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Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: No

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available??>

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: No

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Reviewer #1: The study examines the decrease in life expectancy in Japan during the COVID-19 pandemic. The main focus is on the life expectancy changes in 2021 and 2022. These changes are decomposed by age and major classes of causes of death. A drop in a life-table measure of interindividual life-span equality in 2020 is emphasized. In addition to the national-level analyses, there is also an analysis of associations between the life expectancy changes in 2020-22 and indicators of the population spread of COVID-19 across prefectures.

Herewith, I comment on shortcomings in the manuscript.

1. The Introduction and Discussion sections do not mention some relevant studies on the COVID-related mortality elevation in Japan. Namely, these are:

Hirokazu Tanaka, Shuhei Nomura, Kota Katanoda, Changes in Mortality During the COVID-19 Pandemic in Japan: Descriptive Analysis of National Health Statistics up to 2022, Journal of Epidemiology, 2025, Volume 35, Issue 3, Pages 154-159

Tanaka H, Togawa K, Katanoda K. Impact of the COVID-19 pandemic on mortality trends in Japan: a reversal in 2021? A descriptive analysis of national mortality data, 1995–2021. BMJ Open 2023;13:e071785. doi:10.1136/ bmjopen-2023-071785

Z Shervani,AA Khan, I Khan, A Sherwani, PDM Kumar et al. Marginal Shortening of Life Expectancy in Japan During COVID-19: A Low Pandemic Impact Country Due to Improved Health Infrastructure and Awareness. (2024). European Journal of Medical and Health Sciences, 6(6), 9-17. https://doi.org/10.24018/ejmed.2024.6.6.2214

Zameer Shervani, Aamir Akbar Khan, Intazam Khan et al. Factors Explaining Japan’s Low COVID-19 Mortality: Comparison with Rich and Democratic Countries. (2025). European Journal of Medical and Health Sciences, 7(1), 1-9. https://doi.org/10.24018/ejmed.2025.7.1.2245

Mst Sirajum Munira, Okada Y, Nishiura H. 2023. Life-expectancy changes during the COVID-19 pandemic from 2019–2021: estimates from Japan, a country with low pandemic impact. PeerJ 11:e15784 DOI 10.7717/peerj.15784

These recent studies are thematically close to the present study. They should be mentioned in the Introduction and/or Discussion.

Similarities or differences between the present study and the earlier studies should be noted. The readers should know what novelty the present study adds to the existing literature.

2. The study focuses on annual changes in e0 in 2020-22. However, the full amount of e0 losses in these years can be assessed only through comparisons between the observed e0 values and the counterfactual (predicted) e0 values. Using the Human Mortality Database (HMD) series for Japan, one can see that from 2012 to 2019, the Japanese life expectancy was increasing by 0.18 years per calendar year on average. If we apply this rate of increase to 2020-22, the e0 losses for Japan in 2021 and 2022 would be 0.38 and 0.98 years instead of the observed (annual declines) of 0.13 and 0.48 years.

This difference is important and should be acknowledged. It would also be interesting to see speculations about possible differences between the annual decreases in e0 in 2020-22 and the e0 losses in the same years.

3. The manuscript has a massive Methods section. It is not clear whether it is a methodology paper or a substantive analysis. There are numerous formulae. All of them are known from earlier studies. The first seven equations are completely unnecessary, as they are very conventional and can be found in demography textbooks, such as those by Preston or Keyfitz, or Chiang.

4. The following equations for the analysis of the age at death equality h are less trivial since they are newer.

Maybe the formal definitions of the entropy H, e+ (e-dagger), and the equality h could remain in the main text.

The next four equations related to further analysis of temporal changes in h should be given in a supplementary appendix.

Instead, the reader needs a transparent explanation of all measures and calculation procedures. The substantive sense of h should be clarified. Without a transparent explanation, it's impossible to understand why h is used and what is the public health sense of its decrease by 0.07 between 2019 and 2020.

