Literature DB >> 30268347

Occupied with classification: Which occupational classification scheme better predicts health outcomes?

Emily Eyles1, David Manley2, Kelvyn Jones2.   

Abstract

Health inequalities continue to grow despite continuous policy intervention. Work, one domain of health inequalities, is often included as a component of social class rather than as a determinant in its own right. Many social class classifications are derived from occupation types, but there are other components within them that mean they may not be useful as proxies for occupation. This paper develops the exposome, a life-course exposure model developed by Wild (2005), into the worksome, allowing for the explicit consideration of both physical and psychosocial exposures and effects derived from work and working conditions. The interactions between and within temporal and geographical scales are strongly emphasised, and the interwoven nature of both psychosocial and physical exposures is highlighted. Individuals within an occupational type can be both affected by and effect upon occupation level characteristics and health measures. By using the worksome, occupation types are separated from value-laden social classifications. This paper will empirically examine whether occupation better predicts health measures from the European Working Conditions Survey (EWCS). Logistic regression models using Bayesian MCMC estimation were run for each classification system, for each health measure. Health measures included, for example, whether the respondent felt their work affected their health, their self-rated health, pain in upper or lower limbs, and headaches. Using the Deviance Information Criterion (DIC), a measure of predictive accuracy penalised for model complexity, the models were assessed against one another. The DIC shows empirically which classification system is most suitable for use in modelling. The 2-digit International Standard Classification of Occupations showed the best predictive accuracy for all measures. Therefore, examining the relationship between health and work should be done with classifications specific to occupation or industry rather than socio-economic class classifications. This justifies the worksome, allowing for a conceptual framework to link many forms of work-health research.
Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Entities:  

Keywords:  Class; Classifications; Exposome; Occupational health; Social exposure; Work; Worksome

Mesh:

Year:  2018        PMID: 30268347     DOI: 10.1016/j.socscimed.2018.09.020

Source DB:  PubMed          Journal:  Soc Sci Med        ISSN: 0277-9536            Impact factor:   4.634


  5 in total

1.  What Do the Managers Think of Us? The Older-Worker-Perspective of Managers' Attitudes.

Authors:  Annette Meng; Emil Sundstrup; Lars L Andersen
Journal:  Int J Environ Res Public Health       Date:  2021-04-14       Impact factor: 3.390

2.  Psychosocial stress and musculoskeletal pain among senior workers from nine occupational groups: Cross-sectional findings from the SeniorWorkingLife study.

Authors:  Jonas Vinstrup; Emil Sundstrup; Lars L Andersen
Journal:  BMJ Open       Date:  2021-03-29       Impact factor: 2.692

3.  A case control study of occupation and cardiovascular disease risk in Japanese men and women.

Authors:  Kota Fukai; Yuko Furuya; Shoko Nakazawa; Noriko Kojimahara; Keika Hoshi; Akihiro Toyota; Masayuki Tatemichi
Journal:  Sci Rep       Date:  2021-12-14       Impact factor: 4.379

4.  Strong Labour Market Inequality of Opportunities at the Workplace for Supporting a Long and Healthy Work-Life: The SeniorWorkingLife Study.

Authors:  Lars L Andersen; Per H Jensen; Annette Meng; Emil Sundstrup
Journal:  Int J Environ Res Public Health       Date:  2019-09-05       Impact factor: 3.390

5.  Barriers and opportunities for prolonging working life across different occupational groups: the SeniorWorkingLife study.

Authors:  Lars L Andersen; Per H Jensen; Emil Sundstrup
Journal:  Eur J Public Health       Date:  2020-04-01       Impact factor: 3.367

  5 in total

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