ArticleScandinavian journal of work, environment & health2021
Patterns of working hour characteristics and risk of sickness absence among shift-working hospital employees: a data-mining cohort study.
Article in Scandinavian journal of work, environment & health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.
- Organisational-level risk and health-promoting factors within the healthcare sector-a systematic search and review.Frontiers in medicine · 2024Pooled it
- Article
- Trajectories of sickness absence in healthcare workers: a cohort study in a Spanish hospital between 2018 and 2023.BMC health services research · 2026Article
- Working time traffic light recommendations: development, use, and implementation in the Finnish social and healthcare.Industrial health · 2026Article
- Promoting Sustainable Work Hours Through Participatory Scheduling: A Cross-Sectional Study Employing Objective Working Time Data.Journal of nursing management · 2026Article
- Identifying risk factors of long sickness absences: a registry-based study using explainable AI methods.BMJ open · 2025Article
- Workload and short sickness absences in a cohort of Finnish hospital employees.The European journal of health economics : HEPAC : health economics in prevention and care · 2025Article
- Airport security personnel's working hour characteristics and associations with sickness absence-a retrospective cohort study in 2016-2019.Industrial health · 2025Article
- Under pressure - Nursing staff's perspectives on working hours and recovery during the COVID-19 pandemic: A qualitative study.International journal of nursing studies advances · 2024Article
- Towards the year 2049: The next 25 years of occupational health and safety research.Scandinavian journal of work, environment & health · 2024Review
- Concurrent Trajectories of Objectively Measured Insufficient Recovery and Workload Among a Cohort of Shift Working Hospital Employees: Quantitative Empirical Research.Nursing open · 2024Article
- Working hours and health - key research topics in the past and future.Scandinavian journal of work, environment & health · 2024Review
- Predicting long-term sickness absence with employee questionnaires and administrative records: a prospective cohort study of hospital employees.Scandinavian journal of work, environment & health · 2023Article
- Work unit level personnel working hours and the patients' length of in-hospital stay-An administrative data approach.PLOS digital health · 2023Article
- Article
- Sick Leave Determinants in the Healthcare Sector (Part II): A Review of Organizational-Level Factors.The Brown journal of hospital medicine · 2023Review
- Sequence analysis of the combinations of work shifts and absences in health care - comparison of two years of administrative data.BMC nursing · 2022Article
- Working Time Control and Variability in Europe Revisited: Correlations with Health, Sleep, and Well-Being.International journal of environmental research and public health · 2022Article
- Scientific challenges of studying shift schedule design.Scandinavian journal of work, environment & health · 2022Article
- A survey of train driver schedules, sleep, wellbeing, and driving performance in Australia and New Zealand.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesData 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).
methodsIn 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.
resultsWe identified eight distinct working hour patterns in shift work: (i) regular morning (M)/evening (E) work, weekends off; (ii) irregular M work; (iii) irregular M/E/night (N) work; (iv) regular M work, weekends off; (v) irregular, interrupted M/E/N work; (vi) variable M work, weekends off; (vii) quickly rotating M/E work, non-standard weeks; and (viii) slowly rotating M/E 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.
conclusionsThis data-mining suggests that hypothesis-free approaches can contribute to scientific understanding of healthy working hour characteristics and complement traditional hypothesis-driven approaches.
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