Developing an individualized treatment rule for Veterans with major depressive disorder using electronic health records.

Efforts to develop an individualized treatment rule (ITR) to optimize major depressive disorder (MDD) treatment with antidepressant medication (ADM), psychotherapy, or combined ADM-psychotherapy have been hampered by small samples, small predictor sets, and suboptimal analysis methods. Analyses of large administrative databases designed to approximate experiments followed iteratively by pragmatic trials hold promise for resolving these problems. The current report presents a proof-of-concept study using electronic health records (EHR) of n = 43,470 outpatients beginning MDD treatment in Veterans Health Administration Primary Care Mental Health Integration (PC-MHI) clinics, which offer access not only to ADMs but also psychotherapy and combined ADM-psychotherapy. EHR and geospatial databases were used to generate an extensive baseline predictor set (5,865 variables). The outcome was a composite measure of at least one serious negative event (suicide attempt, psychiatric emergency department visit, psychiatric hospitalization, suicide death) over the next 12 months. Best-practices methods were used to adjust for nonrandom treatment assignment and to estimate a preliminary ITR in a 70% training sample and to evaluate the ITR in the 30% test sample. Statistically significant aggregate variation was found in overall probability of the outcome related to baseline predictors (AU-ROC = 0.68, S.E. = 0.01), with test sample outcome prevalence of 32.6% among the 5% of patients having highest predicted risk compared to 7.1% in the remainder of the test sample. The ITR found that psychotherapy-only was the optimal treatment for 56.0% of patients (roughly 20% lower risk of the outcome than if receiving one of the other treatments) and that treatment type was unrelated to outcome risk among other patients. Change in aggregate treatment costs of implementing this ITR would be negligible, as 16.1% fewer patients would be prescribed ADMs and 2.9% more would receive psychotherapy. A pragmatic trial would be needed to confirm the accuracy of the ITR.

A practical risk calculator for suicidal behavior among transitioning U.S. Army soldiers: results from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS).

BACKGROUND: Risk of suicide-related behaviors is elevated among military personnel transitioning to civilian life. An earlier report showed that high-risk U.S. Army soldiers could be identified shortly before this transition with a machine learning model that included predictors from administrative systems, self-report surveys, and geospatial data. Based on this result, a Veterans Affairs and Army initiative was launched to evaluate a suicide-prevention intervention for high-risk transitioning soldiers. To make targeting practical, though, a streamlined model and risk calculator were needed that used only a short series of self-report survey questions. METHODS: We revised the original model in a sample of n = 8335 observations from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in one of three Army STARRS 2011-2014 baseline surveys while in service and in one or more subsequent panel surveys (LS1: 2016-2018, LS2: 2018-2019) after leaving service. We trained ensemble machine learning models with constrained numbers of item-level survey predictors in a 70% training sample. The outcome was self-reported post-transition suicide attempts (SA). The models were validated in the 30% test sample. RESULTS: Twelve-month post-transition SA prevalence was 1.0% (s.e. = 0.1). The best constrained model, with only 17 predictors, had a test sample ROC-AUC of 0.85 (s.e. = 0.03). The 10-30% of respondents with the highest predicted risk included 44.9-92.5% of 12-month SAs. CONCLUSIONS: An accurate SA risk calculator based on a short self-report survey can target transitioning soldiers shortly before leaving service for intervention to prevent post-transition SA.

Predicting suicide attempts among U.S. Army soldiers after leaving active duty using information available before leaving active duty: results from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS).

