Job-exposure matrices addressing lifestyle to be applied in register-based occupational health studies

Abstract

Objectives: Information about lifestyle factors in register-based occupational health studies is often not available. The objective of this study was therefore to develop gender, age and calendar-time specific job-exposure matrices (JEMs) addressing five selected lifestyle characteristics across job groups as a tool for lifestyle adjustment in register-based studies.

Methods: We combined and harmonised questionnaire and interview data on lifestyle from several Danish surveys in the time period 1981-2013 for 264 054 employees registered with a DISCO-88 code (the Danish version of International Standard Classification of Occupations (ISCO)-88) in a nationwide register-based Danish Occupational Cohort. We modelled the probability of specified lifestyles in mixed models for each level of the four-digit DISCO code with age and sex as fixed effects and assessed variation in terms of intraclass correlation coefficients (ICCs) and exposure-level percentile ratios across jobs for six different time periods from 1981 through 2013.

Results: The ICCs were overall low (0.26%-7.05%) as the within-job group variation was large relative to the between job group variation, but across jobs the calendar period-specific ratios between highest and lowest predicted levels were ranging from 1.2 to 6.9, and for the 95%/1% and the 75%/5% percentile ratios ranges were 1.1-2.8 and 1.1-1.6, respectively, thus indicating substantial contrast for some lifestyle exposures and some occupations.

Conclusions: The lifestyle JEMs may prove a useful tool for control of lifestyle-related confounding in register-based occupational health studies where lacking information on individual lifestyle factors may compromise internal validity.

Keywords: behaviour; bias; cohort study; confounding.

Figure 1

Overview of the data flow linking the respective cohort studies in one data file, where in the lifestyle job-exposure matrices(JEMs) are generated. BMI, body mass index.

Figure 2

The population distribution of the predicted values of the lifestyle job-exposure matrices across all job groups with specification of the three jobs with lowest and highest predicted values for each lifestyle JEM. 1210=directors and chief executives; 1227=production and operations department managers; 2113=chemists; 2114=geologists and geophysicists; 2141=architects, town and traffic planners; 2145=mechanical engineers; 2221=medial doctors; 224=pharmacists; 2310=college, university and higher education teaching professionals; 2419=business professionals; 3213=farming and forestry advisers; 3449=customs, tax and related government associate professionals; 3475=athletes and sportspersons; 5111=travel and attendants; 5121=housekeepers and related workers; 5123=waiters and bartenders; 5133=home based personal care workers; 5162=police officers; 5169=protective service workers; 7136=plumbers and pipe fitters; 7142=varnishes and related painters; 8141=wood processing-plant operators; 8162=steam engine and boiler operators; 8322=car, taxi and van drivers; 8323=bus and tram drivers; 83244=heavy truck and lorry drivers; 8340=ships’, deck crews and related workers; 9312=construction and maintenance labourers; 9313=building construction labourers.