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Predictors of sickness absence in college and university educated self-employed: a historic register study
BACKGROUND: Despite a large proportion of the workforce being self-employed, few studies have been conducted on risk factors for sickness absence in this population. The aim of this study is to identify risk factors for future sickness absence in a population of college and university educated self-...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4108014/ https://www.ncbi.nlm.nih.gov/pubmed/24886527 http://dx.doi.org/10.1186/1471-2458-14-420 |
Sumario: | BACKGROUND: Despite a large proportion of the workforce being self-employed, few studies have been conducted on risk factors for sickness absence in this population. The aim of this study is to identify risk factors for future sickness absence in a population of college and university educated self-employed. METHODS: In a historic register study based on insurance company files risk factors were identified by means of logistic regression analysis. Data collected at application for private disability insurance from 634 applicants were related to subsequent sickness absence periods of 30 days or more during a follow-up period of 7.95 years. Variables studied were self-reported lifestyle variables, variables concerning medical history and present health conditions and variables derived from the general medical examination including blood tests and urinary analysis. RESULTS: Results from analysis of data from 634 applicants for private disability insurance show that previous periods of sickness absence (OR 2.07), female gender (OR 2.04), health complaints listed in the health declaration (OR 1.88), elevated erythrocyte sedimentation rate (ESR) (OR 4.05) and the nature of the profession were related to a higher risk of sickness absence. CONCLUSIONS: Sickness absence was found to be related to demographic variables (gender, profession), medical variables (health complaints and erythrocyte sedimentation rate) and to variables with both a medical and a behavioural component (previous sickness absence). |
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