Reward neurocircuitry predicts longitudinal changes in alcohol use following trauma exposure.

BACKGROUND: Trauma is a risk factor for developing maladaptive alcohol use. Preclinical research has shown that stress alters the processing of midbrain and striatal reward and incentive signals. However, little research has been conducted on alterations in reward-related neurocircuitry post-trauma in humans. Neuroimaging markers may be particularly useful as they can provide insight into the mechanisms that may make an individual vulnerable to developing trauma-related psychopathologies. This study aimed to identify reward-related neural correlates associated with changes in alcohol use after trauma exposure. METHODS: Participants were recruited from U.S. emergency departments for the AURORA study (N=286, 178 female). Trauma-related change in alcohol use at 8 weeks post-trauma relative to pre-trauma was quantified as a change in 30-day total drinking per the PhenX Toolkit Alcohol 30-Day Quantity and Frequency Measure. Reward-related neurocircuitry activation and functional connectivity (FC) were assessed 2 weeks post-trauma using fMRI during a monetary reward task using region of interest and whole-brain voxelwise analyses. RESULTS: Greater increase in alcohol use from pre-trauma to 8 weeks post-trauma was predicted by (1) greater ventral tegmental area (VTA) and (2) greater cerebellum activation during Gain>Loss trials measured 2 weeks post-trauma and (3) greater seed-based FC between the VTA and lateral occipital cortex and precuneus. CONCLUSIONS: Altered VTA activation and FC early post-trauma may be associated with reward-seeking and processing, contributing to greater alcohol use post-trauma. These data provide novel evidence of neural correlates that underlie increased alcohol use early post-trauma that may be targeted via early interventions to prevent the development of maladaptive alcohol use.

Enhancing Suicide Risk Prediction With Polygenic Scores in Psychiatric Emergency Settings: Prospective Study.

BACKGROUND: Despite growing interest in the clinical translation of polygenic risk scores (PRSs), it remains uncertain to what extent genomic information can enhance the prediction of psychiatric outcomes beyond the data collected during clinical visits alone. OBJECTIVE: This study aimed to assess the clinical utility of incorporating PRSs into a suicide risk prediction model trained on electronic health records (EHRs) and patient-reported surveys among patients admitted to the emergency department. METHODS: Study participants were recruited from the psychiatric emergency department at Massachusetts General Hospital. There were 333 adult patients of European ancestry who had high-quality genotype data available through their participation in the Mass General Brigham Biobank. Multiple neuropsychiatric PRSs were added to a previously validated suicide prediction model in a prospective cohort enrolled between February 4, 2015, and March 13, 2017. Data analysis was performed from July 11, 2022, to August 31, 2023. Suicide attempt was defined using diagnostic codes from longitudinal EHRs combined with 6-month follow-up surveys. The clinical risk score for suicide attempt was calculated from an ensemble model trained using an EHR-based suicide risk score and a brief survey, and it was subsequently used to define the baseline model. We generated PRSs for depression, bipolar disorder, schizophrenia, suicide attempt, and externalizing traits using a Bayesian polygenic scoring method for European ancestry participants. Model performance was evaluated using area under the receiver operator curve (AUC), area under the precision-recall curve, and positive predictive values. RESULTS: Of the 333 patients (n=178, 53.5% male; mean age 36.8, SD 13.6 years; n=333, 100% non-Hispanic and n=324, 97.3% self-reported White), 28 (8.4%) had a suicide attempt within 6 months. Adding either the schizophrenia PRS or all PRSs to the baseline model resulted in the numerically highest discrimination (AUC 0.86, 95% CI 0.73-0.99) compared to the baseline model (AUC 0.84, 95% Cl 0.70-0.98). However, the improvement in model performance was not statistically significant. CONCLUSIONS: In this study, incorporating genomic information into clinical prediction models for suicide attempt did not improve patient risk stratification. Larger studies that include more diverse participants are required to validate whether the inclusion of psychiatric PRSs in clinical prediction models can enhance the stratification of patients at risk of suicide attempts.

The case for eliminating excessive worry as a requirement for generalized anxiety disorder: a cross-national investigation.

