Validation of self‑harm prediction models among formerly incarcerated individuals using health data

BACKGROUND: Individuals released from jail die by self‑harm at nearly nine times the rate of the general U.S. POPULATION: Most jails rely on traditional screening methods, such as brief self-report questionnaires, which are often inconsistently administered and have limited sensitivity and predictive accuracy. This highlights the urgent need for alternative self-harm risk identification methods during and after incarceration. OBJECTIVE: To evaluate the feasibility of applying an existing self-harm risk prediction model to jail populations. METHODS: We analyzed data from 4,154 individuals incarcerated in Michigan jails who were enrolled in Medicaid. We applied a prediction model, originally developed by the Mental Health Research Network (MHRN), to identify individuals at elevated risk for self-harm. Predictors included demographics, mental health and substance use diagnoses, medical comorbidities, prior history of self-harm, mental health-related hospitalizations, and dispensing of psychotropic medications. RESULTS: The study cohort was predominantly male (70%) and racially diverse (50% Black, 43% White), with a median jail stay of just one day. Overall, the model demonstrated good discrimination, achieving a C-statistic of 0.77, with 68% sensitivity and 77% specificity, and a 99% negative predictive value. Notably, among individuals with shorter jail stays, predictive performance was better, with the C-statistic increasing to 0.80. CONCLUSIONS: Health records-based models demonstrated good predictive performance and may offer a scalable, data-driven alternative to traditional screening tools in jails. Integrating health records-based risk prediction tools in jails could improve early detection of self-harm risk and support more targeted prevention efforts.