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

Abstract

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.

An epidemic of myopia is taking place in some East Asian countries, rising as a major public health concern.¹ China, for example, has experienced an unprecedented increase in the prevalence of myopia in recent decades, with over 600 million Chinese being myopic.² In previous nationwide studies estimating myopia prevalence, unaided visual acuity (VA) was frequently used as a proxy measure for myopia in children and adolescents^3,4 considering its properties of high sensitivity, specificity, and simplicity.⁵ The prevalence of reduced VA was reported to be highly correlated with the prevalence of myopia, measured by cycloplegic refraction among children and adolescents (correlation coefficient: 0.992).⁶ 

Socioeconomic status (SES) is well known to be associated with myopia.^7,8 Previous studies have identified a variety of individual-level SES measures (e.g., income level and educational level) as risk factors for myopia.^8–10 However, there is a growing awareness that the SES of one's community—namely, area-level SES measures (e.g., the gross domestic product [GDP], hospital bed density, and population density)—may also have an independent impact on reduced VA.^2,11,12 For example, the risk of reduced VA was reported to increase by 20% if the GDP doubled.² Recently, a new metric, nightlight data, has been proven to be another good SES indicator complementary to GDP.^13–15 These area-level SES measures may have influenced the prevalence of myopia by affecting recreational space and access to health care services.¹⁶ Nevertheless, few studies have investigated the relationships between these area-level SES measures and myopia. 

Environmental factors also play an important role in myopia development. For example, green space exposure was reported to be protective against myopia.¹⁷ Abundant evidence has identified increased time spent outdoors as an independent protective factor for myopia due to higher levels of sunlight exposure.¹⁸ It is rational to further postulate that area-level environmental measures such as annual sunlight duration and latitude may also have an effect on myopia, whereas there exists limited evidence for this hypothesis, and the findings are far from conclusive.^19–21 

To our knowledge, most previous analyses have focused on the roles of individual-level measures in explaining visual health disparities, while few studies have explored the problem from the area level, which, from a public health perspective, would have significant implications for policymaking and resource allocation. Therefore, we used data from a nationwide survey in Mainland China to assess the effects of these area-based socioeconomic and environmental measures on reduced VA. 

Materials and Methods

Data Resources and Main Measures

The data on reduced VA prevalence that we used for analysis were derived from the Chinese National Survey on Students’ Constitution and Health in 2014 (CNSSCH 2014),²² which were the most up to date available. In total, 261,833 primary and secondary students aged 7 to 22 years were involved. All of them were randomly selected from 30 of 31 mainland provinces (or municipalities) excluding Tibet using a unified three-stage clustering procedure to obtain a representative sample. The detailed sampling strategy of CNSSCH has been previously described elsewhere.^23,24 

Our study did not involve ethical issues, as we conducted secondary analysis of open data without public involvement. 

Measurement of VA

The measurement of unaided distance VA was conducted for each eye using a retroilluminated logMAR chart consisting of tumbling-E optotypes (Precision Vision, Woodstock, IL, USA) under an illumination of approximately 500 lux. Students were instructed by qualified optometrists to report the direction of the E optotype at 5 m away. Reduced VA was defined as unaided distance VA less than 6/6 in the worse eye. Given the large scope and sample size of this nationwide survey, it was not feasible to measure present VA, best-corrected VA, and cycloplegic refraction. 

Measurement of Exposure Data

The nightlight data we used were originally remote sensing satellite images captured by the Visible Infrared Imaging Radiometer Suite (VIIRS), and they were subsequently converted into a digital number (DN) using a sigmoid function model proposed by Zhao et al.,²⁵ which represents the intensity of recorded lights. We downloaded the monthly “VIIRS Cloud Mask (vcm)” data from the Earth Observation Group (https://eogdata.mines.edu/download_dnb_composites.html) and then calculated the annual mean DN of each province (or municipality) for further analysis. Data on park green space per 10,000 people, GDP, population density, and number of hospital beds per 10,000 people were retrieved from the Chinese Statistical Yearbook. Information on the latitude and average annual sunshine duration of each province (or municipality) was sourced from the National Geomatics Center of China (NGCC) and China Meteorological Administration. All the exposure data were from the same year as the study population. 

Statistical Analysis

The prevalence of reduced VA was estimated for the overall sample and then stratified by age, sex, and area (urban versus rural). We adopted Pearson χ² tests to evaluate the differences in reduced VA prevalence between different groups (age, sex, and area). Both the crude and standardized prevalence rates adjusted for sex and age were calculated for each province or municipality. 

