Question-answering system extracts information on injection drug use from clinical notes.

BACKGROUND: Injection drug use (IDU) can increase mortality and morbidity. Therefore, identifying IDU early and initiating harm reduction interventions can benefit individuals at risk. However, extracting IDU behaviors from patients' electronic health records (EHR) is difficult because there is no other structured data available, such as International Classification of Disease (ICD) codes, and IDU is most often documented in unstructured free-text clinical notes. Although natural language processing can efficiently extract this information from unstructured data, there are no validated tools. METHODS: To address this gap in clinical information, we design a question-answering (QA) framework to extract information on IDU from clinical notes for use in clinical operations. Our framework involves two main steps: (1) generating a gold-standard QA dataset and (2) developing and testing the QA model. We use 2323 clinical notes of 1145 patients curated from the US Department of Veterans Affairs (VA) Corporate Data Warehouse to construct the gold-standard dataset for developing and evaluating the QA model. We also demonstrate the QA model's ability to extract IDU-related information from temporally out-of-distribution data. RESULTS: Here, we show that for a strict match between gold-standard and predicted answers, the QA model achieves a 51.65% F1 score. For a relaxed match between the gold-standard and predicted answers, the QA model obtains a 78.03% F1 score, along with 85.38% Precision and 79.02% Recall scores. Moreover, the QA model demonstrates consistent performance when subjected to temporally out-of-distribution data. CONCLUSIONS: Our study introduces a QA framework designed to extract IDU information from clinical notes, aiming to enhance the accurate and efficient detection of people who inject drugs, extract relevant information, and ultimately facilitate informed patient care.

There are many health risks associated with injection drug use (IDU). Identifying people who inject drugs early can reduce the likelihood of these issues arising. However, extracting information about any possible IDU from a person's electronic health records can be difficult because the information is often in text-based general clinical notes rather than provided in a particular section of the record or as numerical data. Manually extracting information from these notes is time-consuming and inefficient. We used a computational method to train computer software to be able to extract IDU details. Potentially, this approach could be used by healthcare providers to more efficiently and accurately identify people who inject drugs, and therefore provide better advice and medical care.

A framework for inferring and analyzing pharmacotherapy treatment patterns.

BACKGROUND: To discover pharmacotherapy prescription patterns and their statistical associations with outcomes through a clinical pathway inference framework applied to real-world data. METHODS: We apply machine learning steps in our framework using a 2006 to 2020 cohort of veterans with major depressive disorder (MDD). Outpatient antidepressant pharmacy fills, dispensed inpatient antidepressant medications, emergency department visits, self-harm, and all-cause mortality data were extracted from the Department of Veterans Affairs Corporate Data Warehouse. RESULTS: Our MDD cohort consisted of 252,179 individuals. During the study period there were 98,417 emergency department visits, 1,016 cases of self-harm, and 1,507 deaths from all causes. The top ten prescription patterns accounted for 69.3% of the data for individuals starting antidepressants at the fluoxetine equivalent of 20-39 mg. Additionally, we found associations between outcomes and dosage change. CONCLUSIONS: For 252,179 Veterans who served in Iraq and Afghanistan with subsequent MDD noted in their electronic medical records, we documented and described the major pharmacotherapy prescription patterns implemented by Veterans Health Administration providers. Ten patterns accounted for almost 70% of the data. Associations between antidepressant usage and outcomes in observational data may be confounded. The low numbers of adverse events, especially those associated with all-cause mortality, make our calculations imprecise. Furthermore, our outcomes are also indications for both disease and treatment. Despite these limitations, we demonstrate the usefulness of our framework in providing operational insight into clinical practice, and our results underscore the need for increased monitoring during critical points of treatment.

Impact of Implementation Facilitation on the REACH VET Clinical Program for Veterans at Risk for Suicide.