Perhaps it would be better to use e+ instead of h. e+ is a transparent measure of life expectancy losses by Vaupel and Canudas-Romo. e+ is counted in years of life and has a clear public health sense (Shkolnikov et al. 2011).

Shkolnikov V.M., Andreev E.M., Zhang Z., Oeppen J., J.W.Vaupel. 2011. Losses of expected lifetime in the United States and other developed countries: methods and empirical analyses. Demography, 48: 211-239

5. Despite the length of the Methods, they lack a precise description of the data and computational procedures. Which calculations were based on the complete life tables vs. the abridged life tables? It is clearly preferable to use the single-year-age data for calculating dispersion measures such as h or e+.

This information is needed. It can be provided in a supplementary appendix.

6. The analysis of causes of the h changes between 2019 and 2020, 2020 and 2021, 2021 and 2022 is not convincing.

From the methodology point of view, it is possible to see directly the components of changes in h. This could be done by decomposing the change in h by age and CODs with one of the universal decomposition methods (discrete replacement method by Andreev et al. 2002; continuous change method by Horiuchi et al. 2008). These methods estimate additive contributions of different ages and causes of death to a change in any aggregate measure. This has not been done. Looking at changes in M(x) or at the weights W(x) cannot replace the direct decomposition.

On the substantive side, there are also some problems.

First, the alarming drop in h between 2019 and 2020 is not explained. However, Figure 3 reveals moderate mortality increases at young adult ages in 2020. My own quick look at the Human Mortality Database data for Japan also indicates mortality increases at ages between 20 and 29 as well as some ages between 30 and 44 in 2020. So, what happened in 2020? It is important to know. The potential role of the pandemic is doubtful.

Second, increases in h from 2020 to 2021 and from 2021 to 2022 are considered “undesirable” as they were (likely) caused by the mortality elevation at old and very old ages.

At the same time, the h increase in 2021-22 may be considered an advantage of Japan compared to other countries. While in Japan, the mortality increase in 2021 and especially in 2022 was entirely concentrated at old ages, in many other countries (USA, Eastern Europe, Britain, Belgium, and other), the COVID-related mortality increases were also large at midlife and “young old” ages (55-69). The latter pattern results in stagnant or even decreasing values of h.

That is to say that the changes in e0 and e+ in Japan should be considered in context of e0 and e+ changes elsewhere.

7. Discussion about the large increases in mortality from CVD in 2021 and 2022 does not take into account potential direct contributions of COVID-19 to deaths from CVD. COVID-19 could appear on the medical death certificate as an immediate, intermediate, or contributory cause.

It would be good to discuss how large the direct influence of COVID-19 could be in CVD deaths in Japan.

8. The analysis of statistical associations between e0 decreases in 2021 and 2022 and the epidemiological indicators is not fully justified.

The authors should perform statistical tests for normality, heteroscedasticity, and linearity.

Pearson’s r values together with p-values should shown on these graphs.

The regression outcomes are commented as if there is a radical difference between 2021 and 2022. It looks like statistical links exist in 2021 and they do not exist in 2022. However, the scatterplots and the tables show that in both years, the links are very weak, but in 2022 some of them are statistically significant.

9. Overall evaluation of the Japanese fight against death in 2020-22.

The manuscript sounds quite alarming. However, previous studies presented Japan as a country with low/very moderate losses and as a “success story”.

It would be good to address this in the Discussion.

9. Language.

The manuscript would benefit from its further editing by a native English speaker.

For example, the title “Changes in life span equity from 2020-22 in Japan” is confusing. It would be possible to say “in 2020-22” or from “2019 to 2022”.

Reviewer #2: General Assessment

This manuscript presents a detailed demographic analysis of mortality in Japan during the COVID-19 pandemic up to 2022, leveraging prefectural life expectancy estimates, Arriaga decomposition, and life span equality (ℎ) derived from life table entropy. The study updates previous work by the authors and provides novel insights on the role of cardiovascular mortality, the discrepancies between COVID-19 statistics and demographic indicators, and the evolution of lifespan equality.

