Abstract
Background
This study explored the relationship between school-level built environment factors (building density and height) and myopia prevalence.
Methods
In all 16 districts of Shanghai, students from public schools and kindergartens underwent visual acuity testing and non-cycloplegic refraction between 2019 and 2023. Building density and height within 500-metre and 1,000-metre buffers around the schools were obtained using GIS (Geographic Information System)-based spatial and network analyses from the Tianditu database. Demographic and behavioural covariates, such as age, gender, outdoor activity time and near-work time, were collected through questionnaires. Logistic mixed-effect models were used to analyse the association between the building environment and myopia, adjusting for relevant demographic and behavioural factors.
Results
Data from 8 647 375 participants were analysed, with 1 116 851 followed consistently. The myopia rate among students was significantly higher during 2020–2021 compared with 2019, 2022 and 2023 (p<0.05). Myopia was more prevalent in students aged 6 to 12, with a higher average age for myopic individuals (8.13 vs 6.64 years). The average building density was higher at schools of myopic students (15.70%) versus non-myopic students (14.90%) (p<0.001). Building density near schools was negatively correlated with spherical equivalent (p<0.05), while no significant association was found with building height. The risk of myopia increased with higher building density (OR: 1.143–1.202, p<0.05), particularly in boys over 12 years old. A stronger association was observed in inner suburban areas (p<0.001).
Conclusions
Prolonged exposure to high building density may accelerate myopia progression. Urban planning should consider vision health, with regulations on building density near schools to reduce myopia risk.
Keywords: Public health, Risk Factors, Surveys and Questionnaires, Epidemiology
WHAT IS ALREADY KNOWN ON THIS TOPIC
Myopia prevalence has been rising globally, particularly in urban regions. While genetic factors and environmental influences like prolonged near-work and lack of outdoor activity are well-established risk factors, the relationship between built environment factors, such as building density, and myopia remains underexplored. Previous studies indicate urban environments may contribute to higher myopia risk, but the specific impact of building density and height is not fully understood.
WHAT THIS STUDY ADDS
This study establishes a significant association between higher building density and myopia risk, particularly among boys aged 12 and older. Unlike previous research, it finds that the role of outdoor activity in mediating this relationship is minimal. Additionally, the study identifies a stronger effect in inner suburban areas, adding a regional perspective to the understanding of how the built environment influences myopia.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
The findings underscore the importance of considering built environment factors, particularly building density, in urban planning. Future research should explore the underlying mechanisms behind this relationship. Policy implications suggest that regulating building density around schools could be a critical strategy to mitigate myopia risk in children and adolescents, guiding both urban planning and public health interventions.
Introduction
Myopia, a common refractive error primarily developing during childhood and early adulthood, occurs when images are focused in front of the retina, leading to blurred distance vision.1 The global prevalence of myopia has risen alarmingly, from 28.3% in 2010 to 34.0% in 2020, with approximately 2.62 billion individuals affected worldwide.2 This rising trend is particularly pronounced in East and Southeast Asia, where the prevalence among young adults can reach as high as 80%–90%, with a substantial portion suffering from high myopia (10%–20%).2 Myopia negatively impacts the quality of life, causing difficulties in school performance and social interactions and is associated with an estimated productivity loss of $244 billion.3 High myopia increases the risk of severe ocular pathologies such as retinal detachment and myopic maculopathy. In addition, under-corrected refractive errors have become the third leading cause of blindness globally as of 2020.4
Both environmental and genetic factors influence myopia.1 5 Children with myopic parents exhibit a heightened likelihood of developing myopia, underscoring the substantial role of genetic factors.5 Additionally, environmental factors, such as prolonged close work and insufficient outdoor activities, are also crucial in increasing myopia risk.6 Furthermore, lack of outdoor activities and natural light are key factors. Studies showed that spending more time outdoors can help reduce myopia.7 Biological studies suggest that reduced exposure to high-intensity outdoor light can disrupt normal retinal dopamine signalling, which plays a protective role against excessive axial elongation—a key factor in myopia development.8 Experimental evidence from animal models has shown that bright light exposure suppresses axial growth of the eyeball.9 Epidemiological studies in humans also support the association that children exposed to less natural light due to dense urban environments or prolonged indoor activity show greater axial elongation and higher rates of myopia progression.10 The global rise in myopia coincides with urbanisation, with higher prevalence observed in urban areas compared with rural regions.6
The rapid pace of urbanisation and advancements in geographic information technology has raised awareness of how the built environment affects health. The built environment includes all structures built by humans, such as high-rise buildings, residential areas and commercial complexes.11 It is now recognised as one of the critical drivers of non-communicable diseases.11 12 As building density increases, natural light diminishes, and outdoor spaces for physical activity become limited, negatively impacting the visual health of adolescents.13 14 Therefore, we hypothesise that building environment significantly influences myopia by limiting sunlight, outdoor activities and increasing exposure to pollutants.14
Few studies have specifically investigated the relationship between building density/height and myopia, the potential mediating role of outdoor activities or the interaction effects. To address this gap, this study analysed longitudinal primary care data from a large cohort of children and adolescents in Shanghai, China. The data were collected over several years through school-based screening programmes and linked to environmental exposures. The primary aim was to determine whether building density and height are independently associated with myopia and to investigate potential mediating factors and whether age or gender influences these associations.
