A marginal structural model approach to analyse work-related injuries: An example using data from the Health and Retirement Study.

TitleA marginal structural model approach to analyse work-related injuries: An example using data from the Health and Retirement Study.
Publication TypeJournal Article
Year of PublicationForthcoming
AuthorsBaidwan, NKaur, Gerberich, SGoodwin, Kim, H, Ryan, AD, Church, T, Capistrant, B
JournalInjury Prevention
Date Published2019 Apr 24
ISSN Number1475-5785
KeywordsSurvey Methodology, Working conditions
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.

DOI10.1136/injuryprev-2018-043124
User Guide Notes

http://www.ncbi.nlm.nih.gov/pubmed/31018941?dopt=Abstract

Alternate JournalInj. Prev.
Citation Key10046
PubMed ID31018941