Greenness Surrounding Schools and Visual Impairment in Chinese Children and Adolescents

Vol. 129, No. 10 ResearchOpen AccessGreenness Surrounding Schools and Visual Impairment in Chinese Children and Adolescents Bo-Yi Yang, Shanshan Li, Zhiyong Zou, Iana Markevych, Joachim Heinrich, Michael S. Bloom, Ya-Na Luo, Wen-Zhong Huang, Xiang Xiao, Zhaohuan Gui, Wen-Wen Bao, Jin Jing, Jun Ma, Yinghua Ma, Yajun Chen, and Guang-Hui Dong Bo-Yi Yang Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, China , Shanshan Li Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia , Zhiyong Zou Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, China , Iana Markevych Institute of Psychology, Jagiellonian University, Krakow, Poland , Joachim Heinrich Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, Ludwig-Maximilians-Universität München (LMU) Munich, Munich, Germany Comprehensive Pneumology Center Munich, LMU Munich, Munich, Germany German Center for Lung Research, LMU Munich, Munich, Germany , Michael S. Bloom Department of Global and Community Health, George Mason University, Fairfax, Virginia, USA , Ya-Na Luo Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, China , Wen-Zhong Huang Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, China , Xiang Xiao Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, China , Zhaohuan Gui Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangzhou, China , Wen-Wen Bao Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangzhou, China , Jin Jing Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangzhou, China , Jun Ma Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, China , Yinghua Ma Address Correspondence to G.-H. Dong, Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, 74 Zhongshan 2nd Rd., Yuexiu District, Guangzhou 510080, China. Telephone: 86 20 87333409. Email: E-mail Address: [email protected]; or Y. Chen, Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, 74 Zhongshan 2nd Rd., Yuexiu District, Guangzhou 510080, China. Email: E-mail Address: [email protected]; or Y. Ma, Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing 100191, China. Email: E-mail Address: [email protected] Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, China , Yajun Chen Address Correspondence to G.-H. Dong, Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, 74 Zhongshan 2nd Rd., Yuexiu District, Guangzhou 510080, China. Telephone: 86 20 87333409. Email: E-mail Address: [email protected]; or Y. Chen, Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, 74 Zhongshan 2nd Rd., Yuexiu District, Guangzhou 510080, China. Email: E-mail Address: [email protected]; or Y. Ma, Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing 100191, China. Email: E-mail Address: [email protected] Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangzhou, China , and Guang-Hui Dong Address Correspondence to G.-H. Dong, Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, 74 Zhongshan 2nd Rd., Yuexiu District, Guangzhou 510080, China. Telephone: 86 20 87333409. Email: E-mail Address: [email protected]; or Y. Chen, Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, 74 Zhongshan 2nd Rd., Yuexiu District, Guangzhou 510080, China. Email: E-mail Address: [email protected]; or Y. Ma, Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing 100191, China. Email: E-mail Address: [email protected] Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, China Published:27 October 2021CID: 107006https://doi.org/10.1289/EHP8429AboutSectionsPDF Supplemental Materials ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmail AbstractBackground:Evidence concerning the effects of greenness on childhood visual impairment is scarce.Objectives:We aimed to assess whether greenness surrounding schools was associated with visual impairment prevalence and visual acuity levels in Chinese schoolchildren and whether the associations might be explained by reduced air pollution.Methods:In September 2013, we recruited 61,995 children and adolescents 6–18 years of age from 94 schools in seven provinces/municipalities in China. Greenness exposure was assessed using the normalized difference vegetation index (NDVI) and the soil-adjusted vegetation index (SAVI) from July to August 2013. Visual impairment was defined as at least one visual acuity level (dimensionless) lower than 4.9 (Snellen 5/6 equivalent). Three-year annual averages of particulate matter (PM) with an aerodynamic diameter of ≤1μm (PM1) and nitrogen dioxide (NO2) at each school were assessed using machine learning methods. We used generalized linear mixed models to estimate the