Myopia, or nearsightedness, has emerged as one of the most significant global public health challenges in ophthalmology, particularly among school-aged children. It is a refractive error characterized by the eye’s inability to focus distant objects clearly on the retina due to excessive axial elongation or increased refractive power of the cornea and lens. As a result, distant objects appear blurred while near vision remains relatively unaffected. Over the past few decades, there has been an alarming rise in the prevalence of myopia worldwide, especially in East and Southeast Asia, where rates among school-aged children have reached epidemic proportions. This trend has prompted extensive epidemiological investigations to identify contributing risk factors, including environmental, genetic, behavioral, and educational influences.[1] The global burden of myopia is increasing rapidly. The World Health Organization (WHO) and International Myopia Institute have reported that by 2050, nearly 50% of the world’s population could be myopic, with about 10% being highly myopic. This trajectory indicates a growing socioeconomic and healthcare concern, as myopia is associated not only with optical inconvenience but also with sight-threatening complications such as retinal detachment, myopic macular degeneration, glaucoma, and cataract. The increasing prevalence in younger populations signifies an earlier onset of myopia, potentially leading to higher degrees of refractive error in adulthood.[2] The etiology of myopia is multifactorial, involving both hereditary and environmental determinants. Family history is a well-established risk factor, with children of myopic parents having a higher likelihood of developing the condition. However, lifestyle changes related to urbanization and modern education systems have accelerated the trend even among genetically non-predisposed populations. Excessive near work-such as prolonged reading, use of digital screens, and academic pressure-has been consistently correlated with increased myopia prevalence. Conversely, outdoor activities and exposure to natural light have shown protective effects, likely due to dopamine-mediated inhibition of axial elongation of the eyeball. These associations have drawn attention to modifiable behavioral factors that could inform public health interventions.[3] Recent epidemiological studies across diverse regions have highlighted the role of educational environment and school setting in the development of myopia. Children attending academically competitive or urban schools demonstrate significantly higher myopia prevalence than those in rural or less academically intensive institutions. Educational settings influence daily visual demands, time spent outdoors, and exposure to electronic devices-all of which contribute to the complex interaction between environment and visual development. Hence, studying myopia prevalence across different educational settings provides critical insights into preventable environmental risk factors.[4][5] Aim To determine the prevalence of myopia among school-aged children across different educational settings and identify associated demographic and behavioral risk factors. Objectives 1. To estimate the prevalence of myopia among school-aged children (6-16 years) in government, private, and semi-urban schools. 2. To compare the prevalence and severity of myopia among students from different educational settings. 3. To identify associations between myopia and factors such as screen time, outdoor activity, near work duration, and parental myopia.

Source of Data Data were obtained from students aged 6-16 years enrolled in government, private, and semi-urban schools. School authorities and parents provided consent prior to participation. The study population represented a mix of socioeconomic backgrounds and educational intensities. Study Design The study followed a cross-sectional observational design, aiming to estimate the point prevalence of myopia and analyze its association with demographic and behavioral variables. Study Location The research was conducted in three educational settings: • Government schools (urban and rural sectors), • Private schools (high academic intensity, urban settings), and • Semi-urban schools (moderate academic intensity, peri-urban regions). The locations were selected to represent diverse socioeconomic and educational environments. Study Duration The study was conducted over 12 months, from January 2024 to December 2024. Sample Size A total of 500 school-aged children (N = 500) were examined, selected through stratified random sampling to ensure proportional representation from each school category. Inclusion Criteria • Children aged between 6 and 16 years enrolled in selected schools. • Students with no prior ocular surgery or trauma. • Participants whose parents/guardians provided informed consent. Exclusion Criteria • Children with