Abstract
PURPOSE: To construct a risk assessment model for forecasting the likelihood of myopia in elementary school students. DESIGN: A cross-sectional study. METHODS: This study utilized convenient sampling and questionnaire survey to collect data from eligible elementary students and their parents during the coronavirus disease 2019 (COVID-19) pandemic period from March to December 2020. The data were divided into training and testing sets in a 7:3 ratio. Lasso regression was employed to screen variables for inclusion in the model to establish a generalized linear model, with a nomogram model as the final result. RESULTS: The study included 1139 elementary students, comprising 54.5 % male and 45.5 % female participants. A total of 37 variables were obtained, which were analyzed using lasso regression. Cross-validation revealed that the best lambda value was 0.04201788. Five variables affecting myopia were identified: three risk and two protective factors. The three risk factors were student age (OR = 1.32), family location (urban vs. rural, OR = 2.33), and parents' occupation (compared with farmer: worker, OR = 2.03; teacher, OR = 1.62; medical worker, OR = 5.64; self-employed, OR = 1.78; civil servant, OR = 1.65; company employee, OR = 1.45; service industries, OR = 3.38; and others, OR = 3.20). The two protective factors were eye distance score (OR = 0.83) and eye health exercise score (OR = 0.95). The model was verified and showed good accuracy with an AUC of 0.778 and Brier score of 0.122 in addition to satisfactory clinical effects. CONCLUSIONS: The model effectively predicted the risk of myopia in elementary school students during the COVID-19 pandemic. Using this model, high-risk groups can be identified to provide a foundation for early intervention and follow-up, thereby reducing the incidence of myopia in this population.