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[Temporary disability: analysis strategies].

F Benavides1, M Sáez, M Barceló

  • 1Medicina Preventiva i Salut Pública, Universitat Pompeu Fabra, Barcelona, 08003, España.

Gaceta Sanitaria
|September 9, 1999
PubMed
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Poisson regression models are unsuitable for analyzing repeated temporary sick-leave (TSL) data. Generalized Estimating Equations (GEE) and Wei, Lin, and Weissfeld (WLW) regression models also present limitations for this type of analysis.

Area of Science:

  • Occupational Health
  • Biostatistics
  • Epidemiology

Background:

  • Temporary sick-leave (TSL) can occur multiple times per individual, with varying frequencies over observation periods.
  • Accurate statistical modeling is crucial for understanding TSL patterns and their associated risk factors.

Purpose of the Study:

  • To evaluate the suitability of different regression models for estimating rate ratios of recurrent TSL.
  • To identify demographic and occupational factors associated with higher TSL incidence.

Main Methods:

  • Employed 2,306 workers over 1,259 days, analyzing 2,649 TSL episodes.
  • Applied Poisson regression, Generalized Estimating Equations (GEE), and Wei, Lin, and Weissfeld (WLW) modified Cox regression models.
  • Identified high-risk groups including women, younger workers, cleaners, maintenance staff, auxiliary nurses, and shift workers.

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Main Results:

  • Poisson regression assumptions were violated by the TSL data.
  • GEE and WLW models provided wider confidence intervals but did not fundamentally alter the identified high-risk profiles.
  • Specific demographics and occupations were associated with increased TSL incidence.

Conclusions:

  • Standard Poisson regression is inappropriate for analyzing recurrent TSL data.
  • GEE and WLW models are not ideal alternatives for this specific data type.
  • Further exploration of conditional regression models is recommended for accurate TSL analysis.