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Sleep disturbances and predictors of nondeployability among active-duty Army soldiers: an odds ratio analysis of medical healthcare data from fiscal year 2018.

ABSTRACT: BACKGROUND:The impact of sleep disorders on active-duty soldiers' medical readiness is not currently quantified. Patient data generated at military treatment facilities can be accessed to create research reports and thus can be used to estimate the prevalence of sleep disturbances and the role of sleep on overall health in service members. The current study aimed to quantify sleep-related health issues and their impact on health and nondeployability through the analysis of U.S. military healthcare records from fiscal year 2018 (FY2018). METHODS:Medical diagnosis information and deployability profiles (e-Profiles) were queried for all active-duty U.S. Army patients with a concurrent sleep disorder diagnosis receiving medical care within FY2018. Nondeployability was predicted from medical reasons for having an e-Profile (categorized as sleep, behavioral health, musculoskeletal, cardiometabolic, injury, or accident) using binomial logistic regression. Sleep e-Profiles were investigated as a moderator between other e-Profile categories and nondeployability. RESULTS:Out of 582,031 soldiers, 48.4% (n?= 281,738) had a sleep-related diagnosis in their healthcare records, 9.7% (n?= 56,247) of soldiers had e-Profiles, and 1.9% (n?= 10,885) had a sleep e-Profile. Soldiers with sleep e-Profiles were more likely to have had a motor vehicle accident (pOR (prevalence odds ratio) =4.7, 95% CI 2.63-8.39, P?? 0.001) or work/duty-related injury (pOR?=?1.6, 95% CI 1.32-1.94, P?? 0.001). The likelihood of nondeployability was greater in soldiers with a sleep e-Profile and a musculoskeletal e-Profile (pOR?=?4.25, 95% CI 3.75-4.81, P?? 0.001) or work/duty-related injury (pOR?=?2.62, 95% CI 1.63-4.21, P?? 0.001). CONCLUSION:Nearly half of soldiers had a sleep disorder or sleep-related medical diagnosis in 2018, but their sleep problems are largely not profiled as limitations to medical readiness. Musculoskeletal issues and physical injury predict nondeployability, and nondeployability is more likely to occur in soldiers who have sleep e-Profiles in addition to these issues. Addressing sleep problems may prevent accidents and injuries that could render a soldier nondeployable.

SUBMITTER: Devine JK

PROVIDER: S-EPMC7063745 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Sleep disturbances and predictors of nondeployability among active-duty Army soldiers: an odds ratio analysis of medical healthcare data from fiscal year 2018.

Devine Jaime K JK Collen Jacob J Choynowski Jake J JJ Capaldi Vincent V

Military Medical Research 20200310 1

<h4>Background</h4>The impact of sleep disorders on active-duty soldiers' medical readiness is not currently quantified. Patient data generated at military treatment facilities can be accessed to create research reports and thus can be used to estimate the prevalence of sleep disturbances and the role of sleep on overall health in service members. The current study aimed to quantify sleep-related health issues and their impact on health and nondeployability through the analysis of U.S. military ...[more]

PMID: 32151283

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Project description:ObjectiveThe authors sought to identify predictors of imminent suicide attempt (within 30 days) among U.S. Army soldiers following their first documented suicidal ideation.MethodsUsing administrative data from the Army Study to Assess Risk and Resilience in Servicemembers, the authors identified 11,178 active-duty Regular Army enlisted soldiers (2006-2009) with medically documented suicidal ideation and no prior medically documented suicide attempts. The authors examined risk factors for suicide attempt within 30 days of first suicidal ideation using logistic regression analyses, including sociodemographic and service-related characteristics, psychiatric diagnoses, physical health care visits, injuries, and history of family violence or crime perpetration or victimization.ResultsAmong soldiers with first documented suicidal ideation, 830 (7.4%) attempted suicide, 46.3% of whom (N=387) attempted suicide within 30 days (rate, 35.4 per 1,000 soldiers). Following a series of multivariate analyses, the final model identified females (odds ratio=1.3, 95% CI=1.0, 1.8), combat medics (odds ratio=1.6, 95% CI=1.1, 2.2), individuals with an anxiety disorder diagnosis prior to suicidal ideation (odds ratio=1.3, 95% CI=1.0, 1.6), and those who received a sleep disorder diagnosis on the same day as the recorded suicidal ideation (odds ratio=2.3, 95% CI=1.1, 4.6) as being more likely to attempt suicide within 30 days. Black soldiers (odds ratio=0.6, 95% CI=0.4, 0.9) and those who received an anxiety disorder diagnosis on the same day as suicidal ideation (odds ratio=0.7, 95% CI=0.5, 0.9) were less likely.ConclusionsSuicide attempt risk is highest in the first 30 days following ideation diagnosis and is more likely among women, combat medics, and soldiers with an anxiety disorder diagnosis before suicidal ideation and a same-day sleep disorder diagnosis. Black soldiers and those with a same-day anxiety disorder diagnosis were at decreased risk. These factors may help identify soldiers at imminent risk of suicide attempt.

