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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.

Purpose: The aim of this investigation was to document the prevalence and correlates of refusing to answer a US federal health survey item about firearms in the household.Design: The cross-sectional analysis was conducted with 2004 and 2017 Behavioral Risk Factor Surveillance System (BRFSS) survey data from Texas, Oregon, Idaho, California, Kansas, and Utah states whose surveys included items about firearms in the household.Participants: Probability-based samples of adults over the age of 18 (n = 34 488 in 2017 BRFSS; n = 33 136 in 2004 BRFSS).Measures: Dichotomized measure of whether respondents answered versus refused to answer “Are any firearms now kept in or around your home?”Analysis: Weighted multiple logistic regression was used to assess how sociodemographic and health-related characteristics were associated with item refusal.Results: Approximately 1.8% (95% CI: 1.6-2.1) of respondents in 2004 and 3.9% (95% CI: 3.4-4.5) of respondents in 2017 sample refused the firearms item (P< .01). Men were more likely than women (2004: adjusted odds ratio [aOR] = 1.81, 95% CI: 1.24-2.62; 2017: aOR = 1.60, 95% CI = 1.17-2.18) and Latino/a respondents were less likely than white respondents (2004: aOR = 0.24, 95% CI: 0.10-0.60; 2017: aOR = 0.21, 95% CI: 0.13-0.34) to refuse the firearms question. In 2004, refusal was more likely among older than younger respondents, but in 2017, age was not associated with refusal.Conclusions: Refusal to firearm-related survey items along sociodemographic characteristics warrants further research. Community-informed strategies (eg, focus groups, cognitive testing, in-depth interviews) could improve the context and wording of firearm-related items to maximize response to these items in public health surveys.

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.

Early diagnosis and treatment of chronic obstructive pulmonary disease (COPD) can slow disease progression. The Department of Veterans Affairs (VA)/Department of Defense Clinical Practice Guidelines (CPG), established to improve patient outcomes, recommend the use of spirometry in the COPD diagnostic process. The aims of this study were to assess VA health care providers' performance related to CPG-recommended spirometry administration in the evaluation of newly diagnosed COPD among veterans, determine the patient characteristics that may influence the adherence rate, and compare VA concordance rates to those of other health plans.

Abstract.Background:Few studies have considered different messaging strategies that may augment campaign efficacy to generate help-seeking behaviors among populations at increased risk for suicide, mainly US military veterans. Aims:Findings are presented from the pilot evaluation of the It's Your Call campaign implemented by the Department of Veterans Affairs (VA). Three messaging strategies (with varying intensity and mix of messages) were compared to explore which best promote use of the Veterans Crisis Line (VCL) among veteran populations. Method:Daily VCL call data were obtained for 10 US cities during 2011–2012 where the campaign was active, and modeled using Poisson regression to identify changes in utilization patterns associated with the implementation of different messaging strategies. Results:Significant increases in call rates were only evident during the campaign in communities where mixed messages were disseminated. Further, use of mixed messages yielded greater increases in call rates when compared with the other tested strategies. This was an observational study where identification of causal relationships between variables was limited. Conclusion:Findings are encouraging as messaging was associated with help seeking, and they provide insights into strategies that may rapidly promote crisis line use. Results also underscore the need for further research on suicide prevention campaigns and dissemination practices.

An estimated 60% of all Operation Enduring Freedom / Operation Iraqi Freedom (OEF/OIF) veterans who have left the military had used the US Department of Veterans Affairs (VA) for health care services as of March 31, 2015. What is not known, however, are the differences in demographic, military, and health characteristics between OEF/OIF veterans who use the VA for health care and OEF/OIF veterans who do not. We used data from the 2009-2011 National Health Study for a New Generation of US Veterans to explore these differences. We found that VA health care users were more likely than non-VA health care users to be non-Hispanic black, to be unmarried, to have served on active duty and in the army, to have been deployed to OEF/OIF, and to have an annual income less than $35 000. The prevalence of 21 chronic medical conditions was higher among VA health care users than among non-VA health care users. OEF/OIF veterans using the VA for health care differ from nonusers with respect to demographic, military, and health characteristics. These data may be useful for developing programs and policies to address observed health disparities and achieve maximum benefit for the VA beneficiary population.

Objectives: The US Department of Veterans Affairs’ Suicide Prevention Applications Network (SPAN) is a national system for suicide event tracking and case management. The objective of this study was to assess data on suicide attempts among people using Veterans Health Administration (VHA) services.Methods: We assessed the degree of data overlap on suicide attempters reported in SPAN and the VHA’s medical records from October 1, 2010, to September 30, 2014—overall, by year, and by region. Data on suicide attempters in the VHA’s medical records consisted of diagnoses documented with E95 codes from the International Classification of Diseases, Ninth Revision.Results: Of 50 518 VHA patients who attempted suicide during the 4-year study period, data on fewer than half (41%) were reported in both SPAN and the medical records; nearly 65% of patients whose suicide attempt was recorded in SPAN had no data on attempted suicide in the VHA’s medical records.Conclusion: Evaluation of administrative data suggests that use of SPAN substantially increases the collection of data on suicide attempters as compared with the use of medical records alone, but neither SPAN nor the VHA’s medical records identify all suicide attempters. Further research is needed to better understand the strengths and limitations of both systems and how to best combine information across systems.

