Dear Dr Ahmadi-Abhari,

Thank you for submitting your manuscript entitled "Direct and indirect impacts of the COVID-19 pandemic on life-expectancy and person-years of life lost with and without disability: a systematic analysis for 18 European countries, 2020-2022" for consideration by PLOS Medicine.

Your manuscript has now been evaluated by the PLOS Medicine editorial staff and I am writing to let you know that we would like to send your submission out for external peer review.

However, before we can send your manuscript to reviewers, we need you to complete your submission by providing the metadata that is required for full assessment. To this end, please login to Editorial Manager where you will find the paper in the 'Submissions Needing Revisions' folder on your homepage. Please click 'Revise Submission' from the Action Links and complete all additional questions in the submission questionnaire.

Please re-submit your manuscript within two working days, i.e. by Sep 05 2024 11:59PM.

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Feel free to email us at plosmedicine@plos.org if you have any queries relating to your submission.

Kind regards,

Syba Sunny, MBBS, MRes, FRCPath

Associate Editor

PLOS Medicine

ssunny@plos.org

Dear Dr Ahmadi-Abhari,

Many thanks for submitting your manuscript "Direct and indirect impacts of the COVID-19 pandemic on life-expectancy and person-years of life lost with and without disability: a systematic analysis for 18 European countries, 2020-2022" (PMEDICINE-D-24-02916R1) to PLOS Medicine. The paper has been reviewed by subject experts and a statistician; their comments are included below and can also be accessed here: [LINK]

As you will see, the reviewers raised several concerns about the data, its analysis and the modelling used. After discussing the paper with the editorial team and an academic editor with relevant expertise, I'm pleased to invite you to revise the paper in response to the reviewers' comments. We plan to send the revised paper to some or all of the original reviewers, and we cannot provide any guarantees at this stage regarding publication.

When you upload your revision, please include a point-by-point response that addresses all of the reviewer and editorial points, indicating the changes made in the manuscript and either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please also be sure to check the general editorial comments at the end of this letter and include these in your point-by-point response. When you resubmit your paper, please include a clean version of the paper as the main article file and a version with changes tracked as a marked-up manuscript. It may also be helpful to check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper.

We ask that you submit your revision by Oct 25 2024 11:59PM. However, if this deadline is not feasible, please contact me by email, and we can discuss a suitable alternative.

Don't hesitate to contact me directly with any questions (ssunny@plos.org).

Best regards,

Syba

Syba Sunny, MBBS, MRes, FRCPath

Associate Editor

PLOS Medicine

ssunny@plos.org

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Comments from the academic editor:

The academic editor thought that the manuscript looked interesting and relevant. She commented that, whilst the reviewers raised many points, none doubted the value of the work. She was supportive of taking your manuscript forward to the next step.

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Comments from the reviewers:

Reviewer #1: The authors attempted to assess the direct and indirect impacts of the COVID-19 pandemic on life expectancy and years of life lost with and without disabilities in 18 European countries, 2020-2022. The authors have been quite successful in achieving their objective. In fact, I only have two comments.

1.- As the authors state on page 3 of the supplementary material, they estimate transition probabilities by fitting Cox proportional hazards regression models.

However, the authors do not test the fulfillment of the proportional hazards assumption. They should test it (both globally and individually for each predictor) and also provide the corresponding Kaplan-Meier curves.

If it is not fulfilled, they should use methods other than the Cox model.

2.- In the Discussion, the authors point out, as a limitation, that 'another source of error is the accuracy of projections for future mortality rates which is inherently unpredictable' and that 'to partly account for uncertainty in projections in mortality and incidence of disease we accompanied our estimates with 95% uncertainty intervals derived from Monte-Carlo simulations'.

I think this is good, but insufficient. The authors should discuss uncertainty in sufficient detail. That is, for example, are the intervals wider in different countries? Are the intervals different over time? etc.

