Stimulant medication and suicide mortality in attention-deficit hyperactivity disorder.

Patients diagnosed with attention-deficit hyperactivity disorder (ADHD) are at an elevated risk for suicide. No prior work has assessed the association between stimulant prescriptions and death by suicide in this population. This retrospective cohort study included Department of Veterans Affairs patients with an active ADHD diagnosis that received stimulant medications between 2016 and 2019. We found that months with active stimulant medication prescription was associated with decreased risk of suicide mortality compared with months without stimulant medication (odds ratio 0.57, 95% CI 0.36-0.88). Our results suggest that prescribing stimulant medications for patients diagnosed with ADHD is associated with decreased risk of suicide mortality.

Pilot randomized controlled trial of a brief strategy to prevent suicide after discharge from residential addiction treatment.

INTRODUCTION: Veterans are at greater risk for suicide and veterans with substance use disorder (SUD) have an even greater risk. Little research has looked into brief interventions to prevent suicide in this population in residential substance use treatment programs. METHOD: We conducted a pilot, randomized controlled trial of a brief suicide prevention strategy called Veterans Affairs Brief Intervention and Contact Program (VA BIC) in patients participating in the Residential Recovery Center (RRC) SUD 28-day program and deemed at risk for suicide. We measured changes in symptoms at 1-, 3-, and 6-months. We looked at social connectedness, suicidal ideation, hopelessness, thwarted belongingness, perceived burdensomeness, and treatment engagement. RESULTS: The study enrolled twenty patients. One participant withdrew immediately after baseline. We found that adherence to VA BIC components was high, as 100 % of patients (N = 10) completed 70 % or more of the VA BIC visits. Furthermore, 80 % of intervention group patients (N = 8) completed all VA BIC components. During the six-month follow-up, suicidal ideation improved in patients assigned to VA BIC, while it worsened in the standard care arm. Similarly, patients assigned to VA BIC reported a reduction in perceived burdensomeness over the six-month follow-up period while it worsened in the standard care arm. Additionally, VA BIC may modestly improve treatment engagement in the first month postdischarge. CONCLUSION: We were able to recruit and enroll patients from a residential SUD treatment program into a clinical trial of the VA BIC intervention. Our preliminary results suggest that VA BIC may be useful in reducing suicidal ideation and perceived burdensomeness in patients who are discharged from residential SUD treatment programs and increasing treatment engagement. Future trials of VA BIC should determine whether VA BIC can reduce the risk of suicide in patients who are discharged from residential SUD treatment programs.

An Analysis of Irregular Discharges From Residential Treatment Programs in the Department of Veterans Affairs Health Care System.

INTRODUCTION: Veteran populations are frequently diagnosed with mental health conditions such as substance use disorder and PTSD. These conditions are associated with adverse outcomes including a higher risk of suicide. The Veterans Health Administration (VHA) has designed a robust mental health system to address these concerns. Veterans can access mental health treatment in acute inpatient, residential, and outpatient settings. Residential programs play an important role in meeting the needs of veterans who need more structure and support. Residential specialty types in the VHA include general mental health, substance use disorder, PTSD, and homeless/work programs. These programs are affiliated with a DVA facility (i.e., medical center). Although residential care can improve outcomes, there is evidence that some patients are discharged from these settings before achieving the program endpoint. These unplanned discharges are referred to using language such as against medical advice, self-discharge, or irregular discharge. Concerningly, unplanned discharges are associated with patient harm including death by suicide. Although there is some initial evidence to locate factors that predict irregular discharge in VHA residential programs, no work has been done to examine features associated with irregular discharge in each residential specialty. METHODS: We conducted a retrospective cohort study of patients who were discharged from VHA residential treatment programs between January 2018 and September 2022. We included the following covariates: Principal diagnosis, gender, age, race/ethnicity, number of physical health conditions, number of mental health diagnostic categories, marital status, risk of homelessness, urbanicity, and service connection. We considered two discharge types: Regular and irregular. We used logistic regression to determine the odds of irregular discharge using models stratified by bed specialty as well as combined odds ratios and 95% CIs across program specialties. The primary purposes are to identify factors that predict irregular discharge and to determine if the factors are consistent across bed specialties. In a secondary analysis, we calculated facility-level adjusted rates of irregular discharge, limiting to facilities with at least 50 discharges. We identified the amount of residual variation that exists between facilities after adjusting for patient factors. RESULTS: A total of 279 residential programs (78,588 patients representing 124,632 discharges) were included in the analysis. Substance use disorder and homeless/work programs were the most common specialty types. Both in the overall and stratified analyses, the number of mental health diagnostic categories and younger age were predictors of irregular discharge. In the facility analysis, there was substantial variation in irregular discharge rates across residential specialties even after adjusting for all patient factors. For example, PTSD programs had a mean adjusted irregular discharge rate of 15.3% (SD: 7.4; range: 2.1-31.2; coefficient of variation: 48.4%). CONCLUSIONS: Irregular discharge is a key concern in VHA residential care. Patient characteristics do not account for all of the observed variation in rates across residential specialty types. There is a need to develop specialty-specific measures of irregular discharge to learn about system-level factors that contribute to irregular discharge. These data can inform strategies to avoid harms associated with irregular discharge.

