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Article

A Serial Cross-Sectional Analysis of the Prevalence, Risk Factors and Geographic Variations of Reduced Visual Acuity in Primary and Secondary Students from 2000 to 2017 in Hong Kong

by
Perseus W.F. Wong
,
Jimmy S.M. Lai
and
Jonathan C.H. Chan
*
Department of Ophthalmology, L.K.S. Faculty of Medicine, The University of Hong Kong, Hong Kong, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(3), 1023; https://doi.org/10.3390/ijerph17031023
Submission received: 21 October 2019 / Revised: 20 January 2020 / Accepted: 31 January 2020 / Published: 6 February 2020
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Abstract

:
Background: We would like to investigate the prevalence trend, potential risk factors and geographic features of reduced visual acuity (VA) in primary and secondary schoolchildren of Hong Kong. Methods: This was a serial cross-sectional study using historical data of schoolchildren aged 6 to 15 years from the annual health checks conducted at Student Health Service Centers across Hong Kong, for the school years of 2000/2001 to 2016/2017. Results: The prevalence of reduced VA increased from 49.23% (95% CI, 48.99−49.47) in 2000/2001 to 54.34% (95% CI, 54.10−54.58) in 2011/2012 but decreased to 51.42% (95% CI, 51.17−51.66) in 2016/2017. Girls were less susceptible than boys at age 6−7 (and in grade primary 1−2), but more susceptible at older ages. The prevalence in junior grades increased while the risk effect of grade reduced over the past 17 years. Geographic variation on the risk for reduced VA existed and spatial autocorrelation was positive. The difference in prevalence of reduced VA between Hong Kong and mainland China has decreased in recent years. Cross-border students living in mainland China were associated with a lower risk for reduced VA. Conclusions: Further study was proposed to investigate the environmental association between students living in and outside Hong Kong with the prevalence of reduced VA. Multi-level research should also be conducted to investigate the influence of compositional and contextual factors on the prevalence of reduced VA.

1. Introduction

Refractive error was the leading cause of reduced visual acuity (VA) among children and adolescents worldwide. Studies of this century in Vietnam, India and Malaysia have found that refractive error accounted for 80% to 96% of reduced VA [1,2,3]. In mainland China, refractive errors accounted for 85% to 98% of reduced VA in Beijing, Chongqing, Xinjiang, Yunnan, Guangdong and Shanghai [4,5,6,7,8,9,10]. One common cause of refractive errors among schoolchildren was myopia, accounting for 25% to 75% of refractive errors in Saudi Arabia, Nepal, India, Norway and Mexico [11,12,13,14,15]. Myopia accounted for 53% to 67% of refractive errors in Shanghai, Chongqing and Beijing of China [7,9,16].
To our knowledge, three surveys were done for estimating the prevalence of refractive errors or myopia among schoolchildren in Hong Kong (HK). A 2005−2010 survey by Lam et al showed that the prevalence of refractive errors was 34.2% and 66.6% at age 6 and 10, respectively [17]. Another survey found that the prevalence of myopia in local and international schoolchildren in 2001 were 85−88% and 60−66%, respectively, with the highest in the Chinese group (82.8%) and the lowest in the white group (40.5%) [18]. The third survey noted that the prevalence (± standard deviation, SD) of myopia at age 5−16 was 36.71% (±2.87%) from 1998 to 2000 [19]. However, they had three limitations.
First, their sample size was small because large-scale adoption of cycloplegic refraction was not feasible. In mainland China, two retrospective serial cross-sectional studies were conducted to estimate the prevalence of reduced VA in large populations, assuming that reduced VA would be a proxy measure for myopia [20,21]. Their rationale was based on the findings of three previous studies of testing the sensitivity and specificity of using unaided VA to screen for refractive errors among students in Singapore and Australia [22,23,24].
Second, they did not measure geographic variations on the risk for reduced VA and spatial autocorrelation in HK. Previous studies proved the associations of areas of residence (urban/rural region), housing types (flat room/building), living environment (frequency of seeing green) and economic status (socioeconomically advantaged/disadvantaged) with myopia, one major cause of refractive errors [25,26,27,28]. If these variables were similarly or dissimilarly clustered with the spatial location, there would be positive or negative spatial autocorrelation of reduced VA.
Third, they estimated the prevalence at a single time point but did not describe the dynamic changes over a series of time points, e.g., cross-boundary students with distinct sociodemographic features may have led to changes in prevalence over time. In mainland China, two retrospective serial cross-sectional studies were conducted to estimate the prevalence of reduced VA in Guangzhou, China from 1988 to 2007 and the whole of China from 1985 to 2010. In contest, no serial cross-sectional studies, whether prospective or retrospective, have been conducted in HK.
For understanding the epidemiologic trend of reduced VA, we would like to review the historical data of Health Programs at Student Health Service Centers (SHSCs) of the Department of Health, Hong Kong Special Administrative Region (DH, HKSAR) [29]. The objective of our study was to investigate the prevalence trend, potential risk factors and geographic features of reduced VA in primary and secondary students from the school year 2000/2001 to 2016/2017.