The topic is relevant to demography, epidemiology, and public health. The methods are standard and appropriate, the figures are generally clear, and the study addresses an important gap: understanding indirect mortality effects and the changing age pattern of mortality in Japan.

However, the manuscript would benefit from substantial clarification, stronger methodological justification, better contextualization, and more cautious interpretation. Some claims are not fully supported by the analyses provided, and certain demographic measures are introduced without sufficient explanation for a multidisciplinary readership.

Overall, the manuscript is publishable after major revisions.

Major Comments

Interpretation of prefectural analysis needs caution. The manuscript argues that the absence of correlation between prefectural COVID-19 statistics and life expectancy change in 2021–22 indicates “growing ascertainment bias”. With only 47 prefectures, linear regression power is limited. COVID-19 cases, ICU days, and deaths suffer from known reporting issues, but the manuscript does not quantify potential misreporting.

Life expectancy changes include all-cause mortality, not just COVID-19: structural health-system strain, ageing, deferred healthcare, and influenza resurgence might all explain discrepancies. Instead of asserting ascertainment bias, acknowledge it as one possible explanation among others, and emphasize the ecological, multi-cause nature of the prefectural relationships.

Cause-of-death decomposition requires deeper contextualization. The decomposition clearly shows increased contributions of cardiovascular and “other” causes in 2022 (Fig. 3) . However, “other causes” is too broad. Senility, accidents, metabolic disorders? If senility drives most changes, this should be explicitly shown, not only mentioned.

Cardiovascular increases are attributed to COVID-19-related indirect effects, but the manuscript does not present evidence for causal mechanisms. Add a supplemental table with the breakdown of “other causes.” Add a paragraph clarifying whether increases in cardiovascular deaths follow known international post-COVID trends or reflect Japan-specific dynamics.

Life-span equality section is underdeveloped. The discussion of ℎ (life span equality) is technically correct but insufficient for non-specialist readers. Interpretation is vague: “undesirable increase in life span equality” (lines 314-315) may confuse readers unfamiliar with life table entropy.

The explanation linking increased ℎ to higher mortality above threshold age a_h is technically valid but described in overly mathematical terms. Life span equality increased because mortality increases were concentrated at older ages, which compresses the age-at-death distribution. Provide a short, intuitive explanation of life table entropy and its link to lifespan compression.

The Results section sometimes repeats earlier work. Some paragraphs replicate findings already described in the authors’ 2019–2021 study (Munira et al., 2023), but without clearly distinguishing what is new in the update up to 2022.

Add a short subsection explicitly stating, What was already known from 2019–2021? What has changed in 2022? Why 2022 is important for Japan (e.g., Omicron, high vaccine coverage, healthcare saturation)?

Graphical presentation could be improved.

Fig. 4 (Life span equality). The vertical axis lacks interpretation: add labels or annotations explaining what changes of 0.02 in ℎ represent.

Methods need more transparency. Missing clarifications. Were abridged life tables validated against official JMD single-year tables? Authors mention near-identity but show no numbers.

What values were used for ax in 2020–2022? JMD uses specific ax schedules that change in crisis years. Add a short appendix or supplemental note describing the ax assumptions, interpolation methods, quality checks.

Discussion of policy relevance could be strengthened. The manuscript ends with general statements but does not articulate the policy implications of increased cardiovascular mortality, increased senility deaths, decoupling of reported COVID-19 statistics from demographic indicators. A clearer final section would improve impact.

Minor Comments

In the first sentence of the abstract, life expectancy is not wrongly defined. It says that “Life expectancy is a demographic measure of the death structure and its change at the population level.” But LE does not measure the death structure (whatever that means); it is one of the central measures in demography and actuarial science, expressing the average length of life that a newborn (or any person of a given age) is expected to live, if current mortality conditions were to remain constant throughout their remaining lifetime. Formally, life expectancy is a summary measure of mortality derived from a life table, which models the survival pattern of a hypothetical cohort subjected to age-specific mortality rates observed in a particular population and period.

