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Updated: Jul 11, 2025

Demonstration of Cutaneous Allodynia in Association with Chronic Pelvic Pain
Published on: June 23, 2009
Exposomic Signatures of Cervical Pain
Carlos J Maldonado1, Jessica A White-Phillip1, Yuliang Liu2
1Department of Clinical Investigation, Womack Army Medical Center, Fort Bragg, NC 28310, USA.
Introduction:
We evaluated risk factors associated with cervical pain (CP) among officers and enlisted members of the U.S. Army and Marine Aviation community using an exposomic approach. Specifically, we aimed to determine the factors associated with reported CP.
Materials And Methods:
This is a retrospective cohort study that utilized the Medical Assessment and Readiness System housed at Womack Army Medical Center to evaluate the longitudinal data taken from medical and workforce resources. This study included 77,864 active duty AMAC members during October 2015-December 2019. Multivariable mixed-effects logistic regression was used to assess the relationship between the independent variables of rank, service time, deployment, Armed Forces Qualification Test score, tobacco use, alcohol use, age, gender, race, ethnicity, body mass index, marital status, and education level and the dependent variable, incidence occurrence of CP.
Results:
The total analysis included 77,864 individuals with 218,180 person-years of observations. The incidence rate of CP was 18.8 per 100 person-years, with a 12% period prevalence. Cervical pain was independently associated with rank, service time, Armed Forces Qualification Test score, and alcohol use (all P < .05).
Conclusions:
Our longitudinal exposomic signatures-based approach aims to complement the outcomes of data science and analytics from Medical Assessment and Readiness System with validations of objective biochemical indicator species observed in Army and Marine Aviation community members suffering from CP. This initial approach using parallel track complementarity has the potential of substantiating the underlying mechanisms foundational to design prospective personalized algorithms that can be used as a predictive model. Finally, a specific evaluation of occupational risk factors may provide insight into factors not readily ascertained from the civilian literature.
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