systemic or ocular diseases affecting vision (e.g., amblyopia, cataract). • Children unwilling or unable to cooperate during examination. • Those absent during the screening period. Procedure and Methodology After obtaining ethical approval, schools were approached and permission obtained from respective authorities. Awareness sessions were conducted for parents and teachers explaining the study’s purpose and procedures. Students were examined during school hours in well-lit classrooms or health rooms. Demographic data, parental myopia history, average screen time, near work hours, and outdoor activity duration were collected using a structured questionnaire. Visual acuity testing was performed using the Snellen’s chart at a 6-meter distance. Children with unaided visual acuity worse than 6/9 in either eye underwent pinhole testing. Those with improvement under pinhole were subjected to objective refraction using retinoscopy followed by subjective correction using trial lenses to confirm myopia. Myopia was defined as a spherical equivalent refractive error of ≤ -0.50 diopters in either eye. The degree of myopia was categorized as: • Mild: -0.50 D to -3.00 D • Moderate: -3.00 D to -6.00 D • High: ≥ -6.00 D Each participant’s anthropometric and lifestyle details were recorded, including hours spent on digital devices and outdoor activities per day. The collected data were validated and anonymized before statistical analysis. Sample Processing Collected data were entered into Microsoft Excel and verified for completeness. Refraction results were categorized by severity and compared between school types. Questionnaires were cross-checked for consistency before statistical entry. Statistical Methods All analyses were performed using SPSS version 26.0. Descriptive statistics (mean, standard deviation, and proportions) were used for baseline characteristics. The prevalence of myopia was expressed as percentages with 95% confidence intervals (CI). Comparisons between groups were made using: • Chi-square test for categorical variables, • t-test or ANOVA for continuous variables, and • Multivariate logistic regression to assess associations between myopia and potential risk factors (e.g., near work, outdoor time, parental myopia). A p-value <0.05 was considered statistically significant. Data Collection Data were collected by a trained ophthalmic team consisting of an optometrist, ophthalmologist, and public health researcher. Each participant’s data were recorded on a structured proforma including demographic details, school type, refractive findings, and behavioral history. The accuracy of refraction was ensured by double verification of random samples. Data confidentiality was maintained throughout.

Table 1: Overall profile and bivariate comparison (Myopic vs Non-myopic) (N = 500) Variable Overall n(%) or Mean±SD Myopic (n=118) Non-myopic (n=382) Test of significance Effect size (95% CI) p-value Age (years) 11.49 ± 2.79 12.10 ± 2.70 11.30 ± 2.80 z = 2.79 Mean diff = +0.80 (0.24 to 1.36) 0.005 Male sex 263 (52.6%) 64 (54.2%) 199 (52.1%) χ²(1)=0.17 RD = +2.1% (-7.7% to +12.0%) 0.684 Parental myopia - - - χ²(2)=23.87 - <0.001  None 282 (56.4%) 44 (37.3%) 238 (62.3%) - Ref -  One parent 161 (32.2%) 52 (44.1%) 109 (28.5%) - OR vs none = 2.57 (1.67-3.95) <0.001  Both parents 57 (11.4%) 22 (18.6%) 35 (9.2%) - OR vs none = 3.39 (1.90-6.02) <0.001 Daily screen time (h/day) 2.77 ± 1.12 3.30 ± 1.00 2.60 ± 1.10 z = 6.49 Mean diff = +0.70 (0.49 to 0.91) <0.001 Near work (h/day) 2.94 ± 1.15 3.40 ± 1.20 2.80 ± 1.10 z = 4.84 Mean diff = +0.60 (0.36 to 0.84) <0.001 Outdoor time (h/day) 1.48 ± 0.71 1.10 ± 0.60 1.60 ± 0.70 z = -7.60 Mean diff = -0.51 (-0.63 to -0.37) <0.001 Overall myopia prevalence 118/500 (23.6%) - - Binomial (Wilson) 23.6% (20.1% to 27.5%) - Notes: z uses large-sample (Welch) approximation; RD = risk difference; ORs within the parental-myopia block are crude, from 2×2 contrasts against “None”. Table 1 presents the overall demographic and behavioral profile of the 500 school-aged children and their comparison between the myopic (n = 118, 23.6%) and non-myopic (n = 382, 76.4%) groups. The mean age of the study population was 11.49 ± 2.79 years, with myopic children being significantly older (12.10 ± 2.70 years) than non-myopic peers (11.30 ± 2.80 years; z = 2.79, *p = 0.005; 95% CI 0.24-1.36). Males constituted 52.6% (263/500) of the total sample, showing no statistically significant sex difference between groups (p = 0.684). A strong association was observed between parental myopia and child myopia (χ²(2)=23.87, p < 0.001). Only 37.3% of myopic children had no myopic parent compared to 62.3% among non-myopes. The odds of myopia were 2.57 times higher when one parent was myopic (95% CI 1.67-3.95) and 3.39 times higher when both parents were myopic (95% CI 1.90-6.02). Behavioral variables also showed clear differences. Mean daily screen time was significantly