Measuring multimorbidity in a working population: the effect on incident sickness absence.

Ubalde López, Mònica, 1972-; Delclòs i Clanchet, Jordi, 1956-; Benavides, Fernando G. (Fernando García); Calvo Bonacho, Eva; Gimeno Ruiz de Porras, David

doi:http://dx.doi.org/10.1007/s00420-015-1104-4

Measuring multimorbidity in a working population: the effect on incident sickness absence.

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Ubalde-Lopez M, Delclos GL, Benavides FG, Calvo-Bonacho E, Gimeno D. Measuring multimorbidity in a working population: the effect on incident sickness absence. Int Arch Occup Environ Health. 2016 May;89(4):667-78. DOI: 10.1007/s00420-015-1104-4

Abstract

PURPOSE: Multimorbidity research typically focuses on chronic and common diseases in patient and/or older populations. We propose a multidimensional multimorbidity score (MDMS) which incorporates chronic conditions, symptoms, and health behaviors for use in younger, presumably healthier, working populations. METHODS: Cross-sectional study of 372,370 Spanish workers who underwent a standardized medical evaluation in 2006. We computed a MDMS (range 0-100) based on the sex-specific results of a multicorrespondence analysis (MCA). We then used Cox regression models to assess the predictive validity of this MDMS on incident sickness absence (SA) episodes. RESULTS: Two dimensions in the MCA explained about 80 % of the variability in both sexes: (1) chronic cardiovascular conditions and health behaviors, and (2) pain symptoms, in addition to sleep disturbances in women. More men than women had at least one condition (40 vs 15 %) and two or more (i.e., multimorbidity) (12 vs 2 %). The MDMS among those with multimorbidity ranged from 16.8 (SD 2.4) to 51.7 (SD 9.9) in men and 18.5 (SD 5.8) to 43.8 (SD 7.8) in women. We found that the greater the number of health conditions, the higher the risk of SA. A higher MDMS was also a risk factor for incident SA, even after adjusting for prior SA and other covariates. In women, this trend was less evident. CONCLUSIONS: A score incorporating chronic health conditions, behaviors, and symptoms provides a more holistic approach to multimorbidity and may be useful for defining health status in working populations and for predicting key occupational outcomes.