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50 results on '"Belsher, Bradley E."'

14. Implementing collaborative primary care for depression and posttraumatic stress disorder: Design and sample for a randomized trial in the U.S. military health system

Author

Engel, Charles C., Bray, Robert M., Jaycox, Lisa H., Freed, Michael C., Zatzick, Doug, Lane, Marian E., Brambilla, Donald, Rae Olmsted, Kristine, Vandermaas-Peeler, Russ, Litz, Brett, Tanielian, Terri, Belsher, Bradley E., Evatt, Daniel P., Novak, Laura A., Unützer, Jürgen, and Katon, Wayne J.

Published
2014
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18. A systematic analysis and prioritization of suicide prevention research gaps

Author

Workman, Don E., primary, Bellanti, Dawn M., additional, Kelber, Marija S., additional, Beech, Erin H., additional, Smolenski, Derek, additional, Bush, Nigel E., additional, Edwards‐Stewart, Amanda, additional, Skopp, Nancy, additional, Otto, Jean L., additional, Garvey Wilson, Abigail L., additional, Morgan, Maria A., additional, Ojha, Suman, additional, and Belsher, Bradley E., additional

Published
2021
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23. Adjustment disorders: A research gaps analysis.

Author

Morgan, Maria A., primary, Kelber, Marija Spanovic, additional, Workman, Don E., additional, Beech, Erin H., additional, Garvey Wilson, Abigail L., additional, Edwards-Stewart, Amanda, additional, Belsher, Bradley E., additional, Evatt, Daniel P., additional, Otto, Jean, additional, Skopp, Nancy A., additional, Bush, Nigel E., additional, and Campbell, Marjorie, additional

Published
2021
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26. From everyday life predictions to suicide prevention: Clinical and ethical considerations in suicide predictive analytic tools.

Author

Luk, Jeremy W., Pruitt, Larry D., Smolenski, Derek J., Tucker, Jennifer, Workman, Don E., and Belsher, Bradley E.

Subjects
SUICIDE prevention, SUICIDE, HIV, TRAFFIC estimation, DATA analytics
Abstract

Advances in artificial intelligence and machine learning have fueled growing interest in the application of predictive analytics to identify high‐risk suicidal patients. Such application will require the aggregation of large‐scale, sensitive patient data to help inform complex and potentially stigmatizing health care decisions. This paper provides a description of how suicide prediction is uniquely difficult by comparing it to nonmedical (weather and traffic forecasting) and medical predictions (cancer and human immunodeficiency virus risk), followed by clinical and ethical challenges presented within a risk‐benefit conceptual framework. Because the misidentification of suicide risk may be associated with unintended negative consequences, clinicians and policymakers need to carefully weigh the risks and benefits of using suicide predictive analytics across health care populations. Practical recommendations are provided to strengthen the protection of patient rights and enhance the clinical utility of suicide predictive analytics tools. [ABSTRACT FROM AUTHOR]

Published
2022
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27. More than expected? Assessing departures from additivity in health care utilization after mild traumatic brain injury in military personnel with pre-existing mental health conditions

Author

Edwards-Stewart, Amanda, primary, Smolenski, Derek J., additional, Peters, Zachary J., additional, Quah, Ruth F., additional, Bush, Nigel E., additional, Campbell, Marjorie S., additional, Skopp, Nancy A., additional, Belsher, Bradley E., additional, and Kennedy, Carrie H., additional

Published
2020
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31. Prediction Models for Suicide Attempts and Deaths

Author

Belsher, Bradley E., primary, Smolenski, Derek J., additional, Pruitt, Larry D., additional, Bush, Nigel E., additional, Beech, Erin H., additional, Workman, Don E., additional, Morgan, Rebecca L., additional, Evatt, Daniel P., additional, Tucker, Jennifer, additional, and Skopp, Nancy A., additional

Published
2019
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36. Prediction Models for Suicide Attempts and Deaths: A Systematic Review and Simulation.

Author

Smolenski, Derek J., Pruitt, Larry D., Bush, Nigel E., Beech, Erin H., Tucker, Jennifer, Skopp, Nancy A., Belsher, Bradley E., Workman, Don E., Evatt, Daniel P., and Morgan, Rebecca L.

Subjects
SUICIDAL behavior, PREDICTION models, META-analysis, PREDICTIVE validity, SUICIDE statistics
Abstract

Importance: Suicide prediction models have the potential to improve the identification of patients at heightened suicide risk by using predictive algorithms on large-scale data sources. Suicide prediction models are being developed for use across enterprise-level health care systems including the US Department of Defense, US Department of Veterans Affairs, and Kaiser Permanente.Objectives: To evaluate the diagnostic accuracy of suicide prediction models in predicting suicide and suicide attempts and to simulate the effects of implementing suicide prediction models using population-level estimates of suicide rates.Evidence Review: A systematic literature search was conducted in MEDLINE, PsycINFO, Embase, and the Cochrane Library to identify research evaluating the predictive accuracy of suicide prediction models in identifying patients at high risk for a suicide attempt or death by suicide. Each database was searched from inception to August 21, 2018. The search strategy included search terms for suicidal behavior, risk prediction, and predictive modeling. Reference lists of included studies were also screened. Two reviewers independently screened and evaluated eligible studies.Findings: From a total of 7306 abstracts reviewed, 17 cohort studies met the inclusion criteria, representing 64 unique prediction models across 5 countries with more than 14 million participants. The research quality of the included studies was generally high. Global classification accuracy was good (≥0.80 in most models), while the predictive validity associated with a positive result for suicide mortality was extremely low (≤0.01 in most models). Simulations of the results suggest very low positive predictive values across a variety of population assessment characteristics.Conclusions and Relevance: To date, suicide prediction models produce accurate overall classification models, but their accuracy of predicting a future event is near 0. Several critical concerns remain unaddressed, precluding their readiness for clinical applications across health systems. [ABSTRACT FROM AUTHOR]