associations between greenness and prevalent visual impairment and visual acuity levels and used mediation analyses to explore the potential mediating role of air pollution.Results:In the adjusted model, an interquartile range increase in NDVI500m was associated with lower odds of prevalent visual impairment [odds ratio (OR)=0.95; 95% confidence interval (CI): 0.93, 0.97]. The same increase in NDVI500m was also associated with 0.012 (95% CI: 0.008, 0.015) and 0.011 (95% CI: 0.007, 0.015) increases in visual acuity levels for left- and right-eye, respectively. Our results also suggested that PM1 and NO2 significantly mediated the association between NDVI500m and visual impairment. Similar effect estimates were observed for SAVI500m, and our estimates were generally robust in several sensitivity analyses.Discussion:These findings suggest higher greenness surrounding schools might reduce the risk of visual impairment, possibly owing in part to lower PM1 and NO2 in vegetated areas. Further longitudinal studies with more precise greenness assessment are warranted to confirm these findings. https://doi.org/10.1289/EHP8429IntroductionVisual impairment in children and adolescents, largely caused by near-sightedness (myopia), has become a major public health concern worldwide, particularly in Asian countries (Morgan et al. 2012). Currently, one-fifth of the world's population is affected by myopia (Holden et al. 2016), and in the urban areas of some Asian countries, 80–90% of high school children are myopic (Dong et al. 2020; Lin et al. 2004; Pan et al. 2012). There are ∼6.4 million children and adolescents with visual impairment in China alone, and the number is projected to increase to 180.4 million by 2030 (Sun et al. 2015).Visual impairment is a multifactorial condition, involving both genetic and environmental factors (Hysi et al. 2020). Evidence suggests that environmental factors have a more important role in the pathogenesis of visual impairment than genetic factors (Morgan and Rose 2005). Globally, the increase in the prevalence of visual impairment, especially myopia in children and adolescents, has coincided with the rapid increase in urbanization (Morgan et al. 2012; Wolfram et al. 2014), and several studies have reported a higher prevalence of myopia in urban areas than in rural areas (Dong et al. 2020; Ip et al. 2008; Shapira et al. 2019). The exact reasons for the observed rural–urban differences are unclear, yet they may be related to urban lifestyles, urban-related environmental exposures, and differences in medical care. There is substantial evidence that extensive screen time and little time spent outdoors are risk factors for developing myopia (Morgan et al. 2012). In addition, two cohort studies, one performed in children from Barcelona, Spain, and the other in children from both rural and areas of Taiwan, have shown that exposure to nitrogen dioxide (NO2), particulate matter (PM) with an aerodynamic diameter of ≤2.5μm (PM2.5), and black carbon were associated with the increased incidence of myopia (Dadvand et al. 2017a; Wei et al. 2019). A cross-sectional study among the elderly from both rural and urban areas of six developing countries also reported positive associations of PM2.5 and ozone with myopia (Ruan et al. 2019). Furthermore, PM2.5 exposure induced myopia in an experimental hamster study (Wei et al. 2019). Mechanistically, air pollution can induce oxidative stress and inflammation in the eyes (Jung et al. 2018; Torricelli et al. 2011), causing intraocular inflammation and impairing retinal microvasculature function as well as neuro-activity and axial length growth (Adar et al. 2010; Herbort et al. 2011; Louwies et al. 2013; Njie-Mbye et al. 2013), potentially resulting in myopia.Green spaces—such as parks, gardens, and forests—are critical components of both urban and rural areas. Evidence suggests that green space could reduce air pollution levels via filtration and adsorption (Markevych et al. 2017), encourage people to participate in outdoor physical activity (Almanza et al. 2012; Amoly et al. 2014; De la Fuente et al. 2020), and reduce recreational use of television, computers, and video games (i.e., recreational screen time) (Dadvand et al. 2014). Therefore, it is plausible to assume that greenspace could be related to a lower myopia risk. To our knowledge, there is only one previous study, which reported that green space, as measured by general levels of vegetation (greenness), was associated with lower odds of spectacles use, as a surrogate for myopia (Dadvand et al. 2017b). However, that study did not investigate