All-cause mortality according to COVID-19 vaccination status: An analysis of the UK office for National statistics public data

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However, assessment of its real-world effect can be performed by analysis of all-cause mortality by vaccination status. The UK is perhaps the only country which has made publicly available all-cause mortality data by vaccination status. Methods Data from April 2021 to May 2023 published by the UK Office for National Statistics (ONS) were retrospectively analyzed by age groups and vaccination status; the standardized mortality ratio (SMR) for all-cause and non-COVID-19 mortality was calculated against the corresponding unvaccinated groups. Results We found that across all age groups, all-cause mortality SMRs increased from a certain date, dependent on the age group. Across all age groups, all-cause mortality SMRs were initially much lower than 1. However, due to their increase, by a certain date for the 18-39, 80-89 and 90+ age groups they exceeded the reference value. For the other age groups, the date at which the SMR would reach 1 can be predicted, provided the trend is maintained. Non-COVID-19 SMRs’ trends were very similar. Their initial values much lower than 1 are suggestive of significant biases in the ONS dataset, leading to underestimate the risks for the vaccinated people, as it is implausible that COVID-19 vaccines protect against non-COVID-19 deaths. Conclusions The increase over time in all-cause death SMRs in vaccinated people compared to unvaccinated, and their excess from the reference values for certain age groups, should be carefully considered to understand the underlying factors. Furthermore, since the initial values of the SMRs are much lower than 1, we assume the presence of significant biases in the ONS dataset, leading to understimate the risks for the vaccinated people, as it is implausible that COVID-19 vaccines protect against non-COVID-19 deaths. It would be desirable for other major countries to systematically collect all-cause mortality by vaccination status and, in the meantime, a pending indepth investigations, much greater caution should be exercised in promoting mass vaccination campaigns. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/13-886", "name": "All-cause mortality according to COVID-19 vaccination status: An analysis..." } } ] } Home Browse All-cause mortality according to COVID-19 vaccination status: An analysis... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Alessandria M, Malatesta G, Di Palmo G et al. All-cause mortality according to COVID-19 vaccination status: An analysis of the UK office for National statistics public data [version 2; peer review: 2 approved] . F1000Research 2025, 13 :886 ( https://doi.org/10.12688/f1000research.154058.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Revised All-cause mortality according to COVID-19 vaccination status: An analysis of the UK office for National statistics public data [version 2; peer review: 2 approved] Marco Alessandria 1 , Giovanni Malatesta 2 , Giovanni Di Palmo 3 , Marco Cosentino https://orcid.org/0000-0002-6978-7775 4 , Alberto Donzelli https://orcid.org/0000-0002-4365-6814 5 Marco Alessandria 1 , Giovanni Malatesta 2 , [...] Giovanni Di Palmo 3 , Marco Cosentino https://orcid.org/0000-0002-6978-7775 4 , Alberto Donzelli https://orcid.org/0000-0002-4365-6814 5 PUBLISHED 20 Feb 2025 Author details Author details 1 Department of Life Sciences and Systems Biology, University of Turin, Torino, Italy 2 Physics Graduate, Member of the Scientific Committee of the Fondazione Allineare Sanità e Salute, Pistoia, Italy 3 B.E., Data Analysis Specialist, Taranto, Italy 4 Center for Research in Medical Pharmacology, University of Insubria, Varese, Italy 5 MD, Specialist in Hygiene and Preventive Medicine, independent Medical-Scientific Commission; President, Fondazione Allineare Sanità e Salute, Milano, Italy Marco Alessandria Roles: Conceptualization, Formal Analysis, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Giovanni Malatesta Roles: Conceptualization, Formal Analysis, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Giovanni Di Palmo Roles: Conceptualization, Formal Analysis, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Marco Cosentino Roles: Conceptualization, Formal Analysis, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Alberto Donzelli Roles: Conceptualization, Formal Analysis, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract Background The mass vaccination campaign against COVID-19 has been commonly considered the best response to the global COVID-19 pandemic crisis. However, assessment of its real-world effect can be performed by analysis of all-cause mortality by vaccination status. The UK is perhaps the only country which has made publicly available all-cause mortality data by vaccination status. Methods Data from April 2021 to May 2023 published by the UK Office for National Statistics (ONS) were retrospectively analyzed by age groups and vaccination status; the standardized mortality ratio (SMR) for all-cause and non-COVID-19 mortality was calculated against the corresponding unvaccinated groups. Results We found that across all age groups, all-cause mortality SMRs increased from a certain date, dependent on the age group. Across all age groups, all-cause mortality SMRs were initially much lower than 1. However, due to their increase, by a certain date for the 18-39, 80-89 and 90+ age groups they exceeded the reference value. For the other age groups, the date at which the SMR would reach 1 can be predicted, provided the trend is maintained. Non-COVID-19 SMRs’ trends were very similar. Their initial values much lower than 1 are suggestive of significant biases in the ONS dataset, leading to underestimate the risks for the vaccinated people, as it is implausible that COVID-19 vaccines protect against non-COVID-19 deaths. Conclusions The increase over time in all-cause death SMRs in vaccinated people compared to unvaccinated, and their excess from the reference values for certain age groups, should be carefully considered to understand the underlying factors. Furthermore, since the initial values of the SMRs are much lower than 1, we assume the presence of significant biases in the ONS dataset, leading to understimate the risks for the vaccinated people, as it is implausible that COVID-19 vaccines protect against non-COVID-19 deaths. It would be desirable for other major countries to systematically collect all-cause mortality by vaccination status and, in the meantime, a pending indepth investigations, much greater caution should be exercised in promoting mass vaccination campaigns. READ ALL READ LESS Keywords COVID-19; COVID-19 vaccinations; all-cause mortality; Standardized Mortality Ratio Corresponding Author(s) Alberto Donzelli ( [email protected] ) Close Corresponding author: Alberto Donzelli Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2025 Alessandria M et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Alessandria M, Malatesta G, Di Palmo G et al. All-cause mortality according to COVID-19 vaccination status: An analysis of the UK office for National statistics public data [version 2; peer review: 2 approved] . F1000Research 2025, 13 :886 ( https://doi.org/10.12688/f1000research.154058.2 ) First published: 05 Aug 2024, 13 :886 ( https://doi.org/10.12688/f1000research.154058.1 ) Latest published: 20 Feb 2025, 13 :886 ( https://doi.org/10.12688/f1000research.154058.2 ) Revised Amendments from Version 1 The new version has integrated or better explained the text based on the reviewers' requests, with a partial restructuring of the Abstract, the insertion of a new important bibliographic reference, a support from data of the ONS itself for the illustration of the concept of healthy-vaccinee bias, a paragraph on limitations, and a better formulation of the conclusions. The new version has integrated or better explained the text based on the reviewers' requests, with a partial restructuring of the Abstract, the insertion of a new important bibliographic reference, a support from data of the ONS itself for the illustration of the concept of healthy-vaccinee bias, a paragraph on limitations, and a better formulation of the conclusions. See the authors' detailed response to the review by Stefan Pilz See the authors' detailed response to the review by Jarle Aarstad READ REVIEWER RESPONSES 1. Introduction Due to the COVID-19 pandemic crisis and the subsequent COVID-19 mass vaccination campaign, the interest has hugely soared in publicly available data on all-cause mortality. For example, data from England and Wales show in 2022, in comparison to the previous average five-year reference period, an excess mortality with a trend driven by more deaths than expected starting in March 2022. 1 From April 2021 to the end of May 2023 (the period covered by the ONS dataset) the total excess mortality amounted to 129,801 deaths above the five-year average. 2 A similar trend occurs in many other countries in the European Union (EU), as indicated by the graphs and maps provided by the European Mortality Monitoring Project (EuroMoMo), a routine public health mortality monitoring system aimed at detecting and measuring excess deaths related to public health threats across EU countries. According to EuroMoMo, the excess deaths in 2022 were 328,047 in 2022 and 305,301 in 2021 ( Graphs and maps — EUROMOMO , accessed May 1, 2024). This is clearly an anomaly, as previous mortality shocks over the past 120 years have almost always been followed by immediate rebounds back, in one to two years, 3 with normalization of mortality risk. A similar observation has also been made 4 about excess mortality across 47 countries in the Western World since the COVID-19 Pandemic, based on ‘Our World in Data’ estimates of January 2020 to December 2022. Indeed, excess mortality was registered in 87% of countries in 2020, in 89% in 2021 and in 91% in 2022. During 2021, when not only containment measures but also COVID-19 vaccines were used to tackle virus spread and infection, the highest number of excess deaths was recorded. 4 England and Wales benefit from one of the best public health data collection systems in the world, and are therefore uniquely positioned to monitor and investigate the above-mentioned phenomenon. 1 Moreover, the Office for National Statistics (ONS) of the United Kingdom (UK) has published all-cause mortality data in England, 5 stratified according to COVID-19 vaccination status, thus overcoming the intrinsic limitation of just identifying deaths due to COVID-19, as for instance happens so far in Italy and in most if not all EU countries, and allowing a direct assessment of the eventual consequences of COVID-19 vaccination for individual as well as public health in terms of change not only of COVID-19 mortality but also of all-cause mortality. In addition, the UK vaccinated more than 50% of its eligible population in the first four months of 2021 and by the end of 2021, 77% received at least one dose, 6 thus exceeding the aforementioned threshold earlier than most of the other EU countries. It is therefore possible that the trends observed in England and Wales anticipate what will later occur in EU. We decided therefore to analyze the ONS public data on all-cause mortality according to vaccination status, starting from the rates already officially provided by the ONS itself on its website. 5 We calculated the rate ratios RR by vaccination status for every age group. Furthermore, due to month-to-month variation in the populations of individual vaccination status, we decided to calculate Standardized Mortality Ratios (SMRs) for those vaccinated with any dose in the different age groups, and to evaluate any potential emerging trends over time. A previous version of this study, dealing with UK ONS data from January to May 2021, is available on Preprints.org . 7 2. Methods In this retrospective study we collected data from the UK ONS web-based platform. 5 This platform gathers total mortality data by vaccination status from April, 2021 until May, 2023. Data are publicly available under the Open Government license ( https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/ , accessed on 12 February 2024), and can therefore be freely analyzed and published provided that the source is properly acknowledged. Relying on the excel file provided by this platform, we utilized data from the spreadsheet named “Table 2”(extended data), inasmuch as, differently from other spreadsheets, it provides proper stratification by age and vaccination status to perform an estimate of the Standardization Mortality Rate (SMR) and Relative Risks (RR) for the All-causes death and Non-COVID-19 deaths variable. We could not consider Deaths involving COVID-19 inasmuch from the UK ONS dataset, the absolute frequency of deaths in many vaccination statuses and many age group indicated for this variable was <3 mostly for the younger age groups and for many months of the year 2023. We were therefore unable to reliably calculate the RRs and SMRs. The spreadsheet used for this analysis provides seven age groups (18–39, 40–49, 50–59, 60–69, 70–79, 80–89, 90+ years) and each age group is further subdivided into several classes based on vaccination status: • Unvaccinated, • First dose less than 21 days before (1D<21d), • First dose at least 21 days before (1D≥21d), • Second dose less than 21 days before (2D<21d), • Second dose at least 21 days before (2D≥21d), • Third dose or booster less than 21 days before (3D<21d), • Third dose or booster at least 21 days before (3D≥21d). Even if the ONS marks with a “u” (unreliable) any rates arising from a number of death lower than 20, nonetheless we decided to consider groups with a minimum of 10 deaths. Though being aware that the lower the number of deaths, the greater the uncertainty on both the rates and the RRs, this choice allowed us to identify trends of RR over time for each age group. Given the extremely variable nature of the RR trend over time, we decided to understand if this phenomenon was related to the distribution of populations between the various vaccination statuses and for each month of observation. In this regard, we created stacked graphs, in order to analyze the population distributions of the vaccination status for each age group and for each month of the entire observation period, inserting on the y-axis the person-years and on the x-axis the observation months (Supplementary materials, Tables S1-S7(extended data) and Figures S1A/B-S7A/B)((extended data)). From the stacked graphs, a dynamic distribution emerged over the entire observation period for all vaccination statuses. Furthermore, for all age groups was observed an almost constant distribution of the Unvaccinated population over period, unlike the 18-39 and 40-49 age groups where, for the first months of the observation, these groups were more representative compared to other vaccination status. Based on these observations, in order to manage the dynamic distribution of the vaccination statuses month per month, we decided to calculate the SMRs for each observation month. From the calculation of the SMRs boxes indicating <3 deaths were excluded. Finally, for the 18-39, 40-49 age groups we decided do not consider the first six months and the first three months respectively while for the 50-59 we did not consider the first month, in order to compare a roughly constant distribution over time of the Unvaccinated population with the Vaccinated population and obtain a reliable estimate of the SMRs. This decision was made considering a percentage variation of no more than 1% between months in the unvaccinated population. Subsequently, we investigated the relation between SMRs and observation months applying a simple regression model and using SMRs as dependent variable and the observation months as independent variable. Finally, we calculated the intersection of the regression line with the reference line for the unvaccinated (y=1) to identify where possible, or predict where not, the moment in which deaths from all causes in the vaccinated group exceed those of the unvaccinated. 2.1 Statistical analysis To calculate the relative risk (RR) between the vaccinated and unvaccinated populations, we used the age-standardized rates indicated in the excel files provided by the UK ONS. 5 Their 95% confidence intervals (CI) were calculated according to the following formula 8 , 9 : CI 95 ( RR ) = e [ ln ( RR ) ± 1.96 ∗ SE ln ( RR ) ] , where “ln (RR)” is the natural logarithm of the Relative Risk and “SE ln(RR) ” is the standard error of the natural logarithm of the RR. The SE ln(RR) was calculated for each vaccination status of each of the age groups according to the formula: (1) SEln ( RR ) = ( V . Pop . − Stand . D . Exp . V . Pop . ∗ Stand . D . Exp . ) + ( Un . Pop . − Stand . D . Exp . Un . Pop . ∗ Stand . D . Exp . ) , Where, for each year, “V.Pop.” represents the vaccinated population, “Un.Pop.” represents the unvaccinated population, and “Stand.D.Exp.” represents the expected standardized deaths, that is, the deaths that would occur by applying the Age-standardised mortality rates per 100,000 person-years (Note 1, spreadsheet “Note” UK ONS web-based platform 6 ) to the real population, calculated according to the formula: Expected Standardized Deaths = Age − standardized rate ∗ Population of each vaccine status 100.000 . The choice to use the “Expected Standardized Deaths” is justified by the fact that the calculated RR expresses the ratio between two standardized rates based on the European population. The P value was calculated according to Altman and Bland 10 : p = exp ( − 0.717 × z − 0.416 × z 2 ) where z = ln ( RR ) SE and SE is the (1) . To calculate SMR indirect standardization method was applied according to Naing (2000). 