IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0290160.html
   My bibliography  Save this article

Two complementary approaches to estimate an excess of mortality: The case of Switzerland 2022

Author

Listed:
  • Isabella Locatelli
  • Valentin Rousson

Abstract

Objective: During the COVID-19 pandemic, excess mortality has generally been estimated comparing overall mortality in a given year with either past mortality levels or past mortality trends, with different results. Our objective was to illustrate and compare the two approaches using mortality data for Switzerland in 2022, the third year of the COVID-19 pandemic. Methods: Using data from the Swiss Federal Statistical Office, standardized mortality rates and life expectancies in 2022 were compared with those of the last pre-pandemic year 2019 (first approach), as well as with those that would be expected if the pre-pandemic downward trend in mortality had continued during the pandemic (second approach). The pre-pandemic trend was estimated via a Poisson log-linear model on age-specific mortality over the period 2010–19. Results: Using the first approach, we estimated in Switzerland in 2022 an excess mortality of 2.6% (95%CI: 1.0%-4.1%) for men and 2.5% (95%CI: 1.0%-4.0%) for women, while the excess mortality rose to 8.4% (95%CI: 6.9%-9.9%) for men and 6.0% (95%CI: 4.6%-7.5%) for women using the second approach. Age classes over 80 were the main responsible for the excess mortality in 2022 for both sexes using the first approach, although a significant excess mortality was also found in most age classes above 30 using the second approach. Life expectancy in 2022 has been reduced by 2.7 months for men and 2.4 months for women according to the first approach, whereas it was reduced by respectively 8.8 and 6.0 months according to the second approach. Conclusions: The excess mortality and loss of life expectancy in Switzerland in 2022 are around three times greater if the pre-pandemic trend is taken into account than if we simply compare 2022 with 2019. These two different approaches, one being more speculative and the other more factual, can also be applied simultaneously and provide complementary results. In Switzerland, such a dual-approach strategy has shown that the pre-pandemic downward trend in mortality is currently halted, while pre-pandemic mortality levels have largely been recovered by 2022.

