Hearing Impairment and Noise-Induced Hearing Injury in the United States Military

The incidence and severity of Service-related hearing impairment and noise-induced hearing injury (HINIHI) are very high among Department of Defense (DoD) personnel, as evidenced by the growth of related Veterans Benefit Administration disability compensation payments. We will present the latest findings from the DoD Epidemiologic and Economic Burden of Hearing Loss Study (DEEBoHLS) regarding HINIHI rates among active-duty Service Members. DEEBoHLS is a collaborative project between the DoD Hearing Center of Excellence (HCE), The University of Texas School of Public Health (UTSPH), the Veterans Health Administration (VHA), in consultation with occupational epidemiologists, biostatisticians, health economists, decision analysis experts, otolaryngologists, audiologists, and military and Veterans Affairs costing experts. The Geneva Foundation and HCE facilitate data access and ensure that appropriate measures are taken to ensure data security and confidentiality. 

 For the main epidemiological analysis, we included 18-64 year old Active Duty Armed Forces (USAF, USA, USN, USMC, USCG) serving during fiscal years (FY) 2008 to 2012, and who have at least one clinical encounter with selected ICD-9 diagnosis of HINIHI. We will identify cases of HINIHI from direct care (CAPER: Comprehensive Ambulatory/Professional Encounter Record, and SIDR: Standard Inpatient Data Record) and paid provider (TED-I: TRICARE Encounter Data – Institutional and TED-NI: TRICARE Encounter Data – Non Institutional) data sources. We will also identify cases using the Defense Occupational and Environmental Health Readiness System – Hearing Conservation database (DOEHRS-HC), which maintains hearing conservation and audiometric data across the DoD. The Defense Manpower Data Center will provide data on at-risk denominators by the demographic and job-related characteristics.

We will report prevalence and cumulative incidence rates of HINIHI based on several case definitions (e.g., combination of ICD-9 diagnosis codes). We will report rates by demographic characteristics (i.e., gender, age and race/ethnicity), employment characteristics (i.e., branch, grade and rank) and occupational category (i.e., DoD MOC). We will also report on differences in rates by FY. Finally, we will present a comparison with published DoD and civilian rates for comparable case definitions.