Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Influence of Sociodemographic, Economic and Employment-Related Factors on Sick Leave Due To Mental Illness. A Retrospective Study in an Industrialized Region in Southern Europe

Version 1 : Received: 26 May 2021 / Approved: 27 May 2021 / Online: 27 May 2021 (11:36:39 CEST)

How to cite: Fuertes-Guiro, F.; Marqués-Gaspar, C.; Amet, A.; Fernández-Bengoa, L. Influence of Sociodemographic, Economic and Employment-Related Factors on Sick Leave Due To Mental Illness. A Retrospective Study in an Industrialized Region in Southern Europe. Preprints 2021, 2021050665 (doi: 10.20944/preprints202105.0665.v1). Fuertes-Guiro, F.; Marqués-Gaspar, C.; Amet, A.; Fernández-Bengoa, L. Influence of Sociodemographic, Economic and Employment-Related Factors on Sick Leave Due To Mental Illness. A Retrospective Study in an Industrialized Region in Southern Europe. Preprints 2021, 2021050665 (doi: 10.20944/preprints202105.0665.v1).

Abstract

(1) Background: This study identifies and analyzes those variables that may influence sick leave due to mental illness, based on a retrospective descriptive study of a mutual insurance company in the industrialized region of southern Europe (Catalonia). (2) Methods: All workers who were on sick leave due to mental illness during the period 2009-2019 were included in the study. The relationships between sick leave due to mental illness and social/employment-related and economical and demographic factors were analyzed using multivariate logistic regression and Cox regression model. (3) Results: The period studied included 34,764 workers. Anxious-depressive disorders account for 83.3% of the diagnosed mental disorders. The age cohorts between 30 and 50 years represent 60% of the affected workers. Highest income and high population density regions are the most affected. The levels of mental illness are higher in spring and summer. Professions related to manufacturing industry, automobile mechanics companies, the hospitality industry, education and healthcare and social service companies was more heavily affected. (4) Conclusions: Population density and GDP per capita, the age cohort, the season of the year, the type of payment, the type of contract, and the worker’s business and profession can predict the appearance of sick leave due to mental illness. Mutual insurance companies should plan interventions to minimize these factors and avoid the socioeconomic consequences.

Keywords

Sick leave; mental illness; predictors, prevention

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