OBJECTIVE: In 2017, the Veterans Health Administration (VHA) implemented a national suicide prevention program, called Recovery Engagement and Coordination for Health-Veterans Enhanced Treatment (REACH VET), that uses a predictive algorithm to identify, attempt to reach, assess, and care for patients at the highest risk for suicide. The authors aimed to evaluate whether facilitation enhanced implementation of REACH VET at VHA facilities not meeting target completion rates. METHODS: In this hybrid effectiveness-implementation type 2 program evaluation, a quasi-experimental pre-post design was used to assess changes in implementation outcome measures evaluated 6 months before and 6 months after onset of facilitation of REACH VET implementation at 23 VHA facilities. Measures included percentages of patients with documented coordinator and provider acknowledgment of receipt, care evaluation, and outreach attempt. Generalized estimating equations were used to compare differences in REACH VET outcome measures before and after facilitation. Qualitative interviews were conducted with personnel and were explored via template analysis. RESULTS: Time had a significant effect in all outcomes models (p<0.001). An effect of facilitation was significant only for the outcome of attempted outreach. Patients identified by REACH VET had significantly higher odds of having a documented outreach attempt after facilitation of REACH VET implementation, compared with before facilitation. Site personnel felt supported and reported that the external facilitators were helpful and responsive. CONCLUSIONS: Facilitation of REACH VET implementation was associated with an improvement in outreach attempts to veterans identified as being at increased risk for suicide. Outreach is critical for engaging veterans in care.

High dimensional predictions of suicide risk in 4.2 million US Veterans using ensemble transfer learning.

We present an ensemble transfer learning method to predict suicide from Veterans Affairs (VA) electronic medical records (EMR). A diverse set of base models was trained to predict a binary outcome constructed from reported suicide, suicide attempt, and overdose diagnoses with varying choices of study design and prediction methodology. Each model used twenty cross-sectional and 190 longitudinal variables observed in eight time intervals covering 7.5 years prior to the time of prediction. Ensembles of seven base models were created and fine-tuned with ten variables expected to change with study design and outcome definition in order to predict suicide and combined outcome in a prospective cohort. The ensemble models achieved c-statistics of 0.73 on 2-year suicide risk and 0.83 on the combined outcome when predicting on a prospective cohort of [Formula: see text] 4.2 M veterans. The ensembles rely on nonlinear base models trained using a matched retrospective nested case-control (Rcc) study cohort and show good calibration across a diversity of subgroups, including risk strata, age, sex, race, and level of healthcare utilization. In addition, a linear Rcc base model provided a rich set of biological predictors, including indicators of suicide, substance use disorder, mental health diagnoses and treatments, hypoxia and vascular damage, and demographics.

Deploying a national clinical text processing infrastructure.

OBJECTIVES: Clinical text processing offers a promising avenue for improving multiple aspects of healthcare, though operational deployment remains a substantial challenge. This case report details the implementation of a national clinical text processing infrastructure within the Department of Veterans Affairs (VA). METHODS: Two foundational use cases, cancer case management and suicide and overdose prevention, illustrate how text processing can be practically implemented at scale for diverse clinical applications using shared services. RESULTS: Insights from these use cases underline both commonalities and differences, providing a replicable model for future text processing applications. CONCLUSIONS: This project enables more efficient initiation, testing, and future deployment of text processing models, streamlining the integration of these use cases into healthcare operations. This project implementation is in a large integrated health delivery system in the United States, but we expect the lessons learned to be relevant to any health system, including smaller local and regional health systems in the United States.

GWAS Meta-Analysis of Suicide Attempt: Identification of 12 Genome-Wide Significant Loci and Implication of Genetic Risks for Specific Health Factors.

