Life-expectancy changes during the COVID-19 pandemic from 2019–2021: estimates from Japan, a country with low pandemic impact

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Introduction

Materials and Methods

Demographic and epidemiological datasets

Calculation of life expectancy

where wx is the length of discrete interval of age group x. Then, using qx, the number of survivors from birth to each exact age is obtained as follows:

for age group x=0,14,59,110andolder(in years). The number of deaths in age group x is:

and the remaining person-years of life for those in age group x, Tx, is written as:

Decomposing the life expectancy changes

where the first term is the direct contribution, and the second term in brackets is the indirect or interaction contribution to the difference in life expectancy attributable to the corresponding age group. Lx is the number of person-years lived within age group x, and Tx+1 is the total number of person-years lived within age groups x+1 and beyond. The last age group only has a direct effect because there is no later age group for any indirect or interaction effect. The sum of contributions from all age groups should equal the total life expectancy change in years. We used this method for the age groups up to 100+ years old, because the cause of death data for those aged 100–104, 105–109 and 110 years or more were not available. We therefore recalculated the life table for age groups from 0 to 100 years or more to allow decomposition.

where Rix is the proportion of deaths in age group x due to cause i, and mx is the all-cause mortality rate in age group x.

Statistical and numerical analysis

Data sharing statement

Ethical considerations

Results

Discussion

Conclusions

Supplemental Information

Life expectancy in 2020, 2021 in Japan in comparison with pre-pandemic trend from 2000 to 2019.

A to C: life expectancy from 2000 to 2021 in Japan for total, male, and female population are shown, respectively. In each panel, the line shows the best fit from linear regression using data from 2000 to 2019, with life expectancy as the response variable and year as the explanatory variable. For all panels, the shaded areas are 95% confidence intervals from linear regressions.

DOI: 10.7717/peerj.15784/supp-1

Correlation between life expectancy change from 2020–21 and infant mortality rate and natural birth rate in 2021.

(A) Correlation between life expectancy changes in 2021 and infant birth per 100,000 (log scale) in 2021. (B) Correlation between life expectancy change in 2021 and infant mortality rate in 2021. The infant mortality rates are expressed in percentages. For each panel, the line shows the best fit from linear regression, and the shaded area is 95% confidence intervals of the fitted model.

DOI: 10.7717/peerj.15784/supp-2

Correlation between life expectancy change from 2020–21 and daily hospital visit, daily hospital stays, and cancer mortality under age 75 per 100,000 in 2021.

(A) Correlation between life expectancy changes in 2021 and daily hospital visit (excluding those to psychiatric hospitals) per 100,000 (log scale) in 2021. (B) Correlation between life expectancy change in 2021 and daily hospital stays in beds of general use per 100,000 (log scale) in 2021. (C) Correlation between life expectancy change in 2021 and cancer mortality under age 75 per 100,000 (log scale) in 2021. The line shows the best fit from linear regression, and the 95% confidence intervals are shown by shading. No significant correlation was found among these pairs, though cancer mortality showed mild tendency of negative correlation between life expectancy gaps.

DOI: 10.7717/peerj.15784/supp-3

Supplementary Tables.

DOI: 10.7717/peerj.15784/supp-4

Additional Information and Declarations

Competing Interests

Hiroshi Nishiura is an Academic Editor for PeerJ. Otherwise, the authors declare that they have no competing interests.

Author Contributions

Mst Sirajum Munira performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Yuta Okada conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Hiroshi Nishiura conceived and designed the experiments, performed the experiments, authored or reviewed drafts of the article, and approved the final draft.

Data Availability

The following information was supplied regarding data availability:

The cause of death data are available in the Supplemental Tables.

Funding

Hiroshi Nishiura received funding from Health and Labour Sciences Research Grants (20CA2024, 20HA2007, 21HB1002 and 21HA2016); the Japan Agency for Medical Research and Development (AMED; JP20fk0108140, JP20fk0108535, JP21fk0108612 and JP 23fk0108685); the Japan Society for the Promotion of Science KAKENHI (21H03198 and 22K19670); the Environment Research and Technology Development Fund (JPMEERF20S11804) of the Environmental Restoration and Conservation Agency of Japan; the Japan Science and Technology Agency SICORP Program (JPMJSC20U3 and JPMJSC2105), and the RISTEX program for Science of Science, Technology and Innovation Policy (JPMJRS22B4). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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