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Abstract Background Estimating the indirect mortality due to COVID-19 is of the utmost importance to develop adequate public health policy during future outbreaks. Methods From province-wide administrative datasets, we identified British Columbians who tested negative for COVID-19 during the first wave and never tested positive throughout 2020. We obtained a pre-pandemic (2018) cohort matched on age, sex, history of non-communicable disorders (NCDs), multimorbidity, and severity/acuity, and implemented a doubly robust estimation of the effect of the first pandemic wave on mortality. Results The adjusted odds ratio (AOR) of death was 3.2 times higher for a 2020 cohort who tested negative for COVID-19 (n = 123,133), compared to matched pre-pandemic controls. In both cohorts, a majority (72.5%) experienced at least one pre-existing NCD. Stratification by NCD shows an AOR of death ranges between 2–for people with substance use disorders– and 7–for people previously undiagnosed with NCDs (e.g., incident cases that went untreated). The largest subgroup was composed of people with mental disorders (47,413 people), with an AOR of death of 2.5. Though the COVID-19 direct mortality in the general population remained low (1.9 per 10,000), the excess mortality in this COVID-negative cohort was extremely high − 4,085 of the 123,133– which entails a minimum indirect excess mortality death rate of 6.5 per 10,000 in the general population. Conclusions During the first pandemic year, mortality in COVID-negative adults was several times greater than before COVID-19, in people with matched NCD distribution and severity. Our findings suggest that low direct COVID-19 mortality was accompanied by less visible–but much higher– indirect mortality due to undiagnosed and/or untreated NCDs, highlighting the need to focus not only on mitigating the harms of new agents, but also of continuing service delivery for treatable conditions.

Abstract Despite the established association between social and behavioral determinants of health (SBDH) and suicide risk, SBDHs from unstructured electronic health record notes for suicide prediction remain underutilized. This study investigates the impact of SBDH identified from both structured and unstructured data utilizing a natural language processing (NLP) system on suicide prediction at 7, 30, 90, and 180 days post-discharge. Using data from 2,987,006 US Veterans between 1 October 2009, and 30 September 2015, we designed a case-control study demonstrating that structured and NLP-extracted SBDH significantly enhance distinct prediction models’ performance. For example, the random forest model improved its 180-day post-discharge prediction with an area under the receiver operating characteristic curve increase from 83.57% to 84.25% (95% CI = 0.63%–0.98%, p val

Abstract Background High unmet need for treatment of mental disorders exists throughout the world. An understanding of barriers to treatment is needed to develop effective programs to address this problem. Methods Data on barriers were obtained from face-to-face interviews in 22 community surveys across 19 countries (n = 102,812 respondents aged ≥ 18 years, 57.7% female, median age [interquartile range]: 43 [31–57] years; 68.5% weighted average response rate) in the World Mental Health (WMH) surveys. We focus on the n = 5,136 respondents with 12-month DSM-IV anxiety, mood, or substance use disorders with perceived need for treatment. The n = 2,444 such respondents who did not receive treatment were asked about barriers to receiving treatment, whereas the n = 926 respondents who received treatment with a delay were asked about barriers leading to delays. Consistent with previous research, we distinguished five broad classes of barriers: low perceived disorder severity, two types of barriers in the domain of predisposing factors (beliefs/attitudes about treatment ineffectiveness and stigma) and two types in the domain of enabling factors (financial and nonfinancial). Baseline predictors of receiving treatment found in a prior report (i.e., comparing the n = 2,692 respondents who received treatment with the n = 2,444 who did not) were examined as predictors of barriers, while barriers were examined as mediators of associations between these predictors and treatment. Results Most respondents reported multiple barriers. Barriers among respondents who did not receive treatment included low perceived severity (52.9%), perceived treatment ineffectiveness (44.8%), nonfinancial (40.2%) and financial (32.9%) barriers in the domain of enabling factors, and stigma (20.6%). Barriers causing delays in treatment had a similar rank-order but were reported by higher proportions of respondents (X2 1 = 3.8–199.8, p = 0.050−

Abstract 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 Illumina HumanMethylation450 or MethylationEPIC (850 K) BeadChips. 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

Abstract 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

Abstract 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).

Abstract 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.

Objectives: The rate of suicide increased in members of the United States Army since 2011 after the Iraq and Afghanistan wars and continues to be a major concern. In order to reverse this disturbing trend, it is vital to understand the risk and protective factors for suicide death in servicemembers. Methods: Data were obtained from a case-control psychological autopsy study, which compared U.S. Army suicide decedent cases (n = 135) to a probability sample of living controls (n = 255) who are also service members weighted to be representative of the Army. Interviews were conducted with next-of-kin (NOK) and supervisor (SUP) informants. Multivariable logistic regressions models were constructed using predictors significant after controlling for multiple comparisons. Results: The most parsimonious multivariable model controlling for deployment status, as reported by SUP predicting suicide death consisted of four significant variables: a spouse or partner left him or her in the past month (OR = 28.5 [95% CI = 1.8, 442.7] χ² = 5.72, p = .0168); a smaller social network (OR = 4.2 [95% CI = 1.0, 17.3] χ² = 3.97, p = .0462), less likely to seek help from a mental health counselor (OR = 3.4 [95% CI = 1.2, 9.7] χ² = 5.35, p = .0207) and more likely to be described as incautious (OR = 3.8 [95% CI = 1.2, 11.7] χ² = 5.42, p = .0199). The AUC = .88 [95%CI = 0.82, 0.94] for this regression model suggests strong prediction. Conclusions: Our findings suggest that recent relationship problems, especially in soldiers who are less likely to seek out support from others, may be warning signs for detection and prevention of imminent risk of suicide and according to supervisor informant surveys, had neither evidence of a mental health disorder, nor disclosed suicidal ideation or self-harm. Implications for suicide prevention are discussed.

Abstract Background Mental health service providers are increasingly interested in patient perspectives. We examined rates and predictors of patient-reported satisfaction and perceived helpfulness in a cross-national general population survey of adults with 12-month DSM-IV disorders who saw a provider for help with their mental health. Methods Data were obtained from epidemiological surveys in the World Mental Health Survey Initiative. Respondents were asked about satisfaction with treatments received from up to 11 different types of providers (very satisfied, satisfied, neither satisfied nor dissatisfied, somewhat dissatisfied, very dissatisfied) and helpfulness of the provider (a lot, some, a little, not at all). We modelled predictors of satisfaction and helpfulness using a dataset of patient-provider observations (n = 5,248). Results Most treatment was provided by general medical providers (37.4%), psychiatrists (18.4%) and psychologists (12.7%). Most patients were satisfied or very satisfied (65.9-87.5%, across provider) and helped a lot or some (64.4-90.3%). Spiritual advisors and healers were most often rated satisfactory and helpful. Social workers in human services settings were rated lowest on both dimensions. Patients also reported comparatively low satisfaction with general medical doctors and psychiatrists/psychologists and found general medical doctors less helpful than other providers. Men and students reported lower levels of satisfaction than women and nonstudents. Respondents with high education reported higher satisfaction and helpfulness than those with lower education. Type of mental disorder was unrelated to satisfaction but in some cases (depression, bipolar spectrum disorder, social phobia) was associated with low perceived helpfulness. Insurance was unrelated to either satisfaction or perceived helpfulness but in some cases was associated with elevated perceived helpfulness for a given level of satisfaction. Conclusions Satisfaction with and perceived helpfulness of treatment varied as a function of type of provider, service setting, mental status, and socio-demographic variables. Invariably, caution is needed in combining data from multiple countries where there are cultural and service delivery variations. Even so, our findings underscore the utility of patient perspectives in treatment evaluation and may also be relevant in efforts to match patients to treatments.