Materials and methods
Study design and population
This study used data from the Shanghai child and adolescent large-scale eye study project (SCALE), covering myopia screenings from 2019 to 2023 across public schools and kindergartens in 16 districts. The scientific rationale, study design and survey methods for this project have been reported previously.15 The study population included students aged 3 to 20 years. The study population included students aged 3 to 20 years. Individuals with pre-existing eye conditions, or missing data on the building environment or eye health, were excluded. In total, 8 914 614 data records over the years were included in final analysis (online supplemental figure S2).
The study protocol was approved by the Institutional Ethics Committee of Shanghai General Hospital and follows the tenets of the Declaration of Helsinki for experimentation on humans. Parents or guardians of all participants were contacted and informed of the study purpose and procedures, and informed consent was obtained.
Outcome measurement
Each child enrolled in the study was required to undergo distance visual acuity (VA) and non-cycloplegic refraction by trained staff using standard procedures at school sites. Uncorrected VA was measured using a logarithmic E chart at 5 metres, with luminance of 80–320 cd/m2. VA was recorded in decimal notation and was transformed into five-grade notation for statistical calculation, which adhered to the National Standard of China (GB 11 533–1989).16 It could be equivalently mapped to other notations, including Snellen decimal/fraction and logMAR (online supplemental table S1). The procedure followed international criteria for recording VA.17
Non-cycloplegic auto-refraction was conducted using either the Topcon KR-8900 (Tokyo, Japan), Nidek AR-330A (Nagoya, Japan) or HUVITZ HRK-7000A (Gemjeong-dong, South Korea) auto-refractors. The consistency of the inter-instrument among the three auto-refractors was acceptable.15 An average of three consecutive readings was recorded for each eye. If any two measurements varied by more than 0.50 dioptre, the readings were discarded, and the eye remeasured. Calibration of the instruments was performed at the start of each examination day. The municipal and district-level project groups organised field supervision and quality control across all study sites.
The primary outcome was prevalent myopia. We calculated the non-cycloplegic SE for each eye using the formula spherical power +0.5* cylindrical power. Myopia was defined as VA <5.0 (in five-grade notation) and non-cycloplegic SE <−0.5D of either eye.18 Although cycloplegic eye drops were not used, we used well-established screening methods from prior studies to predict myopia, and the consistency of the screening methods and cycloplegic refraction has been previously explored.19 20
Measurement of school environment exposures
Myopia is often referred to as a ‘classroom disease’, with most students spending significant time at school. Therefore, we primarily analysed the impact of the built environment around schools on myopia. Based on prior literature and hypothesised understandings, the primary exposures of interest were school-level built environment characteristics, building density and building height.1421,24 Environmental exposure metrics were developed through GIS-based spatial and network analyses of data within a 1000-metre or 500-metre radius of participants’ schools or kindergartens from multiple databases. The selection of such a distance area for measuring environmental exposures was based on previous reports, corresponding to 5–10 min of walking at a moderate pace.22
Building density and height are the key parameters for assessing land use and spatial breadth. Building density was defined as the ratio of the total footprint area of all buildings within a defined buffer zone (500 m or 1000 m radius around each school) to the total land area of the buffer zone. Mathematically, this was calculated as: Building Density (%) = (Σ Building Footprint Area within Buffer) / (Total Buffer Area)*100%. For our Shanghai study area, building footprint data were primarily obtained from the authoritative Tianditu database, with OpenStreetMap (2023 dataset) used for completeness verification. Building height data were derived from Tianditu’s 3D building models, representing the average height of all buildings within each buffer zone. All spatial analyses were performed using ArcGIS 10.3, employing the ‘Buffer’ tool to create analysis zones and the ‘Zonal Statistics’ function to aggregate building characteristics.
