Workforce Diversity, Equity, and Inclusion: A Crucial Component of Professionalism in Psychiatry.

Documented disparities have profoundly impacted the training and careers of physicians from socially and historically marginalized groups, including women, people with disabilities, people who identify with racial and ethnic minority groups, and the lesbian, gay, bisexual, transgender, and queer or questioning+ community. Professionalism is a core component of medical training and practice, yet a focus on workforce diversity, equity, and inclusion is often absent. This report aims to encourage the adoption of workforce diversity, equity, and inclusion as a crucial component of professionalism, with an emphasis on the field of psychiatry.

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.

Workforce Diversity, Equity, and Inclusion: A Crucial Component of Professionalism in Psychiatry.

Documented disparities have profoundly impacted the training and careers of physicians from socially and historically marginalized groups, including women, people with disabilities, people who identify with racial and ethnic minority groups, and the lesbian, gay, bisexual, transgender, and queer or questioning+ community. Professionalism is a core component of medical training and practice, yet a focus on workforce diversity, equity, and inclusion is often absent. This report aims to encourage the adoption of workforce diversity, equity, and inclusion as a crucial component of professionalism, with an emphasis on the field of psychiatry.

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.

Assessment of a Risk Index for Suicide Attempts Among US Army Soldiers With Suicide Ideation: Analysis of Data From the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS).

Importance: The Department of Veterans Affairs recently began requiring annual suicide ideation (SI) screening of all patients and additional structured questions for patients reporting SI. Related changes are under consideration at the Department of Defense. These changes will presumably lead to higher SI detection, which will require hiring additional clinical staff and/or developing a clinical decision support system to focus in-depth suicide risk assessments on patients considered high risk. Objective: To carry out a proof-of-concept study for whether a brief structured question battery from a survey of US Army soldiers can help target in-depth suicide risk assessments by identifying soldiers with self-reported lifetime SI who are at highest risk of subsequent administratively recorded nonfatal suicide attempts (SAs). Design, Setting, and Participants: Cohort study with prospective observational design. Data were collected from May 2011 to February 2013. Participants were followed up through December 2014. Analyses were conducted from March to November 2018. A logistic regression model was used to assess risk for subsequent administratively recorded nonfatal SAs. A total of 3649 Regular Army soldiers in 3 Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) surveys who reported lifetime SI were followed up for 18 to 45 months from baseline to assess administratively reported nonfatal SAs. Main Outcomes and Measures: Outcome was administratively recorded nonfatal SAs between survey response and December 2014. Predictors were survey variables. Results: The 3649 survey respondents were 80.5% male and had a median (interquartile range) age of 29 (25-36) years (range, 18-55 years); 69.4% were white non-Hispanic, 14.6% were black, 9.0% were Hispanic, 7.0% were another racial/ethnic group. Sixty-five respondents had administratively recorded nonfatal SAs between survey response and December 2014. One additional respondent died by suicide without making a nonfatal SA but was excluded from analysis based on previous evidence that predictors are different for suicide death and nonfatal SAs. Significant risk factors were SI recency (odds ratio [OR], 7.2; 95% CI, 2.9-18.0) and persistence (OR, 2.6; 95% CI, 1.0-6.8), positive screens for mental disorders (OR, 26.2; 95% CI, 6.1-112.0), and Army career characteristics (OR for junior enlisted rank, 30.0; 95% CI, 3.3-272.5 and OR for senior enlisted rank, 6.7; 95% CI, 0.8-54.9). Cross-validated area under the curve was 0.78. The 10% of respondents with highest estimated risk accounted for 39.2% of subsequent SAs. Conclusions and Relevance: Results suggest the feasibility of developing a clinically useful risk index for SA among soldiers with SI using a small number of self-report questions. If implemented, a continuous quality improvement approach should be taken to refine the structured question series.

Subjective social status and substance use severity in a young adult sample.

Subjective social status (SSS), an individual's subjective view of standing in society, has been shown to better predict health outcomes compared to objective measures of socioeconomic status (SES), including educational attainment and income. This study examines the relationship between SSS and severity of tobacco, alcohol, and marijuana use after controlling for objective measures of SES. Young adults (N = 1,987) aged 18-25 who reported smoking at least one cigarette in the past 30 days were recruited and surveyed anonymously online. Three separate structural equation models examined whether SSS was associated with severity of tobacco, alcohol, and marijuana use, controlling for personal and household income, years of education, employment status, and parental education. Household income (b = .31), employment status (b = .07), years of education (b = .09), and parental education (b = .16) were positively associated with SSS (all p values < .001); personal income was not significantly associated with SSS (p = .11). All three models adequately fit the data. SSS was negatively associated with severity of tobacco (b = -.13, p < .001) and marijuana use (b = -36, p = .02), but not alcohol use severity (b = .01, p = .56). Among young adults, higher subjective social status is associated with less severe tobacco and marijuana use, whereas alcohol use severity appears to be similar across socioeconomic class.

Marijuana and tobacco co-use in young adults: patterns and thoughts about use.

OBJECTIVE: We examined the frequency and intensity of tobacco use and thoughts about abstinence among young adults in the United States as a function of their use of marijuana. We hypothesized that heavier marijuana use would be associated with heavier tobacco use and fewer attempts to quit smoking, and we explored relationships between marijuana use and ratings of intentions and thoughts related to quitting tobacco. METHOD: This was a cross-sectional survey consisting of online recruitment and anonymous self-report. Participants were English literate, were between the ages of 18 and 25 years, and reported past-month tobacco use. More than half (53%) had smoked marijuana in the past 30 days. Tobacco use (quantity/frequency, Heavy Smoking Index, past-year quit attempt), thoughts about tobacco use (outcome expectancies, desire, self-efficacy, difficulty of quitting, abstinence goal, pros and cons, stage of change), alcohol use, and other drug use were assessed. RESULTS: Compared with those who smoked only tobacco, cousers were younger and had smoked for fewer years; had higher household income; were more likely to be male, multiethnic, and nondaily smokers; and reported greater alcohol and other drug use. The variable of days using marijuana in the past 30 days was associated with multiple measures of tobacco use intensity/frequency. Only one association was significant between marijuana use and tobacco-related cognitions: Cousers had a lower likelihood of planning to quit tobacco for good (odds ratio = 0.75, 95% CI [0.58, 0.98]). CONCLUSIONS: Findings support the association between tobacco and marijuana use among young people but speak to the importance of addressing tobacco cognitions in young adult smokers regardless of level of marijuana use.