Spatial Technology Assessment of Green Space Exposure and Myopia

Yang, Yahan; Chen, Wenben; Xu, Andi; Zhao, Lanqin; Ding, Xiaohu; Li, Jiawei; Chen, Chuan; Long, Erping; Liu, Zhenzhen; Wang, Xun; Li, Xuelong; Zhang, Xingying; Jiang, Zhiyu; He, Hangen; Wang, Guojun; Jin, Ling; Liao, Huipeng; Yun, Dongyuan; Yu‐Wai‐Man, Patrick; Yan, Pisong; Wang, Ruixin; Li, Zhongwen; Xie, Yi; Liu, Yuantao; Wang, Xiaodan; Zhang, Qingling; Wang, Jiantao; Nie, Danyao; Zhang, Shaochong; Ting, Daniel Shu Wei; Wong, Tien Yin; He, Mingguang; Liu, Yizhi; Morgan, Ian G.

doi:10.1016/j.ophtha.2021.07.031

Cited by 16 publications

(16 citation statements)

References 6 publications

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“…Similarly, studies conducted in Africa projected an increase in childhood myopia prevalence in urban settings . Additionally, the presence of green spaces within school campuses is associated with a lower prevalence of myopia at the school level and potentially with reduced risk of myopia development in individuals . In our study, we systematically examined various environmental variables with population density as a reference point.…”

Section: Discussionmentioning

confidence: 99%

“…It is important to note that urban residents often have better access to health care compared with those in rural areas . While previous studies have explored the correlation between specific environmental factors associated with urban living, such as green areas, and myopia, there is a lack of understanding regarding the broader effects of the overall urban living environment on myopia . It is essential to consider the collective influence of various environmental factors within urbanization.…”

Section: Introductionmentioning

confidence: 99%

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Urban Living Environment and Myopia in Children

Li,

Qin

et al. 2023

JAMA Netw Open

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ImportanceThe global prevalence of myopia has shown a steady increase over recent decades, with urban areas seemingly experiencing a more significant impact.ObjectiveTo assess the association between urbanization and the prevalence, incidence, progression, and severity of myopia.Design, Setting, and ParticipantsThis cohort study included students in grades 1 to 6 in Tianjin, China, who underwent 3 vision examinations conducted over a 2-year period, from March 1, 2021, to March 31, 2023. Participants from grades 1 to 4 completed the 2-year follow-up.ExposuresUrban living environmentMain Outcomes and MeasuresThe association of urbanization with the incidence, progression, prevalence, and severity of myopia. To quantify urbanization, an urban score was constructed using satellite data and an iterative exploratory factor analysis.ResultsOf 177 894 students (51.7% male; mean [SD] age, 10.27 [1.75] years) included in the study, 137 087 students (52.3% male; mean [SD] age, 8.97 [1.21] years) were followed up for 2 years. A positive association was identified between myopia incidence and urbanization. Specifically, each 1-unit increment in the urban score was associated with an increased risk of myopia over a 1-year period (odds ratio [OR], 1.09; 95% CI, 1.01-1.15; P = .02) and a 2-year period (OR, 1.53; 95% CI, 1.50-1.57; P &lt; .001). Conversely, each 1-unit increase in the urban score was associated with a significant decrease in myopia progression at 1 year (OR, 0.84; 95% CI, 0.82-0.86; P &lt; .001) and 2 years (OR, 0.73; 95% CI, 0.70-0.75, P &lt; .001). In a cross-sectional data analysis, the urban score was positively associated with myopia prevalence (OR, 1.62; 95% CI, 1.08-2.42; P = .02) and negatively associated with myopia severity, as indicated by spherical equivalent refraction (OR, 1.46; 95% CI, 1.07-1.99; P = .02).Conclusions and RelevanceThis study exploring urban living environments and myopia revealed dual associations of urban living with both the incidence and the progression of myopia. The observed patterns emphasize the urgency of promptly implementing myopia control strategies in less urbanized regions, where myopia progression may be accentuated.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Urban Living Environment and Myopia in Children

Li,

Qin

et al. 2023

JAMA Netw Open

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“…We extracted factors such as age, sex, location, MAP, BMI, and sexual maturity from the data set for further analysis and found that age was the most important risk factor, followed by MAP, GDP, sexual maturity, BMI, and sex. Behavioral and environmental factors such as economic development, time spent outdoors, physical activity, green scenery exposure, sunlight exposure, near-work activity, smartphone overuse, diet, and sleep, which have been reported as the main factors of myopia onset and progression [20,[22][23][24] cannot be investigated in large-scale vision screenings. Our analysis of risk factors of high myopia is based on a review by the International Myopia Institute, with the conclusion that the two major risk factors for myopia are high educational pressures and deprivation of time outdoors [25].…”

Section: Principal Findingsmentioning

confidence: 99%

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

Guan¹,

Zhu²,

Hu³

et al. 2023

J Med Internet Res

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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.

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“…In methodology, it is used to measure the consistency of the spatial distribution pattern of the dependent variable and the independent variable, that is, the correlation between exposure and outcomes by the Pearson correlation coefficient or nonparametric test ( 31 ). Yang et al used normalized difference vegetation index (NDVI) as a measure of green space exposure, which was defined as the ratio of the difference between near-infrared reflectance and red visible reflectance to their sum, which ranges between −1 and 1, with a higher value indicating more greenness ( 22 ). They found that there was a negative association between green space exposure and myopia, giving a guide for the development of prevention strategies targeting the onset of myopia.…”

Section: Application In Ophthalmologymentioning

confidence: 99%

Progress on application of spatial epidemiology in ophthalmology

Liu

Chen

Yang

et al. 2022

Front. Public Health

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Most ocular diseases observed with cataract, chlamydia trachomatis, diabetic retinopathy, and uveitis, have their associations with environmental exposures, lifestyle, and habits, making their distribution has certain temporal and spatial features based essentially on epidemiology. Spatial epidemiology focuses on the use of geographic information systems (GIS), global navigation satellite systems (GNSS), and spatial analysis to map spatial distribution as well as change the tendency of diseases and investigate the health services status of populations. Recently, the spatial epidemic approach has been applied in the field of ophthalmology, which provides many valuable key messages on ocular disease prevention and control. This work briefly reviewed the context of spatial epidemiology and summarized its progress in the analysis of spatiotemporal distribution, non-monitoring area data estimation, influencing factors of ocular diseases, and allocation and utilization of eye health resources, to provide references for its application in the prevention and control of ocular diseases in the future.

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Spatial Technology Assessment of Green Space Exposure and Myopia

Cited by 16 publications

References 6 publications

Urban Living Environment and Myopia in Children

Urban Living Environment and Myopia in Children

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

Progress on application of spatial epidemiology in ophthalmology

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