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2016 | 29 | 6 | 973-989

Article title

Effort–reward imbalance, overcommitment and their associations with all-cause and mental disorder long-term sick leave – A case-control study of the Swedish working population

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Content

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Abstracts

EN
Objectives To investigate if effort–reward imbalance (ERI) and overcommitment (OC) are associated with all-cause and mental disorder long-term sick leave (LS), and to identify differences in associations between genders, private versus public sector employees and socioeconomic status groups. Material and Methods The study uses a cross-sectional case-control design with a sample of 3477 persons on long-term sick leave of more than 59 days and a control group of 2078 in employment. Data on sick leave originate from social insurance registers, while data on health, working and living conditions were gathered through a survey. The binary logistic regression was used to test the multivariate associations. Results Effort–reward imbalance was associated with all-cause LS among the women (odds ratio (OR) = 1.58, 95% CI: 1.2–2.08), but not among the men. Associations for mental disorder LS were evident for both ERI and OC among both genders (ERI/OC: women OR = 2.76/2.82; men OR = 2.18/2.92). For the men these associations were driven by high effort, while for the women it was low job esteem in public sector and low job security in private sector. Among the highly educated women, ERI was strongly related to mental disorder LS (OR = 6.94, 95% CI: 3.2–15.04), while the highly educated men seemed to be strongly affected by OC for the same outcome (OR = 5.79, 95% CI: 1.48–22.57). Conclusions The study confirmed the independent roles of ERI and OC for LS, with stronger associations among the women and for mental disorders. The ERI model is a promising tool that can contribute to understanding the prevailing gender gap in sick leave and increasing sick leave due to mental disorders. Int J Occup Med Environ Health 2016;29(6):973–989

Year

Volume

29

Issue

6

Pages

973-989

Physical description

Dates

published
2016

Contributors

author
  • Swedish Social Insurance Agency, Stockholm, Sweden (Department for Analysis and Forecast, Statistical Analysis Unit)
  • Karolinska Institutet, Stockholm, Sweden (Department of Clinical Neuroscience, Division of Insurance Medicine)

References

Document Type

Publication order reference

Identifiers

Biblioteka Nauki
2168388

YADDA identifier

bwmeta1.element.ojs-doi-10_13075_ijomeh_1896_00712
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