Refractive Error Coverage and Related Sociodemographic Factors: A Study of 1.1 Million Students in Schools of Hubei...

This study aims to investigate the prevalence of refractive error coverage and its multidimensional sociodemographic influencing factors among 1.1 million school-aged students in Hubei Province, China.

This population-based, cross-sectional study (2022-2024) included 1,113,548 students selected via stratified cluster sampling across Hubei Province, China. Standardized clinical assessments comprised distance visual acuity (VA) and noncycloplegic autorefraction. Students were defined as eligible for correction if presenting with functional visual impairment (uncorrected VA >0.2 logMAR) accompanied by myopia (≤-0.50 diopters [D]), hyperopia (≥+2.00 D), or astigmatism (≥0.75 D). Refractive error coverage was calculated as the proportion of eligible students possessing vision-improving glasses. A nested questionnaire substudy (n = 17,014) evaluated multidimensional sociodemographic determinants. Predictors of coverage were identified using both multivariate logistic regression and a random forest machine learning algorithm.

Of the 1,113,548 students, 41.26% were eligible for correction, and the overall refractive error coverage was 57.92%. Coverage was significantly positively associated with educational level and inversely with uncorrected visual acuity (UCVA) severity (P < 0.001). Logistic regression revealed higher coverage was significantly associated with female gender, non-left-behind status (odds ratio = 2.30), better academic performance, higher parental education, and parental glasses wear. Random forest analysis identified bilateral UCVA and grade level as the top predictors of coverage.

Refractive error coverage among Hubei students remains suboptimal. Improving these rates requires targeted public health interventions prioritizing males, lower-grade students, and rural/left-behind children, alongside initiatives to enhance parental eye health literacy.

By translating data from over 1.1 million students into predictive models, this study provides a robust, evidence-based foundation to optimize targeted clinical vision screening protocols for vulnerable pediatric populations.