Reaching Target Communities in a Community Preschool Vision Screening Program.

PURPOSE: To develop a method to identify preschools with the greatest need for vision screening, correlations between socioeconomic status, preschool capacity, and rates of pediatric vision screenings performed by a community vision screening program were investigated. Geoinformatics mapping software was used to visually display the areas of greatest need. METHODS: Vision screening data from a community vision screening program, child care facility data from California Department of Social Services, and income data from the U.S. Census Bureau through ArcGIS software (Esri) were collected. When possible, data were consolidated at the ZIP code level. Kolmogorov-Smirnov analysis was used to determine correlations between data elements. Licensed child care facilities were scored on a scale (from 1 to 5) based on the socioeconomic status of the ZIP code and the facility capacity. The scoring system prioritized larger facilities in lower income communities to most efficiently use vision screening program resources. RESULTS: There was a positive correlation between the capacity of the child care facility and the median household income (P = .005). Second, we found a positive correlation between child care capacity and the median household income (P = .005). Licensed child care facilities were mapped and colored using GIS software according to their cumulative score. CONCLUSIONS: Challenges to vision screening in under-served communities include the lack of child care facilities and smaller facility size. The use of a scoring system and mapping software can direct vision screening programs to reach a greater number of children with the most efficient use of resources. [J Pediatr Ophthalmol Strabismus. 2022;59(6):375-379.].

Access to Pediatric Eye Care Following Vision Screening.

PURPOSE: To quantify the accessibility of eye care providers from photoscreening centers within the vision screening region in relation to population density and median household income. METHODS: Driving times between vision screening locations and eye care centers were mapped and analyzed using OpenStreetMap software (Open Street Map Foundation). U.S. Census Bureau data of population density and median household income were linked with screening centers using ArcGIS Online (Esri) to determine correlations with driving times. RESULTS: A total of 290 driving times for 145 photo-screening centers, 147 optometrists, and 7 pediatric ophthalmologists were calculated and mapped. Median driving times from a photoscreening center to the nearest optometrist and ophthalmologist were 4.74 and 25.10 minutes, respectively, with 90% of the screening centers residing within 12.46 and 67.19 minutes of the nearest optometrist and ophthalmologist, respectively. Driving times to optometrists are far less than times to pediatric ophthalmologists due to the greater number of optometrists. Decreasing driving times with increasing population and median household income indicate the concentration of optometrists and pediatric ophthalmologists within urbanized areas. CONCLUSIONS: Most photoscreening centers reside within 5 and 70 minutes of the nearest optometrist and pediatric ophthalmologist, respectively. Driving times indicate the region's greater accessibility to optometrists than to pediatric ophthalmologists. Eye care centers tend to be localized within urbanized areas with higher population densities and higher median household incomes. [J Pediatr Ophthalmol Strabismus. 2022;59(6):369-374.].