potential underlying mechanisms. To help fill this literature gap, our hypothesis-driven study aimed to explore the relationship between greenness exposure and visual impairment in children and adolescents and whether lower air pollution might explain this hypothesized association.MethodsStudy PopulationIn September 2013, we undertook a large cross-sectional investigation in seven Chinese provinces/province-level municipalities (Figure 1). A four-stage stratified clustering sampling scheme was adopted to recruit study participants (Figure 2). First, seven Mainland China provinces or municipalities were randomly selected. Second, one or two cities were randomly selected from each province or municipality, yielding nine cities. Third, 3–17 schools were randomly selected from each city, generating 94 total schools. Fourth, all students in the selected schools and residing at their current address for ≥1y were eligible to participate in the study.Figure 1. Map of China showing study locations. Four provinces (Liaoning, Ningxia, Hunan, and Guangdong) and three municipalities (Tianjin, Chongqing, and Shanghai) were sampled. Base map data were obtained from ArcMap (version 10.4; Esri), HERE, Garmin, and the GIS User Community.Figure 2. Flowchart of the national cross-sectional study of Chinese school children and adolescents (6–18 years of age) selected from seven provinces or municipalities in 2013 (n=61,995).After obtaining permission from each school's principal, we provided classroom teachers with information packages (including child and parent questionnaires, study descriptions, and consent forms) to distribute to students or their parents (or legal guardians). We carefully emphasized the voluntary nature of participation to students and their parents/guardians. For children 6–8 years of age, their parents/guardians completed and delivered both the child and parent questionnaires to the classroom teachers. Children and adolescents 9–18 years of age completed the child questionnaires themselves, and their parents/guardians completed only the parent questionnaires. The questionnaire included items on sociodemographic and lifestyle factors.All participants or their parents/guardians gave written informed consent before participation. The study protocol was approved by the ethnical committee of the Peking University Health Science Center (reference no. IRB000010523034).Visual Acuity Measurement and Visual ImpairmentWe measured the visual acuity of each eye according to the Standard for Logarithmic Visual Acuity Charts set by the Standardization Administration of China (GB11533-2011). This standard recommends a five-mark record for Chinese children and adolescents, which equals to five minus the logarithm of the minimum angle of resolution (LogMAR) (Standardization Administration of the People's Republic of China 2014). The visual acuity presented as dimensionless values from 4.0 to 5.3, with higher values indicating better visual acuity. In brief, in a well-lit room, experienced eye care professionals measured visual acuity for each eye using a retroilluminated logMAR chart with trumling-E optotypes at a distance of 5 m. If a child correctly identified four or more of five top-line optotypes (4.0 in a 5-mark record, Snellen 5/50 equivalent), they were reexamined at 4.3 (Snellen 6/30 equivalent), 4.6 (Snellen 5/13 equivalent), and then line by line to 5.3 (Snellen 5/2.5 equivalent). We recorded visual acuity for each eye as the lowest line on which four of five optotypes were identified correctly. If a child was unable to read the top line at 5 m, the assessment was repeated at a distance of 1 m and the visual measurements were divided by 4 (Sun et al. 2015). Before measuring visual acuity, we asked the children and adolescents whether they wore glasses, contact lenses, or orthokeratology contact lenses. If a child wore glasses or contact lenses, we first tested visual acuity 30 min after they removed their glasses/contacts (i.e., without correction) and then repeated the measurements with the child wearing glasses/contacts (i.e., with best correction). The measurements without correction were used in the present analysis.Following the International Council of Ophthalmology population survey recommendation set forth in collaboration with the World Health Organization and the International Agency for the Prevention of Blindness (Colenbrander 2002), we defined visual impairment as at least one eye with visual acuity lower than 4.9 (Snellen 5/6). Both continuous visual acuity levels and dichotomous visual impairment were used as outcome