Managed care updates of subscriber jail release to prompt community suicide prevention: clinical trial protocol.

BACKGROUND: Recent jail detention is a marker for trait and state suicide risk in community-based populations. However, healthcare providers are typically unaware that their client was in jail and few post-release suicide prevention efforts exist. This protocol paper describes an effectiveness-implementation trial evaluating community suicide prevention practices triggered by advances in informatics that alert CareSource, a large managed care organization (MCO), when a subscriber is released from jail. METHODS: This randomized controlled trial investigates two evidence-based suicide prevention practices triggered by CareSource's jail detention/release notifications, in a partial factorial design. The first phase randomizes ~ 43,000 CareSource subscribers who pass through any Ohio jail to receive Caring Contact letters sent by CareSource or to Usual Care after jail release. The second phase (running simultaneously) involves a subset of ~ 6,000 of the 43,000 subscribers passing through jail who have been seen in one of 12 contracted behavioral health agencies in the 6 months prior to incarceration in a stepped-wedge design. Agencies will receive: (a) notifications of the client's jail detention/release, (b) instructions for re-engaging these clients, and (c) training in suicide risk assessment and the Safety Planning Intervention for use at re-engagement. We will track suicide-related and service linkage outcomes 6 months following jail release using claims data. CONCLUSIONS: This design allows us to rigorously test two intervention main effects and their interaction. It also provides valuable information on the effects of system-level change and the scalability of interventions using big data from a MCO to flag jail release and suicide risk. TRIAL REGISTRATION: The trial is registered at clinicaltrials.gov (NCT05579600). Registered 27 June, 2023.

From court to the community: improving access to evidence-based treatment for underserved youth involved in the juvenile legal system at-risk for suicide.

BACKGROUND: Juvenile legal involved youth (JLIY) experience disproportionately high rates of suicidal and self-injurious thoughts and behaviors (SSITB). Many JLIY lack access to evidence-based treatment specifically designed to treat SSITB, thereby increasing the overall risk of suicide. The overwhelming majority of JLIY are not placed in secure facilities and almost all incarcerated youth are eventually released to the community. Consequently, SSITB are a major concern of JLIY residing in the community and it is critical that this population has access to evidence-based treatment for SSITB. Unfortunately, most community mental health providers who treat JLIY have not been trained in evidence-based interventions that are specifically designed to SSITB, which often leads to youth experiencing prolonged periods of SSITB. Training community mental health providers who serve JLIY in the detection and treatment of SSITB shows promise for decreasing the overall suicide risk for JLIY. METHODS: The current proposal aims to reduce SSITB among JLIY, and thus reduce mental health disparities in this vulnerable and underserved youth population, by increasing access to evidence-based treatment strategies specifically designed to treat SSITB behaviors. We will implement an agency-wide training among at least 9 distinct community mental health agencies that serve JLIY referred to treatment by a statewide court system in the Northeast. Agencies will be trained in an adapted version of the COping, Problem Solving, Enhancing life, Safety, and Parenting (COPES+) intervention. Training will be implemented via a cluster-randomized stepped wedge trial that proceeds through multiple phases. DISCUSSION: This research engages multiple systems (i.e., juvenile legal and mental health systems) serving JLIY and has the potential to directly inform treatment practices in juvenile legal and mental health systems. The current protocol has significant public health implications as the primary goals are to reduce SSITB among adolescents involved in the juvenile legal system. By implementing a training protocol with community-based providers to help them learn an evidence-based intervention, this proposal aims to reduce mental health disparities in a marginalized and underserved population. TRIAL REGISTRATION: osf.io/sq9zt.

Evaluation of a Model to Target High-risk Psychiatric Inpatients for an Intensive Postdischarge Suicide Prevention Intervention.

Importance: The months after psychiatric hospital discharge are a time of high risk for suicide. Intensive postdischarge case management, although potentially effective in suicide prevention, is likely to be cost-effective only if targeted at high-risk patients. A previously developed machine learning (ML) model showed that postdischarge suicides can be predicted from electronic health records and geospatial data, but it is unknown if prediction could be improved by adding additional information. Objective: To determine whether model prediction could be improved by adding information extracted from clinical notes and public records. Design, Setting, and Participants: Models were trained to predict suicides in the 12 months after Veterans Health Administration (VHA) short-term (less than 365 days) psychiatric hospitalizations between the beginning of 2010 and September 1, 2012 (299 050 hospitalizations, with 916 hospitalizations followed within 12 months by suicides) and tested in the hospitalizations from September 2, 2012, to December 31, 2013 (149 738 hospitalizations, with 393 hospitalizations followed within 12 months by suicides). Validation focused on net benefit across a range of plausible decision thresholds. Predictor importance was assessed with Shapley additive explanations (SHAP) values. Data were analyzed from January to August 2022. Main Outcomes and Measures: Suicides were defined by the National Death Index. Base model predictors included VHA electronic health records and patient residential data. The expanded predictors came from natural language processing (NLP) of clinical notes and a social determinants of health (SDOH) public records database. Results: The model included 448 788 unique hospitalizations. Net benefit over risk horizons between 3 and 12 months was generally highest for the model that included both NLP and SDOH predictors (area under the receiver operating characteristic curve range, 0.747-0.780; area under the precision recall curve relative to the suicide rate range, 3.87-5.75). NLP and SDOH predictors also had the highest predictor class-level SHAP values (proportional SHAP = 64.0% and 49.3%, respectively), although the single highest positive variable-level SHAP value was for a count of medications classified by the US Food and Drug Administration as increasing suicide risk prescribed the year before hospitalization (proportional SHAP = 15.0%). Conclusions and Relevance: In this study, clinical notes and public records were found to improve ML model prediction of suicide after psychiatric hospitalization. The model had positive net benefit over 3-month to 12-month risk horizons for plausible decision thresholds. Although caution is needed in inferring causality based on predictor importance, several key predictors have potential intervention implications that should be investigated in future studies.

