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Design of the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS)

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2013

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Wiley-Blackwell
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Kessler, Ronald C., Lisa J. Colpe, Carol S. Fullerton, Nancy Gebler, James A. Naifeh, Matthew K. Nock, Nancy A. Sampson, et al. 2013. “Design of the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS).” International Journal of Methods in Psychiatric Research 22 (4) (December): 267–275. doi:10.1002/mpr.1401.

Abstract

The Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS) is a multi-component epidemiological and neurobiological study designed to generate actionable evidence-based recommendations to reduce U.S. Army suicides and increase basic knowledge about the determinants of suicidality. This report presents an overview of the designs of the six component Army STARRS studies. These include: an integrated study of historical administrative data systems (HADS) designed to provide data on significant administrative predictors of suicides among the more than 1.6 million soldiers on active duty in 2004–2009; retrospective case-control studies of suicide attempts and fatalities; separate large-scale cross-sectional studies of new soldiers (i.e., those just beginning Basic Combat Training [BCT], who completed self-administered questionnaires [SAQ] and neurocognitive tests and provided blood samples) and soldiers exclusive of those in BCT (who completed SAQs); a pre-post deployment study of soldiers in three Brigade Combat Teams about to deploy to Afghanistan (who completed SAQs and provided blood samples) followed multiple times after returning from deployment; and a platform for following up Army STARRS participants who have returned to civilian life. DoD/Army administrative data records are linked with SAQ data to examine prospective associations between self-reports and subsequent suicidality. The presentation closes with a discussion of the methodological advantages of cross-component coordination.

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Suicide, mental disorders, U.S. Army, epidemiologic research design, design effects, sample bias, sample weights, survey design efficiency, survey sampling

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