Abstract Background Mental health problems are common and impairing among university students, yet only a minority of students with psychological disorders access treatment. Understanding barriers to treatment is integral to planning services, especially in resource constrained settings like South Africa (SA). Methods Data collected across 17 institutions in the online SA National Student Mental Health Survey were used to: (1) estimate 12-month prevalence of common mental health problems and self-harm; (2) estimate the proportion of students receiving treatments for the various mental health problems; (3) explore barriers to treatment; and (4) investigate sociodemographic predictors of treatment mediated through the various barriers endorsed by students with mental health problems. Prevalence analyses were carried out using cross-tabulations and prediction analyses using modified Poisson regression models. Results Prevalence of clinically significant mental health problems is high relative to international comparisons, with the prevalence of severe, mild and moderate symptoms of any disorder and/or self-harm of 24.8% (SD = 0.3), 18.8% (SD = 0.3) and 27.6% (SD = 0.4) respectively. Treatment rates were 35.2% (S.E. = 0.6) among students with mental health problems who perceived need for treatment and 21.3% (S.E. = 0.4) irrespective of perceived need. Treatment rates were highest for mood disorders (29.9%, S.E. = 0.6) and lowest for externalising disorders (23.8%, S.E. = 0.5). Treatment rates were much less variable across disorder types among students with perceived need than irrespective of perceived need, indicating that perceived need mediated the associations of disorder types with received treatment. Adjusting for disorder profile, probability of obtaining treatment was significantly and positively associated with older age, female gender, study beyond the first year, traditional sexual orientation, and diverse indicators of social advantage (full-time study, high parent education, and attending Historically White Institutions). Among students with mental health problems, numerous barriers to treatment were reported adjusting for disorder profile, including lack of perceived need (39.5%, S.E. = 0.5) and, conditional on perceived need, psychological (54.4%, S.E. = 1.0), practical (77.3%, S.E. = 1.1), and other (79.1%, S.E. = 1.1) barriers. Typically, students reported multiple barriers to treatment. Differences in perceived need explained the gender difference in treatment, whereas practical barriers were most important in accounting for the other predictors of treatment. Conclusion Mental health problems are highly prevalent but seldom treated among SA university students. Although many barriers were reported, practical barriers were especially important in accounting for the associations of social disadvantage with low rates of treatment. Many of these practical barriers are however addressable.

Abstract Patients exposed to trauma often experience high rates of adverse post-traumatic neuropsychiatric sequelae (APNS). The biological mechanisms promoting APNS are currently unknown, but the microbiota-gut-brain axis offers an avenue to understanding mechanisms as well as possibilities for intervention. Microbiome composition after trauma exposure has been poorly examined regarding neuropsychiatric outcomes. We aimed to determine whether the gut microbiomes of trauma-exposed emergency department patients who develop APNS have dysfunctional gut microbiome profiles and discover potential associated mechanisms. We performed metagenomic analysis on stool samples (n = 51) from a subset of adults enrolled in the Advancing Understanding of RecOvery afteR traumA (AURORA) study. Two-, eight- and twelve-week post-trauma outcomes for post-traumatic stress disorder (PTSD) (PTSD checklist for DSM-5), normalized depression scores (PROMIS Depression Short Form 8b) and somatic symptom counts were collected. Generalized linear models were created for each outcome using microbial abundances and relevant demographics. Mixed-effect random forest machine learning models were used to identify associations between APNS outcomes and microbial features and encoded metabolic pathways from stool metagenomics. Microbial species, including Flavonifractor plautii, Ruminococcus gnavus and, Bifidobacterium species, which are prevalent commensal gut microbes, were found to be important in predicting worse APNS outcomes from microbial abundance data. Notably, through APNS outcome modeling using microbial metabolic pathways, worse APNS outcomes were highly predicted by decreased L-arginine related pathway genes and increased citrulline and ornithine pathways. Common commensal microbial species are enriched in individuals who develop APNS. More notably, we identified a biological mechanism through which the gut microbiome reduces global arginine bioavailability, a metabolic change that has also been demonstrated in the plasma of patients with PTSD.

Abstract Objectives To estimate 12‐month prevalence, persistence, severity, and treatment of mental disorders and socio‐demographic correlates in Qatar. Methods We conducted the first national population‐based telephone survey of Arab adults between 2019 and 2022 using the Composite International Diagnostic Interview and estimated 12‐month DSM‐5 mood and anxiety disorders and their persistence (the proportion of lifetime cases who continue to meet 12‐month criteria). Results The 12‐month prevalence of any disorder was 21.1% (10.4% mild, 38.7% moderate, and 50.9% severe) and was associated with: younger age, female, previously married, and with persistence of any disorder. Persistence was 74.7% (64.0% mood and 75.6% anxiety) and was significantly associated with secondary education or lower. Minimally adequate treatment received among those with any 12‐month mental disorder was 10.6% (74.6% in healthcare and 64.6% non‐healthcare sectors). Severity and the number of disorders significantly associated with each other and with treatment received (χ 2 = 7.24, p = 0.027) including adequate treatment within the mental health specialty sector (χ 2 = 21.42, p

Abstract Objectives Lifetime DSM‐5 diagnoses generated by the lay‐administered Composite International Diagnostic Interview for DSM‐5 (CIDI) in the World Mental Health Qatar (WMHQ) study were compared to diagnoses based on blinded clinician‐administered reappraisal interviews. Methods Telephone follow‐up interviews used the non‐patient edition of the Structured Clinician Interview for DSM‐5 (SCID) oversampling respondents who screened positive for five diagnoses in the CIDI: major depressive episode, mania/hypomania, panic disorder, generalized anxiety disorder, and obsessive‐compulsive disorder. Concordance was also examined for a diagnoses of post‐traumatic stress disorder based on a short‐form versus full version of the PTSD Checklist for DSM‐5 (PCL‐5). Results Initial CIDI prevalence estimates differed significantly from the SCID for most diagnoses ( χ 1 2 = 6.6–31.4, p = 0.010

Abstract Objectives To estimate lifetime prevalence, risk, and treatment for mental disorders and their correlates in Qatar's general population for the first time. Methods We conducted a national phone survey of 5,195 Qatari and Arab residents in Qatar (2019–2022) using the Composite International Diagnostic Interview Version 3.3 and estimated lifetime mood and anxiety defined diagnoses. Survival‐based discrete time models, lifetime morbid risk, and treatment projections were estimated. Results Lifetime prevalence of any disorder was 28.0% and was associated with younger cohorts, females, and migrants, but lower formal education. Treatment contact in the year of disorder onset were 13.5%. The median delay in receiving treatment was 5 years (IQR = 2–13). Lifetime treatment among those with a lifetime disorder were 59.9% for non‐healthcare and 63.5% for healthcare; it was 68.1% for any anxiety and 80.1% for any mood disorder after 50 years of onset. Younger cohorts and later age of onset were significantly predictors of treatment. Conclusions Lifetime prevalence of mental disorders in Qatar is comparable to other countries. Treatment is significantly delayed and delivered largely in non‐healthcare sectors thus the need for increased literacy of mental illness to reduce stigma and improve earlier help‐seeking in healthcare settings.

Abstract This study examined the latent structure of the broad range of complex neuropsychiatric morbidities occurring 1 year after COVID-19 infection. As part of the CU-COVID19 study, 248 (response rate=39.3%) of 631 adults hospitalized for COVID-19 infection in Hong Kong completed an online survey between March-2021 and January-2022. Disorder prevalence was compared against a random non-infected household sample (n=1834). 248 surveys were received on average 321 days post-infection (Mean age: 48.9, 54% female, moderate/severe/critical infection: 58.2%). 32.4% were screened to have at least one mental disorder, 78.7% of whom had concurrent fatigue/subjective cognitive impairment (SCI). Only PTSD (19.1%) was significantly more common than control (14%, p=0.047). Latent profile analysis classified individuals into P1 (12·4%)-no current neuropsychiatric morbidities, P2 (23.1%)-SCI/fatigue, P3 (45.2%)-anxiety/PTSD, P4 (19.3%)-depression. SCI and fatigue pervaded in all profiles (P2-4) with neuropsychiatric morbidities one-year post-infection. PTSD, anxiety and depressive symptoms were most important in differentiating P2-4. Past mental health and P4 independently predicted functional impairment. Neuropsychiatric morbidity was associated with past mental health, reduced resilience, financial problems, but not COVID-19 severity. Their confluence with depressive and anxiety symptoms predicted impairment and are associated with psychological and environmental factors.