Objective: We examined the sociodemographic, military, and health characteristics of current cigarette smokers, former smokers, and nonsmokers among Operation Enduring Freedom (OEF) / Operation Iraqi Freedom (OIF) veterans and estimated smoking prevalence to better understand cigarette use in this population.Methods: We analyzed data from the US Department of Veterans Affairs (VA) 2009-2011 National Health Study for a New Generation of US Veterans. On the basis of a stratified random sample of 60 000 OEF/OIF veterans, we sought responses to a 72-item questionnaire via mail, telephone, or Internet. Cigarette smoking status was based on self-reported cigarette use in the past year. We used multinomial logistic regression to evaluate associations between smoking status and sociodemographic, military, and health characteristics.Results: Among 19 911 veterans who provided information on cigarette smoking, 5581 were current smokers (weighted percentage: 32.5%, 95% confidence interval [CI]: 31.7-33.2). Current smokers were more likely than nonsmokers or former smokers to be younger, to have less education or income, to be separated/divorced or never married/single, and to have served on active duty or in the army. Comparing current smokers and nonsmokers, some significant associations from adjusted analyses included the following: having a Mental Component Summary score (a measure of overall mental health) above the mean of the US population relative to below the mean (adjusted odds ratio [aOR] = 0.81, 95% CI: 0.73-0.90); having physician-diagnosed depression (aOR = 1.52, 95% CI: 1.33-1.74), respiratory conditions (aOR = 1.16, 95% CI: 1.04-1.30), or repeated seizures/blackouts/convulsions (aOR = 1.80, 95% CI: 1.22-2.67); heavy alcohol use vs never use (aOR = 5.49, 95% CI: 4.57-6.59); a poor vs excellent perception of overall health (aOR = 3.79, 95% CI: 2.60-5.52); and being deployed vs nondeployed (aOR = 0.87, 95% CI: 0.78-0.96). Using health care services from the VA protected against current smoking.Conclusion: Mental and physical health, substance use, and military service characteristics shape cigarette-smoking patterns in OEF/OIF veterans.

This study presents preliminary evidence that exposure to different health campaigns enhance intentions to seek help from telephone hotlines among Veteran households. Data were collected from telephone surveys (N = 8,756) conducted with both Veteran (n = 3,904), and for comparison, non-Veteran households (n = 4,852). Cox proportional hazard models were used to identify associations between message exposure variables ("type" or "number") with a high intent to use different hotlines (e.g., suicide prevention, domestic violence). As the number of types of messages an individual was exposed to increased, reported high intent for hotline use also increased. This remained significant across hotline type and for both Veteran and non-Veterans households. Results underscore the need for further research on dissemination strategies of public messaging and their impact on health behavior among Veteran populations.

Objective. The Transit Authority Suicide Prevention (TASP) campaign was launched by the Department of Veterans Affairs (VA) in a limited number of U.S. cities to promote the use of crisis lines among veterans of military service.Methods. We obtained the daily number of calls to the VCL and National Suicide Prevention Lifeline (NSPL) for six implementation cities (where the campaign was active) and four control cities (where there was no TASP campaign messaging) for a 14-month period. To identify changes in call volume associated with campaign implementation, VCL and NSPL daily call counts for three time periods of equal length (pre-campaign, during campaign, and post-campaign) were modeled using a Poisson log-linear regression with inference based on the generalized estimating equations.Results. Statistically significant increases in calls to both the VCL and the NSPL were reported during the TASP campaign in implementation cities, but were not reported in control cities during or following the campaign. Secondary outcome measures were also reported for the VCL and included the percentage of callers who are veterans, and calls resulting in a rescue during the study period.Conclusions. Results from this study reveal some promise for suicide prevention messaging to promote the use of telephone crisis services and contribute to an emerging area of research examining the effects of campaigns on help seeking.

This study sought to compare patterns of alcohol use between male veterans and nonveterans.

Objective. This study examined whether youth who live in an urban, disadvantaged community are significantly more likely than youth representing the nation to engage in a range of health-compromising behaviors.Methods. Analyses were based on the Youth Violence Survey conducted in 2004 and administered to students (n=4,131) in a high-risk school district. Students in ninth grade (n=1,114) were compared with ninth-grade students in the 2003 national Youth Risk Behavior Survey (n=3,674) and the National Longitudinal Study of Adolescent Health conducted in 1995/1996 (n=3,523). Analyses assessed the differences in prevalence of risk and protective factors among ninth-grade students from the three studies using Chi-square tests.Results. The results showed that youth in this urban, disadvantaged community were significantly more likely than their peers across the country to report vandalism, theft, violence, and selling drugs. Youth in this community also reported significantly less support from their homes and schools, and less monitoring by their parents. Moreover, youth in this community were significantly less likely to binge drink or initiate alcohol use prior to age 13 than youth across the U.S.Conclusions. Youth who live in this urban, disadvantaged community reported significantly higher prevalence of some, but not all, risky behaviors than nationally representative U.S. youth. These findings highlight that some caution is justified when defining what might constitute high risk and that demographic and other characteristics need to be carefully considered when targeting certain high-risk behaviors.