Quantifying Classification Exposure Dose From the Pilot Veteran Military Occupational and Environmental Assessment Tool.

Authors

Calvin Lu, Lily Reck, Charity B Breneman, Kyle Pietro, Timothy Chun, Nathaniel Allen, Owen Killy, Kamila Pollin, Robert Forsten, Jose Ortiz, Immanuel Samuel, Michelle Prisco, Mathew Reinhard, John Barrett, Michelle Costanzo

Published in

Military medicine. Volume 191. Issue Supplement_1. Pages 502-510. Aug 01, 2026.

Abstract

Military personnel and Veterans are frequently exposed to complex environmental hazards; however, quantifying these exposures remains a challenge because of the limited availability of objective data and the reliance on subjective reporting. This study introduces the Classification Exposure Dose (Class-ED) framework, a novel method for estimating exposure dose using self-reported data on duration, frequency, and route of exposure.
Class-ED scores were calculated across 3 domains-fuel-related (FUEL), air quality (AQ), and radiation (RADI)-and analyzed in relation to neurobehavioral symptoms and the subdomains of cognitive, affective, somatosensory, and vestibular, along with neurological quality of life in a sample of 52 post-deployed, treatment-seeking Veterans.
Generalized linear models revealed Class-ED FUEL exposure was the most consistent environmental predictor of neurological symptom burden, showing significant associations with global neurobehavioral symptoms, and subdomains of cognitive, affective, and vestibular symptoms. Class-ED RADI was significantly associated with subdomains of cognitive symptoms. Class-ED AQ was not significantly associated with most neurobehavioral symptom domains. For quality of life, only Class-ED FUEL showed a marginal negative trend. Additionally, depression emerged as a strong, consistent predictor across all neurobehavioral symptoms and neurological quality of life.
This analysis demonstrates the utility of the Class-ED approach for quantifying aggregate, self-reported military environmental exposures and linking them to neurobehavioral symptom patterns, offering a scalable framework for future research and veteran care integration.

PMID:
42560279
Bibliographic data and abstract were imported from PubMed on 06 Aug 2026.

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