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BRIEF RESEARCH ARTICLE
Year : 2023 | Volume
: 67
| Issue : 1 | Page : 170-173
Socio-Ecological determinants of myopia in rural school students in North India: Results from a nurse-led program
Latika Rohilla1, Limalemla Jamir2, Vaibhav Miglani3, Parul Chawla Gupta4, K Aruna Devi5, Mona Duggal6
1 Public Health Nursing Officer, Advanced Pediatrics Centre, Postgraduate Institute of Medical Education and Research, Chandigarh, India
2 Senior Resident, School of Public Health, Post Graduate Institute of Medical Education and Research, Chandigarh, India
3 Data Scientist, Department of Hematology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
4 Assistant Professor, Department of Ophthalmology, Postgraduate Institute of Medical Education and Research, Chandigarh, India
5 Junior Resident, School of Public Health, Post Graduate Institute of Medical Education and Research, Chandigarh, India
6 Assistant Professor, Department of Ophthalmology, Community Ophthalmology Unit, Postgraduate Institute of Medical Education and Research, Chandigarh, India
Correspondence Address:
Mona Duggal
Community Ophthalmology Unit, Department of Ophthalmology, Postgraduate Institute of Medical Education and Research, Chandigarh
India
Source of Support: None, Conflict of Interest: None
CheckDOI: 10.4103/ijph.ijph_1017_22
Abstract
Socio-ecological determinants of high myopia incidence among school students largely remain unexplored, especially in developing countries. A cross-sectional study was conducted in rural schools in North India to assess the relationship between these determinants and myopia among adolescent students. A public health nurse used a pre-tested questionnaire (demographics, family ocular status, and screen time) and Snellen's chart for testing visual acuity, and referred suspected cases for cycloplegic refraction assessment. Among the total of 955 students, the median (range) age was 14 (13–15) years. The prevalence of myopia was 5.03% (95% confidence interval [CI]: 4.99–5.07). Myopia was found to be associated with computer usage at school (P = 0.058), malnutrition (P = 0.001), and familial myopia (P = 0.079) in the bivariate analysis. Significant predictors of myopia in the regression model were females (odd ratio [OR]: 6.29; 95% CI: 2.69–14.72), higher maternal age (OR: 1.09; 95% CI: 1–1.17), and reading distance <20 cm (OR: 1.98; 95% CI: 1.01–3.87).
Keywords: Myopia, Rural, school health services, Sunlight, Vision screening
Refractive error is the most common cause of visual impairment. The prevalence of myopia is expected to increase by 200 million between 2000 and 2050, with a higher disease burden in Asia.[1] Refractive errors among children differ by urban (4%–30.5%) and rural (2.6%–13.6%) residence, ethnicity, social class, hours spent in natural sunlight, reading, or using screen-based media.[2],[3]
Previous studies among school students in India have studied the effect of outdoor activity and near work including screen time on refractive error.[2],[4] This is perhaps the first study from the region to be a public health nurse-led screening program for the visual acuity of school children. The study aimed to examine the association between myopia among students in rural areas by multiple factors in the physical and social environment.
This school-based, cross-sectional study was conducted over a period of 1 year (2017–18) in Naraingarh block, district Ambala, Haryana, India. LR, a public health nurse who led the study, first attended 2-day training under an ophthalmologist (author: PCG). The study population comprised high school students, aged 10–18 years. Students who refused to participate or presented with organic defects in the eye such as corneal opacity, the opacity of the lens, choroid, or retinal disorders were excluded from the study. The study was conducted after obtaining ethical clearance from the “Institutional Ethics Committee,” PGIMER, Chandigarh. Assent and informed consent were obtained from the students and parents, respectively.
A pre-tested, bilingual (English and Hindi), questionnaire comprising sociodemographics; time spent on studies, screen-based media and outdoor activity; and vision-related problems was used. Questionnaire on birth history (preterm and maternal age) and vision disorders in the family was filled out by parents at home and submitted back within 1–2 days. Nutritional status of the participants was obtained through anthropometric measurements and body mass index (BMI; weight [kg]/height [m2]). The Asian BMI cutoff levels were considered to categorize nutritional status as underweight (<18.5 kg/m2), normal (18.5–22.9 kg/m2), overweight (23.0–24.9 kg/m2), and obese (≥25 kg/m2). For testing visual acuity, a portable Snellen's chart was used for grading the vision as 6/6–6/36 which was hung on a wall at eye level for the students at a distance of 6 m (20 feet). Myopia in the students was defined as a visual acuity <6/6.
For data collection, a well-ventilated, well-lit room was selected to facilitate eye testing. After the students filled out the questionnaire, they were taken for physical examination and myopia screening. The students found to have a myopic refractive error were referred to an optometrist of the study institute for cycloplegic (2% homatropine eye drops) refraction assessment.
Stata version 15.1 (StataCorp. 2017. Stata Statistical Software: Release 15. College Station, TX: StataCorp LP) was used for the data analysis. Multivariable logistic regression analysis was used to predict the effects of various risk factors on myopia. The presence of myopia (yes/no) was used as a dependent variable, and gender, school type, father's occupation, caste, reading distance, mother's age at birth, and duration of study under the sun as independent variables. Multicollinearity was tested using variation inflation factor (VIF), and a mean VIF cutoff of 4 was used to accept the model's collinearity and to avoid any inflation of effect sizes. Specification error test was done to check if the model is properly specified or to check whether there are not any additional predictors which could be added to give a meaningful effect.
