RESUMEN
PRECIS: Higher neighborhood-level poverty is associated with greater odds of missing a free eye disease screening appointment, underscoring the importance of community-based interventions to address upstream social determinants of health. PURPOSE: To investigate the association between neighborhood-level characteristics and attendance for a free eye disease screening. PATIENTS AND METHODS: The MI-SIGHT program is conducted in two community clinics in Southeastern Michigan. Participant-level demographics were extracted from electronic health records. Neighborhood level-characteristics including Area Deprivation Index (ADI), median household income (HHI), percent of households with >30% rent burden, percent of households without vehicles, percent of households in subsidized housing, and energy burden were obtained from the Wisconsin Neighborhood Atlas and the United States census. Logistic regression was used to model the probability of clinic visit attendance, which was the main outcome measure. RESULTS: 1431 participants were scheduled for screening appointments between July 2020 to November 2021, with a no-show rate of 23%. Individuals lived an average of 7.7 miles from each clinic (SD=8.1) and in neighborhoods with a mean ADI of 6.8 (SD=3.2, 1-10 scale where 10 is the most deprived). After adjusting for age, sex, race, and ethnicity, participants from neighborhoods with higher deprivation were more likely to have missed clinic visits. For example, there was an 8% higher odds of missed clinic visits for every 1-point increase in ADI (odds ratio, OR=1.08, P=0.020) and an 18% higher odds of a missed visit with every 10% increase in households without a vehicle (OR=1.18, P=0.013). CONCLUSION: Higher neighborhood-level poverty was associated with greater odds of missing a free eye disease screening appointment after adjusting for individual characteristics. Increased neighborhood-level resources are likely needed to bolster engagement in preventive eye care.
RESUMEN
PURPOSE: To investigate contrast sensitivity (CS) as a screening tool to detect eye disease and assess its association with both eye disease and vision-related quality of life. DESIGN: Cross-sectional study. METHODS: Setting and population: Adults receiving care from a free clinic and a Federally Qualified Health Center in Michigan. MAIN OUTCOME MEASURES: Screening positive for eye disease and Visual Function Questionnaire (VFQ) score. OBSERVATION: Participants received a vision exam reviewed via telemedicine for disease, completed a demographic survey, and the 9-item VFQ. The ability of CS to predict eye disease was explored and area under the curve (AUC) is reported. Logistic and linear regression were used to investigate the continuous effect of CS on the probability of screening positive for eye disease and VFQ score, respectively, adjusting for age and visual acuity. RESULTS: 1159 included participants were, on average, 54.9 ± 14.5 years old, 62% identified as female, 34% as White, 54% as Black, 10% as Hispanic/Latino, and reported mean VFQ score of 79.7 ± 15.3. CS ranged from 0.00 to 1.95 log units (mean = 1.54 ± 0.24), 21% of eyes had glaucoma, 19% cataract, 6% DR, and 2% AMD. AUCs were 0.53 to 0.73. A 0.3 log unit decrease in better eye CS was associated with increased odds of glaucoma (odds ratio [OR] = 1.35, confidence interval [CI] = 1.09-1.67), cataract (OR = 1.35, CI = 1.05-1.72), DR (OR = 2.05, CI = 1.51-2.77), and AMD (OR = 2.08, CI = 1.10-3.91). A 0.3 log unit increase in better eye CS was associated with a 5.9 unit increase in VFQ. CONCLUSION: While CS alone is not sufficient to identify people with eye disease, it is an important measure of visual function that can add value to comprehensive eye screening.