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Original article
A marginal structural model approach to analyse work-related injuries: an example using data from the health and retirement study
  1. Navneet Kaur Baidwan1,
  2. Susan Goodwin Gerberich2,
  3. Hyun Kim3,
  4. Andrew D Ryan4,
  5. Timothy Church3,
  6. Benjamin Capistrant5
  1. 1Division of Environmental Health Sciences, University of Minnesota, Minneapolis, Minnesota, USA
  2. 2Division of Environmental Health Sciences, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA
  3. 3Division of Environmental Health Sciences, School of Public Health, University of Minnesita, Minneapolis, Minnesota, USA
  4. 4Division of Environmental Health Sciences, School of Public Health, Midwest Center for Occupational Health and Safety, Regional Injury Prevention Research Center and Center for Violence Prevention and Control, University of Minnesota, Minneapolis, Minnesota, USA
  5. 5School of Social Work, Smith College, Smith College, Northampton, Massachusetts, USA
  1. Correspondence to Professor Susan Goodwin Gerberich, Division of Environmental Health Sciences, School of Public Health, University of Minnesota, Minneapolis MN 55455, USA; gerbe001{at}umn.edu

Abstract

Background Biases may exist in the limited longitudinal data focusing on work-related injuries among the ageing workforce. Standard statistical techniques may not provide valid estimates when the data are time-varying and when prior exposures and outcomes may influence future outcomes. This research effort uses marginal structural models (MSMs), a class of causal models rarely applied for injury epidemiology research to analyse work-related injuries.

Methods 7212 working US adults aged ≥50 years, obtained from the Health and Retirement Study sample in the year 2004 formed the study cohort that was followed until 2014. The analyses compared estimates measuring the associations between physical work requirements and work-related injuries using MSMs and a traditional regression model. The weights used in the MSMs, besides accounting for time-varying exposures, also accounted for the recurrent nature of injuries.

Results The results were consistent with regard to directionality between the two models. However, the effect estimate was greater when the same data were analysed using MSMs, built without the restriction for complete case analyses.

Conclusions MSMs can be particularly useful for observational data, especially with the inclusion of recurrent outcomes as these can be incorporated in the weights themselves.

  • work-related injuries
  • time-varying data
  • inverse probability weighting
  • marginal structural models

https://doi.org/10.1136/injuryprev-2018-043124

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    Footnotes

    • Contributors NKB: primarily responsible for acquiring a comprehensive knowledge of the intricacies of the very complex HRS database and designing the relevant methodological approach, conducting the data analyses and preparing a draft manuscript following regular meetings and discussions with the research team of co-authors who also contributed to the manuscript. SGG and HK: mentored the primary author regarding study design and analysis during the entire research project, together with AR who additionally provided mentorship relevant to database management and analysis. TC: biostatistian, provided insights and feedback on the overall project. BC: with experience and expertise with the HRS provided key input to this very complex and important effort.

    • Funding This research was funded by the Midwest Center for Occupational Health and Safety (MCOHS), Education and Research Center, Pilot Projects Research Training Program, supported by the National Institute for Occupational Safety and Health (NIOSH), Centers for Disease Control and Prevention (OH008434).

    • Disclaimer The contents of this effort are solely the responsibility of the authors and do not necessarily represent the official view of the National Institute for Occupational Safety and Health, Centers for Disease Control and Prevention, or other associated entities.

    • Competing interests None declared.

    • Patient consent for publication Not required.

    • Ethics approval Approval to conduct this study, using publically available data, was obtained from the Institutional Review Board, University of Minnesota, under the exempt review process (approval number: 1606E89582).

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

    • Data sharing statement Data are available in a public, open access repository.