variables.Greenness AssessmentWe estimated greenness surrounding schools using two satellite-based vegetation indices: the normalized difference vegetation index (NDVI) (Tucker 1979) and the soil-adjusted vegetation index (SAVI) (Huete 1988). Both indices were derived from Landsat 8 Operational Land Imager satellite images at a 30×30m resolution. NDVI and SAVI were calculated based on the land surface reflectance of the visible (red) and near-infrared parts of the electromagnetic spectrum, and SAVI further incorporated a correction factor to minimize influences of soil background. Both indices range from −1 to 1, with higher values indicating higher vegetation levels, values close to 0 representing barren areas, and values close to −1 corresponding to bodies of water. We downloaded cloud-free satellite images taken from July to August 2013, the period of maximum vegetation cover for the study areas and the time closest to the health data collection. For each school, we used one image, and a total of nine images were used. For Liaoning province and Chongqing municipality, we used two images each, and for the remaining five provinces or municipalities, one image each (detailed information about the images is shown in Table S1). Negative pixel values were excluded to avoid the potential confounding effects of water. Greenness surrounding schools was estimated as the mean NDVI and SAVI values in buffers of 500 and 1,000m around the centroid of each school. We used ArcGIS (version 10.4; Esri) to perform these calculations.Confounders and Potential MediatorsFor a variable to be considered as a confounder, the following criteria had to be satisfied: a) the variable had to be a risk factor for visual impairment; b) it had to be related to greenness exposure; and c) it could not be a mediator on the pathway between greenness and visual impairment (Jager et al. 2008). We constructed a directed acyclic graph (DAG; Figure S1) to retain a minimally sufficient set of confounders in regression models. The following confounders were selected: children's age (in years), children's sex (boy vs. girl), children's ethnicity (Han vs. others), urbanicity (urban vs. rural), parental education level (defined as the highest degree of either parent: below senior high school vs. completed senior high school vs. completed junior college vs. completed college or above), and district/county-level gross domestic product (GDP) (<63,511 vs. ≥63,511 Yuan/capita; 1 US dollar=6.13 Yuan in 2013), and district/county-level population density (<2,011 vs. ≥2,011 persons/km2). All of the above confounders were self-reported except for the district/county-level GDP and population density. More specifically, information on ethnicity was collected from the parents of children ≤8 years of age and from children and adolescents ≥9 years of age by the question: "What is your child's/your ethnicity?" The potential responses were: Han, Hui, Tibet, Mongol, and "other" ethnicities. If "other" ethnicities was chosen, then the parent/guardian or child was asked for more detailed information on the exact ethnicity. For participants who were unsure about their ethnicity or belonged to multiple ethnic groups, we advised them to provide the ethnicity information listed on their national identification card. Because of the limited numbers of Hui, Tibet, Mongol, and other ethnicities, we dichotomized ethnicity into Han vs. others and then modeled the new classification. The reasons for incorporating ethnicity as a potential confounder included the following: a) people with different ethnicities may have different genetic backgrounds, which are closely correlated with myopia risk (Hysi et al. 2020); and b) people with different ethnicities may live in different areas and have different lifestyles, which may affect greenness exposure. Urbanicity was divided into urban and rural areas at the school level according to 2013 administrative divisions, which were provided by the National Bureau of Statistics ( http://www.stats.gov.cn/tjsj/ndsj/2013/indexch.htm). Information on GDP (Yuan per capita) and population density (persons per kilometer squared) in the districts (or counties) containing the study schools was also obtained from the National Bureau of Statistics in 2013 and then presented and modeled as a dichotomous variable. Districts (urban area) and counties (rural area) are sub–city-level administrative divisions, and the size of the districts and counties ranged from 29 to 1,414km2 and from 2,912 to 6,264km2, respectively, in our study.Further, based on the DAG (Figure S1), air pollution at