OBJECTIVE: Suicidal behavior is heritable and is a major cause of death worldwide. Two large-scale genome-wide association studies (GWASs) recently discovered and cross-validated genome-wide significant (GWS) loci for suicide attempt (SA). The present study leveraged the genetic cohorts from both studies to conduct the largest GWAS meta-analysis of SA to date. Multi-ancestry and admixture-specific meta-analyses were conducted within groups of significant African, East Asian, and European ancestry admixtures. METHODS: This study comprised 22 cohorts, including 43,871 SA cases and 915,025 ancestry-matched controls. Analytical methods across multi-ancestry and individual ancestry admixtures included inverse variance-weighted fixed-effects meta-analyses, followed by gene, gene-set, tissue-set, and drug-target enrichment, as well as summary-data-based Mendelian randomization with brain expression quantitative trait loci data, phenome-wide genetic correlation, and genetic causal proportion analyses. RESULTS: Multi-ancestry and European ancestry admixture GWAS meta-analyses identified 12 risk loci at p values <5×10-8. These loci were mostly intergenic and implicated DRD2, SLC6A9, FURIN, NLGN1, SOX5, PDE4B, and CACNG2. The multi-ancestry SNP-based heritability estimate of SA was 5.7% on the liability scale (SE=0.003, p=5.7×10-80). Significant brain tissue gene expression and drug set enrichment were observed. There was shared genetic variation of SA with attention deficit hyperactivity disorder, smoking, and risk tolerance after conditioning SA on both major depressive disorder and posttraumatic stress disorder. Genetic causal proportion analyses implicated shared genetic risk for specific health factors. CONCLUSIONS: This multi-ancestry analysis of suicide attempt identified several loci contributing to risk and establishes significant shared genetic covariation with clinical phenotypes. These findings provide insight into genetic factors associated with suicide attempt across ancestry admixture populations, in veteran and civilian populations, and in attempt versus death.

Impact of Mandated Case Review Policy on Opioid Discontinuation and Mortality Among High-Risk Long-Term Opioid Therapy Patients: The STORM Stepped-Wedge Cluster Randomized Controlled Trial.

BACKGROUND: Although long-term opioid therapy (LTOT) has its own risks, opioid discontinuation could pose harm for high-risk Veterans Health Administration (VHA) patients receiving LTOT. There is limited information on the impact of a mandate requiring providers to perform case reviews on high-risk patients with an active opioid prescription (ie, mandated case review policy) on opioid discontinuation and mortality. METHODS: Our study is a secondary data analysis of a 23-month stepped-wedge cluster randomized controlled trial between April 2018 and March 2020. The study included 10 685 LTOT patients with a predicted risk of a serious adverse event between the top 1% to 5% nationally who entered the risk range between 4/18/2018 and 11/9/2019. We examined whether the mandated case review policy had an impact on opioid discontinuation and mortality for the patients. RESULTS: Among 10 685 LTOT patients (88.2% male; mean [SD] age, 61.1 [11.7] years), 29.1% experienced discontinuation and the mortality rate was 9.5%. Patients under mandated case review had a decreased risk of opioid discontinuation (average marginal effect [AME], -11.16 [95% CI, -15.30 to -7.01] percentage points) and all-cause mortality (AME, -3.31 [95% CI, -5.63 to -1.00] percentage points), relative to patients who were not under the mandate. CONCLUSIONS: The VHA mandated case review policy was associated with lower probability of discontinuation and all-cause mortality for high-risk patients receiving LTOT. Interventions that maintain care engagement while optimizing pain management for high-risk patients may be beneficial for minimizing mortality and other risks associated with discontinuation.

Healthcare costs and use before and after opioid overdose in Veterans Health Administration patients with opioid use disorder.