Covariates
The study adjusted for demographic covariates (age and gender). Additionally, several schools covering about ten per cent of the total students every year were randomly selected and invited to complete a previously validated questionnaire. The questionnaire included daily time spent near-work and outdoor activities during school days and weekends. Weekly time was calculated by multiplying weekdays by five and weekends by two.
Statistical analysis
Because of the multilevel nature of the data, this study used linear mixed effect models to explore the relationship between school-level built environment and individual-level refractive outcome—screening myopia. Building density and height within 500/1000 metres were set as fixed effect predictors, with schools as random effects. The response variable was myopia rate. In model I, we included building density and height. Model II adjusted for average age and gender ratio. Model III was further adjusted for daily outdoor time and near-work time. Model IV included the survey year as an additional fixed effect. OR and 95% CIs were presented, and a forest plot was created to illustrate the relationship between myopia and building density. Then, stratified analyses by gender, age (<6, 6–12, >12 years) and geographical location were performed. A two-sided P value of <0.05 was considered significant. Subsequently, we analysed individuals followed up for over 3 years. We employed age, grade, school and gender at the onset of the follow-up as independent variables, and whether they developed myopia as the dependent variable, to construct a multifactor logistic regression model examining the influence of these variables on the onset of myopia. All spatial analyses were conducted with ArcGIS V.10.3, and statistical analysis was performed with R V.4.1.2.
Results
Complete data on myopia and built environment indicators were available for 8 914 614 participants from the SCALE project, conducted across multiple years (2019–2023). Among these, valid questionnaire data were available for 8 647 375 participants (online supplemental figure S2).
From 2019 to 2023, the majority of participants in the myopia screening were children aged 6–12 years, with primary and junior high school students accounting for the largest proportions (table 1). Gender distribution remained consistent, with boys representing a similar proportion each year, though the differences were statistically significant. The prevalence of myopia showed an initial increase, peaking in 2021, followed by a gradual decline (p<0.05), which may be attributed to the increased visual load associated with widespread online learning during the COVID-19 pandemic. Although daily outdoor time and near-work duration did not show significant differences across the years, environmental factors such as building height within 0.5 km and 1 km of schools exhibited a gradual increase over time, while building density showed no significant changes. Meanwhile, refractive parameters, including spherical equivalent refraction (SER) and axial length, remained relatively stable with minimal fluctuations across the study period.
Table 1. Baseline characteristics of children and adolescents participating in myopia screening in Shanghai, 2019–2023.