BACKGROUND: Around the world, people living in objectively difficult circumstances who experience symptoms of generalized anxiety disorder (GAD) do not qualify for a diagnosis because their worry is not 'excessive' relative to the context. We carried out the first large-scale, cross-national study to explore the implications of removing this excessiveness requirement. METHODS: Data come from the World Health Organization World Mental Health Survey Initiative. A total of 133 614 adults from 12 surveys in Low- or Middle-Income Countries (LMICs) and 16 surveys in High-Income Countries (HICs) were assessed with the Composite International Diagnostic Interview. Non-excessive worriers meeting all other DSM-5 criteria for GAD were compared to respondents meeting all criteria for GAD, and to respondents without GAD, on clinically-relevant correlates. RESULTS: Removing the excessiveness requirement increases the global lifetime prevalence of GAD from 2.6% to 4.0%, with larger increases in LMICs than HICs. Non-excessive and excessive GAD cases worry about many of the same things, although non-excessive cases worry more about health/welfare of loved ones, and less about personal or non-specific concerns, than excessive cases. Non-excessive cases closely resemble excessive cases in socio-demographic characteristics, family history of GAD, and risk of temporally secondary comorbidity and suicidality. Although non-excessive cases are less severe on average, they report impairment comparable to excessive cases and often seek treatment for GAD symptoms. CONCLUSIONS: Individuals with non-excessive worry who meet all other DSM-5 criteria for GAD are clinically significant cases. Eliminating the excessiveness requirement would lead to a more defensible GAD diagnosis.

Design of a multicenter randomized controlled trial of a post-discharge suicide prevention intervention for high-risk psychiatric inpatients: The Veterans Coordinated Community Care Study.

BACKGROUND: The period after psychiatric hospital discharge is one of elevated risk for suicide-related behaviors (SRBs). Post-discharge clinical outreach, although potentially effective in preventing SRBs, would be more cost-effective if targeted at high-risk patients. To this end, a machine learning model was developed to predict post-discharge suicides among Veterans Health Administration (VHA) psychiatric inpatients and target a high-risk preventive intervention. METHODS: The Veterans Coordinated Community Care (3C) Study is a multicenter randomized controlled trial using this model to identify high-risk VHA psychiatric inpatients (n = 850) randomized with equal allocation to either the Coping Long Term with Active Suicide Program (CLASP) post-discharge clinical outreach intervention or treatment-as-usual (TAU). The primary outcome is SRBs over a 6-month follow-up. We will estimate average treatment effects adjusted for loss to follow-up and investigate the possibility of heterogeneity of treatment effects. RESULTS: Recruitment is underway and will end September 2024. Six-month follow-up will end and analysis will begin in Summer 2025. CONCLUSION: Results will provide information about the effectiveness of CLASP versus TAU in reducing post-discharge SRBs and provide guidance to VHA clinicians and policymakers about the implications of targeted use of CLASP among high-risk psychiatric inpatients in the months after hospital discharge. CLINICAL TRIALS REGISTRATION: ClinicalTrials.Gov identifier: NCT05272176 (https://www. CLINICALTRIALS: gov/ct2/show/NCT05272176).

Interactive effects of genetic liability and combat exposure on risk of alcohol use disorder among US service members.

BACKGROUND: An improved understanding of pathways to alcohol use disorder (AUD) among service members may inform efforts to reduce the substantial impact of AUD on this population. This study examined whether the relationship between a service-related risk factor (combat exposure) and later AUD varied based on individual differences in genetic liability to AUD. METHODS: The sample consisted of 1203 US Army soldiers of genetically determined European ancestry who provided survey and genomic data in the Army STARRS Pre/Post Deployment Study (PPDS; 2012-2014) and follow-up survey data in wave 1 of the STARRS Longitudinal Study (2016-2018). Logistic regression was used to estimate the conditional effect of combat exposure level (self-reported in PPDS) on odds of probable AUD diagnosis at follow-up, as a function of a soldier's polygenic risk score (PRS) for AUD. RESULTS: The direct effect of combat exposure on AUD risk was non-significant (AOR=1.12, 95 % CI=1.00-1.26, p=.051); however, a significant combat exposure x PRS interaction was observed (AOR=1.60, 95 % CI=1.03-2.46, p=.033). Higher combat exposure was more strongly associated with elevated AUD risk among soldiers with heightened genetic liability to AUD. CONCLUSIONS: The effect of combat exposure on AUD risk appeared to vary based on a service member's level of genetic risk for AUD. Continued investigation is warranted to determine whether PRS can help stratify AUD risk within stress-exposed groups such as combat-deployed soldiers. Such efforts might reveal opportunities to focus prevention efforts on smaller subgroups at the intersection of having both environmental exposures and genetic vulnerability to AUD.