AIMS: To compare healthcare costs and use between United States (US) Veterans Health Administration (VHA) patients with opioid use disorder (OUD) who experienced an opioid overdose (OD cohort) and patients with OUD who did not experience an opioid overdose (non-OD cohort). DESIGN: This is a retrospective cohort study of administrative and clinical data. SETTING: The largest integrated national health-care system is the US Veterans Health Administration's healthcare systems. PARTICIPANTS: We included VHA patients diagnosed with OUD from October 1, 2017 through September 30, 2018. We identified the index date of overdose for patients who had an overdose. Our control group, which included patients with OUD who did not have an overdose, was randomly assigned an index date. A total of 66 513 patients with OUD were included for analysis (OD cohort: n = 1413; non-OD cohort: n = 65 100). MEASUREMENTS: Monthly adjusted healthcare-related costs and use in the year before and after the index date. We used generalized estimating equation models to compare patients with an opioid overdose and controls in a difference-in-differences framework. FINDINGS: Compared with the non-OD cohort, an opioid overdose was associated with an increase of $16 890 [95% confidence interval (CI) = $15 611-18 169; P < 0.001] in healthcare costs for an estimated $23.9 million in direct costs to VHA (95% CI = $22.1 million, $25.7 million) within the 30 days following overdose after adjusting for baseline characteristics. Inpatient costs ($13 515; 95% CI = $12 378-14 652; P < 0.001) reflected most of this increase. Inpatient days (+6.15 days; 95% CI, = 5.33-6.97; P < 0.001), inpatient admissions (+1.01 admissions; 95% CI = 0.93-1.10; P < 0.001) and outpatient visits (+1.59 visits; 95% CI = 1.34-1.84; P < 0.001) also increased in the month after opioid overdose. Within the overdose cohort, healthcare costs and use remained higher in the year after overdose compared with pre-overdose trends. CONCLUSIONS: The US Veterans Health Administration patients with opioid use disorder (OUD) who have experienced an opioid overdose have increased healthcare costs and use that remain significantly higher in the month and continuing through the year after overdose than OUD patients who have not experienced an overdose.

Veterans Health Administration Outpatient Psychiatry Staffing Model: Longitudinal Analysis on Mental Health Performance.

BACKGROUND: An adequate supply of mental health (MH) professionals is necessary to provide timely access to MH services. Veterans Health Administration (VHA) continues to prioritize the expansion of the MH workforce to meet increasing demand for services. OBJECTIVE: Validated staffing models are essential to ensure timely access to care, to plan for future demand, to ensure delivery of high-quality care, and to balance the demands of fiscal responsibility and strategic priorities. DESIGN: Longitudinal retrospective cohort of VHA outpatient psychiatry, fiscal years 2016-2021. PARTICIPANTS: Outpatient VHA psychiatrists. MAIN MEASURES: Quarterly outpatient staff-to-patient ratios (SPRs), defined as the number of full-time equivalent clinically assigned providers per 1000 veterans receiving outpatient MH care, were calculated. Longitudinal recursive partitioning models were created to identify optimal cut-offs for the outpatient psychiatry SPR associated with success on VHA's measures of quality, access, and satisfaction. KEY RESULTS: Among outpatient psychiatry staff, the root node identified an outpatient SPR of 1.09 for overall performance (p < 0.001). For metrics associated with Population Coverage, a root node identified an SPR of 1.36 (p < 0.001). Metrics associated with continuity of care and satisfaction were associated with a root node of 1.10 and 1.07 (p < 0.001), respectively. In all analyses, the lowest SPRs were associated with the lowest group performance on VHA MH metrics of interest. CONCLUSIONS: Establishing validated staffing models associated with high-quality MH care is critical given the national psychiatry shortage and increasing demand for services. Analyses support VHA's current recommended minimum outpatient psychiatry-specific SPR of 1.22 as a reasonable target to provide high-quality care, access, and satisfaction.

Identification of Novel, Replicable Genetic Risk Loci for Suicidal Thoughts and Behaviors Among US Military Veterans.

