antonio Arcadipane
    20. Patrick Archambault
    21. Lukas Arenz
    22. Christel Arnold-Day
    23. Ana Aroca
    24. Rakesh Arora
    25. Diptesh Aryal
    26. Elizabeth A Ashley
    27. AM Udara Lakshan Attanyake
    28. Benjamin Bach
    29. J Kenneth Baillie
    30. Valeria Balan
    31. Irene Bandoh
    32. Renata Barbalho
    33. Wendy S Barclay
    34. Michaela Barnikel
    35. Joaquín Baruch
    36. Diego Fernando Bautista Rincon
    37. Abigail Beane
    38. John Beca
    39. Netta Beer
    40. Husna Begum
    41. David Bellemare
    42. Anna Berenguera
    43. Hazel Bergin
    44. Amar Bhatt
    45. Claudia Bianco
    46. Moirangthem Bikram Singh
    47. Felwa Bin Humaid
    48. Jonathan Bitton
    49. Catherine Blier
    50. Lucille Blumberg
    51. Debby Bogaert
    52. Patrizia Bonelli
    53. Dounia Bouhmani
    54. Thipsavanh Bounphiengsy
    55. Latsaniphone Bountthasavong
    56. Bianca Boxma
    57. Kathy Brickell
    58. Aidan Burrell
    59. Ingrid G Bustos
    60. Eder Caceres
    61. Caterina Caminiti
    62. João Camões
    63. Cecilia Canepa
    64. Janice Caoili
    65. Francesca Carlacci
    66. Gayle Carney
    67. Inês Carqueja
    68. François Martin Carrier
    69. Gail Carson
    70. Silvia Castañeda
    71. Nidyanara Castanheira
    72. Roberta Cavalin
    73. Muge Cevik
    74. Bounthavy Chaleunphon
    75. Adrienne Chan
    76. Meera Chand
    77. Alfredo Antonio Chetta
    78. Julian Chica
    79. Danoy Chommanam
    80. Yock Ping Chow
    81. Nathaniel Christy
    82. Barbara Wanjiru Citarella
    83. Sara Clohisey
    84. Perren J Cobb
    85. Cassidy Codan
    86. Marie Connor
    87. Graham S Cooke
    88. Mary Copland
    89. Amanda Corley
    90. Gloria Crowl
    91. Paula Custodio
    92. Ana da Silva Filipe
    93. Andrew Dagens
    94. Peter Daley
    95. Heidi Dalton
    96. Jo Dalton
    97. Nick Daneman
    98. Emmanuelle A Dankwa
    99. Frédérick D'Aragon
    100. Thushan de Silva
    101. Jillian Deacon
    102. William Dechert
    103. Emmanuelle Denis
    104. Santi Dewayanti
    105. Pathik Dhanger
    106. Yael Dishon
    107. Annemarie B Docherty
    108. Arjen M Dondorp
    109. Maria Donnelly
    110. Christl A Donnelly
    111. Chloe Donohue
    112. Peter Doran
    113. Phouvieng Douangdala
    114. James Joshua Douglas
    115. Joanne Downey
    116. Tom Drake
    117. Murray Dryden
    118. Susanne Dudman
    119. Jake Dunning
    120. Lucian Durham
    121. Anne Margarita Dyrhol-Riise
    122. Marco Echeverria-Villalobos
    123. Giorgio Economopoulos
    124. Michael Edelstein
    125. Martina Escher
    126. Mariano Esperatti
    127. Lorinda Essuman
    128. Amna Faheem
    129. Arabella Fahy
    130. Cameron J Fairfield
    131. Laura Feeney
    132. Carlo Ferrari
    133. Sílvia Ferreira
    134. Claudia Figueiredo-Mello
    135. Juan Fiorda
    136. Tom Fletcher
    137. Brigid Flynn
    138. Federica Fogliazza
    139. Patricia Fontela
    140. Simon Forsyth
    141. Robert A Fowler
    142. Marianne Fraher
    143. Diego Franch-Llasat
    144. John F Fraser
    145. Christophe Fraser
    146. Ana Freitas Ribeiro
    147. Nora Fuentes
    148. G Argin
    149. Sérgio Gaião
    150. Linda Gail Skeie
    151. Phil Gallagher
    152. Carrol Gamble
    153. Julia Garcia-Diaz
    154. Esteban Garcia-Gallo
    155. Federica Garofalo
    156. Jess Gibson
    157. Michelle Girvan
    158. Geraldine Goco
    159. Joan Gómez-Junyent
    160. Bronner P Gonçalves
    161. Alicia Gonzalez
    162. Patricia Gordon
    163. Margarite Grable
    164. Christopher A Green
    165. William Greenhalf
    166. Fiona Griffiths