Published
2019
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38. Centrally Assisted Collaborative Telecare for Posttraumatic Stress Disorder and Depression Among Military Personnel Attending Primary Care

Author

Engel, Charles C., primary, Jaycox, Lisa H., additional, Freed, Michael C., additional, Bray, Robert M., additional, Brambilla, Donald, additional, Zatzick, Douglas, additional, Litz, Brett, additional, Tanielian, Terri, additional, Novak, Laura A., additional, Lane, Marian E., additional, Belsher, Bradley E., additional, Olmsted, Kristine L. Rae, additional, Evatt, Daniel P., additional, Vandermaas-Peeler, Russ, additional, Unützer, Jürgen, additional, and Katon, Wayne J., additional

Published
2016
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39. Development of Longitudinal Data Analysis in Mental Health Research For Military Service Members.

Author

Liu, Xian, Evatt, Daniel P, and Belsher, Bradley E

Abstract

Since the 1991 Gulf War, mental health conditions of military Service members have received increasing public attention and are a major focus for the U.S. government. A substantial proportion of military health research has been devoted to examining the pattern of change over time in mental health symptoms and diagnostic status among Service members. Unfortunately, many researchers continue to use somewhat obsolete methods to analyze trends and transitions in mental health, despite advances in statistical methodology that permit attention to the unique features inherent in longitudinal data.

Published
2019
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47. Evidence Brief: Safety and Effectiveness of Telehealth-delivered Mental Health Care

Author

Beech EH, Young S, Anderson JK, Belsher BE, and Parr NJ

Abstract

The Evidence Synthesis Program (ESP) Coordinating Center is responding to a request from the US Department of Veterans Affairs (VA) Office of Mental Health and Suicide Prevention (OMHSP) for an Evidence Brief on the safety and effectiveness of evidence-based mental health care delivered via telehealth modalities. Findings from this Evidence Brief will be used to inform delivery of mental health care across the VA system.

Published
2022

48. Adjustment disorders: A research gaps analysis.

Author

Morgan MA, Kelber MS, Workman DE, Beech EH, Garvey Wilson AL, Edwards-Stewart A, Belsher BE, Evatt DP, Otto J, Skopp NA, Bush NE, and Campbell M

Subjects
Humans, Adjustment Disorders diagnosis, Adjustment Disorders epidemiology, Adjustment Disorders therapy
Abstract

Adjustment disorders are among the most commonly diagnosed mental health disorders in both civilian and military clinical settings. Despite their high prevalence, adjustment disorders have received little research attention. The many gaps in our understanding of this group of disorders hinder the development of adequate, evidence-based treatment protocols. This study utilizes a systematic methodology to identify and prioritize research gaps in adjustment disorders. We used authoritative source reports to identify gaps in research domains from foundational science to services research. Subject-matter experts conducted literature searches to substantiate and refine research gaps, and stakeholders assessed the importance and impact of this work for researchers and policy-makers. We identified 254 possible research-needs statements, which were ultimately reduced to 11 final, prioritized research gaps. Two gaps addressed prevention and screening and three addressed treatment and services research. Six gaps addressed foundational science, epidemiology, and etiology research domains, highlighting the need for basic research. Until some of the basic science questions are resolved (e.g., diagnostic clarity, valid screening, and assessment measures) about adjustment disorders, we may not be able to develop adequate evidence-based interventions for the disorders, and it will be difficult to understand the trajectory of these disorders throughout treatment. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Published
2022
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49. Evidence Brief: Treatment of Comorbid Conditions

Author

Beech EH, Rahman B, Ward R, Anderson J, Belsher BE, and Parr NJ

Abstract

The Evidence Synthesis Program (ESP) Coordinating Center is responding to a request from the Office of Mental Health and Suicide Prevention (OMHSP) for an Evidence Brief on treatment of comorbid mental health conditions, or mental health conditions comorbid with a traumatic brain injury, substance use disorder, or chronic pain among Veterans and military Service members. Findings from this Evidence Brief will be used to inform the development of a clinical provider toolkit, as required by the Commander John Scott Hannon Veterans Mental Health Care Improvement Act (S.785 2019). The toolkit is intended to enhance clinical provider training in delivery of comprehensive care for Veterans and military Service members with comorbid conditions.

Published
2021

50. Posttraumatic Stress Disorder, Depression, and Other Comorbidities: Clinical and Systems Approaches to Diagnostic Uncertainties.

Author

Colston MJ, Belsher BE, Beech EH, Curry JC, Tyberg YA, Melmed AZ, McGraw KL, and Stoltz RF

Abstract

Overlap in the clinical presentation and significant rates of comorbidity complicate effective management of depression and PTSD, each presenting major health burdens for veterans and active-duty service members., Competing Interests: Author disclosures The authors report no actual or potential conflicts of interest with regard to this article.

Published
2016

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