A prediction model for differential resilience to the effects of combat-related stressors in US army soldiers.

OBJECTIVES: To develop a composite score for differential resilience to effects of combat-related stressors (CRS) on persistent DSM-IV post-traumatic stress disorder (PTSD) among US Army combat arms soldiers using survey data collected before deployment. METHODS: A sample of n = 2542 US Army combat arms soldiers completed a survey shortly before deployment to Afghanistan and then again two to three and 8-9 months after redeployment. Retrospective self-reports were obtained about CRS. Precision treatment methods were used to determine whether differential resilience to persistent PTSD in the follow-up surveys could be developed from pre-deployment survey data in a 60% training sample and validated in a 40% test sample. RESULTS: 40.8% of respondents experienced high CRS and 5.4% developed persistent PTSD. Significant test sample heterogeneity was found in resilience (t = 2.1, p = 0.032), with average treatment effect (ATE) of high CRS in the 20% least resilient soldiers of 17.1% (SE = 5.5%) compared to ATE = 3.8% (SE = 1.2%) in the remaining 80%. The most important predictors involved recent and lifetime pre-deployment distress disorders. CONCLUSIONS: A reliable pre-deployment resilience score can be constructed to predict variation in the effects of high CRS on persistent PTSD among combat arms soldiers. Such a score could be used to target preventive interventions to reduce PTSD or other resilience-related outcomes.

Further evidence that peritraumatic 17ß-estradiol levels influence chronic posttraumatic pain outcomes in women, data from both humans and animals.

ABSTRACT: Chronic posttraumatic pain (CPTP) is common after traumatic stress exposure (TSE) and disproportionately burdens women. We previously showed across 3 independent longitudinal cohort studies that, in women, increased peritraumatic 17ß-estradiol (E2) levels were associated with substantially lower CPTP over 1 year. Here, we assessed this relationship in a fourth longitudinal cohort and also assessed the relationship between E2 and CPTP at additional time points post-TSE. Furthermore, we used a well-validated animal model of TSE to determine whether exogenous E2 administration protects against mechanical hypersensitivity. Using nested samples and data from the Advancing Understanding of RecOvery afteR traumA study (n = 543 samples, 389 participants), an emergency department-based prospective study of TSE survivors, we assessed the relationship between circulating E2 levels and CPTP in women and men using multivariate repeated-measures mixed modeling. Male and ovariectomized female Sprague Dawley rats were exposed to TSE and administered E2 either immediately after or 3 days post-TSE. Consistent with previous results, we observed an inverse relationship between peritraumatic E2 and longitudinal CPTP in women only (ß = -0.137, P = 0.033). In animals, E2 protected against mechanical hypersensitivity in female ovariectomized rats only if administered immediately post-TSE. In conclusion, peritraumatic E2 levels, but not those at post-TSE time points, predict CPTP in women TSE survivors. Administration of E2 immediately post TSE protects against mechanical hypersensitivity in female rats. Together with previous findings, these data indicate that increased peritraumatic E2 levels in women have protective effects against CPTP development and suggest that immediate post-TSE E2 administration in women could be a promising therapeutic strategy for reducing risk of CPTP.

Undetected suicide attempts among U.S. soldiers: results from the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS).

