| Literature DB >> 28545424 |
Robert J Ursano1, Ronald C Kessler2, James A Naifeh3, Holly Herberman Mash3, Carol S Fullerton3, Tsz Hin Hinz Ng3, Pablo A Aliaga3, Gary H Wynn3, Hieu M Dinh3, James E McCarroll3, Nancy A Sampson2, Tzu-Cheg Kao4, Michael Schoenbaum5, Steven G Heeringa6, Murray B Stein7,8.
Abstract
BACKGROUND: The U.S. Army suicide attempt rate increased sharply during the wars in Iraq and Afghanistan. Risk may vary according to occupation, which significantly influences the stressors that soldiers experience.Entities:
Keywords: Military; Occupation; Suicide attempt
Mesh:
Year: 2017 PMID: 28545424 PMCID: PMC5445296 DOI: 10.1186/s12888-017-1350-y
Source DB: PubMed Journal: BMC Psychiatry ISSN: 1471-244X Impact factor: 3.630
Multivariate association of military occupation with suicide attempt among Regular Army enlisted soldiers, adjusting for socio-demographic and service-related variablesa, b
| OR | (95% CI) | Cases ( | Total ( | Rated | Pop %e | SREf | |
|---|---|---|---|---|---|---|---|
| Occupation | |||||||
| Combat arms | 1.2* | (1.1–1.2) | 2506 | 7,159,106 | 420 | 23.3 | 417 |
| Special forces | 0.3* | (0.2–0.5) | 16 | 368,016 | 52 | 1.2 | 102 |
| Combat medic | 1.4* | (1.3–1.5) | 682 | 1,470,882 | 556 | 4.8 | 504 |
| Other | 1.0 | – | 6446 | 21,716,246 | 356 | 70.7 | 357 |
| χ2 3 | 126.2* | ||||||
aThe sample of enlisted soldiers (n = 9650 cases, 153,523 control person-months) is a subset of the total sample (n = 193,617 person-months) from the Army STARRS Historical Administrative Data Study (HADS). Control person-months were assigned a weight of 200 to adjust for under-sampling
bLogistic regression models included gender, age at Army entry, current age, race/ethnicity, education, marital status, time in service (≤ 1 year, 2 years, 3–4 years, 5–10 years, >10 years), deployment status (never, currently, or previously deployed), and military occupation. The model also included a dummy predictor variable for calendar month and year to control for secular trends
cTotal includes both cases (i.e., soldiers with a suicide attempt) and control person-months
dRate per 100,000 person-years, calculated based on n1/n2, where n1 is the unique number of soldiers within each category and n2 is the annual number of person-years, not person-months, in the population (n = 3.08 million)
ePop % = percent of the population of enlisted soldiers
fSRE = Standardized risk estimates (suicide attempters per 100,000 person-years) were calculated assuming other predictors were at their sample-wide means
*p < 0.05
Multivariate associations of military occupation with suicide attempt among Regular Army enlisted soldiers stratified by deployment statusa, b
| Deployment Status | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Never Deployed | Currently Deployed | Previously Deployed | |||||||
| OR | (95% CI) | SREc | OR | (95% CI) | SREc | OR | (95% CI) | SREc | |
| Occupation | |||||||||
| Combat arms | 1.1* | (1.0–1.2) | 610 | 1.0 | (0.9–1.2) | 159 | 1.3* | (1.2–1.4) | 358 |
| Combat medic | 1.5* | (1.3–1.6) | 801 | 1.3* | (1.0–1.8) | 208 | 1.2* | (1.0–1.5) | 352 |
| Other | 1.0 | – | 546 | 1.0 | – | 155 | 1.0 | – | 283 |
| χ2 2 | 67.2* | 4.5 | 32.8* | ||||||
aThe sample of enlisted soldiers (n = 9650 cases, 153,523 control person-months) is a subset of the total sample (n = 193,617 person-months) from the Army STARRS Historical Administrative Data Study (HADS). Control person-months were assigned a weight of 200 to adjust for under-sampling
bLogistic regression models included gender, age at Army entry, current age, race/ethnicity, education, marital status, time in service (≤ 1 year, 2 years, 3–4 years, 5–10 years, >10 years), and military occupation. The models also included a dummy predictor variable for calendar month and year to control for secular trends
cSRE = standardized risk estimates (suicide attempters per 100,000 person-years) were calculated assuming other predictors were at their sample-wide means
*p < 0.05
Multivariate associations of military occupation with suicide attempt among Regular Army enlisted soldiers stratified by time in servicea, b
| Time in Service | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ≤ 1 Year | 2 Years | 3–4 Years | 5–10 Years | >10 Years | |||||||||||
| OR | (95% CI) | SREc | OR | (95% CI) | SREc | OR | (95% CI) | SREc | OR | (95% CI) | SREc | OR | (95% CI) | SREc | |
| Occupation | |||||||||||||||
| Combat arms | 1.1 | (1.0–1.2) | 931 | 1.2* | (1.0–1.3) | 657 | 1.2* | (1.1–1.3) | 453 | 1.4* | (1.2–1.5) | 279 | 1.1 | (0.9–1.4) | 76 |
| Combat medic | 1.5* | (1.3–1.7) | 1313 | 1.4* | (1.2–1.7) | 795 | 1.3* | (1.1–1.6) | 494 | 1.2 | (1.0–1.5) | 253 | 1.4 | (1.0–2.1) | 98 |
| Other | 1.0 | – | 868 | 1.0 | – | 565 | 1.0 | – | 378 | 1.0 | – | 207 | 1.0 | – | 69 |
| χ2 2 | 44.9* | 19.1* | 18.7* | 25.4* | 3.7 | ||||||||||
aThe sample of enlisted soldiers (n = 9650 cases, 153,523 control person-months) is a subset of the total sample (n = 193,617 person-months) from the Army STARRS Historical Administrative Data Study (HADS). Control person-months were assigned a weight of 200 to adjust for under-sampling
bLogistic regression models included gender, age at Army entry, current age, race/ethnicity, education, marital status, deployment status (never, currently, or previously deployed), and military occupation. The models also included a dummy predictor variable for calendar month and year to control for secular trends
cSRE = standardized risk estimates (suicide attempters per 100,000 person-years) were calculated assuming other predictors were at their sample-wide means
*p < 0.05
Fig. 1Monthly risk of suicide attempt by military occupation and among Regular Army enlisted soldiers in their first year of service1,2 1The sample of enlisted soldiers in their first year of service (combat arms, n=6,853; combat medic, n=1,450; other, n=17,483) is a subset of the total sample of enlisted soldiers (n=163,173 person-months) from the Army STARRS Historical Administrative Data Study (HADS). Control person-months were assigned a weight of 200 to adjust for under-sampling. 2 Monthly risk based on hazard rates and linear spline models