Abstract Objective The standard method of generating disorder‐specific disability scores has lay raters make rankings between pairs of disorders based on brief disorder vignettes. This method introduces bias due to differential rater knowledge of disorders and inability to disentangle the disability due to disorders from the disability due to comorbidities. Methods We propose an alternative, data‐driven, method of generating disorder‐specific disability scores that assesses disorders in a sample of individuals either from population medical registry data or population survey self‐reports and uses Generalized Random Forests (GRF) to predict global (rather than disorder‐specific) disability assessed by clinician ratings or by survey respondent self‐reports. This method also provides a principled basis for studying patterns and predictors of heterogeneity in disorder‐specific disability. We illustrate this method by analyzing data for 16 disorders assessed in the World Mental Health Surveys (n = 53,645). Results Adjustments for comorbidity decreased estimates of disorder‐specific disability substantially. Estimates were generally somewhat higher with GRF than conventional multivariable regression models. Heterogeneity was nonsignificant. Conclusions The results show clearly that the proposed approach is practical, and that adjustment is needed for comorbidities to obtain accurate estimates of disorder‐specific disability. Expansion to a wider range of disorders would likely find more evidence for heterogeneity.

Abstract Objective The Mental and Substance Use Disorders Prevalence Study (MDPS) builds upon previous epidemiologic studies to provide estimates of prevalence and treatment rates of mental and substance use disorders among adults aged 18–65 in the U.S. The study background and methods are described. Method The MDPS employed novel techniques such as the inclusion of household, prison, homeless and state psychiatric hospital populations, a semi‐structured clinical interview administered by trained clinical interviewers to assess disorders, the assessment of both past year and lifetime schizophrenia spectrum disorder (SSD) using full Diagnostic and Statistical Manual 5 criteria, as well as other mental and substance use disorders, and video‐based interviewing. Population specific and combined sample weights were developed to estimate nationally representative prevalence and treatment rates. Results Data collection was conducted between October 2020 and October 2022 resulting in 5679 clinical interviews. The statistical weighting and analytic plan are described. Weighted response rates and reasons for non‐response are provided for each study population. Conclusions The MDPS successfully developed and employed novel techniques to estimate the prevalence and treatment rates of mental and substance use disorders in both household and non‐household populations, including some of the most impairing disorders such as SSD.

Abstract Background Understanding mental health predictors of imminent suicide attempt (SA; within 30 days) among soldiers with depression and no prior suicide ideation (SI) can inform prevention and treatment. The current study aimed to identify sociodemographic and service-related characteristics and mental disorder predictors associated with imminent SA among U.S. Army soldiers following first documented major depression diagnosis (MDD) with no history of SI. Methods In this case-control study using Army Study to Assess Risk and Resilience in Servicemembers (STARRS) administrative data, we identified 101,046 active-duty Regular Army enlisted soldiers (2010–2016) with medically-documented MDD and no prior SI (MDD/No-SI). We examined risk factors for SA within 30 days of first MDD/No-SI using logistic regression analyses, including socio-demographic/service-related characteristics and psychiatric diagnoses. Results The 101,046 soldiers with documented MDD/No-SI were primarily male (78.0%),

Abstract Background Posttraumatic stress disorder (PTSD) is associated with significant morbidity, but efficacious pharmacotherapy and psychotherapy are available. Data from the World Mental Health Surveys were used to investigate extent and predictors of treatment coverage for PTSD in high-income countries (HICs) as well as in low- and middle-income countries (LMICs). Methods Seventeen surveys were conducted across 15 countries (9 HICs, 6 LMICs) by the World Health Organization (WHO) World Mental Health Surveys. Of 35,012 respondents, 914 met DSM-IV criteria for 12-month PTSD. Components of treatment coverage analyzed were: (a) any mental health service utilization; (b) adequate pharmacotherapy; (c) adequate psychotherapy; and (d) effective treatment coverage. Regression models investigated predictors of treatment coverage. Results 12-month PTSD prevalence in trauma exposed individuals was 1.49 (S.E., 0.08). A total of 43.0% (S.E., 2.2) received any mental health services, with fewer receiving adequate pharmacotherapy (13.5%), adequate psychotherapy (17.2%), or effective treatment coverage (14.4%), and with all components of treatment coverage lower in LMICs than HICs. In a multivariable model having insurance (OR = 2.31, 95 CI 1.17, 4.57) and severity of symptoms (OR = .35, 95% CI 0.18, 0.70) were predictive of effective treatment coverage. Conclusion There is a clear need to improve pharmacotherapy and psychotherapy coverage for PTSD, particularly in those with mild symptoms, and especially in LMICs. Universal health care insurance can be expected to increase effective treatment coverage and therefore improve outcomes.

Objectives To identify the extent to which the presence of recent stressful events are risk factors for suicide among active‐duty soldiers as reported by informants. Methods Next‐of‐kin (NOK) and supervisors (SUP) of active duty soldiers (n = 135) who died by suicide and two groups of living controls: propensity‐matched (n = 128) and soldiers who reported suicidal ideation in the past year, but did not die (SI) (n = 108) provided data via structured interviews from the Study to Assess Risk and Resilience in Servicemembers (Army STARRS). Multivariate logistic regression analyses were used to create a risk score for suicide. Results The odds of suicide increased significantly for soldiers experiencing relationship problems, military punishment, and perceived failure or humiliation in the month prior to death. Suicide risk models with these risk factors predicted suicide death among those who reported SI in the past year (OR = 5.9, [95% CI = 1.5, 24.0] χ2 = 6.24, p = 0.0125, AUC, 0.73 (0.7, 0.8) NOK) and (OR = 8.6, [95% CI = 1.4, 51.5] χ2 = 5.49, p = 0.0191, AUC, 0.78 (0.7, 0.8); SUP) suggesting the combination of these recent stressors may contribute to the transition from ideation to action. Conclusions Our findings suggest for the first time recent stressors distinguished suicide ideating controls from suicide decedents in the month prior to death as reported by informants. Implications for preventive intervention efforts for clinicians, supervisors and family members in identifying the transition from ideation to action are discussed.

Abstract The authors sought to characterize adverse posttraumatic neuropsychiatric sequelae (APNS) symptom trajectories across ten symptom domains (pain, depression, sleep, nightmares, avoidance, re-experiencing, anxiety, hyperarousal, somatic, and mental/fatigue symptoms) in a large, diverse, understudied sample of motor vehicle collision (MVC) survivors. More than two thousand MVC survivors were enrolled in the emergency department (ED) and completed a rotating battery of brief smartphone-based surveys over a 2-month period. Measurement models developed from survey item responses were used in latent growth curve/mixture modeling to characterize homogeneous symptom trajectories. Associations between individual trajectories and pre-trauma and peritraumatic characteristics and traditional outcomes were compared, along with associations within and between trajectories. APNS across all ten symptom domains were common in the first two months after trauma. Many risk factors and associations with high symptom burden trajectories were shared across domains. Both across and within traditional diagnostic boundaries, APNS trajectory intercepts, and slopes were substantially correlated. Across all domains, symptom severity in the immediate aftermath of trauma (trajectory intercepts) had the greatest influence on the outcome. An interactive data visualization tool was developed to allow readers to explore relationships of interest between individual characteristics, symptom trajectories, and traditional outcomes ( http://itr.med.unc.edu/aurora/parcoord/ ). Individuals presenting to the ED after MVC commonly experience a broad constellation of adverse posttraumatic symptoms. Many risk factors for diverse APNS are shared. Individuals diagnosed with a single traditional outcome should be screened for others. The utility of multidimensional categorizations that characterize individuals across traditional diagnostic domains should be explored.