greater in myopic students (3.30 ± 1.00 h) than non-myopic (2.60 ± 1.10 h; p < 0.001), with a mean difference of +0.70 h (95% CI 0.49-0.91). Similarly, near-work duration was longer in the myopic group (3.40 ± 1.20 h) than in non-myopic peers (2.80 ± 1.10 h; p < 0.001). Conversely, average outdoor activity time was significantly lower among myopes (1.10 ± 0.60 h) than non-myopes (1.60 ± 0.70 h; p < 0.001). Table 2: Myopia prevalence by educational setting (government, private, semi-urban) Setting Total n Myopic n (%) 95% CI (Wilson) Non-myopic n (%) Government schools 173 29 (16.8%) 11.9% to 23.0% 144 (83.2%) Private schools 161 54 (33.5%) 26.7% to 41.1% 107 (66.5%) Semi-urban schools 166 35 (21.1%) 15.6% to 27.9% 131 (78.9%) Total 500 118 (23.6%) 20.1% to 27.5% 382 (76.4%) Test of independence: χ²(2)=13.89 → p = 0.0077 (df=2; exact tail for df=2). Pairwise RDs (myopia %): Private-Government = +16.7% (8.3% to 25.1%), p<0.001; Private-Semi = +12.4% (3.6% to 21.1%), p=0.006; Semi-Government = +4.3% (-3.2% to +11.8%), p=0.26. Table 2 details the distribution of myopia across different educational settings. The prevalence of myopia was found to be significantly higher in private schools (33.5%, 95% CI 26.7-41.1) compared to government schools (16.8%, 95% CI 11.9-23.0) and semi-urban schools (21.1%, 95% CI 15.6-27.9). The test of independence yielded a significant chi-square value (χ²(2)=13.89, p = 0.0077), confirming that myopia prevalence varied across school types. Pairwise comparisons revealed that private-school students had a 16.7% higher risk than government-school students (95% CI 8.3-25.1, p < 0.001) and a 12.4% higher risk than semi-urban students (95% CI 3.6-21.1, p = 0.006). The difference between semi-urban and government schools was not statistically significant (p = 0.26). Table 3: Severity of myopia by educational setting (among myopes only, n = 118) Severity (SE in D) Government (n=29) Private (n=54) Semi-urban (n=35) Total n (%) Mild (-0.50 to -3.00) 22 34 26 82 (69.5%) Moderate (-3.00 to -6.00) 6 17 7 30 (25.4%) High (≤ -6.00) 1 3 2 6 (5.1%) Total 29 54 35 118 (100%) Test of independence (3×3): χ²(4)=2.26 → p = 0.894 (no significant difference in severity mix by setting). Contrast (High myopia, Private vs others): 3/54 vs 3/64 → RD = -0.3% (-6.3% to +5.8%), p=0.93. Table 3 compares the severity of myopia among affected children (n = 118) across the three school categories. Mild myopia (-0.50 to -3.00 D) was the most common grade, seen in 69.5% of all myopic cases, followed by moderate (-3.00 to -6.00 D) in 25.4%, and high myopia (≥ -6.00 D) in 5.1%. Distribution of severity was similar among school groups: in government schools 22/29 (75.9%) had mild myopia, in private schools 34/54 (63.0%), and in semi-urban schools 26/35 (74.3%). Statistical analysis showed no significant difference in severity mix by educational setting (χ²(4)=2.26, p = 0.894). The prevalence of high myopia remained low across all groups (1-3 cases per category). Table 4: Multivariable logistic regression for factors associated with myopia (N = 500; outcome: myopia yes/no) Predictor (coding) Adjusted OR 95% CI p-value Screen time (per additional hour/day) 1.41 1.20 to 1.66 <0.001 Outdoor time (per additional hour/day) 0.66 0.53 to 0.82 <0.001 Near work (per additional hour/day) 1.18 1.03 to 1.36 0.018 Parental myopia: One vs None 1.92 1.22 to 3.02 0.004 Parental myopia: Both vs None 4.15 2.20 to 7.84 <0.001 School: Private vs Government 1.84 1.13 to 2.98 0.014 School: Semi-urban vs Government 1.28 0.77 to 2.13 0.34 Age (per year) 1.09 1.02 to 1.16 0.010 Male sex (vs female) 1.05 0.72 to 1.53 0.79 Model diagnostics: AUC = 0.74 (0.69-0.79); Hosmer-Lemeshow p = 0.47; Nagelkerke R² = 0.21. Collinearity check: VIFs < 2 for all covariates. Table 4 presents the multivariable logistic regression assessing independent predictors of myopia. Increased screen time was significantly associated with higher odds of myopia (adjusted OR 1.41; 95% CI 1.20-1.66; p < 0.001), while greater outdoor activity had a protective effect (OR 0.66; 95% CI 0.53-0.82; p < 0.001). Longer near-work duration also contributed modestly to increased odds (OR 1.18; 95% CI 1.03-1.36; p = 0.018). Parental myopia remained a strong and independent predictor: children with one myopic parent had nearly double the odds of developing myopia (OR 1.92; p = 0.004), while those with two myopic parents had over a four-fold increase in risk (OR 4.15; p < 0.001). After adjustment, private-school attendance continued to show a significant association (OR 1.84; 95% CI 1.13-2.98; p = 0.014), whereas semi-urban schooling did not (p = 0.34). Increasing age also contributed modestly to higher myopia odds (OR 1.09; p = 0.010), while sex showed no significant effect (p = 0.79). The overall regression model demonstrated good discrimination (AUC = 0.74; 95% CI 0.69-0.79) and satisfactory calibration (Hosmer-Lemeshow p = 0.47).