BACKGROUND: While previous studies have reported high rates of documented suicide attempts (SAs) in the U.S. Army, the extent to which soldiers make SAs that are not identified in the healthcare system is unknown. Understanding undetected suicidal behavior is important in broadening prevention and intervention efforts. METHODS: Representative survey of U.S. Regular Army enlisted soldiers (n = 24 475). Reported SAs during service were compared with SAs documented in administrative medical records. Logistic regression analyses examined sociodemographic characteristics differentiating soldiers with an undetected SA v. documented SA. Among those with an undetected SA, chi-square tests examined characteristics associated with receiving a mental health diagnosis (MH-Dx) prior to SA. Discrete-time survival analysis estimated risk of undetected SA by time in service. RESULTS: Prevalence of undetected SA (unweighted n = 259) was 1.3%. Annual incidence was 255.6 per 100 000 soldiers, suggesting one in three SAs are undetected. In multivariable analysis, rank ⩾E5 (OR = 3.1[95%CI 1.6-5.7]) was associated with increased odds of undetected v. documented SA. Females were more likely to have a MH-Dx prior to their undetected SA (Rao-Scott χ21 = 6.1, p = .01). Over one-fifth of undetected SAs resulted in at least moderate injury. Risk of undetected SA was greater during the first four years of service. CONCLUSIONS: Findings suggest that substantially more soldiers make SAs than indicated by estimates based on documented attempts. A sizable minority of undetected SAs result in significant injury. Soldiers reporting an undetected SA tend to be higher ranking than those with documented SAs. Undetected SAs require additional approaches to identifying individuals at risk.

Predicting Suicides Among US Army Soldiers After Leaving Active Service.

Importance: The suicide rate of military servicemembers increases sharply after returning to civilian life. Identifying high-risk servicemembers before they leave service could help target preventive interventions. Objective: To develop a model based on administrative data for regular US Army soldiers that can predict suicides 1 to 120 months after leaving active service. Design, Setting, and Participants: In this prognostic study, a consolidated administrative database was created for all regular US Army soldiers who left service from 2010 through 2019. Machine learning models were trained to predict suicides over the next 1 to 120 months in a random 70% training sample. Validation was implemented in the remaining 30%. Data were analyzed from March 2023 through March 2024. Main outcome and measures: The outcome was suicide in the National Death Index. Predictors came from administrative records available before leaving service on sociodemographics, Army career characteristics, psychopathologic risk factors, indicators of physical health, social networks and supports, and stressors. Results: Of the 800 579 soldiers in the cohort (84.9% male; median [IQR] age at discharge, 26 [23-33] years), 2084 suicides had occurred as of December 31, 2019 (51.6 per 100 000 person-years). A lasso model assuming consistent slopes over time discriminated as well over all but the shortest risk horizons as more complex stacked generalization ensemble machine learning models. Test sample area under the receiver operating characteristic curve ranged from 0.87 (SE = 0.06) for suicides in the first month after leaving service to 0.72 (SE = 0.003) for suicides over 120 months. The 10% of soldiers with highest predicted risk accounted for between 30.7% (SE = 1.8) and 46.6% (SE = 6.6) of all suicides across horizons. Calibration was for the most part better for the lasso model than the super learner model (both estimated over 120-month horizons.) Net benefit of a model-informed prevention strategy was positive compared with intervene-with-all or intervene-with-none strategies over a range of plausible intervention thresholds. Sociodemographics, Army career characteristics, and psychopathologic risk factors were the most important classes of predictors. Conclusions and relevance: These results demonstrated that a model based on administrative variables available at the time of leaving active Army service can predict suicides with meaningful accuracy over the subsequent decade. However, final determination of cost-effectiveness would require information beyond the scope of this report about intervention content, costs, and effects over relevant horizons in relation to the monetary value placed on preventing suicides.

Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts.

BACKGROUND: Incorporating genomic data into risk prediction has become an increasingly popular approach for rapid identification of individuals most at risk for complex disorders such as PTSD. Our goal was to develop and validate Methylation Risk Scores (MRS) using machine learning to distinguish individuals who have PTSD from those who do not. METHODS: Elastic Net was used to develop three risk score models using a discovery dataset (n = 1226; 314 cases, 912 controls) comprised of 5 diverse cohorts with available blood-derived DNA methylation (DNAm) measured on the Illumina Epic BeadChip. The first risk score, exposure and methylation risk score (eMRS) used cumulative and childhood trauma exposure and DNAm variables; the second, methylation-only risk score (MoRS) was based solely on DNAm data; the third, methylation-only risk scores with adjusted exposure variables (MoRSAE) utilized DNAm data adjusted for the two exposure variables. The potential of these risk scores to predict future PTSD based on pre-deployment data was also assessed. External validation of risk scores was conducted in four independent cohorts. RESULTS: The eMRS model showed the highest accuracy (92%), precision (91%), recall (87%), and f1-score (89%) in classifying PTSD using 3730 features. While still highly accurate, the MoRS (accuracy = 89%) using 3728 features and MoRSAE (accuracy = 84%) using 4150 features showed a decline in classification power. eMRS significantly predicted PTSD in one of the four independent cohorts, the BEAR cohort (beta = 0.6839, p=0.006), but not in the remaining three cohorts. Pre-deployment risk scores from all models (eMRS, beta = 1.92; MoRS, beta = 1.99 and MoRSAE, beta = 1.77) displayed a significant (p < 0.001) predictive power for post-deployment PTSD. CONCLUSION: The inclusion of exposure variables adds to the predictive power of MRS. Classification-based MRS may be useful in predicting risk of future PTSD in populations with anticipated trauma exposure. As more data become available, including additional molecular, environmental, and psychosocial factors in these scores may enhance their accuracy in predicting PTSD and, relatedly, improve their performance in independent cohorts.

