Pre-deployment predictors of suicide attempt during and after combat deployment: Results from the Army Study to Assess Risk and Resilience in Servicemembers
Deployment-related experiences might be risk factors for soldier suicides, in which case identification of vulnerable soldiers before deployment could inform preventive efforts. We investigated this possibility by using pre-deployment survey and administrative data in a sample of US Army soldiers to...
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creator | Zuromski, Kelly L. Bernecker, Samantha L. Chu, Carol Wilks, Chelsey R. Gutierrez, Peter M. Joiner, Thomas E. Liu, Howard Naifeh, James A. Nock, Matthew K. Sampson, Nancy A. Zaslavsky, Alan M. Stein, Murray B. Ursano, Robert J. Kessler, Ronald C. Steven Heeringa Wagner, James Kessler, Ronald C. Cox, Kenneth Aliaga, Pablo A. Benedek, David M. Campbell-Sills, Laura Fullerton, Carol S. Gebler, Nancy Gifford, Robert K. House, Meredith Hurwitz, Paul E. Jain, Sonia Kao, Tzu-Cheg Lewandowski-Romps, Lisa Mash, Holly Herberman McCarroll, James E. Naifeh, James A. Hinz Ng, Tsz Hin Nock, Matthew K. Sampson, Nancy A. Santiago, Patcho Wynn, Gary H. Zaslavsky, Alan M. |
description | Deployment-related experiences might be risk factors for soldier suicides, in which case identification of vulnerable soldiers before deployment could inform preventive efforts. We investigated this possibility by using pre-deployment survey and administrative data in a sample of US Army soldiers to develop a risk model for suicide attempt (SA) during and shortly after deployment.
Data came from the Army Study to Assess Risk and Resilience in Servicemembers Pre-Post Deployment Survey (PPDS). Soldiers completed a baseline survey shortly before deploying to Afghanistan in 2011–2012. Survey measures were used to predict SAs, defined using administrative and subsequent survey data, through 30 months after deployment. Models were built using penalized regression and ensemble machine learning methods.
Significant pre-deployment risk factors were history of traumatic brain injury, 9 + mental health treatment visits in the 12 months before deployment, young age, female, previously married, and low relationship quality. Cross-validated AUC of the best penalized and ensemble models were .75–.77. 21.3–40.4% of SAs occurred among the 5–10% of soldiers with highest predicted risk and positive predictive value (PPV) among these high-risk soldiers was 4.4–5.7%.
SA can be predicted significantly from pre-deployment data, but intervention planning needs to take PPV into consideration. |
doi_str_mv | 10.1016/j.jpsychires.2019.12.003 |
format | Article |
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Data came from the Army Study to Assess Risk and Resilience in Servicemembers Pre-Post Deployment Survey (PPDS). Soldiers completed a baseline survey shortly before deploying to Afghanistan in 2011–2012. Survey measures were used to predict SAs, defined using administrative and subsequent survey data, through 30 months after deployment. Models were built using penalized regression and ensemble machine learning methods.
Significant pre-deployment risk factors were history of traumatic brain injury, 9 + mental health treatment visits in the 12 months before deployment, young age, female, previously married, and low relationship quality. Cross-validated AUC of the best penalized and ensemble models were .75–.77. 21.3–40.4% of SAs occurred among the 5–10% of soldiers with highest predicted risk and positive predictive value (PPV) among these high-risk soldiers was 4.4–5.7%.
