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.
Bibliographical noteFunding Information:
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).
© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.
Copyright 2020 Elsevier B.V., All rights reserved.
- inverse probability weighting
- marginal structural models
- time-varying data
- work-related injuries
PubMed: MeSH publication types
- Journal Article
- Research Support, U.S. Gov't, P.H.S.