2. Materials and Methods

2.1. Study Area

The Student Health Service (SHS) of the DH, HKSAR has been offering annual health checks for primary and secondary students (including local and international schools) of HK since the 1995/1996 school year for primary students and the 1996/1997 school year for secondary students. This includes regular VA assessment, with the aim to detect any VA problems of students and whether these problems need referrals or have been appropriately corrected. Clinical data pertaining to visual assessment of these students were obtained from 12 SHSCs throughout HK.
The Hong Kong Special Administrative Region is divided into 18 administrative districts, which are: Central and Western, Wan Chai, Eastern, Southern, Yau Tsim Mong, Sham Shui Po, Kowloon City, Wong Tai Sin, Kwun Tong, Kwai Tsing, Tsuen Wan, Tuen Mun, Yuen Long, North, Tai Po, Sha Tin, Sai Kung and Islands [30].

2.2. Study Design

This is a retrospective, serial, cross-sectional study covering primary and secondary students aged 6 to 15 years attending the annual health check offered by the SHS from the school year of 2000/2001 to 2016/2017, with the exception of 2009/2010, since in that school year, SHS had to take part in the Human Swine Influenza Vaccination Program, and therefore, annual appointments were only provided to Primary 1 (P1) to Secondary 1 (S1) students.

2.3. Source of Data

Available records between 2000/2001 and 2016/2017 from the SHS database were extracted for relevant data; including age, sex, school type, grade of student, school district, location of SHSCs, type of housing, home district and VA test result. The relative age and sex composition of the study population was retrieved from the Student Enrollment Statistics of Education Bureau [31].

2.4. Definitions

VA testing was performed for distant vision using a Logarithm of Minimum Angle of Resolution (logMAR) VA display chart at 3 or 4 meters. Students with an unaided logMAR VA of 0.3 or better were recorded as having satisfactory vision. The test was done with visual aid if the students was wearing glass or contact lens. Reduced VA was defined as having an unaided logMAR VA of >0.3, or wearing glasses or contact lenses (excluding Ortho-K lenses).

