Sociodemographic Factors, Clinical Profiles and Social Determinants of Suicides Involving Opioids

Involvement of opioids in suicides has doubled during the past two decades, which poses a major public health concern in the United States. There is also a heightened concern for opioid-related suicides during the current COVID-19 pandemic. In order to inform better prevention approaches, it is important to understand specific characteristics of suicides involving opioids. The goal of the proposed study is to examine the patterns of sociodemographic factors, clinical profiles, and social determinants that characterize suicides involving opioids in order to enhance targeted efforts for suicide prevention. The proposed study will leverage data linkage, predictive modeling, and spatial clustering techniques to identify individual-level factors as well as aggregate-level factors linked with opioid-related suicides. First, we will develop predictive analytics to identify decedents’ individual factors that characterize suicides involving opioids. We hypothesize that multivariable predictive modeling will identify individual characteristics linked with opioid-related suicides. Second, we will develop spatial algorithms to identify geo-derived neighborhood attributes linked with opioid-related suicides and visualize geo-clusters of opioid-related suicides relative to geo-derived neighborhood attributes. We hypothesize that spatial algorithms will identify neighborhood attributes linked with opioid-related suicides. Third, we will examine whether and how individual-level and aggregate-level attributes of opioid-involved suicides differ by age, sex and race. We hypothesize that heterogeneous individual-level and aggregate-level attributes of opioid-involved suicides will be identified across different age groups, sexes, races. This study will use the sample of suicide decedents with valid toxicology results (N = 7,294) from the Office of the Chief Medical Examiner (OCME) data in Maryland (2006-2019), where specificity in classification of suicides is particularly high among all US states. The innovation of the proposed project is to leverage data linkage of the population-level mortality data from the OCME with extensive medical records drawn from Health Information Exchange in the State of Maryland – Chesapeake Regional Information System for our Patients. Given there is scarce knowledge with regard to the patterns and attributes of opioid-related suicides, multivariable predictive modeling will help identify particular characteristics associated with opioid-related suicides at both individual level (Aim 1) and at aggregate level (Aim 2). Together with the efforts to detect the geo-clustering of opioid-related suicides (Aim 2), the findings will help better inform targeted interventions for suicides particularly involving opioids. The Aim 3 analyses will help better understand complex interconnections between opioid use and suicide across different demographic subgroups. All three aims in the proposed study will also add important insights into polysubstance involvement in opioid-related suicides, especially given that majority of opioid fatalities involved other substances. The findings related to polysubstance opioid suicides will provide implications for screening of concurrent use of opioids and other substances and clinical practice of co-prescription of opioids and other medications. These findings could potentially aid in the development of suicide prevention and intervention approaches targeting opioid users. The findings will also help develop a set of policies and guidelines that will better integrate suicide risk screening among opioid users in clinical and community settings.