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

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.

Spatial Vision Inequalities: A Literature Review of the Impact of Place on Vision and Eye Health Outcomes.

"Neighborhood and built environment" is one of the five domains of social determinants of health that has been outlined by Healthy People 2030, and this domain impacts an individual's well-being, health, and quality of life. Social risk factors (SRFs) in the neighborhood and built environment domain include unstable or unsafe housing, poor access to transportation, lack of green spaces, pollution, safety concerns, and neighborhood measures of inequity. In this narrative literature review, we assess the relationship between neighborhood and built environment SRFs and eye health and vision outcomes. We explain how mapping neighborhood-level SRFs may be used to advance health equity in the field of eye health and vision care.

Social Risk Factor Associations With Presenting Visual Acuity in Patients With Microbial Keratitis.

Importance: Neighborhood-level social risk factors may contribute to health disparities in microbial keratitis (MK) disease presentation. Understanding neighborhood-level factors may identify areas for revised health policies to address inequities that impact eye health. Objective: To investigate if social risk factors were associated with presenting best-corrected visual acuity (BCVA) for patients with MK. Design, Setting, and Participants: This was a cross-sectional study of patients with a diagnosis of MK. Patients presenting to the University of Michigan with a diagnosis of MK between August 1, 2012, and February 28, 2021, were included in the study. Patient data were obtained from the University of Michigan electronic health record. Main Outcomes and Measures: Individual-level characteristics (age, self-reported sex, self-reported race and ethnicity), presenting log of the minimum angle of resolution (logMAR) BCVA, and neighborhood-level factors, including measures on deprivation, inequity, housing burden, and transportation at the census block group, were obtained. Univariate associations of presenting BCVA (< 20/40 vs ≥20/40) with individual-level characteristics were assessed with 2-sample t, Wilcoxon, and χ2 tests. Logistic regression was used to test associations of neighborhood-level characteristics with the probability of presenting BCVA worse than 20/40 after adjustment for patient demographics. Results: A total of 2990 patients with MK were identified and included in the study. Patients had a mean (SD) age of 48.6 (21.3) years, and 1723 were female (57.6%). Patients self-identified with the following race and ethnicity categories: 132 Asian (4.5%), 228 Black (7.8%), 99 Hispanic (3.5%), 2763 non-Hispanic (96.5%), 2463 White (84.4%), and 95 other (3.3%; included any race not previously listed). Presenting BCVA had a median (IQR) value of 0.40 (0.10-1.48) logMAR units (Snellen equivalent, 20/50 [20/25-20/600]), and 1508 of 2798 patients (53.9%) presented with BCVA worse than 20/40. Patients presenting with logMAR BCVA less than 20/40 were older than those who presented with 20/40 or higher (mean difference, 14.7 years; 95% CI, 13.3-16.1; P < .001). Furthermore, a larger percentage of male vs female sex patients presented with logMAR BCVA less than 20/40 (difference, 5.2%; 95% CI, 1.5-8.9; P = .04), as well as Black race (difference, 25.7%; 95% CI, 15.0%-36.5%;P < .001) and White race (difference, 22.6%; 95% CI, 13.9%-31.3%; P < .001) vs Asian race, and non-Hispanic vs Hispanic ethnicity (difference, 14.6%; 95% CI, 4.5%-24.8%; P = .04). After adjusting for age, self-reported sex, and self-reported race and ethnicity, worse Area Deprivation Index (odds ratio [OR], 1.30 per 10-unit increase; 95% CI, 1.25-1.35; P < .001), increased segregation (OR, 1.44 per 0.1-unit increase in Theil H index; 95% CI, 1.30-1.61; P < .001), higher percentage of households with no car (OR, 1.25 per 1 percentage point increase; 95% CI, 1.12-1.40; P = .001), and lower average number of cars per household (OR, 1.56 per 1 less car; 95% CI, 1.21-2.02; P = .003) were associated with increased odds of presenting BCVA worse than 20/40. Conclusion and Relevance: Findings of this cross-sectional study suggest that in a sample of patients with MK, patient characteristics and where they live were associated with disease severity at presentation. These findings may inform future research on social risk factors and patients with MK.

Seeing Red: Associations between Historical Redlining and Present-Day Visual Impairment and Blindness.

PURPOSE: Redlining was a discriminatory housing policy in the United States that began in 1933 and denoted neighborhoods with high proportions of Black individuals as "undesirable" and a high risk for lending, which therefore excluded people from obtaining traditional insured mortgages to purchase a home. Simultaneously, realtors discouraged Black individuals from purchasing homes in predominantly non-Black neighborhoods. This resulted in decreased home ownership and wealth accumulation among Black individuals and neighborhoods with high proportions of Black individuals. This study investigated rates of visual impairment and blindness (VIB) in neighborhoods that at one time were graded for redlining. DESIGN: Secondary data analysis of American Community Survey data and historical grades for redlining. PARTICIPANTS: United States census tracts (CTs) from 2010 with historical grades for redlining. METHODS: One-way analysis of variance, Kruskal-Wallis test, chi-square test, and logistic regression modeling. MAIN OUTCOME MEASURES: The main outcome was CT percentage of residents reporting VIB and the association with historical grades for redlining. Grades were converted to numeric values (1 to 4, with higher values indicating worse grade) and aggregated over a CT based on the distribution of grades within to obtain a redlining score. Logistic regression was used to model the effect of redlining on the probability of having VIB. RESULTS: Eleven thousand six hundred sixty-eight CTs were analyzed. Logistic regression found that a 1-unit increase in average redlining score was associated with a 13.4% increased odds of VIB after controlling for CT measures of age, sex, people of color (any non-White race), state, and population size (odds ratio [OR], 1.134; 95% confidence interval [CI], 1.131-1.138; P < 0.001). Similar results were observed for an additional model that adjusted for the estimated percentage of Black residents within a CT (OR, 1.180; 95% CI, 1.177-1.183; P < 0.001). CONCLUSIONS: Historical government-sanctioned residential segregation through redlining was found to be associated with higher proportions of people living with VIB in these neighborhoods today. Understanding how neighborhood segregation impacts eye health is important for planning improved mechanisms of eye care delivery to mitigate health disparities. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.