    167. Anja Grosse Lordemann
    168. Anne-Marie Guerguerian
    169. Daniel Haber
    170. Hannah Habraken
    171. Matthew Hall
    172. Sophie Halpin
    173. Summer Hamza
    174. Rashan Haniffa
    175. Hayley Hardwick
    176. Ewen M Harrison
    177. Janet Harrison
    178. Alan Hartman
    179. Madiha Hashmi
    180. Leanne Hays
    181. Lars Hegelund
    182. Lars Heggelund
    183. Ross Hendry
    184. Liv Hesstvedt
    185. Astarini Hidayah
    186. Rupert Higgins
    187. Samuel Hinton
    188. Antonia Ho
    189. Jan Cato Holter
    190. Peter Horby
    191. Juan Pablo Horcajada
    192. Abby Hurd
    193. Samreen Ijaz
    194. Clare Jackson
    195. Nina Jamieson
    196. Waasila Jassat
    197. Synne Jenum
    198. Philippe Jouvet
    199. Dafsah Juzar
    200. Chris Kandel
    201. Christiana Kartsonaki
    202. Anant Kataria
    203. Kevin Katz
    204. Hannah Keane
    205. Seán Keating
    206. Yvelynne Kelly
    207. Sadie Kelly
    208. Kalynn Kennon
    209. Sharma Keshav
    210. Imrana Khalid
    211. Michelle E Kho
    212. Saye Khoo
    213. Peter Kiiza
    214. Beathe Kiland Granerud
    215. Anders Benjamin Kildal
    216. Anders Kildal
    217. Paul Klenerman
    218. Gry Kloumann Bekken
    219. Stephen R Knight
    220. Robin Kobbe
    221. Paa Kobina Forson
    222. Chamira Kodippily
    223. Franklina Korkor Abebrese
    224. Volkan Korten
    225. Karolina Krawczyk
    226. Deepali Kumar
    227. Demetrios Kutsogiannis
    228. Ama Kwakyewaa Bedu-Addo
    229. François Lamontagne
    230. Marina Lanza
    231. Nicola Latronico
    232. Andy Law
    233. Teresa Lawrence
    234. James Lee
    235. Jennifer Lee
    236. Gary Leeming
    237. Amy Lester-Grant
    238. Andrew Letizia
    239. Gianluigi Li Bassi
    240. Janet Liang
    241. Wei Shen Lim
    242. Andreas Lind
    243. Ruth Lyons
    244. Giuseppe Maglietta
    245. Maria Majori
    246. Paddy Mallon
    247. Patrizia Mammi
    248. Frank Manetta
    249. Ceila Maria Sant Ana Malaque
    250. Daniel Marino
    251. Carlos Cañada Illana
    252. Catherine Marquis
    253. Hannah Marrinan
    254. Laura Marsh
    255. John Marshall
    256. Dori-Ann Martin
    257. Ignacio Martin-Loeches
    258. Alejandro Martin-Quiros
    259. Alejandro Martín-Quiros
    260. Caroline Martins Rego
    261. Gennaro Martucci
    262. Eva Miranda Marwali
    263. David Maslove
    264. Sabina Mason
    265. Henrique Mateus Fernandes
    266. Romans Matulevics
    267. Mayfong Mayxay
    268. Colin McArthur
    269. Anne McCarthy
    270. Rachael McConnochie
    271. Sarah E McDonald
    272. Allison McGeer
    273. Johnny McKeown
    274. Kenneth A McLean
    275. Elaine McPartlan
    276. Edel Meaney
    277. Kusum Menon
    278. Alexander J Mentzer
    279. Laura Merson
    280. Tiziana Meschi
    281. Dan Meyer
    282. Alison M Meynert
    283. Efstathia Mihelis
    284. Elena Molinos
    285. Brenda Molloy
    286. Claudia Montes
    287. Shona C Moore
    288. Sarah Moore
    289. Lina Morales Cely
    290. Caroline Mudara
    291. Fredrik Müller
    292. Karl Erik Müller
    293. Laveena Munshi
    294. Lorna Murphy
    295. Srinivas Murthy
    296. Himed Musaab
    297. Carlotta Mutti
    298. Himasha Muvindi
    299. Mangala Narasimhan
    300. Matthew Nelder
    301. Emily Neumann
    302. Alistair Nichol
    303. Lisa Norman
    304. Mahdad Noursadeghi
    305. Giovanna Occhipinti
    306. Derbrenn OConnor
    307. Katie O'Hearn
    308. Piero L Olliaro
    309. David SY Ong
    310. Wilna Oosthuyzen
    311. Peter Openshaw
    312. Linda O'Shea
    313. Massimo Palmarini
    314. Giovanna Panarello
    315. Prasan Kumar Panda
