Objective To assess construct and discriminant validity of four health-related work productivity loss questionnaires in relation to employer productivity metrics, and to describe variation in economic estimates of productivity loss provided by the questionnaires in healthy workers. Methods 58 billing office workers completed surveys including health information and four productivity loss questionnaires. Employer productivity metrics and work hours were also obtained. Results Productivity loss questionnaires were weakly to moderately correlated with employer productivity metrics. Workers with more health complaints reported greater health-related productivity loss than healthier workers, but showed no loss on employer productivity metrics. Economic estimates of productivity loss showed wide variation among questionnaires, yet no loss of actual productivity. Conclusions Additional studies are needed comparing questionnaires with objective measures in larger samples and other industries, to improve measurement methods for health-related productivity loss.

BackgroundThere is growing use of a job exposure matrix (JEM) to provide exposure estimates in studies of work-related musculoskeletal disorders; few studies have examined the validity of such estimates, nor did compare associations obtained with a JEM with those obtained using other exposures.ObjectiveThis study estimated upper extremity exposures using a JEM derived from a publicly available data set (Occupational Network, O*NET), and compared exposure-disease associations for incident carpal tunnel syndrome (CTS) with those obtained using observed physical exposure measures in a large prospective study.Methods2393 workers from several industries were followed for up to 2.8 years (5.5 person-years). Standard Occupational Classification (SOC) codes were assigned to the job at enrolment. SOC codes linked to physical exposures for forceful hand exertion and repetitive activities were extracted from O*NET. We used multivariable Cox proportional hazards regression models to describe exposure-disease associations for incident CTS for individually observed physical exposures and JEM exposures from O*NET.ResultsBoth exposure methods found associations between incident CTS and exposures of force and repetition, with evidence of dose–response. Observed associations were similar across the two methods, with somewhat wider CIs for HRs calculated using the JEM method.ConclusionExposures estimated using a JEM provided similar exposure-disease associations for CTS when compared with associations obtained using the ‘gold standard’ method of individual observation. While JEMs have a number of limitations, in some studies they can provide useful exposure estimates in the absence of individual-level observed exposures.

ObjectivesJob exposure matrices (JEMs) can be constructed from expert-rated assessments, direct measurement and self-reports. This paper describes the construction of a general population JEM based on self-reported physical exposures, its ability to create homogeneous exposure groups (HEG) and the use of different exposure metrics to express job-level estimates.MethodsThe JEM was constructed from physical exposure data obtained from the Cohorte des consultants des Centres d’examens de santé (CONSTANCES). Using data from 35 526 eligible participants, the JEM consisted of 27 physical risk factors from 407 job codes. We determined whether the JEM created HEG by performing non-parametric multivariate analysis of variance (NPMANOVA). We compared three exposure metrics (mean, bias-corrected mean, median) by calculating within-job and between-job variances, and by residual plots between each metric and individual reported exposure.ResultsNPMANOVA showed significantly higher between-job than within-job variance among the 27 risk factors (F(253,21964)=61.33, p<0.0001, r2=41.1%). The bias-corrected mean produced more favourable HEG as we observed higher between-job variance and more explained variance than either means or medians. When compared with individual reported exposures, the bias-corrected mean led to near-zero mean differences and lower variance than other exposure metrics.ConclusionsCONSTANCES JEM using self-reported data yielded HEGs, and can thus classify individual participants based on job title. The bias-corrected mean metric may better reflect the shape of the underlying exposure distribution. This JEM opens new possibilities for using unbiased exposure estimates to study the effects of workplace physical exposures on a variety of health conditions within a large general population study.

ObjectivesJob exposure matrices (JEMs) are increasingly used to estimate physical workplace exposures. We conducted a cross-national comparison of exposure estimates from two general population JEMs to aid the interpretation of exposure–outcome associations across countries and to explore the feasibility of cross-national application of JEMs to provide workplace physical exposure estimates.MethodsWe compared physical exposure estimates from two general population JEMs created from the FrenchCohorte des consultants des Centres d’examens de santé study (27 exposure variables) and the American Occupational Information Network database (21 exposure variables). These exposure variables were related to physical demands or ergonomic risk factors for musculoskeletal disorders. We used a crosswalk to match French Profession et Catégorie Sociale job codes with American Standard Occupational Classification job codes and calculated Spearman’s correlations and Cohen’s kappa values for exposure variable pairs between these French and American JEMs. We defined a priori 50 matched French and American JEM variable pairs that measured similar exposures.ResultsAll variable pairs measuring similar physical exposures demonstrated positive correlations. Among the 50 matched pairs, 33 showed high correlation (ρ≥0.70) and 46 showed at least moderate agreement (κ≥0.41). Exposures expected to be mutually exclusive (manual work vs office work) showed strongly negative correlations.ConclusionsFrench and American general population physical exposure JEMs were related, sharing moderate to high association and moderate to substantial agreement between the majority of variable pairs measuring similar exposures. These findings will inform cross-national comparisons of study results and support some uses of general population JEMs outside their countries of origin.

Background This research aimed to improve residential construction foremen’s communication skills and safety behaviors of their crewmembers when working at heights. Methods Eighty-four residential construction foremen participated in the 8-hour fall prevention and safety communication training. We compared pre-intervention surveys from foremen and their crewmembers to measure the effect of training. Results Foremen and crewmembers’ ratings showed improvements in fall prevention knowledge, behaviors, and safety communication and were sustained 6-months post-training, with emphasized areas demonstrating larger increases. Ratings were similar between foremen and crewmembers, suggesting that the foremen effectively taught their crew and assigned accurate ratings. Based upon associations between safety behaviors and reported falls observed in prior research, we would expect a 16.6% decrease in the one year cumulative incidence of self-reported falls post-intervention. Conclusions This intervention improved safety knowledge and behaviors of a large number of workers by training construction foremen in fall prevention and safety communication skills.

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Background: Construction workers have high rates of work-related musculoskeletal disorders, which lead to frequent opioid use and opioid use disorder (OUD). This paper quantified the incidence of opioid use and OUD among construction workers with and without musculoskeletal disorders. Methods: We conducted a retrospective study using union health claims from January 2015 to June 2018 from 19,909 construction workers. Claims for diagnoses of chronic musculoskeletal disorders, acute musculoskeletal injuries, musculoskeletal surgery, and other conditions were linked to new opioid prescriptions. We examined the effects of high doses (≥50 morphine mg equivalents per day), large supply (more than 7 days per fill), long-term opioid use (60 or more days supplied within a calendar quarter), and musculoskeletal disorders, on the odds of a future OUD. Results: There were high rates (42.8% per year) of chronic musculoskeletal disorders among workers, of whom 24.1% received new opioid prescriptions and 6.3% received long-term opioid prescriptions per year. Workers receiving opioids for chronic musculoskeletal disorders had the highest odds of future OUD: 4.71 (95% confidence interval 3.09-7.37); workers prescribed long-term opioids in any calendar quarter had a nearly 10-fold odds of developing an OUD. Conclusions: Among construction workers, opioids initiated for musculoskeletal pain were strongly associated with incident long-term opioid use and OUD. Musculoskeletal pain from physically demanding work is likely one driver of the opioid epidemic in occupations like construction. Prevention of work injuries and alternative pain management are needed for workers at risk for musculoskeletal injuries.

A higher proportion of employees working for smaller businesses use available supports than employees of larger businesses. Supports offered by employers should target the needs and interests of the workforce, particularly for the higher risk low-income employees.