Citation

Kessler RC, Colpe LJ, Fullerton CS, Gebler N, Naifeh JA, Nock MK, Sampson NA, Schoenbaum M, Zaslavsky AM, Stein MB, Ursano RJ, Heeringa SG. Int. J. Methods Psychiatr. Res. 2013; 22(4): 267-275.

Affiliation

Department of Health Care Policy, Harvard Medical School, Boston, MA, USA.

Copyright

(Copyright © 2013, John Wiley and Sons)

DOI

10.1002/mpr.1401

PMID

24318217

PMCID

PMC3992857

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 US Army suicides and increase basic knowledge about the determinants of suicidality. This report presents an overview of the designs of the six components of the Army STARRS. These include: an integrated analysis of the Historical Administrative Data Study (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 [SAQs] 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. Department of Defense/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.

Copyright © 2013 John Wiley & Sons, Ltd.

Language: en

Keywords

Suicide; US Army; design effects; epidemiologic research design; mental disorders; sample bias; sample weights; survey design efficiency; survey sampling