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.

Development of a model to predict psychotherapy response for depression among Veterans.

BACKGROUND: Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan. METHODS: This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018-2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample. RESULTS: 32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables. CONCLUSIONS: Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.

Development of a model to predict antidepressant treatment response for depression among Veterans.

BACKGROUND: Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA). METHODS: A 2018-2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample. RESULTS: In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors. CONCLUSIONS: Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

Are Veterans Getting Their Preferred Depression Treatment? A National Observational Study in the Veterans Health Administration.

BACKGROUND: Physician responsiveness to patient preferences for depression treatment may improve treatment adherence and clinical outcomes. OBJECTIVE: To examine associations of patient treatment preferences with types of depression treatment received and treatment adherence among Veterans initiating depression treatment. DESIGN: Patient self-report surveys at treatment initiation linked to medical records. SETTING: Veterans Health Administration (VA) clinics nationally, 2018-2020. PARTICIPANTS: A total of 2582 patients (76.7% male, mean age 48.7 years, 62.3% Non-Hispanic White) MAIN MEASURES: Patient self-reported preferences for medication and psychotherapy on 0-10 self-anchoring visual analog scales (0="completely unwilling"; 10="completely willing"). Treatment receipt and adherence (refilling medications; attending 3+ psychotherapy sessions) over 3 months. Logistic regression models controlled for socio-demographics and geographic variables. KEY RESULTS: More patients reported strong preferences (10/10) for psychotherapy than medication (51.2% versus 36.7%, McNemar χ21=175.3, p<0.001). A total of 32.1% of patients who preferred (7-10/10) medication and 21.8% who preferred psychotherapy did not receive these treatments. Patients who strongly preferred medication were substantially more likely to receive medication than those who had strong negative preferences (odds ratios [OR]=17.5; 95% confidence interval [CI]=12.5-24.5). Compared with patients who had strong negative psychotherapy preferences, those with strong psychotherapy preferences were about twice as likely to receive psychotherapy (OR=1.9; 95% CI=1.0-3.5). Patients who strongly preferred psychotherapy were more likely to adhere to psychotherapy than those with strong negative preferences (OR=3.3; 95% CI=1.4-7.4). Treatment preferences were not associated with medication or combined treatment adherence. Patients in primary care settings had lower odds of receiving (but not adhering to) psychotherapy than patients in specialty mental health settings. Depression severity was not associated with treatment receipt or adherence. CONCLUSIONS: Mismatches between treatment preferences and treatment type received were common and associated with worse treatment adherence for psychotherapy. Future research could examine ways to decrease mismatch between patient preferences and treatments received and potential effects on patient outcomes.

Designing mental health promotion campaigns: segmenting U.S. Veteran audiences to address public stigma.

BACKGROUND: Public stigma is a significant deterrent to mental health service use for U.S. veterans. Media campaigns are often used to dispel stigmatizing beliefs and actions. Segmentation is an evidence-based practice for their effective use; however, little data has been published on veteran segments to target with anti-stigma messages. AIMS: This article aims to identify and describe initial typologies of stigmatizing attitudes within a group of U.S. military veterans. METHODS: Telephone-based cross-sectional surveys were conducted with a national random sample of veterans from 2014 to 2016 (N = 2142). Stigma outcomes were measured using a brief, validated instrument used in population-based surveys of public perceptions toward people with mental illness. Cluster analysis was conducted to identify specific groupings along multiple dimensions. RESULTS: A final four-cluster solution was identified among veterans with distinct patterns of attitudes toward mental illness and include: 1) the undecided, 2) the influencer, 3) the ambivalent, and 4) the potential ally. Several strategies were also identified for designing anti-stigma messaging toward these segments. CONCLUSIONS: This research demonstrates veterans can be segmented by attitudes to target with anti-stigma campaign messages.

Invited Commentary: New Directions in Machine Learning Analyses of Administrative Data to Prevent Suicide-Related Behaviors.

