基于Framingham模型的苏州市9～18岁学生近视风险模型的构建

目的　探讨苏州市学生近视主要影响因素，建立近视的风险模型，为学生近视综合防控提供依据。方法　分层整群随机抽样招募苏州市四至十二年级学生为调查对象，通过标准化问卷收集学生年龄、性别、饮食习惯、用眼环境、读写习惯、近距离用眼、户外活动、睡眠及父母近视史等，现场测量教室照明，对每名学生进行屈光检测和裸眼远视力检查。Logistic回归分析近视的影响因素，Framingham模型构建近视风险评分模型。结果　本次纳入9～18岁学生2 859人，近视2 264人，近视检出率为79.19%。Logistic回归分析显示，女性（OR = 1.544，95%CI:1.258～1.896）、年龄（OR = 1.404，95%CI:1.325～1.487）、父母近视（一人近视:OR = 2.114，95%CI:1.694～2.638；均近视:OR = 3.450，95%CI:2.590～4.594）、黑板面照度不合格（OR = 1.316，95%CI:1.017～1.703）和躺着或趴着看书或电子屏幕（OR = 1.464，95%CI:1.099～1.950）是中小学生近视的危险因素。Framingham近视风险模型ROC曲线下面积为0.755（95%CI:0.733～0.777），最佳临界值点为6.6，灵敏度为68.3%，特异度为70.9%，约登指数为39.21%（95%CI:34.90%～42.35%）。结论　Framingham近视风险评分模型具有较好的近视预测能力，可用于近视的健康管理和干预。

Establishment of myopia risk model among students aged 9 to 18 years in Suzhou based on Framingham model

Objective To explore the main influencing factors of students’ myopia in Suzhou, establish a risk model of myopia, and provide evidence for comprehensive prevention and control of students’ myopia. Methods Grades 4 to 12 students were collected by stratified cluster random sampling method in 2020. Health questionnaires were used to collect the students’ information including gender, age, eating habits, eye healthy environments, reading and writing habits, close eye using behaviors, outdoor activity times, sleep times, and parents’ myopia. Illumination levels in classroom were measured on site. Uncorrected visual acuity and refraction were tested for each participating student. Logistic regression analysis was applied to explore myopia-related factors. Framingham model was used to establish myopia risk model. Results In total 2 859 students aged 9 to 18 years were enrolled, with myopia prevalence of 79.19%. Logistic regression analysis showed that female (OR=1.544, 95%CI: 1.258-1.896), age (OR=1.404, 95%CI: 1.325-1.487), parents’ myopia (one with myopia: OR=2.114, 95%CI: 1.694-2.638; both with myopia: OR=3.450, 95%CI: 2.590-4.594), illumination level of blackboard below standard (OR=1.316, 95%CI: 1.017-1.703), and lying down reading books or watching electronic screen (OR=1.464, 95%CI: 1.099-1.950) were risk factors of myopia. Area under the ROC curve of Framingham myopia risk model was 0.755 (95%CI: 0.733-0.777), optimum critical value was 6.6, sensitivity was 68.3%, specificity was 70.9%, and Youden index was 39.21% (95%CI: 34.90%-42.35%). Conclusion Framingham myopia risk model showed good predictive power, and it can be used for health management and intervention of myopia.