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Related Experiment Video

Updated: Jun 10, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

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

Jan Hagberg1, Marjan Vaez, Kristina Alexanderson

  • 1Section of Personal Injury Prevention, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden. jan.hagberg@ki.se

Work (Reading, Mass.)
|August 5, 2010
PubMed
Summary

Changes in sick-leave diagnoses were linked to future disability pension (DP). Individuals with fewer diagnosis changes and more musculoskeletal issues were more likely to receive DP. Statistical information theory methods can analyze these nominal data patterns.

Related Experiment Videos

Last Updated: Jun 10, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

Area of Science:

  • Occupational health
  • Biostatistics
  • Public health

Background:

  • Analyzing longitudinal sick-leave data presents methodological challenges due to recurring spells, varying diagnoses, and dependence on prior episodes.
  • Standard statistical methods are often unsuitable for nominal-scale variables like sick-leave diagnoses, especially for repeated measurements.

Purpose of the Study:

  • To investigate the association between the number and pattern of sick-leave diagnosis changes and future disability pension (DP).
  • To evaluate analytical methods for repeated nominal data in the context of sick-leave diagnoses.

Main Methods:

  • Utilized data from a 12-year prospective cohort study involving 213 individuals aged 25-34 with initial back diagnoses.
  • Employed entropies, adjusted uncertainty coefficients for repeated measurements, and transition matrices to analyze changes in sick-leave diagnoses.

Main Results:

  • 22% of participants were granted DP within 12 years.
  • Individuals receiving DP exhibited less frequent sick-leave diagnosis changes and a higher incidence of new sick-leave periods with musculoskeletal diagnoses.
  • The variation in diagnoses and the dependence between consecutive diagnoses were significantly associated with DP.

Conclusions:

  • Traditional statistical tools, often linear and requiring numerical variables, are inadequate for analyzing repeated measurements on nominal data like sick-leave diagnoses.
  • Statistical information theory offers beneficial tools for analyzing complex patterns in discrete, repeated nominal data, providing insights into health-related outcomes.