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The objective of this study was to define the risk of hearing loss among US military members in relation to their deployment experiences. Data were drawn from the Millennium Cohort Study. Self-reported data and objective military service data were used to assess exposures and outcomes. Among all 48,540 participants, 7.5% self-reported new-onset hearing loss. Self-reported hearing loss showed moderate to substantial agreement (k = 0.57-0.69) with objective audiometric measures. New-onset hearing loss was associated with combat deployment (adjusted odds ratio [AOR] = 1.63, 95% confidence interval [CI] = 1.49-1.77), as well as male sex and older age. Among deployers, new-onset hearing loss was also associated with proximity to improvised explosive devices (AOR = 2.10, 95% CI = 1.62-2.73) and with experiencing a combat-related head injury (AOR = 6.88, 95% CI = 3.77-12.54). These findings have implications for health care and disability planning, as well as for prevention programs.

Background Complete and accurate ascertainment of mortality is critically important in any longitudinal study. Tracking of mortality is particularly essential among US military members because of unique occupational exposures (e.g., worldwide deployments as well as combat experiences). Our study objectives were to describe the early mortality experience of Panel 1 of the Millennium Cohort, consisting of participants in a 21-year prospective study of US military service members, and to assess data sources used to ascertain mortality. Methods A population-based random sample (n = 256,400) of all US military service members on service rosters as of October 1, 2000, was selected for study recruitment. Among this original sample, 214,388 had valid mailing addresses, were not in the pilot study, and comprised the group referred to in this study as the invited sample. Panel 1 participants were enrolled from 2001 to 2003, represented all armed service branches, and included active-duty, Reserve, and National Guard members. Crude death rates, as well as age- and sex-adjusted overall and age-adjusted, category-specific death rates were calculated and compared for participants (n = 77,047) and non-participants (n = 137,341) based on data from the Social Security Administration Death Master File, Department of Veterans Affairs (VA) files, and the Department of Defense Medical Mortality Registry, 2001-2006. Numbers of deaths identified by these three data sources, as well as the National Death Index, were compared for 2001-2004. Results There were 341 deaths among the participants for a crude death rate of 80.7 per 100,000 person-years (95% confidence interval [CI]: 72.2,89.3) compared to 820 deaths and a crude death rate of 113.2 per 100,000 person-years (95% CI: 105.4, 120.9) for non-participants. Age-adjusted, category-specific death rates highlighted consistently higher rates among study non-participants. Although there were advantages and disadvantages for each data source, the VA mortality files identified the largest number of deaths (97%). Conclusions The difference in crude and adjusted death rates between Panel 1 participants and non-participants may reflect healthier segments of the military having the opportunity and choosing to participate. In our study population, mortality information was best captured using multiple data sources.

Context High rates of alcohol misuse after deployment have been reported among personnel returning from past conflicts, yet investigations of alcohol misuse after return from the current wars in Iraq and Afghanistan are lacking. Objectives To determine whether deployment with combat exposures was associated with new-onset or continued alcohol consumption, binge drinking, and alcohol-related problems. Design, Setting, and Participants Data were from Millennium Cohort Study participants who completed both a baseline (July 2001 to June 2003; n=77 047) and follow-up (June 2004 to February 2006; n=55 021) questionnaire (follow-up response rate = 71.4%). After we applied exclusion criteria, our analyses included 48 481 participants (active duty, n = 26 613; Reserve or National Guard, n = 21 868). Of these, 5510 deployed with combat exposures, 5661 deployed without combat exposures, and 37 310 did not deploy. Main Outcome Measures New-onset and continued heavy weekly drinking, binge drinking, and alcohol-related problems at follow-up. Results Baseline prevalence of heavy weekly drinking, binge drinking, and alcohol-related problems among Reserve or National Guard personnel who deployed with combat exposures was 9.0%, 53.6%, and 15.2%, respectively; follow-up prevalence was 12.5%, 53.0%, and 11.9%, respectively; and new-onset rates were 8.8%, 25.6%, and 7.1%, respectively. Among active-duty personnel, new-onset rates were 6.0%, 26.6%, and 4.8%, respectively. Reserve and National Guard personnel who deployed and reported combat exposures were significantly more likely to experience new-onset heavy weekly drinking (odds ratio [OR], 1.63; 95% confidence interval [CI], 1.36-1.96), binge drinking (OR, 1.46; 95% CI, 1.24-1.71), and alcohol-related problems (OR, 1.63; 95% CI, 1.33-2.01) compared with nondeployed personnel. The youngest members of the cohort were at highest risk for all alcohol-related outcomes. Conclusion Reserve and National Guard personnel and younger service members who deploy with reported combat exposures are at increased risk of new-onset heavy weekly drinking, binge drinking, and alcohol-related problems.

Background Nonresponse bias in a longitudinal study could affect the magnitude and direction of measures of association. We identified sociodemographic, behavioral, military, and health-related predictors of response to the first follow-up questionnaire in a large military cohort and assessed the extent to which nonresponse biased measures of association. Methods Data are from the baseline and first follow-up survey of the Millennium Cohort Study. Seventy-six thousand, seven hundred and seventy-five eligible individuals completed the baseline survey and were presumed alive at the time of follow-up; of these, 54,960 (71.6%) completed the first follow-up survey. Logistic regression models were used to calculate inverse probability weights using propensity scores. Results Characteristics associated with a greater probability of response included female gender, older age, higher education level, officer rank, active-duty status, and a self-reported history of military exposures. Ever smokers, those with a history of chronic alcohol consumption or a major depressive disorder, and those separated from the military at follow-up had a lower probability of response. Nonresponse to the follow-up questionnaire did not result in appreciable bias; bias was greatest in subgroups with small numbers. Conclusions These findings suggest that prospective analyses from this cohort are not substantially biased by non-response at the first follow-up assessment.

Background Self-reported medical history data are frequently used in epidemiological studies. Self-reported diagnoses may differ from medical record diagnoses due to poor patient-clinician communication, self-diagnosis in the absence of a satisfactory explanation for symptoms, or the "health literacy" of the patient. Methods The US Department of Defense military health system offers a unique opportunity to evaluate electronic medical records with near complete ascertainment while on active duty. This study compared 38 self-reported medical conditions to electronic medical record data in a large population-based US military cohort. The objective of this study was to better understand challenges and strengths in self-reporting of medical conditions. Results Using positive and negative agreement statistics for less-prevalent conditions, near-perfect negative agreement and moderate positive agreement were found for the 38 diagnoses. Conclusion This report highlights the challenges of using self-reported medical data and electronic medical records data, but illustrates that agreement between the two data sources increases with increased surveillance period of medical records. Self-reported medical data may be sufficient for ruling out history of a particular condition whereas prevalence studies may be best served by using an objective measure of medical conditions found in electronic healthcare records. Defining medical conditions from multiple sources in large, long-term prospective cohorts will reinforce the value of the study, particularly during the initial years when prevalence for many conditions may still be low.