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

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  • 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
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    References listed on IDEAS

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    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.
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