Reviewer #2: Our knowledge of the impact of the pandemic on mortality is still fragmentary, despite a large number of publications on COVID-19. The manuscript aims to provide useful information by providing estimates of person-years of life lost (PYLL) and quantifying them by disability status. The authors use an excess mortality approach with the baseline mortality level estimated using the multi-state Markov chain model. Nevertheless, I have some doubts about the outcome.

1. Relatively old data (last year 2017) is used to estimate disability-free proportion. The assumption that the pandemic didn't change any trends in disability (especially at most affected old ages) is relatively strong and not necessarily correct.

2. The period used to fit the model (since 1998) might be too long. This would be OK for mid- and long-term forecasts. The short-term forecast used to estimate the expected level of mortality in 2020-2022 is more sensitive to recent changes in mortality trends.

3. In 2020, the number of COVID-19 deaths by country is not comparable between countries. At the beginning of the pandemic, information on deaths from COVID-19 was biased by large differences in approaches to testing SARS-CoV-2 and recoding of COVID-19 as a cause of death. While some countries tend to attribute to COVID-19 all or nearly all deaths of those with positive tests for the virus, others applied more conservative approaches with an emphasis on pre-existing co-morbidities.

4. The importance of GDP seems to be overestimated by the authors. For example, several publications noted an East-West gradient in pandemic losses in 2021. It corresponds to the same gradient in mortality. The correlation between GDP and mortality was described by Presto many years ago but it doesn't explain why Western countries had lower losses in 2021. In addition, the correlation between GDP and losses doesn't work in 2020. In this light, factors such as vaccination coverage and trust in science and government seem to be more important.

Finally, the description of the model in the supplementary material is not detailed enough and is poorly structured. It is impossible to evaluate the model and its possible limitations.

Reviewer #3: 1- While the use of a Markov model is appropriate for modeling longitudinal transitions, could the authors provide more justification for choosing this model over other potential approaches (e.g., Cox proportional hazards models for time-to-event data or microsimulation models)? Even though the Markov model captures state transitions well, a brief explanation for this choice, particularly given the model's memoryless property (i.e., transitions depend only on the current state and not on past history), would be beneficial.

2- The manuscript outlines the assumptions of the model clearly. However, I suggest including a brief discussion of the limitations these assumptions might impose, especially regarding the transition probabilities derived from datasets that predate the pandemic.

3- The use of population numbers and mortality rates from sources like STMF and the WHO is appropriate, and the explanation of these inputs is clear. However, it would be helpful to explain more clearly how country-specific variations in data availability and quality were handled, as these differences could affect the robustness of the model's outputs.

4- The use of ELSA and SHARE data to estimate transition probabilities is well justified. However, the manuscript would benefit from including sensitivity analyses that account for variability in these inputs, as the reliability of data may differ between countries.

5- The authors mention Monte Carlo simulations to generate uncertainty intervals, but providing a more detailed explanation of how variation in transition probabilities across different countries was managed would enhance the transparency of the model.

6- The use of Monte Carlo simulations to estimate uncertainty intervals is appropriate given the model's complexity and reliance on probabilistic transitions. However, it could be helpful to explain why 500 iterations were chosen and whether higher or lower numbers of iterations were tested to assess their impact on the stability of the uncertainty estimates.

7- The estimation of PYLL (person-years of life lost) with and without disability is a strength of the analysis. However, given the lack of direct data on disability-related mortality in many countries, the assumptions used to estimate these inputs should be better justified. It would be useful to explain why hazard ratios from the UK were applied across other countries.

8- The two extreme scenarios—either assuming all reductions in non-COVID deaths were real or assuming they resulted from misclassification as COVID-19 deaths—offer a reasonable approach to managing uncertainty in cause of death. However, the analysis could benefit from more discussion of the likely midpoint between these extremes, as well as an exploration of intermediate assumptions and how they might impact the final results.