SA can be predicted significantly from pre-deployment data, but intervention planning needs to take PPV into consideration.</description><identifier>ISSN: 0022-3956</identifier><identifier>EISSN: 1879-1379</identifier><identifier>DOI: 10.1016/j.jpsychires.2019.12.003</identifier><identifier>PMID: 31865211</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Adolescent ; Adult ; Age Factors ; Brain Injuries, Traumatic - epidemiology ; Deployment ; Female ; Health Surveys ; Humans ; Male ; Marital Status - statistics & numerical data ; Mental Disorders - epidemiology ; Military personnel ; Military Personnel - statistics & numerical data ; Models, Statistical ; Predictive modeling ; Prognosis ; Resilience, Psychological ; Risk Assessment ; Risk Factors ; Sex Factors ; Suicide ; Suicide, Attempted - statistics & numerical data ; United States - epidemiology ; Young Adult</subject><ispartof>Journal of psychiatric research, 2020-02, Vol.121, p.214-221</ispartof><rights>2019 Elsevier Ltd</rights><rights>Copyright © 2019 Elsevier Ltd. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c479t-cc0c9a4bf58176215cbf18c26d1dfcd3de8f493be8388b558c6cd33f17021a723</citedby><cites>FETCH-LOGICAL-c479t-cc0c9a4bf58176215cbf18c26d1dfcd3de8f493be8388b558c6cd33f17021a723</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0022395619309987$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>230,314,776,780,881,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31865211$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zuromski, Kelly L.</creatorcontrib><creatorcontrib>Bernecker, Samantha L.</creatorcontrib><creatorcontrib>Chu, Carol</creatorcontrib><creatorcontrib>Wilks, Chelsey R.</creatorcontrib><creatorcontrib>Gutierrez, Peter M.</creatorcontrib><creatorcontrib>Joiner, Thomas E.</creatorcontrib><creatorcontrib>Liu, Howard</creatorcontrib><creatorcontrib>Naifeh, James A.</creatorcontrib><creatorcontrib>Nock, Matthew K.</creatorcontrib><creatorcontrib>Sampson, Nancy A.</creatorcontrib><creatorcontrib>Zaslavsky, Alan M.</creatorcontrib><creatorcontrib>Stein, Murray B.</creatorcontrib><creatorcontrib>Ursano, Robert J.</creatorcontrib><creatorcontrib>Kessler, Ronald C.</creatorcontrib><creatorcontrib>Steven Heeringa</creatorcontrib><creatorcontrib>Wagner, James</creatorcontrib><creatorcontrib>Kessler, Ronald C.</creatorcontrib><creatorcontrib>Cox, Kenneth</creatorcontrib><creatorcontrib>Aliaga, Pablo A.</creatorcontrib><creatorcontrib>Benedek, David M.</creatorcontrib><creatorcontrib>Campbell-Sills, Laura</creatorcontrib><creatorcontrib>Fullerton, Carol S.</creatorcontrib><creatorcontrib>Gebler, Nancy</creatorcontrib><creatorcontrib>Gifford, Robert K.</creatorcontrib><creatorcontrib>House, Meredith</creatorcontrib><creatorcontrib>Hurwitz, Paul E.</creatorcontrib><creatorcontrib>Jain, Sonia</creatorcontrib><creatorcontrib>Kao, Tzu-Cheg</creatorcontrib><creatorcontrib>Lewandowski-Romps, Lisa</creatorcontrib><creatorcontrib>Mash, Holly Herberman</creatorcontrib><creatorcontrib>McCarroll, James E.</creatorcontrib><creatorcontrib>Naifeh, James A.</creatorcontrib><creatorcontrib>Hinz Ng, Tsz Hin</creatorcontrib><creatorcontrib>Nock, Matthew K.</creatorcontrib><creatorcontrib>Sampson, Nancy A.</creatorcontrib><creatorcontrib>Santiago, Patcho</creatorcontrib><creatorcontrib>Wynn, Gary H.</creatorcontrib><creatorcontrib>Zaslavsky, Alan M.</creatorcontrib><creatorcontrib>Army liaison/consultant</creatorcontrib><creatorcontrib>Other team members</creatorcontrib><creatorcontrib>Site Principal Investigators</creatorcontrib><title>Pre-deployment predictors of suicide attempt during and after combat deployment: Results from the Army Study to Assess Risk and Resilience in Servicemembers</title><title>Journal of psychiatric research</title><addtitle>J Psychiatr Res</addtitle><description>Deployment-related experiences might be risk factors for soldier suicides, in which case identification of vulnerable soldiers before deployment could inform preventive efforts. We investigated this possibility by using pre-deployment survey and administrative data in a sample of US Army soldiers to develop a risk model for suicide attempt (SA) during and shortly after deployment.
Data came from the Army Study to Assess Risk and Resilience in Servicemembers Pre-Post Deployment Survey (PPDS). Soldiers completed a baseline survey shortly before deploying to Afghanistan in 2011–2012. Survey measures were used to predict SAs, defined using administrative and subsequent survey data, through 30 months after deployment. Models were built using penalized regression and ensemble machine learning methods.
Significant pre-deployment risk factors were history of traumatic brain injury, 9 + mental health treatment visits in the 12 months before deployment, young age, female, previously married, and low relationship quality. Cross-validated AUC of the best penalized and ensemble models were .75–.77. 21.3–40.4% of SAs occurred among the 5–10% of soldiers with highest predicted risk and positive predictive value (PPV) among these high-risk soldiers was 4.4–5.7%.