| 2019 (n=1 456 865) | 2020 (n=1 677 514) | 2021 (n=1 802 170) | 2022 (n=1 736 703) | 2023 (n=1 974 123) | P value | |
|---|---|---|---|---|---|---|
| Age, mean (SD) | 9.79 (3.38) | 9.48 (3.66) | 9.73 (3.57) | 9.77 (3.70) | 10.07 (3.59) | >0.05 |
| < 6, n (%) | 140 296 (9.63) | 243 910 (14.54) | 197 338 (10.95) | 228 550 (13.16) | 165 629 (8.39) | <0.05 |
| 6–12, n (%) | 993 873 (68.22) | 1 050 202 (62.61) | 1 170 149 (64.93) | 1 073 456 (61.81) | 1 290 484 (65.37) | <0.05 |
| > 12, n (%) | 322 696 (22.15) | 383 312 (22.85) | 434 683 (24.12) | 434 697 (25.03) | 518 010 (26.24) | <0.05 |
| Gender/boys, n (%) | 761 503 (52.27) | 874 152 (52.11) | 938 750 (52.09) | 783 600 (45.12) | 886 973 (44.93) | < 0.05 |
| Grade, n (%) | ||||||
| Kindergarten | 341 489 (23.44) | 426 257 (25.41) | 463 158 (25.70) | 415 942 (23.95) | 466 880 (23.65) | <0.05 |
| Primary school | 771 847 (52.98) | 802 019 (47.81) | 854 589 (47.42) | 814 342 (46.89) | 937 906 (47.51) | <0.05 |
| Junior high school | 234 867 (16.12) | 289.539 (17.26) | 310 694 (17.24) | 307 396 (17.70) | 352 381 (17.85) | <0.05 |
| High school | 108 682 (7.46) | 159 699 (9.52) | 173 729 (9.64) | 198 853 (11.45) | 216 956 (10.99) | <0.05 |
| Myopia, n (%) | 663 868 (45.57) | 824 495 (49.15) | 905 873 (50.27) | 738 850 (42.54) | 822 381 (41.66) | <0.05 |
| VAR, median (P25, P75) | 4.80 (4.60, 5.00) | 4.80 (4.60, 5.00) | 4.80 (4.60, 5.00) | 4.80 (4.60, 5.00) | 4.80 (4.60, 5.00) | <0.05 |
| SER, mean (SD) | −1.21 (2.05) | −1.22 (2.08) | −1.22 (2.08) | −1.29 (2.08) | −1.27 (2.06) | >0.05 |
| ALR, median (P25, P75) | 23.86 (22.74, 24.88) | 23.88 (22.78, 24.86) | 23.87 (22.77, 24.88) | 23.84 (22.77, 24.82) | 23.88 (22.84, 24.83) | >0.05 |
| Daily outdoor time, mean (SD) | 1.32 (0.89) | 1.34 (0.87) | 1.43 (0.88) | 1.41 (0.78) | 1.39 (0.82) | > 0.05 |
| Daily near work time, mean (SD) | 2.32 (1.58) | 2.34 (1.62) | 2.36 (1.43) | 2.42 (1.60) | 2.37 (1.73) | >0.05 |
| Building height within 0.5 km, median (P25, P75) | 34.21 (24.43, 39.89) | 34.78 (24.72, 40.41) | 34.95 (24.82, 40.54) | 35.51 (25.09, 40.92) | 36.09 (25.23, 41.05) | <0.05 |
| Building height within 1.0 km, median (P25, P75) |
33.48 (26.19, 38.37) | 34.19 (26.34, 38.68) | 34.28 (26.40, 38.70) | 34.67 (26.49, 39.02) | 35.25 (26.67, 39.21) | <0.05 |
| Building density within 0.5 km, mean (SD) | 16.27 (6.71) | 16.08 (6.87) | 15.93 (6.86) | 16.09 (7.01) | 15.74 (6.90) | >0.05 |
| Building density within 1.0 km, mean (SD) | 15.29 (6.24) | 15.20 (6.35) | 15.06 (6.34) | 15.17 (6.47) | 14.83 (6.37) | >0.05 |
ALR, axial length of the right eye; SER, spherical equivalent refraction; VAR, visual acuity of the right eye.
Based on the entire study population, we identified a fixed cohort of 1 116 851 participants who were consistently followed over 3 years. The results revealed that the average age of myopic individuals (8.13 years) was higher than that of non-myopic individuals (6.64 years), with myopia being more prevalent among those aged 6 to 12. Additionally, the residential building density for myopic individuals was significantly higher than that for non-myopic individuals (15.70 vs 14.90, p<0.001) (online supplemental table S2).
