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Derek Wong, Leila Khazaeni, Justin Herling, Leah Shin, Hatem Jaber, Parisah Moghaddampour, Jennifer Dunbar; Demographics of Patients with Diabetic Eye Disease Presenting to the Emergency Department. Invest. Ophthalmol. Vis. Sci. 2020;61(7):3847.
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© ARVO (1962-2015); The Authors (2016-present)
Establishing preventive measures for diabetic retinopathy may be difficult for geographically isolated or low income communities, leading residents of such areas to seek care through the emergency department once disease progression has occurred. This study uses population data and a geographic information system to analyze the demographics of patients who presented with complications of diabetic eye disease to a single level 1 trauma center.
Ophthalmology consults in the emergency department were reviewed from the years 2014-2017. Inclusion criteria were: a completed ophthalmology consult note, age ≥ 18 years, a chief complaint related to complications of diabetic eye disease, and an address of residence in the study area. Diabetes prevalence, racial/ethnicity demographics, and level of education by zip code were attained from the 2014 California Health Interview Survey. Median household income and uninsured rate by zip code were attained from the 2013-2017 American Community Survey 5-Year Estimates. Ophthalmologist office zip codes were extracted from the American Board of Ophthalmology and the American Academy of Ophthalmology websites. ArcGIS (Esri, Redlands, CA) was used to analyze the driving distances of the study patients to the nearest ophthalmologist. For each sociodemographic category, zip codes were divided into 4 quartiles based on approximately equal total populations. The proportion of study patients by zip code was compared using chi-squared test to determine significance. Level of alpha was set at p≤0.05.
Of 1047 total emergency ophthalmology consults evaluated, 170 patients (16.2%) presented to the emergency department with complications of diabetic eye disease. There was a statistically significant difference in the proportion of study patients for the following sociodemographic categories: diabetes prevalence, median household income, race/ethnicity, education, and uninsured rate (p<0.01). When driving distances to nearest ophthalmologist were analyzed, there was no statistical significance found (p=0.84).
The data helps to identify communities that could potentially benefit from preventive measures such as diabetic eye screenings. A zip code’s diabetic prevalence could be more useful in consideration as opposed to a patient’s driving distance to nearest ophthalmologist.
This is a 2020 ARVO Annual Meeting abstract.
Table 1. Proportion of study patients for each sociodemographic category
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