SA can be predicted significantly from pre-deployment data, but intervention planning needs to take PPV into consideration.</description><subject>Adolescent</subject><subject>Adult</subject><subject>Age Factors</subject><subject>Brain Injuries, Traumatic - epidemiology</subject><subject>Deployment</subject><subject>Female</subject><subject>Health Surveys</subject><subject>Humans</subject><subject>Male</subject><subject>Marital Status - statistics & numerical data</subject><subject>Mental Disorders - epidemiology</subject><subject>Military personnel</subject><subject>Military Personnel - statistics & numerical data</subject><subject>Models, Statistical</subject><subject>Predictive modeling</subject><subject>Prognosis</subject><subject>Resilience, Psychological</subject><subject>Risk Assessment</subject><subject>Risk Factors</subject><subject>Sex Factors</subject><subject>Suicide</subject><subject>Suicide, Attempted - statistics & numerical data</subject><subject>United States - epidemiology</subject><subject>Young Adult</subject><issn>0022-3956</issn><issn>1879-1379</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkdtu1DAQhiMEokvhFZAvuUnwIQeHC6SlKgepEqiFa8uxJ10vSRw8zkp5Fx4Wt1sWuOLK0vifb-z5sowwWjDK6tf7Yj_janYuABacsrZgvKBUPMo2TDZtzkTTPs42lHKei7aqz7JniHtKacNZ-TQ7E0zWFWdsk_38EiC3MA9-HWGKZA5gnYk-IPE9wcUZZ4HoGGGcI7FLcNMt0ZMluo8QiPFjp1P9BHhDrgGXISLpgx9J3AHZhnElN3GxK4mebBEBkVw7_H7PSXE3OJgMEDeRGwgHZ2CEsYOAz7MnvR4QXjyc59m395dfLz7mV58_fLrYXuWmbNqYG0NNq8uuryRras4q0_VMGl5bZntjhQXZl63oQAopu6qSpk5V0bOGcqYbLs6zt0fuvHQjWJP-EfSg5uBGHVbltVP_3kxup279QdVtJRrWJMCrB0DwPxbAqEaHBoZBT-AXVFwISuuykjRF5TFqgkcM0J_GMKru5Kq9-iNX3clVjKskN7W-_PuZp8bfNlPg3TEAaVkHB0GhuV-tTSwTlfXu_1N-AVqawMU</recordid><startdate>20200201</startdate><enddate>20200201</enddate><creator>Zuromski, Kelly L.</creator><creator>Bernecker, Samantha L.</creator><creator>Chu, Carol</creator><creator>Wilks, Chelsey R.</creator><creator>Gutierrez, Peter M.</creator><creator>Joiner, Thomas E.</creator><creator>Liu, Howard</creator><creator>Naifeh, James A.</creator><creator>Nock, Matthew K.</creator><creator>Sampson, Nancy A.</creator><creator>Zaslavsky, Alan M.</creator><creator>Stein, Murray B.</creator><creator>Ursano, Robert J.</creator><creator>Kessler, Ronald C.</creator><creator>Steven Heeringa</creator><creator>Wagner, James</creator><creator>Kessler, Ronald C.</creator><creator>Cox, Kenneth</creator><creator>Aliaga, Pablo A.</creator><creator>Benedek, David M.</creator><creator>Campbell-Sills, Laura</creator><creator>Fullerton, Carol S.</creator><creator>Gebler, Nancy</creator><creator>Gifford, Robert K.</creator><creator>House, Meredith</creator><creator>Hurwitz, Paul E.</creator><creator>Jain, Sonia</creator><creator>Kao, Tzu-Cheg</creator><creator>Lewandowski-Romps, Lisa</creator><creator>Mash, Holly Herberman</creator><creator>McCarroll, James E.</creator><creator>Naifeh, James A.</creator><creator>Hinz Ng, Tsz Hin</creator><creator>Nock, Matthew K.</creator><creator>Sampson, Nancy A.</creator><creator>Santiago, Patcho</creator><creator>Wynn, Gary H.</creator><creator>Zaslavsky, Alan M.</creator><general>Elsevier Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20200201</creationdate><title>Pre-deployment predictors of suicide attempt during and after combat deployment: Results from the Army Study to Assess Risk and Resilience in Servicemembers</title><author>Zuromski, Kelly L. ; Bernecker, Samantha L. ; Chu, Carol ; Wilks, Chelsey R. ; Gutierrez, Peter M. ; Joiner, Thomas E. ; Liu, Howard ; Naifeh, James A. ; Nock, Matthew K. ; Sampson, Nancy A. ; Zaslavsky, Alan M. ; Stein, Murray B. ; Ursano, Robert J. ; Kessler, Ronald C. ; Steven Heeringa ; Wagner, James ; Kessler, Ronald C. ; Cox, Kenneth ; Aliaga, Pablo A. ; Benedek, David M. ; Campbell-Sills, Laura ; Fullerton, Carol S. ; Gebler, Nancy ; Gifford, Robert K. ; House, Meredith ; Hurwitz, Paul E. ; Jain, Sonia ; Kao, Tzu-Cheg ; Lewandowski-Romps, Lisa ; Mash, Holly Herberman ; McCarroll, James E. ; Naifeh, James A. ; Hinz Ng, Tsz Hin ; Nock, Matthew K. ; Sampson, Nancy A. ; Santiago, Patcho ; Wynn, Gary H. ; Zaslavsky, Alan M.