The spatial distribution maps of adolescent myopia prevalence in Shanghai from 2019 to 2023 reveal distinct patterns across Shanghai, China (figure 1). The southern districts, particularly Minhang and Pudong, consistently exhibited the highest myopia prevalence, exceeding 40% annually. In contrast, central and northern districts, such as Jing’an and Huangpu, displayed moderate to high prevalence rates, ranging from 30% to 40%, with a noticeable upward trend over the 5 years, indicating an increasingly severe public health concern. The 2023 building density map highlights that the highest density was concentrated in central districts, especially Jing’an, Huangpu and Xuhui districts, where the overlap between high building density and elevated myopia prevalence suggests a potential correlation between urban environmental factors and the rising myopia rates among adolescents.
Figure 1. Relationship between myopia prevalence and building density in 16 districts of Shanghai. (A–E) The distribution of myopia prevalence across 16 districts of Shanghai from 2019 to 2023. (F) Building density distribution in 16 districts of Shanghai.
Higher building density within 0.5 km and 1 km of schools was consistently associated with more severe myopia, as indicated by a significant negative correlation with the SER (p<0.05) (table 2, online supplemental table S3 and S4). In contrast, no significant relationship was found between building height and SER. The risk of myopia significantly increased with higher building density (OR: 1.143–1.202 in models I-IV, p<0.05). Although the direct effect of building density on myopia was significant (OR=1.246, p<0.001), the mediation effect through outdoor time was minimal and not significant (p=0.510) (table 3). This indicated that the influence of building density on myopia was primarily direct, with limited mediation by outdoor activities.
Table 2. Associations between building height/density within 1 km and myopia-related outcomes: multilevel regression results.
| Model I* | Model II† | |||||
|---|---|---|---|---|---|---|
| N | Coefficient (SD) | P value | N | Coefficient (SD) | P value | |
| Spherical equivalent of right eye | ||||||
| Building height within 1 km | 6 917 900 | 0 (0.001) | 0.718 | 4 844 925 | 0 (0) | 0.291 |
| Building density within 1 km | −0.004 (0.002) | 0.012* | −0.004 (0.001) | <0.001‡ | ||
| Axial length | ||||||
| Building height within 1 km | 2 332 687 | 0.006 (0.005) | 0.169 | 1 632 881 | 0.001 (0.003) | 0.877 |
| Building density within 1 km | 0.007 (0.009) | 0.413 | 0.004 (0.006) | 0.484 | ||
| Screening myopia | ||||||
| Building height within 1 km | 8 647 375 | 1.011 (0.974, 1.049) | 0.576 | 4 844 925 | 1.005 (0.982, 1.028) | 0.662 |
| Building density within 1 km | 1.202 (1.094, 1.320) | 0.001* | 1.181 (1.118, 1.248) | <0.001‡ | ||
Model Ⅰ adjusted for random effect of schools.
Model Ⅱ adjusted for fixed effect of age, gender, grade, daily outdoor time, daily near work time, years and random effect of schools.
p<0.05.
Table 3. Building density(1 km)-outdoor time-myopia related outcomes mediation effect analysis.
| Effect* | P value | |
|---|---|---|
| Spherical equivalent of right eye | ||
| Indirect | −0.003 (−0.009, 0.003) | 0.290 |
| Direct | −0.003 (−0.006, 0.001) | 0.075 |
| Total | −0.006 (−0.010, −0.001) | 0.030† |
| Myopia | ||
| Indirect | −0.065 (−0.336, 0.140) | 0.510 |
| Direct | 1.246 (1.108, 1.455) | <0.001† |
| Total | 1.181 (1.118, 1.248) | <0.001† |
| Axial length | ||
| Indirect | 0 (−0.006, 0.006) | 0.923 |
| Direct | 0.003 (−0.001, 0.012) | 0.150 |
| Total | 0.003 (0.001, 0.006) | 0.062 |
Adjusted for fixed effect of age, gender, grade, daily outdoor time, daily near work time and random effect of schools.
P<0.05.
