Background : Behavioral health outpatients are at risk for self-harm. Identifying individuals or combination of risk factors could discriminate those at elevated risk for self-harm. Methods : The study population (N=248,491) included New York State Medicaid-enrolled individuals aged 10 to 64 with mental health specialty clinic visits 11/1/15-11/1/16. Self-harm episodes were defined using ICD-10 codes from emergency department and inpatient visits. Multi-predictor logistic regression models were fit on a subsample of the data and compared to a testing sample based on discrimination performance (Area Under the Curve or AUC). Results : Of N=248,491 patients, 4,224 (1.70%) had an episode of intentional self-harm. Factors associated with increased self-harm risk were age17-25, being female and having recent diagnoses of depression (AOR=4.3, 95%CI: 3.6-5.0), personality disorder (AOR=4.2, 95%CI: 2.9-6.1), or substance use disorder (AOR=3.4, 95%CI: 2.7-4.3) within the last month. A multi-predictor logistic regression model including demographics and new psychiatric diagnoses within 90 days prior to index date had good discrimination and outperformed competitor models on a testing sample (AUC=0.86, 95%CI:0.85-0.87). Limitations : New York State Medicaid data may not be generalizable to the entire U.S population. ICD-10 codes do not allow distinction between self-harm with and without intent to die. Conclusions : Our results highlight the usefulness of recency of new psychiatric diagnoses, in predicting the magnitude and timing of intentional self-harm risk. An algorithm based on this finding could enhance clinical assessments support screening, intervention and outreach programs that are at the heart of a Zero Suicide prevention model.
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