AimsTo explore the prevalence and risk factors for myopia and uncorrected myopia in schoolchildren in southern China.MethodsThe government-led Shantou Myopia Study was conducted from September 2020 to June 2021. Non-cycloplegic refraction was performed. Uncorrected visual acuity (UCVA) was measured along with presenting visual acuity if participants wore spectacles. Spherical equivalent refraction (SER) is defined as the spherical dioptres added to half of the cylindrical dioptres. Myopia is defined as SER <−0.50 dioptre with UCVA of <20/20 in at least one eye.ResultsThis study enrolled 724 828 schoolchildren (77.8% of all schoolchildren in Shantou) from 901 schools. Data from 721 032 schoolchildren (99.5%) were analysed (mean age 11.53±3.13 years, 6–20 years, 373 230 boys and 347 802 girls). Among them, 373 459 (51.8%) had myopia: 37.1% of 465 696 children in primary schools, 75.4% of 170 164 children in junior high schools and 84.8% of 85 172 children in senior high schools. The prevalence of myopia increases non-linearly with age. Older age, female and urban living environment were independently associated with myopia prevalence and myopic SER. Among the 373 459 children with myopia, 60.0% had no refractive correction: 74.9%, 53.9% and 35.5% in primary, junior high and senior high schools, respectively.ConclusionThe overall prevalence of myopia among schoolchildren in Shantou was 51.8%, higher than the national average in China. The proportion of uncorrected myopia is high, especially in primary schools. Our results indicate the need for public education on eye care among schoolchildren even in a municipal city.

AimsTo assess the global burden and economic inequalities in the distribution of blindness and vision loss between 1990 and 2019.MethodsA secondary analysis of the Global Burden of Diseases, Injuries and Risk Factors Study (GBD) 2019. Data for disability-adjusted life-years (DALYs) due to blindness and vision loss were extracted from the GBD 2019. Data for gross domestic product per capita were extracted from the World Bank database. Slope index of inequality (SII) and concentration index were computed to assess absolute and relative cross-national health inequality, respectively.ResultsCountries with high, high-middle, middle, low-middle and low Socio-demographic Index (SDI) had decline of age-standardised DALY rate of 4.3%, 5.2%, 16.0%, 21.4% and 11.30% from 1990 to 2019, respectively. The poorest 50% of world citizens bore 59.0% and 66.2% of the burden of blindness and vision loss in 1990 and 2019, respectively. The absolute cross-national inequality (SII) fell from −303.5 (95% CI −370.8 to −236.2) in 1990 to −256.0 (95% CI −288.1 to −223.8) in 2019. The relative inequality (concentration index) for global blindness and vision loss remained essentially constant between 1991 (−0.197, 95% CI −0.234 to −0.160) and 2019 (−0.193, 95% CI −0.216 to −0.169).ConclusionThough countries with middle and low-middle SDI were the most successful in decreasing burden of blindness and vision loss, a high level of cross-national health inequality persisted over the past three decades. More attention must be paid to the elimination of avoidable blindness and vision loss in low-income and middle-income countries.

Purpose To establish a machine learning (ML) model for predicting future spherical equivalent refraction (SER) and its annual progression rate in Chinese schoolchildren. Methods A total of 23,832 participants were analyzed, including 12,514 females and 11,318 males. The follow-up visits were conducted between February 2008 and June 2021. The SER value at a specific future time point was predicted based on the results of at least two cycloplegic refraction measurements. The prediction model was established using a random forest (RF) model. A five-fold cross-validation was used to select the parameters and train an optimal RF model for prediction. Axial position, SER values, and age were selected as predictors. The performance of the algorithm was evaluated using the coefficient of determination (R2), mean squared error (MSE), and mean absolute error (MAE). Results The RF algorithm had a great advantage in the detection of high myopia, with an R2 value of 0.944 in the training datasets. The established model achieved clinically acceptable predictions of SER values at a specific future time point. For the performance of the algorithm, the R2 achieved 0.994 in the training datasets, and 0.963 in the validation datasets, respectively. For the testing datasets, the MAE, MSE, and R2 achieved 0.489, 0.502, and 0.964 for the entire population, respectively. Conclusions Our ML model performs well in the prediction of SER and myopia progression in Chinese schoolchildren. Random forest algorithm has potential advantages in the detection of high myopia and thus provides evidence for precise individual intervention and control of myopia.