11 Before to use the simple regression model, all the assumption of the model was verified: scatter plot was created to verify the linear relationship between variables, Shapiro-Wilk normality test was used to verify the residual distributions and Breusch-Pagan test was used to verify homoscedasticity of the variance of errors. To calculate the intersection of the regression line with the reference line of the unvaccinated for each age group we indicated the observation months with a progressive number and solved the equation of the regression line for x and assigning y=1. The x value obtained was compared with the numbers assigned of the observation months so that we could identify a specific moment in the observation period. Data was processed using R studio (version 2023.09.0). 3. Results 3.1 About ONS dataset The UK ONS dataset we investigated is the latest version available. 5 It is based on the population in Census 2021, linking Census deidentified records to National Health Service (NHS) numbers. People with no NHS number or multiple entries are not included. The individuals were then linked via NHS number to vaccination data from the National Immunisation Management Service (NIMS) and ONS death registrations. The population was restricted to people in England, alive on 1 April 2021. Overall, ONS dataset population (51,786,812 people) covers 91.6% of the England population on Census Day 2021. The excluded population therefore amount to almost 4,600,000 people. Furthermore, 103,142 were excluded due to erroneous or inconsistent vaccination data, so the overall excluded population amounts to almost 4,700,000 people. Finally, of the 1,149,784 deaths that occurred in England between 1 April 2021 and 31 May 2023, 90.6% (1,041,524) could be linked to individuals in the 2021 Census. 3.2 Population distributions The stacked graphs showed a dynamic distribution over all observation period whose percentages and absolute frequencies are reported in Supplementary Material (Tables S1-S7 and Figures S1A/B–S7A/B) (Extended data). 39 3.3 Rate Ratios (RR) All-causes mortality rate ratios RR according to vaccination status are shown in Supplementary, Tables S8-S14(Extended data) 39 and Figures S8-S14(Extended data). 39 Similarly, RRs for non-COVID19 related deaths are shown in Tables S15-S21(Extended data) 39 and Figures S15-S21(Extended data). 39 Main results for both mortality causes are summarized below. Deaths from all causes : In all age groups, those vaccinated with the first dose at least 21 days ago have a significantly higher risk of death from all causes than those not vaccinated in almost all months of the entire period, except for the 18-39 age group in which the RRs are significatively higher than 1 in half the months. The average RR values in all age groups are between 1.7 and 2.3, except for the 18-39 years group where the average is 1.5. In the age groups 18-39, 60-69, and older RRs present initial peaks higher than 3, up to a maximum of 5.5 in the 70-79 age group. As regards those vaccinated with 2 doses at least 21 days ago, in the age groups starting from 60 years, the risk of death from all causes in the initial months is much lower than the unvaccinated, with a tendency to increase. Since around a third of the entire period (between October and December 2021) RRs significantly exceeds the reference value, remaining higher in almost all the remaining months, although not significatively in the last few months for 60-69 and 90+ years age groups. The RRs for those vaccinated with three doses at least 21 days ago, for the age groups 60-69 years and older, present a growth trend which, starting from values much lower than one, reaches and exceeds the reference value: • for the 60-69 years age group, the risk of death significantly exceeds that of the unvaccinated in the months of November and December 2022 and remains in the following months not significantly different from that of the unvaccinated; • for the 70-79 years age group the RR exceeds the reference value in June 2022 and always remains significantly higher, apart from the months of September 2022 and May 2023 where the values are not significative; • for the 80-89 years age group the RR exceeds the reference value in April 2022, reaches the maximum value (RR = 2.29, CI 95 = 2.04-2.58) and then stabilizes on values always significantly higher than 1; • for vaccinated people aged 90 years and over, the reference value is exceeded in April 2022 (RR = 1.13, CI 95 = 1.02-1.26), then remaining at values always significantly higher than 1, with a maximum of 1.85 in November 2022. Deaths non correlated to COVID19 The rate ratios (RR) from non-Covid causes follow the trends already seen for deaths from all causes, with slightly higher values. Therefore, the above considerations can be repeated. 3.4 Standardized mortality ratios Age groups The results of all regression models performed for the all-causes deaths and non-COVID19 deaths variable and for each age group are summarized in Table 1 . Furthermore, in table 1 are indicated the intersection of the regression line with the reference line of the unvaccinated also in the graphs where is not possible to visualize the intersection. Table 1. Coefficient of determination (R2) of the regression analysis of SMRs and observation months and p-value of the regression line for each age group and for the All-causes death and Non-COVID-19 deaths variable; intersection: intersection of the regression line with the reference line for the unvaccinated (y=1). Age group Variables R 2 p-value Intersection 18-39 All-causes death 0.601 <0.0001 January, 2023 Non-COVID-19 deaths 0.500 0.0005 January, 2023 40-49 All-causes death 0.712 <0.0001 September, 2023 Non-COVID-19 deaths 0.463 0.0003 April, 2023 50-59 All-causes death 0.734 <0.0001 July, 2024 Non-COVID-19 deaths 0.609 <0.0001 January, 2025 60-69 All-causes death 0.847 <0.0001 February, 2024 Non-COVID-19 deaths 0.745 <0.0001 May, 2024 70-79 All-causes death 0.860 <0.0001 January, 2024 Non-COVID-19 deaths 0.764 <0.0001 May, 2024 80-89 All-causes death 0.784 <0.0001 April, 2023 Non-COVID-19 deaths 0.706 <0.0001 January, 2023 90+ All-causes death 0.695 <0.0001 September, 2022 Non-COVID-19 deaths 0.705 <0.0001 May, 2022 The regression model for 18-39 age group showed coefficient of determination R 2 =0.601 and a p-value <0.0001 for the All-causes death variable. For the non-COVID19 deaths regression model showed a R 2 =0.500 and a p-value=0.0005 ( Figure 1 ). Both variables showed the intersection of the regression line in January 2023. Figure 1. Regression line of the SMR trend of the 18-39 age group. The regression model for 40-49 age group showed coefficient of determination R 2 =0.712 and a p-value <0.0001 for the All-causes death variable. For the non-COVID19 deaths regression model showed a R 2 =0.463 and a p-value=0.0003 ( Figure 2 ). The All-causes death variables showed the intersection of the regression line in September 2023 while the non-COVID19 deaths showed the intersection of the regression line in April 2023. Figure 2. Regression line of the SMR trend of the 40-49 age group. The regression model for 50-59 age group showed coefficient of determination R 2 =0.734 and a p-value <0.0001 for the All-causes death variable. For the non-COVID19 deaths regression model showed a R 2 =0.609 and a p-value<0.0001 ( Figure 3 ). The All-causes death variables showed the intersection of the regression line in July 2023 while the non-COVID19 deaths showed the intersection of the regression line in January 2025. Figure 3. Regression line of the SMR trend of the 50-59 age group. The regression model for 60-69 age group showed coefficient of determination R 2 =0.847 and a p-value <0.0001 for the All-causes death variable. For the non-COVID19 deaths regression model showed a R 2 =0.745 and a p-value<0.0001 ( Figure 4 ). The All-causes death variables showed the intersection of the regression line in February 2024 while the non-COVID19 deaths showed the intersection of the regression line in May 2024. Figure 4. Regression line of the SMR trend of the 60-69 age group. The regression model for 70-79 age group showed coefficient of determination R 2 =0.860 and a p-value <0.0001 for the All-causes death variable. For the non-COVID19 deaths regression model showed a R 2 =0.764 and a p-value<0.0001 ( Figure 5 ). The All-causes death variables showed the intersection of the regression line in January 2024 while the non-COVID19 deaths showed the intersection of the regression line in May 2024. Figure 5. Regression line of the SMR trend of the 70-79 age group. The regression model for 80-89 age group showed coefficient of determination R 2 =0.784 and a p-value <0.0001 for the All-causes death variable. For the non-COVID19 deaths regression model showed a R 2 =0.706 and a p-value<0.0001 ( Figure 6 ). The All-causes death variables showed the intersection of the regression line in April 2023 while the non-COVID19 deaths showed the intersection of the regression line in January 2023. Figure 6. Regression line of the SMR trend of the 80-89 age group. The regression model for 90+ age group showed coefficient of determination R 2 =0.695 and a p-value <0.0001 for the All-causes death variable. For the non-COVID19 deaths regression model showed a R 2 =0.705 and a p-value<0.0001 ( Figure 7 ). The All-causes death variables showed the intersection of the regression line in September 2022 while the non-COVID19 deaths showed the intersection of the regression line in May 2022. Figure 7. Regression line of the SMR trend of the 90+ age group. 4. Discussion In the present study, we analyzed the UK ONS data on all-cause mortality according to vaccination status, which are publicly available on the ONS institutional website. 5 The main findings of our analysis are: (a) compared with unvaccinated, vaccinated with one or two doses show, in the period April 2021-May 2023, a substantially higher risk of all-causes and non-COVID-19 deaths . Indeed, for both causes people vaccinated with one dose show a RR significatively higher than 1 through almost the whole period and in any of the age groups, except the 18-39 years, in which it is significatively higher in half the period. In people who received the second dose, from 60 to 90+ years of age the risk of all-causes and non-COVID-19 death is significatively higher than in unvaccinated people through about the final two thirds of the period. It should be noted that from 50 to 90+ years of age the RRs of all-causes and non-COVID-19 death have implausibly low initial values. (b) Also vaccinated with three doses have incredibly low initial values of RR both for all-causes and for non-COVID-19 deaths. Their RRs, for people aged 60-69 and older progressively grow up to reach and exceed the reference value from 60 years of age, and from 70 years and over the RRs remain significantly greater than 1. (c) A linear growth trend is revealed by regression analysis of Standardized Mortality Ratios of people vaccinated with any dose compared to the unvaccinated across all age groups, for both all-cause and non-COVID-19 deaths. The regression lines, both for all-cause deaths and for non-COVID-19 ones, start from very low values for all the age groups. For the age groups 18-39, 80-89 and 90+ years both regression lines intersect the reference line of unvaccinated during the study period. The same occurs for the age 50-59, limited to non-COVID-19 deaths, For the other age groups, we have predicted the month of the intersection (see Table 1 ). The results found for the RRs of the first and second doses are confirmed in other studies, e.g. in two studies carried out in an Italian province. 12 , 13 Both studies show a significantly higher risk of death from all causes for those vaccinated with one and two doses compared to the unvaccinated. Furthermore, they show an implausible high protection of the vaccine against deaths from all causes, too high to be attributed to protection from deaths from COVID-19, which are a minority percentage of total deaths. Both studies, however, are affected by important biases. The main one is the so-called Immortal Time Bias (ITB), as highlighted in an intervention published in an Italian epidemiology journal. 14 After correction of ITB, the unlikely protection provided by the third dose against death from all causes disappears entirely. Another recent study, 15 was based on the same dataset kindly provided by the authors of the article, 13 but corrected for the ITB. In its multivariable analysis, this study shows higher all-cause death hazard ratios (HRs) for individuals vaccinated with one and two doses compared to unvaccinated, and no protection against all-cause deaths for population vaccinated with 3 or more doses. Furthermore, the study 15 found a small but statistically significant reduction in life expectancy for vaccinated people with two and three or more doses The present study also shows extremely low initial risks of all-causes and nonCOVID-19 death, as well in the analysis of the RRs for those vaccinated with two and three doses, as in that of the SMRs for those vaccinated with any dose compared to the unvaccinated. These results appear difficult to justify, especially when referring to non-COVID-19 deaths, unless admitting the presence of some important selection bias. In fact, if the populations being compared were homogeneous, the risk difference of death from causes other than COVID-19 should be about zero, and both the RRs and SMRs for non-COVID deaths should not differ significantly from 1. Unfortunately, there is a lack of information on the health status of populations or about other factors that influence the risk of death, so we can only formulate some hypotheses, that are not necessarily alternative. 4.1 Underestimation of the unvaccinated population One hypothesis might be an underestimation of the unvaccinated population in the ONS dataset. We have seen that the population included in this dataset does not cover the entire population of England recorded in the 2021 census. The population left out of the dataset is around 4,700,000 people. There would be a selection bias with systematic effects throughout the period if this proportion of the total population were not equally distributed between vaccinated and unvaccinated, and if it included a greater proportion of unvaccinated. We can see from the last two ONS reports that, although they cover a larger population than previous reports, the criteria for inclusion (and therefore exclusion) are the same as those based on the previous census. They are therefore subject to the same limitations. The main one is that the excluded population is not randomly selected, and therefore the population covered by the dataset is not representative of the general population. The key to inclusion is having a National Health Service (NHS) number. The probability of a vaccinated person not having an NHS number is virtually zero, because without a number you cannot be vaccinated. However, this is not the case for the unvaccinated, some of whom may not have been registered with a General Practitioner (GP) and therefore do not have an NHS number. As deaths among the unvaccinated are certified in the same way as those among the vaccinated, they cannot escape ONS registration. This would result in a relative overestimation of mortality rates for the unvaccinated and consequently an underestimation of RRs and SMRs for the vaccinated. 4.2 The healthy-vaccinee bias Another possible contributory hypotheses is the so called healthy-vaccinee bias. 16 – 19 In 2021 a lower mortality among the vaccinated can also be explained partly by the healthy-adherer effect, or the healthy-vaccinee bias in the vaccination field. This effect is much more powerful than commonly thought: in fact, voluntary adherence to a treatment can be associated with a nearly halved mortality, 20 – 22 and even with a mortality reduction of 2.5 to 3 or more times 16 , 18 , 23 – 25 compared to the mortality of those who do not adhere. This effect is independent of the type of treatment to which one adheres voluntarily, being also found in randomized controlled trials in the placebo adherers (compared with placebo non-adherers). This effect may have several explanations. In the short time, individuals contingently ill tend to postpone vaccination. 25 , 26 In addition, doctors can renounce to vaccinate people considered close to death, whose subsequent death burdens the unvaccinated cohort disproportionately. However, the healthy-adherer effect can be detected over many years, 16 , 18 , 20 , 23 because subjects adhering to preventive treatments are usually at the same time more likely to engage in healthy lifestyles than patients not adhering. 17 , 21 A healthy lifestyle includes diet, exercise, lower tobacco and alcohol consumption, less risky behaviors, 27 and the search for better health care. These features—difficult to capture in administrative databases—are associated with morbidity and mortality outcomes in observational studies. Moreover, the trust in the intervention to which one adheres can exert a beneficial placebo effect. The healthy-adherer bias is more difficult to correct than the opposite effect of confounding by indication (subjects in worse health conditions are vaccinated first), which is relatively easier to correct, provided it is known e.g. the number of comorbidities, or the Charlson comorbidity index of the groups to be compared. 28 Unfortunately, the UK ONS public data do not include any information about comorbidities. It is likely that the healthy-vaccinee bias effect will continue to operate to varying degrees in 2022 and 2023, albeit to a diminishing extent during periods in which vaccination mandates have been in force. Moreover, it is plausible that the increase in the number of the vaccinated has diluted the opposite effect of confounding by indication. 