Machine Learning to Differentiate Risk of Suicide Attempt and Self-harm After General Medical Hospitalization of Women With Mental Illness.

BackgroundSuicide prevention is a public health priority, but risk factors for suicide after medical hospitalization remain understudied. This problem is critical for women, for whom suicide rates in the United States are disproportionately increasing.ObjectiveTo differentiate the risk of suicide attempt and self-harm following general medical hospitalization among women with depression, bipolar disorder, and chronic psychosis.MethodsWe developed a machine learning algorithm that identified risk factors of suicide attempt and self-harm after general hospitalization using electronic health record data from 1628 women in the University of California Los Angeles Integrated Clinical and Research Data Repository. To assess replicability, we applied the algorithm to a larger sample of 140,848 women in the New York City Clinical Data Research Network.ResultsThe classification tree algorithm identified risk groups in University of California Los Angeles Integrated Clinical and Research Data Repository (area under the curve 0.73, sensitivity 73.4, specificity 84.1, accuracy 0.84), and predictor combinations characterizing key risk groups were replicated in New York City Clinical Data Research Network (area under the curve 0.71, sensitivity 83.3, specificity 82.2, and accuracy 0.84). Predictors included medical comorbidity, history of pregnancy-related mental illness, age, and history of suicide-related behavior. Women with antecedent medical illness and history of pregnancy-related mental illness were at high risk (6.9%-17.2% readmitted for suicide-related behavior), as were women below 55 years old without antecedent medical illness (4.0%-7.5% readmitted).ConclusionsPrevention of suicide attempt and self-harm among women following acute medical illness may be improved by screening for sex-specific predictors including perinatal mental health history

Risk of suicide attempts and self-harm after 1.4 million general medical hospitalizations of men with mental illness

BackgroundThe short-term risk of suicide after medical hospital discharge is four times higher among men compared with women. As previous work has identified female-specific antecedents of suicide-related behavior after medical hospitalization of women with serious mental illness, we examined predictors among a similar population of men with multimorbidity.MethodsClassification and regression tree (CART) models were developed and validated using electronic health records (EHRs) from 1,423,161 medical (non-psychiatric) hospitalizations of men ≥ 18-years-old with an existing diagnosis of a depressive disorder, bipolar disorder, or chronic psychosis. Hospitalizations occurred between 2009 and 2017. Risk groups were evaluated using an independent testing set. The primary outcome was readmission within one year associated with ICD-9 or -10 code for self-harm or attempt.ResultsThe 1-year readmission rate for intentional self-harm and suicide attempt was 3.9% (55,337/1,423,161 hospitalizations). The classification model discriminated risk with area under the curve (AUC) 0.73 (Confidence Interval [95%CI] 0.68-0.74), accuracy 0.82 (95%CI 0.71-0.83), sensitivity 82.6% (95%CI 81.2-84), and specificity 83.1% (95%CI 81.7-84.5). Strongest predictors were medical comorbidity, prior self-harm, age, and prior hospitalization. Men with greater medical comorbidity burden and prior self-harm were at highest risk (Odds Ratio [OR] 3.10, 95%CI 3.02-3.18), as were men < 62-years-old with few medical comorbidities (OR 1.11 95%CI 1.08-1.13).LimitationsThe study focused on medical hospitalizations for suicide attempt and thus captured only severe attempts resulting in hospitalization.ConclusionsAfter medical hospitalization, men with serious mental illness experienced a high risk of self-harm (1:25 hospitalizations). Risk was particularly elevated among younger patients without prior medical conditions and older patients with medical comorbidity and prior self-harm

Mental Health‐Related Emergency Department Visits Among Children During The Early COVID‐19 Pandemic

ObjectiveTo measure univariate and covariate-adjusted trends in children's mental health-related emergency department (MH-ED) use across geographically diverse areas of the U.S. during the first wave of the Coronavirus-2019 (COVID-19) pandemic.MethodThis is a retrospective, cross-sectional cohort study using electronic health records from four academic health systems, comparing percent volume change and adjusted risk of child MH-ED visits among children aged 3-17 years, matched on 36-week (3/18/19-11/25/19 vs. 3/16/20-11/22/20) and 12-week seasonal time intervals. Adjusted incidence rate ratios (IRR) were calculated using multivariate Poisson regression.ResultsVisits declined during spring-fall 2020 (n = 3892 vs. n = 5228, -25.5%) and during spring (n = 1051 vs. n = 1839, -42.8%), summer (n = 1430 vs. n = 1469, -2.6%), and fall (n = 1411 vs. n = 1920, -26.5%), compared with 2019. There were greater declines among males (28.2% vs. females -22.9%), children 6-12-year (-28.6% vs. -25.9% for 3-5 years and -22.9% for 13-17 years), and Black children (-34.8% vs. -17.7% to -24.9%). Visits also declined for developmental disorders (-17.0%) and childhood-onset disorders (e.g., attention deficit and hyperactivity disorders; -18.0%). During summer-fall 2020, suicide-related visits rose (summer +29.8%, fall +20.4%), but were not significantly elevated from 2019 when controlling for demographic shifts. In contrast, MH-ED use during spring-fall 2020 was significantly reduced for intellectual disabilities (IRR 0.62 [95% CI 0.47-0.86]), developmental disorders (IRR 0.71 [0.54-0.92]), and childhood-onset disorders (IRR 0.74 [0.56-0.97]).ConclusionsThe early pandemic brought overall declines in child MH-ED use alongside co-occurring demographic and diagnostic shifts. Children vulnerable to missed detection during instructional disruptions experienced disproportionate declines, suggesting need for future longitudinal research in this population