Exploring the intersectionality of characteristics among those who experienced opioid overdoses: A cluster analysis.

Abstract

As Canada continues to experience an opioid crisis, it is important to understand the intersection between the demographic, socioeconomic and service use characteristics of those experiencing opioid overdoses to better inform prevention and treatment programs. The Statistics Canada British Columbia Opioid Overdose Analytical File (BCOOAF) represents people's opioid overdoses between January 2014 and December 2016 (n = 13,318). The BCOOAF contains administrative health data from British Columbia linked to Statistics Canada data, including on health, employment, social assistance and police contacts. Cluster analysis was conducted using the k-prototypes algorithm. 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 male, used non-opioid prescription medications and had varying levels of employment. Individuals in Cluster A1 were employed, worked mostly in construction, had high incomes 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; were mostly taking prescription opioids, with about one quarter or less receiving opioid agonist treatment (OAT); mostly had precarious to no employment; and had low to no income. People in Cluster B1 were primarily middle-aged (45 to 65 years) and on social assistance, while people in Cluster B2 were older, more frequently used health services and had no social assistance income. Individuals in Group C were primarily younger males aged 24 to 44 years, with higher prevalence of having experienced multiple overdoses, were medium to high users of health care services, were mostly unemployed and were recipients of social assistance. Most had multiple contacts with police. Those in Cluster C1 predominantly had no documented use of prescription opioid medications, and all had no documented OAT, while all individuals in Cluster C2 were on OAT. The application of machine learning techniques to a multidimensional database enables an intersectional approach to study those experiencing opioid overdoses. The results revealed distinct patient profiles that can be used to better target interventions and treatment.