28 A commonly used argument is the better known confounding by indication effect 28 : it is likely that fragile subjects with multiple diseases have been vaccinated as a priority, followed by the others. However, as the vaccination campaigns proceed, the composition of the vaccinated and unvaccinated populations should result less unbalanced with respect to the pre-existing state of health. The ONS declare that “Changes in non-COVID-19 mortality by vaccination status are largely driven by the changing composition of the vaccination status groups. This is because of the priority given to clinically extremely vulnerable people or with underlying health conditions, and differences in timing of vaccination among eligible people". 3 However, a priority was also given to the healthier population of health workers. Moreover, the most fragile part of the population prioritized for the vaccination is a smaller portion (especially in the younger ages), and the composition of each age group progressively tends to be similar to that of the unvaccinated, in terms of general health conditions. Therefore, a decreasing trend would be expected, both because of the decreasing weight of the fragile fraction compared to the overall group and because of the harvesting effect, described below. The healthy-vaccinee/un-healthy-un-vaccinee bias might be further supported by the web page by the Office for National Statistics, 31 showing that unvaccinated have higher tendency: to live in more deprived areas, urban areas, or social rented housing, to be not born in the UK or do not have English as a main language, to have never worked or to be long-term unemployed, to be more limited by a disability, and to be male (more men die than women). 31 4.3 The harvesting effect The RRs of the first doses generally show high initial mortality peaks, possibly linked to the priority given to the fragile subjects. The early death of the most fragile causes that those who move on to the second dose are healthier overall. It is unfortunate that the last two ONS datasets do not provide data for the first three months of 2021, corresponding to the start of the vaccination programme. In fact, since vaccinations began with the older classes, in April 2021 the latter had not only completed the vaccination with the first dose, but had already started the second. Therefore our hypothesis can be confirmed only in the age groups 18-39 and 40-49, where one can clearly perceive the initial mortality peak of the first doses, probably already decreasing, followed by the initial mortality peak of the second doses, starting from lower values and with a lower maximum compared to that of the first doses. As regards the third doses, the initial mortality peak disappears in all age groups, suggesting that many of the most “fragile” people have already died, and that a ‘healthy-vaccinee effect’ might partly explain the initial very low RR values. Examining the SMR graphs, we note that, apart from the elderly, the points relating to all-cause deaths and non-COVID-19 deaths tend to overlap over time. This may indicate that the impact of COVID-19-related deaths vanishes, and that the risk of all-cause death and of deaths not related to COVID-19 is nearly the same. In fact, in the ONS dataset, approaching the end of the observation period, the COVID-19-deaths for the different vaccination statuses show an ever-increasing number of values indicated with <3, what prevents one from calculating both the RRs and the SMRs. This justifies the choice not to take into consideration COVID-19 deaths, but only all-cause and non-COVID-19 deaths. Hence, the insistent push towards further vaccinations seems hardly motivated. 4.4 Loss of protective vaccine effectiveness and lower lethality of new variants Again, examining the SMRs, the fact that initially the regression line of non-COVID-19 deaths is above that of all-cause deaths might indicate that the vaccine initially has a protective effect on COVID-19 deaths, thus lowering the risk of all-cause deaths, that include those COVID-19 related, among vaccinated people. The fact that they subsequently converge may indicate either that the vaccine gradually loses its protective effectiveness, or that the risk of COVID-19 deaths decreases due to the increasingly lower lethality of the new variants, or that the two causes act together. The SMR graphs allow one to make a further consideration: in the 80-89 age group the convergence of the lines of all-cause deaths is much attenuated, and in the 90+ age group the lines are almost parallel, as one can see from the regression coefficients that differ less and less. This might indicate that COVID-19 still represents a risk for the elderly and that the vaccine therefore protects them by reducing the risk of all-cause deaths. Yet, it might also be due to the fact that the lower lethality of the new variants is offset by the fact that the vaccinated people get infected more than unvaccinated 29 – 36 and that by taking additional doses they are temporarily protected from the risk of dying from COVID-19. 4.5 Unintended effects of COVID-19 vaccines on the increasing deaths Last but not least: why are the SMRs of non-COVID-19 related deaths increasing? Why should the risk of those vaccinated with any dose increase compared to that of the unvaccinated? Apart from the risk of immediate adverse reactions/events, the doubt naturally arises that the vaccine , might cause damage to the immune system, exposing the vaccinated to a greater risk of death from pathologies non-COVID-19 related 37 , 38 5. Limitation Although this study shows an increase in SMR for all causes of death in vaccinated compared to unvaccinated individuals over time, we acknowledge some limitations. First, the lack of individual data published by the UK Office for National Statistics (ONS), which would have allowed us to determine the exact time at which each subject transitioned to a different vaccination status, did not allow us to use a different statistical approach that could provide more robust measures of association. Second, the extremely low baseline risk for all causes of death and death from non-COVID-19 is likely due to differences in the representativeness of comorbidity between groups, which the UK ONS does not provide (and likely does not possess to a large extent: see healthy-vaccinee bias). Therefore, it was not possible to adjust the estimation of the model for these covariates. 6. Conclusions The English all-cause and non-COVID-19 mortality data by vaccination status, released by the UK ONS for the 26 months from April 2021 to May 2023, were analyzed by age group and vaccination status. Our findings show that all-cause deaths SMRs were increasing in any of the age groups considered. All-cause death SMRs, initially well below 1 for every age group, due to their increase, since a certain date exceeded the reference value of the unvaccinated people for the age groups 18-39, 80-89 and 90+. For the other age groups, it is possible to predict the date in which the SMR would reach the value 1, intersecting the unvaccinated level, provided that this trend is consistently maintained. Non-COVID-19 SMR values show a very similar trend: initially they are much lower than 1, but it is not plausible such a vaccine protection from non-COVID-19 deaths. Therefore, this suggests significant biases in the ONS dataset, leading to an underestimation of the risks for the vaccinated. Regardless of the interpretative hypotheses, the fact that all-cause mortality SMRs in vaccinated increase over time compared to those of unvaccinated requires further, urgent investigation. In any case, we hope that the ONS will resume the publication of the mortality data series by vaccination status, interrupted in May 2023, and that its example will be followed by other countries. Moreover, the precautionary principle should suggest much greater caution in promoting extensive vaccination campaigns, pending the acquisition of valid explanations of the alarming phenomenon observed. Institutional review board statement Not applicable. Informed consent statement Not applicable. Author contributions Conceptualization, M.A., G.M., G.D.P., M.C. and A.D.; methodology, formal analysis and writing—original draft preparation, M.A., G.M., G.D.P., M.C. and A.D.; writing—review and editing, M.A., G.M., G.D.P., M.C. and A.D. All authors have read and agreed to the published version of the manuscript. Disclaimer/publisher’s note The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. Data availability statement The data presented in this study are openly available on the UK ONS web page entitled “Deaths by vaccination status, England”, available online: https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/deathsbyvaccinationstatusengland (accessed on 1 May 2024). Extended data Zenodo: Supplementary Materials , https://doi.org/10.5281/zenodo.13080361 39 This project contains the following extended data: • Supplementary Tables 1-7 (population).xlsx • Supplementary Tables 15-21 (mortality non-Covid deaths).xlsx • Supplementary Tables 8-14 (mortality all causes).xlsx • Supplemetary figure.pptx • Table 2 Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Acknowledgement Dr. Chiara Giove, for administrative support References 1. Hussain Z: England and Wales have seen rise in excess deaths in 2022. BMJ. 2022; 378 : o2283. Publisher Full Text 2. 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Berrino F, Donzelli A, Bellavite P, et al. : COVID-19 vaccination and all-cause and non-COVID-19 mortality. A revaluation of a study carried out in an Italian Province. Epidemiol. Prev. 2023 Nov-Dec; 47 (6): 374–378. English. PubMed Abstract | Publisher Full Text 15. Alessandria M, Malatesta GM, Berrino F, et al. : A Critical Analysis of All-Cause Deaths during COVID-19 Vaccination in an Italian Province. Microorganisms. 2024; 12 : 1343. PubMed Abstract | Publisher Full Text | Free Full Text 16. Pinsky PF, Miller A, Kramer BS, et al. : Evidence of a healthy volunteer effect in the prostate, lung, colorectal, and ovarian cancer screening trial. Am. J. Epidemiol. 2007; 165 : 874–881. PubMed Abstract | Publisher Full Text 17. Dormuth CR, Patrick AR, Shrank WH: Statin adherence and risk of accidents: a cautionary tale. Circulation. 2009; 119 : 2051–2057. PubMed Abstract | Publisher Full Text | Free Full Text 18. 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Chemaitelly H, Ayoub HH, Tang P, et al. : Long-term COVID-19 booster effectiveness by infection history and clinical vulnerability and immune imprinting: a retrospective population-based cohort study. Lancet Infect. Dis. 2023 Jul; 23 (7): 816–827. PubMed Abstract | Publisher Full Text | Free Full Text 32. Tamandjou C, Auvigne V, Schaeffer J, et al. : Effectiveness of second booster compared to first booster and protection conferred by previous SARS-CoV-2 infection against symptomatic Omicron BA.2 and BA.4/5 in France. Vaccine. 2023 Apr 24; 41 (17): 2754–2760. PubMed Abstract | Publisher Full Text | Free Full Text 33. Shrestha NK, Burke PC, Nowacki AS, et al. : Effectiveness of the Coronavirus Disease 2019 Bivalent Vaccine. Open Forum Infect. Dis. 2023 Apr 19; 10 (6): ofad209. PubMed Abstract | Publisher Full Text | Free Full Text 34. Qassim SH, Chemaitelly H, Ayoub HH, et al. : Population immunity of natural infection, primary-series vaccination, and booster vaccination in Qatar during the COVID-19 pandemic: an observational study. EClinicalMedicine. 2023 Jul 20; 62 : 102102. PubMed Abstract | Publisher Full Text | Free Full Text 35. Chalupka A, Richter L, Chakeri A, et al. : Effectiveness of a fourth SARS-CoV-2 vaccine dose in previously infected individuals from Austria. Eur. J. Clin. Investig. 2024 Mar; 54 (3): e14136. PubMed Abstract | Publisher Full Text 36. Shrestha NK, Burke PC, Nowacki AS, et al. : Risk of Coronavirus Disease 2019 (COVID-19) among those up-to-date and not up-to-date on COVID-19 vaccination by US CDC criteria. PLoS One. 2023 Nov 8; 18 (11): e0293449. PubMed Abstract | Publisher Full Text | Free Full Text 37. Wang H, Xu Q, Zhao C, et al. : An immune evasion mechanism with IgG4 playing an essential role in cancer and implication for immunotherapy. J. Immunother. Cancer. 2020 Aug; 8 (2): e000661. PubMed Abstract | Publisher Full Text | Free Full Text 38. Uversky VN, Redwan EM, Makis W, et al. : IgG4 Antibodies Induced by Repeated Vaccination May Generate Immune Tolerance to the SARS-CoV-2 Spike Protein. Vaccines (Basel). 2023 May 17; 11 (5): 991. PubMed Abstract | Publisher Full Text | Free Full Text 39. Alessandria M: Supplementary material. Zenodo. 2024. Publisher Full Text Comments on this article Comments (1) Version 2 VERSION 2 PUBLISHED 20 Feb 2025 Revised Reader Comment 09 Aug 2025 Betty Oostenbrink , Chemistry, Avans University of Applied Science - Tilburg, Tilburg, The Netherlands 09 Aug 2025 Reader Comment Given the high concentration factor (Nvacc/Nunvacc) in older age groups—exceeding 40—it is not meaningful that the SMR ratio is below 1. In younger age groups, the concentration factor is also ... Continue reading Given the high concentration factor (Nvacc/Nunvacc) in older age groups—exceeding 40—it is not meaningful that the SMR ratio is below 1. In younger age groups, the concentration factor is also above 1: 5 (18–39 years), 9 (40–49 years), 15 (50–59 years), and 18 (60–69 years). Therefore, the focus should be on the difference between non-COVID and COVID SMR ratios. The fact that the SMR ratio is below 1 has no special meaning with these high concentration factors. Analysis of the differences in both SMR ratio reveals that the vaccine’s effect takes time to manifest: 18–39 years: Effect observed from the 6th month (no prior data). 40–49 years: Effect observed from the 4th month (no prior data). 50–59 years: Clear difference from the 4th to 5th month. 60–69 and 70–79 years: Effect from the 3rd month. 80–89 years: Effect from the 4th month. 90+ years: Effect from the 4th or 5th month. For individuals aged 60 and older, the vaccine appears effective shortly after the second dose. In younger age groups, efficacy is observed after a single dose. Moreover, comparing COVID and non-COVID mortality suggests no justification for additional vaccination rounds: For younger age groups (18–39, 40–49, and 50–59), the vaccine’s effect disappeared after approximately 14 months. For all younger age groups ((18–39, 40–49, and 50–59, 60-69): the initial reduction in non-COVID mortality among vaccinated individuals disappeared after the third dose.The third dose either had no impact or counteracted the effects of the first two doses, as the percentage of COVID deaths remained unchanged. In the 70-79 age groups the effect almost disappeared after the 3rd vaccination but not completely. In the 80–89 and 90+ age groups, a difference between non-COVID and COVID mortality persists, but it diminishes after the third dose. Based on these findings, the third and fourth vaccine doses appear ineffective for those under 70. Furthermore, the necessity of a third dose remains unclear, as the differences in mortality outcomes decrease and by comparing non-COVID and COVID mortality SMR no visible decline in difference between the two in time was visible before the 3rd vaccination started. Given the high concentration factor (Nvacc/Nunvacc) in older age groups—exceeding 40—it is not meaningful that the SMR ratio is below 1. In younger age groups, the concentration factor is also above 1: 5 (18–39 years), 9 (40–49 years), 15 (50–59 years), and 18 (60–69 years). Therefore, the focus should be on the difference between non-COVID and COVID SMR ratios. The fact that the SMR ratio is below 1 has no special meaning with these high concentration factors. Analysis of the differences in both SMR ratio reveals that the vaccine’s effect takes time to manifest: 18–39 years: Effect observed from the 6th month (no prior data). 40–49 years: Effect observed from the 4th month (no prior data). 50–59 years: Clear difference from the 4th to 5th month. 60–69 and 70–79 years: Effect from the 3rd month. 80–89 years: Effect from the 4th month. 90+ years: Effect from the 4th or 5th month. For individuals aged 60 and older, the vaccine appears effective shortly after the second dose. In younger age groups, efficacy is observed after a single dose. Moreover, comparing COVID and non-COVID mortality suggests no justification for additional vaccination rounds: For younger age groups (18–39, 40–49, and 50–59), the vaccine’s effect disappeared after approximately 14 months. For all younger age groups ((18–39, 40–49, and 50–59, 60-69): the initial reduction in non-COVID mortality among vaccinated individuals disappeared after the third dose.The third dose either had no impact or counteracted the effects of the first two doses, as the percentage of COVID deaths remained unchanged. In the 70-79 age groups the effect almost disappeared after the 3rd vaccination but not completely. In the 80–89 and 90+ age groups, a difference between non-COVID and COVID mortality persists, but it diminishes after the third dose. Based on these findings, the third and fourth vaccine doses appear ineffective for those under 70. Furthermore, the necessity of a third dose remains unclear, as the differences in mortality outcomes decrease and by comparing non-COVID and COVID mortality SMR no visible decline in difference between the two in time was visible before the 3rd vaccination started. Competing Interests: No competing interests were disclosed. Close Report a concern Comment ADD YOUR COMMENT Author details Author details 1 Department of Life Sciences and Systems Biology, University of Turin, Torino, Italy 2 Physics Graduate, Member of the Scientific Committee of the Fondazione Allineare Sanità e Salute, Pistoia, Italy 3 B.E., Data Analysis Specialist, Taranto, Italy 4 Center for Research in Medical Pharmacology, University of Insubria, Varese, Italy 5 MD, Specialist in Hygiene and Preventive Medicine, independent Medical-Scientific Commission; President, Fondazione Allineare Sanità e Salute, Milano, Italy Marco Alessandria Roles: Conceptualization, Formal Analysis, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Giovanni Malatesta Roles: Conceptualization, Formal Analysis, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Giovanni Di Palmo Roles: Conceptualization, Formal Analysis, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Marco Cosentino Roles: Conceptualization, Formal Analysis, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Alberto Donzelli Roles: Conceptualization, Formal Analysis, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (2) version 2 Revised Published: 20 Feb 2025, 13:886 https://doi.org/10.12688/f1000research.154058.2 version 1 Published: 05 Aug 2024, 13:886 https://doi.org/10.12688/f1000research.154058.1 Copyright © 2025 Alessandria M et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Alessandria M, Malatesta G, Di Palmo G et al. All-cause mortality according to COVID-19 vaccination status: An analysis of the UK office for National statistics public data [version 2; peer review: 2 approved] . F1000Research 2025, 13 :886 ( https://doi.org/10.12688/f1000research.154058.