9- The incorporation of time-specific transition probabilities based on pre-pandemic trends enhances the dynamic nature of the model. However, since the COVID-19 pandemic significantly disrupted healthcare access, it would be useful to discuss how these disruptions were accounted for in the model beyond mortality rates (e.g., access to preventive care or management of chronic diseases).

10- Conducting sensitivity analyses using Monte Carlo simulations adds robustness to the findings, but additional information on the distribution assumptions for the parameters would be helpful. For example, are transition probabilities assumed to follow a normal or log-normal distribution?

11- The authors mention that the methods and assumptions used in this Markov model have been validated for the UK. It would strengthen the manuscript to elaborate on any cross-country validation performed to ensure the assumptions are valid across the 18 European countries, given the variations in healthcare systems, demographic structures, and responses to COVID-19. If such validation was not feasible, this limitation should be explicitly stated.

12- The manuscript could benefit from a more explicit discussion of how missing data (if any) were handled in the model. Given that longitudinal data sources like ELSA and SHARE may contain incomplete information, it is important to explain how this was addressed (e.g., through imputation, exclusion, or other methods).

13- Given that the study spans the period from 2020 to 2022, which included multiple waves of COVID-19 and the emergence of different variants (e.g., Alpha, Delta, Omicron), it would be helpful to discuss how, if at all, these evolving factors were considered in the modeling. Was the pandemic treated as a uniform event, or did time-varying effects of different variants impact mortality and healthcare access?

14- The model assumes discrete health states with transitions between them. It would be useful to clarify if and how interactions between health states (e.g., cardiovascular disease co-occurring with dementia) were handled in the model. Can individuals occupy multiple health states simultaneously, and how might this affect transitions and mortality risk?

15- The manuscript mentions the indirect impacts of the pandemic due to disruptions in healthcare services. It would be beneficial to expand on whether other external shocks (e.g., changes in policy or healthcare infrastructure strain) were modelled, particularly in relation to non-COVID causes of death. For example, were the lagged effects of disruptions (e.g., delayed cancer screenings) on long-term mortality, which may not fully manifest within the 2020-2022 window, considered?

16- The model stratifies by age groups (35+, 65+, etc.), but it might be helpful to discuss whether age-cohort effects (i.e., differences between generations) were considered. For instance, older cohorts may have different health risks and exposures compared to younger cohorts. Even though the model is time-based, it could be valuable to discuss how aging dynamics and cohort-specific vulnerabilities were factored in or could affect the results.

17- The mention of a bespoke R package developed by one of the authors is a positive step toward reproducibility. However, it would be useful to clarify whether this code will be made publicly available, as doing so would enhance the transparency of the modeling process.

18- The supplementary material contains valuable information such as tables and figures that aid in understanding the modeling assumptions and results. It is important to ensure that the main manuscript clearly directs readers to these tables and figures, especially for those who may not read the supplementary material in full detail.

Any attachments provided with reviews can be seen via the following link: [LINK]

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General editorial requests:

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Dear Dr. Ahmadi-Abhari,

Thank you very much for re-submitting your manuscript "Direct and indirect impacts of the COVID-19 pandemic on life-expectancy and person-years of life lost with and without disability: a systematic analysis for 18 European countries, 2020-2022" (PMEDICINE-D-24-02916R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by 2 of the original reviewers (including the statistician). I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We expect to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

Please note, when your manuscript is accepted, an uncorrected proof of your manuscript will be published online ahead of the final version, unless you've already opted out via the online submission form. If, for any reason, you do not want an earlier version of your manuscript published online or are unsure if you have already indicated as such, please let the journal staff know immediately at plosmedicine@plos.org.

If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by Jan 17 2025 11:59PM. But please do let us know if you need more time.

