Place-Based Measures of Inequity and Vision Difficulty and Blindness.

ABSTRACT

Importance Known social risk factors associated with poor visual and systemic health in the US include segregation, income inequality, and persistent poverty. Objective: To investigate the association of vision difficulty, including blindness, in neighborhoods with measures of inequity (Theil H index, Gini index, and persistent poverty). Design, Setting, and Participants: This cross-sectional study used data from the 2012-2016 American Community Survey and 2010 US census tracts as well as Theil H index, Gini index, and persistent poverty measures from PolicyMap. Data analysis was completed in July 2023. Main Outcomes and Measures: The main outcome was the number of census tract residents reporting vision difficulty and blindness (VDB) and the association with the Theil H index, Gini index, or persistent poverty, assessed using logistic regression. Results: In total, 73 198 census tracts were analyzed. For every 0.1-unit increase in Theil H index and Gini index, there was an increased odds of VDB after controlling for census tract-level median age, the percentage of the population that identified as female sex, the percentage of the population that identified as a member of a racial or ethnic minority group, state, and population size (Theil H index odds ratio [OR], 1.14 [95% CI, 1.14-1.14; P < .001]; Gini index OR, 1.15 [95% CI, 1.15-1.15; P < .001]). Persistent poverty was associated with an increased odds of VDB after controlling for census tract-level median age, the percentage of the population that identified as female sex, the percentage of the population that identified as a member of a racial or ethnic minority group, state, and population size compared with nonpersistent poverty (OR, 1.36; 95% CI, 1.35-1.36; P < .001). Conclusions and Relevance In this cross-sectional study, residential measures of inequity through segregation, income inequality, or persistent poverty were associated with a greater number of residents living with VDB. It is essential to understand and address how neighborhood characteristics can impact rates of VDB.