New innovative research provides a better understanding of the intersection of characteristics associated with opioid overdose
Opioid-related overdoses continue to be a public health crisis with deaths significantly increasing since the COVID-19 pandemic began. Previous studies have identified a range of individual characteristics associated with a higher risk of opioid overdoses including low income, precarious employment and contacts with police, but information on the intersection of these characteristics, that is, the combination of characteristics to identify profiles of individuals that may be at higher risk, has been missing. Identifying these profiles can help healthcare providers better target prevention and harm reduction programs.
A new study, "Exploring the intersectionality of characteristics among those who experienced opioid overdoses: A cluster analysis" released today in Health Reports, builds on previous work by applying machine learning techniques to examine the intersectionality of characteristics, to provide more comprehensive profiles.
In this new study, cluster analysis was applied to a range of factors—including frequency and timing of contacts with health care and justice systems, use of prescription medications including opioids, employment and income and social assistance use—among a cohort of individuals who experienced an opioid overdose in British Columbia between 2014 and 2016.
The results revealed a six-cluster solution, composed of three groups (A, B and C), each with two distinct clusters (1 and 2). Individuals in Group A were predominantly working-age males who used non-opioid prescription medications with little to no use of hospital services. Individuals in Cluster A1 were employed with high incomes, many working in construction, and had a high rate of fatal overdoses, while individuals in Cluster A2 were precariously employed and had varying levels of income. Individuals in Group B were predominantly female, mostly taking prescription opioids, precarious to no employment with low to no income. Those in Cluster B1 were primarily middle-aged and receiving social assistance, while those in Cluster B2 were older, more frequent users of health care services and very low to no income. Finally, individuals in Group C were primarily younger males with a higher rate of multiple overdoses, mostly unemployed and most had multiple contacts with police and the healthcare system. Those in Cluster C1 predominantly had no documented use of prescription opioid medications, and no documented use of opioid agonist treatment (OAT), while all individuals in Cluster C2 were on OAT.
This type of analysis enables the identification of distinct profiles of individuals experiencing opioid overdoses as well as information on the systems (i.e., healthcare and justice) that they contact on a frequent basis. This type of information can be used by public health to better target and tailor programs and identify points of intervention to support treatment and lessen harms.
The article "Exploring the intersectionality of characteristics among those who experienced opioid overdoses: A cluster analysis" is now available in the March 2023 online issue of Health Reports, Vol. 34, No. 3 (82-003-X).
This issue of Health Reports also contains the article "Daily accelerometer-measured physical activity patterns and associations with cardiometabolic health among Canadian working adults."
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