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Measuring resilience and fatality rate during the first wave of COVID-19 pandemic in Northern Italy: a note

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  • Guccio, Calogero

Abstract

During the first wave of the COVID-19 pandemic, much emphasis was placed on mortality indicators such as case-fatality rate and crude mortality rates to offer a preliminary assessment of the resilience between healthcare systems. The paper aims to contribute to the debate on the resilience of the healthcare systems during the pandemic by discussing whether the case-fatality rate and crude mortality indicators are appropriate for assessing resilience or whether other indicators should be employed. Comparing data obtained with different approaches based on statistical inference and large-scale serological survey, the article highlight, that great care must be taken when using case-fatality and crude mortality data, which in the absence of careful analysis, can lead to erroneous conclusions on the assessment of the resilience between healthcare systems. Furthermore, it shows that even in the absence of detailed epidemiological data new advancement in statistical methods can be useful to provide a more sounding evaluation of resilience.

Suggested Citation

  • Guccio, Calogero, 2021. "Measuring resilience and fatality rate during the first wave of COVID-19 pandemic in Northern Italy: a note," EconStor Preprints 231374, ZBW - Leibniz Information Centre for Economics.
    • Handle: RePEc:zbw:esprep:231374
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    File URL: https://www.econstor.eu/bitstream/10419/231374/1/Guccio-WP-resilience.pdf
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    References listed on IDEAS

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    1. Connelly, Luke B. & Birch, Stephen, 2022. "Answers in search of questions: what does the comparison of COVID19 data among regions in Northern Italy tell us?," Health Economics, Policy and Law, Cambridge University Press, vol. 17(2), pages 224-226, April.
    2. Manski, Charles F. & Molinari, Francesca, 2021. "Estimating the COVID-19 infection rate: Anatomy of an inference problem," Journal of Econometrics, Elsevier, vol. 220(1), pages 181-192.
    3. Domenico Depalo, 2021. "True COVID-19 mortality rates from administrative data," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(1), pages 253-274, January.
    4. Durante, Ruben & Guiso, Luigi & Gulino, Giorgio, 2021. "Asocial capital: Civic culture and social distancing during COVID-19," Journal of Public Economics, Elsevier, vol. 194(C).
    5. Nicola Borri & Francesco Drago & Chiara Santantonio & Francesco Sobbrio, 2021. "The “Great Lockdown”: Inactive workers and mortality by Covid‐19," Health Economics, John Wiley & Sons, Ltd., vol. 30(10), pages 2367-2382, September.
    6. Hortaçsu, Ali & Liu, Jiarui & Schwieg, Timothy, 2021. "Estimating the fraction of unreported infections in epidemics with a known epicenter: An application to COVID-19," Journal of Econometrics, Elsevier, vol. 220(1), pages 106-129.
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    More about this item

    Keywords

    Resilience; Missing data; Epidemiology; Novel coronavirus; Italy; Healthcare governance;
    All these keywords.

    JEL classification:

    • I10 - Health, Education, and Welfare - - Health - - - General
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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