Purpose Myopia treatment using orthokeratology (ortho-k) slows myopia progression. However, it is not equally effective in all patients. We aimed to predict the treatment effect of ortho-k using a machine-learning-assisted (ML) prediction model. Methods Of the 119 patients who started ortho-k treatment between January 1, 2019, and January 1, 2022, 91 met the inclusion criteria and were included in the model. Ocular parameters and clinical characteristics were collected. A logistic regression model with least absolute shrinkage and selection operator regression was used to select factors associated with the treatment effect. Results Age, baseline axial length, pupil diameter, lens wearing time, time spent outdoors, time spent on near work, white-to-white distance, anterior corneal flat keratometry, and posterior corneal astigmatism were selected in the model (aera under curve: 0.949). The decision curve analysis showed beneficial effects. The C-statistic of the predictive model was 0.821 (95% CI: 0.815, 0.827). Conclusion Ocular parameters and clinical characteristics were used to predict the treatment effect of ortho-k. This ML-assisted model may assist ophthalmologists in making clinical decisions for patients, improving myopia control, and predicting the clinical effect of ortho-k treatment via a retrospective non-intervention trial.

/pdf/machine-learning-for-predicting-the-treatment-effect-of-2ix722yf.pdf

Machine learning for predicting the treatment effect of orthokeratology in children

Background Patients with high myopia have an increased lifetime risk of complications. The prevalence patterns of high myopia in children and adolescents in southern China are unclear. Early identification of high-risk individuals is critical for reducing the occurrence and development of high myopia and avoiding the resulting complications. Objective This study aimed to determine the prevalence of high myopia in children and adolescents in southern China via real-world screening data and to predict its onset by studying the risk factors for high myopia based on machine learning. Methods This retrospective school-based study was conducted in 13 cities with different gross domestic products in southern China. Through data acquisition and filtering, we analyzed the prevalence of high myopia and its association with age, school stage, gross domestic product, and risk factors. A random forest algorithm was used to predict high myopia among schoolchildren and then assessed in an independent hold-out group. Results There were 1,285,609 participants (mean age 11.80, SD 3.07, range 6-20 years), of whom 658,516 (51.2%) were male. The overall prevalence of high myopia was 4.48% (2019), 4.88% (2020), and 3.17% (2021), with an increasing trend from the age of 11 to 17 years. The rates of high myopia increased from elementary schools to high schools but decreased at all school stages from 2019 to 2021. The coastal and southern cities had a higher proportion of high myopia, with an overall prevalence between 2.60% and 5.83%. Age, uncorrected distance visual acuity, and spherical equivalents were predictive factors for high myopia onset in schoolchildren. The random forest algorithm achieved a high accuracy of 0.948. The area under the receiver operator characteristic curve (AUC) was 0.975. Both indicated sufficient model efficacy. The performance of the model was validated in an external test with high accuracy (0.971) and a high AUC (0.957). Conclusions High myopia had a high incidence in Guangdong Province. Its onset in children and adolescents was well predicted with the random forest algorithm. Efficient use of real-world data can contribute to the prevention and early diagnosis of high myopia.

/pdf/prevalence-patterns-and-onset-prediction-of-high-myopia-for-2qcr9mh1.pdf

Prevalence Patterns and Onset Prediction of High Myopia for Children and Adolescents in Southern China via Real-World Screening Data: Retrospective School-Based Study