Suicide risk is elevated among military service members who recently transitioned to civilian life. Identifying high-risk service members before this transition could facilitate provision of targeted preventive interventions. We investigated the feasibility of doing this by attempting to develop a prediction model for self-reported suicide attempts (SAs) after leaving or being released from active duty in the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS). This study included two self-report panel surveys (LS1: 2016-2018, LS2: 2018-2019) administered to respondents who previously participated while on active duty in one of three Army STARRS 2011-2014 baseline self-report surveys. We focus on respondents who left active duty >12 months before their LS survey (n = 8899). An ensemble machine learning model using predictors available prior to leaving active duty was developed in a 70% training sample and validated in a 30% test sample. The 12-month self-reported SA prevalence (SE) was 1.0% (0.1). Test sample AUC (SE) was 0.74 (0.06). The 15% of respondents with highest predicted risk included nearly two-thirds of 12-month SAs and over 80% of medically serious 12-month SAs. These results show that it is possible to identify soldiers at high post-transition self-report SA risk before the transition. Future model development is needed to examine prediction of SAs assessed by administrative data and using surveys administered closer to the time of leaving active duty.

An individualized treatment rule to optimize probability of remission by continuation, switching, or combining antidepressant medications after failing a first-line antidepressant in a two-stage randomized trial.

BACKGROUND: There is growing interest in using composite individualized treatment rules (ITRs) to guide depression treatment selection, but best approaches for doing this are not widely known. We develop an ITR for depression remission based on secondary analysis of a recently published trial for second-line antidepression medication selection using a cutting-edge ensemble machine learning method. METHODS: Data come from the SUN(^_^)D trial, an open-label, assessor blinded pragmatic trial of previously-untreated patients with major depressive disorder from 48 clinics in Japan. Initial clinic-level randomization assigned patients to 50 or 100 mg/day sertraline. We focus on the 1549 patients who failed to remit within 3 weeks and were then rerandomized at the individual-level to continuation with sertraline, switching to mirtazapine, or combining mirtazapine with sertraline. The outcome was remission 9 weeks post-baseline. Predictors included socio-demographics, clinical characteristics, baseline symptoms, changes in symptoms between baseline and week 3, and week 3 side effects. RESULTS: Optimized treatment was associated with significantly increased cross-validated week 9 remission rates in both samples [5.3% (2.4%), p = 0.016 50 mg/day sample; 5.1% (2.7%), p = 0.031 100 mg/day sample] compared to randomization (30.1-30.8%). Optimization was also associated with significantly increased remission in both samples compared to continuation [24.7% in both: 11.2% (3.8%), p = 0.002 50 mg/day sample; 11.7% (3.9%), p = 0.001 100 mg/day sample]. Non-significant gains were found for optimization compared to switching or combining. CONCLUSIONS: An ITR can be developed to improve second-line antidepressant selection, but replication in a larger study with more comprehensive baseline predictors might produce stronger and more stable results.

Pre-marital predictors of marital violence in the WHO World Mental Health (WMH) Surveys.

PURPOSE: Intimate partner violence (IPV) is a pervasive public health problem. Existing research has focused on reports from victims and few studies have considered pre-marital factors. The main objective of this study was to identify pre-marital predictors of IPV in the current marriage using information obtained from husbands and wives. METHODS: Data from were obtained from married heterosexual couples in six countries. Potential predictors included demographic and relationship characteristics, adverse childhood experiences, dating violence, and psychiatric disorders. Reports of IPV and other characteristics from husbands and wives were considered independently and in relation to spousal reports. RESULTS: Overall, 14.4% of women were victims of IPV in the current marriage. Analyses identified ten significant variables including age at first marriage (husband), education, relative number of previous marriages (wife), history of one or more categories of childhood adversity (husband or wife), history of dating violence (husband or wife), early initiation of sexual intercourse (husband or wife), and four combinations of internalizing and externalizing disorders. The final model was moderately predictive of marital violence, with the 5% of women accounting for 18.6% of all cases of marital IPV. CONCLUSIONS: Results from this study advance understanding of pre-marital predictors of IPV within current marriages, including the importance of considering differences in the experiences of partners prior to marriage and may provide a foundation for more targeted primary prevention efforts.

Childhood adversities and post-traumatic stress disorder: evidence for stress sensitisation in the World Mental Health Surveys.