Abstract Background The Composite International Diagnostic Interview (CIDI) has been clinically reappraised in several studies conducted mainly in the US and Europe. This report describes the methodology used to conduct one of the Middle East's largest clinical reappraisal studies. The study was carried out in conjunction with the World Mental Health Qatar—the first national psychiatric epidemiological study of common mental disorders in the country. This study aimed to evaluate the diagnostic consistency of core modules of the newly translated and adapted Arabic version of the CIDI 5.0 against the independent clinical diagnoses based on the Structured Clinical Interview for DSM‐5 (SCID‐5). Methods Telephone follow‐up interviews were administered by trained clinicians using the latest research edition of the SCID for DSM‐5. Telephone administered interviews were key in the data collection, as the study took place during the COVID‐19 pandemic. Results Overall, within 12 months, 485 interviews were completed. The response rate was 52%. Quality control monitoring documented excellent adherence of clinical interviews to the rating protocol. Conclusions The overall methods used in this study proved to be efficient and effective. For future research, instrument cultural adaptation within the cultural context is highly recommended.

Abstract Visual components of trauma memories are often vividly re-experienced by survivors with deleterious consequences for normal function. Neuroimaging research on trauma has primarily focused on threat-processing circuitry as core to trauma-related dysfunction. Conversely, limited attention has been given to visual circuitry which may be particularly relevant to posttraumatic stress disorder (PTSD). Prior work suggests that the ventral visual stream is directly related to the cognitive and affective disturbances observed in PTSD and may be predictive of later symptom expression. The present study used multimodal magnetic resonance imaging data (n = 278) collected two weeks after trauma exposure from the AURORA study, a longitudinal, multisite investigation of adverse posttraumatic neuropsychiatric sequelae. Indices of gray and white matter were combined using data fusion to identify a structural covariance network (SCN) of the ventral visual stream 2 weeks after trauma. Participant’s loadings on the SCN were positively associated with both intrusion symptoms and intensity of nightmares. Further, SCN loadings moderated connectivity between a previously observed amygdala-hippocampal functional covariance network and the inferior temporal gyrus. Follow-up MRI data at 6 months showed an inverse relationship between SCN loadings and negative alterations in cognition in mood. Further, individuals who showed decreased strength of the SCN between 2 weeks and 6 months had generally higher PTSD symptom severity over time. The present findings highlight a role for structural integrity of the ventral visual stream in the development of PTSD. The ventral visual stream may be particularly important for the consolidation or retrieval of trauma memories and may contribute to efficient reactivation of visual components of the trauma memory, thereby exacerbating PTSD symptoms. Potentially chronic engagement of the network may lead to reduced structural integrity which becomes a risk factor for lasting PTSD symptoms.

Abstract Background Major depressive disorder (MDD) is a leading cause of disease morbidity. Combined treatment with antidepressant medication (ADM) plus psychotherapy yields a much higher MDD remission rate than ADM only. But 77% of US MDD patients are nonetheless treated with ADM only despite strong patient preferences for psychotherapy. This mismatch is due at least in part to a combination of cost considerations and limited availability of psychotherapists, although stigma and reluctance of PCPs to refer patients for psychotherapy are also involved. Internet-based cognitive behaviorial therapy (i-CBT) addresses all of these problems. Methods Enrolled patients (n = 3360) will be those who are beginning ADM-only treatment of MDD in primary care facilities throughout West Virginia, one of the poorest and most rural states in the country. Participating treatment providers and study staff at West Virginia University School of Medicine (WVU) will recruit patients and, after obtaining informed consent, administer a baseline self-report questionnaire (SRQ) and then randomize patients to 1 of 3 treatment arms with equal allocation: ADM only, ADM + self-guided i-CBT, and ADM + guided i-CBT. Follow-up SRQs will be administered 2, 4, 8, 13, 16, 26, 39, and 52 weeks after randomization. The trial has two primary objectives: to evaluate aggregate comparative treatment effects across the 3 arms and to estimate heterogeneity of treatment effects (HTE). The primary outcome will be episode remission based on a modified version of the patient-centered Remission from Depression Questionnaire (RDQ). The sample was powered to detect predictors of HTE that would increase the proportional remission rate by 20% by optimally assigning individuals as opposed to randomly assigning them into three treatment groups of equal size. Aggregate comparative treatment effects will be estimated using intent-to-treat analysis methods. Cumulative inverse probability weights will be used to deal with loss to follow-up. A wide range of self-report predictors of MDD heterogeneity of treatment effects based on previous studies will be included in the baseline SRQ. A state-of-the-art ensemble machine learning method will be used to estimate HTE. Discussion The study is innovative in using a rich baseline assessment and in having a sample large enough to carry out a well-powered analysis of heterogeneity of treatment effects. We anticipate finding that self-guided and guided i-CBT will both improve outcomes compared to ADM only. We also anticipate finding that the comparative advantages of adding i-CBT to ADM will vary significantly across patients. We hope to develop a stable individualized treatment rule that will allow patients and treatment providers to improve aggregate treatment outcomes by deciding collaboratively when ADM treatment should be augmented with i-CBT. Trial registration ClinicalTrials.gov NCT04120285 . Registered on October 19, 2019.

Abstract Background Most individuals with major depressive disorder (MDD) receive either no care or inadequate care. The aims of this study is to investigate potential determinants of effective treatment coverage. Methods In order to examine obstacles to providing or receiving care, the type of care received, and the quality and use of that care in a representative sample of individuals with MDD, we analyzed data from 17 WHO World Mental Health Surveys conducted in 15 countries (9 high-income and 6 low/middle-income). Of 35,012 respondents, 3341 had 12-month MDD. We explored the association of socio-economic and demographic characteristics, insurance, and severity with effective treatment coverage and its components, including type of treatment, adequacy of treatment, dose, and adherence. Results High level of education (OR = 1.63; 1.19, 2.24), private insurance (OR = 1.62; 1.06, 2.48), and age (30–59yrs; OR = 1.58; 1.21, 2.07) predicted effective treatment coverage for depression in a multivariable logistic regression model. Exploratory bivariate models further indicate that education may follow a dose—response relation; that people with severe depression are more likely to receive any services, but less likely to receive adequate services; and that in low and middle-income countries, private insurance (the only significant predictor) increased the likelihood of receiving effective treatment coverage four times. Conclusions In the regression models, specific social determinants predicted effective coverage for major depression. Knowing the factors that determine who does and does not receive treatment contributes to improve our understanding of unmet needs and our ability to develop targeted interventions.

Abstract Background Major depressive disorder (MDD) and generalized anxiety disorder (GAD) are highly prevalent among university students and predict impaired college performance and later life role functioning. Yet most students do not receive treatment, especially in low-middle-income countries (LMICs). We aim to evaluate the effects of expanding treatment using scalable and inexpensive Internet-delivered transdiagnostic cognitive behavioral therapy (iCBT) among college students with symptoms of MDD and/or GAD in two LMICs in Latin America (Colombia and Mexico) and to investigate the feasibility of creating a precision treatment rule (PTR) to predict for whom iCBT is most effective. Methods We will first carry out a multi-site randomized pragmatic clinical trial (N = 1500) of students seeking treatment at student mental health clinics in participating universities or responding to an email offering services. Students on wait lists for clinic services will be randomized to unguided iCBT (33%), guided iCBT (33%), and treatment as usual (TAU) (33%). iCBT will be provided immediately whereas TAU will be whenever a clinic appointment is available. Short-term aggregate effects will be assessed at 90 days and longer-term effects 12 months after randomization. We will use ensemble machine learning to predict heterogeneity of treatment effects of unguided versus guided iCBT versus TAU and develop a precision treatment rule (PTR) to optimize individual student outcome. We will then conduct a second and third trial with separate samples (n = 500 per arm), but with unequal allocation across two arms: 25% will be assigned to the treatment determined to yield optimal outcomes based on the PTR developed in the first trial (PTR for optimal short-term outcomes for Trial 2 and 12-month outcomes for Trial 3), whereas the remaining 75% will be assigned with equal allocation across all three treatment arms. Discussion By collecting comprehensive baseline characteristics to evaluate heterogeneity of treatment effects, we will provide valuable and innovative information to optimize treatment effects and guide university mental health treatment planning. Such an effort could have enormous public-health implications for the region by increasing the reach of treatment, decreasing unmet need and clinic wait times, and serving as a model of evidence-based intervention planning and implementation. Trial status IRB Approval of Protocol Version 1.0; June 3, 2020. Recruitment began on March 1, 2021. Recruitment is tentatively scheduled to be completed on May 30, 2024. Trial registration ClinicalTrials.gov NCT04780542 . First submission date: February 28, 2021.

