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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

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 s...

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Autores principales: Devine, Jaime K., Collen, Jacob, Choynowski, Jake J., Capaldi, Vincent
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7063745/
https://www.ncbi.nlm.nih.gov/pubmed/32151283
http://dx.doi.org/10.1186/s40779-020-00239-7
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author Devine, Jaime K.
Collen, Jacob
Choynowski, Jake J.
Capaldi, Vincent
author_facet Devine, Jaime K.
Collen, Jacob
Choynowski, Jake J.
Capaldi, Vincent
author_sort Devine, Jaime K.
collection PubMed
description 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.
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spelling pubmed-70637452020-03-13 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. Collen, Jacob Choynowski, Jake J. Capaldi, Vincent Mil Med Res Research 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. BioMed Central 2020-03-10 /pmc/articles/PMC7063745/ /pubmed/32151283 http://dx.doi.org/10.1186/s40779-020-00239-7 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Devine, Jaime K.
Collen, Jacob
Choynowski, Jake J.
Capaldi, Vincent
Sleep disturbances and predictors of nondeployability among active-duty Army soldiers: an odds ratio analysis of medical healthcare data from fiscal year 2018
title Sleep disturbances and predictors of nondeployability among active-duty Army soldiers: an odds ratio analysis of medical healthcare data from fiscal year 2018
title_full Sleep disturbances and predictors of nondeployability among active-duty Army soldiers: an odds ratio analysis of medical healthcare data from fiscal year 2018
title_fullStr Sleep disturbances and predictors of nondeployability among active-duty Army soldiers: an odds ratio analysis of medical healthcare data from fiscal year 2018
title_full_unstemmed Sleep disturbances and predictors of nondeployability among active-duty Army soldiers: an odds ratio analysis of medical healthcare data from fiscal year 2018
title_short Sleep disturbances and predictors of nondeployability among active-duty Army soldiers: an odds ratio analysis of medical healthcare data from fiscal year 2018
title_sort sleep disturbances and predictors of nondeployability among active-duty army soldiers: an odds ratio analysis of medical healthcare data from fiscal year 2018
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7063745/
https://www.ncbi.nlm.nih.gov/pubmed/32151283
http://dx.doi.org/10.1186/s40779-020-00239-7
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