This study demonstrated the feasibility of a trained public health nurse successfully leading a vision screening program at the community level. Out of 1106 participants invited, data analysis was done for 955 (99.5%), as per completeness of data. The mean standard deviation age of the students was 13.79 (1.43) years (median, range: 14 years, 13–15). Out of the total, 420 (43.97%) were males. The relationship of myopia with various sociodemographic variables is presented in [Table 1]. The overall prevalence of myopia was 5.03% (95% confidence interval [CI] 4.99–5.07). Myopia was found to be higher among females (P = 0.000) as compared to males. The prevalence of myopia among school students was significantly higher in lower birth order (P = 0.000). One hundred and thirty participants were referred to the optometrist for cycloplegic refraction. Of this, 45 (34.6%) were diagnosed with refractive error and needing spectacles.
Table 1: Refractive error and sociodemographic characteristics of the study participants (n=955)Among the lifestyle factors, a significant association was seen between nutritional status and myopia (P = 0.001). Forty-two participants had been prescribed spectacles before the study but only 32 (76.2%) reported wearing spectacles. Further, 18 (56.3%) of these were found to have myopic refractive error even with spectacles (unable to read Snellen's chart with spectacles). The students wore spectacles for reading 33 (78.6%), writing 21 (50%), and watching television 21 (50%). Reasons for not using spectacles were forgetting to wear them (30.8%), fear of losing spectacles (35%), does not look good on the face (30.8%), uncomfortable to wear (25%), friends tease about spectacles (25%), wearing spectacles will not correct the vision (12.8%), and wish to wear contact lenses instead (10%).
Among the family-related factors, myopia was significantly associated with preterm birth (P = 0.000) and higher maternal age at birth (P = 0.006). Myopia was not related to reported tobacco smoking among their parents as well as the use of spectacles among parents or siblings.
Multiple logistic regression model using 100% observations followed the assumptions of heteroscedasticity, multicollinearity, and autocorrelation [Table 2]. This model is able to explain 10% variability in the data (coefficient of determination). Females have six times more odds to suffer from myopia as compared to males (P = 0.000). When we remove gender from this model, the coefficient of determination falls to 4%, which means that gender plays an important role in myopia (odd ratio [OR]: 6.29; CI: 2.69–14.72). Private school students have 1.38 times more odds to get myopia as compared to government school students (OR: 1.38; CI: 0.69–2.76). If the child reads at a distance of <20 cm, he/she has almost 2 times more odds to get myopia as compared to children who read at more than 20 cm distance, and this is a statistically significant result (OR: 1.98; CI: 1.01–3.87). For every increase in the mother's age at birth by 1 year, the odds of a child having myopia increase by 1.09 times (OR: 1.09; CI: 1–1.17) and this is a statistically significant result. If the child studies under the sun for <2 h, there are 1.23 more odds as compared to children who study for more than 2 h that the child will have myopia (OR: 1.23; CI: 0.67–2.27).
Table 2: Multiple logistic regression models to assess refractive error of the study participants (appropriate model with goodness-of-fit mentioned)This study provides evidence for an association between specific socioenvironmental factors and myopia among school students. This is one of the few studies to assess whether individual (gender, preterm birth, nutritional status, and screen-based media use), familial (maternal age at birth, parental smoking, and parental/sibling refractive error), and environmental (exposure to natural light and reading distance) factors influence the prevalence of myopia among school going children in rural India.
The overall prevalence of myopia among school students in the present study was found to be lower than the pooled prevalence obtained from a recent meta-analysis of Indian data for the last four decades.[5] In the present study, the female gender emerged as a risk factor for myopia. This finding is congruent with studies from India and supports the possibility that female children spending more time indoors on near work than their male counterparts predisposes them to vision problems.[4],[6] However, the overall evidence of gender and myopia has been inconclusive so far with higher prevalence reported among males/females and vice versa by different studies.[2],[5]
As reported by other studies also, preterm birth, birth order, and higher maternal age at birth were independent risk factors for refractive error and the possible dynamics of adverse perinatal outcomes including preterm birth with advanced maternal age holds true in this study.[7] In contrast to other studies, parental education, parental smoking, and spectacle use by family members did not emerge as risk factors of myopia.[2] This is possibly due to other nonfamilial underlying factors such as near work and the method of self-report adopted in the study.[8] Another controversial risk factor in the pathogenesis of refractive error is nutritional status.[9] The present study did find nutritional status to be an independent predictor of refractive error, similar to some previous studies.[10] More detailed analysis (macro-and micronutrients) of the role of this important parameter in myopia, needs to be done in the future. As reported by several other studies, studying in natural light and computer/mobile use was not found to be significantly associated with myopia in the present study.[2],[4] In the regression analysis, reading distance (<20 cm) persisted as an independent risk factor for myopia.
The strengths of this study are its sample size, high response rate, and being a nurse-led screening program. Study limitations include cross-sectional study design and convenient enrollment of only school students which reduces the generalizability as we missed school drop-outs.
In conclusion, this nurse-led screening program found a significant difference in the prevalence of myopia between male and female students in rural areas. Other predictors of myopia were preterm birth, birth order, maternal age at birth, reading distance (<20 cm), and nutritional status.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
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