Trajectory analyses of sickness absence among industrial and municipal employees

Occup Med (Lond). 2017 Mar 1;67(2):109-113. doi: 10.1093/occmed/kqw104.

Abstract

Background: Compared with the public sector, the private sector is more susceptible to changes in the economic environment and associated threats of downsizing, outsourcing and transfers of production. This might be assumed to be associated with more restrictive sickness absence practices.

Aims: To investigate whether this difference is reflected in higher sickness absence rates in the public sector and to explore the potential of trajectory analysis in researching such absences.

Methods: The sample consisted of industrial and municipal employees. Latent groups of differential sickness absence during a 6-year study period were searched with a two-response trajectory analysis that jointly captured the spells and the days. Multinomial logistic regressions were used to assess associations of the labour market sector with the set of trajectories obtained.

Results: There were 2207 industrial and 3477 municipal employees in the study group. The analysis assigned the employees to three trajectory groups, the 'low-level', 'middle-range' and 'high-range' groups. The relative risk ratios for the middle-range and the high-range trajectories of public sector employees were not higher after controlling for age, gender and occupational.

Conclusions: In this study, the labour market sector was not a major independent determinant of sickness absence practices. Trajectory analysis can be recommended as a way to determine differential absence practices. The trajectory approach might help occupational health services to identify more accurately the employees who need support to maintain their work ability.

Keywords: Finland; register study; trajectory analysis..

Publication types

  • Comparative Study

MeSH terms

  • Absenteeism*
  • Adult
  • Female
  • Government Agencies*
  • Humans
  • Industry*
  • Male
  • Middle Aged
  • Public Sector
  • Return to Work / statistics & numerical data*
  • Sick Leave / statistics & numerical data*
  • Socioeconomic Factors