Methods for analysing individual changes in sick-leave diagnoses over time.
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.


METHODS
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 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.
Hagberg
Jan
J
Section of Personal Injury Prevention, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden. jan.hagberg@ki.se
Vaez
Marjan
M
Alexanderson
Kristina
K
D000328:Adult; D006233:Persons with Disabilities; D006801:Humans; D015233:Models, Statistical; D010415:Pensions; D011446:Prospective Studies; D012044:Regression Analysis; D018582:Sick Leave; D013548:Sweden