2.5. Data Analysis

We used multiple imputation to create 20 data sets with imputed values for handling the missing data. The means and odds ratios (ORs) were calculated by using SAS PROC SURVEYMEANS and PROC SURVEYLOGISTIC, respectively. We combined the outcomes of 20 datasets and provided the final estimates with 95% confidence interval (CI) in SAS PROC MIANALYZE.
The prevalence of reduced VA was reported in crude rate, age-sex adjusted rate after adjusting the sampling weight of age and sex composition to poststratification weight for each school year, and standardized rate for selecting the study population for 2000/2001 as a standard population. We divided the sample data into 10 strata (5 age groups (6−7, 8−9, 10−11, 12−13 and 14–15) × 2 sex groups (boys and girls)), and combined them with the population data from Education Bureau to do poststratification analysis and direct standardization. Mann-Kendall trend test is a non-parametric test used to analyze the data for consistently increasing or decreasing trends in the series. We tested the null hypothesis that there was no monotonic trend for the prevalence of reduced VA over the study period. Subgroup analyses stratified by age (6−7, 8−9, 10−11, 12−13 and 14−15) and grade (P1-P2, P3-P4, P5-P6, S1-S2 and S3-S4) were also done to identify either consistency of or large differences in the prevalence and ORs of reduced VA among different categories of students.
We performed univariate logistic analysis with and without adjusting for age and sex and fitted all independent variables into multivariate logistic analysis to measure the strength of their association with reduced VA. The results were presented in four time intervals: 2000/2001−2003/2004, 2004/2005−2007/2008, 2008/2009−2012/2013 and 2013/2014−2016/2017. Chi-square independence test was used to test whether the presence of reduced VA and the 18 districts (home or school) were independent when Kruskal–Wallis test were used to test whether the prevalence of reduced VA were identical among the 18 districts (home or school). Mantel test was computed to test whether there was a relationship between the community distance from Central and Western District (where the city center is located) and the prevalence (or AORs) of reduced VA. Moran’s Index was calculated to test whether there was a spatial clustering of the prevalence (or AORs) of reduced VA associated with the community distance from Central and Western District and also measure the overall spatial autocorrelation.
All data extraction, processing and analyses were performed using the R package version 3.5.1 (R Development Core Team, 2018) and SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). The results were reported in estimate with 95% CI and with p-value < 0.05 being considered statistically significant.

2.6. Ethics Approval

Ethics approval was granted by the Institutional Review Board (IRB) and Ethics Committees (EC) of the Hong Kong University/Hospital Authority Hong Kong West Cluster (UW 18-071) on 11 January, 2018. Availability of data and computing code: due to the ownership, the original dataset would not be available without authorization from the Department of Health in Hong Kong but the computing code are available from the corresponding author on reasonable request.

3. Results

3.1. Demographics

The overall frequency of schoolchildren meeting the inclusion criteria were 6.75 million from 2000/2001 to 2016/2017. On average, the frequency was 0.422 million per school year. The participation rate ranged from 55.73% in 2000/2001 to 69.76% in 2013/2014. The range of mean (±SD) age was from 9.72 (±2.49) in 2000/2001 to 10.64 (±2.73) in 2010/2011. The proportion of boys increased from 49.93% in 2000/2001 to 51.37% in 2016/2017. The mean (±SD) presenting VA of right and left eyes were 0.229 (±0.142) and 0.235 (±0.136), respectively, in 2000/2001 and changed to 0.173 (±0.18) and 0.171 (±0.175), respectively, in 2016/2017. The proportions of wearing glasses or contact lenses ranged from 32.43% in 2000/2001 to 42.9% in 2010/2011. The percentage frequency of missing data of three variables (i.e., home district, presenting VA and usage of visual aids) was almost less than 1%. (Supplementary Materials Tables S1 and S2)

3.2. Prevalence

The standardized prevalence of reduced VA rose from 49.23% (95% CI, 48.99–49.47) in 2000/2001 to 54.34% (95% CI, 54.10–54.58) in 2011/2012 but dropped to 51.42% (95% CI, 51.17–51.66) in 2016/2017. Its correlations with the proportion of primary to secondary students attending SHS and enrolled students were −0.875 and −0.885, respectively. Mann-Kendall trend tests showed that the prevalence trend was rising from 2000/2001 to 2011/2012 (tau, 0.927; p-value, <0.001) but was null from 2012/2013 to 2016/2017. The overall prevalence trend remained increasing (tau, 0.383; p-value, 0.043) (Table 1).
Stratified by age, the standardized prevalence rose from 26.08% (95% CI, 25.76−26.40) in 2000/2001 to 31.13% (95% CI, 30.70−31.55) in 2011/2012 but dropped to 24.47% (95% CI, 24.13−24.81) in 2016/2017 for age 6−7. Moreover, it rose from 66.22% (95% CI, 65.37−67.07) in 2000/2001 to 69.46% (95% CI, 68.81−70.10) in 2011/2012 but dropped to 68.89% (95% CI, 68.14−69.63) in 2016/2017 for age 14−15. Mann-Kendall trend tests showed that the prevalence trend for all age groups was rising from 2000/2001 to 2011/2012 (p-value, <0.001) and was null from 2012/2013 to 2016/2017, except age 12−13 (tau, 1; p-value, 0.027). The overall prevalence trend was increasing for age 10−13 only. (Supplementary Table S3)
Stratified by grade, the standardized prevalence rose from 27.40% (95% CI, 27.11−27.70) in 2000/2001 to 33.76% (95% CI, 33.35−34.18) in 2011/2012, but dropped to 27.36% (95% CI, 27.01−27.70) in 2016/2017 for grade P1-P2. Moreover, it rose from 69.05% (95% CI, 68.06−70.05) in 2000/2001 to 70.48% (95% CI, 69.81−71.16) in 2011/2012, but dropped to 69.44% (95% CI, 68.67−70.20) in 2016/2017 for grade S3-S4. Mann-Kendall trend tests showed that the prevalence trend for all age groups was rising from 2000/2001 to 2011/2012 (p-value, <0.001) and dropping from 2012/2013 to 2016/2017 for grade P3-P4 (tau, −1; p-value, 0.027). The overall prevalence trend was increasing for grade P3-P6 only.