The stratified analysis revealed significant age and gender differences in the association between building density and myopia risk (figure 2 and online supplemental figure S1). Among boys aged 6 years or younger, each unit increase in building density was associated with an 18.9% increase in myopia risk within 0.5 km (OR=1.189, p<0.001) and a 21.1% increase within 1 km (OR=1.211, p<0.001). In contrast, no significant association was observed for girls in the same age group (p>0.05). For children aged 6 to 12 years, building density significantly impacted both boys and girls, with boys showing slightly higher odds ratios (1.200 to 1.304) than girls (OR=1.173 to 1.272). Notably, among boys over 12 years old, the impact of building density was even more pronounced, with ORs of 1.276 (p=0.004) and 1.311 (p=0.003) within 0.5 km and 1 km, respectively. However, for girls over 12, no significant association was found (p>0.05). Overall, the results indicate that boys, particularly those older than 12, are more susceptible to the effects of building density on myopia risk, with the impact intensifying with age.
Figure 2. Effect of building density at 1 km on myopia incidence across age and gender groups. Model I adjusted for random effect of schools. Model II adjusted for fixed effect of grade and random effect of schools. *p<0.05.
This study found that the association between building density and the risk of myopia varied across different regions (online supplemental table S5). Building density in inner suburban areas had the most pronounced impact on adolescent myopia risk. Higher density within both the 1 km and 0.5 km radii significantly increased myopia incidence (p<0.001). In comparison, while central urban areas demonstrated some association with myopia risk, the effect was less substantial (OR=1.080, p=0.008).
Discussion
Myopia is a significant concern for individuals worldwide, yet the relationship between building density and myopia has yet to be explored. In this large, objectively measured study, we identified a significant association between higher building density and increased prevalence of myopia, particularly among boys aged 12 years and older. In contrast, building height had no significant effect. While building density had a substantial direct effect on myopia, outdoor time played a minor role. This suggested that the influence of building density on myopia is primarily direct, with outdoor activity playing a limited role in this process. Additionally, there were significant age and gender differences in the association between building density and myopia risk, particularly among boys aged 12 and older, where the impact of building density was most pronounced. As age increased, this influence on myopia risk gradually intensified for boys, while no significant association was observed in their female peers.
This study found a significant association between higher building density around schools and increased myopia prevalence among students. In contrast, average building height within the same buffer zones was not significantly associated with myopia risk. Our study identified a significant association between higher building density and the incidence of myopia among children and adolescents, which several potential mechanisms can explain. First, high-density areas often reduce green spaces and outdoor activity opportunities, which protect against myopia.14 21 25 However, our findings showed that outdoor time had a limited mediating effect between building density and myopia. Second, high building density often obstructs natural light exposure, limiting the amount of outdoor light children in these areas receive, thereby reducing the protective effect of light on eye growth.13 26 Additionally, areas with higher building density typically have increased levels of air pollution, which may contribute to myopia development by inducing ocular inflammation or directly irritating eye tissues.27 28 Finally, high building density often obstructs long-distance views, which can discourage children from engaging in activities that promote distance vision, further exacerbating the risk of myopia. Children living in densely built areas may be more prone to prolonged screen time and near-work.29 30 Thus, our findings support the hypothesis that building density negatively impacts myopia development through environmental and behavioural mechanisms. The mediation effect analysis of building density and outdoor time on myopia-related outcomes highlighted a strong association between urban building density and myopia among schoolchildren, suggesting that effective interventions may need to address factors beyond merely increasing outdoor activity.
The increased effects of building density in participants aged 7 to 12 years might be associated with rapid refractive development and hormonal changes characteristic of this age group.31 32 The study indicated that boys, particularly those over 12 years old, were more sensitive to the impact of building density on myopia risk. However, previous studies suggested that girls were more affected by high building density, which contrasted with the findings of this study.33 This discrepancy might have been due to differences in behaviour, as girls typically engage in more indoor activities during and after adolescence, while boys are more likely to participate in outdoor activities, potentially leading to differing sensitivities to environmental factors.33 34 Therefore, further research is needed to better understand the influence of building density on myopia across different age groups and genders.
The consistency observed between outcome measures and building density within a 500-metre radius provides compelling evidence supporting a potential causal relationship. In contrast, no significant association was found between building height and myopia, suggesting that horizontal building density has a more substantial impact on myopia development than vertical height. Furthermore, the influence of building environments on myopia risk varied across different regions. This study demonstrated that, within both the 1 km and 0.5 km radii, higher building density in inner suburban areas significantly increased the risk of myopia in children and adolescents. The mixed nature of inner suburban areas, which lack both the extensive outdoor activity spaces of outer suburbs and the abundant recreational facilities of central urban areas, may explain the more pronounced effect of building density on myopia in these regions.
