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Modeling sickness absence data: A scoping review.

Tom Duchemin1,2, Mounia N Hocine1

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Summary

Analyzing sick leave data requires careful statistical tool selection due to its complex frequency and duration. This review highlights a lack of model evaluation, suggesting joint models could offer deeper insights into sick leave patterns.

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Area of Science:

  • Multidisciplinary research encompassing economics, psychology, health, and social behavior.
  • Focus on statistical modeling for individual sick leave analysis.

Background:

  • Sick leave determinants impact worker well-being and societal costs.
  • Analyzing sick leave data presents statistical challenges due to its complex structure (frequency and duration) and numerous influencing factors.

Purpose of the Study:

  • To conduct a scoping review of statistical approaches for analyzing individual sick leave data.
  • To synthesize key insights and identify research gaps in sick leave statistical modeling.

Main Methods:

  • Systematic scoping review following PRISMA methodology.
  • Searched major databases (Medline, Web of Science, etc.) for statistical modeling studies on sick leave (1981-2019).
  • Selected 469 articles from 5983 retrieved publications.

Main Results:

  • Identified three main modeling types: univariate (438 articles, mostly count models), bivariate (14 articles), and others like multistate/SEM (22 articles).
  • Significant lack of model evaluation: predictive accuracy assessed in only 18 articles, explanatory accuracy in 43.
  • Univariate count models dominate the literature.

Conclusions:

  • Current statistical approaches for sick leave data are predominantly univariate, with limited evaluation of predictive or explanatory accuracy.
  • Further research utilizing joint models is recommended to comprehensively analyze sick leave spells by considering both frequency and duration.