    316. Hem Paneru
    317. Paolo Parducci
    318. Rachael Parke
    319. Melissa Parker
    320. Laura Patrizi
    321. Lisa Patterson
    322. Mical Paul
    323. William A Paxton
    324. Mare Pejkovska
    325. Luis Periel
    326. Michele Petrovic
    327. Frank Olav Pettersen
    328. Scott Pharand
    329. Ooyanong Phonemixay
    330. Soulichanya Phoutthavong
    331. Roberta Pisi
    332. Riinu Pius
    333. Simone Piva
    334. Georgios Pollakis
    335. Andra-Maris Post
    336. Jeff Powis
    337. Viladeth Praphasiri
    338. Mark G Pritchard
    339. Gamage Dona Dilanthi Priyadarshani
    340. Matteo Puntoni
    341. Vilmaris Quinones-Cardona
    342. Else Quist-Paulsen
    343. Anais Rampello
    344. Rajavardhan Rangappa
    345. Elena Ranza
    346. Aasiyah Rashan
    347. Thalha Rashan
    348. Indrek Rätsep
    349. Cornelius Rau
    350. Francesco Rausa
    351. Brenda Reeve
    352. Liadain Reid
    353. Dag Henrik Reikvam
    354. Jordi Rello
    355. Oleksa Rewa
    356. Luis Felipe Reyes
    357. Asgar Rishu
    358. Maria Angelica Rivera Nuñez
    359. Stephanie Roberts
    360. David L Robertson
    361. Ferran Roche-Campo
    362. Amanda Rojek
    363. Roberto Roncon-Albuquerque
    364. Matteo Rossetti
    365. Sandra Rossi
    366. Clark D Russell
    367. Aleksander Rygh Holten
    368. Luca Sacchelli
    369. Musharaf Sadat
    370. Valla Sahraei
    371. Leonardo Salazar
    372. Kizy Sanchez de Oliveira
    373. Vanessa Sancho-Shimizu
    374. Gyan Sandhu
    375. Zulfiqar Sandhu
    376. Oana Sandulescu
    377. Marlene Santos
    378. Shirley Sarfo-Mensah
    379. Iam Claire E Sarmiento
    380. Sree Satyapriya
    381. Rumaisah Satyawati
    382. Egle Saviciute
    383. Gary Schwartz
    384. Janet T Scott
    385. James Scott-Brown
    386. Malcolm G Semple
    387. Ellen Shadowitz
    388. Shaikh Sharjeel
    389. Catherine A Shaw
    390. Victoria Shaw
    391. Rajesh Mohan Shetty
    392. Haixia Shi
    393. Mohiuddin Shiekh
    394. Sally Shrapnel
    395. Moses Siaw-Frimpong
    396. Bountoy Sibounheuang
    397. Louise Sigfrid
    398. Piret Sillaots
    399. Budha Charan Singh
    400. Pompini Agustina Sitompul
    401. Vegard Skogen
    402. Sue Smith
    403. Michelle Smyth
    404. Tom Solomon
    405. Rima Song
    406. BP Sanka Ruwan Sri Darshana
    407. Shiranee Sriskandan
    408. Stephanie-Susanne Stecher
    409. Trude Steinsvik
    410. Birgitte Stiksrud
    411. Adrian Streinu-Cercel
    412. Anca Streinu-Cercel
    413. David Stuart
    414. Jacky Y Suen
    415. Charlotte Summers
    416. Jaques Sztajnbok
    417. Maria Lawrensia Tampubolon
    418. Richard S Tedder
    419. Hubert Tessier-Grenier
    420. Shaun Thompson
    421. David Thomson
    422. Emma C Thomson
    423. Ryan S Thwaites
    424. Andrea Ticinesi
    425. Paul Tierney
    426. Tirupakuzhi Vijayaraghavan
    427. Kristian Tonby
    428. Rosario Maria Torres Santos-Olmo
    429. Lance CW Turtle
    430. Anders Tveita
    431. PG Ishara Udayanga
    432. Alberto Uribe
    433. Timothy M Uyeki
    434. Ilaria Valzano
    435. Pooja Varghese
    436. Michael Varrone
    437. Sebastian Vencken
    438. James Vickers
    439. José Ernesto Vidal
    440. Judit Villar
    441. Andrea Villoldo
    442. Chiara Vitiello
    443. Manivanh Vongsouvath
    444. Steve Webb
    445. Jia Wei
    446. Sanne Wesselius
    447. Murray Wham
    448. Nicole White
    449. Surya Otto Wijaya
    450. Evert-Jan Wils
    451. Xin Ci Wong
    452. Stephanie Yerkovich
    453. Touxiong Yiaye
    454. Obada Yousif
    455. Saptadi Yuliarto
    456. Maram Zahran
    457. Maria Zambon