BackgroundAlthough childhood adversities are known to predict increased risk of post-traumatic stress disorder (PTSD) after traumatic experiences, it is unclear whether this association varies by childhood adversity or traumatic experience types or by age.AimsTo examine variation in associations of childhood adversities with PTSD according to childhood adversity types, traumatic experience types and life-course stage.MethodEpidemiological data were analysed from the World Mental Health Surveys (n = 27 017).ResultsFour childhood adversities (physical and sexual abuse, neglect, parent psychopathology) were associated with similarly increased odds of PTSD following traumatic experiences (odds ratio (OR) = 1.8), whereas the other eight childhood adversities assessed did not predict PTSD. Childhood adversity-PTSD associations did not vary across traumatic experience types, but were stronger in childhood-adolescence and early-middle adulthood than later adulthood.ConclusionsChildhood adversities are differentially associated with PTSD, with the strongest associations in childhood-adolescence and early-middle adulthood. Consistency of associations across traumatic experience types suggests that childhood adversities are associated with generalised vulnerability to PTSD following traumatic experiences.

Sexual Assault Victimization and Mental Health Treatment, Suicide Attempts, and Career Outcomes Among Women in the US Army.

OBJECTIVES: To examine associations of administratively recorded sexual assault victimization during military service with subsequent mental health and negative career outcomes among US Army women controlling for nonrandom victimization exposure. METHODS: We used data from the Army Study to Assess Risk and Resilience in Servicemembers to apply propensity score methods to match all 4238 female Regular Army soldiers with administratively recorded sexual assault victimization during 2004 to 2009 to 5 controls per case with similar composite victimization risk. We examined associations of this victimization measure with administratively recorded mental health treatment, suicide attempt, and Army career outcomes over the subsequent 12 months by using survival analysis for dichotomous outcomes and conditional generalized linear models for continuous outcomes. RESULTS: Women with administratively recorded sexual assault had significantly elevated odds ratios (ORs) of subsequent mental health treatment (any, OR = 2.5; 95% confidence interval [CI] = 2.4, 2.6; specialty, OR = 3.1; 95% CI = 2.9, 3.3; inpatient, OR = 2.8; 95% CI = 2.5, 3.1), posttraumatic stress disorder treatment (any, OR = 6.3; 95% CI = 5.7, 6.9; specialty, OR = 7.7; 95% CI = 6.8, 8.6; inpatient, OR = 6.8; 95% CI = 5.4, 8.6), suicide attempt (OR = 3.0; 95% CI = 2.5, 3.6), demotion (OR = 2.1; 95% CI = 1.9, 2.3), and attrition (OR = 1.2; 95% CI = 1.1, 1.2). CONCLUSIONS: Sexual assault victimization is associated with considerable suffering and likely decreased force readiness.

Posttraumatic stress disorder associated with unexpected death of a loved one: Cross-national findings from the world mental health surveys.

BACKGROUND: Unexpected death of a loved one (UD) is the most commonly reported traumatic experience in cross-national surveys. However, much remains to be learned about posttraumatic stress disorder (PTSD) after this experience. The WHO World Mental Health (WMH) survey initiative provides a unique opportunity to address these issues. METHODS: Data from 19 WMH surveys (n = 78,023; 70.1% weighted response rate) were collated. Potential predictors of PTSD (respondent sociodemographics, characteristics of the death, history of prior trauma exposure, history of prior mental disorders) after a representative sample of UDs were examined using logistic regression. Simulation was used to estimate overall model strength in targeting individuals at highest PTSD risk. RESULTS: PTSD prevalence after UD averaged 5.2% across surveys and did not differ significantly between high-income and low-middle income countries. Significant multivariate predictors included the deceased being a spouse or child, the respondent being female and believing they could have done something to prevent the death, prior trauma exposure, and history of prior mental disorders. The final model was strongly predictive of PTSD, with the 5% of respondents having highest estimated risk including 30.6% of all cases of PTSD. Positive predictive value (i.e., the proportion of high-risk individuals who actually developed PTSD) among the 5% of respondents with highest predicted risk was 25.3%. CONCLUSIONS: The high prevalence and meaningful risk of PTSD make UD a major public health issue. This study provides novel insights into predictors of PTSD after this experience and suggests that screening assessments might be useful in identifying high-risk individuals for preventive interventions.