Background Myopia is a serious public health issue. High school–aged adolescents in Beijing have an alarming prevalence of myopia. Therefore, determining myopia protective factors is essential. Green space has a certain association with myopia protective factors that can protect against myopia. Objective This study aims to examine the effects of green space around schools on individual myopia risk in high school–aged adolescents and the school-level myopia prevalence. Methods Green space was measured using the normalized difference vegetation index (NDVI). A total of 13,380 samples of 51 high schools were selected from a 2021 Beijing Municipal Health Commission survey. Adolescent myopia was defined as a spherical equivalent of ≤–1.00 diopters in the worse eye. Generalized linear mixed models with a binomial error structure were used to analyze the effects of the NDVI on personal myopia risk and adjust them by other factors, such as demographics, exposure time, and outdoor exercise. The effects of the NDVI on school-level myopia prevalence with adjusted demographics and the relative position factors of trees were analyzed through quasibinomial regression. Results The overall prevalence of myopia was 80.61% (10,785/13,380, 95% CI 79.93%-81.27%). Although with a 0.1 increase in the 500 and 1000 m buffer NDVIs adjusted by demographic and other factors, the high school–aged personal myopia risk significantly dropped by 16% (odds ratio [OR] 0.84, 95% CI 0.73-0.97) and 12% (OR 0.88, 95% CI 0.79-0.99), respectively. However, only the adjusted 500 m buffer NDVI (by demographics and the position of trees) with a 0.1 increase significantly reduced the school-level myopia prevalence by 15% (OR 0.85, 95% CI 0.74-0.98). Subgroup analysis showed that the adjusted effects of the 500 m buffer NDVI are significant in schoolgirls (OR 0.82, 95% CI 0.72-0.93), juniors (OR 0.82, 95% CI 0.72-0.94), the Han nationality (OR 0.84, 95% CI 0.72-0.97), 1-year exposure (OR 0.84, 95% CI 0.71-0.99) and 3-year exposure (OR 0.78, 95% CI 0.65-0.94). Conclusions The greenness of a 500 m buffer around schools is associated with a lower personal myopia risk among adolescents and a lower prevalence of myopia in schools. With regard to prevention and control activities, green space within a 500 m buffer around schools is suggested as an independent protective factor for adolescent myopia.

https://jmir.org/api/download?alt_name=publichealth_v9i1e42694_app1.pdf&filename=b2f3a5171d22e3374608519ced000dee.pdf

Effects of Greenness on Myopia Risk and School-Level Myopia Prevalence Among High School–Aged Adolescents: Cross-sectional Study

Purpose To assess the impacts of area-level socioeconomic and environmental measures on reduced visual acuity (VA). Methods This ecological study used the nationally representative cross-sectional data from the Chinese National Survey on Students’ Constitution and Health in 2014 (CNSSCH 2014), which included 261,833 participants aged 7 to 22 years randomly selected from 30 mainland provinces in China. Area-level socioeconomic measures assessed included gross domestic product (GDP), population density, density of hospital beds, and nighttime light data measured as the mean digital number (DN) of each region; environmental factors assessed included latitude, annual sunlight duration, and park green space density. The main outcome measure was the prevalence of reduced VA in each province of Mainland China. Results GDP (coefficient: 0.221; P < 0.001), mean DN (coefficient: 0.461; P < 0.001), latitude (coefficient: 0.093; P < 0.001), and annual sunlight duration (coefficient: 0.112; P < 0.001) were positively associated with the prevalence of reduced VA, while population density (coefficient: −0.256; P < 0.001), park green space per 10,000 people (coefficient: −0.145; P < 0.001), and number of hospital beds per 10,000 people (coefficient: −0.146; P < 0.001) were negatively associated with reduced VA prevalence. Factor analysis indicated a marginally nonsignificant positive correlation between socioeconomic factors and the prevalence of reduced VA (coefficient: 0.034; P = 0.07). Conclusions Increased GDP and mean DN, which reflect economic development, were associated with a higher prevalence of reduced VA, while larger park green space and number of hospital beds per 10,000 people seemed to play a protective role, which could be targeted to design preventive strategies for myopia.

Impact of Area-Level Socioeconomic and Environmental Measures on Reduced Visual Acuity Among Children and Adolescents

Association of Built Environment Attributes with Screening Myopia Among Children and Adolescents: Large-Sample, Cross-Sectional, Observational Evidence from Shanghai, China

Spatial Technology Assessment of Green Space Exposure and Myopia.

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Spatial Technology Assessment of Green Space Exposure and Myopia.