Detecting Disparities in Vision Difficulty Care through Regression Analysis

Abstract

Due to the progressive nature of preventable vision loss, annual examinations are necessary to address early stages of diseases. While studies have focused on risk factors leading to preventable vision loss, little work has been done to understand prevalence of vision difficulty in regard to availability of services and factors such as age, health insurance, and poverty. This study demonstrates geographic trends in vision difficulty to broaden the understanding of disparities in vision care accessibility in the United States. American Community Survey 2014 5-Year Estimate disability data were analyzed alongside Urban Influence Codes and National Provider Identifier registry data for optometrists and ophthalmologists to investigate correlations between accessibility to eye care and prevalence of vision difficulties. Through ArcMap software, ordinary least squares analysis of county-level data of eye care providers and other factors produced the standard residuals for the model used to identify vision care disparities. Vision care disparities were detected in 107 total counties between all twelve Urban Influence Codes classifications using county-level data. This study focuses only on the first of three phases of addressing equal health care access and establishes necessary background material for the next two phases by geographically identifying the locations of vision care disparities.