Our study has several limitations. First, we could not assess the impact of environmental factors near participants’ homes, although the proximity between home and school environments in Shanghai might suggest similarities. Second, potential student mobility between schools could have led to misclassification of environmental exposure. Additionally, conducted in a single, densely populated city, the study may limit the generalisability of our findings. Finally, we lacked detailed information on some potential confounders, which could have introduced bias. Our study did not adjust for certain socioeconomic confounders, such as parental education, household income and home lighting quality, which are known to be associated with myopia risk. These unmeasured variables may influence children’s time spent on near work, screen use and access to outdoor environments, thereby potentially biasing the observed associations. Future studies incorporating detailed individual- or household-level socioeconomic data will be essential to better isolate the independent effect of building density on myopia development. In addition, we did not account for variations in school infrastructure, such as classroom lighting, screen exposure policies or the availability of outdoor activity facilities, which could differ across schools and regions and influence the observed associations. Neighbourhood-level socioeconomic status may also affect both the built environment and myopia risk by shaping access to resources, recreational spaces and parental awareness of vision health. Future research incorporating detailed individual and contextual socioeconomic data is essential to better isolate the independent effect of building density on myopia development. These factors may co-vary with building density and independently influence myopia development through behavioural and environmental pathways. Without controlling for these contextual variables, the observed associations between building density and myopia risk may partly reflect these unmeasured influences.
However, this study also has significant strengths. The use of 5-year longitudinal data allowed us to observe the long-term effects of the built environment on myopia, which many studies cannot achieve. The unique scale and extensive geographical coverage of the SCALE project provided robust statistical power to detect effects and explore interactions. Our study is among the first to explore the relationship between building density and myopia, using objective measurements to minimise bias. Built environment factors may function as the distal cause of myopia, characterised by introducing weak and long-term effects that will influence a wide population and range of diseases.35 We also examined the mediating role of outdoor time, offering valuable insights for future research. Finally, our findings stress the importance of including public health in urban planning to support eye health.
Conclusions
This study suggests that higher building density may have long-term adverse effects on the eye health of children and adolescents. The study underscores the importance of incorporating public health considerations into urban planning, especially in designing school and urban environments that support eye health. Future research should further explore these findings, focusing on controlling additional potential confounders to better understand the long-term impact of the built environment on myopia and to inform effective prevention strategies.
Supplementary material
Acknowledgements
The authors thank the teams that collected data and the individuals who participated in this study.
Footnotes
Funding: This study was supported by the National Natural Science Foundation of China (no. 82003562); the Shanghai Municipal Three Year Action Plan for Strengthening the Construction of Public Health System (2023-2025); the Excellent Youth Talent Training Plan (no. GWVI-11.2-YQ27); the Project of Shanghai Health Commission (no.202340065); and the Shanghai Health Science Popularization Youth Talent Special Project (no. JKKPYC-2023-B10).
Provenance and peer review: Not commissioned; externally peer-reviewed.
Patient consent for publication: Not applicable.
Ethics approval: This study involved human participants. The study protocol was approved by the Institutional Ethics Committee of Shanghai General Hospital (2015KY149) and followed the tenets of the Declaration of Helsinki for experimentation on humans. Participants gave informed consent to participate in the study before taking part.
Data availability free text: After publication of this study, study protocols, statistical code and requested questionnaire data may be made available to interested investigators after approval of Shanghai eye disease prevention and treatment centre. NDVI data can be modelled from Landsat satellite data. Linked electronic eye health records require separate permission from the Shanghai Municipal Health Commission.
Map disclaimer: The depiction of boundaries on this map does not imply the expression of any opinion whatsoever on the part of BMJ (or any member of its group) concerning the legal status of any country, territory, jurisdiction or area or of its authorities. This map is provided without any warranty of any kind, either express or implied.
Data availability statement
Data are available upon reasonable request.
References
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Data Availability Statement
Data are available upon reasonable request.