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Structural equation models in occupational health: an application to exposure modelling
  1. Mary E Davis1,2
  1. 1Department of Urban and Environmental Policy and Planning, Tufts University, Medford, Massachusetts, USA
  2. 2Exposure, Epidemiology and Risk Program, Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA
  1. Correspondence to Mary E Davis, Assistant Professor, Department of Urban and Environmental Policy and Planning, Tufts University, 97 Talbot Avenue, Medford, MA 02155, USA; mary.davis{at}tufts.edu

Abstract

Objectives Many occupational hygiene surveys are designed to collect pollutant monitoring data from multiple locations simultaneously to better reflect the reality of work-related exposure. The exposure model must account for the complexity inherent in this study design, as well as be flexible to extrapolating exposures across an occupational cohort for dose–response modelling and risk assessment. This paper explores the structural equation model (SEM) as a tool to analyse pollutant monitoring data from occupational studies with multiple concurrent sampling across exposure locations.

Methods This study uses exposure data from a comprehensive assessment of diesel exhaust in the US trucking industry to test the strength of SEMs over more standard analytical approaches such as ordinary least squares (OLS). The exposure data consist of concurrent sampling of elemental carbon from multiple co-located monitors on individual workers, work area and background levels at 36 different trucking terminals across the USA.

Results The SEM is compared with two separate OLS specifications—one that focuses only on predicting personal exposure and excludes data from the additional monitoring sites, and a second that estimates three separate OLS specifications. When compared with the OLS specifications, the SEM provided a better fit to these layered exposure data. The OLS specifications suffered from bias in the coefficients, including downward bias in the work area and background exposure levels and overstatement of the smoking effect. Additionally, many theoretically valid covariates were significant only in the SEM.

Conclusions This study provides evidence in favour of more widespread use of SEMs in occupational health. SEMs represent a more robust and realistic framework for modelling multiple exposure pathways and have the potential to reduce exposure misclassification bias and strengthen the linkages between studies of exposure and disease outcomes.

  • Hygiene/occupational hygiene
  • statistics
  • exposure assessment
  • exposure monitoring
  • diesel fumes

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Footnotes

  • Funding The research was supported in part by National Institutes of Health/National Cancer Institute (R01 CA90792) and Health Effects Institute (4705-RFA03-1/04-1).

  • Competing interests None.

  • Patient consent Obtained.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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