Funding

UK Foreign, Commonwealth and Development Office

  • Bronner P Gonçalves
  • Peter Horby
  • Gail Carson
  • Piero L Olliaro
  • Valeria Balan
  • Barbara Wanjiru Citarella
  • Janice Caoili
  • Madiha Hashmi

Wellcome Trust (215091/Z/18/Z)

  • Bronner P Gonçalves
  • Peter Horby
  • Gail Carson
  • Piero L Olliaro
  • Valeria Balan
  • Barbara Wanjiru Citarella

Wellcome Trust (222410/Z/21/Z)

  • Bronner P Gonçalves
  • Peter Horby
  • Gail Carson
  • Piero L Olliaro
  • Valeria Balan
  • Barbara Wanjiru Citarella

Wellcome Trust (225288/Z/22/Z)

  • Bronner P Gonçalves
  • Peter Horby
  • Gail Carson
  • Piero L Olliaro
  • Valeria Balan
  • Barbara Wanjiru Citarella

Wellcome Trust (222048/Z/20/Z)

  • Janice Caoili
  • Madiha Hashmi

Bill and Melinda Gates Foundation (OPP1209135)

  • Peter Horby
  • Gail Carson
  • Piero L Olliaro
  • Joaquín Baruch

University of Oxford's COVID-19 Research Fund (0009146)

  • Laura Merson

Branco Weiss Fellowship

  • Moritz UG Kraemer

Google.org

  • Moritz UG Kraemer

Oxford Martin School, University of Oxford

  • Moritz UG Kraemer

Rockefeller Foundation

  • Moritz UG Kraemer

European Union Horizon 2020 (874850)

  • Moritz UG Kraemer

CIHR Coronavirus Rapid Research Funding Opportunity (OV2170359)

  • François Martin Carrier
  • Srinivas Murthy
  • Asgar Rishu
  • Rob Fowler
  • James Joshua Douglas

Bevordering Onderzoek Franciscus

  • David SY Ong

Italian Ministry of Health "Fondi Ricerca corrente-L1P6"

