Project 3: Suicide Risk Identification in Jails using Data Linkage and Automation

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Abstract Project #3 Although professional and accreditation standards exist to guide identification of suicide risk, few jails effectively screen for such risk at booking (Intercept 2). Given that individuals booking into jails may be less forthcoming in reporting thoughts and behaviors to correctional officers, current identification practices are insufficient. It may be possible to enhance identification methods in jails, replicating a method developed in community health systems. Using a general population sample from seven health systems, the Mental Health Research Network developed a suicide risk model to predict suicide attempts/deaths using electronic health records and insurance claims data. Claims records were used to create the model that resulted in a risk score that could be available for medical personnel, alerting them to the possibility of heightened suicide risk. Replicating this validation, using a jail population with integrated Medicaid claims data, could result in a similar identification process for justice- involved individuals available at jail intake (booking) that could assist in detecting who among those entering jail could be at risk for suicide attempts and suicide deaths. Risk identified through the model will be compared to the practice-as-usual identification within the jail. Because there is no standardized process for identification of suicide risk within jails, each jail’s screening process will be assessed separately. This proposal would leverage three geographically and demographically diverse jails in one state, increasing the generalizability of the findings. Aim 1. Validate the suicide risk model with a jail population sample (three jails; on all of those who enter during a specific length of time), using Medicaid claims and vital record data. Aim 2. Compare the risk flag to the current suicide risk identification process (e.g. practice as usual) within 3 diverse jails. Aim 3. Evaluate implementation factors to inform the design of a future hybrid trial and integration within jails, working with state Medicaid and the Department of Health and Human Services. Improved suicide risk identification in jails could decrease the adverse impacts that suicide has on those who are detained, family members, correctional staff, the institution and community (i.e. liability, costs). Our long-term goal of this research targets jail systems by implementing an automated ‘suicide risk flag’ – derived from prior health records, resulting in improved detection at intake that would lead to intervention to reduce suicide attempts and suicide deaths within the jail. The assembled team has experience with development of the model, familiarity and experience implementing screening tools within jails, and integrating and analyzing jail and Medicaid data. The project leverages an established partnership between the team and criminal justice system. This project will inform an R01 hybrid effectiveness-implementation trial to assess whether the use of a suicide risk flag derived from this algorithm results in access to evidence based intervention within the jail resulting in a reduction in suicide attempts and death within these jail settings and post-release.