Acceptability of potential interventions to increase firearm safety among patients in VA mental health treatment.

OBJECTIVE: Veterans in mental health care have high rates of firearm-related suicide; reducing firearm access during high-risk periods could save lives. We assessed veteran patients' attitudes towards voluntary interventions to reduce access. METHODS: Descriptive data came from surveys mailed to random samples of veterans receiving mental health care in five geographically diverse VA facilities. Survey items inquired about the acceptability of seven voluntary health system interventions to address firearm access, ranging from lower-intensity interventions that addressed safety but might not reduce access (i.e., clinician screening; distribution of gunlocks) to interventions substantially limiting access (i.e., storage of firearms offsite; gun disposal). Mailings occurred between 5/11/15 and 10/19/15; 677 of 1354 veterans (50%) returned the surveys. RESULTS: 93.2% of respondents endorsed one or more health system interventions addressing firearm access; 75.0% endorsed interventions substantially limiting access. Although veterans with household firearms were less likely to endorse interventions, fully 50.4% would personally participate in at least one intervention that substantially limited access. DISCUSSION: A majority of veterans in VA mental health care endorse voluntary health system interventions addressing firearm access during high-risk periods for suicide. Approximately half of veterans with firearms would personally participate in an intervention that substantially limited firearm access.

Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder.

BACKGROUNDS: Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful. METHOD: We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalised) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments. RESULTS: Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention v. control) or differential treatment outcomes (i.e., intervention A v. intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalised treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials. CONCLUSIONS: Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.

Predicting suicides after outpatient mental health visits in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS).

The 2013 US 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 not known 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 nondeployed Regular US Army soldiers because they account for the vast majority of such suicides. Four machine learning classifiers (naive Bayes, random forests, support vector regression and elastic net penalized regression) were explored. Of the Army suicides in 2004-2009, 41.5% occurred among 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.

Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports.

Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. Although 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 1056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared with observed scores assessed 10-12 years after baseline. ML model prediction accuracy was also compared with that of conventional logistic regression models. Area under the receiver operating characteristic curve based on ML (0.63 for high chronicity and 0.71-0.76 for the other prospective outcomes) was consistently higher than for the logistic models (0.62-0.70) despite the latter models including more predictors. A total of 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.

Characteristics of homicide followed by suicide incidents in multiple states, 2003-04.

OBJECTIVE: To calculate the prevalence of homicide followed by suicide (homicide/suicide) and provide contextual information on the incidents and demographic information about the individuals involved using data from a surveillance system that is uniquely equipped to study homicide/suicide. METHODS: Data are from the National Violent Death Reporting System (NVDRS). This active state-based surveillance system includes data from seven states for 2003 and 13 states for 2004. The incident-level structure facilitates identification of homicide/suicide incidents. RESULTS: Within participating states, 65 homicide/suicide incidents (homicide rate = 0.230/100,000) occurred in 2003 and 144 incidents (homicide rate = 0.238/100,000) occurred in 2004. Most victims (58%) were a current or former intimate partner of the perpetrator. Among all male perpetrators of intimate partner homicide 30.6% were also suicides. A substantial proportion of the victims (13.7%) were the children of the perpetrator. Overall, most victims (74.6%) were female and most perpetrators were male (91.9%). A recent history of legal problems (25.3%), or financial problems (9.3%) was common among the perpetrators. CONCLUSIONS: The results support earlier research documenting the importance of intimate partner violence (IPV) and situational stressors on homicide/suicide. These results suggest that efforts to provide assistance to families in crisis and enhance the safety of IPV victims are needed to reduce risk for homicide/suicide. The consistency of the results from the NVDRS with those from past studies and the comprehensive information available in the NVDRS highlight the promise of this system for studying homicide/suicide incidents and for evaluating the impact of prevention policies and programs.