This issue contains a thoughtful report by Gradus et al. (Am J Epidemiol. 2021;190(12):2517-2527) on a machine learning analysis of administrative variables to predict suicide attempts over 2 decades throughout Denmark. This is one of numerous recent studies that document strong concentration of risk of suicide-related behaviors among patients with high scores on machine learning models. The clear exposition of Gradus et al. provides an opportunity to review major challenges in developing, interpreting, and using such models: defining appropriate controls and time horizons, selecting comprehensive predictors, dealing with imbalanced outcomes, choosing classifiers, tuning hyperparameters, evaluating predictor variable importance, and evaluating operating characteristics. We close by calling for machine-learning research into suicide-related behaviors to move beyond merely demonstrating significant prediction-this is by now well-established-and to focus instead on using such models to target specific preventive interventions and to develop individualized treatment rules that can be used to help guide clinical decisions to address the growing problems of suicide attempts, suicide deaths, and other injuries and deaths in the same spectrum.

Suicide prediction models: a critical review of recent research with recommendations for the way forward.

Suicide is a leading cause of death. A substantial proportion of the people who die by suicide come into contact with the health care system in the year before their death. This observation has resulted in the development of numerous suicide prediction tools to help target patients for preventive interventions. However, low sensitivity and low positive predictive value have led critics to argue that these tools have no clinical value. We review these tools and critiques here. We conclude that existing tools are suboptimal and that improvements, if they can be made, will require developers to work with more comprehensive predictor sets, staged screening designs, and advanced statistical analysis methods. We also conclude that although existing suicide prediction tools currently have little clinical value, and in some cases might do more harm than good, an even-handed assessment of the potential value of refined tools of this sort cannot currently be made because such an assessment would depend on evidence that currently does not exist about the effectiveness of preventive interventions. We argue that the only way to resolve this uncertainty is to link future efforts to develop or evaluate suicide prediction tools with concrete questions about specific clinical decisions aimed at reducing suicides and to evaluate the clinical value of these tools in terms of net benefit rather than sensitivity or positive predictive value. We also argue for a focus on the development of individualized treatment rules to help select the right suicide-focused treatments for the right patients at the right times. Challenges will exist in doing this because of the rarity of suicide even among patients considered high-risk, but we offer practical suggestions for how these challenges can be addressed.

Mental Disorders, Gun Ownership, and Gun Carrying Among Soldiers After Leaving the Army, 2016-2019.

Objectives. To examine associations of current mental and substance use disorders with self-reported gun ownership and carrying among recently separated US Army soldiers. Veterans have high rates of both gun ownership and mental disorders, the conjunction of which might contribute to the high suicide rate in this group. Methods. Cross-sectional survey data were collected in 2018-2019 from 5682 recently separated personnel who took part in the Army Study to Assess Risk and Resilience in Servicemembers. Validated measures assessed recent mood, anxiety, substance use, and externalizing disorders. Logistic regression models examined associations of sociodemographic characteristics, service characteristics, and mental disorders with gun ownership and carrying. Results. Of the participants, 50% reported gun ownership. About half of owners reported carrying some or most of the time. Mental disorders were not associated significantly with gun ownership. However, among gun owners, major depressive disorder, panic disorder, posttraumatic stress disorder, and intermittent explosive disorder were associated with significantly elevated odds of carrying at least some of the time. Conclusions. Mental disorders are not associated with gun ownership among recently separated Army personnel, but some mental disorders are associated with carrying among gun owners. (Am J Public Health. 2021;111(10):1855-1864. https://doi.org/10.2105/AJPH.2021.306420).

Social Determinants and Military Veterans' Suicide Ideation and Attempt: a Cross-sectional Analysis of Electronic Health Record Data.

BACKGROUND: Health care systems struggle to identify risk factors for suicide. Adverse social determinants of health (SDH) are strong predictors of suicide risk, but most electronic health records (EHR) do not include SDH data. OBJECTIVE: To determine the prevalence of SDH documentation in the EHR and how SDH are associated with suicide ideation and attempt. DESIGN: This cross-sectional analysis included EHR data spanning October 1, 2015-September 30, 2016, from the Veterans Integrated Service Network Region 4. PARTICIPANTS: The study included all patients with at least one inpatient or outpatient visit (n = 293,872). MAIN MEASUREMENTS: Adverse SDH, operationalized using Veterans Health Administration (VHA) coding for services and International Statistical Classification of Diseases and Related Health Problems (ICD)-10 codes, encompassed seven types (violence, housing instability, financial/employment problems, legal problems, familial/social problems, lack of access to care/transportation, and nonspecific psychosocial needs). We defined suicide morbidity by ICD-10 codes and data from the VHA's Suicide Prevention Applications Network. Logistic regression assessed associations of SDH with suicide morbidity, adjusting for socio-demographics and mental health diagnoses (e.g., major depression). Statistical significance was assessed with p < .01. KEY RESULTS: Overall, 16.4% of patients had at least one adverse SDH indicator. Adverse SDH exhibited dose-response-like associations with suicidal ideation and suicide attempt: each additional adverse SDH increased odds of suicidal ideation by 67% (AOR = 1.67, 99%CI = 1.60-1.75; p < .01) and suicide attempt by 49% (AOR = 1.49, 99%CI = 1.33-1.68; p < .01). Independently, each adverse SDH had strong effect sizes, ranging from 1.86 (99%CI = 1.58-2.19; p < .01) for legal issues to 3.10 (99%CI = 2.74-3.50; p < .01) for non-specific psychosocial needs in models assessing suicidal ideation and from 1.58 (99%CI = 1.10-2.27; p < .01) for employment/financial problems to 2.90 (99%CI = 2.30-4.16; p < .01) for violence in models assessing suicide attempt. CONCLUSIONS: SDH were strongly associated with suicidal ideation and suicide attempt even after adjusting for mental health diagnoses. Integration of SDH data in EHR could improve suicide prevention.