Abstract Background A major component of illness burden is role impairment. As part of the recently-completed Saudi National Mental Health Survey (SNMHS), we compare the number of days out of role in the Saudi population associated with ten core mental disorders assessed in the survey to those associated with ten commonly occurring chronic physical disorders. Methods The SNMHS was a household survey that assessed prevalence of ten common anxiety, mood, disruptive behavior, and eating disorders in a nationally representative sample of n = 1981 citizens of the Kingdom of Saudi Arabia (KSA) ages 15–65. Comparison information was obtained on prevalence of ten common chronic physical disorders and number of health-related days out of role (DOR) in the 30 days before interview. Generalized linear models were used to examine univariate and multivariable associations of disorders with DOR and to calculate population attributable risk (PAR) separately and overall for the disorders controlling for socio-demographics. Results 19.9% of respondents had one or more of the selected mental disorders and 47.1% had one or more of the selected physical disorders. Nine mental disorders and two physical disorders were associated with increased DOR. PAR was 32.9% for mental disorders, 27.0% for physical disorders, and 59.9% for both combined. Conclusions Mental disorders are associated with a substantial proportion of all health-related DOR in the Kingdom of Saudi Arabia. Programs to detect and treat mental disorders might lead to substantially decreased role impairment in the Kingdom.

Abstract Background Mental healthcare is delivered across service sectors that differ in level of specialization and intervention modalities typically offered. Little is known about the perceived helpfulness of the combinations of service sectors that patients use. Methods Respondents 18 + years with 12-month DSM-IV mental or substance use disorders who saw a provider for mental health problems in the year before interview were identified from WHO World Mental Health surveys in 17 countries. Based upon the types of providers seen, patients were grouped into nine mutually exclusive single-sector or multi-sector ‘treatment profiles’. Perceived helpfulness was defined as the patient’s maximum rating of being helped (‘a lot’, ‘some’, ‘a little’ or ‘not at all’) of any type of provider seen in the profile. Logistic regression analysis was used to examine the joint associations of sociodemographics, disorder types, and treatment profiles with being helped ‘a lot’. Results Across all surveys combined, 29.4% (S.E. 0.6) of respondents with a 12-month disorder saw a provider in the past year (N = 3221). Of these patients, 58.2% (S.E. 1.0) reported being helped ‘a lot’. Odds of being helped ‘a lot’ were significantly higher (odds ratios [ORs] = 1.50–1.89) among the 12.9% of patients who used specialized multi-sector profiles involving both psychiatrists and other mental health specialists, compared to other patients, despite their high comorbidities. Lower odds of being helped ‘a lot’ were found among patients who were seen only in the general medical, psychiatrist, or other mental health specialty sectors (ORs = 0.46–0.71). Female gender and older age were associated with increased odds of being helped ‘a lot’. In models stratified by country income group, having 3 or more disorders (high-income countries only) and state-funded health insurance (low/middle-income countries only) were associated with increased odds of being helped ‘a lot’. Conclusions Patients who received specialized, multi-sector care were more likely than other patients to report being helped ‘a lot’. This result is consistent with previous research suggesting that persistence in help-seeking is associated with receiving helpful treatment. Given the nonrandom sorting of patients by types of providers seen and persistence in help-seeking, we cannot discount that selection bias may play some role in this pattern.

COVID-19 has had far-reaching economic, social and health consequences, with vulnerable groups disproportionally affected. Even before the COVID-19 pandemic, concern was expressed about university students’ mental health, with global data suggesting students are more vulnerable than the general population to mental disorders. Yet, it is unclear what the pandemic’s impact has been on the mental health of students in South Africa. We examined the impact of COVID-19 on first-year students at two universities in South Africa by analysing changes in the prevalence and age-of-onset of three common mental disorders (namely major depressive episode, generalised anxiety disorder, and suicidal ideation) before and during the pandemic, and comparing these to changes between 2015 and 2017. Our analysis of cross-sectional survey data collected in 2015, 2017 and 2020 shows no clear or consistent pattern of increases in prevalence of common mental disorders following the start of the pandemic. Lifetime prevalence rates of common mental disorders among students have been steadily increasing since 2015, and where increases before and during COVID-19 were observed, they are not consistently larger than increases between 2015 and 2020. No significant changes were observed in the 12-month prevalence of common mental disorders before and during COVID-19, except for an increase in prevalence of depression at one institution, and a decrease in suicidal ideation at the other. Findings suggest that in the context of ongoing adversity and disruptions on South African university campuses in recent years, COVID-19 may be just one more stressor local students face and that its impact on student mental health may not have been as marked in South Africa compared to other regions. Significance: • This study is the first to explore COVID-19’s impact on university students’ mental health in South Africa, using data collected before and during the pandemic. • High rates of psychopathology confirm the need for sustainable campus-based interventions to support student well-being. • Rates of mental disorders among students have been increasing since 2015, and increases observed in 2020 were no larger than those observed in prior years. • In the context of disruptions on university campuses in recent years, COVID-19 is just one more stressor for students, and its impact may not have been as marked in South Africa compared to other regions.

Abstract Aim To investigate the occurrence of traumatic stress symptoms (TSS) among healthcare workers active during the COVID-19 pandemic and to obtain insight as to which pandemic-related stressful experiences are associated with onset and persistence of traumatic stress. Methods This is a multicenter prospective cohort study. Spanish healthcare workers (N = 4,809) participated at an initial assessment (i.e., just after the first wave of the Spain COVID-19 pandemic) and at a 4-month follow-up assessment using web-based surveys. Logistic regression investigated associations of 19 pandemic-related stressful experiences across four domains (infection-related, work-related, health-related and financial) with TSS prevalence, incidence and persistence, including simulations of population attributable risk proportions (PARP). Results Thirty-day TSS prevalence at T1 was 22.1%. Four-month incidence and persistence were 11.6% and 54.2%, respectively. Auxiliary nurses had highest rates of TSS prevalence (35.1%) and incidence (16.1%). All 19 pandemic-related stressful experiences under study were associated with TSS prevalence or incidence, especially experiences from the domains of health-related (PARP range 88.4–95.6%) and work-related stressful experiences (PARP range 76.8–86.5%). Nine stressful experiences were also associated with TSS persistence, of which having patient(s) in care who died from COVID-19 had the strongest association. This association remained significant after adjusting for co-occurring depression and anxiety. Conclusions TSSs among Spanish healthcare workers active during the COVID-19 pandemic are common and associated with various pandemic-related stressful experiences. Future research should investigate if these stressful experiences represent truly traumatic experiences and carry risk for the development of post-traumatic stress disorder.

