Background: Recent military conflicts in Iraq (Operation Iraqi Freedom), Afghanistan (Operation Enduring Freedom), and elsewhere have been associated with psychological impacts among military personnel. However, relatively little is known about the relationship between those conflicts and psychological health of military health care professionals. Previous work has shown certain demographic factors associated with diagnosed mental health conditions after deployment. However, unique exposures in the deployed environment may be present that are also associated. Understanding the relationship between the demographic factors, exposures, and post-deployment mental health (PDMH) conditions has not been investigated. The purpose of this study was to determine the association between occupational and/or environmental exposures and incident PDMH conditions in a defined population of United States Air Force health care personnel returning from the deployed environment (i.e., deployment-related exposures).
Methods: A nested case-control study compared cohort members with (N = 4,114) and without (N = 14,073) a PDMH condition in terms of deployment-related occupational and/or environmental exposures. PDMH conditions were identified using the electronic health record and exposures were determined using post-deployment health assessments. Demographic-adjusted multivariable logistic regression models were used to compute odds ratios (ORs).
Results: The final regression model comprised five exposure and 12 demographic variables. Reported exposures were not strongly associated with incident PDMH conditions (OR ranged from 1.22 to 1.38) and were lower than some demographic factors. Demographic characteristics with relatively large effect sizes (ORs less than 0.5 or greater than 1.5) included the protective factors of Air Force Guardsman (OR: 0.45), reservists (OR: 0.34), and surgeons (OR: 0.32), as well as the risk factor of nurses (OR: 1.51). All model parameters had a p-value less than 0.0001 and the area under the receiver operating characteristic curve was 0.668.
Conclusions: Given the low area under the receiver operating characteristic, the final statistical model had only marginal performance in its ability to correctly identify cases. Thus, other factors should be studied to identify additional predictors for PDMH conditions.