school address [i.e., PM with an aerodynamic diameter of ≤1μm (PM1) and NO2] was selected as a candidate mediator of greenness–visual impairment associations. Three-year (2010–2012) average concentrations of PM1 and NO2 for each school were estimated at a 0.1°×0.1° resolution using machine learning methods, which combined satellite-based aerosol optical depth or tropospheric NO2 data, ground-monitored air pollutants data, land cover, meteorology, and other spatial predictors (Chen et al. 2018; Zhan et al. 2018). All children and adolescents at a given school shared the same PM1 and NO2 concentrations.Statistical AnalysisWe employed generalized linear mixed models to evaluate the associations between greenness, prevalent visual impairment, and visual acuity levels, in which provinces (or province-level municipalities) were incorporated as a random effect and greenness metrics and confounders were incorporated as fixed effects. Associations of greenness indices with visual impairment [odds ratios (ORs) and 95% confidence intervals (CIs)] and visual acuity levels [regression coefficients (βs) and 95% CIs] were presented both corresponding to an interquartile range (IQR) difference in greenness indices and by quartiles because natural cubic regression splines [gam-function in R (version 4.1.0; R Development Core Team)] indicated slightly nonlinear relationships (Figures S2–S4). We used NDVI and SAVI within a 500-m buffer of schools in the main regression models and adjusted the main models for the potential confounders listed previously.To assess the robustness of our results, we performed a set of sensitivity analyses. First, we repeated the analysis excluding participants whose parents had myopia (defined as at least one parent reporting myopia; modeled as a dichotomous variable: yes vs. no) (Morgan et al. 2012; Low et al. 2010), those born with low birth weight (defined as parental report of birth weight <2,500g [WHO 1977]; modeled as a dichotomous variable: yes vs. no) (O'Connor et al. 2002), or studying in the first grade of school (exposure time to school environments was short), as well as by excluding each province or municipality one at a time. Second, we additionally individually adjusted the main models for household income (Lim et al. 2012), parental smoking (Iyer et al. 2012), sleep time (Liu et al. 2021), and lifestyle interventions for weight loss (Lim et al. 2010; Morgan et al. 2012) (which are factors that are potentially associated with myopia risk). Household income was collected from parents by the question: "What is your household income per month?" The potential responses were: <2,000, 2,000–4,999, 5,000–7,999, 8,000–11,999, 12,000–14,999, and ≥15,000 Yuan. We presented and dichotomized household income as a dichotomous variable: <5,000 vs. ≥5,000 Yuan. Parental smoking was collected from parents by the question: "On how many days of the last month did you smoke one or more cigarettes?" Smokers were defined as those who smoked at least 1 d during the last month. Parental smoking was defined as at least one parent being a smoker. This variable was presented and modeled as a dichotomous variable: yes vs. no. Sleep time was collected from the parents of children ≤8 years of age and from children and adolescents ≥9 years of age by the question: "How many hours do your child/you sleep per day?" The potential responses were: <7, 7–9, 10–11, and >11h. We dichotomized and modeled this variable as: <9 vs. ≥9h. Lifestyle interventions for weight loss were reported by the parents of children ≤8 years of age and by children and adolescents ≥9 years of age by answering the question: "During the last 30 days, has/have your child/you ever tried to lose weight via changing dietary habits, increasing physical activity, taking weight-loss drugs, or going on a diet?" The potential responses were yes or no and were modeled as a dichotomous variable. Third, we estimated NDVI and SAVI in larger (1,000-m) buffers to assess the impact of a more distant green space. Finally, we estimated average levels of NDVI500m in the 10 months when students were at school (NDVI products were downloaded from the National Aeronautics and Space Administration's MODerate-resolution Imaging Spectroadiometer at https://modis.ornl.gov/data/modis_webservice.html) to assess the potential impact of holiday break (i.e., February and July). SAVI products were not available and thus were not estimated.We used a two-way decomposition method to assess the potential mediating effects of air pollution (3-y annual average concentrations of PM1 and NO2 at each school and