Predictors of Intentional Self -Harm Among Medicaid Mental Health Clinic Clients In New York

Abstract

BackgroundBehavioral health outpatients are at risk for self-harm. Identifying individuals or combination of risk factors could discriminate those at elevated risk for self-harm. MethodsThe study population (N = 248,491) included New York State Medicaid-enrolled individuals aged 10 to 64 with mental health clinic services between November 1, 2015 to November 1, 2016. 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). ResultsOf N = 248,491 patients, 4,224 (1.70%) had an episode of intentional self-harm. Factors associated with increased self-harm risk were age 17–25, 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). LimitationsNew 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. ConclusionsOur 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.