Abstract Background Treatment guidelines for generalized anxiety disorder (GAD) are based on a relatively small number of randomized controlled trials and do not consider patient-centered perceptions of treatment helpfulness. We investigated the prevalence and predictors of patient-reported treatment helpfulness for DSM-5 GAD and its two main treatment pathways: encounter-level treatment helpfulness and persistence in help-seeking after prior unhelpful treatment. Methods Data came from community epidemiologic surveys in 23 countries in the WHO World Mental Health surveys. DSM-5 GAD was assessed with the fully structured WHO Composite International Diagnostic Interview Version 3.0. Respondents with a history of GAD were asked whether they ever received treatment and, if so, whether they ever considered this treatment helpful. Number of professionals seen before obtaining helpful treatment was also assessed. Parallel survival models estimated probability and predictors of a given treatment being perceived as helpful and of persisting in help-seeking after prior unhelpful treatment. Results The overall prevalence rate of GAD was 4.5%, with lower prevalence in low/middle-income countries (2.8%) than high-income countries (5.3%); 34.6% of respondents with lifetime GAD reported ever obtaining treatment for their GAD, with lower proportions in low/middle-income countries (19.2%) than high-income countries (38.4%); 3) 70% of those who received treatment perceived the treatment to be helpful, with prevalence comparable in low/middle-income countries and high-income countries. Survival analysis suggested that virtually all patients would have obtained helpful treatment if they had persisted in help-seeking with up to 10 professionals. However, we estimated that only 29.7% of patients would have persisted that long. Obtaining helpful treatment at the person-level was associated with treatment type, comorbid panic/agoraphobia, and childhood adversities, but most of these predictors were important because they predicted persistence rather than encounter-level treatment helpfulness. Conclusions The majority of individuals with GAD do not receive treatment. Most of those who receive treatment regard it as helpful, but receiving helpful treatment typically requires persistence in help-seeking. Future research should focus on ensuring that helpfulness is included as part of the evaluation. Clinicians need to emphasize the importance of persistence to patients beginning treatment.

Abstract Substance dependence diagnoses (SDs) are important risk factors for suicidality. We investigated the associations of multiple SDs with different suicidality outcomes, testing how genetic background moderates these associations. The Yale-Penn cohort (N = 15,557) was recruited to investigate the genetics of SDs. The Army STARRS (Study to Assess Risk and Resilience in Servicemembers) cohort (N = 11,236) was recruited to evaluate mental health risk and resilience among Army personnel. We applied multivariate logistic regression to investigate the associations of SDs with suicidality and, in the Yale-Penn cohort, we used the structured linear mixed model (StructLMM) to study multivariate gene–environment interactions. In Yale-Penn, lifetime polysubstance dependence was strongly associated with lifetime suicidality: having five SDs showed an association with suicidality, from odds ratio (OR) = 6.77 (95% confidence interval, CI = 5.74–7.99) for suicidal ideation (SI) to OR = 3.61 (95% CI = 2.7–4.86) for suicide attempt (SA). In Army STARRS, having multiple substance use disorders for alcohol and/or drugs was associated with increased suicidality ranging from OR = 2.88 (95% CI = 2.6–3.19) for SI to OR = 3.92 (95% CI = 3.19–4.81) for SA. In Yale-Penn, we identified multivariate gene–environment interactions (Bayes factors, BF > 0) of SI with respect to a gene cluster on chromosome 16 (LCAT, p = 1.82 × 10–7; TSNAXIP1, p = 2.13 × 10−7; CENPT, p = 2.32 × 10−7; PARD6A, p = 5.57 × 10−7) for opioid dependence (BF = 12.2), cocaine dependence (BF = 12.1), nicotine dependence (BF = 9.2), and polysubstance dependence (BF = 2.1). Comorbidity of multiple SDs is a significant associated with suicidality and heritability of suicidality is partially moderated by multivariate gene interactions.

PTSD has been associated with DNA methylation of specific loci in the genome, but studies have been limited by small sample sizes. Here, the authors perform a meta-analysis of DNA methylation data from 10 different cohorts and identify CpGs in AHRR that are associated with PTSD.

Background: College students face several sources of stress. Self-guided stress management interventions offer an excellent opportunity for scaling up evidence-based interventions for self-management of these stresses. However, little is known about the overall effects of these interventions. Increasing this understanding is essential because self-guided stress management interventions might be a cost-effective and acceptable way of providing help to this important segment of the population during a critical life course stage. Methods: We carried out a systematic literature search of bibliographical databases (PubMed, PsycINFO, Embase, and Cochrane Library) for randomized controlled trials (RCTs) of self-guided stress management interventions published up through April 2020. We conducted two separate meta-analyses for perceived stress, depression, and anxiety. The first included interventions for general college student samples. The second included studies for students with high levels of perceived stress. Results: The first meta-analysis included 26 studies with 29 intervention-control comparisons based on a total of 4468 students. The pooled effect size was small but statistically significant (g = 0.19; 95% CI [0.10, 0.29]; p

Abstract Objectives To illustrate the use of machine learning methods to search for heterogeneous effects of a target modifiable risk factor on suicide in observational studies. The illustration focuses on secondary analysis of a matched case‐control study of vitamin D deficiency predicting subsequent suicide. Methods We describe a variety of machine learning methods to search for prescriptive predictors; that is, predictors of significant variation in the association between a target risk factor and subsequent suicide. In each case, the purpose is to evaluate the potential value of selective intervention on the target risk factor to prevent the outcome based on the provisional assumption that the target risk factor is causal. The approaches illustrated include risk modeling based on the super learner ensemble machine learning method, Least Absolute Shrinkage and Selection Operator (Lasso) penalized regression, and the causal forest algorithm. Results The logic of estimating heterogeneous intervention effects is exposited along with the illustration of some widely used methods for implementing this logic. Conclusions In addition to describing best practices in using the machine learning methods considered here, we close with a discussion of broader design and analysis issues in planning an observational study to investigate heterogeneous effects of a modifiable risk factor.

Background: Cross-sectional studies have found that individuals with posttraumatic stress disorder (PTSD) exhibit deficits in autonomic functioning. While PTSD rates are twice as high in women compared to men, sex differences in autonomic functioning are relatively unknown among trauma-exposed populations. The current study used a prospective design to examine sex differences in posttraumatic autonomic functioning. Methods: 192 participants were recruited from emergency departments following trauma exposure (Mean age = 35.88, 68.2% female). Skin conductance was measured in the emergency department; fear conditioning was completed two weeks later and included measures of blood pressure (BP), heart rate (HR), and high frequency heart rate variability (HF-HRV). PTSD symptoms were assessed 8 weeks after trauma. Results: 2-week systolic BP was significantly higher in men, while 2-week HR was significantly higher in women, and a sex by PTSD interaction suggested that women who developed PTSD demonstrated the highest HR levels. Two-week HF-HRV was significantly lower in women, and a sex by PTSD interaction suggested that women with PTSD demonstrated the lowest HF-HRV levels. Skin conductance response in the emergency department was associated with 2-week HR and HF-HRV only among women who developed PTSD. Conclusions: Our results indicate that there are notable sex differences in autonomic functioning among trauma-exposed individuals. Differences in sympathetic biomarkers (BP and HR) may have implications for cardiovascular disease risk given that sympathetic arousal is a mechanism implicated in this risk among PTSD populations. Future research examining differential pathways between PTSD and cardiovascular risk among men versus women is warranted.