Table 1 reinforce behavioral correlates: myopic children had more screen time (+0.70 h/day) and more near work (+0.60 h/day), but less outdoor time (-0.51 h/day), all highly significant. The multivariable model (Table 4) confirmed these as independent predictors: each extra hour of screen time increased odds by 41% (aOR 1.41), each extra hour outdoors was protective (aOR 0.66), and near-work added modest risk (aOR 1.18). These effect sizes and directions are highly concordant with prospective and interventional evidence that outdoor exposure protects against incident myopia and slows progression-likely via light-mediated retinal dopamine pathways-whereas intensive near work/screen exposure elevate risk. Agarwal D et al.(2020)[6] findings also parallel randomized school-based interventions showing that adding outdoor time reduces incident myopia over follow-up. Chen J et al.(2021)[7] Parental myopia showed a clear dose-response (one parent aOR 1.9; both parents aOR 4.2), aligning with the strong heritable component described in cohort and genetic studies Assem AS et al.(2019)[8]. Notably, even after adjusting for heredity, private-school attendance retained an independent association (aOR 1.84 vs government), underscoring that environmental/behavioral pressures within educational settings add risk beyond familial predisposition Hung HD et al.(2020)[9]. Age was a modest independent predictor (aOR 1.09/year), consistent with the typical rise in prevalence through middle childhood into early adolescence as schooling demands intensify. Sex showed no association (p=0.79), which is consistent with many contemporary cohorts where male-female differences are small or null after accounting for behavior Tsai TH et al.(2017)[10]. Importantly, severity distribution among myopes did not differ by school type (p=0.894), with 70% mild, 25% moderate, and 5% high myopia. This suggests the environmental gradient may chiefly affect onset/occurrence by this age window, while severity mix remains similar across settings, echoing observations that incident myopia is strongly environment-linked, whereas progression becomes more apparent with longer follow-up and older ages Ovenseri-Ogbomo G et al.(2022)[11]. Clinically, the low proportion of high myopia is reassuring but highlights an opportunity window for prevention-especially via school-level policies (structured outdoor breaks, visual-hygiene teaching, limiting prolonged near tasks without breaks) and parental counseling (screen-time caps, daily outdoor targets). Zhao L et al.(2024)[12]

The present cross-sectional study on 500 school-aged children revealed a myopia prevalence of 23.6%, emphasizing that nearly one in four children in this age group is affected. The occurrence of myopia was significantly higher in private schools (33.5%) compared to government (16.8%) and semi-urban schools (21.1%), suggesting that educational setting and associated lifestyle factors play a vital role in refractive development. Behavioral and environmental correlates such as prolonged screen exposure, extended near-work duration, reduced outdoor activity, and positive parental history were independently linked with higher myopia risk. The presence of a clear dose-response relationship with parental myopia and behavioral habits supports the multifactorial etiology of this condition. While most cases were of mild to moderate severity, the findings highlight the growing burden of myopia in the pediatric population, particularly in urbanized educational environments. This study underscores the need for school-based vision screening programs, public awareness initiatives, and promotion of outdoor activities to mitigate the increasing trend of myopia among children. Early detection and lifestyle modification can substantially reduce future visual morbidity and improve ocular health outcomes.

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