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01.12.2015 | Research Article | Ausgabe 1/2015 Open Access

BMC Public Health 1/2015

Causal inference in multi-state models–sickness absence and work for 1145 participants after work rehabilitation

Zeitschrift:
BMC Public Health > Ausgabe 1/2015
Autoren:
Jon Michael Gran, Stein Atle Lie, Irene Øyeflaten, Ørnulf Borgan, Odd O. Aalen
Wichtige Hinweise

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

JMG was involved in the planning of the study, prepared the data, performed the statistical analysis and wrote the manuscript. SAL was involved in the planning of the study, prepared the data on sickness benefits and contributed with background information based on earlier work on multi-state models for sickness absence. IØ was involved in the planning of the study, prepared the cohort Ddata of patients on work rehabilitation and contributed with background information on the cohort and other based on earlier work on sickness absence. ØB contributed with background information on the statistical methods. OOA was involved with the planning of the study and contributed with background information on the statistical methods. All authors read and approved the final manuscript.

Abstract

Background

Multi-state models, as an extension of traditional models in survival analysis, have proved to be a flexible framework for analysing the transitions between various states of sickness absence and work over time. In this paper we study a cohort of work rehabilitation participants and analyse their subsequent sickness absence using Norwegian registry data on sickness benefits. Our aim is to study how detailed individual covariate information from questionnaires explain differences in sickness absence and work, and to use methods from causal inference to assess the effect of interventions to reduce sickness absence. Examples of the latter are to evaluate the use of partial versus full time sick leave and to estimate the effect of a cooperation agreement on a more inclusive working life.

Methods

Covariate adjusted transition intensities are estimated using Cox proportional hazards and Aalen additive hazards models, while the effect of interventions are assessed using methods of inverse probability weighting and G-computation.

Results

Results from covariate adjusted analyses show great differences in sickness absence and work for patients with assumed high risk and low risk covariate characteristics, for example based on age, type of work, income, health score and type of diagnosis. Causal analyses show small effects of partial versus full time sick leave and a positive effect of having a cooperation agreement, with about 5 percent points higher probability of returning to work.

Conclusions

Detailed covariate information is important for explaining transitions between different states of sickness absence and work, also for patient specific cohorts. Methods for causal inference can provide the needed tools for going from covariate specific estimates to population average effects in multi-state models, and identify causal parameters with a straightforward interpretation based on interventions.
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