Abstract Background Previous work has indicated that post-traumatic stress disorder (PTSD) symptoms, measured by the Clinician-Administered PTSD Scale (CAPS) within 60 days of trauma exposure, can reliably produce likelihood estimates of chronic PTSD among trauma survivors admitted to acute care centers. Administering the CAPS is burdensome, requires skilled professionals, and relies on symptoms that are not fully expressed upon acute care admission. Predicting chronic PTSD from peritraumatic responses, which are obtainable upon acute care admission, has yielded conflicting results, hence the rationale for a stepwise screening-and-prediction practice. This work explores the ability of peritraumatic responses to produce risk likelihood estimates of early CAPS-based PTSD symptoms indicative of chronic PTSD risk. It specifically evaluates the Peritraumatic Dissociative Experiences Questionnaire (PDEQ) as a risk-likelihood estimator. Methods We used individual participant data (IPD) from five acute care studies that used both the PDEQ and the CAPS (n = 647). Logistic regression calculated the probability of having CAPS scores ≥ 40 between 30 and 60 days after trauma exposure across the range of initial PDEQ scores, and evaluated the added contribution of age, sex, trauma type, and prior trauma exposure. Brier scores, area under the receiver-operating characteristic curve (AUC), and the mean slope of the calibration line evaluated the accuracy and precision of the predicted probabilities. Results Twenty percent of the sample had CAPS ≥ 40. PDEQ severity significantly predicted having CAPS ≥ 40 symptoms (p

Abstract Background Addressing inequalities in mental healthcare utilisation among university students is important for socio-political transformation, particularly in countries with a history of educational exclusion. Methods As part of the WHO World Mental Health International College Student Initiative, we investigated inequalities in mental healthcare utilisation among first-year students at two historically “White” universities in South Africa. Data were collected via a web-based survey from first-year university students (n = 1402) to assess 12-month mental healthcare utilisation, common mental disorders, and suicidality. Multivariate logistic regression models were used to estimate associations between sociodemographic variables and mental healthcare utilisation, controlling for common mental disorders and suicidality. Results A total of 18.1% of students utilised mental healthcare in the past 12 months, with only 28.9% of students with mental disorders receiving treatment (ranging from 28.1% for ADHD to 64.3% for bipolar spectrum disorder). Of those receiving treatment, 52.0% used psychotropic medication, 47.3% received psychotherapy, and 5.4% consulted a traditional healer. Treatment rates for suicidal ideation, plan and attempt were 25.4%, 41.6% and 52.9%, respectively. In multivariate regression models that control for the main effects of mental health variables and all possible joint effects of sociodemographic variables, the likelihood of treatment was lower among males (aOR = 0.57) and Black students (aOR = 0.52). An interaction was observed between sexual orientation and first generation status; among second-generation students, the odds of treatment were higher for students reporting an atypical sexual orientation (aOR = 1.55), while among students with atypical sexual orientations, the likelihood of mental healthcare utilisation was lower for first-generation students (aOR = 0.29). Odds of treatment were significantly elevated among students with major depressive disorder (aOR = 1.88), generalised anxiety disorder (aOR = 2.34), bipolar spectrum disorder (aOR = 4.07), drug use disorder (aOR = 3.45), suicidal ideation (without plan or attempt) (aOR = 2.00), suicide plan (without attempt) (aOR = 3.64) and suicide attempt (aOR = 4.57). Likelihood of treatment increased with level of suicidality, but not number of mental disorders. Conclusion We found very low mental healthcare treatment utilisation among first-year university students in South Africa, with enduring disparities among historically marginalised groups. Campus-based interventions are needed to promote mental healthcare utilisation by first-year students in South Africa, especially among male and Black students and first-generation students with atypical sexual orientations.

Post-traumatic stress disorder (PTSD) is a common mental health problem. Here, the authors report a GWAS from the Psychiatric Genomics Consortium in which they identify two risk loci in European ancestry and one locus in African ancestry individuals and find that PTSD is genetically correlated with several other psychiatric traits.

Abstract Background There is growing awareness of the need for effective prevention, early detection, and novel treatment approaches for common mental disorders (CMDs) among university students. Reliable epidemiological data on prevalence and correlates are the cornerstones of planning and implementing effective health services and adopting a public health approach to student wellness. Yet, there is a comparative lack of sound psychiatric epidemiological studies on CMDs among university students in low- and middle-income countries, like South Africa (SA). It is also unclear if historically marginalised groups of students are at increased risk for mental health problems in post-apartheid SA. The objective of the study was to investigate the prevalence and sociodemographic correlates of lifetime and 12-month CMDs among university students in SA, with a particular focus on vulnerability among students in historically excluded and marginalised segments of the population. Methods Data were collected via self-report measures in an online survey of first-year students registered at two large universities (n = 1402). CMDs were assessed with previously-validated screening scales. Data were weighted and analysed using multivariate statistical methods. Results A total of 38.5% of respondents reported at least one lifetime CMD, the most common being major depressive disorder (24.7%). Twelve-month prevalence of any CMD was 31.5%, with generalised anxiety disorder being the most common (20.8%). The median age of onset for any disorder was 15 years. The median proportional annual persistence of any disorder was 80.0%. Female students, students who reported an atypical sexual orientation, and students with disabilities were at significantly higher risk of any lifetime or 12-month disorder. Female gender, atypical sexual orientation, and disability were associated with elevated risk of internalising disorders, whereas male gender, identifying as White, and reporting an atypical sexual orientation were associated with elevated risk of externalising disorders. Older age, atypical sexual orientation, and disability were associated with elevated risk of bipolar spectrum disorder. Conclusions Despite advances to promote greater social inclusion in post-apartheid SA, students who identify as female, students with atypical sexual orientations, and students with disabilities are nonetheless at increased risk of CMDs, although students who identify as Black and first-generation students are not.

Abstract Background Social anxiety disorder (SAD) is highly prevalent among university students, but the majority of affected students remain untreated. Internet- and mobile-based self-help interventions (IMIs) may be a promising strategy to address this unmet need. This study aims to investigate the efficacy and cost-effectiveness of an unguided internet-based treatment for SAD among university students. The intervention is optimized for the treatment of university students and includes one module targeting fear of positive evaluations that is a neglected aspect of SAD treatment. Methods The study is a two arm randomized controlled trial in which 200 university students with a primary diagnosis of SAD will be assigned randomly to either a wait-list control group (WLC) or the intervention group (IG). The intervention consists of 9 sessions of an internet-based cognitive-behavioral treatment, which also includes a module on fear of positive evaluation (FPE). Guidance is delivered only on the basis of standardized automatic messages, consisting of positive reinforcements for session completion, reminders, and motivational messages in response to non-adherence. All participants will additionally have full access to treatment as usual. Diagnostic status will be assessed through Structured Clinical Interviews for DSM Disorders (SCID). Assessments will be completed at baseline, 10 weeks and 6-month follow-up. The primary outcome will be SAD symptoms at post-treatment, assessed via the Social Phobia Scale (SPS) and the Social Interaction Anxiety Scale (SIAS). Secondary outcomes will include diagnostic status, depression, quality of life and fear of positive evaluation. Cost-effectiveness and cost-utility analyses will be evaluated from a societal and health provider perspective. Discussion Results of this study will contribute to growing evidence for the efficacy and cost-effectiveness of unguided IMIs for the treatment of SAD in university students. Consequently, this trial may provide valuable information for policy makers and clinicians regarding the allocation of limited treatment resources to such interventions. Trial registration DRKS00011424 (German Clinical Trials Register (DRKS)) Registered 14/12/2016.