Importance: Suicide is a leading cause of death; however, the molecular genetic basis of suicidal thoughts and behaviors (SITB) remains unknown. Objective: To identify novel, replicable genomic risk loci for SITB. Design, Setting, and Participants: This genome-wide association study included 633 778 US military veterans with and without SITB, as identified through electronic health records. GWAS was performed separately by ancestry, controlling for sex, age, and genetic substructure. Cross-ancestry risk loci were identified through meta-analysis. Study enrollment began in 2011 and is ongoing. Data were analyzed from November 2021 to August 2022. Main Outcome and Measures: SITB. Results: A total of 633 778 US military veterans were included in the analysis (57 152 [9%] female; 121 118 [19.1%] African ancestry, 8285 [1.3%] Asian ancestry, 452 767 [71.4%] European ancestry, and 51 608 [8.1%] Hispanic ancestry), including 121 211 individuals with SITB (19.1%). Meta-analysis identified more than 200 GWS (P < 5 × 10-8) cross-ancestry risk single-nucleotide variants for SITB concentrated in 7 regions on chromosomes 2, 6, 9, 11, 14, 16, and 18. Top single-nucleotide variants were largely intronic in nature; 5 were independently replicated in ISGC, including rs6557168 in ESR1, rs12808482 in DRD2, rs77641763 in EXD3, rs10671545 in DCC, and rs36006172 in TRAF3. Associations for FBXL19 and AC018880.2 were not replicated. Gene-based analyses implicated 24 additional GWS cross-ancestry risk genes, including FURIN, TSNARE1, and the NCAM1-TTC12-ANKK1-DRD2 gene cluster. Cross-ancestry enrichment analyses revealed significant enrichment for expression in brain and pituitary tissue, synapse and ubiquitination processes, amphetamine addiction, parathyroid hormone synthesis, axon guidance, and dopaminergic pathways. Seven other unique European ancestry-specific GWS loci were identified, 2 of which (POM121L2 and METTL15/LINC02758) were replicated. Two additional GWS ancestry-specific loci were identified within the African ancestry (PET112/GATB) and Hispanic ancestry (intergenic locus on chromosome 4) subsets, both of which were replicated. No GWS loci were identified within the Asian ancestry subset; however, significant enrichment was observed for axon guidance, cyclic adenosine monophosphate signaling, focal adhesion, glutamatergic synapse, and oxytocin signaling pathways across all ancestries. Within the European ancestry subset, genetic correlations (r > 0.75) were observed between the SITB phenotype and a suicide attempt-only phenotype, depression, and posttraumatic stress disorder. Additionally, polygenic risk score analyses revealed that the Million Veteran Program polygenic risk score had nominally significant main effects in 2 independent samples of veterans of European and African ancestry. Conclusions and Relevance: The findings of this analysis may advance understanding of the molecular genetic basis of SITB and provide evidence for ESR1, DRD2, TRAF3, and DCC as cross-ancestry candidate risk genes. More work is needed to replicate these findings and to determine if and how these genes might impact clinical care.

Outpatient provider staffing ratios: Binary recursive models associated with quality, access, and satisfaction.

Veterans Health Administration (VHA) continues to expand the mental health (MH) workforce to meet increasing demand for services. In the present study, longitudinal unbiased recursive partitioning models (conditional inference trees) were created to identify optimal cutoffs for outpatient staffing ratios associated with success on VHA's measures of quality, access, and satisfaction. Quarterly Staff-to-Patient Ratios (SPRs), defined as the number of full-time equivalent providers per 1,000 veterans receiving outpatient mental health care, were calculated for 12 quarters from fiscal years 2016-2018. Associations between VHA metrics associated with quality, access, and satisfaction were evaluated in relation to the overall outpatient SPR. The root node identified an overall outpatient SPR of 7.39 as the split for optimal MH performance. Root nodes associated with metrics addressing population coverage, continuity of care, and experience of care identified SPRs of 7.87, 6.81, and 7.42, respectively. In all analyses, the lowest SPRs were associated with the lowest performance on VHA MH metrics, while the highest SPRs were associated with the highest performance. Analyses support VHA's current recommended minimum outpatient SPR of 7.72 as a reasonable target to provide high-quality care, access, and satisfaction. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Effect of a Predictive Analytics-Targeted Program in Patients on Opioids: a Stepped-Wedge Cluster Randomized Controlled Trial.