  • Andrea Angheben

National Institute for Health Research (CO-CIN-01)

  • J Kenneth Baillie
  • Malcolm G Semple
  • Ewen M Harrison

Medical Research Council (MC_PC_19059)

  • J Kenneth Baillie
  • Malcolm G Semple
  • Ewen M Harrison

NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections (200907)

  • J Kenneth Baillie
  • Malcolm G Semple
  • Ewen M Harrison

NIHR HRPU in Respiratory Infections (200927)

  • J Kenneth Baillie
  • Malcolm G Semple
  • Ewen M Harrison

Liverpool Experimental Cancer Medicine Centre (C18616/A25153)

  • Malcolm G Semple

NIHR Biomedical Research Centre (IS-BRC-1215-20013)

  • J Kenneth Baillie
  • Malcolm G Semple
  • Ewen M Harrison

NIHR Clinical Research Network

  • J Kenneth Baillie
  • Malcolm G Semple
  • Ewen M Harrison

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. For the purpose of Open Access, the authors have applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.

Acknowledgements

The investigators acknowledge the philanthropic support of the donors to the University of Oxford’s COVID-19 Research Response Fund; COVID clinical management team, AIIMS, Rishikesh, India; COVID-19 Clinical Management team, Manipal Hospital Whitefield, Bengaluru, India; Italian Ministry of Health “Fondi Ricerca corrente–L1P6” to IRCCS Ospedale Sacro Cuore–Don Calabria; and Preparedness work conducted by the Short Period Incidence Study of Severe Acute Respiratory Infection.

This work uses data provided by patients and collected by the NHS as part of their care and support #DataSavesLives. The data used for this research were obtained from ISARIC4C. We are extremely grateful to the 2648 frontline NHS clinical and research staff and volunteer medical students who collected these data in challenging circumstances; and the generosity of the patients and their families for their individual contributions in these difficult times. We also acknowledge the support of Jeremy J Farrar and Nahoko Shindo.

Ethics

Ethics Committee approval for this work was given by the World Health Organisation Ethics Review Committee (RPC571 and RPC572 on 25 April 2013). Institutional approval was additionally obtained by participating sites including the South Central Oxford C Research Ethics Committee in England (Ref 13/SC/0149) and the Scotland A Research Ethics Committee (Ref 20/SS/0028) for the United Kingdom and the Human Research Ethics Committee (Medical) at the University of the Witwatersrand in South Africa as part of a national surveillance programme (M160667) collectively representing the majority of the data. Other institutional and national approvals are in place as per local requirements.

Senior Editor

  1. Eduardo Franco, McGill University, Canada

Reviewing Editor

  1. Talía Malagón, McGill University, Canada

Reviewer

  1. Matthew Whitaker, Imperial College London, United Kingdom

Publication history

  1. Received: May 25, 2022
  2. Preprint posted: June 22, 2022 (view preprint)
  3. Accepted: September 7, 2022
  4. Version of Record published: October 5, 2022 (version 1)

Copyright

© 2022, Gonçalves et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Bronner P Gonçalves
  2. Matthew Hall
  3. Waasila Jassat
  4. Valeria Balan
  5. Srinivas Murthy
  6. Christiana Kartsonaki
  7. Malcolm G Semple
  8. Amanda Rojek
  9. Joaquín Baruch
  10. Luis Felipe Reyes
  11. Abhishek Dasgupta
  12. Jake Dunning
  13. Barbara Wanjiru Citarella
  14. Mark Pritchard
  15. Alejandro Martín-Quiros
  16. Uluhan Sili
  17. J Kenneth Baillie
  18. Diptesh Aryal
  19. Yaseen Arabi
  20. Aasiyah Rashan
  21. Andrea Angheben
  22. Janice Caoili
  23. François Martin Carrier
  24. Ewen M Harrison
  25. Joan Gómez-Junyent
  26. Claudia Figueiredo-Mello
  27. James Joshua Douglas
  28. Mohd Basri Mat Nor
  29. Yock Ping Chow
  30. Xin Ci Wong
  31. Silvia Bertagnolio
  32. Soe Soe Thwin
  33. Anca Streinu-Cercel
  34. Leonardo Salazar
  35. Asgar Rishu
  36. Rajavardhan Rangappa
  37. David SY Ong
  38. Madiha Hashmi
  39. Gail Carson
  40. Janet Diaz
  41. Rob Fowler
  42. Moritz UG Kraemer
  43. Evert-Jan Wils
  44. Peter Horby
  45. Laura Merson
  46. Piero L Olliaro
  47. ISARIC Clinical Characterisation Group
(2022)
An international observational study to assess the impact of the Omicron variant emergence on the clinical epidemiology of COVID-19 in hospitalised patients
eLife 11:e80556.
https://doi.org/10.7554/eLife.80556
  1. Further reading