Antecedents, reasons for, and consequences of suicide attempts: Results from a qualitative study of 89 suicide attempts among army soldiers.

Most studies aimed at understanding suicidal behavior have focused on quantifying the associations between putative risk factors and suicidal behavior in comparative studies of cases and controls. The current study, in comparison, exclusively focused on cases-89 Army soldiers presenting for hospital care following a suicide attempt-and attempted to reveal the antecedents of, reasons for, and consequences of suicide attempts. This mixed-methods study using qualitative interviews and self-report surveys/interviews revealed that in most cases, the most recent onset of suicidal thoughts began shortly before the suicide attempt and were not disclosed to others, limiting opportunities for intervention via traditional approaches. The primary reason given for attempting suicide was to escape from psychologically aversive conditions after concluding that no other effective strategies or options were available. Participants reported both negative (e.g., self-view, guilt) and positive (e.g., learning new skills, receiving support) consequences of their suicide attempt-and described things they believe would have prevented them from making the attempt. These findings provide new insights into the motivational and contextual factors for suicidal behavior and highlight several novel directions for prevention and intervention efforts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Differences in self-reported disruptions in mental health treatment during COVID-19 in a national household sample: impact of severity of functional impairment.

Objective: This report uses data from Mental Disorders Prevalence Study (MDPS), a large epidemiologic study that provided national prevalence estimates of seven mental disorders based on the Structured Clinical Interview for DSM-5 (SCID), to assess the odds of treatment disruption during COVID for SMI and non-SMI groups. Methods: This cross-sectional study conducted from 2020 to 2022 included 2,810 household participants with any lifetime mental health treatment. Weighted logistic regressions estimated the odds of reporting disruptions in access to mental health care or psychotropic prescriptions due to COVID. SMI was broadly defined as having an MDP diagnosis and serious functional impairment (GAF ≤50, a validated and widely used cutoff). Non-SMI groups were a mental diagnosis without serious impairment (MDPS diagnosis, GAF >50) and any lifetime treatment and no serious impairment (no MDPS diagnosis, GAF >50). Results: The SMI and mental disorder without serious impairment groups had approximately 6.4- and 2.4-greater odds, respectively, of reporting inability to access mental health care and 4- and 3- greater odds, respectively, of having prescriptions delayed, relative to the group with any lifetime treatment. Among those with serious mental illness, having Medicare insurance increased the odds of reporting inability to access mental health care. Conclusions: Individuals with SMI were much more likely to experience treatment disruptions throughout the pandemic than non-SMI groups.

Clinical characteristics and functioning of adults with bipolar I disorder: Evidence from the mental and substance use disorders prevalence study.

