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Relationship between Mental Health and Socio-Economic, Demographic and Environmental Factors in the COVID-19 Lockdown Period—A Multivariate Regression Analysis

Author

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  • Stefano Bonnini

    (Department of Economics and Management, University of Ferrara, 44121 Ferrara, Italy)

  • Michela Borghesi

    (Department of Economics and Management, University of Ferrara, 44121 Ferrara, Italy)

Abstract

Amongst the several consequences of the COVID-19 pandemic, we should include psychological effects on the population. The mental health consequences of lockdown are affected by several factors. The most important are: the duration of the social isolation period, the characteristics of the living space, the number of online (virtual) and offline (physical) contacts and perceived contacts’ closeness, individual characteristics, and the spread of infection in the geographical area of residence. In this paper, we investigate the possible effects of environmental, social and individual characteristics (predictors) on mental health (response) during the COVID-19 lockdown period. The relationship between mental health and predictors can be studied with a multivariate linear regression model, because “mental health” is a multidimensional concept. This work provides a contribution to the debate about the factors affecting mental health in the period of the COVID-19 lockdown, with the application of an innovative approach based on a multivariate regression analysis and a combined permutation test on data collected in a survey conducted in Italy in 2020.

Suggested Citation

  • Stefano Bonnini & Michela Borghesi, 2022. "Relationship between Mental Health and Socio-Economic, Demographic and Environmental Factors in the COVID-19 Lockdown Period—A Multivariate Regression Analysis," Mathematics, MDPI, vol. 10(18), pages 1-15, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:18:p:3237-:d:908072
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    References listed on IDEAS

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    Cited by:

    1. Bonnini, S. & Borghesi, M. & Giacalone, M., 2024. "Semi-parametric approach for modelling overdispersed count data with application to Industry 4.0," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    2. Stefano Bonnini & Michela Borghesi & Massimiliano Giacalone, 2024. "Advances on multisample permutation tests for “V-shaped” and “U-shaped” alternatives with application to circular economy," Annals of Operations Research, Springer, vol. 342(3), pages 1655-1670, November.

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