To assess the geographical distribution of reduced VA in Mainland China, a map of reduced VA prevalence in 30 provinces was compiled. In addition, we analyzed the overall distribution patterns (clustered/dispersed) through global spatial autocorrelation, and the Global Moran's I index was calculated using the Spatial Statistics Tools to describe the spatial distribution features.²⁶ Multiple linear regression models were fitted to assess the association between each potential risk factor and the prevalence of reduced VA. The variance inflation factor (VIF) was also calculated, and collinearity was considered present if VIF >10.¹¹ To address the problem of collinearity and summarize multiple risk factors into fewer independent variables, we conducted factor analysis, which extracted the common factor hidden behind a set of original factors using the principal component method.²⁷ Factors were accepted if the eigenvalues were larger than 1. Moreover, varimax orthogonal rotation was implemented to increase the factors’ interpretability. We reperformed multiple linear regression after factor analysis, and both the regression coefficients before and after the factor analysis were estimated. To further reveal the trends of data that might be difficult to model with parametric curves, smoothing spline plots were created to explore the relationships between these area-level measures and the prevalence of reduced VA.^28,29 A smooth curve was fitted with each independent variable adjusted for other confounding factors, including age, area, sex, GDP, annual sunlight duration, latitude, park green space density, mean DN, population density, and number of hospital beds per 10,000 people. 

The global spatial autocorrelation and the mapping of reduced VA prevalence were performed using ArcMap 10.7 (Environmental Systems Research Institute, Redland, CA, USA). All statistical analyses were conducted in SAS 9.4 (SAS Institute, Cary, NC, USA), and the smooth curve fitting was performed using R version 3.3.1 and EmpowerStats software (http://www.empowerstats.com). A two-tailed P value less than 0.05 was considered statistically significant. 

Results

This study involved 261,833 primary and secondary students from 26 provinces and four municipalities in mainland China. Table 1 displays the prevalence of reduced VA categorized by age, sex, and area. The overall reduced VA prevalence of all students aged 7 to 22 years was 66.67% (95% confidence interval [CI], 66.64%–66.69%). Reduced VA was more prevalent among girls than among boys (P < 0.001), and the prevalence rate was higher in urban than in rural regions (P < 0.001). The prevalence of reduced VA increased with age (P < 0.001), and the prevalence reached the highest value of 86.40% (95% CI, 86.36%–86.44%) among adolescents aged 19 to 22 years. 

Table 1.

 

Age- and Sex-Standardized Prevalence of Reduced Visual Acuity in Mainland China

 

The standardized reduced VA prevalence of each province or municipality is shown in Supplementary Table S1, which varied from 50.64% (95% CI, 50.50%–50.77%) in Hainan Province to 76.58% (95% CI, 76.46%–76.69%) in Jiangsu Province. The geographical variation in the provincial-level prevalence of reduced VA is fully illustrated in the map (Fig. 1). As indicated by the result of the global spatial autocorrelation analysis, the distribution of reduced VA prevalence is clustered rather than randomly distributed (Moran's I = 0.29, P < 0.05, z score = 4.22). The results indicated that clustered regions with a high prevalence of reduced VA were provinces in the north, such as Gansu and Inner Mongolia, and eastern coastal provinces, such as Shandong, Jiangsu, Zhejiang, and Shanghai. 

Figure 1.

 

Choropleth maps of reduced visual acuity prevalence in Mainland China by province. The Global Moran's I index represents the spatial autocorrelation and distribution pattern of the global incidence. The z score and P value are both measures of statistical significance and are used to determine whether to reject the null hypothesis on a factor-by-element basis. If Moran's I index is greater than 0, and P < 0.05, z > 1.96, then it indicates that the study area has spatial correlation and its distribution is clustered.²⁶ In this study, the result of spatial autocorrelation analysis of reduced visual acuity prevalence in Mainland China is the following: Moran's I = 0.29, P < 0.001, z score = 4.22; distribution is clustered.

Supplementary Table S2 shows the results of the Pearson correlation analysis. The prevalence of reduced VA was positively associated with increasing GDP (coefficient = 0.48, P < 0.01). Although the prevalence rate was positively associated with annual sunlight duration, latitude, population density, and mean DN and negatively associated with park green space per 10,000 people and number of hospital beds per 10,000 people, these associations did not reach statistical significance (all P > 0.05). 

Two factors were extracted from the factor analysis, which explained nearly 80% of the total variance (Table 2). Factor 1 included the number of hospital beds per 10,000 people, population density, and mean DN, which were all related to socioeconomic status and thus could be named the “socioeconomic factor.” Factor 2 included park green space per 10,000 people, annual sunlight duration, and latitude, which were mainly associated with the environment and thus could be named the “environmental factor.” 

Table 2.

 

Extracted Factors Separated by Group

 

Multiple linear regression before factor analysis showed that GDP, annual sunlight duration, latitude, and mean DN were positively associated with the prevalence of reduced VA, while park green space per 10,000 people, number of hospital beds per 10,000 people, and population density were negatively associated with the prevalence of reduced VA (all P < 0.001). After performing the factor analysis, we found that all the VIFs were significantly reduced. Both extracted factors were positively associated with the prevalence of reduced VA, whereas neither of them reached statistical significance (P = 0.07 and 0.14) (Table 3). 

Table 3.

 

Results of Multiple Linear Regression Before and After Factor Analysis

 

Smooth curves were fitted to intuitively display the relationships between area-level measures and the prevalence of reduced VA (Fig. 2). The results revealed that the prevalence of reduced VA continued to rise as the GDP, latitude, and mean DN increased. However, the prevalence rate continued to fall with increasing population density and sharply decreased when the park green space per 10,000 people and the number of hospital beds per 10,000 people increased. 

