Hierarchizing Determinants of Sick Leave: Insights From a Survey on Health and Well-being at the Workplace.


Journal

Journal of occupational and environmental medicine
ISSN: 1536-5948
Titre abrégé: J Occup Environ Med
Pays: United States
ID NLM: 9504688

Informations de publication

Date de publication:
08 2019
Historique:
pubmed: 28 7 2019
medline: 15 12 2020
entrez: 27 7 2019
Statut: ppublish

Résumé

We hierarchized a range of individual and occupational factors impacting the occurrence of very short (1-3 days), short (4 days to 1 month), or long-term (more than a month) sick leave spells. Data were collected from a repeated cross-sectional survey conducted in the French private sector over the period 2011 to 2017. Fifty one sick leave determinants were ranked using a conditional random forest approach. The main determinants of long-term sick leaves were mainly health-related characteristics, such as perceived health, but also work-related covariates such as supervisor acknowledgment. On the contrary, very short-term spells were mainly defined by sociodemographic covariates. These results could be useful for devising appropriate actions to prevent against sick leave at the workplace, particularly long-term spells. Random forest approach is a promising approach for ranking correlated covariates from large datasets.

Identifiants

pubmed: 31348419
doi: 10.1097/JOM.0000000000001643
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e340-e347

Commentaires et corrections

Type : CommentIn
Type : CommentIn

Auteurs

Tom Duchemin (T)

MESuRS laboratory (Modelling, Epidemiology and Surveillance of Health Risks), Conservatoire National des Arts et Métiers (National Conservatory of Arts and Crafts) (Mr Duchemin, Dr Dab, Dr Hocine); Malakoff médéric humanis, (Mr Duchemin, Ms Lounissi); CEDRIC laboratory (Centre for Studies and Research in Computer Science), Conservatoire National des Arts et Métiers (National Conservatory of Arts and Crafts) (Dr Bar-Hen), Paris, France.

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Classifications MeSH