Post-traumatic stress disorder associated with life-threatening motor vehicle collisions in the WHO World Mental Health Surveys.

BACKGROUND: Motor vehicle collisions (MVCs) are a substantial contributor to the global burden of disease and lead to subsequent post-traumatic stress disorder (PTSD). However, the relevant literature originates in only a few countries, and much remains unknown about MVC-related PTSD prevalence and predictors. METHODS: Data come from the World Mental Health Survey Initiative, a coordinated series of community epidemiological surveys of mental disorders throughout the world. The subset of 13 surveys (5 in high income countries, 8 in middle or low income countries) with respondents reporting PTSD after life-threatening MVCs are considered here. Six classes of predictors were assessed: socio-demographics, characteristics of the MVC, childhood family adversities, MVCs, other traumatic experiences, and respondent history of prior mental disorders. Logistic regression was used to examine predictors of PTSD. Mental disorders were assessed with the fully-structured Composite International Diagnostic Interview using DSM-IV criteria. RESULTS: Prevalence of PTSD associated with MVCs perceived to be life-threatening was 2.5 % overall and did not vary significantly across countries. PTSD was significantly associated with low respondent education, someone dying in the MVC, the respondent or someone else being seriously injured, childhood family adversities, prior MVCs (but not other traumatic experiences), and number of prior anxiety disorders. The final model was significantly predictive of PTSD, with 32 % of all PTSD occurring among the 5 % of respondents classified by the model as having highest PTSD risk. CONCLUSION: Although PTSD is a relatively rare outcome of life-threatening MVCs, a substantial minority of PTSD cases occur among the relatively small proportion of people with highest predicted risk. This raises the question whether MVC-related PTSD could be reduced with preventive interventions targeted to high-risk survivors using models based on predictors assessed in the immediate aftermath of the MVCs.

APPROXIMATING A DSM-5 DIAGNOSIS OF PTSD USING DSM-IV CRITERIA.

BACKGROUND: Diagnostic criteria for DSM-5 posttraumatic stress disorder (PTSD) are in many ways similar to DSM-IV criteria, raising the possibility that it might be possible to closely approximate DSM-5 diagnoses using DSM-IV symptoms. If so, the resulting transformation rules could be used to pool research data based on the two criteria sets. METHODS: The pre-post deployment study (PPDS) of the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) administered a blended 30-day DSM-IV and DSM-5 PTSD symptom assessment based on the civilian PTSD Checklist for DSM-IV (PCL-C) and the PTSD Checklist for DSM-5 (PCL-5). This assessment was completed by 9,193 soldiers from three US Army Brigade Combat Teams approximately 3 months after returning from Afghanistan. PCL-C items were used to operationalize conservative and broad approximations of DSM-5 PTSD diagnoses. The operating characteristics of these approximations were examined compared to diagnoses based on actual DSM-5 criteria. RESULTS: The estimated 30-day prevalence of DSM-5 PTSD based on conservative (4.3%) and broad (4.7%) approximations of DSM-5 criteria using DSM-IV symptom assessments were similar to estimates based on actual DSM-5 criteria (4.6%). Both approximations had excellent sensitivity (92.6-95.5%), specificity (99.6-99.9%), total classification accuracy (99.4-99.6%), and area under the receiver operating characteristic curve (0.96-0.98). CONCLUSIONS: DSM-IV symptoms can be used to approximate DSM-5 diagnoses of PTSD among recently deployed soldiers, making it possible to recode symptom-level data from earlier DSM-IV studies to draw inferences about DSM-5 PTSD. However, replication is needed in broader trauma-exposed samples to evaluate the external validity of this finding.

Lifetime prevalence of DSM-IV mental disorders among new soldiers in the U.S. Army: results from the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS).