BACKGROUND: Knowledge of clinical, treatment and life circumstances of individuals with bipolar I disorder (BP-I) in US households is informed by decades old epidemiological surveys. METHODS: The Mental and Substance Use Disorders Prevalence Study was conducted October 2020-October 2022. Clinicians administered the Structured Clinical Interview for the DSM-5 diagnosing 12-month prevalence of BP-I and other mental health disorders (MHD) among 4764 adults aged 18-65 years and collected sociodemographic information. We examined clinical characteristics, differences by sex and age among adults with BP-I, and compared adults with BP-I versus no MHD regarding sociodemographic characteristics, functioning, and substance use disorders (SUDs). RESULTS: Prevalence of BP-I in the MDPS was 1.5 %. Among those with BP-I, 73.4 % had comorbid psychiatric disorders, and 43.4 % had comorbid SUDs. Alcohol use disorder was higher in those with BP-I versus no MHD (33.0 % vs. 6.3 %). Mean Global Assessment of Functioning scores were lower among those with BP-I versus no MHD (53.2 vs. 77.0). Of individuals with BP-I, 64.9 % had past-year outpatient, 5.4 % inpatient, and 18.7 % minimally adequate treatment (≥1 antimanic agent and ≥ 4 outpatient visits). Individuals with BP-I were less likely to be employed (37.3 % vs. 63.0 %) and have a family income ≥$20,000 (48.2 % vs. 81.9 %) versus no MDPS MHD. LIMITATIONS: The survey response rate was low. CONCLUSIONS: In this sample, many individuals with BP-I had psychiatric and SUD comorbidities, lived in poverty and had functional impairment. Few received adequate treatment; women and younger individuals were particularly disadvantaged. Early detection and treatment represent substantial opportunities to improve outcomes.

Epigenome-wide association studies identify novel DNA methylation sites associated with PTSD: A meta-analysis of 23 military and civilian cohorts.

Background: The occurrence of post-traumatic stress disorder (PTSD) following a traumatic event is associated with biological differences that can represent the susceptibility to PTSD, the impact of trauma, or the sequelae of PTSD itself. These effects include differences in DNA methylation (DNAm), an important form of epigenetic gene regulation, at multiple CpG loci across the genome. Moreover, these effects can be shared or specific to both central and peripheral tissues. Here, we aim to identify blood DNAm differences associated with PTSD and characterize the underlying biological mechanisms by examining the extent to which they mirror associations across multiple brain regions. Methods: As the Psychiatric Genomics Consortium (PGC) PTSD Epigenetics Workgroup, we conducted the largest cross-sectional meta-analysis of epigenome-wide association studies (EWASs) of PTSD to date, involving 5077 participants (2156 PTSD cases and 2921 trauma-exposed controls) from 23 civilian and military studies. PTSD diagnosis assessments were harmonized following the standardized guidelines established by the PGC-PTSD Workgroup. DNAm was assayed from blood using either Illumina HumanMethylation450 or MethylationEPIC (850K) BeadChips. A common QC pipeline was applied. Within each cohort, DNA methylation was regressed on PTSD, sex (if applicable), age, blood cell proportions, and ancestry. An inverse variance-weighted meta-analysis was performed. We conducted replication analyses in tissue from multiple brain regions, neuronal nuclei, and a cellular model of prolonged stress. Results: We identified 11 CpG sites associated with PTSD in the overall meta-analysis (1.44e-09 < p < 5.30e-08), as well as 14 associated in analyses of specific strata (military vs civilian cohort, sex, and ancestry), including CpGs in AHRR and CDC42BPB. Many of these loci exhibit blood-brain correlation in methylation levels and cross-tissue associations with PTSD in multiple brain regions. Methylation at most CpGs correlated with their annotated gene expression levels. Conclusions: This study identifies 11 PTSD-associated CpGs, also leverages data from postmortem brain samples, GWAS, and genome-wide expression data to interpret the biology underlying these associations and prioritize genes whose regulation differs in those with PTSD.

Social Vulnerability and Prevalence and Treatment for Mental Health and Substance Use Disorders.

