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

Individuals released from jail die by self‑harm at nearly nine times the rate of the general U.S.

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.

To evaluate the feasibility of applying an existing self-harm risk prediction model to jail populations.

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.

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.

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.