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Predictors of Sickness Absence in a Clinical Population With Chronic Pain
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- Lo Martire, Riccardo (författare)
- Karolinska Institutet,Högskolan Dalarna,Medicinsk vetenskap,Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Huddinge, Sweden; School of Health and Welfare, Dalarna University, Falun, Sweden,Karolinska Inst, Dept Neurobiol Care Sci & Soc, Div Physiotherapy, Alfred Nobels Alle 23, S-14183 Huddinge, Sweden.;Dalarna Univ, Sch Hlth & Welf, Falun, Sweden.
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- Dahlström, Örjan, 1973- (författare)
- Linköpings universitet,Psykologi,Handikappvetenskap,Filosofiska fakulteten,Institutet för handikappvetenskap (IHV),Linköpings universitet, Psykologi,Linköping Univ, Dept Behav Sci & Learning, Linköping, Sweden.
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- Björk, Mathilda, 1977- (författare)
- Linköpings universitet,Avdelningen för prevention, rehabilitering och nära vård,Medicinska fakulteten,Region Östergötland, Smärt och rehabiliteringscentrum,Linköpings universitet, Avdelningen för prevention, rehabilitering och nära vård,Linköping Univ, Pain & Rehabil Ctr, Linköping, Sweden.;Linköping Univ, Dept Hlth Med & Caring Sci, Linköping, Sweden.
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- Philadelphia, PA, United States : Churchill Livingstone, 2021
- 2021
- Engelska.
- Ingår i: Journal of Pain. - Philadelphia, PA, United States : Churchill Livingstone. - 1526-5900 .- 1528-8447. ; 22:10, s. 1180-1194
- Tidskriftsartikel (refereegranskat)
Abstract
Ämnesord
Stäng
- Chronic pain-related sickness absence is an enormous socioeconomic burden globally. Optimized interventions are reliant on a lucid understanding of the distribution of social insurance benefits and their predictors. This register-based observational study analyzed data for a 7-year period from a population-based sample of 44,241 chronic pain patients eligible for interdisciplinary treatment (IDT) at specialist clinics. Sequence analysis was used to describe the sickness absence over the complete period and to separate the patients into subgroups based on their social insurance benefits over the final 2 years. The predictive performance of features from various domains was then explored with machine learning-based modeling in a nested cross-validation procedure. Our results showed that patients on sickness absence increased from 17% 5 years before to 48% at the time of the IDT assessment, and then decreased to 38% at the end of follow-up. Patients were divided into 3 classes characterized by low sickness absence, sick leave, and disability pension, with eight predictors of class membership being identified. Sickness absence history was the strongest predictor of future sickness absence, while other predictors included a 2008 policy, age, confidence in recovery, and geographical location. Information on these features could guide personalized intervention in the specialized healthcare. PERSPECTIVE: This study describes sickness absence in patients who visited a Swedish pain specialist interdisciplinary treatment clinic during the period 2005 to 2016. Predictors of future sickness absence are also identified that should be considered when adapting IDT programs to the patient's needs.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Hälsovetenskap -- Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Health Sciences -- Public Health, Global Health, Social Medicine and Epidemiology (hsv//eng)
Nyckelord
- Chronic pain
- epidemiology
- machine learning
- productivity loss
- sickness absence
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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- Frumento, Paolo
- Constan, Lea
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