Occupational Patterns of Opioid-Related Overdose Deaths Among Arizona Medicaid Enrollees, 2008–2017

Occupational patterns of opioid-related harms comparing a cohort of formerly injured workers to the general population in Ontario, Canada

“…Results from this study are also consistent with the findings of previous studies identifying occupational patterns in opioid-related deaths among the general working population. Prior studies, primarily from the USA, have also found workers in jobs characterized by high levels of physical labour to be at greater risk of opioid-related mortality (Aram et al, 2020;Chalasani et al, 2020;Hawkins et al, 2019;Harduar Morano et al, 2018;Scagos et al, 2019;BC Coroners Service Death Review Panel, 2022;Gomes et al, 2022;Billock et al, 2023). In particular, in our study and others, workers in construction occupations have consistently ranked as having some of the highest rates of opioidrelated deaths.…”

“…In our study, elevated risks of opioidrelated harms were also observed among workers in occupations in medicine and health, sales, and service. While less consistently noted, some previous studies have also found higher rates of opioid use and related deaths in these occupations, particularly among food preparation service, personal care, and health care support occupations (Aram et al, 2020;Hawkins et al, 2019;Harduar Morano et al, 2018;Chalasani et al, 2020;Billock et al, 2023). In addition to higher underlying risks of work injuries in many of these occupations, job insecurity, precarious employment, financial instability, psychosocial work environment factors, and substance use workplace norms and availability may also be potential contributing factors, though few studies have specifically examined the contribution of these factors to the development of opioid-related harms among workers (Hawkins et al, 2021;Shaw et al, 2020).…”

“…The role of work-related injuries and pain as a determinant of opioid-related harms has been previously hypothesized (Shaw et al, 2020). Consistent with this hypothesis, individuals in traditionally blue-collar, labour-intensive occupations, such as construction and trades, transportation, manufacturing, maintenance and repair, and natural resource occupations, are known to be at greater risk for occupational injuries and musculoskeletal pain and have been found to have a high prevalence of opioid-related mortality (Aram et al, 2020;Chalasani et al, 2020;Hawkins et al, 2019;Harduar Morano et al, 2018;Scagos et al, 2019;BC Coroners Service Death Review Panel, 2022;Gomes et al, 2022;Billock et al, 2023). In one study, occupations classified as having high injury rates also exhibited higher rates of opioid-related deaths (Hawkins et al, 2021).…”

Developing and validating a machine-learning algorithm to predict opioid overdose in Medicaid beneficiaries in two US states: a prognostic modelling study

“…Third, we used Arizona Medicaid beneficiaries death certificate data to identify fatal opioid overdoses that did not receive medical attention to determine whether the prediction algorithm worked to predict fatal overdoses using International Classification of Diseases, Tenth Revision (ICD-10) underlying cause-of-death codes X42, X44, Y12, and Y14 for accidental and undetermined overdose and multiple cause-of-death codes T40.1 (heroin), T40.2 (natural and semisynthetic opioids), T40.3 (methadone), and T40.4 (synthetic opioids other than methadone). 22 …”