The vision health of primary and secondary school students has become a growing concern due to increasing academic pressure, the widespread use of electronic devices, and poor eye care habits. High myopia, as an advanced stage of myopia, emerging as a global public health issue. While many existing studies have explored the risk factors for myopia, few have specifically addressed the complex non-linear relationships between these factors and the development of high myopia. This study combines a nested case-control study with restricted cubic splines to develop a risk prediction model for high myopia in primary and secondary school students. By identifying high-risk individuals early, this model aims to delay or prevent the progression of high myopia, contributing to tertiary prevention of myopia, and positively impacting the academic and life quality of students.

To investigate the prevalence and risk factors of high myopia in primary and secondary school students, and conduct a risk prediction model to provide a scientific basis for myopia prevention.

A nested case-control study was conducted in 2023, involving students with moderate myopia from 12 schools in Hangzhou to establish a cohort. Myopia status among the students was monitored in accordance with the National Monitoring and Intervention Program for Common Diseases and Health-Related Factors in Students. Students who progressed to high myopia were classified as the case group, while the others formed the control group, vision care behaviors were surveyed for both groups. Lasso regression was used to select feature variables, followed by multivariate logistic regression analysis to identify factors influencing the development of high myopia among primary and secondary school students. A Nomogram was employed to visualize the risk prediction model. The model's performance was evaluated using the Hosmer-Lemeshow test, receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Additionally, restricted cubic splines were used to further clarify the relationship between age and the risk of high myopia.

A total of 2468 students from 12 primary and secondary schools were enrolled. Among them, 1 293 students did not progress to high myopia, while 1 175 students with moderate myopia progressed to high myopia, resulting in a high myopia incidence rate of 47.61% (1 175/2 468). Significant differences were observed between the two groups in terms of age, grade level, BMI, daily sleep time, geographic region, outdoor activity time, electronic device usage time, after-school homework time, and household lamp usage (P<0.05). Lasso regression identified eight feature variables: grade level, BMI, daily sleep time, geographic region, outdoor activity time, electronic device usage time, after-school homework time, and household lamp usage. Multivariate Logistic regression analysis revealed that the risk factors for high myopia in primary and secondary school students included being in middle school (OR=2.612, 95%CI=2.185-3.127), being overweight or obese (OR=2.140, 95%CI=1.458-3.169), being underweight (OR=1.807, 95%CI=1.430-2.290), sleeping after 22: 00 daily (OR=1.408, 95%CI=1.188-1.670), engaging in outdoor activities for 1-2 h/d (OR=1.371, 95%CI=1.122-1.675) or <1 h/d (OR=1.648, 95%CI=1.342-2.027), using electronic devices >2 h/d (OR=1.440, 95%CI=1.1191.856), and spending 1-2 h/d (OR=1.461, 95%CI=1.126-1.899) or >2 h/d (OR=1.534, 95%CI=1.218-1.935) on after-school homework (P<0.05). In contrast, being in high school was identified as a protective factor against high myopia (OR=0.560, 95%CI=0.419-0.743, P<0.05). A risk prediction model was constructed based on six variables: grade level, BMI, daily sleep time, outdoor activity time, electronic device usage time, and after-school homework time. The model achieved an area under the ROC curve of 0.840 (95%CI=0.825-0.855), demonstrating good fit, consistency, and applicability. Additionally, restricted cubic spline analysis indicated that the age group of 13~15 years was the high-risk period for developing high myopia.

The incidence of high myopia among primary and secondary school students was notably high. The risk prediction model could provide a scientific basis for the prevention and control of high myopia. Strengthening myopia prevention and control measures in middle school, along with improving students' vision care behaviors, was essential for reducing the occurrence of high myopia.