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Results 1-7 (7)
 

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author:(Robert bossart)
1.  Predicting suicides after outpatient mental health visits in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) 
Molecular psychiatry  2016;10.1038/mp.2016.110.
The 2013 U.S. Veterans Administration/Department of Defense Clinical Practice Guidelines (VA/DoD CPG) require comprehensive suicide risk assessments for VA/DoD patients with mental disorders but provide minimal guidance on how to carry out these assessments. Given that clinician-based assessments are known not to be strong predictors of suicide, we investigated whether a precision medicine model using administrative data after outpatient mental health specialty visits could be developed to predict suicides among outpatients. We focused on male non-deployed Regular U.S. Army soldiers because they account for the vast majority of such suicides. Four machine learning classifiers (naïve Bayes, random forests, support vector regression, elastic net penalized regression) were explored. 41.5% of Army suicides in 2004-2009 occurred among the 12.0% of soldiers seen as outpatient by mental health specialists, with risk especially high within 26 weeks of visits. An elastic net classifier with 10-14 predictors optimized sensitivity (45.6% of suicide deaths occurring after the 15% of visits with highest predicted risk). Good model stability was found for a model using 2004-2007 data to predict 2008-2009 suicides, although stability decreased in a model using 2008-2009 data to predict 2010-2012 suicides. The 5% of visits with highest risk included only 0.1% of soldiers (1047.1 suicides/100,000 person-years in the 5 weeks after the visit). This is a high enough concentration of risk to have implications for targeting preventive interventions. An even better model might be developed in the future by including the enriched information on clinician-evaluated suicide risk mandated by the VA/DoD CPG to be recorded.
doi:10.1038/mp.2016.110
PMCID: PMC5247428  PMID: 27431294
Army; machine learning; military; predictive modeling; risk assessment; suicide
2.  Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports 
Molecular psychiatry  2016;21(10):1366-1371.
Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. While efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity, and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1,056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared to observed scores assessed 10–12 years after baseline. ML model prediction accuracy was also compared to that of conventional logistic regression models. Area under the receiver operating characteristic curve (AUC) based on ML (.63 for high chronicity and .71–.76 for the other prospective outcomes) was consistently higher than for the logistic models (.62–.70) despite the latter models including more predictors. 34.6–38.1% of respondents with subsequent high persistence-chronicity and 40.8–55.8% with the severity indicators were in the top 20% of the baseline ML predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML predicted risk distribution. These results confirm that clinically useful MDD risk stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models.
doi:10.1038/mp.2015.198
PMCID: PMC4935654  PMID: 26728563
3.  Lifetime Prevalence of Respiratory Diseases and Exposures Among Veterans of Operation Enduring Freedom and Operation Iraqi Freedom Veterans 
Objective
The objective of this study was to determine the prevalence of respiratory exposures and the association between respiratory exposures and respiratory disease among veterans deployed to Operation Enduring Freedom and Operation Iraqi Freedom (OEF/OIF) compared with nondeployed veterans of this era.
Methods
Data come from a national health survey of 20,563 deployed and nondeployed OEF/OIF era veterans. Prevalence estimates and adjusted odds ratios were calculated. Results were weighted to represent the population.
Results
Prevalence of at least one respiratory exposure was high among both deployed and nondeployed groups (95% and 70%, respectively). In both groups, those with any respiratory exposure were at an increased risk for reporting a respiratory disease.
Conclusion
Respiratory exposures are highly prevalent and are associated with increased odds of respiratory diseases among the OEF/OIF era population.
doi:10.1097/JOM.0000000000000885
PMCID: PMC5482227  PMID: 27930474
4.  VA Suicide Prevention Applications Network 
Public Health Reports  2016;131(6):816-821.
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.
doi:10.1177/0033354916670133
PMCID: PMC5230828  PMID: 28123228
veterans; suicide; prevention
5.  Demographic, Military, and Health Characteristics of VA Health Care Users and Nonusers Who Served in or During Operation Enduring Freedom or Operation Iraqi Freedom, 2009-2011 
Public Health Reports  2016;131(6):839-843.
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.
doi:10.1177/0033354916676279
PMCID: PMC5230837  PMID: 28123232
OEF/OIF; veterans; Department of Veterans Affairs
6.  Cigarette Smoking and Sociodemographic, Military, and Health Characteristics of Operation Enduring Freedom and Operation Iraqi Freedom Veterans 
Public Health Reports  2016;131(5):714-727.
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.
doi:10.1177/0033354916664864
PMCID: PMC5230820  PMID: 28123213
veterans; cigarettes; smoking; OEF/OIF; Operation Iraqi Freedom; Operation Enduring Freedom; health; Afghanistan; Iraq

Results 1-7 (7)