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The Army Study to Assess Risk and
Resilience in Servicemembers
(Army STARRS)
Introduction
• US military suicide rate historically lower than
civilian rate
• Climbs steadily since beginning of the Ira...
• 2 main goals
1. Evaluate hypotheses about modifiable risk and
resilience factors for suicidality
– could be used to targ...
Historical Administrative Data Study (HADS)
• Examines patterns and
correlates of suicide in
an integrated data
system com...
HADS
• All 975057 Regular Army soldiers
– On active duty between 01 Jan 04 & 31 Dec 09
• 569 deaths classified as suicides...
Variables Evaluated
Sociodemographic Army Variables
•Sex
•Age
•Marital Status
•Race
•Religion
•Education
•AFQT Score Categ...
Results
• Women have consistently lower suicide risk than men
– sex difference narrows during deployment because of a
disp...
Results
• The youngest soldiers have markedly elevated suicide risk
during and after deployment
– not among those never de...
Results
• Accession waivers significantly associated with suicide risk
in the Army overall
– mainly artifact of missing wa...
Step 2
Predicting Suicides After Psychiatric
Hospitalization in US Army Soldiers
Introduction
• US military administrative data document
– 8-fold elevated suicide risk in the 3 months after psychiatric
h...
Method
• 53 769 regular US Army hospitalizations
from January 1, 2004, through December 31, 2009, with any
ICD-9-CM psychi...
Variables Evaluated
Sociodemographic Army Variables
Sex
Age
Marital Status
Race
Religion
Education
AFTQ Category
Accession...
Method
• 131 of the 421 bivariate associations (31.1%)
between individual predictors and suicides were
significant at the ...
Components of Predictive Model
•Male sex (yes/no) 7.9
•Age of enlistment ≥27 y (yes/no) 1.9
•AFQT score >50th percentile (...
Hospitalized in a civilian
psychiatric hospital or civilian
facility with a psychiatric unit
(yes/no) 1.6
Disorders diagno...
52.9% of suicides occurred
after the 5% of
hospitalizations with highest
predicted risk
• CR in highest-risk stratum was stable unaffected by
1. Hospitalization was in a facility with a mental health
inpatient ...
Unanswered Questions
• Does risk algorithm improve on clinical judgment?
– Study was unable to ascertain because the US Ar...
References
• Predicting Suicides After Psychiatric Hospitalization in US Army
Soldiers The Army Study to Assess Risk and R...
US Army STARRS - The Army Study to Assess Risk and Resilience in Servicemembers
US Army STARRS - The Army Study to Assess Risk and Resilience in Servicemembers
US Army STARRS - The Army Study to Assess Risk and Resilience in Servicemembers
US Army STARRS - The Army Study to Assess Risk and Resilience in Servicemembers
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US Army STARRS - The Army Study to Assess Risk and Resilience in Servicemembers

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This is a seminar presentation I made on the US Army STARRS study to assess causality and make a predictive algorithm for suicides among veterans. It was made as part of my Psychiatry residency.

Publié dans : Santé & Médecine
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US Army STARRS - The Army Study to Assess Risk and Resilience in Servicemembers