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 2 VERSION 2 PUBLISHED 20 Feb 2025 Revised Views 0 Cite How to cite this report: Aarstad J. Reviewer Report For: All-cause mortality according to COVID-19 vaccination status: An analysis of the UK office for National statistics public data [version 2; peer review: 2 approved] . F1000Research 2025, 13 :886 ( https://doi.org/10.5256/f1000research.177063.r367845 ) The direct URL for this report is: https://f1000research.com/articles/13-886/v2#referee-response-367845 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 27 Feb 2025 Jarle Aarstad , HVL Business School, Western Norway University of Applied Science, Bergen, Norway Approved VIEWS 0 https://doi.org/10.5256/f1000research.177063.r367845 Dear authors, I now understand better the issue of standardized mortality ... Continue reading READ ALL Dear authors, I now understand better the issue of standardized mortality ratio (SMR) among vaccinated against the corresponding unvaccinated groups and find the paper acceptable for indexing. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Applied statistical analysis. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Aarstad J. Reviewer Report For: All-cause mortality according to COVID-19 vaccination status: An analysis of the UK office for National statistics public data [version 2; peer review: 2 approved] . F1000Research 2025, 13 :886 ( https://doi.org/10.5256/f1000research.177063.r367845 ) The direct URL for this report is: https://f1000research.com/articles/13-886/v2#referee-response-367845 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 05 Aug 2024 Views 0 Cite How to cite this report: Pilz S. Reviewer Report For: All-cause mortality according to COVID-19 vaccination status: An analysis of the UK office for National statistics public data [version 2; peer review: 2 approved] . F1000Research 2025, 13 :886 ( https://doi.org/10.5256/f1000research.169038.r342218 ) The direct URL for this report is: https://f1000research.com/articles/13-886/v1#referee-response-342218 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 07 Jan 2025 Stefan Pilz , Medical University of Graz, Graz, Austria Approved VIEWS 0 https://doi.org/10.5256/f1000research.169038.r342218 This is an important retrospective study on the mortality rate according to vaccination status against SARS-CoV-2 in the UK. Based on publicly available national health data the authors show that mortality risk is, in general (for most time periods and ... Continue reading READ ALL This is an important retrospective study on the mortality rate according to vaccination status against SARS-CoV-2 in the UK. Based on publicly available national health data the authors show that mortality risk is, in general (for most time periods and age groups analyzed), higher in vaccinated versus unvaccinated individuals. SMRs in vaccinated individuals are lower than one in early periods of their observation period, but progressively increase over time and exceed one in several age groups suggesting that relative mortality risk progressively increases over time after vaccination. The discussion of these findings is well-written and includes several interesting views on potential biases in their findings. Overall, this is an interesting and important paper. I have only minor comments. The authors should aim to carefully explain very clearly the underlying analyses of their Tables and Figures so that they can also stand alone: e.g. in Table 1 it may not be clear for the reader that this is a regression analysis of SMRs and observation months (this has only been described in the text). The discussion is great, but a brief overall limitations section may improve the quality of the paper (e.g., retrospective study design, only age group data but no individual participant data, etc.). Several limitations have been nicely discussed but I would nevertheless suggest such a brief limitations section. The authors may check whether this article meets all the requirements of the STROBE guidelines. I do not request a STROBE guideline Table as a Supplement although this may be considered. The authors should carefully re-check the spelling and grammar of this article as there are some slight improvements required. E.g., Page 10, second paragraph, second line from the bottom “the risk of death” should be corrected to “the risk difference of death…”. In addition, some parts of the article may be difficult to read and understand for the reader. Consider improving this. In the Abstract the authors write that “the best way can be the analysis of all-cause mortality by vaccination”. I would rather be cautious to state “the best way” and re-phrase this to something like” Assessment of its real-world effect can be performed by analysis of all-cause mortality by vaccination status”. In the results section of the Abstract the last sentence is rather a discussion of the data than a results presentation. Consider revising and/or removing some parts to the discussion of the Abstract. Regarding data analyses and discussion it would be great (if possible depending on statistical power) to focus also on groups with vaccinations less than 21 days before, as for these groups hardly any (or only minimal) vaccine effectiveness on COVID-19 mortality can be assumed so that any mortality difference may well reflect some sort of bias. No problem if the authors are not willing to address this. Regarding the overall conclusions I would suggest being more cautious regarding strong statements (such as a moratorium on promoting mass vaccination campaigns should be implemented) as the findings are only observational and require further confirmation and more in-depth investigations at best with inclusion of more potential confounders and IPD. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Epidemiology and clinical research in endocrinology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Pilz S. Reviewer Report For: All-cause mortality according to COVID-19 vaccination status: An analysis of the UK office for National statistics public data [version 2; peer review: 2 approved] . F1000Research 2025, 13 :886 ( https://doi.org/10.5256/f1000research.169038.r342218 ) The direct URL for this report is: https://f1000research.com/articles/13-886/v1#referee-response-342218 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 20 Feb 2025 Alberto Donzelli , Fondazione "Allineare Sanità e Salute", Italy 20 Feb 2025 Author Response Dear Dr. Stefan Pilz, we would like to thank you for your suggestions. Although you have kindly indicated that the acceptance of your suggestions is at our discretion, we have ... Continue reading Dear Dr. Stefan Pilz, we would like to thank you for your suggestions. Although you have kindly indicated that the acceptance of your suggestions is at our discretion, we have decided willingly to include them in the text, because we believe they are useful for improving the article This is an important retrospective study on the mortality rate according to vaccination status against SARS-CoV-2 in the UK. Based on publicly available national health data the authors show that mortality risk is, in general (for most time periods and age groups analyzed), higher in vaccinated versus unvaccinated individuals. SMRs in vaccinated individuals are lower than one in early periods of their observation period, but progressively increase over time and exceed one in several age groups suggesting that relative mortality risk progressively increases over time after vaccination. The discussion of these findings is well-written and includes several interesting views on potential biases in their findings. Overall, this is an interesting and important paper. I have only minor comments. Q . The authors should aim to carefully explain very clearly the underlying analyses of their Tables and Figures so that they can also stand alone: e.g. in Table 1 it may not be clear for the reader that this is a regression analysis of SMRs and observation months (this has only been described in the text). Author’s response : Thank you for this advice. We have added a sentence that specifics this aspect. Q . The discussion is great, but a brief overall limitations section may improve the quality of the paper (e.g., retrospective study design, only age group data but no individual participant data, etc.). Several limitations have been nicely discussed but I would nevertheless suggest such a brief limitations section. Author’s response: Thank you for this advice. We have added the “Limitation” section. Q . The authors may check whether this article meets all the requirements of the STROBE guidelines. I do not request a STROBE guideline Table as a Supplement although this may be considered. Author’s response : Before submitting our paper, we checked the STROBE guidelines, but the most part of the items of the "STROBE Statements" are not applicable in our paper as we show a critical analysis of the data collected and provided by the ONS and the data analyzed was not collected by us. In the "Methods" section we described in detail what and how we used the information contained in the ONS dataset, but we cannot know how the ONS collected this data. Q . The authors should carefully re-check the spelling and grammar of this article as there are some slight improvements required. E.g., Page 10, second paragraph, second line from the bottom “the risk of death” should be corrected to “the risk difference of death…”. In addition, some parts of the article may be difficult to read and understand for the reader. Consider improving this. Author’s respons e : We inserted your suggestion. Q . In the Abstract the authors write that “the best way can be the analysis of all-cause mortality by vaccination”. I would rather be cautious to state “the best way” and re-phrase this to something like” Assessment of its real-world effect can be performed by analysis of all-cause mortality by vaccination status”. Author’s response : You are right, we have reworded the sentence according to your suggestion Q . In the results section of the Abstract the last sentence is rather a discussion of the data than a results presentation. Consider revising and/or removing some parts to the discussion of the Abstract. Author’s response : Thank you, we decided to revise and move this sentence in “Conclusion” section of the abstract. Q . Regarding data analyses and discussion it would be great (if possible depending on statistical power) to focus also on groups with vaccinations less than 21 days before, as for these groups hardly any (or only minimal) vaccine effectiveness on COVID-19 mortality can be assumed so that any mortality difference may well reflect some sort of bias. Author’s response : In the data analysis phase we also considered groups with vaccinations less than 21 days before, but it was not possible to perform any calculations as most of the boxes indicated <3 deaths. No problem if the authors are not willing to address this. Q . Regarding the overall conclusions I would suggest being more cautious regarding strong statements (such as a moratorium on promoting mass vaccination campaigns should be implemented) as the findings are only observational and require further confirmation and more in-depth investigations at best with inclusion of more potential confounders and IPD. Author’s response : Thank you, we did it Dear Dr. Stefan Pilz, we would like to thank you for your suggestions. Although you have kindly indicated that the acceptance of your suggestions is at our discretion, we have decided willingly to include them in the text, because we believe they are useful for improving the article This is an important retrospective study on the mortality rate according to vaccination status against SARS-CoV-2 in the UK. Based on publicly available national health data the authors show that mortality risk is, in general (for most time periods and age groups analyzed), higher in vaccinated versus unvaccinated individuals. SMRs in vaccinated individuals are lower than one in early periods of their observation period, but progressively increase over time and exceed one in several age groups suggesting that relative mortality risk progressively increases over time after vaccination. The discussion of these findings is well-written and includes several interesting views on potential biases in their findings. Overall, this is an interesting and important paper. I have only minor comments. Q . The authors should aim to carefully explain very clearly the underlying analyses of their Tables and Figures so that they can also stand alone: e.g. in Table 1 it may not be clear for the reader that this is a regression analysis of SMRs and observation months (this has only been described in the text). Author’s response : Thank you for this advice. We have added a sentence that specifics this aspect. Q . The discussion is great, but a brief overall limitations section may improve the quality of the paper (e.g., retrospective study design, only age group data but no individual participant data, etc.). Several limitations have been nicely discussed but I would nevertheless suggest such a brief limitations section. Author’s response: Thank you for this advice. We have added the “Limitation” section. Q . The authors may check whether this article meets all the requirements of the STROBE guidelines. I do not request a STROBE guideline Table as a Supplement although this may be considered. Author’s response : Before submitting our paper, we checked the STROBE guidelines, but the most part of the items of the "STROBE Statements" are not applicable in our paper as we show a critical analysis of the data collected and provided by the ONS and the data analyzed was not collected by us. In the "Methods" section we described in detail what and how we used the information contained in the ONS dataset, but we cannot know how the ONS collected this data. Q . The authors should carefully re-check the spelling and grammar of this article as there are some slight improvements required. E.g., Page 10, second paragraph, second line from the bottom “the risk of death” should be corrected to “the risk difference of death…”. In addition, some parts of the article may be difficult to read and understand for the reader. Consider improving this. Author’s respons e : We inserted your suggestion. Q . In the Abstract the authors write that “the best way can be the analysis of all-cause mortality by vaccination”. I would rather be cautious to state “the best way” and re-phrase this to something like” Assessment of its real-world effect can be performed by analysis of all-cause mortality by vaccination status”. Author’s response : You are right, we have reworded the sentence according to your suggestion Q . In the results section of the Abstract the last sentence is rather a discussion of the data than a results presentation. Consider revising and/or removing some parts to the discussion of the Abstract. Author’s response : Thank you, we decided to revise and move this sentence in “Conclusion” section of the abstract. Q . Regarding data analyses and discussion it would be great (if possible depending on statistical power) to focus also on groups with vaccinations less than 21 days before, as for these groups hardly any (or only minimal) vaccine effectiveness on COVID-19 mortality can be assumed so that any mortality difference may well reflect some sort of bias. Author’s response : In the data analysis phase we also considered groups with vaccinations less than 21 days before, but it was not possible to perform any calculations as most of the boxes indicated <3 deaths. No problem if the authors are not willing to address this. Q . Regarding the overall conclusions I would suggest being more cautious regarding strong statements (such as a moratorium on promoting mass vaccination campaigns should be implemented) as the findings are only observational and require further confirmation and more in-depth investigations at best with inclusion of more potential confounders and IPD. Author’s response : Thank you, we did it Competing Interests: None Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 20 Feb 2025 Alberto Donzelli , Fondazione "Allineare Sanità e Salute", Italy 20 Feb 2025 Author Response Dear Dr. Stefan Pilz, we would like to thank you for your suggestions. Although you have kindly indicated that the acceptance of your suggestions is at our discretion, we have ... Continue reading Dear Dr. Stefan Pilz, we would like to thank you for your suggestions. Although you have kindly indicated that the acceptance of your suggestions is at our discretion, we have decided willingly to include them in the text, because we believe they are useful for improving the article This is an important retrospective study on the mortality rate according to vaccination status against SARS-CoV-2 in the UK. Based on publicly available national health data the authors show that mortality risk is, in general (for most time periods and age groups analyzed), higher in vaccinated versus unvaccinated individuals. SMRs