Importance: Community-level social vulnerability (SV) is associated with physical illness and premature mortality. Its association with mental health (MH) and substance use disorders (SUDs) needs further study. Objective: To study associations of SV with clinical diagnoses of MH disorders, SUDs, and related treatments in the US noninstitutionalized population of adults aged 18 years and older. Design, Setting, and Participants: A survey of adults in a national sample of US households between October 2020 and October 2022. Participants drawn from a multistage, clustered, and stratified area probability sample of US households were included, excluding adults older than 65 years because of the difficulty of differentiating mental disorders from symptoms of dementia. The sample also included adults living in prisons, state psychiatric hospitals, and homeless shelters who were excluded from the sample of US households used in these analyses. Each sample household was sent a letter explaining the study and offering the option to complete the household roster online, by phone, or by email. Of the 12 906 adults selected for clinical interviewing in the household sample, 4674 completed clinical interviews. Main Outcomes and Measures: Main outcomes were Structured Clinical Interview for DSM-5 past-year diagnoses of MH disorders and SUDs and responses to survey questions regarding treatment received. The Social Vulnerability Metric (SVM) and the Area Deprivation Index (ADI) were used to determine SV at the residential zip code level. Results: The analysis involved 4674 participants (2904 [62.13%] female and 1770 [37.87%] male; mean [SD] age, 41.51 [13.41] years). Controlling for measured confounders, the SVM was significantly associated with diagnoses of schizophrenia spectrum disorder (SSD; adjusted odds ratio [aOR], 17.22; 95% CI, 3.05-97.29), opioid use disorder (OUD; aOR, 9.47; 95% CI, 2.30-39.02), stimulant use disorder (aOR, 6.60; 95% CI, 2.01-21.67), bipolar I disorder (aOR, 2.39; 95% CI, 1.19-4.80), posttraumatic stress disorder (aOR, 1.63; 95% CI, 1.06-2.50), and any MH disorder (aOR, 1.44; 95% CI, 1.14-1.83), but not major depressive disorder (MDD), generalized anxiety disorder (GAD), or any SUD. Results were similar for the ADI but generally of lower magnitude (SSD aOR, 11.38; 95% CI, 1.61-80.58; OUD aOR, 2.05; 95% CI, 0.30-14.10; stimulant use disorder aOR, 2.18; 95% CI, 0.52-9.18). Among participants with SSDs, SV was associated with reduced MH treatment (aOR, 0.001; 95% CI, 0.00-0.18) and reduced SUD treatment in participants with OUD or stimulant use disorder (aOR, 0.24; 95% CI, 0.02-2.80). Conclusions and Relevance: In contrast to previous studies using nonclinical symptom-based survey data, we found no association between SV and GAD or MDD. By contrast, there were associations of SV with prevalence of SSD, stimulant use disorder, and OUD with corresponding decreases in treatment. These results suggest that the SVM might assist in developing more comprehensive care models that integrate medical and social care for MH disorders and SUDs.

Neighborhood Resources Associated With Psychological Trajectories and Neural Reactivity to Reward After Trauma.

Importance: Research on resilience after trauma has often focused on individual-level factors (eg, ability to cope with adversity) and overlooked influential neighborhood-level factors that may help mitigate the development of posttraumatic stress disorder (PTSD). Objective: To investigate whether an interaction between residential greenspace and self-reported individual resources was associated with a resilient PTSD trajectory (ie, low/no symptoms) and to test if the association between greenspace and PTSD trajectory was mediated by neural reactivity to reward. Design, Setting, and Participants: As part of a longitudinal cohort study, trauma survivors were recruited from emergency departments across the US. Two weeks after trauma, a subset of participants underwent functional magnetic resonance imaging during a monetary reward task. Study data were analyzed from January to November 2023. Exposures: Residential greenspace within a 100-m buffer of each participant's home address was derived from satellite imagery and quantified using the Normalized Difference Vegetation Index and perceived individual resources measured by the Connor-Davidson Resilience Scale (CD-RISC). Main Outcome and Measures: PTSD symptom severity measured at 2 weeks, 8 weeks, 3 months, and 6 months after trauma. Neural responses to monetary reward in reward-related regions (ie, amygdala, nucleus accumbens, orbitofrontal cortex) was a secondary outcome. Covariates included both geocoded (eg, area deprivation index) and self-reported characteristics (eg, childhood maltreatment, income). Results: In 2597 trauma survivors (mean [SD] age, 36.5 [13.4] years; 1637 female [63%]; 1304 non-Hispanic Black [50.2%], 289 Hispanic [11.1%], 901 non-Hispanic White [34.7%], 93 non-Hispanic other race [3.6%], and 10 missing/unreported [0.4%]), 6 PTSD trajectories (resilient, nonremitting high, nonremitting moderate, slow recovery, rapid recovery, delayed) were identified through latent-class mixed-effect modeling. Multinominal logistic regressions revealed that for individuals with higher CD-RISC scores, greenspace was associated with a greater likelihood of assignment in a resilient trajectory compared with nonremitting high (Wald z test = -3.92; P < .001), nonremitting moderate (Wald z test = -2.24; P = .03), or slow recovery (Wald z test = -2.27; P = .02) classes. Greenspace was also associated with greater neural reactivity to reward in the amygdala (n = 288; t277 = 2.83; adjusted P value = 0.02); however, reward reactivity did not differ by PTSD trajectory. Conclusions and Relevance: In this cohort study, greenspace and self-reported individual resources were significantly associated with PTSD trajectories. These findings suggest that factors at multiple ecological levels may contribute to the likelihood of resiliency to PTSD after trauma.

