Cross sectional, surveillance and longitudinal study of poor visual acuity in Gui an New Distinct of Guizhou Province
10.16835/j.cnki.1000-9817.2023.02.030
- VernacularTitle:贵州省贵安新区小学生视力不良横断面和监测与纵向追踪数据分析
- Author:
HE Wanya, ZHU Yan, TANG Xin, QIN Huiling, CAI Jinghui, NIE Ying
1
Author Information
1. School of Public Health, Guizhou Medical University, Guiyang (550025) , China
- Publication Type:Journal Article
- Keywords:
Vision,low;
Cross sectional studies;
Longitudinal studies;
Students
- From:
Chinese Journal of School Health
2023;44(2):291-294
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To understand the development trend of poor vision among primary students through cross sectional, surveillance and longitudinal analysis, so as to put forward some suggestions on adolescents growth and health.
Methods:Visual data of 3 753 pupils were inclucled for analysis from Gui an New Distinct, Guizhou Province in autumn semester 2021, and were compared with data collected during the year of 2016-2021. The curve, increment and contribution rate of poor vision from each grade of the three designs were contrasted.
Results:In 2021, poor vision rate among pupils in this town was 25.6%. The curve of poor vision rate in cross sectional data was U shaped with significant rise followed by decline which was different from monitoring and longitudinal tracking data, and the trend of poor vision rate of monitoring and longitudinal tracking data were linear with continued increases. The cross sectional data in 2021 showed that the highest contribution rate of poor vision rate of pupils was in grade 1(87.0% ), while other data showed that those were both in grade 4(45.0%, 33.9%).
Conclusion:The accuracy of the development trend of poor vision is lowest in cross sectional analysis and highest in longitudinal analysis. However, data acquisition and preservation is easy in cross sectional study and difficult in longitudinal study. It is necessary to improve the electronic information system based on cross sectional data to gradually form a complete monitoring and longitudinal tracking data, and combine different data to provide more accurate information.