in vaccinated individuals are lower than one in early periods of their observation period, but progressively increase over time and exceed one in several age groups suggesting that relative mortality risk progressively increases over time after vaccination. The discussion of these findings is well-written and includes several interesting views on potential biases in their findings. Overall, this is an interesting and important paper. I have only minor comments. Q . The authors should aim to carefully explain very clearly the underlying analyses of their Tables and Figures so that they can also stand alone: e.g. in Table 1 it may not be clear for the reader that this is a regression analysis of SMRs and observation months (this has only been described in the text). Author’s response : Thank you for this advice. We have added a sentence that specifics this aspect. Q . The discussion is great, but a brief overall limitations section may improve the quality of the paper (e.g., retrospective study design, only age group data but no individual participant data, etc.). Several limitations have been nicely discussed but I would nevertheless suggest such a brief limitations section. Author’s response: Thank you for this advice. We have added the “Limitation” section. Q . The authors may check whether this article meets all the requirements of the STROBE guidelines. I do not request a STROBE guideline Table as a Supplement although this may be considered. Author’s response : Before submitting our paper, we checked the STROBE guidelines, but the most part of the items of the "STROBE Statements" are not applicable in our paper as we show a critical analysis of the data collected and provided by the ONS and the data analyzed was not collected by us. In the "Methods" section we described in detail what and how we used the information contained in the ONS dataset, but we cannot know how the ONS collected this data. Q . The authors should carefully re-check the spelling and grammar of this article as there are some slight improvements required. E.g., Page 10, second paragraph, second line from the bottom “the risk of death” should be corrected to “the risk difference of death…”. In addition, some parts of the article may be difficult to read and understand for the reader. Consider improving this. Author’s respons e : We inserted your suggestion. Q . In the Abstract the authors write that “the best way can be the analysis of all-cause mortality by vaccination”. I would rather be cautious to state “the best way” and re-phrase this to something like” Assessment of its real-world effect can be performed by analysis of all-cause mortality by vaccination status”. Author’s response : You are right, we have reworded the sentence according to your suggestion Q . In the results section of the Abstract the last sentence is rather a discussion of the data than a results presentation. Consider revising and/or removing some parts to the discussion of the Abstract. Author’s response : Thank you, we decided to revise and move this sentence in “Conclusion” section of the abstract. Q . Regarding data analyses and discussion it would be great (if possible depending on statistical power) to focus also on groups with vaccinations less than 21 days before, as for these groups hardly any (or only minimal) vaccine effectiveness on COVID-19 mortality can be assumed so that any mortality difference may well reflect some sort of bias. Author’s response : In the data analysis phase we also considered groups with vaccinations less than 21 days before, but it was not possible to perform any calculations as most of the boxes indicated <3 deaths. No problem if the authors are not willing to address this. Q . Regarding the overall conclusions I would suggest being more cautious regarding strong statements (such as a moratorium on promoting mass vaccination campaigns should be implemented) as the findings are only observational and require further confirmation and more in-depth investigations at best with inclusion of more potential confounders and IPD. Author’s response : Thank you, we did it Dear Dr. Stefan Pilz, we would like to thank you for your suggestions. Although you have kindly indicated that the acceptance of your suggestions is at our discretion, we have decided willingly to include them in the text, because we believe they are useful for improving the article This is an important retrospective study on the mortality rate according to vaccination status against SARS-CoV-2 in the UK. Based on publicly available national health data the authors show that mortality risk is, in general (for most time periods and age groups analyzed), higher in vaccinated versus unvaccinated individuals. SMRs in vaccinated individuals are lower than one in early periods of their observation period, but progressively increase over time and exceed one in several age groups suggesting that relative mortality risk progressively increases over time after vaccination. The discussion of these findings is well-written and includes several interesting views on potential biases in their findings. Overall, this is an interesting and important paper. I have only minor comments. Q . The authors should aim to carefully explain very clearly the underlying analyses of their Tables and Figures so that they can also stand alone: e.g. in Table 1 it may not be clear for the reader that this is a regression analysis of SMRs and observation months (this has only been described in the text). Author’s response : Thank you for this advice. We have added a sentence that specifics this aspect. Q . The discussion is great, but a brief overall limitations section may improve the quality of the paper (e.g., retrospective study design, only age group data but no individual participant data, etc.). Several limitations have been nicely discussed but I would nevertheless suggest such a brief limitations section. Author’s response: Thank you for this advice. We have added the “Limitation” section. Q . The authors may check whether this article meets all the requirements of the STROBE guidelines. I do not request a STROBE guideline Table as a Supplement although this may be considered. Author’s response : Before submitting our paper, we checked the STROBE guidelines, but the most part of the items of the "STROBE Statements" are not applicable in our paper as we show a critical analysis of the data collected and provided by the ONS and the data analyzed was not collected by us. In the "Methods" section we described in detail what and how we used the information contained in the ONS dataset, but we cannot know how the ONS collected this data. Q . The authors should carefully re-check the spelling and grammar of this article as there are some slight improvements required. E.g., Page 10, second paragraph, second line from the bottom “the risk of death” should be corrected to “the risk difference of death…”. In addition, some parts of the article may be difficult to read and understand for the reader. Consider improving this. Author’s respons e : We inserted your suggestion. Q . In the Abstract the authors write that “the best way can be the analysis of all-cause mortality by vaccination”. I would rather be cautious to state “the best way” and re-phrase this to something like” Assessment of its real-world effect can be performed by analysis of all-cause mortality by vaccination status”. Author’s response : You are right, we have reworded the sentence according to your suggestion Q . In the results section of the Abstract the last sentence is rather a discussion of the data than a results presentation. Consider revising and/or removing some parts to the discussion of the Abstract. Author’s response : Thank you, we decided to revise and move this sentence in “Conclusion” section of the abstract. Q . Regarding data analyses and discussion it would be great (if possible depending on statistical power) to focus also on groups with vaccinations less than 21 days before, as for these groups hardly any (or only minimal) vaccine effectiveness on COVID-19 mortality can be assumed so that any mortality difference may well reflect some sort of bias. Author’s response : In the data analysis phase we also considered groups with vaccinations less than 21 days before, but it was not possible to perform any calculations as most of the boxes indicated <3 deaths. No problem if the authors are not willing to address this. Q . Regarding the overall conclusions I would suggest being more cautious regarding strong statements (such as a moratorium on promoting mass vaccination campaigns should be implemented) as the findings are only observational and require further confirmation and more in-depth investigations at best with inclusion of more potential confounders and IPD. Author’s response : Thank you, we did it Competing Interests: None Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Aarstad J. Reviewer Report For: All-cause mortality according to COVID-19 vaccination status: An analysis of the UK office for National statistics public data [version 2; peer review: 2 approved] . F1000Research 2025, 13 :886 ( https://doi.org/10.5256/f1000research.169038.r311454 ) The direct URL for this report is: https://f1000research.com/articles/13-886/v1#referee-response-311454 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 19 Aug 2024 Jarle Aarstad , HVL Business School, Western Norway University of Applied Science, Bergen, Norway Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.169038.r311454 Dear Editor and authors, Thank you for giving me the opportunity to referee the manuscript entitled “All-cause mortality according to COVID-19 vaccination status: An analysis of the UK office for National statistics public data”. I find ... Continue reading READ ALL Dear Editor and authors, Thank you for giving me the opportunity to referee the manuscript entitled “All-cause mortality according to COVID-19 vaccination status: An analysis of the UK office for National statistics public data”. I find the article very interesting and illuminating, particularly comparing all-cause mortality with non-covid mortality, and along with a very good discussion, it increases the study’s validity and adds value to the study of covid-vaccines’ potential mortality effect. Concerning the Introduction, the authors relevantly address the research question and study objective. I believe, however, that the study by Mostert et al. (2024)(Ref-1) merits attention concerning excess mortality in Western countries. I assume the calculations are correct regarding the statistical analyses of the RRs CIs, but I recommend one or two references on which you base the calculations. Also, I did not understand what you mean by “standardized rate”. Altogether, I would like you to explain that part better, particularly for an audience unfamiliar with the approach, eventually in an appendix. I follow the logic in section 3.4, but I suspect you compare never vaccinated with ever vaccinated. Is that correct? I assume yes, but it was a bit unclear for me. In the Figures, you include the number of months, but would it be better to include their names? E.g., Apr. 2021, etc. In Figures 2, 3, 4, 5, and partly 6, the intersections are out of the sample, which you should state in the text. Very good and illuminating discussion. As you apply English/Welsh data, you may refer to the following web page by the Office for National Statistics, showing that unvaccinated tended to live in more deprived areas, urban areas, or social rented housing, were not born in the UK or did not have English as a main language, have never worked or are long-term unemployed, limited a lot by a disability, and are male (among you more men die than women). https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/articles/coronaviruscovid19latestinsights/vaccines Addressing the above mentioned issues adequately, I argue the study will meet the indexing standards. I wish you all the best when reviewing the manuscript. Sincerely, Referee 1. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes References 1. Mostert S, Hoogland M, Huibers M, Kaspers G: Excess mortality across countries in the Western World since the COVID-19 pandemic: ‘Our World in Data’ estimates of January 2020 to December 2022. BMJ Public Health . 2024; 2 (1). Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: Applied statistical analysis. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Aarstad J. Reviewer Report For: All-cause mortality according to COVID-19 vaccination status: An analysis of the UK office for National statistics public data [version 2; peer review: 2 approved] . F1000Research 2025, 13 :886 ( https://doi.org/10.5256/f1000research.169038.r311454 ) The direct URL for this report is: https://f1000research.com/articles/13-886/v1#referee-response-311454 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 20 Feb 2025 Alberto Donzelli , Fondazione "Allineare Sanità e Salute", Italy 20 Feb 2025 Author Response Authors’ responses Dear Dr. Jarle Aarstad, We want to thank you for your advice which contribute to improve our paper. Below the answers to your questions. Reviewer response: Dear ... Continue reading Authors’ responses Dear Dr. Jarle Aarstad, We want to thank you for your advice which contribute to improve our paper. Below the answers to your questions. Reviewer response: Dear Editor and authors, Thank you for giving me the opportunity to referee the manuscript entitled “All-cause mortality according to COVID-19 vaccination status: An analysis of the UK office for National statistics public data”. I find the article very interesting and illuminating, particularly comparing all-cause mortality with non-covid mortality, and along with a very good discussion, it increases the study’s validity and adds value to the study of covid-vaccines’ potential mortality effect. Concerning the Introduction, the authors relevantly address the research question and study objective. I believe, however, that the study by Mostert et al. (2024)(Ref-1) merits attention concerning excess mortality in Western countries. Author’s response : Thank you for your valuable feedback. We have added the reference in the "Introduction" section. Reviewer response: I assume the calculations are correct regarding the statistical analyses of the RRs CIs, but I recommend one or two references on which you base the calculations. Author’s response : Sorry, but we think do not understand, the reference regarding the statistical analyses of the RRs Cis are indicated just before the colons in the sentence above the formula and they are mentioned as 7 and 8 references. Reviewer response: Also, I did not understand what you mean by “standardized rate”. Author’s response : Sorry, you are right. For “standardized rate” we mean the Age-standardized mortality rates per 100,000 person-years, standardized to the 2013 European Standard Population using five-year age groups from those aged 10 years and over as defined in "Note 1" of the spreadsheet called "Notes" of the ONS dataset. We changed the sentence. Reviewer response: Altogether, I would like you to explain that part better, particularly for an audience unfamiliar with the approach, eventually in an appendix. Author’s response : Sorry but we are not sure we understand, could you possibly be more specific? We inserted all the steps of the calculations performed by us, and we are not sure what other steps we could describe in a further appendix. However, we also interpreted your perplexity as due to the fact that we have inserted a bibliographic reference in Italian, less useful to international researchers. We have therefore replaced the current entry n. 9 with the same reference text in English. Reviewer response: I follow the logic in section 3.4, but I suspect you compare never vaccinated with ever vaccinated. Is that correct? I assume yes, but it was a bit unclear for me. Author’s response : Exactly, you are correct, we used the same terminology used in in the Table 2 of the ONS dataset. Reviewer response: In the Figures, you include the number of months, but would it be better to include their names? E.g., Apr. 2021, etc. Author’s response : Thank you, we replaced the numbers with the names. Reviewer response: In Figures 2, 3, 4, 5, and partly 6, the intersections are out of the sample, which you should state in the text. Author’s response : Sorry, you are right. We added a sentence that specifics this aspect. Reviewer response: Very good and illuminating discussion. As you apply English/Welsh data, you may refer to the following web page by the Office for National Statistics, showing that unvaccinated tended to live in more deprived areas, urban areas, or social rented housing, were not born in the UK or did not have English as a main language, have never worked or are long-term unemployed, limited a lot by a disability, and are male (among you more men die than women). https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/articles/coronaviruscovid19latestinsights/vaccines Addressing the above mentioned issues adequately, I argue the study will meet the indexing standards. I wish you all the best when reviewing the manuscript. Author’s response : Thank you for this advice. We have added a reflection on this aspect in the “Discussion” section. Authors’ responses Dear Dr. Jarle Aarstad, We want to thank you for your advice which contribute to improve our paper. Below the answers to your questions. Reviewer response: Dear Editor and authors, Thank you for giving me the opportunity to referee the manuscript entitled “All-cause mortality according to COVID-19 vaccination status: An analysis of the UK office for National statistics public data”. I find the article very interesting and illuminating, particularly comparing all-cause mortality with non-covid mortality, and along with a very good discussion, it increases the study’s validity and adds value to the study of covid-vaccines’ potential mortality effect. Concerning the Introduction, the authors relevantly