BACKGROUND: Risk of overdose, suicide, and other adverse outcomes are elevated among sub-populations prescribed opioid analgesics. To address this, the Veterans Health Administration (VHA) developed the Stratification Tool for Opioid Risk Mitigation (STORM)-a provider-facing dashboard that utilizes predictive analytics to stratify patients prescribed opioids based on risk for overdose/suicide. OBJECTIVE: To evaluate the impact of the case review mandate on serious adverse events (SAEs) and all-cause mortality among high-risk Veterans. DESIGN: A 23-month stepped-wedge cluster randomized controlled trial in all 140 VHA medical centers between 2018 and 2020. PARTICIPANTS: A total of 44,042 patients actively prescribed opioid analgesics with high STORM risk scores (i.e., percentiles 1% to 5%) for an overdose or suicide-related event. INTERVENTION: A mandate requiring providers to perform case reviews on opioid analgesic-prescribed patients at high risk of overdose/suicide. MAIN MEASURES: Nine serious adverse events (SAEs), case review completion, number of risk mitigation strategies, and all-cause mortality. KEY RESULTS: Mandated review inclusion was associated with a significant decrease in all-cause mortality within 4 months of inclusion (OR: 0.78; 95% CI: 0.65-0.94). There was no detectable effect on SAEs. Stepped-wedge analyses found that mandated review patients were five times more likely to receive a case review than non-mandated patients with similar risk (OR: 5.1; 95% CI: 3.64-7.23) and received more risk mitigation strategies than non-mandated patients (0.498; CI: 0.39-0.61). CONCLUSIONS: Among VHA patients prescribed opioid analgesics, identifying high risk patients and mandating they receive an interdisciplinary case review was associated with a decrease in all-cause mortality. Results suggest that providers can leverage predictive analytic-targeted population health approaches and interdisciplinary collaboration to improve patient outcomes. TRIAL REGISTRATION: ISRCTN16012111.

The Veterans Health Administration REACH VET Program: Suicide Predictive Modeling in Practice.

The U.S. Veterans Health Administration developed a suicide prediction statistical model and implemented a novel clinical program, Recovery Engagement and Coordination for Health-Veterans Enhanced Treatment (REACH VET). This high-value suicide prevention program aims to efficiently identify patients at risk and connect them with care. Starting in April 2017, national REACH VET metric data were collected from electronic health records to evaluate required task completion. By October 2020, 98% of veterans identified (N=6,579) were contacted by providers and had their care evaluated. In the nation's largest health care system, it was feasible to implement a clinical program based on a suicide prediction model.

Impact of Informed Consent and Education on Care Engagement After Opioid Initiation in the Veterans Health Administration.

Objective: To ensure all patients receiving long-term opioid therapy (LTOT) understand the risks, benefits and treatment alternatives, the Veterans Health Administration (VHA) released a national policy in 2014 to standardize a signature informed consent (SIC) process. We evaluated the impact of this policy on medical follow-up after LTOT initiation, a guideline recommended practice. Methods: Using VHA administrative data, we identified patients initiating LTOT between May 2013 and May 2016. We used an interrupted time series design to compare the monthly rates of medical follow-up within 30 days and primary care visits within 3 months after LTOT initiation across three periods: 12 months before the policy (Year 1); 12 months after policy release (Year 2); and 12-24 months after policy release, when the SIC process was mandatory (Year 3). Results: Among the 409,895 patients who experienced 758,416 LTOT initiations, medical follow-up within 30 days and primary care engagement within 3 months increased by 4% between Year 1 and Year 3. Compared to Year 1, patients in Year 3 were 1.12 times more likely to have any medical follow-up (95% CI: 1.10, 1.13) and 1.13 times more likely to have a primary care visit (95% CI: 1.12, 1.15). Facilities with a greater proportion of patients receiving SIC had increased medical follow-up (RR: 1.04, 95% CI: 1.01, 1.07) and primary care engagement (RR: 1.06, 95% CI: 1.03, 1.10). Conclusion: The VHA's SIC policy is associated with increased medical follow-up among patients initiating LTOT, which may result in improved patient safety and has implications for other healthcare settings.