Premature Death, Suicide, and Nonlethal Intentional Self-Harm After Psychiatric Discharge.

Importance: There is a need for representative research on serious adverse outcomes following discharge from psychiatric hospitalization. Objective: To compare rates of premature death, suicide, and nonlethal intentional self-harm after psychiatric discharge with rates in the general population and investigate associations of these outcomes with relevant variables associated with the index psychiatric hospitalization. Design, Setting, and Participants: This retrospective cohort study included all residents from Catalonia, Spain (7.6 million population), who had psychiatric hospitalizations between January 1, 2014, and December 31, 2018, and were older than 10 years at the index (first) hospitalization. Follow-up was until December 31, 2019. Statistical analysis was performed from December 1, 2022, through April 11, 2024. Exposures: Socioeconomic status, psychiatric diagnoses, duration of index hospitalization, and number of previous psychiatric hospitalizations. Main Outcomes and Measures: Postdischarge premature death (ie, all-cause death before age 70 years) and suicide (International Statistical Classification of Diseases and Related Health Problems, Tenth Revision [ICD-10] code range X60-X84), identified using mortality data, and postdischarge nonlethal intentional self-harm, identified using electronic health record and self-harm case register data. Standardized mortality ratios (SMRs) compared rates of premature death and suicide between the cohort and the general population. Fully adjusted, multivariable, cause-specific Cox proportional hazards regression models for the 3 outcomes were fitted. Results: A total of 49 108 patients discharged from psychiatric hospitalization were included (25 833 males [52.6%]; mean [SD] age at discharge, 44.2 [18.2] years). During follow-up, 2260 patients (4.6%) died prematurely, 437 (0.9%) died by suicide, and 4752 (9.7%) had an episode of nonlethal intentional self-harm. The overall SMR for premature death was 7.5 (95% CI, 7.2-7.9). For suicide, SMR was 32.9 (95% CI, 29.9-36.0) overall and was especially high among females (47.6 [95% CI, 40.2-54.9]). In fully adjusted sex-stratified hazard models, postdischarge premature death was associated with cognitive disorders (adjusted hazard ratio [AHR], 2.89 [95% CI, 2.24-3.74] for females; 2.59 [95% CI, 2.17-3.08] for males) and alcohol-related disorders (AHR, 1.41 [95% CI, 1.18-1.70] for females; 1.22 [95% CI, 1.09-1.37] for males). Postdischarge suicide was associated with postdischarge intentional self-harm (AHR, 2.83 [95% CI, 1.97-4.05] for females; 3.29 [95% CI, 2.47-4.40] for males), with depressive disorders (AHR, 2.13 [95% CI, 1.52-2.97]) and adjustment disorders (AHR, 1.94 [95% CI, 1.32-2.83]) among males, and with bipolar disorder among females (AHR, 1.94 [95% CI, 1.21-3.09]). Postdischarge intentional self-harm was associated with index admissions for intentional self-harm (AHR, 1.95 [95% CI, 1.73-2.21] for females; 2.62 [95% CI, 2.20-3.13] for males) as well as for adjustment disorders (AHR, 1.48 [95% CI, 1.33-1.65] for females; 1.99 [95% CI, 1.74-2.27] for males), anxiety disorders (AHR, 1.24 [95% CI, 1.10-1.39] for females; 1.36 [95% CI, 1.18-1.58] for males), depressive disorders (AHR, 1.54 [95% CI, 1.40-1.69] for females; 1.80 [95% CI, 1.58-2.04] for males), and personality disorders (AHR, 1.59 [95% CI, 1.46-1.73] for females; 1.43 [95% CI, 1.28-1.60] for males). Conclusions and Relevance: In this cohort study of patients discharged from psychiatric hospitalization, risk for premature death and suicide was significantly higher compared with the general population, suggesting individuals discharged from psychiatric inpatient care are a vulnerable population for premature death and suicidal behavior.