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  4. Vol 4, Issue 2, 2024

Discriminant Analysis of Nomogram Prediction Model for Myopia in Primary and Secondary School Students

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DOI: 10.23977/phpm.2024.040219 | Downloads: 12 | Views: 726

Author(s)

Chen Yan 1, Li Jun 1,2

Affiliation(s)

1 School of Public Health, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, 712046, China
2 Xi'an Center for Disease Control and Prevention, Xi'an, Shaanxi, 710054, China

Corresponding Author

Li Jun

ABSTRACT

The aim is to create a model of myopia risk forecasting for primary and secondary school pupils in Xi'an City, and to supply a scientific foundation for the prevention and regulation of myopia in primary and secondary school students. In 2023, 12 schools in Xi'an City were chosen to be the focus of monitoring common student diseases, and stratified whole group sampling was conducted for each grade of primary school (4~6), junior high, and senior high. The current state of myopia screening was outlined, and multifactorial logistic regression was used to analyse the correlates of myopia, as well as to construct a nomogram prediction model. Resultly, a total of 3531 students aged 9~18 years were selected, with 2776 myopes and a myopia rate of 78.6%. The multifactorial logistic regression model showed that females (OR=1.34, 95% CI: 1.13~1.59), school level (junior high school: OR=2.13, 95% CI: 1.72~2.66; senior high school: OR=3.91, 95% CI: 3.00~5.11), and number of eye exercises performed per day (2: OR=0.68, 95% CI: 0.49~0.94; 3: OR=0.61, 95% CI: 0.42~0.91), eyes one foot away from books (always: OR=0.53, 95% CI: 0.31 to 0.91), and parental myopia (b2222oth myopic: OR=3.61, 95% CI: 2.66-4.98; father's myopia only: OR=1.96, 95% CI: 1.51 -2.56; mother's myopia only: OR=1.81, 95% CI: 1.44-2.30), and the number of vision examinations in the previous year (≥2: OR=1.41, 95% CI: 1.17-1.70) were statistically significant (all P values <0.05). The column chart prediction model showed that the risk factors for myopia among primary and secondary school students in Xi'an were gender, school year, frequency of eye exercises/day, eye distance from books, parental myopia, and the number of previous year's visual acuity examinations, respectively. In conclusion, a graphical prediction model of column-line design can accurately detect risk elements for myopia and offer scientific backing for the prevention and regulation of myopia in primary and secondary school students.

KEYWORDS

Myopia; risk factors; column charts; primary and secondary school students; prediction models

CITE THIS PAPER

Chen Yan, Li Jun, Discriminant Analysis of Nomogram Prediction Model for Myopia in Primary and Secondary School Students. MEDS Public Health and Preventive Medicine (2024) Vol. 4: 139-146. DOI: http://dx.doi.org/10.23977/phpm.2024.040219.

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