BACKGROUND: The prevalence of 30-day mental disorders with retrospectively reported early onsets is significantly higher in the U.S. Army than among socio-demographically matched civilians. This difference could reflect high prevalence of preenlistment disorders and/or high persistence of these disorders in the context of the stresses associated with military service. These alternatives can to some extent be distinguished by estimating lifetime disorder prevalence among new Army recruits. METHODS: The New Soldier Study (NSS) in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) used fully structured measures to estimate lifetime prevalence of 10 DSM-IV disorders in new soldiers reporting for Basic Combat Training in 2011-2012 (n = 38,507). Prevalence was compared to estimates from a matched civilian sample. Multivariate regression models examined socio-demographic correlates of disorder prevalence and persistence among new soldiers. RESULTS: Lifetime prevalence of having at least one internalizing, externalizing, or either type of disorder did not differ significantly between new soldiers and civilians, although three specific disorders (generalized anxiety, posttraumatic stress, and conduct disorders) and multimorbidity were significantly more common among new soldiers than civilians. Although several socio-demographic characteristics were significantly associated with disorder prevalence and persistence, these associations were uniformly weak. CONCLUSIONS: New soldiers differ somewhat, but not consistently, from civilians in lifetime preenlistment mental disorders. This suggests that prior findings of higher prevalence of current disorders with preenlistment onsets among soldiers than civilians are likely due primarily to a more persistent course of early-onset disorders in the context of the special stresses experienced by Army personnel.

Guns, Impulsive Angry Behavior, and Mental Disorders: Results from the National Comorbidity Survey Replication (NCS-R).

Analyses from the National Comorbidity Study Replication provide the first nationally representative estimates of the co-occurrence of impulsive angry behavior and possessing or carrying a gun among adults with and without certain mental disorders and demographic characteristics. The study found that a large number of individuals in the United States self-report patterns of impulsive angry behavior and also possess firearms at home (8.9%) or carry guns outside the home (1.5%). These data document associations of numerous common mental disorders and combinations of angry behavior with gun access. Because only a small proportion of persons with this risky combination have ever been involuntarily hospitalized for a mental health problem, most will not be subject to existing mental health-related legal restrictions on firearms resulting from a history of involuntary commitment. Excluding a large proportion of the general population from gun possession is also not likely to be feasible. Behavioral risk-based approaches to firearms restriction, such as expanding the definition of gun-prohibited persons to include those with violent misdemeanor convictions and multiple DUI convictions, could be a more effective public health policy to prevent gun violence in the population.

DSM-5 and ICD-11 definitions of posttraumatic stress disorder: investigating "narrow" and "broad" approaches.

BACKGROUND: The development of the Diagnostic and Statistical Manual of Mental Disorders 5th edition (DSM-5) and ICD-11 has led to reconsideration of diagnostic criteria for posttraumatic stress disorder (PTSD). The World Mental Health (WMH) Surveys allow investigation of the implications of the changing criteria compared to DSM-IV and ICD-10. METHODS: WMH Surveys in 13 countries asked respondents to enumerate all their lifetime traumatic events (TEs) and randomly selected one TE per respondent for PTSD assessment. DSM-IV and ICD-10 PTSD were assessed for the 23,936 respondents who reported lifetime TEs in these surveys with the fully structured Composite International Diagnostic Interview (CIDI). DSM-5 and proposed ICD-11 criteria were approximated. Associations of the different criteria sets with indicators of clinical severity (distress-impairment, suicidality, comorbid fear-distress disorders, PTSD symptom duration) were examined to investigate the implications of using the different systems. RESULTS: A total of 5.6% of respondents met criteria for "broadly defined" PTSD (i.e., full criteria in at least one diagnostic system), with prevalence ranging from 3.0% with DSM-5 to 4.4% with ICD-10. Only one-third of broadly defined cases met criteria in all four systems and another one third in only one system (narrowly defined cases). Between-system differences in indicators of clinical severity suggest that ICD-10 criteria are least strict and DSM-IV criteria most strict. The more striking result, though, is that significantly elevated indicators of clinical significance were found even for narrowly defined cases for each of the four diagnostic systems. CONCLUSIONS: These results argue for a broad definition of PTSD defined by any one of the different systems to capture all clinically significant cases of PTSD in future studies.