Project description:Studies have suggested that sickle cell trait elevates the risks of exertional rhabdomyolysis and death. We conducted a study of sickle cell trait in relation to these outcomes, controlling for known risk factors for exertional rhabdomyolysis, in a large population of active persons who had undergone laboratory tests for hemoglobin AS (HbAS) and who were subject to exertional-injury precautions.We used Cox proportional-hazards models to test whether the risks of exertional rhabdomyolysis and death varied according to sickle cell trait status among 47,944 black soldiers who had undergone testing for HbAS and who were on active duty in the U.S. Army between January 2011 and December 2014. We used the Stanford Military Data Repository, which contains comprehensive medical and administrative data on all active-duty soldiers.There was no significant difference in the risk of death among soldiers with sickle cell trait, as compared with those without the trait (hazard ratio, 0.99; 95% confidence interval [CI], 0.46 to 2.13; P=0.97), but the trait was associated with a significantly higher adjusted risk of exertional rhabdomyolysis (hazard ratio, 1.54; 95% CI, 1.12 to 2.12; P=0.008). This effect was similar in magnitude to that associated with tobacco use, as compared with no use (hazard ratio, 1.54; 95% CI, 1.23 to 1.94; P<0.001), and to that associated with having a body-mass index (BMI; the weight in kilograms divided by the square of the height in meters) of 30.0 or more, as compared with a BMI of less than 25.0 (hazard ratio, 1.39; 95% CI, 1.04 to 1.86; P=0.03). The effect was less than that associated with recent use of a statin, as compared with no use (hazard ratio, 2.89; 95% CI, 1.51 to 5.55; P=0.001), or an antipsychotic agent (hazard ratio, 3.02; 95% CI, 1.34 to 6.82; P=0.008).Sickle cell trait was not associated with a higher risk of death than absence of the trait, but it was associated with a significantly higher risk of exertional rhabdomyolysis. (Funded by the National Heart, Lung, and Blood Institute and the Uniformed Services University of the Health Sciences.).

Project description:The US Army experienced a sharp increase in soldier suicides beginning in 2004. Administrative data reveal that among those at highest risk are soldiers in the 12 months after inpatient treatment of a psychiatric disorder.To develop an actuarial risk algorithm predicting suicide in the 12 months after US Army soldier inpatient treatment of a psychiatric disorder to target expanded posthospitalization care.There were 53,769 hospitalizations of active duty soldiers from January 1, 2004, through December 31, 2009, with International Classification of Diseases, Ninth Revision, Clinical Modification psychiatric admission diagnoses. Administrative data available before hospital discharge abstracted from a wide range of data systems (sociodemographic, US Army career, criminal justice, and medical or pharmacy) were used to predict suicides in the subsequent 12 months using machine learning methods (regression trees and penalized regressions) designed to evaluate cross-validated linear, nonlinear, and interactive predictive associations.Suicides of soldiers hospitalized with psychiatric disorders in the 12 months after hospital discharge.Sixty-eight soldiers died by suicide within 12 months of hospital discharge (12.0% of all US Army suicides), equivalent to 263.9 suicides per 100,000 person-years compared with 18.5 suicides per 100,000 person-years in the total US Army. The strongest predictors included sociodemographics (male sex [odds ratio (OR), 7.9; 95% CI, 1.9-32.6] and late age of enlistment [OR, 1.9; 95% CI, 1.0-3.5]), criminal offenses (verbal violence [OR, 2.2; 95% CI, 1.2-4.0] and weapons possession [OR, 5.6; 95% CI, 1.7-18.3]), prior suicidality [OR, 2.9; 95% CI, 1.7-4.9], aspects of prior psychiatric inpatient and outpatient treatment (eg, number of antidepressant prescriptions filled in the past 12 months [OR, 1.3; 95% CI, 1.1-1.7]), and disorders diagnosed during the focal hospitalizations (eg, nonaffective psychosis [OR, 2.9; 95% CI, 1.2-7.0]). A total of 52.9% of posthospitalization suicides occurred after the 5% of hospitalizations with highest predicted suicide risk (3824.1 suicides per 100,000 person-years). These highest-risk hospitalizations also accounted for significantly elevated proportions of several other adverse posthospitalization outcomes (unintentional injury deaths, suicide attempts, and subsequent hospitalizations).The high concentration of risk of suicide and other adverse outcomes might justify targeting expanded posthospitalization interventions to soldiers classified as having highest posthospitalization suicide risk, although final determination requires careful consideration of intervention costs, comparative effectiveness, and possible adverse effects.

Dataset Information

Sleep disturbances and predictors of nondeployability among active-duty Army soldiers: an odds ratio analysis of medical healthcare data from fiscal year 2018.

Publications

Sleep disturbances and predictors of nondeployability among active-duty Army soldiers: an odds ratio analysis of medical healthcare data from fiscal year 2018.

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