Randomized Policy Evaluation of the Veterans Health Administration Stratification Tool for Opioid Risk Mitigation (STORM).

BACKGROUND: The Veterans Health Administration (VHA) developed a dashboard Stratification Tool for Opioid Risk Mitigation (STROM) to guide clinical practice interventions. VHA released a policy mandating that high-risk patients of an adverse event based on the STORM dashboard are to be reviewed by an interdisciplinary team of clinicians. AIM: Randomized program evaluation to evaluate if patients in the oversight arm had a lower risk of opioid-related serious adverse events (SAEs) or death compared to those in the non-oversight arm. SETTING AND PARTICIPANTS: One-hundred and forty VHA facilities (aka medical centers) were randomly assigned to two groups: oversight and non-oversight arms. VHA patients who were prescribed opioids between April 18, 2018, and November 8, 2019, were included in the cohort. PROGRAM DESCRIPTION: We hypothesized that patients cared for by VHA facilities that received the policy with the oversight accountability language would achieve lower opioid-related SAEs or death. PROGRAM EVALUATION: We did not observe a relationship between the oversight arm and opioid-related SAEs or death. Patients in the non-oversight arm had a significantly higher chance of receiving a case review compared to those in the oversight arm. DISCUSSION: Even though our findings were unexpected, the STORM policy overall was likely successful in focusing the provider's attention on very high-risk patients.

A genome-wide association study of suicide attempts in the million veterans program identifies evidence of pan-ancestry and ancestry-specific risk loci.

To identify pan-ancestry and ancestry-specific loci associated with attempting suicide among veterans, we conducted a genome-wide association study (GWAS) of suicide attempts within a large, multi-ancestry cohort of U.S. veterans enrolled in the Million Veterans Program (MVP). Cases were defined as veterans with a documented history of suicide attempts in the electronic health record (EHR; N = 14,089) and controls were defined as veterans with no documented history of suicidal thoughts or behaviors in the EHR (N = 395,064). GWAS was performed separately in each ancestry group, controlling for sex, age and genetic substructure. Pan-ancestry risk loci were identified through meta-analysis and included two genome-wide significant loci on chromosomes 20 (p = 3.64 × 10-9) and 1 (p = 3.69 × 10-8). A strong pan-ancestry signal at the Dopamine Receptor D2 locus (p = 1.77 × 10-7) was also identified and subsequently replicated in a large, independent international civilian cohort (p = 7.97 × 10-4). Additionally, ancestry-specific genome-wide significant loci were also detected in African-Americans, European-Americans, Asian-Americans, and Hispanic-Americans. Pathway analyses suggested over-representation of many biological pathways with high clinical significance, including oxytocin signaling, glutamatergic synapse, cortisol synthesis and secretion, dopaminergic synapse, and circadian rhythm. These findings confirm that the genetic architecture underlying suicide attempt risk is complex and includes both pan-ancestry and ancestry-specific risk loci. Moreover, pathway analyses suggested many commonly impacted biological pathways that could inform development of improved therapeutics for suicide prevention.

Surveying the Landscape of Quality-of-Care Measures for Mental and Substance Use Disorders.