Evaluating policies to improve access to mental health services in rural areas.

PURPOSE: The United States Department of Veterans Affairs (VA) has gradually implemented policies to increase access to mental health care outside of VA medical centers. Most notably, this included requirements to offer mental health services at VA-administered community-based clinics in 2008 and increased access to VA-paid care in the community beginning in 2014. Our objective was to understand how mental health service use patterns changed for rural VA patients during this time. METHODS: We developed a longitudinal cohort of all rural patients who used VA services between 2002 and 2019 (N = 3,345,862). We examined individual, health care, and contextual predictors of mental health service use as well as modalities of mental health service use during policy-relevant time periods using descriptive statistics. FINDINGS: Access to mental health services increased with each policy change. The annual percentage of rural VA patients accessing mental health services increased from 11.4% in the earliest years (2002-2004) to 19.8% in the latest years (2017-2019). The most rapid period of increase followed a requirement for availability of mental health services at VA-administered community clinics. Increasing access to VA-paid care in the community had less effect. By the end of the evaluation, gaps remained in the delivery of care to elderly patients over the age of 75. CONCLUSIONS: Rural patients use mental health services when they become available. Access was the highest with a combination of changes to both delivery modalities and payment methods. Continued, and perhaps different efforts are required to address a persistent disparity for older patients.

Optimizing Data on Race and Ethnicity for Veterans Affairs Patients.

INTRODUCTION: Maintaining accurate race and ethnicity data among patients of the Veterans Affairs (VA) healthcare system has historically been a challenge. This work expands on previous efforts to optimize race and ethnicity values by combining multiple VA data sources and exploring race- and ethnicity-specific collation algorithms. MATERIALS AND METHODS: We linked VA patient data from 2000 to 2018 with race and ethnicity data from four administrative and electronic health record sources: VA Medical SAS files (MedSAS), Corporate Data Warehouse (CDW), VA Centers for Medicare extracts (CMS), and VA Defense Identity Repository Data (VADIR). To assess the accuracy of each data source, we compared race and ethnicity values to self-reported data from the Survey of Health Experiences of Patients (SHEP). We used Cohen's Kappa to assess overall (holistic) source agreement and positive predictive values (PPV) to determine the accuracy of sources for each race and ethnicity separately. RESULTS: Holistic agreement with SHEP data was excellent (K > 0.80 for all sources), while race- and ethnicity-specific agreement varied. All sources were best at identifying White and Black users (average PPV = 0.94, 0.93, respectively). When applied to the full VA user population, both holistic and race-specific algorithms substantially reduced unknown values, as compared to single-source methods. CONCLUSIONS: Combining multiple sources to generate race and ethnicity values improves data accuracy among VA patients. Based on the overall agreement with self-reported data, we recommend using non-missing values from sources in the following order to fill in race values-SHEP, CMS, CDW, MedSAS, and VADIR-and in the following order to fill in ethnicity values-SHEP, CDW, MedSAS, VADIR, and CMS.