modeled as continuous variables) on the associations of NDVI500m and SAVI500m with visual impairment. The mediation analyses were performed using the CAUSALMED procedure (counterfactual approach) in SAS, and the total effects of greenness on visual impairment was divided into two components that were attributable to a) the greenness directly (direct effect) and b) mediation (indirect effect) (Valeri and VanderWeele 2013). Standard errors (SEs) were calculated using the delta method (VanderWeele and Vansteelandt 2009). The CAUSALMED procedure does not allow for incorporating random effects, so we did not account for the multilevel structure of the data in the mediation analysis. Alternatively, we adjusted for province/municipality as a fixed effect to partially accommodate the issue of bias in the SEs of the estimates due to the clustered outcomes.In addition, we performed moderated mediation analysis using the CAUSALMED procedure to explore whether the mediating effects of air pollution were modified by children's sex (boy vs. girl), children's age (6–11 vs. 12–18 y), and parents' highest education level (completed senior high school or below vs. completed junior college or above). We tested the significance of the moderation effects using a two-sample z-test based on the point estimates and SEs (Altman and Bland 2003).We further explored potential modification of the associations between NDVI500m and SAVI500m with prevalent visual impairment and visual acuity levels by outdoor exercise time and screen time. Children and adolescents' outdoor exercise time was evaluated by asking parents of children ≤8 years of age and children and adolescents ≥9 years of age the following question: "How long do you spend in doing outdoor exercise per day?" The potential responses were: <1 , 1–2, 2–4, and >4h/d. We further categorized the participants into two subgroups: low outdoor exercise group (<2h/d) and high outdoor exercise group (≥2h/d). Children and adolescents' screen time was collected by asking parents of children ≤8 years of age and children and adolescents ≥9 years of age the following questions: a) "How many minutes do you spend in watching TV per day?"; and b) "How many minutes do you spend in using a computer or play video games per day?" We calculated screen time for each participant by summing the time watching TV and using computer or playing video games and further categorized the data into two subgroups: <1 and ≥1h/d (mean value of screen time). We fitted separate regression models to the data for each subgroup and obtained subgroup-specific effect estimates. We assessed differences in the associations between subgroups using a two-sample z-test, based on the point estimates and SEs (Altman and Bland 2003).All statistical analyses were performed in SAS statistical software (version 9.4; SAS Institute Inc.) unless otherwise stated. A p<0.05 for a two-tailed test denoted statistical significance.ResultsStudy Participants and Greenness ExposuresWe initially invited 65,437 children and adolescents to participate in the study, of whom 62,517 completed the questionnaire (response rate 95.5%). After excluding 522 participants with missing visual acuity measurements, 61,995 children and adolescents were included in the present analysis. The distribution of basic participant characteristics were comparable before and after excluding those missing visual acuity measurements (Table S2).The mean (SD) age of the 61,995 participants was 11.0 (3.3) y and nearly half of them (48.5%) were girls (Table 1). Most participants were of Han ethnicity (92.3%) and born to parents who graduated from senior high school or below (71.4%). Approximately half of the participants (48.3%) exercised for <2h/d. In total, 34,216 (55.2%) children and adolescents were diagnosed with visual impairment, and both mean (SD) left- and right-eye visual acuity levels were 4.8 (0.4). Compared with participants without visual impairment, those with visual impairment were more likely to be older (12.3 vs. 9.7 y), girls (52.3% vs. 43.7%), live in urban areas (67.3% vs. 64.6%), live in areas with higher GDP (GDP ≥65,311 Yuan/capita: 49.7% vs. 48.4%) or population density (population density ≥2,011 persons/km2: 58.2% vs. 49.0%), have parents with higher education levels (parental highest education level of junior college or above: 29.6% vs. 27.4%), have less outdoor exercise time (≥2h/d: 50.9% vs. 52.8%), and have less screen time (≥1h/d: 40.5% vs. 47.2%).Table 1 Distribution of sociodemographic, lifestyle, environmental, and visual characteristics of the study population collected fro