Cumulative traumas and risk thresholds: 12-month PTSD in the World Mental Health (WMH) surveys.

BACKGROUND: Clinical research suggests that posttraumatic stress disorder (PTSD) patients exposed to multiple traumatic events (TEs) rather than a single TE have increased morbidity and dysfunction. Although epidemiological surveys in the United States and Europe also document high rates of multiple TE exposure, no population-based cross-national data have examined this issue. METHODS: Data were analyzed from 20 population surveys in the World Health Organization World Mental Health Survey Initiative (n = 51,295 aged 18+). The Composite International Diagnostic Interview (3.0) assessed 12-month PTSD and other common DSM-IV disorders. Respondents with 12-month PTSD were assessed for single versus multiple TEs implicated in their symptoms. Associations were examined with age of onset (AOO), functional impairment, comorbidity, and PTSD symptom counts. RESULTS: 19.8% of respondents with 12-month PTSD reported that their symptoms were associated with multiple TEs. Cases who associated their PTSD with four or more TEs had greater functional impairment, an earlier AOO, longer duration, higher comorbidity with mood and anxiety disorders, elevated hyperarousal symptoms, higher proportional exposures to partner physical abuse and other types of physical assault, and lower proportional exposure to unexpected death of a loved one than cases with fewer associated TEs. CONCLUSIONS: A risk threshold was observed in this large-scale cross-national database wherein cases who associated their PTSD with four or more TEs presented a more "complex" clinical picture with substantially greater functional impairment and greater morbidity than other cases of PTSD. PTSD cases associated with four or more TEs may merit specific and targeted intervention strategies.

Major depressive disorder subtypes to predict long-term course.

BACKGROUND: Variation in the course of major depressive disorder (MDD) is not strongly predicted by existing subtype distinctions. A new subtyping approach is considered here. METHODS: Two data mining techniques, ensemble recursive partitioning and Lasso generalized linear models (GLMs), followed by k-means cluster analysis are used to search for subtypes based on index episode symptoms predicting subsequent MDD course in the World Mental Health (WMH) surveys. The WMH surveys are community surveys in 16 countries. Lifetime DSM-IV MDD was reported by 8,261 respondents. Retrospectively reported outcomes included measures of persistence (number of years with an episode, number of years with an episode lasting most of the year) and severity (hospitalization for MDD, disability due to MDD). RESULTS: Recursive partitioning found significant clusters defined by the conjunctions of early onset, suicidality, and anxiety (irritability, panic, nervousness-worry-anxiety) during the index episode. GLMs found additional associations involving a number of individual symptoms. Predicted values of the four outcomes were strongly correlated. Cluster analysis of these predicted values found three clusters having consistently high, intermediate, or low predicted scores across all outcomes. The high-risk cluster (30.0% of respondents) accounted for 52.9-69.7% of high persistence and severity, and it was most strongly predicted by index episode severe dysphoria, suicidality, anxiety, and early onset. A total symptom count, in comparison, was not a significant predictor. CONCLUSIONS: Despite being based on retrospective reports, results suggest that useful MDD subtyping distinctions can be made using data mining methods. Further studies are needed to test and expand these results with prospective data.

Alcohol abuse in developed and developing countries in the World Mental Health Surveys: Socially defined consequences or psychiatric disorder?