OBJECTIVE: Quality measures that are used to evaluate health care services have a central role in monitoring and incentivizing quality improvement and the provision of evidence-based treatment. This systematic scan aimed to catalog quality-of-care measures for mental and substance use disorders and assess gaps and redundancies to inform efforts to develop and retire measures. METHODS: Quality measure inventories were analyzed from six organizations that evaluate health care quality in the United States. Measures were included if they were defined via symptoms or diagnoses of mental and substance use disorders or specialty treatments or treatment settings for adults. RESULTS: Of 4,420 measures analyzed, 635 (14%) met inclusion criteria, and 376 unique quality-of-care measure constructs were cataloged and characterized. Symptoms or diagnoses of disorders were most commonly used to define measures (46%, N=172). Few measures were available for certain disorders (e.g., anxiety disorders), evidence-based treatments (e.g., psychotherapy), and quality domains (e.g., equity). Only one in four measures was endorsed by the National Quality Forum, which independently and critically evaluates quality measures. Among measures that were actively in use for national quality improvement initiatives (N=319), process measures (57%) were most common, followed by outcome measures (30%), the latter of which focused most often on experience of care. CONCLUSIONS: A vast landscape of mental and substance use disorder quality-of-care measures currently exists, and continued efforts to harmonize duplicative measures and to develop measures for underrepresented evidence-based treatments and quality domains are warranted. The authors recommend reinvesting in a national, centralized system for measure curation, with a stakeholder-centered process for independent measure review and endorsement.

Evaluating and Improving Engagement in Care After High-Intensity Stays for Mental or Substance Use Disorders.

OBJECTIVE: This interrupted time-series analysis examined whether activating a quality measure, supported by education and a population management tool, was associated with higher postdischarge engagement (PDE) in outpatient care after inpatient and residential stays for mental or substance use disorder care. METHODS: Discharges from October 2016 to May 2019 were identified from national Veterans Health Administration (VHA) records representing all 140 VHA health care systems. Engagement was defined as multiple mental or substance use disorder outpatient visits in the 30 days postdischarge. The number of such visits required to meet the engagement definition depended on a patient's suicide risk and acuity level of inpatient or residential treatment. Health care system-level performance was calculated as the percentage of qualifying discharges with 30-day PDE. A segmented mixed-effects linear regression model tested whether monthly health care system performance changed significantly after activation of the PDE measure (activation rollout period, October-December 2017). RESULTS: A total of 322,344 discharges qualified for the measure. In the regression model, average health care system performance was 65.6% at the beginning of the preactivation period (October 2016) and did not change significantly in the following 12 months. Average health care system performance increased by 5.7% (SE=0.8%, p<0.001) after PDE measure activation and did not change significantly thereafter-a difference representing 11,464 more patients engaging in care than would have without activation of the measure. CONCLUSIONS: Results support use of this measure, along with education, technical assistance, and population management tools, to improve engagement after discharge from residential and inpatient mental and substance use disorder treatment.

Mental health treatment and the role of tele-mental health at the veterans health administration during the COVID-19 pandemic.

To quantify overall trends in patients treated for mental health disorders and adverse events, including via tele-mental health (TMH) and psychopharmacology during pandemic-related health care transformation. Longitudinal observational study including veterans receiving mental health treatment at a Veterans Health Administration (VHA) facility from January 1, 2017 to June 16, 2020. Observed and expected patient care for on-going and new treatment of depression, posttraumatic stress, substance use disorder, severe mental illness diagnoses, overdose, and suicide attempts, and psychotropic prescriptions for antidepressant, antipsychotic, benzodiazepine, opioid, and mood stabilizing medications are depicted. Percent change between actual and expected counts in the early months of the COVID pandemic (March 18-May 5, 2020) are computed. Decreases in counts of patients receiving mental health treatment early in the pandemic ranged from 7% to 20% for on-going treatment, and 28% to 37% for new treatment. TMH rapidly expanded across VHA, becoming the primary means by which encounters were delivered. Counts of patients receiving on-going care for suicide attempts were stable, and for overdoses, decreased by 17%. Counts of patients initiating care for suicide attempts and overdoses decreased by 30% and 38%, respectively. Weekly prescriptions and medication on-hand for psychotropics ranged from a 2% decrease to a 4% increase. New patient prescribing decreased 21%-50%. VHA and other large healthcare systems will need to expand outreach and continue to develop TMH services to maintain care continuity and initiate care for existing and new patients during COVID-19 and future large-scale outbreaks, epidemics, and disasters. (PsycInfo Database Record (c) 2022 APA, all rights reserved).