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.

Two Studies of Connectedness to Parents and Suicidal Thoughts and Behavior in Children and Adolescents.

We tested hypotheses that greater connectedness to parent(s) is associated with lower risk for nonlethal suicidal thoughts and behavior (STB), termed direct protective effects, and that parent connectedness serves to moderate (lower) the risk for STB associated with psychopathology including major depressive episode (MDE), termed moderating protective effects. Independent samples of children and adolescents recruited for a multicenter study of familial alcoholism were studied. Generalized estimating equation models were used that adjusted for age, sex, and youth psychopathology variables. The sample for Study 1 was assessed at baseline and about 2- and 4-year follow-ups, with baseline characteristics of n = 921, M age = 14.3 ± 1.8 years, and 51.8% female. The sample for Study 2 was assessed at baseline and about 5-year follow-up, with baseline characteristics of n = 867, M age = 12.0 ± 3.2 years, and 51.0% female. In both studies, increased perceived connectedness to father but not mother was associated with lower risk for measures of STB, consistent with direct protective effects. In Study 1, measures of parent connectedness were associated with lower risk for STB but only for youth that did not experience MDE (or alcohol use disorder), inconsistent with moderating protective effects. Study 2 showed that connectedness to fathers was associated with lower risk for suicide plans or attempts (severe STB) but not frequent thoughts of death or dying (nonsevere STB). Improved connectedness to fathers may lower risk for STB in children and adolescents, consistent with direct protective effects. Hypotheses about moderating protective effects were not supported.

Predictive Modeling and Concentration of the Risk of Suicide: Implications for Preventive Interventions in the US Department of Veterans Affairs.

OBJECTIVES: The Veterans Health Administration (VHA) evaluated the use of predictive modeling to identify patients at risk for suicide and to supplement ongoing care with risk-stratified interventions. METHODS: Suicide data came from the National Death Index. Predictors were measures from VHA clinical records incorporating patient-months from October 1, 2008, to September 30, 2011, for all suicide decedents and 1% of living patients, divided randomly into development and validation samples. We used data on all patients alive on September 30, 2010, to evaluate predictions of suicide risk over 1 year. RESULTS: Modeling demonstrated that suicide rates were 82 and 60 times greater than the rate in the overall sample in the highest 0.01% stratum for calculated risk for the development and validation samples, respectively; 39 and 30 times greater in the highest 0.10%; 14 and 12 times greater in the highest 1.00%; and 6.3 and 5.7 times greater in the highest 5.00%. CONCLUSIONS: Predictive modeling can identify high-risk patients who were not identified on clinical grounds. VHA is developing modeling to enhance clinical care and to guide the delivery of preventive interventions.

Suicidal ideation and mental distress among adults with military service history: results from 5 U.S. states, 2010.

OBJECTIVES: We examined the association of military service history with past-year suicidal ideation and past-30-days mental distress in a probability-based sample of adults. METHODS: We gathered 2010 Behavioral Risk Factor Surveillance System data from 5 states that asked about past-year suicidal ideation. Military service was defined as current or former active-duty service or National Guard or Reserves service. We stratified analyses into 18 to 39 years, 40 to 64 years, and 65 years and older age groups and used multiple logistic regression analyses, adjusted for demographic confounders, to discern the association of military service history with past-year suicidal ideation and past-30-days mental distress. RESULTS: Among the 26,736 respondents, 13.1% indicated military service history. After adjusting for several confounders, we found military history status among those aged 40 to 64 years was associated with both past-year suicidal ideation and past-30-days mental distress. We found no significant associations among the younger or older age groups. CONCLUSIONS: Differences in suicidal ideation between military and nonmilitary individuals may occur in midlife. Future research should examine the possibility of cohort effects, service era effects, or both.