Figure 2.

 

The relationships between prevalence of reduced visual acuity and area-level socioeconomic and environmental measures. We equally divided the records into three subgroups in each panel according to the value of the factor from the minimum to the maximum. The black dot represents the means of the corresponding factor and the reduced VA prevalence of each subgroup, and the solid black line represents the standard deviation of the reduced VA prevalence. The black dotted line is the fitting result of the smooth curve.

Discussion

The present study evaluated the burden of reduced VA, a surrogate for myopia in children and adolescence, in 2014 in Mainland China and assessed the impacts of socioeconomic and environmental measures on the prevalence of reduced VA at the provincial level. The overall estimated age- and sex-adjusted prevalence of reduced VA among Chinese students aged 7 to 22 years was 66.67% (95% CI, 66.64%–66.69%). The growth of GDP and mean DN and the relative lack of hospital beds and park green space per 10,000 people were associated with a higher risk of reduced VA. 

Provinces with a high prevalence of reduced VA were clustered in the north, such as Beijing, and coastal areas in the east, such as Jiangsu and Shanghai, which were mainly economically developed provinces. Reduced VA was more prevalent in urban than in rural regions. In addition, as indicators of area-level SES, GDP and mean DN were both positively associated with the prevalence of reduced VA. All these results indicate a correlation between socioeconomic measures and the prevalence of reduced VA in Mainland China, which is consistent with the results of multiple linear regression. Previous studies have also reported that economic development was associated with an increased prevalence of myopia,^2,11 which supports our findings. Several possible explanations have been proposed. First, the booming economy of China has changed people's lifestyles in many ways. Increased near work is required by modern society, and outdoor activity hours have been constantly reduced, which are both well-accepted risk factors for myopia.² Second, the development of the economy and technology has provided more access to digital products, which could be detrimental to eyesight.³⁰ Furthermore, the property gap has been widened during economic development, especially in economically developed provinces, which created a competitive environment pushing individuals to pursue wealth through education.¹¹ Consequently, a heavy burden has been placed on students, and intensive schooling with less time playing outdoors has significantly increased the prevalence of myopia in China.³¹ Our findings may help explain the myopia boom in Singapore, South Korea, and other East Asia countries with similar conditions.^32,33 However, it is worth noting that some developed countries such as the United States have high GDP and DN but a low myopia rate.³⁴ It suggests that economic development cannot provide the whole explanation. Further studies are warranted to investigate the real effects of economic development on myopia prevalence. 

It was observed that the prevalence of reduced VA sharply decreased when the number of hospital beds per 10,000 people increased, which may imply that there were fewer myopic patients in regions with sufficient health care resources. However, it was reported that there was still inequity in the geographical distribution of hospital beds and other medical resources.³⁵ The recent epidemic of COVID-19 also uncovered the enormous supply and demand imbalance in health care resources in China.³⁶ Given the negative association between the number of hospital beds per 10,000 people and the prevalence of reduced VA, it is essential to further expand and reasonably allocate related resources. 

For environmental factors, both the results of smooth curve fitting and multiple linear regression showed that provinces with vast park green space per 10,000 people had a lower reduced VA prevalence. Yang et al.¹⁷ reported similar findings that there was a negative correlation between green space exposure and myopia. The results could provide new insights for developing strategies to prevent or delay the onset of myopia. 

Sunlight exposure has been identified as a protective factor against myopia,¹⁸ and Leng et al.¹⁹ reported a higher prevalence of myopia linked with shorter light exposure and lower latitude, whereas we obtained the opposite results. One possible explanation is that those environmental factors are less powerful than factors related to human activities, such as time spent outdoors, and several confounding factors have increased the complexity of the relationship. Further research is warranted to address these issues. 

The major strength of the present study are our large, representative data obtained from a nationwide survey. However, limitations should also be noted. First, VA was used as a surrogate for myopia, but it may not be completely accurate. Nevertheless, cycloplegic refraction was not feasible because of limited resources. Second, we used data from 2014 because up-to-date data were not available. Third, a causal relationship could not be established due to the cross-sectional design, and ecological fallacy may have existed. 

In conclusion, increased socioeconomic measures, including GDP and mean DN, were associated with a higher prevalence of reduced VA. Park green space per 10,000 people and the number of hospital beds per 10,000 people were negatively associated with the prevalence of reduced VA. Our results provide valuable information for understanding the myopia epidemic in China and may help develop health care policies and preventive measures such as (1) reinforcing myopia surveillance especially in high-risk provinces to prevent high myopia through early intervention, (2) increasing park green space, and (3) expanding the supply of health care resources and prioritizing the equitable distribution of hospital beds and doctors. The findings may also be useful for other developing countries with rapid economic growth in recent years. 

Acknowledgments

Supported by the National Natural Science Foundation of China (82122059). 

Disclosure: J.-Y. Kai, None; D.-L. Li, None; H.-H. Hu, None; X.-F. Zhang, None; C.-W. Pan, None