Mental health and clinical psychological science in the time of COVID-19: Challenges, opportunities, and a call to action.

COVID-19 presents significant social, economic, and medical challenges. Because COVID-19 has already begun to precipitate huge increases in mental health problems, clinical psychological science must assert a leadership role in guiding a national response to this secondary crisis. In this article, COVID-19 is conceptualized as a unique, compounding, multidimensional stressor that will create a vast need for intervention and necessitate new paradigms for mental health service delivery and training. Urgent challenge areas across developmental periods are discussed, followed by a review of psychological symptoms that likely will increase in prevalence and require innovative solutions in both science and practice. Implications for new research directions, clinical approaches, and policy issues are discussed to highlight the opportunities for clinical psychological science to emerge as an updated, contemporary field capable of addressing the burden of mental illness and distress in the wake of COVID-19 and beyond. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Using Administrative Data to Predict Suicide After Psychiatric Hospitalization in the Veterans Health Administration System.

There is a very high suicide rate in the year after psychiatric hospital discharge. Intensive postdischarge case management programs can address this problem but are not cost-effective for all patients. This issue can be addressed by developing a risk model to predict which inpatients might need such a program. We developed such a model for the 391,018 short-term psychiatric hospital admissions of US veterans in Veterans Health Administration (VHA) hospitals 2010-2013. Records were linked with the National Death Index to determine suicide within 12 months of hospital discharge (n=771). The Super Learner ensemble machine learning method was used to predict these suicides for time horizon between 1 week and 12 months after discharge in a 70% training sample. Accuracy was validated in the remaining 30% holdout sample. Predictors included VHA administrative variables and small area geocode data linked to patient home addresses. The models had AUC=.79-.82 for time horizons between 1 week and 6 months and AUC=.74 for 12 months. An analysis of operating characteristics showed that 22.4%-32.2% of patients who died by suicide would have been reached if intensive case management was provided to the 5% of patients with highest predicted suicide risk. Positive predictive value (PPV) at this higher threshold ranged from 1.2% over 12 months to 3.8% per case manager year over 1 week. Focusing on the low end of the risk spectrum, the 40% of patients classified as having lowest risk account for 0%-9.7% of suicides across time horizons. Variable importance analysis shows that 51.1% of model performance is due to psychopathological risk factors accounted, 26.2% to social determinants of health, 14.8% to prior history of suicidal behaviors, and 6.6% to physical disorders. The paper closes with a discussion of next steps in refining the model and prospects for developing a parallel precision treatment model.

Medication burden in bipolar disorder: a chart review of patients at psychiatric hospital admission.

Individuals with bipolar disorder (BD) often receive complex polypharmacy regimens as part of treatment, yet few studies have sought to evaluate patient characteristics associated with this high medication burden. This retrospective chart review study examined rates of complex polypharmacy (i.e., ≥4 psychotropic medications), patterns of psychotropic medication use, and their demographic and clinical correlates in a naturalistic sample of adults with bipolar I disorder (BDI; N=230) presenting for psychiatric hospital admission. Using a computer algorithm, a hospital administrator extracted relevant demographic, clinical, and community treatment information for analysis. Patients reported taking an average of 3.31 (S.D.=1.46) psychotropic medications, and 5.94 (S.D.=3.78) total medications at intake. Overall, 82 (36%) met criteria for complex polypharmacy. Those receiving complex polypharmacy were significantly more likely to be female, to be depressed, to have a comorbid anxiety disorder, and to have a history of suicide attempt. Women were significantly more likely than men to be prescribed antidepressants, benzodiazepines, and stimulants, even after controlling for mood episode polarity. Study data highlight the high medication burden experienced by patients with BD, especially those who are acutely symptomatic. Data also highlight the particularly high medication burden experienced by women with BD; a burden not fully accounted for by depression.

Likelihood of suicidality at varying levels of depression severity: a re-analysis of NESARC data.

Although it is clear that increasing depression severity is associated with more risk for suicidality, less is known about at what levels of depression severity the risk for different suicide symptoms increases. We used item response theory to estimate the likelihood of endorsing suicide symptoms across levels of depression severity in an epidemiological data set. Regardless of depression severity, suicide attempts were less frequently endorsed than ideation, which was less frequently endorsed than feeling like one wanted to die. All suicide symptoms were generally less likely to be endorsed than other depression symptoms. There was a low probability of suicidality at depression levels that likely would not merit a diagnosis of major depression.