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c479t-cc0c9a4bf58176215cbf18c26d1dfcd3de8f493be8388b558c6cd33f17021a723</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adolescent</topic><topic>Adult</topic><topic>Age Factors</topic><topic>Brain Injuries, Traumatic - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of psychiatric research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zuromski, Kelly L.</au><au>Bernecker, Samantha L.</au><au>Chu, Carol</au><au>Wilks, Chelsey R.</au><au>Gutierrez, Peter M.</au><au>Joiner, Thomas E.</au><au>Liu, Howard</au><au>Naifeh, James A.</au><au>Nock, Matthew K.</au><au>Sampson, Nancy A.</au><au>Zaslavsky, Alan M.</au><au>Stein, Murray B.</au><au>Ursano, Robert J.</au><au>Kessler, Ronald C.</au><au>Steven Heeringa</au><au>Wagner, James</au><au>Kessler, Ronald C.</au><au>Cox, Kenneth</au><au>Aliaga, Pablo A.</au><au>Benedek, David M.</au><au>Campbell-Sills, Laura</au><au>Fullerton, Carol S.</au><au>Gebler, Nancy</au><au>Gifford, Robert K.</au><au>House, Meredith</au><au>Hurwitz, Paul E.</au><au>Jain, Sonia</au><au>Kao, Tzu-Cheg</au><au>Lewandowski-Romps, Lisa</au><au>Mash, Holly Herberman</au><au>McCarroll, James E.</au><au>Naifeh, James A.</au><au>Hinz Ng, Tsz Hin</au><au>Nock, Matthew K.</au><au>Sampson, Nancy A.</au><au>Santiago, Patcho</au><au>Wynn, Gary H.</au><au>Zaslavsky, Alan M.</au><aucorp>Army liaison/consultant</aucorp><aucorp>Other team members</aucorp><aucorp>Site Principal Investigators</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Pre-deployment predictors of suicide attempt during and after combat deployment: Results from the Army Study to Assess Risk and Resilience in Servicemembers</atitle><jtitle>Journal of psychiatric research</jtitle><addtitle>J Psychiatr Res</addtitle><date>2020-02-01</date><risdate>2020</risdate><volume>121</volume><spage>214</spage><epage>221</epage><pages>214-221</pages><issn>0022-3956</issn><eissn>1879-1379</eissn><abstract>Deployment-related experiences might be risk factors for soldier suicides, in which case identification of vulnerable soldiers before deployment could inform preventive efforts. We investigated this possibility by using pre-deployment survey and administrative data in a sample of US Army soldiers to develop a risk model for suicide attempt (SA) during and shortly after deployment.
Data came from the Army Study to Assess Risk and Resilience in Servicemembers Pre-Post Deployment Survey (PPDS). Soldiers completed a baseline survey shortly before deploying to Afghanistan in 2011–2012. Survey measures were used to predict SAs, defined using administrative and subsequent survey data, through 30 months after deployment. Models were built using penalized regression and ensemble machine learning methods.
Significant pre-deployment risk factors were history of traumatic brain injury, 9 + mental health treatment visits in the 12 months before deployment, young age, female, previously married, and low relationship quality. Cross-validated AUC of the best penalized and ensemble models were .75–.77. 21.3–40.4% of SAs occurred among the 5–10% of soldiers with highest predicted risk and positive predictive value (PPV) among these high-risk soldiers was 4.4–5.7%.
SA can be predicted significantly from pre-deployment data, but intervention planning needs to take PPV into consideration.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>31865211</pmid><doi>10.1016/j.jpsychires.2019.12.003</doi><tpages>8</tpages><oa>free_for_read</oa></addata></record> |
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ispartof | Journal of psychiatric research, 2020-02, Vol.121, p.214-221 |
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source | MEDLINE; Elsevier ScienceDirect Journals |
subjects | Adolescent Adult Age Factors Brain Injuries, Traumatic - epidemiology Deployment Female Health Surveys Humans Male Marital Status - statistics & numerical data Mental Disorders - epidemiology Military personnel Military Personnel - statistics & numerical data Models, Statistical Predictive modeling Prognosis Resilience, Psychological Risk Assessment Risk Factors Sex Factors Suicide Suicide, Attempted - statistics & numerical data United States - epidemiology Young Adult |
title | Pre-deployment predictors of suicide attempt during and after combat deployment: Results from the Army Study to Assess Risk and Resilience in Servicemembers |
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