Dynamic prediction of reoffending in individuals given community sentences: Development and validation of a novel risk monitoring assessment tool (oxMore).

OBJECTIVE: This study aimed to develop and validate a dynamic risk assessment tool for individuals serving community sentences that accounts for the effects of acute adverse events and desistance from crime. HYPOTHESES: Dynamic risk prediction models that incorporate updated data on mental health relapses, incidents of victimization, and desistance from crime will produce more accurate risk stratification for reoffending than models lacking dynamic measurement of risk factors. METHOD: We analyzed a national cohort of 59,676 individuals given community sentences in Sweden, of whom 23,879 (45%) had prior psychiatric diagnoses and 18,546 (31%) had substance use disorder diagnoses. Model development tested prespecified criminal history, sociodemographic, and clinical risk factors. Employing landmarking methods for time-to-event data, we modeled the effects of new health care episodes during community supervision, changes in a supervised individual's circumstances, and the impact of crime desistance. We validated the model in a geographically distinct population. RESULTS: During follow up, 18,307 (31%) were reconvicted, 4,416 (7%) committed a violent offense, and 5,381 (9%) were hospitalized with a psychiatric diagnosis. The model demonstrated strong calibration and discrimination performance (c-index = 0.74 for violent reoffending, c-index = 0.69 for general reoffending). It also outperformed comparison models that did not incorporate dynamic data. The final model was translated into an online risk calculator (OxMore). CONCLUSIONS: Implementation of dynamic models could lead to more accurate risk stratification for individuals under community supervision, including those with psychiatric and substance use disorders, potentially improving resource allocation, and linkage to interventions that reduce recidivism rates. (PsycInfo Database Record (c) 2025 APA, all rights reserved).