BACKGROUND: Previous single country research has raised concerns that: (1) the DSM-IV diagnosis of alcohol abuse (AA) is met primarily through the hazardous use criterion related to drinking and driving and (2) that the hazardous use and social consequences AA criteria primarily reflect varying socioeconomic and cultural factors rather than psychiatric disorder. METHODS: Using representative cross-national data from the 21 countries in the World Mental Health surveys, adults meeting DSM-IV lifetime criteria for AA but not dependence from 10 developed (n=46,071) and 11 developing (n=49,761) countries were assessed as meeting AA with the hazardous use or the social consequences criteria. RESULTS: Between 29.3% (developed) and 16.2% (developing) of respondents with AA met only the hazardous use criterion. AA cases with and without hazardous use were similar in age-of-onset, course, predictors, and psychopathological consequences in both developed and developing countries. DISCUSSION AND CONCLUSIONS: Despite some associations of the AA criteria with socioeconomic factors, the hazardous use and social consequences criteria were significantly associated with psychiatric predictors and sequelae. The findings indicate that these criteria reflect psychiatric disorder and are appropriate for inclusion as DSM-5 Alcohol Use Disorder criteria. SCIENTIFIC SIGNIFICANCE: These findings support a psychiatric rather than a sociocultural view of the hazardous use and social consequences symptoms and provide evidence that they are appropriate diagnostic criteria cross-nationally with utility in a wide range of socioeconomic environments. This suggests consideration for their adoption by ICD-11. Further research is needed on the implications of these results for prevention and treatment.

Posttraumatic stress disorder in a nationally representative mexican community sample.

This study describes the public health burden of trauma exposure and posttraumatic stress disorder (PTSD) in relation to the full range of traumatic events to identify the conditional risk of PTSD from each traumatic event experienced in the Mexican population and other risk factors. The representative sample comprised a subsample (N = 2,362) of the urban participants of the Mexican National Comorbidity Survey (2001-2002). We used the World Health Organization's Composite International Diagnostic Interview (CIDI) to assess exposure to trauma and the presence of PTSD according to the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; American Psychiatric Association, ) in each respondents' self-reported worst traumatic event, as well as a randomly selected lifetime trauma. The results showed that traumatic events were extremely common in Mexico (68.8%). The estimate of lifetime PTSD in the whole population was 1.5%; among only those with a traumatic event it was 2.1%. The 12-month prevalence of PTSD in the whole population was 0.6%; among only those with a traumatic event it was 0.8%. Violence-related events were responsible for a large share of PTSD. Sexual violence, in particular, was one of the greatest risks for developing PTSD. These findings support the idea that trauma in Mexico should be considered a public health concern.

Comparative effectiveness of remote digital gamified and group CBT skills training interventions for anxiety and depression among college students: Results of a three-arm randomised controlled trial.

Digital interventions can enhance access to healthcare in under-resourced settings. However, guided digital interventions may be costly for low- and middle-income countries, despite their effectiveness. In this randomised control trial, we evaluated the effectiveness of two digital interventions designed to address this issue: (1) a Cognitive Behavioral Therapy Skills Training (CST) intervention that increased scalability by using remote online group administration; and (2) the SuperBetter gamified self-guided CBT skills training app, which uses other participants rather than paid staff as guides. The study was implemented among anxious and/or depressed South African undergraduates (n = 371) randomised with equal allocation to Remote Group CST, SuperBetter, or a MoodFlow mood monitoring control. Symptoms were assessed with the Generalized Anxiety Disorder-7 (GAD-7) and the Patient Health Questionnaire-9 (PHQ-9). Intention-to-treat analysis found effect sizes at the high end of prior digital intervention trials, including significantly higher adjusted risk differences (ARD; primary outcome) in joint anxiety/depression remission at 3-months and 6-months for Remote Group CST (ARD = 23.3-18.9%, p = 0.001-0.035) and SuperBetter (ARD = 12.7-22.2%, p = 0.047-0.006) than MoodFlow and mean combined PHQ-9/GAD-7 scores (secondary outcome) significantly lower for Remote Group CST and SuperBetter than MoodFlow. These results illustrate how innovative delivery methods can increase the scalability of standard one-on-one guided digital interventions. PREREGISTRATION INTERNATIONAL STANDARD RANDOMISED CONTROLLED TRIAL NUMBER (ISRTCN) SUBMISSION #: 47,089,643.

