Patterns of working hour characteristics and risk of sickness absence among shift-working hospital employees : a data-mining cohort study

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Rosenström , T , Härmä , M , Kivimäki , M , Ervasti , J , Virtanen , M , Hakola , T , Koskinen , A & Ropponen , A 2021 , ' Patterns of working hour characteristics and risk of sickness absence among shift-working hospital employees : a data-mining cohort study ' , Scandinavian Journal of Work, Environment & Health , vol. 47 , no. 5 , pp. 395-403 . https://doi.org/10.5271/sjweh.3957

Title: Patterns of working hour characteristics and risk of sickness absence among shift-working hospital employees : a data-mining cohort study
Author: Rosenström, Tom; Härmä, Mikko; Kivimäki, Mika; Ervasti, Jenni; Virtanen, Marianna; Hakola, Tarja; Koskinen, Aki; Ropponen, Annina
Contributor: University of Helsinki, Department of Psychology and Logopedics
University of Helsinki, Department of Public Health
University of Helsinki, Finnish Institute of Occupational Health
Date: 2021
Language: eng
Number of pages: 9
Belongs to series: Scandinavian Journal of Work, Environment & Health
ISSN: 0355-3140
URI: http://hdl.handle.net/10138/333900
Abstract: Objectives Data mining can complement traditional hypothesis-based approaches in characterizing unhealthy work exposures. We used it to derive a hypothesis-free characterization of working hour patterns in shift work and their associations with sickness absence (SA). Methods In this prospective cohort study, complete payroll-based work hours and SA dates were extracted from a shift-scheduling register from 2008 to 2019 on 6029 employees from a hospital district in Southwestern Finland. We applied permutation distribution clustering to time series of successive shift lengths, between-shift rest periods, and shift starting times to identify clusters of similar working hour patterns over time. We examined associations of clusters spanning on average 23 months with SA during the following 23 months. Results We identified eight distinct working hour patterns in shift work: (i) regular morning (Myevening (E) work, weekends off; (ii) irregular M work; (iii) irregular M/E/night (N) work; (iv) regular M work, weekends off; (v) irregular, interrupted WE/N work; (vi) variable M work, weekends off, (vii) quickly rotating WE work, non-standard weeks; and (viii) slowly rotating WE work, non-standard weeks. The associations of these eight working-hour clusters with risk of future SA varied. The cluster of irregular, interrupted M/E/N work was the strongest predictor of increased SA (days per year) with an incidence rate ratio of 1.77 (95% confidence interval 1.74-1.80) compared to regular M/E work, weekends off. Conclusions This data-mining suggests that hypothesis-free approaches can contribute to scientific understanding of healthy working hour characteristics and complement traditional hypothesis-driven approaches.
Subject: employee scheduling
nurse rostering
occupational health
permutation distribution clustering
sick leave
shift Work
shift worker
R PACKAGE
TIME
HEALTH
LEAVE
3142 Public health care science, environmental and occupational health
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