address the research question and study objective. I believe, however, that the study by Mostert et al. (2024)(Ref-1) merits attention concerning excess mortality in Western countries. Author’s response : Thank you for your valuable feedback. We have added the reference in the "Introduction" section. Reviewer response: I assume the calculations are correct regarding the statistical analyses of the RRs CIs, but I recommend one or two references on which you base the calculations. Author’s response : Sorry, but we think do not understand, the reference regarding the statistical analyses of the RRs Cis are indicated just before the colons in the sentence above the formula and they are mentioned as 7 and 8 references. Reviewer response: Also, I did not understand what you mean by “standardized rate”. Author’s response : Sorry, you are right. For “standardized rate” we mean the Age-standardized mortality rates per 100,000 person-years, standardized to the 2013 European Standard Population using five-year age groups from those aged 10 years and over as defined in "Note 1" of the spreadsheet called "Notes" of the ONS dataset. We changed the sentence. Reviewer response: Altogether, I would like you to explain that part better, particularly for an audience unfamiliar with the approach, eventually in an appendix. Author’s response : Sorry but we are not sure we understand, could you possibly be more specific? We inserted all the steps of the calculations performed by us, and we are not sure what other steps we could describe in a further appendix. However, we also interpreted your perplexity as due to the fact that we have inserted a bibliographic reference in Italian, less useful to international researchers. We have therefore replaced the current entry n. 9 with the same reference text in English. Reviewer response: I follow the logic in section 3.4, but I suspect you compare never vaccinated with ever vaccinated. Is that correct? I assume yes, but it was a bit unclear for me. Author’s response : Exactly, you are correct, we used the same terminology used in in the Table 2 of the ONS dataset. Reviewer response: In the Figures, you include the number of months, but would it be better to include their names? E.g., Apr. 2021, etc. Author’s response : Thank you, we replaced the numbers with the names. Reviewer response: In Figures 2, 3, 4, 5, and partly 6, the intersections are out of the sample, which you should state in the text. Author’s response : Sorry, you are right. We added a sentence that specifics this aspect. Reviewer response: Very good and illuminating discussion. As you apply English/Welsh data, you may refer to the following web page by the Office for National Statistics, showing that unvaccinated tended to live in more deprived areas, urban areas, or social rented housing, were not born in the UK or did not have English as a main language, have never worked or are long-term unemployed, limited a lot by a disability, and are male (among you more men die than women). https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/articles/coronaviruscovid19latestinsights/vaccines Addressing the above mentioned issues adequately, I argue the study will meet the indexing standards. I wish you all the best when reviewing the manuscript. Author’s response : Thank you for this advice. We have added a reflection on this aspect in the “Discussion” section. Competing Interests: None Close Report a concern Author Response 16 Jan 2025 Alberto Donzelli , Fondazione "Allineare Sanità e Salute", Italy 16 Jan 2025 Author Response Dear Dr. Jarle Aarstad, We want to thank you for your advice which contribute to improve our paper. Below are the answers to your questions. Dear Editor and authors, ... Continue reading Dear Dr. Jarle Aarstad, We want to thank you for your advice which contribute to improve our paper. Below are the answers to your questions. Dear Editor and authors, Thank you for giving me the opportunity to referee the manuscript entitled “All-cause mortality according to COVID-19 vaccination status: An analysis of the UK office for National statistics public data”. I find the article very interesting and illuminating, particularly comparing all-cause mortality with non-covid mortality, and along with a very good discussion, it increases the study’s validity and adds value to the study of covid-vaccines’ potential mortality effect. Q . Concerning the Introduction, the authors relevantly address the research question and study objective. I believe, however, that the study by Mostert et al. (2024)(Ref-1) merits attention concerning excess mortality in Western countries. Author’s response : Thank you for your valuable feedback. We have added the reference in the "Introduction" section. Q . I assume the calculations are correct regarding the statistical analyses of the RRs CIs, but I recommend one or two references on which you base the calculations. Author’s response : sorry, but we think do not understand, the reference regarding the statistical analyses of the RRs CIs are indicated just before the colons in the sentence above the formula and they are mentioned as 8 and 9 references. Q . Also, I did not understand what you mean by “standardized rate”. Author’s response : sorry, you are right. For “standardized rate” we mean the Age-standardised mortality rates per 100,000 person-years, standardised to the 2013 European Standard Population using five-year age groups from those aged 10 years and over as defined in "Note 1" of the spreadsheet called "Notes" of the ONS dataset. We changed the sentence. Q . Altogether, I would like you to explain that part better, particularly for an audience unfamiliar with the approach, eventually in an appendix. Author’s response : sorry but we think do not understand, could you be more specific? We inserted all the steps of the calculations performed by us and we would not know which other steps we could describe in a further appendix. Q . I follow the logic in section 3.4, but I suspect you compare never vaccinated with ever vaccinated. Is that correct? I assume yes, but it was a bit unclear for me. Author’s response : exactly, you are correct, we used the same terminology used in the Table 2 of the ONS dataset. Q. In the Figures, you include the number of months, but would it be better to include their names? E.g., Apr. 2021, etc. Author’s response : thank you, we replaced the numbers with the names. Q . In Figures 2, 3, 4, 5, and partly 6, the intersections are out of the sample, which you should state in the text. Author’s response : sorry, you are right. We added a sentence that specifics this aspect. Q. Very good and illuminating discussion. As you apply English/Welsh data, you may refer to the following web page by the Office for National Statistics, showing that unvaccinated tended to live in more deprived areas, urban areas, or social rented housing, were not born in the UK or did not have English as a main language, have never worked or are long-term unemployed, limited a lot by a disability, and are male (among you more men die than women). https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/articles/coronaviruscovid19latestinsights/vaccines Addressing the above mentioned issues adequately, I argue the study will meet the indexing standards. Author’s response : thank you for this advice. We have added a reflection on this aspect in the “Discussion” section. I wish you all the best when reviewing the manuscript. Dear Dr. Jarle Aarstad, We want to thank you for your advice which contribute to improve our paper. Below are the answers to your questions. Dear Editor and authors, Thank you for giving me the opportunity to referee the manuscript entitled “All-cause mortality according to COVID-19 vaccination status: An analysis of the UK office for National statistics public data”. I find the article very interesting and illuminating, particularly comparing all-cause mortality with non-covid mortality, and along with a very good discussion, it increases the study’s validity and adds value to the study of covid-vaccines’ potential mortality effect. Q . Concerning the Introduction, the authors relevantly address the research question and study objective. I believe, however, that the study by Mostert et al. (2024)(Ref-1) merits attention concerning excess mortality in Western countries. Author’s response : Thank you for your valuable feedback. We have added the reference in the "Introduction" section. Q . I assume the calculations are correct regarding the statistical analyses of the RRs CIs, but I recommend one or two references on which you base the calculations. Author’s response : sorry, but we think do not understand, the reference regarding the statistical analyses of the RRs CIs are indicated just before the colons in the sentence above the formula and they are mentioned as 8 and 9 references. Q . Also, I did not understand what you mean by “standardized rate”. Author’s response : sorry, you are right. For “standardized rate” we mean the Age-standardised mortality rates per 100,000 person-years, standardised to the 2013 European Standard Population using five-year age groups from those aged 10 years and over as defined in "Note 1" of the spreadsheet called "Notes" of the ONS dataset. We changed the sentence. Q . Altogether, I would like you to explain that part better, particularly for an audience unfamiliar with the approach, eventually in an appendix. Author’s response : sorry but we think do not understand, could you be more specific? We inserted all the steps of the calculations performed by us and we would not know which other steps we could describe in a further appendix. Q . I follow the logic in section 3.4, but I suspect you compare never vaccinated with ever vaccinated. Is that correct? I assume yes, but it was a bit unclear for me. Author’s response : exactly, you are correct, we used the same terminology used in the Table 2 of the ONS dataset. Q. In the Figures, you include the number of months, but would it be better to include their names? E.g., Apr. 2021, etc. Author’s response : thank you, we replaced the numbers with the names. Q . In Figures 2, 3, 4, 5, and partly 6, the intersections are out of the sample, which you should state in the text. Author’s response : sorry, you are right. We added a sentence that specifics this aspect. Q. Very good and illuminating discussion. As you apply English/Welsh data, you may refer to the following web page by the Office for National Statistics, showing that unvaccinated tended to live in more deprived areas, urban areas, or social rented housing, were not born in the UK or did not have English as a main language, have never worked or are long-term unemployed, limited a lot by a disability, and are male (among you more men die than women). https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/articles/coronaviruscovid19latestinsights/vaccines Addressing the above mentioned issues adequately, I argue the study will meet the indexing standards. Author’s response : thank you for this advice. We have added a reflection on this aspect in the “Discussion” section. I wish you all the best when reviewing the manuscript. Competing Interests: None Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 20 Feb 2025 Alberto Donzelli , Fondazione "Allineare Sanità e Salute", Italy 20 Feb 2025 Author Response Authors’ responses Dear Dr. Jarle Aarstad, We want to thank you for your advice which contribute to improve our paper. Below the answers to your questions. Reviewer response: Dear ... Continue reading Authors’ responses Dear Dr. Jarle Aarstad, We want to thank you for your advice which contribute to improve our paper. Below the answers to your questions. Reviewer response: Dear Editor and authors, Thank you for giving me the opportunity to referee the manuscript entitled “All-cause mortality according to COVID-19 vaccination status: An analysis of the UK office for National statistics public data”. I find the article very interesting and illuminating, particularly comparing all-cause mortality with non-covid mortality, and along with a very good discussion, it increases the study’s validity and adds value to the study of covid-vaccines’ potential mortality effect. Concerning the Introduction, the authors relevantly address the research question and study objective. I believe, however, that the study by Mostert et al. (2024)(Ref-1) merits attention concerning excess mortality in Western countries. Author’s response : Thank you for your valuable feedback. We have added the reference in the "Introduction" section. Reviewer response: I assume the calculations are correct regarding the statistical analyses of the RRs CIs, but I recommend one or two references on which you base the calculations. Author’s response : Sorry, but we think do not understand, the reference regarding the statistical analyses of the RRs Cis are indicated just before the colons in the sentence above the formula and they are mentioned as 7 and 8 references. Reviewer response: Also, I did not understand what you mean by “standardized rate”. Author’s response : Sorry, you are right. For “standardized rate” we mean the Age-standardized mortality rates per 100,000 person-years, standardized to the 2013 European Standard Population using five-year age groups from those aged 10 years and over as defined in "Note 1" of the spreadsheet called "Notes" of the ONS dataset. We changed the sentence. Reviewer response: Altogether, I would like you to explain that part better, particularly for an audience unfamiliar with the approach, eventually in an appendix. Author’s response : Sorry but we are not sure we understand, could you possibly be more specific? We inserted all the steps of the calculations performed by us, and we are not sure what other steps we could describe in a further appendix. However, we also interpreted your perplexity as due to the fact that we have inserted a bibliographic reference in Italian, less useful to international researchers. We have therefore replaced the current entry n. 9 with the same reference text in English. Reviewer response: I follow the logic in section 3.4, but I suspect you compare never vaccinated with ever vaccinated. Is that correct? I assume yes, but it was a bit unclear for me. Author’s response : Exactly, you are correct, we used the same terminology used in in the Table 2 of the ONS dataset. Reviewer response: In the Figures, you include the number of months, but would it be better to include their names? E.g., Apr. 2021, etc. Author’s response : Thank you, we replaced the numbers with the names. Reviewer response: In Figures 2, 3, 4, 5, and partly 6, the intersections are out of the sample, which you should state in the text. Author’s response : Sorry, you are right. We added a sentence that specifics this aspect. Reviewer response: Very good and illuminating discussion. As you apply English/Welsh data, you may refer to the following web page by the Office for National Statistics, showing that unvaccinated tended to live in more deprived areas, urban areas, or social rented housing, were not born in the UK or did not have English as a main language, have never worked or are long-term unemployed, limited a lot by a disability, and are male (among you more men die than women). https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/articles/coronaviruscovid19latestinsights/vaccines Addressing the above mentioned issues adequately, I argue the study will meet the indexing standards. I wish you all the best when reviewing the manuscript. Author’s response : Thank you for this advice. We have added a reflection on this aspect in the “Discussion” section. Authors’ responses Dear Dr. Jarle Aarstad, We want to thank you for your advice which contribute to improve our paper. Below the answers to your questions. Reviewer response: Dear Editor and authors, Thank you for giving me the opportunity to referee the manuscript entitled “All-cause mortality according to COVID-19 vaccination status: An analysis of the UK office for National statistics public data”. I find the article very interesting and illuminating, particularly comparing all-cause mortality with non-covid mortality, and along with a very good discussion, it increases the study’s validity and adds value to the study of covid-vaccines’ potential mortality effect. Concerning the Introduction, the authors relevantly address the research question and study objective. I believe, however, that the study by Mostert et al. (2024)(Ref-1) merits attention concerning excess mortality in Western countries. Author’s response : Thank you for your valuable feedback. We have added the reference in the "Introduction" section. Reviewer response: I assume the calculations are correct regarding the statistical analyses of the RRs CIs, but I recommend one or two references on which you base the calculations. Author’s response : Sorry, but we think do not understand, the reference regarding the statistical analyses of the RRs Cis are indicated just before the colons in the sentence above the formula and they are mentioned as 7 and 8 references. Reviewer response: Also, I did not understand what you mean by “standardized rate”. Author’s response : Sorry, you are right. For “standardized rate” we mean the Age-standardized mortality rates per 100,000 person-years, standardized to the 2013 European Standard Population using five-year age groups from those aged 10 years and over as defined in "Note 1" of the spreadsheet called "Notes" of the ONS dataset. We changed the sentence. Reviewer response: Altogether, I would like you to explain that part better, particularly for an audience unfamiliar with the approach, eventually in an appendix. Author’s response : Sorry but we are not sure we understand, could you possibly be more specific? We inserted all the steps of the calculations performed by us, and we are not sure what other steps we could describe in a further appendix. However, we also interpreted your perplexity as due to the fact that we