Abstract Background Specific phobia (SP) is a relatively common disorder associated with high levels of psychiatric comorbidity. Because of its early onset, SP may be a useful early marker of internalizing psychopathology, especially if generalized to multiple situations. This study aimed to evaluate the association of childhood generalized SP with comorbid internalizing disorders. Methods We conducted retrospective analyses of the cross-sectional population-based World Mental Health Surveys using the Composite International Diagnostic Interview. Outcomes were lifetime prevalence, age of onset, and persistence of internalizing disorders; past-month disability; lifetime suicidality; and 12-month serious mental illness. Logistic and linear regressions were used to assess the association of these outcomes with the number of subtypes of childhood-onset (

Psychological problems like procrastination, perfectionism, low self-esteem, test anxiety and stress are common among college students. There are evidence-based interventions available for these problems that not only have direct effects on these problems, but also indirect effects on mental disorders such as depression and anxiety disorders. Targeting these psychological problems may offer new opportunities to prevent and treat mental disorders in a way that is less stigmatizing to students. In this study we examined the association of five psychological problems with five common mental disorders (panic, generalized anxiety, bipolar, major depressive, and substance use disorder) in a sample of 2,449 students from two Dutch universities. Psychological problems were measured with one item for each problem and mental disorders were measured with the Composite International Diagnostic Interview Screening Scales. Associations were examined with Poisson regression models as relative risks (RR) of the disorders as a function of the psychological problems. The population attributable fraction (PAF) indicates by what percentage the prevalence of the mental disorder would be reduced if the psychological problem was addressed successfully by an intervention. Especially generalized anxiety disorder was strongly associated with psychological problems (strong associations with stress and low self-esteem and moderately with test anxiety). The group with three or more psychological problems had a strongly increased risk for generalized anxiety (RR = 11.25; 95% CI: 7.51–16.85), and a moderately increase risk for major depression (RR = 3.22; 95% CI: 2.63–3.95), panic disorder (RR = 3.19; 95% CI: 1.96–5.20) and bipolar disorder (RR = 3.66; 95% CI: 2.40–5.58). The PAFs for having any of the psychological problems (one or more) were considerable, especially for generalized anxiety (60.8%), but also for panic disorder (35.1%), bipolar disorder (30.6%) and major depression (34.0%). We conclude that common psychological problems are associated with mental disorders and with each other. After adjustment, psychological problems are associated with different patterns of mental disorders. If the impact of the psychological problems could be taken away, the prevalence of several mental disorders would be reduced considerably. The psychological problems may provide a promising target to indirectly prevent and intervene in psychopathology in hard to reach college students with mental disorders.

Abstract Objectives A small country in the Arabian Peninsula, Qatar experienced rapid economic growth in the last 3 decades accompanied by major socio‐demographic shifts towards a younger and more highly educated population. To date, no national epidemiological study has examined the prevalence, associated factors, or sequelae of mental disorders in Qatar's general population. Methods The World Mental Health Qatar (WMHQ) is a national mental health needs assessment survey and is the first carried out in collaboration with the World Mental Health Survey initiative to assess the prevalence and burden of psychiatric illnesses among the full Arabic speaking population (nationals and non‐nationals) within the same country. Results Standard translation and harmonization procedures were used to develop the WMHQ instrument. A survey quality control system with standard performance indicators was developed to ensure interviewer adherence to standard practices. A pilot study was then carried out just prior to the COVID‐19 pandemic. Endorsement from public health authorities and sequential revision of the interview schedule led to full survey completion (as opposed to partial completion) and good overall response rate. Conclusions The WMHQ survey will provide timely and actionable information based on quality enhancement procedures put in place during the development and piloting of the study.

Abstract Objectives To develop and test an internationally applicable mapping function for converting WHODAS‐2.0 scores to disability weights, thereby enabling WHODAS‐2.0 to be used in cost‐utility analyses and sectoral decision‐making. Methods Data from 14 countries were used from the WHO Multi‐Country Survey Study on Health and Responsiveness, administered among nationally representative samples of respondents aged 18+ years who were non‐institutionalized and living in private households. For the combined total of 92,006 respondents, available WHODAS‐2.0 items (for both 36‐item and 12‐item versions) were mapped onto disability weight estimates using a machine learning approach, whereby data were split into separate training and test sets; cross‐validation was used to compare the performance of different regression and penalized regression models. Sensitivity analyses considered different imputation strategies and compared overall model performance with that of country‐specific models. Results Mapping functions converted WHODAS‐2.0 scores into disability weights; R‐squared values of 0.700–0.754 were obtained for the test data set. Penalized regression models reached comparable performance to standard regression models but with fewer predictors. Imputation had little impact on model performance. Model performance of the generic model on country‐specific test sets was comparable to model performance of country‐specific models. Conclusions Disability weights can be generated with good accuracy using WHODAS 2.0 scores, including in national settings where health state valuations are not directly available, which signifies the utility of WHODAS as an outcome measure in evaluative studies that express intervention benefits in terms of QALYs gained.

Background: Self-report data on mental distress indicate a deterioration of population mental health in many countries during the COVID-19 pandemic. A Norwegian epidemiological diagnostic psychiatric interview survey was conducted from January to September 2020, allowing for comparison of mental disorder and suicidal ideation prevalence from before through different pandemic periods. Prevalence of suicide deaths were compared between 2020 and 2014–2018. Methods: Participants from the Trøndelag Health Study (HUNT) in Trondheim were recruited through repeated probability sampling. Using the Composite International Diagnostic Interview (CIDI 5.0) (n = 2154), current prevalence of mental disorders and suicidal ideation was examined in repeated cross-sectional analyzes. Data on suicide deaths was retrieved from the Norwegian Cause of Death Registry and compared for the months March to May in 2014–2018 and 2020. Findings: Prevalence of current mental disorders decreased significantly from the pre-pandemic period (January 28th to March 11th 2020; 15•3% (95% CI 12•4–18•8)) to the first pandemic period (March 12th – May 31st; 8•7% (6•8–11•0)). Prevalences were similar between the pre-pandemic period and the interim (June 1st July 31st; 14•2% (11•4–17•5)) and second periods (August 1st-September 18th; 11•9% (9•0–15•6)). No significant differences were observed in suicidal ideation or in suicide deaths. Interpretation: Except for a decrease in mental disorders in the first pandemic period, the findings suggest stable levels of mental disorders, suicidal ideation and suicide deaths during the first six months of the COVID-19 pandemic compared to pre-pandemic levels. Potential methodological and contextual explanations of these findings compared with findings from other studies are discussed. Funding: None.

The college years are stressful for many students. Identifying the sources of stress and their relative importance in leading to clinically significant emotional problems may assist in the development of targeted stress management interventions. The current report examines the distribution and associations of perceived stress across major life areas with 12-month prevalence of common mental disorders in a cross-national sample of first-year college students. The 20,842 respondents were from 24 universities in 9 countries that participated in the World Health Organization World Mental Health International College Student Initiative. Logistic regression analysis examined associations of current perceived stress in six life areas (financial situation, health, love life, relationships with family, relationships at work/school, problems experienced by loved ones) with six types of 12-month mental disorders (major depressive disorder, bipolar disorder, generalized anxiety disorder, panic disorder, alcohol use disorder, drug use disorder). Population attributable risk proportions (PARPs) were calculated to estimate the upper-bound potential effects of interventions focused on perceived stress in reducing prevalence of mental disorders. The majority of students (93.7%) reported at least some stress in at least one of the six areas. A significant dose-response association was found between extent of stress in each life area and increased odds of at least one of the six disorders. The multivariable models that included all stress measures were significant for all disorders (F = 20.6–70.6, p < 0.001). Interpretation of PARPs as representing causal effects of stresses on disorders suggests that up to 46.9–80.0% of 12-month disorder prevalence might be eliminated if stress prevention interventions were developed to block the associations of stress with these disorders.

Background: Despite equivalent or lower lifetime and past-year prevalence of mental disorder among racial/ethnic minorities compared to non-Latino Whites in the United States, evidence suggests that mental disorders are more persistent among minorities than non-Latino Whites. But, it is unclear how nativity and socioeconomic status contribute to observed racial/ethnic differences in prevalence and persistence of mood, anxiety, and substance disorders. Method: Data were examined from a coordinated series of four national surveys that together assessed 21,024 Asian, non-Latino Black, Latino, and non-Latino White adults between 2001 and 2003. Common DSM-IV mood, anxiety, and substance disorders were assessed using the Composite International Diagnostic Interview. Logistic regression analyses examined how several predictors (e.g., race/ethnicity, nativity, education, income) and the interactions between those predictors were associated with both 12-month disorder prevalence and 12-month prevalence among lifetime cases. For the second series of analyses, age of onset and time since onset were used as additional control variables to indirectly estimate disorder persistence. Results: Non-Latino Whites demonstrated the highest unadjusted 12-month prevalence of all disorder types (p

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.