Motor Vehicle Crash-associated Eye Injuries Presenting to U.S. Emergency Departments

Grayson W. Armstrong, BA; Allison J. Chen, BA; James G. Linakis, MD, PhD; Michael J. Mello, MD, MPH; Paul B. Greenberg, MD

Disclosures

Western J Emerg Med. 2014;15(6):693-700. 

In This Article

Methods

This study received local institutional review committee exemption; review was not indicated for use of the NEISS-AIP database. This study adhered to the Declaration of Helsinki and all federal and state laws.

Data Source

The National Electronic Injury Surveillance System All Injury Program (NEISS-AIP) is a database containing national, weighted, annualized estimates for non-fatal injuries treated in U.S. EDs.[9] Data from 66 of the 100 NEISS hospitals with both trauma and non-trauma center EDs are included in the NEISS-AIP. These hospitals each have an ED with a minimum of six beds, are open 24 hours per day, and are used as a nationally representative, stratified probability sample of the roughly 5,000 hospitals in the U.S. with an ED of the same parameters. Each year, the NEISS-AIP collects data on approximately 500,000 non-fatal injury- and consumer-related cases. The NEISS-AIP defines non-fatal injuries as bodily harm resulting from severe exposure to an external force, substance, or submission.

Hospitals in the NEISS-AIP network provide data from injury-related ED cases daily. Each case report consists of coded variables describing characteristics of the injury, including demographic information, disposition upon ED discharge, principal diagnoses, primary body part affected, and the locale where the injury took place. Coders only report ED injury cases to the NEISS-AIP if specific mechanistic, diagnostic, and mortality criteria are met.[10] NEISS-AIP quality assurance coders code the cause, assault-relatedness, and transportation- and traffic-relatedness of each injury. The NEISS-AIP defines traffic-related injuries as those precipitating from MVCs occurring on a public highway, street, or road as opposed to any other location. Causes of injury are classified into major external cause and intent-of-injury groupings from the International Classification of Disease (ICD) 9-CM. We chose this national dataset over other potential databases due to its focus on ED-injury case data, which our study sought to address.

Data Analysis

This was a retrospective cross-sectional study using the NEISS-AIP database to examine eye injuries sustained by occupants of MVCs treated in EDs from 2001 through 2008 (the most recent year data were available at time of analysis).[9]

The inclusion criteria for this study were cases where the "eyeball" was identified as the primary body part injured and where "motor vehicle occupant" (MV-occupant) was identified as the precipitating cause of injury. Motorcycle and pedestrian injuries were excluded from our study.

We derived national injury estimates using the sample weights representing the inverse probability of selection for each case seen in the 66 NEISS-AIP hospitals. Weighted counts of injuries serve as representative numbers for national injuries and are derived from the NEISS-AIP dataset. We estimated projected incidences of injury along with their associated 95% confidence intervals (CIs) using STATA SE, version 10.0 (STATA Corporation, College Station, Texas, USA). The program's Survey commands (svy) are capable of accounting for the sampling weight structure present in the NEISS-AIP database. The types, dispositions, and mechanisms of eye injuries, as well as the race and ethnicity of patients, were tabulated. We created figures using Microsoft Excel, version 14.2.3. The program's 'Add Trendline…' linear regression function was used to create associated trendlines.

We determined rates of injury among the general population using national population estimates from the U.S. Census Bureau.[11] The estimated population data for July 1, 2004, and July 1, 2005 were averaged, yielding a population estimate for January 1, 2005, the midpoint of our study. We used U.S. Census Bureau data for annual as well as race and ethnicity population estimates where appropriate.

We determined rates of injury specifically among drivers in MVCs using both NEISS-AIP national estimates of MVC-related eye injuries among drivers and U.S. Department of Transportation Federal Highway Administration estimates of licensed drivers in the U.S.[12–14] The NEISS-AIP records the driver (and passenger occupant) status of ED-treated injury cases. We averaged the estimated U.S. licensed driver population data for 2004 and 2005, yielding an estimate for January 1, 2005, the midpoint of our study.

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