Sincerely,

Syba

Syba Sunny, MBBS, MRes, FRCPath

Senior Editor

PLOS Medicine

plosmedicine.org

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Requests from Editors:

Thank you for submitting your revised manuscript. As you can see, the reviewers were positive about your amendments and we are pleased to be moving forward with your submission. One of the reviewers (Reviewer 3) provided some additional suggestions. In your next revision, we ask that you kindly provide a point-by-point response to these comments as well as the editorial requests outlined below.

(1) Data Availability Statement

Thank you for providing a Data Availability Statement. However, we kindly ask that you provide some extra information for the benefit of our readers. For data residing with any third party, authors are required to provide instructions with contact information (web or email address) for obtaining the data. Please provide this information with respect to the ELSA and SHARE studies. Please also note that a study author cannot be the contact person for any data. Please direct readers to a non-author institutional point of contact, such as a data access or ethics committee. Providing a durable point of contact ensures data will be accessible even if an author changes email addresses, institutions, or becomes unavailable to answer requests.

(2) Author Summary:

We ask that you kindly amend your Author Summary a little more. Please use bullet points. (Ideally each sub-heading should contain 2-3 single sentence, concise bullet points containing the most salient points from your study.) Please provide an expansion of GDP at first use in the Author Summary. The section entitled ‘What Did the Researchers Do and Find?’ would benefit from being shortened further. We also ask if the authors could revise the first 2 sentences of the last section to include a more concrete example of a study limitation. It may be best to remove the first 2 sentences entirely, start this section with the sentence ‘ The increasing PYLL…’ and write about a study limitation towards the end of this section instead. The authors could say something along the lines of ‘Limitations of this study include the lack of mortality data by disability status that was available during the pandemic and the subsequent reliance on estimates.’

Comments from Reviewers:

Reviewer #1: The authors have responded very well not only to my comments but also to those of the other reviewers. In addition, they have included many of them in the new version of the manuscript. I have no further comments.

Reviewer #3: Thank you for thoroughly addressing the comments and suggestions I provided in my initial review. The revisions have significantly improved the clarity, depth, and methodological rigor of the manuscript. I appreciate the detailed responses to each point, particularly the explanations regarding the modelling choices, data handling, and statistical analyses. Below are some additional comments that, if addressed, could further strengthen the manuscript.

- While the Markov model uses age- and sex-specific strata, the authors could discuss how granularity in the inputs (e.g., socioeconomic or regional differences within countries) might impact the outputs.

- While the authors chose 500 iterations for Monte Carlo simulations, it may be useful to present results of convergence diagnostics or explore whether higher iterations (e.g., 1000) would meaningfully change uncertainty intervals.

-Provide additional justification for the use of normal distributions for transition probabilities. For some events (e.g., rare transitions), log-normal or beta distributions might be more suitable. Explain why normal distributions were deemed appropriate in this context.

- Given that pre-pandemic data trends were assumed to be linear or log-linear, a brief explanation of how non-linear patterns (if present) were tested and ruled out would strengthen the methodology.

Any attachments provided with reviews can be seen via the following link:

[LINK]

Dear Dr Ahmadi-Abhari, 

On behalf of my colleagues and the Academic Editor, Professor Mirjam Kretzschmar, I am pleased to inform you that we have agreed to publish your manuscript "Direct and indirect impacts of the COVID-19 pandemic on life-expectancy and person-years of life lost with and without disability: a systematic analysis for 18 European countries, 2020-2022" (PMEDICINE-D-24-02916R3) in PLOS Medicine.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. Please be aware that it may take several days for you to receive this email; during this time no action is required by you. Once you have received these formatting requests, please note that your manuscript will not be scheduled for publication until you have made the required changes.

In the meantime, please log into Editorial Manager at http://www.editorialmanager.com/pmedicine/, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production process. 

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To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Thank you again for submitting to PLOS Medicine. We look forward to publishing your paper. 

Sincerely, 

Syba

Syba Sunny, MBBS, MRes, FRCPath 

Associate Editor 

PLOS Medicine