A marginal structural model approach to analyse work-related injuries: an example using data from the health and retirement study

• Published in: Injury prevention Vol. 26; no. 3; pp. 248 - 253
• Main Authors: Baidwan, Navneet Kaur, Gerberich, Susan Goodwin, Kim, Hyun, Ryan, Andrew D, Church, Timothy, Capistrant, Benjamin
• Format: Journal Article
• Language: English
• Published: England BMJ Publishing Group LTD 01.06.2020
• Subjects: Aged; Aging; Bias; Cohort Studies; Epidemiology; Female; Humans; Injuries; Male; Medical research; Middle Aged; Models, Statistical; Occupational Injuries - epidemiology; Probability; Retirement; Risk Factors; Structural models; Surveys and Questionnaires; Variables; Workforce; United States > US; work-related injuries; marginal structural models; inverse probability weighting; time-varying data
• ISSN: 1353-8047; 1475-5785
• DOI: 10.1136/injuryprev-2018-043124

BackgroundBiases 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.Methods7212 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.ResultsThe 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.ConclusionsMSMs can be particularly useful for observational data, especially with the inclusion of recurrent outcomes as these can be incorporated in the weights themselves.