  1. 1. The Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS)
  2. 2. Introduction • US military suicide rate historically lower than civilian rate • Climbs steadily since beginning of the Iraq and Afghanistan conflicts • By 2008 it has exceeded the demographically matched civilian rate • Department of the Army enters agreement with National Institute of Mental Health (NIMH) to fund jointly the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS)
  3. 3. • 2 main goals 1. Evaluate hypotheses about modifiable risk and resilience factors for suicidality – could be used to target effective preventive interventions for Army suicides 2. Expand basic scientific understanding of psychosocial and neurobiological risk and resilience factors for suicidal behaviors and their psychopathologic correlates • Army STARRS includes a number of coordinated component studies designed to facilitate – nonexperimental hypothesis generation and testing – intervention targeting – intervention evaluation
  4. 4. Historical Administrative Data Study (HADS) • Examines patterns and correlates of suicide in an integrated data system combining information from 39 Army and Department of Defense administrative databases • All soldiers who served in the Army between January 1, 2004, and December 31, 2009
  5. 5. HADS • All 975057 Regular Army soldiers – On active duty between 01 Jan 04 & 31 Dec 09 • 569 deaths classified as suicides • Accession waivers – Acceptance of applicants who do not fully meet Army admission standards (drugs, medical, AFTQ score) • Stop loss orders – Requirement that soldiers serve past their original obligation
  6. 6. Variables Evaluated Sociodemographic Army Variables •Sex •Age •Marital Status •Race •Religion •Education •AFQT Score Category •Accession waiver status •Current Rank •History of demotions in past 02 years •Stop loss orders •Length of Army Service •Deployment Category •Reporting of suicide rate per 100 000 person-years (ie, per 1.2 million person-months) •Cases were person-months of suicide or accident death and controls comprising a 1 per 400 stratified probability sample of all other person-months
  7. 7. Results • Women have consistently lower suicide risk than men – sex difference narrows during deployment because of a disproportionately greater increase in risk for deployed women than men • Suicide risk inversely associated with educational attainment – highest risk among soldiers who did not receive either a high school diploma or general equivalency diploma or had an alternative education certificate, which includes vocational training and other alternate schooling – Association not significant during deployment • Suicide risk varies significantly by religious affiliation – Highest in ‘Other Religions / Religion unknown’ • Suicide rates are elevated among soldiers who were demoted within the prior 2 years across all deployment categories
  8. 8. Results • The youngest soldiers have markedly elevated suicide risk during and after deployment – not among those never deployed • Among enlisted soldiers, suicide is inversely related to rank – suicide rates among currently and previously deployed E1 and E2 (junior enlisted) soldiers are among the highest • Suicide risk is inversely related to length of Army service, with particularly high risk among currently and previously deployed soldiers in their first 2 years of service • Suicide risk lower in married soldiers and those with other dependents than in than unmarried soldiers without dependents – during deployment but not among either the never deployed or previously deployed • Suicide risk is also inversely related to AFQT categories – this association is most pronounced during deployment
  9. 9. Results • Accession waivers significantly associated with suicide risk in the Army overall – mainly artifact of missing waiver data for almost all officers and for most soldiers with more than 5 years of Army service (both groups with low suicide risk) • Within the sub-sample of enlisted soldiers with 5 or fewer years of service no statistically significant association found between suicide risk and receiving waiver • Suicide risk for soldiers serving under a current stop loss order does not differ consistently from that for other soldiers – However, the small number of never-deployed soldiers who previously served under a stop loss order, a group that by definition includes only soldiers who chose to reenlist after having been released from a stop loss order, have significantly elevated suicide risk
  10. 10. Step 2 Predicting Suicides After Psychiatric Hospitalization in US Army Soldiers
  11. 11. Introduction • US military administrative data document – 8-fold elevated suicide risk in the 3 months after psychiatric hospitalization – 5-fold elevated risk for the remainder of the 12 months after hospitalization • Use administrative data available during hospitalization to generate an actuarial post- hospitalization suicide risk algorithm – Research has revealed that actuarial suicide prediction is much more accurate than prediction based on clinical judgment • Strong risk algorithm might be developed in the US Army because of integrated administrative data for all US Army personnel – Absence of such data in the general population is widely recognized as an impediment
  12. 12. Method • 53 769 regular US Army hospitalizations from January 1, 2004, through December 31, 2009, with any ICD-9-CM psychiatric admission diagnosis – Involved 40820 soldiers 1. Functional bivariate associations were examined and predictors transformed to explore nonlinear multivariate association 2. All predictors were discretized and analyzed with 100 regression trees in distinct bootstrap pseudo-samples using the Rpackage rpart program to prevent over-fitting and allow detecting interactions among predictors 3. Predictors having significant bivariate associations and interactions emerging in 10% or more of regression trees were included as predictors in multivariate survival models
  13. 13. Variables Evaluated Sociodemographic Army Variables Sex Age Marital Status Race Religion Education AFTQ Category Accession waiver status Current Rank History of demotions in past 02 years Stop loss orders Length of Army Service Deployment Category •Reporting of suicide rate per 100 000 person-years (ie, per 1.2 million person-months) •Cases were person-months of suicide or accident death and controls comprising a 1 per 400 stratified probability sample of all other person-months
  14. 14. Method • 131 of the 421 bivariate associations (31.1%) between individual predictors and suicides were significant at the .05 level • Forward stepwise analysis selected a more stable set of predictors in a reduced logistic model • This model contained 20 predictors – had a slightly lower AUC (AUC, 0.84) and CR (CR, 50.0%) in the highest-risk ventile
  15. 15. Components of Predictive Model •Male sex (yes/no) 7.9 •Age of enlistment ≥27 y (yes/no) 1.9 •AFQT score >50th percentile (yes/no) 3.3 Access to firearms •No. of registered pistols 1.3 Crime perpetration •No. of verbal assault offenses in past 12 mo 2.2 •Any nonviolent weapons offense in past 24 mo (yes/no) 5.6 Suicidal behavior •Any prior suicide attempt since enlistment (yes/no) 2.9 •No. of outpatient visits with suicidal ideation in past 12 mo 1.6 Other prior treatment •≥6 Outpatient visits with a mental health professional in past 12 mo (yes/no) 1.9 •No. of antidepressant prescriptions filled in past 12 mo 1.3 •No. of psychiatric hospitalizations/time in service >50% percentile (yes/no) 0.3 •Any prior inpatient psychiatric treatment in past 12 mo (yes/no) 1.8 No. of inpatient days in past 12 mo by diagnosis •Major depression 2.2 •Somatoform or dissociative disorder 5.6
  16. 16. Hospitalized in a civilian psychiatric hospital or civilian facility with a psychiatric unit (yes/no) 1.6 Disorders diagnosed during current hospitalization (yes/no) •PTSD 0.4 •Suicidal ideation 2.4 •Nonaffective psychosis 2.9 •Somatoform or dissociative disorder 3.6 •Hearing loss 6.0
  17. 17. 52.9% of suicides occurred after the 5% of hospitalizations with highest predicted risk
  18. 18. • CR in highest-risk stratum was stable unaffected by 1. Hospitalization was in a facility with a mental health inpatient unit vs a general medical facility without such a unit (P = .19) 2. Suicide occurred before vs after September 1, 2008 (P = .12) 3. Suicide did vs did not occur within 3 months of hospital discharge (P = .99)
  19. 19. Unanswered Questions • Does risk algorithm improve on clinical judgment? – Study was unable to ascertain because the US Army electronic medical record does not include a field where suicide risk assessments are recorded – Documentation of suicide risk assessment in clinical notes was not consistent • Is suicide is sufficiently common in the highest-risk stratum and available interventions sufficiently powerful to make targeted post-hospitalization interventions efficient? – Results shed no light on this question • The potential for harm to be considered – intensive post-hospitalization interventions might lead to undue scrutiny by nonmedical leaders that adversely affect soldier careers
  20. 20. References • Predicting Suicides After Psychiatric Hospitalization in US Army Soldiers The Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) Kessler R et al JAMA Psychiatry. doi:10.1001/jamapsychiatry.2014.1754 • Predictors of Suicide and Accident Death in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) Results From the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) Shoenbaum M et al JAMA Psychiatry. 2014;71(5):493-503. doi:10.1001/jamapsychiatry.2013.4417

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