Suggested Citation

  • Isabella Locatelli & Valentin Rousson, 2023. "Two complementary approaches to estimate an excess of mortality: The case of Switzerland 2022," PLOS ONE, Public Library of Science, vol. 18(8), pages 1-15, August.
    • Handle: RePEc:plo:pone00:0290160
    DOI: 10.1371/journal.pone.0290160
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0290160
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0290160&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0290160?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Cameron,A. Colin & Trivedi,Pravin K., 2013. "Regression Analysis of Count Data," Cambridge Books, Cambridge University Press, number 9781107667273, Enero.
    2. William Msemburi & Ariel Karlinsky & Victoria Knutson & Serge Aleshin-Guendel & Somnath Chatterji & Jon Wakefield, 2023. "The WHO estimates of excess mortality associated with the COVID-19 pandemic," Nature, Nature, vol. 613(7942), pages 130-137, January.
    3. Valentin Rousson & Fred Paccaud & Isabella Locatelli, 2022. "Comparing the loss of life expectancy at birth during the 2020 and 1918 pandemics in six European countries," Vienna Yearbook of Population Research, Vienna Institute of Demography (VID) of the Austrian Academy of Sciences in Vienna, vol. 20(1), pages 527-542.
    4. Heather Booth & Rob Hyndman & Piet de Jong & Leonie Tickle, 2006. "Lee-Carter mortality forecasting: a multi-country comparison of variants and extensions," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 15(9), pages 289-310.
    5. Xiao Dong & Brandon Milholland & Jan Vijg, 2016. "Evidence for a limit to human lifespan," Nature, Nature, vol. 538(7624), pages 257-259, October.
    6. Nan Li & Ronald Lee, 2005. "Coherent mortality forecasts for a group of populations: An extension of the lee-carter method," Demography, Springer;Population Association of America (PAA), vol. 42(3), pages 575-594, August.
    7. Justin T. Denney & Robert McNown & Richard G. Rogers & Steven Doubilet, 2013. "Stagnating Life Expectancies and Future Prospects in an Age of Uncertainty," Social Science Quarterly, Southwestern Social Science Association, vol. 94(2), pages 445-461, June.
    8. Carter, Lawrence R. & Lee, Ronald D., 1992. "Modeling and forecasting US sex differentials in mortality," International Journal of Forecasting, Elsevier, vol. 8(3), pages 393-411, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rob Hyndman & Heather Booth & Farah Yasmeen, 2013. "Coherent Mortality Forecasting: The Product-Ratio Method With Functional Time Series Models," Demography, Springer;Population Association of America (PAA), vol. 50(1), pages 261-283, February.
    2. Hong Li & Johnny Siu-Hang Li, 2017. "Optimizing the Lee-Carter Approach in the Presence of Structural Changes in Time and Age Patterns of Mortality Improvements," Demography, Springer;Population Association of America (PAA), vol. 54(3), pages 1073-1095, June.
    3. Hyndman, Rob J. & Booth, Heather, 2008. "Stochastic population forecasts using functional data models for mortality, fertility and migration," International Journal of Forecasting, Elsevier, vol. 24(3), pages 323-342.
    4. Marie-Pier Bergeron-Boucher & James E. Oeppen & Vladimir Canudas-Romo & James W. Vaupel, 2017. "Coherent forecasts of mortality with compositional data analysis," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 37(17), pages 527-566.
    5. Feng, Lingbing & Shi, Yanlin & Chang, Le, 2021. "Forecasting mortality with a hyperbolic spatial temporal VAR model," International Journal of Forecasting, Elsevier, vol. 37(1), pages 255-273.
    6. Basellini, Ugofilippo & Camarda, Carlo Giovanni & Booth, Heather, 2023. "Thirty years on: A review of the Lee–Carter method for forecasting mortality," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1033-1049.
    7. Wang, Hsin-Chung & Yue, Ching-Syang Jack & Chong, Chen-Tai, 2018. "Mortality models and longevity risk for small populations," Insurance: Mathematics and Economics, Elsevier, vol. 78(C), pages 351-359.
    8. repec:osf:socarx:5zr2k_v1 is not listed on IDEAS
    9. Thilini Dulanjali Kularatne & Jackie Li & Yanlin Shi, 2022. "Forecasting Mortality Rates with a Two-Step LASSO Based Vector Autoregressive Model," Risks, MDPI, vol. 10(11), pages 1-23, November.
    10. Yanlin Shi & Sixian Tang & Jackie Li, 2020. "A Two-Population Extension of the Exponential Smoothing State Space Model with a Smoothing Penalisation Scheme," Risks, MDPI, vol. 8(3), pages 1-18, June.
    11. Massimiliano Menzietti & Maria Francesca Morabito & Manuela Stranges, 2019. "Mortality Projections for Small Populations: An Application to the Maltese Elderly," Risks, MDPI, vol. 7(2), pages 1-25, March.
    12. Carlo Maccheroni & Samuel Nocito, 2017. "Backtesting the Lee–Carter and the Cairns–Blake–Dowd Stochastic Mortality Models on Italian Death Rates," Risks, MDPI, vol. 5(3), pages 1-23, July.
    13. Hong Li & Yanlin Shi, 2021. "Mortality Forecasting with an Age-Coherent Sparse VAR Model," Risks, MDPI, vol. 9(2), pages 1-19, February.
    14. Marie-Pier Bergeron-Boucher & James E. Oeppen & James W. Vaupel & Søren Kjærgaard, 2019. "The impact of the choice of life table statistics when forecasting mortality," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(43), pages 1235-1268.
    15. Jia Liu & Jackie Li, 2019. "Beyond the highest life expectancy: construction of proxy upper and lower life expectancy bounds," Journal of Population Research, Springer, vol. 36(2), pages 159-181, June.
    16. Christina Bohk-Ewald & Marcus Ebeling & Roland Rau, 2017. "Lifespan Disparity as an Additional Indicator for Evaluating Mortality Forecasts," Demography, Springer;Population Association of America (PAA), vol. 54(4), pages 1559-1577, August.
    17. Pascariu, Marius D. & Canudas-Romo, Vladimir & Vaupel, James W., 2018. "The double-gap life expectancy forecasting model," Insurance: Mathematics and Economics, Elsevier, vol. 78(C), pages 339-350.
    18. Søren Kjærgaard & Yunus Emre Ergemen & Malene Kallestrup-Lamb & Jim Oeppen & Rune Lindahl-Jacobsen, 2019. "Forecasting Causes of Death using Compositional Data Analysis: the Case of Cancer Deaths," CREATES Research Papers 2019-07, Department of Economics and Business Economics, Aarhus University.
    19. Shang, Han Lin & Haberman, Steven, 2017. "Grouped multivariate and functional time series forecasting:An application to annuity pricing," Insurance: Mathematics and Economics, Elsevier, vol. 75(C), pages 166-179.
    20. Tim J. Boonen & Hong Li, 2017. "Modeling and Forecasting Mortality With Economic Growth: A Multipopulation Approach," Demography, Springer;Population Association of America (PAA), vol. 54(5), pages 1921-1946, October.
    21. Basellini, Ugofilippo & Camarda, Carlo Giovanni & Booth, Heather, 2022. "Thirty years on: A review of the Lee-Carter method for forecasting mortality," SocArXiv 8u34d, Center for Open Science.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0290160. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.