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Methods for analysing individual changes in sick-leave diagnoses over time
Karolinska Institute.
Karolinska Institute.
Linköping University, Department of Medicine and Health Sciences, Division of Preventive and Social Medicine and Public Health Science. Linköping University, Faculty of Health Sciences.
2010 (English)In: WORK-A JOURNAL OF PREVENTION ASSESSMENT and REHABILITATION, ISSN 1051-9815, Vol. 36, no 3, 283-293 p.Article in journal (Refereed) Published
Abstract [en]

Several methodological challenges arise when attempting to analyse individual data on changes of sick-leave diagnoses over several years. Sick-leave spells for a person can recur, have different sick-leave diagnoses, and both these aspects are dependent of previous episodes, the numbers of repeated periods vary across subjects, and standard statistical methods are not valid for variables on nominal scales, e. g. sick-leave diagnoses. Objective: Our aim was to ascertain whether the number and pattern of changes in sick-leave diagnoses are associated with future disability pension (DP) and to test methods for analysis of repeated measurements on nominal data. Participants: Data from a 12-year prospective cohort study of the 8000 sick-leave periods of the 213 persons aged 25-34 who, in 1985, had a new sick-leave spell andgt;= 28 days with back diagnoses were used. Methods: We used entropies, uncertainty coefficients adjusted for repeated measurements, and transition matrices to examine the changes in sick-leave diagnoses that occurred during follow up. Results: In the 12 years 22% were granted DP and they had changed sick-leave diagnosis less frequently and more often had new sick-leave periods with musculoskeletal diagnoses than the others. The variation in diagnoses and the degree of dependence between consecutive diagnoses were associated with DP. Conclusions: Many tools in statistics are based on linear methods that require numerical variables, but such methods are not valid for repeated measurements on discrete variables on nominal scales, as for sick-leave diagnosis. In such cases, it can be beneficial to use tools that are applied in statistical information theory.

Place, publisher, year, edition, pages
IOS PRESS, NIEUWE HEMWEG 6B, 1013 BG AMSTERDAM, NETHERLANDS , 2010. Vol. 36, no 3, 283-293 p.
National Category
Medical and Health Sciences
URN: urn:nbn:se:liu:diva-58635DOI: 10.3233/WOR-2010-1030ISI: 000280557400004OAI: diva2:344899
Available from: 2010-08-22 Created: 2010-08-20 Last updated: 2010-08-22

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Alexanderson, Kristina
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Division of Preventive and Social Medicine and Public Health ScienceFaculty of Health Sciences
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