JMIR Publications

ABSTRACT

Background:

Early identification of high-risk individuals for high myopia is critical. We used real-world big data collected from vision screening to illustrate the prevalence patterns of high myopia in children and adolescents in southern China and efficiently predicted the onset of high myopia in children and adolescents in large-scale screening for the first time.

Objective:

To determine the prevalence of high myopia in children and adolescents in southern China and predict its onset by studying the risk factors for high myopia based on the machine learning method.

Methods:

This cross-sectional, school-based study was conducted in 13 cities with different gross domestic products in southern China. By data acquisition and filtering, we analyzed the prevalence of high myopia and its association with age, school stage, gross domestic product; and risk factors. A Random Forest algorithm was used to predict high myopia among schoolchildren.

Results:

There were 1,285,609 participants (average age 11.80 ± 3.07 years, range 6–20 years), of whom 658,516 (51.2%) were men. The overall prevalence of high myopia was 4.48% (2019), 4.88% (2020), and 3.17% (2021), with an increasing trend from the age of 11 to 17 years. The rates of high myopia increased from elementary schools to high schools but decreased at all school stages from 2019 to 2021. The coastal and southern cities have a higher proportion of high myopia, with an overall prevalence between 2.60% and 5.83%. Age was the most important risk factor. Random Forest achieved high accuracy of 0.948. Areas under the receiver operator characteristic curve were 0.975. Both indicated sufficient model efficacy.

Conclusions:

High myopia had a high incidence in Guangdong Province. Its onset in children and adolescents was well predicted by the Random Forest algorithm. Efficient use of real-world data can contribute to the prevention and early diagnosis of high myopia.