IDEAS home Printed from https://ideas.repec.org/a/spr/qualqt/v57y2023i2d10.1007_s11135-022-01603-6.html
   My bibliography  Save this article

A quantitative analysis on the effect of COVID-19 in a private health insurance plan expenditure

Author

Listed:
  • Davide Biancalana

    (Sapienza University of Rome: Universita degli Studi di Roma La Sapienza)

  • Fabio Baione

    (Sapienza University of Rome: Universita degli Studi di Roma La Sapienza)

Abstract

This paper explores the effect of COVID-19 on health care expenditure using data from a private Health Insurance Plan (HIP). As well known, at the beginning of the COVID-19 pandemic, governments had to rely on Non-Pharmaceutical Interventions against the spread of the virus. However, the stringency of lockdowns differed across space and time as governments had to adjust their strategy dynamically to the country-specific development of the crisis. These strategies have strongly changed the policyholders’ behavior; however, after this period, a fundamental question is whether the policyholder behavior will return to a status quo (i.e. in traditional care delivery). We analyze these effects using a “pre-post” quantitative study using longitudinal data collected from 2017 to 2021. We consider as a consumption measure the health care expenditure amount within several types of health services, coming from a group of insured persons, followed overtime every quarter, and separating the effect per gender and age. Moving in this direction, the purpose of our contribution is to investigate if the traditional actuarial approach for assessing the loss cost, based on the Generalized Linear Models, could predict the effect on the health care expenditure due to COVID-19 and the capacity to which a HIP can anticipate these uncertainties. Our results provide a comprehensive picture of the different effects of COVID-19 on the health services offered by the HIP, as well as on the behavior ofpolicyholders during and after the pandemic period.

Suggested Citation

  • Davide Biancalana & Fabio Baione, 2023. "A quantitative analysis on the effect of COVID-19 in a private health insurance plan expenditure," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(2), pages 173-187, December.
    • Handle: RePEc:spr:qualqt:v:57:y:2023:i:2:d:10.1007_s11135-022-01603-6
    DOI: 10.1007/s11135-022-01603-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11135-022-01603-6
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11135-022-01603-6?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Edward W. Frees & Gee Lee & Lu Yang, 2016. "Multivariate Frequency-Severity Regression Models in Insurance," Risks, MDPI, vol. 4(1), pages 1-36, February.
    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. Lu Yang & Claudia Czado, 2022. "Two‐part D‐vine copula models for longitudinal insurance claim data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(4), pages 1534-1561, December.
    2. Šoltés Erik & Zelinová Silvia & Bilíková Mária, 2019. "General Linear Model: An Effective Tool For Analysis Of Claim Severity In Motor Third Party Liability Insurance," Statistics in Transition New Series, Statistics Poland, vol. 20(4), pages 13-31, December.
    3. Youn Ahn, Jae & Jeong, Himchan & Lu, Yang, 2021. "On the ordering of credibility factors," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 626-638.
    4. Tzougas, George & Jeong, Himchan, 2021. "An expectation-maximization algorithm for the exponential-generalized inverse Gaussian regression model with varying dispersion and shape for modelling the aggregate claim amount," LSE Research Online Documents on Economics 108210, London School of Economics and Political Science, LSE Library.
    5. Martin Eling & Kwangmin Jung, 2022. "Heterogeneity in cyber loss severity and its impact on cyber risk measurement," Risk Management, Palgrave Macmillan, vol. 24(4), pages 273-297, December.
    6. George Tzougas & Himchan Jeong, 2021. "An Expectation-Maximization Algorithm for the Exponential-Generalized Inverse Gaussian Regression Model with Varying Dispersion and Shape for Modelling the Aggregate Claim Amount," Risks, MDPI, vol. 9(1), pages 1-17, January.
    7. Emilio Gómez-Déniz & Enrique Calderín-Ojeda, 2021. "A Priori Ratemaking Selection Using Multivariate Regression Models Allowing Different Coverages in Auto Insurance," Risks, MDPI, vol. 9(7), pages 1-18, July.
    8. Oh, Rosy & Jeong, Himchan & Ahn, Jae Youn & Valdez, Emiliano A., 2021. "A multi-year microlevel collective risk model," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 309-328.
    9. Calcetero Vanegas, Sebastián & Badescu, Andrei L. & Lin, X. Sheldon, 2024. "Effective experience rating for large insurance portfolios via surrogate modeling," Insurance: Mathematics and Economics, Elsevier, vol. 118(C), pages 25-43.
    10. Pechon, Florian & Denuit, Michel & Trufin, Julien, 2018. "Multivariate Modelling of Multiple Guarantees in Motor Insurance of a Household," LIDAM Discussion Papers ISBA 2018019, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    11. Fung, Tsz Chai & Badescu, Andrei L. & Lin, X. Sheldon, 2019. "A class of mixture of experts models for general insurance: Theoretical developments," Insurance: Mathematics and Economics, Elsevier, vol. 89(C), pages 111-127.
    12. Gómez-Déniz, E., 2016. "Bivariate credibility bonus–malus premiums distinguishing between two types of claims," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 117-124.
    13. Verschuren, Robert Matthijs, 2022. "Frequency-severity experience rating based on latent Markovian risk profiles," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 379-392.
    14. Lee, Gee Y. & Shi, Peng, 2019. "A dependent frequency–severity approach to modeling longitudinal insurance claims," Insurance: Mathematics and Economics, Elsevier, vol. 87(C), pages 115-129.
    15. Garrido, J. & Genest, C. & Schulz, J., 2016. "Generalized linear models for dependent frequency and severity of insurance claims," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 205-215.
    16. Massimo Costabile & Fabio Viviano, 2021. "Modeling the Future Value Distribution of a Life Insurance Portfolio," Risks, MDPI, vol. 9(10), pages 1-17, October.
    17. Yves Staudt & Joël Wagner, 2021. "Assessing the Performance of Random Forests for Modeling Claim Severity in Collision Car Insurance," Risks, MDPI, vol. 9(3), pages 1-28, March.
    18. Park, Sojung C. & Kim, Joseph H.T. & Ahn, Jae Youn, 2018. "Does hunger for bonuses drive the dependence between claim frequency and severity?," Insurance: Mathematics and Economics, Elsevier, vol. 83(C), pages 32-46.
    19. Chen, Zezhun Chen & Dassios, Angelos & Tzougas, George, 2024. "EM estimation for bivariate mixed poisson INAR(1) claim count regression models with correlated random effects," LSE Research Online Documents on Economics 118826, London School of Economics and Political Science, LSE Library.
    20. Eling, Martin & Jung, Kwangmin, 2018. "Copula approaches for modeling cross-sectional dependence of data breach losses," Insurance: Mathematics and Economics, Elsevier, vol. 82(C), pages 167-180.

    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:spr:qualqt:v:57:y:2023:i:2:d:10.1007_s11135-022-01603-6. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.