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.

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.

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).

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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.

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. 4^th 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 4^th 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).

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

   "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

   "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

    "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

    "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

    "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

    "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

    "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

    "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

    "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
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