Social behavioral analysis of the influence of residential area and grade on pupils' myopia rate: a cross-sectional survey in Nanjing, China.

Objective: This study aims to examine the prevalence of myopia among pupils in different residential areas (city center, nearby suburbs, and far suburbs) and grade levels (lower, middle, and upper), analyzing social behaviors and risk factors to guide early intervention.

Methods: A cross-sectional survey was conducted with elementary students across various regions in Nanjing. A total of 2,342 valid questionnaires were collected. Univariate and multivariate logistic regression analyses were used to identify and assess risk factors for myopia and their variations across regions and grade levels.

Results: The survey revealed an overall myopia prevalence of 35.65% (95% CI = 33.71-37.59) among pupils in Nanjing, with the highest rate observed in nearby suburbs (41.34, 95% CI = 38.37-44.31). Myopia prevalence increased with grade level. Multivariate logistic regression analysis identified 8 significant risk factors for myopia, including visual fatigue, frequent eye rubbing, late bedtimes, heavy study burdens, insufficient time spent outdoors, insufficient device breaks, mobile phone use, and infrequent vision checks. Risk factors for myopia varied by region and grade level. Common risk factors across all groups included visual fatigue and infrequent vision checks. City center exhibited unique risk factors, such as late bedtimes and insufficient time spent outdoors. Nearby suburbs displayed unique risk factors, including heavy study burdens and entertainment-oriented electronic device use. Far suburbs displayed a unique risk factor in the prevalent use of mobile phones. Besides, lower grade students showed notably higher entertainment-oriented electronic device use, while upper grade students were more likely to have late bedtimes and insufficient device breaks.

Conclusion: Different social behavioral factors play a key role in the occurrence of myopia among pupils in different regions and grades, and targeted intervention measures need to be developed based on regional characteristics and grade level features.