The objective of the manuscript “…the present study focuses on interpreting changes in the nature of mortality not only by life expectancy but also by the age distribution of mortality in Japan” is confusing by how it is written. I think the focus goes beyond that.

Copy-editing is needed.

Neoplastics does not exist as a cause of death; perhaps you mean neoplasms.

In the Abstract says: “Beyond this measure that have been heavily applied…” must be “…has been heavily applied.”

Clarify what life-span equality measures in one sentence.

Introduction

Provide more background on Japanese COVID-19 mortality, including excess mortality patterns reported by other sources.

Methods

Add citations when introducing the formulas. Perhaps Preston, Heuveline and Guillot (2001). Actually, you could discard the explanation of the life table construction; just refer to it , for example, Preston et al. (2001).

Preston, S. H., Heuveline, P., & Guillot, M. (2001). Demography: Measuring and modeling population processes . Blackwell Publishers.

Clarify whether decomposition was symmetric or stepwise.

Results

Avoid repeating numeric results from S1 Table in multiple places streamline. You started the results by quoting it. If it is important, just include it in the text, not in the appendix.

Discussion

Statements such as “not surprising” should be replaced with evidence-based justification.

References

Some citations appear duplicated or misformatted.

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Reviewer #1: No

Reviewer #2: No

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Dear Dr. Nishiura,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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We look forward to receiving your revised manuscript.

Kind regards,

Claudio Alberto Dávila-Cervantes, Ph.D.

Academic Editor

PLOS One

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously? -->?>

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available??>

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.-->

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English??>

Reviewer #1: Yes

Reviewer #2: Yes

**********

Reviewer #1: After the revision, the manuscript looks much better. All my comments were taken into account and addressed through changes to the text and/or additional analyses (decomposition of the h change and others). More of the earlier literature is cited; the Methods section was shortened and is free of trivial/textbook equations; the counterfactual approach was added to avoid underestimating life expectancy losses; and the analysis of statistical associations across prefectures is described more comprehensively. Importantly, it is mentioned now that COVID-19 may underlie some of the increases in deaths from CVD, senility, and some other causes.

This is all good. Nevertheless, I have noticed a few minor problems to be corrected before publication of the study.

L63-L64. “…measurable mortality impact…” – better to say “considerable” or “substantial” impact.

L85-L86. “We also decomposed the year-on-year life expectancy change from 2019-22 …” sounds confusing. It should be “We also decomposed annual life expectancy changes in 2019-2022.”

L101. “The deaths counts by cause of death in Japan is only available…” It should be “The death counts by cause of death in Japan are only available…”

L135. “from 2000-2022” It should be “from 2000 to 2022” or “for the period 2000-2022”

L141-L143. It is still unclear why it is important (from the public health viewpoint) to know whether a relative e0 change is larger or smaller than the relative e-dagger change.

L225-L226. It is better to say “Life expectancy changes from 2020 to 2021 and from 2021 to 2022”. The column heading “Year” is confusing. It is better to say “Period”.

L233-L235. “… more eminent in 2021-22 than in 2020-21”. This is unclear. It would be better to say here “more eminent in the change from 2021 to 2022 than in the change from 2020 to 2021”.

L328. “The unclear correlation …. indicators of COVID-19…”. It is better to say “The unclear correlations across prefectures … indicators of COVID-19 …”.

Reviewer #2: (No Response)

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what does this mean?). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our Privacy Policy..-->

Reviewer #1: Yes: Vladimir M. ShkolnikovVladimir M. ShkolnikovVladimir M. ShkolnikovVladimir M. Shkolnikov

Reviewer #2: No

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Changes in life expectancy and life span equality during the COVID-19 epidemic in 2020-22 in Japan

PONE-D-25-54870R2

Dear Dr. Nishiura,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Claudio Alberto Dávila-Cervantes, Ph.D.

Academic Editor

PLOS One

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