Further reading

    1. Epidemiology and Global Health
    2. Immunology and Inflammation
    James A Hay, Stephen M Kissler ... Yonatan H Grad
    Research Article Updated

    Background:

    The combined impact of immunity and SARS-CoV-2 variants on viral kinetics during infections has been unclear.

    Methods:

    We characterized 1,280 infections from the National Basketball Association occupational health cohort identified between June 2020 and January 2022 using serial RT-qPCR testing. Logistic regression and semi-mechanistic viral RNA kinetics models were used to quantify the effect of age, variant, symptom status, infection history, vaccination status and antibody titer to the founder SARS-CoV-2 strain on the duration of potential infectiousness and overall viral kinetics. The frequency of viral rebounds was quantified under multiple cycle threshold (Ct) value-based definitions.

    Results:

    Among individuals detected partway through their infection, 51.0% (95% credible interval [CrI]: 48.3–53.6%) remained potentially infectious (Ct <30) 5 days post detection, with small differences across variants and vaccination status. Only seven viral rebounds (0.7%; N=999) were observed, with rebound defined as 3+days with Ct <30 following an initial clearance of 3+days with Ct ≥30. High antibody titers against the founder SARS-CoV-2 strain predicted lower peak viral loads and shorter durations of infection. Among Omicron BA.1 infections, boosted individuals had lower pre-booster antibody titers and longer clearance times than non-boosted individuals.

    Conclusions:

    SARS-CoV-2 viral kinetics are partly determined by immunity and variant but dominated by individual-level variation. Since booster vaccination protects against infection, longer clearance times for BA.1-infected, boosted individuals may reflect a less effective immune response, more common in older individuals, that increases infection risk and reduces viral RNA clearance rate. The shifting landscape of viral kinetics underscores the need for continued monitoring to optimize isolation policies and to contextualize the health impacts of therapeutics and vaccines.

    Funding:

    Supported in part by CDC contract #200-2016-91779, a sponsored research agreement to Yale University from the National Basketball Association contract #21-003529, and the National Basketball Players Association.

    1. Epidemiology and Global Health
    2. Microbiology and Infectious Disease
    Paul Tupper, Shraddha Pai ... Caroline Colijn
    Research Article Updated

    The role of schools in the spread of SARS-CoV-2 is controversial, with some claiming they are an important driver of the pandemic and others arguing that transmission in schools is negligible. School cluster reports that have been collected in various jurisdictions are a source of data about transmission in schools. These reports consist of the name of a school, a date, and the number of students known to be infected. We provide a simple model for the frequency and size of clusters in this data, based on random arrivals of index cases at schools who then infect their classmates with a highly variable rate, fitting the overdispersion evident in the data. We fit our model to reports from four Canadian provinces, providing estimates of mean and dispersion for cluster size, as well as the distribution of the instantaneous transmission parameter β, whilst factoring in imperfect ascertainment. According to our model with parameters estimated from the data, in all four provinces (i) more than 65% of non-index cases occur in the 20% largest clusters, and (ii) reducing instantaneous transmission rate and the number of contacts a student has at any given time are effective in reducing the total number of cases, whereas strict bubbling (keeping contacts consistent over time) does not contribute much to reduce cluster sizes. We predict strict bubbling to be more valuable in scenarios with substantially higher transmission rates.