Estimated Average Treatment Effect of Psychiatric Hospitalization in Patients With Suicidal Behaviors: A Precision Treatment Analysis.

Importance: Psychiatric hospitalization is the standard of care for patients presenting to an emergency department (ED) or urgent care (UC) with high suicide risk. However, the effect of hospitalization in reducing subsequent suicidal behaviors is poorly understood and likely heterogeneous. Objectives: To estimate the association of psychiatric hospitalization with subsequent suicidal behaviors using observational data and develop a preliminary predictive analytics individualized treatment rule accounting for heterogeneity in this association across patients. Design, Setting, and Participants: A machine learning analysis of retrospective data was conducted. All veterans presenting with suicidal ideation (SI) or suicide attempt (SA) from January 1, 2010, to December 31, 2015, were included. Data were analyzed from September 1, 2022, to March 10, 2023. Subgroups were defined by primary psychiatric diagnosis (nonaffective psychosis, bipolar disorder, major depressive disorder, and other) and suicidality (SI only, SA in past 2-7 days, and SA in past day). Models were trained in 70.0% of the training samples and tested in the remaining 30.0%. Exposures: Psychiatric hospitalization vs nonhospitalization. Main Outcomes and Measures: Fatal and nonfatal SAs within 12 months of ED/UC visits were identified in administrative records and the National Death Index. Baseline covariates were drawn from electronic health records and geospatial databases. Results: Of 196 610 visits (90.3% men; median [IQR] age, 53 [41-59] years), 71.5% resulted in hospitalization. The 12-month SA risk was 11.9% with hospitalization and 12.0% with nonhospitalization (difference, -0.1%; 95% CI, -0.4% to 0.2%). In patients with SI only or SA in the past 2 to 7 days, most hospitalization was not associated with subsequent SAs. For patients with SA in the past day, hospitalization was associated with risk reductions ranging from -6.9% to -9.6% across diagnoses. Accounting for heterogeneity, hospitalization was associated with reduced risk of subsequent SAs in 28.1% of the patients and increased risk in 24.0%. An individualized treatment rule based on these associations may reduce SAs by 16.0% and hospitalizations by 13.0% compared with current rates. Conclusions and Relevance: The findings of this study suggest that psychiatric hospitalization is associated with reduced average SA risk in the immediate aftermath of an SA but not after other recent SAs or SI only. Substantial heterogeneity exists in these associations across patients. An individualized treatment rule accounting for this heterogeneity could both reduce SAs and avert hospitalizations.

Predicting Homelessness Among Transitioning U.S. Army Soldiers.

INTRODUCTION: This study develops a practical method to triage Army transitioning service members (TSMs) at highest risk of homelessness to target a preventive intervention. METHODS: The sample included 4,790 soldiers from the Study to Assess Risk and Resilience in Servicemembers-Longitudinal Study (STARRS-LS) who participated in 1 of 3 Army STARRS 2011-2014 baseline surveys followed by the third wave of the STARRS-LS online panel surveys (2020-2022). Two machine learning models were trained: a Stage-1 model that used administrative predictors and geospatial data available for all TSMs at discharge to identify high-risk TSMs for initial outreach; and a Stage-2 model estimated in the high-risk subsample that used self-reported survey data to help determine highest risk based on additional information collected from high-risk TSMs once they are contacted. The outcome in both models was homelessness within 12 months after leaving active service. RESULTS: Twelve-month prevalence of post-transition homelessness was 5.0% (SE=0.5). The Stage-1 model identified 30% of high-risk TSMs who accounted for 52% of homelessness. The Stage-2 model identified 10% of all TSMs (i.e., 33% of high-risk TSMs) who accounted for 35% of all homelessness (i.e., 63% of the homeless among high-risk TSMs). CONCLUSIONS: Machine learning can help target outreach and assessment of TSMs for homeless prevention interventions.