have inserted a bibliographic reference in Italian, less useful to international researchers. We have therefore replaced the current entry n. 9 with the same reference text in English. Reviewer response: I follow the logic in section 3.4, but I suspect you compare never vaccinated with ever vaccinated. Is that correct? I assume yes, but it was a bit unclear for me. Author’s response : Exactly, you are correct, we used the same terminology used in in the Table 2 of the ONS dataset. Reviewer response: In the Figures, you include the number of months, but would it be better to include their names? E.g., Apr. 2021, etc. Author’s response : Thank you, we replaced the numbers with the names. Reviewer response: In Figures 2, 3, 4, 5, and partly 6, the intersections are out of the sample, which you should state in the text. Author’s response : Sorry, you are right. We added a sentence that specifics this aspect. Reviewer response: Very good and illuminating discussion. As you apply English/Welsh data, you may refer to the following web page by the Office for National Statistics, showing that unvaccinated tended to live in more deprived areas, urban areas, or social rented housing, were not born in the UK or did not have English as a main language, have never worked or are long-term unemployed, limited a lot by a disability, and are male (among you more men die than women). https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/articles/coronaviruscovid19latestinsights/vaccines Addressing the above mentioned issues adequately, I argue the study will meet the indexing standards. I wish you all the best when reviewing the manuscript. Author’s response : Thank you for this advice. We have added a reflection on this aspect in the “Discussion” section. Competing Interests: None Close Report a concern Author Response 16 Jan 2025 Alberto Donzelli , Fondazione "Allineare Sanità e Salute", Italy 16 Jan 2025 Author Response Dear Dr. Jarle Aarstad, We want to thank you for your advice which contribute to improve our paper. Below are the answers to your questions. Dear Editor and authors, ... Continue reading Dear Dr. Jarle Aarstad, We want to thank you for your advice which contribute to improve our paper. Below are the answers to your questions. Dear Editor and authors, Thank you for giving me the opportunity to referee the manuscript entitled “All-cause mortality according to COVID-19 vaccination status: An analysis of the UK office for National statistics public data”. I find the article very interesting and illuminating, particularly comparing all-cause mortality with non-covid mortality, and along with a very good discussion, it increases the study’s validity and adds value to the study of covid-vaccines’ potential mortality effect. Q . Concerning the Introduction, the authors relevantly address the research question and study objective. I believe, however, that the study by Mostert et al. (2024)(Ref-1) merits attention concerning excess mortality in Western countries. Author’s response : Thank you for your valuable feedback. We have added the reference in the "Introduction" section. Q . I assume the calculations are correct regarding the statistical analyses of the RRs CIs, but I recommend one or two references on which you base the calculations. Author’s response : sorry, but we think do not understand, the reference regarding the statistical analyses of the RRs CIs are indicated just before the colons in the sentence above the formula and they are mentioned as 8 and 9 references. Q . Also, I did not understand what you mean by “standardized rate”. Author’s response : sorry, you are right. For “standardized rate” we mean the Age-standardised mortality rates per 100,000 person-years, standardised to the 2013 European Standard Population using five-year age groups from those aged 10 years and over as defined in "Note 1" of the spreadsheet called "Notes" of the ONS dataset. We changed the sentence. Q . Altogether, I would like you to explain that part better, particularly for an audience unfamiliar with the approach, eventually in an appendix. Author’s response : sorry but we think do not understand, could you be more specific? We inserted all the steps of the calculations performed by us and we would not know which other steps we could describe in a further appendix. Q . I follow the logic in section 3.4, but I suspect you compare never vaccinated with ever vaccinated. Is that correct? I assume yes, but it was a bit unclear for me. Author’s response : exactly, you are correct, we used the same terminology used in the Table 2 of the ONS dataset. Q. In the Figures, you include the number of months, but would it be better to include their names? E.g., Apr. 2021, etc. Author’s response : thank you, we replaced the numbers with the names. Q . In Figures 2, 3, 4, 5, and partly 6, the intersections are out of the sample, which you should state in the text. Author’s response : sorry, you are right. We added a sentence that specifics this aspect. Q. Very good and illuminating discussion. As you apply English/Welsh data, you may refer to the following web page by the Office for National Statistics, showing that unvaccinated tended to live in more deprived areas, urban areas, or social rented housing, were not born in the UK or did not have English as a main language, have never worked or are long-term unemployed, limited a lot by a disability, and are male (among you more men die than women). https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/articles/coronaviruscovid19latestinsights/vaccines Addressing the above mentioned issues adequately, I argue the study will meet the indexing standards. Author’s response : thank you for this advice. We have added a reflection on this aspect in the “Discussion” section. I wish you all the best when reviewing the manuscript. Dear Dr. Jarle Aarstad, We want to thank you for your advice which contribute to improve our paper. Below are the answers to your questions. Dear Editor and authors, Thank you for giving me the opportunity to referee the manuscript entitled “All-cause mortality according to COVID-19 vaccination status: An analysis of the UK office for National statistics public data”. I find the article very interesting and illuminating, particularly comparing all-cause mortality with non-covid mortality, and along with a very good discussion, it increases the study’s validity and adds value to the study of covid-vaccines’ potential mortality effect. Q . Concerning the Introduction, the authors relevantly address the research question and study objective. I believe, however, that the study by Mostert et al. (2024)(Ref-1) merits attention concerning excess mortality in Western countries. Author’s response : Thank you for your valuable feedback. We have added the reference in the "Introduction" section. Q . I assume the calculations are correct regarding the statistical analyses of the RRs CIs, but I recommend one or two references on which you base the calculations. Author’s response : sorry, but we think do not understand, the reference regarding the statistical analyses of the RRs CIs are indicated just before the colons in the sentence above the formula and they are mentioned as 8 and 9 references. Q . Also, I did not understand what you mean by “standardized rate”. Author’s response : sorry, you are right. For “standardized rate” we mean the Age-standardised mortality rates per 100,000 person-years, standardised to the 2013 European Standard Population using five-year age groups from those aged 10 years and over as defined in "Note 1" of the spreadsheet called "Notes" of the ONS dataset. We changed the sentence. Q . Altogether, I would like you to explain that part better, particularly for an audience unfamiliar with the approach, eventually in an appendix. Author’s response : sorry but we think do not understand, could you be more specific? We inserted all the steps of the calculations performed by us and we would not know which other steps we could describe in a further appendix. Q . I follow the logic in section 3.4, but I suspect you compare never vaccinated with ever vaccinated. Is that correct? I assume yes, but it was a bit unclear for me. Author’s response : exactly, you are correct, we used the same terminology used in the Table 2 of the ONS dataset. Q. In the Figures, you include the number of months, but would it be better to include their names? E.g., Apr. 2021, etc. Author’s response : thank you, we replaced the numbers with the names. Q . In Figures 2, 3, 4, 5, and partly 6, the intersections are out of the sample, which you should state in the text. Author’s response : sorry, you are right. We added a sentence that specifics this aspect. Q. Very good and illuminating discussion. As you apply English/Welsh data, you may refer to the following web page by the Office for National Statistics, showing that unvaccinated tended to live in more deprived areas, urban areas, or social rented housing, were not born in the UK or did not have English as a main language, have never worked or are long-term unemployed, limited a lot by a disability, and are male (among you more men die than women). https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/articles/coronaviruscovid19latestinsights/vaccines Addressing the above mentioned issues adequately, I argue the study will meet the indexing standards. Author’s response : thank you for this advice. We have added a reflection on this aspect in the “Discussion” section. I wish you all the best when reviewing the manuscript. Competing Interests: None Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (1) Version 2 VERSION 2 PUBLISHED 20 Feb 2025 Revised Reader Comment 09 Aug 2025 Betty Oostenbrink , Chemistry, Avans University of Applied Science - Tilburg, Tilburg, The Netherlands 09 Aug 2025 Reader Comment Given the high concentration factor (Nvacc/Nunvacc) in older age groups—exceeding 40—it is not meaningful that the SMR ratio is below 1. In younger age groups, the concentration factor is also ... Continue reading Given the high concentration factor (Nvacc/Nunvacc) in older age groups—exceeding 40—it is not meaningful that the SMR ratio is below 1. In younger age groups, the concentration factor is also above 1: 5 (18–39 years), 9 (40–49 years), 15 (50–59 years), and 18 (60–69 years). Therefore, the focus should be on the difference between non-COVID and COVID SMR ratios. The fact that the SMR ratio is below 1 has no special meaning with these high concentration factors. Analysis of the differences in both SMR ratio reveals that the vaccine’s effect takes time to manifest: 18–39 years: Effect observed from the 6th month (no prior data). 40–49 years: Effect observed from the 4th month (no prior data). 50–59 years: Clear difference from the 4th to 5th month. 60–69 and 70–79 years: Effect from the 3rd month. 80–89 years: Effect from the 4th month. 90+ years: Effect from the 4th or 5th month. For individuals aged 60 and older, the vaccine appears effective shortly after the second dose. In younger age groups, efficacy is observed after a single dose. Moreover, comparing COVID and non-COVID mortality suggests no justification for additional vaccination rounds: For younger age groups (18–39, 40–49, and 50–59), the vaccine’s effect disappeared after approximately 14 months. For all younger age groups ((18–39, 40–49, and 50–59, 60-69): the initial reduction in non-COVID mortality among vaccinated individuals disappeared after the third dose.The third dose either had no impact or counteracted the effects of the first two doses, as the percentage of COVID deaths remained unchanged. In the 70-79 age groups the effect almost disappeared after the 3rd vaccination but not completely. In the 80–89 and 90+ age groups, a difference between non-COVID and COVID mortality persists, but it diminishes after the third dose. Based on these findings, the third and fourth vaccine doses appear ineffective for those under 70. Furthermore, the necessity of a third dose remains unclear, as the differences in mortality outcomes decrease and by comparing non-COVID and COVID mortality SMR no visible decline in difference between the two in time was visible before the 3rd vaccination started. Given the high concentration factor (Nvacc/Nunvacc) in older age groups—exceeding 40—it is not meaningful that the SMR ratio is below 1. In younger age groups, the concentration factor is also above 1: 5 (18–39 years), 9 (40–49 years), 15 (50–59 years), and 18 (60–69 years). Therefore, the focus should be on the difference between non-COVID and COVID SMR ratios. The fact that the SMR ratio is below 1 has no special meaning with these high concentration factors. Analysis of the differences in both SMR ratio reveals that the vaccine’s effect takes time to manifest: 18–39 years: Effect observed from the 6th month (no prior data). 40–49 years: Effect observed from the 4th month (no prior data). 50–59 years: Clear difference from the 4th to 5th month. 60–69 and 70–79 years: Effect from the 3rd month. 80–89 years: Effect from the 4th month. 90+ years: Effect from the 4th or 5th month. For individuals aged 60 and older, the vaccine appears effective shortly after the second dose. In younger age groups, efficacy is observed after a single dose. Moreover, comparing COVID and non-COVID mortality suggests no justification for additional vaccination rounds: For younger age groups (18–39, 40–49, and 50–59), the vaccine’s effect disappeared after approximately 14 months. For all younger age groups ((18–39, 40–49, and 50–59, 60-69): the initial reduction in non-COVID mortality among vaccinated individuals disappeared after the third dose.The third dose either had no impact or counteracted the effects of the first two doses, as the percentage of COVID deaths remained unchanged. In the 70-79 age groups the effect almost disappeared after the 3rd vaccination but not completely. In the 80–89 and 90+ age groups, a difference between non-COVID and COVID mortality persists, but it diminishes after the third dose. Based on these findings, the third and fourth vaccine doses appear ineffective for those under 70. Furthermore, the necessity of a third dose remains unclear, as the differences in mortality outcomes decrease and by comparing non-COVID and COVID mortality SMR no visible decline in difference between the two in time was visible before the 3rd vaccination started. Competing Interests: No competing interests were disclosed. Close Report a concern Comment ADD YOUR COMMENT keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 2 (revision) 20 Feb 25 read Version 1 05 Aug 24 read read Jarle Aarstad , Western Norway University of Applied Science, Bergen, Norway Stefan Pilz , Medical University of Graz, Graz, Austria Comments on this article All Comments (1) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Aarstad J. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 27 Feb 2025 | for Version 2 Jarle Aarstad , HVL Business School, Western Norway University of Applied Science, Bergen, Norway 0 Views copyright © 2025 Aarstad J. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Dear authors, I now understand better the issue of standardized mortality ratio (SMR) among vaccinated against the corresponding unvaccinated groups and find the paper acceptable for indexing. Competing Interests No competing interests were disclosed. Reviewer Expertise Applied statistical analysis. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Aarstad J. Peer Review Report For: All-cause mortality according to COVID-19 vaccination status: An analysis of the UK office for National statistics public data [version 2; peer review: 2 approved] . F1000Research 2025, 13 :886 ( https://doi.org/10.5256/f1000research.177063.r367845) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-886/v2#referee-response-367845 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Pilz S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 07 Jan 2025 | for Version 1 Stefan Pilz , Medical University of Graz, Graz, Austria 0 Views copyright © 2025 Pilz S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This is an important retrospective study on the mortality rate according to vaccination status against SARS-CoV-2 in the UK. Based on publicly available national health data the authors show that mortality risk is, in general (for most time periods and age groups analyzed), higher in vaccinated versus unvaccinated individuals. SMRs in vaccinated individuals are lower than one in early periods of their observation period, but progressively increase over time and exceed one in several age groups suggesting that relative mortality risk progressively increases over time after vaccination. The discussion of these findings is well-written and includes several interesting views on potential biases in their findings. Overall, this is an interesting and important paper. I have only minor comments. The authors should aim to carefully explain very clearly the underlying analyses of their Tables and Figures so that they can also stand alone: e.g. in Table 1 it may not be clear for the reader that this is a regression analysis of SMRs and observation months (this has only been described in the text). The discussion is great, but a brief overall limitations section may improve the quality of the paper (e.g., retrospective study design, only age group data but no individual participant data, etc.). Several limitations have been nicely discussed but I would nevertheless suggest such a brief limitations section. The authors may check whether this article meets all the requirements of the STROBE guidelines. I do not request a STROBE guideline Table as a Supplement although this may be considered. The authors should carefully re-check the spelling and grammar of this article as there are some slight improvements required. E.g., Page 10, second paragraph, second line from the bottom “the risk of death” should be corrected to “the risk difference of death…”. In addition, some parts of the article may be difficult to read and understand for the reader. Consider improving this. In the Abstract the authors write that “the best way can be the analysis of all-cause mortality by vaccination”. I would rather be cautious to state “the best way” and re-phrase this to something like” Assessment of its real-world effect can be performed by analysis of all-cause mortality by vaccination status”. In the results section of the Abstract the last sentence is rather a discussion of the data than a results presentation. Consider revising and/or removing some parts to the discussion of the Abstract. Regarding data analyses and discussion it would be great (if possible depending on statistical power) to focus also on groups with vaccinations less than 21 days before, as for these groups hardly any (or only minimal) vaccine effectiveness on COVID-19 mortality can be assumed so that any mortality difference may well reflect some sort of bias. No problem if the authors are not willing to address this. Regarding the overall conclusions I would suggest being more cautious regarding strong statements (such as a moratorium on promoting mass vaccination campaigns should be implemented) as the findings are only observational and require further confirmation and more in-depth investigations at best with inclusion of more potential confounders and IPD. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Epidemiology and clinical research in endocrinology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (1) Author Response 20 Feb 2025 Alberto Donzelli, Fondazione "Allineare Sanità e Salute", Italy Dear Dr. Stefan Pilz, we would like to thank you for your suggestions. Although you have kindly indicated that the acceptance of your suggestions is at our discretion, we have decided willingly to include them in the text, because we believe they are useful for improving the article This is an important retrospective study on the mortality rate according to vaccination status against SARS-CoV-2 in the UK. Based on publicly available national health data the authors show that mortality risk is, in general (for most time periods and age groups analyzed), higher in vaccinated versus unvaccinated individuals. SMRs in vaccinated individuals are lower than one in early periods of their observation period, but progressively increase over time and exceed one in several age groups suggesting that relative mortality risk progressively increases over time after vaccination. The discussion of these findings is well-written and includes several interesting views on potential biases in their findings. Overall, this is an interesting and important paper. I have only minor comments. Q . The authors should aim to carefully explain very clearly the underlying analyses of their Tables and Figures so that they can also stand alone: e.g. in Table 1 it may not be clear for the reader that this is a regression analysis of SMRs and observation months (this has only been described in the text). Author’s response : Thank you for this advice. We have added a sentence that specifics this aspect. Q . The discussion is great, but a brief overall limitations section may improve the quality of the paper (e.g., retrospective study design, only age group data but no individual participant data, etc.). Several limitations have been nicely discussed but I would nevertheless suggest such a brief limitations section. Author’s response: Thank you for this advice. We have added the “Limitation” section. Q . The authors may check whether this article meets all the requirements of the STROBE guidelines. I do not request a STROBE guideline Table as a Supplement although this may be considered. Author’s response : Before submitting our paper, we checked the STROBE guidelines, but the most part of the items of the "STROBE Statements" are not applicable in our paper as we show a critical analysis of the data collected and provided by the ONS and the data analyzed was not collected by us. In the "Methods" section we described in detail what and how we used the information contained in the ONS dataset, but we cannot know how the ONS collected this data. Q . The authors should carefully re-check the spelling and grammar of this article as there are some slight improvements required. E.g., Page 10, second paragraph, second line from the bottom “the risk of death” should be corrected to “the risk difference of death…”. In addition, some parts of the article may be difficult to read and understand for the reader. Consider improving this. Author’s respons e : We inserted your suggestion. Q . In the Abstract the authors write that “the best way can be the analysis of all-cause mortality by vaccination”. I would rather be cautious to state “the best way” and re-phrase this to something like” Assessment of its real-world effect can be performed by analysis of all-cause mortality by vaccination status”. Author’s response : You are right, we have reworded the sentence according to your suggestion Q . In the results section of the Abstract the last sentence is rather a discussion of the data than a results presentation. Consider revising and/or removing some parts to the discussion of the Abstract. Author’s response : Thank you, we decided to revise and move this sentence in “Conclusion” section of the abstract. Q . Regarding data analyses and discussion it would be great (if possible depending on statistical power) to focus also on groups with vaccinations less than 21 days before, as for these groups hardly any (or only minimal) vaccine effectiveness on COVID-19 mortality can be assumed so that any mortality difference may well reflect some sort of bias. Author’s response : In the data analysis phase we also considered groups with vaccinations less than 21 days before, but it was not possible to perform any calculations as most of the boxes indicated <3 deaths. No problem if the authors are not willing to address this. Q . Regarding the overall conclusions I would suggest being more cautious regarding strong statements (such as a moratorium on promoting mass vaccination campaigns should be implemented) as the findings are only observational and require further confirmation and more in-depth investigations at best with inclusion of more potential confounders and IPD. Author’s response : Thank you, we did it View more View less Competing Interests None reply Respond Report a concern Pilz S. Peer Review Report For: All-cause mortality according to COVID-19 vaccination status: An analysis of the UK office for National statistics public data [version 2; peer review: 2 approved] . F1000Research 2025, 13 :886 ( https://doi.org/10.5256/f1000research.169038.r342218) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-886/v1#referee-response-342218 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Aarstad J. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 19 Aug 2024 | for Version 1 Jarle Aarstad , HVL Business School, Western Norway University of Applied Science, Bergen, Norway 0 Views copyright © 2024 Aarstad J. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (2) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Dear Editor and authors, Thank you for giving me the opportunity to referee the manuscript entitled “All-cause mortality according to COVID-19 vaccination status: An analysis of the UK office for National statistics public data”. I find the article very interesting and illuminating, particularly comparing all-cause mortality with non-covid mortality, and along with a very good discussion, it increases the study’s validity and adds value to the study of covid-vaccines’ potential mortality effect. Concerning the Introduction, the authors relevantly address the research question and study objective. I believe, however, that the study by Mostert et al. (2024)(Ref-1) merits attention concerning excess mortality in Western countries. I assume the calculations are correct regarding the statistical analyses of the RRs CIs, but I recommend one or two references on which you base the calculations. Also, I did not understand what you mean by “standardized rate”. Altogether, I would like you to explain that part better, particularly for an audience unfamiliar with the approach, eventually in an appendix. I follow the logic in section 3.4, but I suspect you compare never vaccinated with ever vaccinated. Is that correct? I assume yes, but it was a bit unclear for me. In the Figures, you include the number of months, but would it be better to include their names? E.g., Apr. 2021, etc. In Figures 2, 3, 4, 5, and partly 6, the intersections are out of the sample, which you should state in the text. Very good and illuminating discussion. As you apply English/Welsh data, you may refer to the following web page by the Office for National Statistics, showing that unvaccinated tended to live in more deprived areas, urban areas, or social rented housing, were not born in the UK or did not have English as a main language, have never worked or are long-term unemployed, limited a lot by a disability, and are male (among you more men die than women). https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/articles/coronaviruscovid19latestinsights/vaccines Addressing the above mentioned issues adequately, I argue the study will meet the indexing standards. I wish you all the best when reviewing the manuscript. Sincerely, Referee 1. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes References 1. Mostert S, Hoogland M, Huibers M, Kaspers G: Excess mortality across countries in the Western World since the COVID-19 pandemic: ‘Our World in Data’ estimates of January 2020 to December 2022. BMJ Public Health . 2024; 2 (1). Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise Applied statistical analysis. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (2) Author Response 20 Feb 2025 Alberto Donzelli, Fondazione "Allineare Sanità e Salute", Italy Authors’ responses Dear Dr. Jarle Aarstad, We want to thank you for your advice which contribute to improve our paper. Below the answers to your questions. Reviewer response: Dear Editor and authors, Thank you for giving me the opportunity to referee the manuscript entitled “All-cause mortality according to COVID-19 vaccination status: An analysis of the UK office for National statistics public data”. I find the article very interesting and illuminating, particularly comparing all-cause mortality with non-covid mortality, and along with a very good discussion, it increases the study’s validity and adds value to the study of covid-vaccines’ potential mortality effect. Concerning the Introduction, the authors relevantly address the research question and study objective. I believe, however, that the study by Mostert et al. (2024)(Ref-1) merits attention concerning excess mortality in Western countries. Author’s response : Thank you for your valuable feedback. We have added the reference in the "Introduction" section. Reviewer response: I assume the calculations are correct regarding the statistical analyses of the RRs CIs, but I recommend one or two references on which you base the calculations. Author’s response : Sorry, but we think do not understand, the reference regarding the statistical analyses of the RRs Cis are indicated just before the colons in the sentence above the formula and they are mentioned as 7 and 8 references. Reviewer response: Also, I did not understand what you mean by “standardized rate”. Author’s response : Sorry, you are right. For “standardized rate” we mean the Age-standardized mortality rates per 100,000 person-years, standardized to the 2013 European Standard Population using five-year age groups from those aged 10 years and over as defined in "Note 1" of the spreadsheet called "Notes" of the ONS dataset. We changed the sentence. Reviewer response: Altogether, I would like you to explain that part better, particularly for an audience unfamiliar with the approach, eventually in an appendix. Author’s response : Sorry but we are not sure we understand, could you possibly be more specific? We inserted all the steps of the calculations performed by us, and we are not sure what other steps we could describe in a further appendix. However, we also interpreted your perplexity as due to the fact that we have inserted a bibliographic reference in Italian, less useful to international researchers. We have therefore replaced the current entry n. 9 with the same reference text in English. Reviewer response: I follow the logic in section 3.4, but I suspect you compare never vaccinated with ever vaccinated. Is that correct? I assume yes, but it was a bit unclear for me. Author’s response : Exactly, you are correct, we used the same terminology used in in the Table 2 of the ONS dataset. Reviewer response: In the Figures, you include the number of months, but would it be better to include their names? E.g., Apr. 2021, etc. Author’s response : Thank you, we replaced the numbers with the names. Reviewer response: In Figures 2, 3, 4, 5, and partly 6, the intersections are out of the sample, which you should state in the text. Author’s response : Sorry, you are right. We added a sentence that specifics this aspect. Reviewer response: Very good and illuminating discussion. As you apply English/Welsh data, you may refer to the following web page by the Office for National Statistics, showing that unvaccinated tended to live in more deprived areas, urban areas, or social rented housing, were not born in the UK or did not have English as a main language, have never worked or are long-term unemployed, limited a lot by a disability, and are male (among you more men die than women). https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/articles/coronaviruscovid19latestinsights/vaccines Addressing the above mentioned issues adequately, I argue the study will meet the indexing standards. I wish you all the best when reviewing the manuscript. Author’s response : Thank you for this advice. We have added a reflection on this aspect in the “Discussion” section. View more View less Competing Interests None reply Respond Report a concern Author Response 16 Jan 2025 Alberto Donzelli, Fondazione "Allineare Sanità e Salute", Italy Dear Dr. Jarle Aarstad, We want to thank you for your advice which contribute to improve our paper. Below are the answers to your questions. Dear Editor and authors, Thank you for giving me the opportunity to referee the manuscript entitled “All-cause mortality according to COVID-19 vaccination status: An analysis of the UK office for National statistics public data”. I find the article very interesting and illuminating, particularly comparing all-cause mortality with non-covid mortality, and along with a very good discussion, it increases the study’s validity and adds value to the study of covid-vaccines’ potential mortality effect. Q . Concerning the Introduction, the authors relevantly address the research question and study objective. I believe, however, that the study by Mostert et al. (2024)(Ref-1) merits attention concerning excess mortality in Western countries. Author’s response : Thank you for your valuable feedback. We have added the reference in the "Introduction" section. Q . I assume the calculations are correct regarding the statistical analyses of the RRs CIs, but I recommend one or two references on which you base the calculations. Author’s response : sorry, but we think do not understand, the reference regarding the statistical analyses of the RRs CIs are indicated just before the colons in the sentence above the formula and they are mentioned as 8 and 9 references. Q . Also, I did not understand what you mean by “standardized rate”. Author’s response : sorry, you are right. For “standardized rate” we mean the Age-standardised mortality rates per 100,000 person-years, standardised to the 2013 European Standard Population using five-year age groups from those aged 10 years and over as defined in "Note 1" of the spreadsheet called "Notes" of the ONS dataset. We changed the sentence. Q . Altogether, I would like you to explain that part better, particularly for an audience unfamiliar with the approach, eventually in an appendix. Author’s response : sorry but we think do not understand, could you be more specific? We inserted all the steps of the calculations performed by us and we would not know which other steps we could describe in a further appendix. Q . I follow the logic in section 3.4, but I suspect you compare never vaccinated with ever vaccinated. Is that correct? I assume yes, but it was a bit unclear for me. Author’s response : exactly, you are correct, we used the same terminology used in the Table 2 of the ONS dataset. Q. In the Figures, you include the number of months, but would it be better to include their names? E.g., Apr. 2021, etc. Author’s response : thank you, we replaced the numbers with the names. Q . In Figures 2, 3, 4, 5, and partly 6, the intersections are out of the sample, which you should state in the text. Author’s response : sorry, you are right. We added a sentence that specifics this aspect. Q. Very good and illuminating discussion. As you apply English/Welsh data, you may refer to the following web page by the Office for National Statistics, showing that unvaccinated tended to live in more deprived areas, urban areas, or social rented housing, were not born in the UK or did not have English as a main language, have never worked or are long-term unemployed, limited a lot by a disability, and are male (among you more men die than women). https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/articles/coronaviruscovid19latestinsights/vaccines Addressing the above mentioned issues adequately, I argue the study will meet the indexing standards. Author’s response : thank you for this advice. We have added a reflection on this aspect in the “Discussion” section. I wish you all the best when reviewing the manuscript. View more View less Competing Interests None reply Respond Report a concern Aarstad J. 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