A reconsideration of the correlation between veteran status and firearm suicide in the general population.

BACKGROUND: The relationship between veteran status and firearm suicide has been evaluated previously, but multiple sources of bias limit conclusions. This study aimed to clarify the relationship between a history of military service and firearm suicide. METHODS: Data obtained from suicide death certificates from nine states (1999-2009) were analysed. History of military service was validated using data obtained from the Department of Veterans Affairs and Department of Defense. Modified Poisson Regression with robust SEs was used to compute adjusted prevalence ratios for the common outcome of firearm use among suicide decedents. RESULTS: Male veteran suicide decedents were 6% more likely to use firearms, and female veteran suicide decedents were 18% more likely to use firearms compared with their non-veteran peers. CONCLUSIONS: Prior estimates based on logistic regression and death certificate reporting of veteran status may have overestimated the relationship between veteran status and firearm use among suicide decedents.

Using media to promote suicide prevention hotlines to Veteran households.

This article presents preliminary evidence that media campaigns are valuable in promoting suicide prevention hotlines to Veteran households by reporting data from 2526 telephone surveys. Findings from this study underscore the need for further investigation of the use of media campaigns to support suicide prevention initiatives aimed at Veteran populations.

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.

Housing instability and mental distress among US veterans.

Evidence has suggested increased risk for homelessness and suicide among US veterans, but little is known about the associations between housing instability and psychological distress (including suicidal ideation). We examined frequent mental distress (FMD) and suicidal ideation among a probability-based sample of 1767 Nebraska veterans who participated in the 2010 Behavioral Risk Factor Surveillance Survey who had and had not experienced housing instability in the past 12 months. Veterans experiencing housing instability had increased odds of FMD and suicidal ideation.

Prevalence of gender identity disorder and suicide risk among transgender veterans utilizing veterans health administration care.

OBJECTIVES: We estimated the prevalence and incidence of gender identity disorder (GID) diagnoses among veterans in the Veterans Health Administration (VHA) health care system and examined suicide risk among veterans with a GID diagnosis. METHODS: We examined VHA electronic medical records from 2000 through 2011 for 2 official ICD-9 diagnosis codes that indicate transgender status. We generated annual period prevalence estimates and calculated incidence using the prevalence of GID at 2000 as the baseline year. We cross-referenced GID cases with available data (2009-2011) of suicide-related events among all VHA users to examine suicide risk. RESULTS: GID prevalence in the VHA is higher (22.9/100 000 persons) than are previous estimates of GID in the general US population (4.3/100 000 persons). The rate of suicide-related events among GID-diagnosed VHA veterans was more than 20 times higher than were rates for the general VHA population. CONCLUSIONS: The prevalence of GID diagnosis nearly doubled over 10 years among VHA veterans. Research is needed to examine suicide risk among transgender veterans and how their VHA utilization may be enhanced by new VA initiatives on transgender care.

Psychosocial characteristics associated with frequent physical fighting: findings from the 2009 National Youth Risk Behavior Survey.

The goal of the current study was to determine the prevalence and psychosocial correlates associated with frequent fighting among US high school students. Cross-sectional analyses were conducted using the 2009 Youth Risk Behavior Survey (N=16 410). Multivariate logistic regression analyses determined associations between demographic and psychosocial correlates of frequent fighting. Among students, 13.6% reported fighting once, 15.3% reported fighting 2-11 times and 2.6% reported fighting 12 or more times in the past year. Risk factors associated with frequent fighting were weapon carrying (adjusted OR=10.55; 95% CI 7.40 to 15.05), suicide attempt (adjusted OR=6.16; 95% CI 3.70 to 10.28), binge drinking (adjusted OR=3.15; 95% CI 2.16 to 4.59) and feeling too unsafe to go to school (adjusted OR=3.09; 95% CI 2.00 to 4.77). There is a clear need to better understand the patterns and psychosocial characteristics of frequent physical fighting and the prevention and interventions strategies that may be most relevant for these vulnerable youth.