3.3. Age and Sex

Overall, girls have higher risk for reduced VA relative to boys with stable OR of around 1.1 throughout the study period in univariate and multivariate logistic analysis.
If stratified by age, girls were less susceptible to reduced VA at 6−7 years but became more susceptible at 8−9 years from 2000/2001 to 2008/2009 and at 10−11 years from 2010/2011 to 2016/2017. In 2000/2001, the ORs of girls were 0.937 (95% CI, 0.911−0.964) for age 6−7, 1.033 (95% CI, 1.008−1.057) for age 8−9 and 1.175 (95% CI, 1.124−1.228) for age 14−15. Ten years later, in 2010/11, the ORs of girls were 0.928 (95% CI, 0.898−0.960) for age 6−7, 1.126 (95% CI, 1.097−1.156) for age 10−11 and 1.157 (95% CI, 1.123−1.193) for age 14−15 (Supplementary Table S4).
If stratified by grade, girls were also less susceptible to reduced VA at grade P1–P2 but again became more susceptible at grade P3–P4 from 2000/2001 to 2010/2011 and grade P5–P6 from 2011/2012 to 2016/2017. In 2000/2001, the ORs of girls were 0.957 (95% CI, 0.933−0.982) for grade P1–P2, 1.046 (95% CI, 1.023−1.070) for grade P3–P4 and 1.169 (95% CI, 1.111−1.231) for grade S3–S4. Just over 10 years later, in 2011/2012, the ORs of girls were 0.929 (95% CI, 0.902−0.957) for grade P1–P2, 1.124 (95% CI, 1.094−1.155) for grade 5−6 and 1.215 (95% CI, 1.173−1.259) for grade S3–S4.
The result showed that an interaction effect between age (or grade) and sex was statistically significant. Sex would modify the effect of age (or grade) on reduced VA. Students with older age (or more senior grade) were associated with a higher risk of reduced VA, regardless of their sex. Being a girl meant a fixed reduction of risk regardless of age (or grade), but with an additional incremental risk according to the interaction term between age (or grade) and sex. The net effect of being a girl (compared to being a boy) was initially negative, but subsequently became positive, with regard to the risk of having reduced VA. For example, in 2000/2001, relative to boys at age 6, the corresponding ORs of boys at age 7, 8 and 15 were 1.163, 1.613 and 5.525, respectively. Compared to boys of the same age, girls have a lower risk of reduced VA at age 7 (0.893 × 1.095 = 0.978), but higher risk from age 8 (0.893 × 1.151 = 1.028) to 15 (0.893 × 1.361 = 1.215). Similarly, relative to boys at grade P1, the corresponding ORs of boys at grade P2, P3 and S4 were 1.292, 1.805 and 6.827, respectively. Again, girls have a lower risk of reduced VA than boys at grade P2 (0.909 × 1.085 = 0.986), but higher risk from grade P3 (0.909 × 1.137 = 1.034) to S4 (0.909 × 1.273 = 1.157) (Table 2).
Older age was significantly associated with an increasingly higher risk for having reduced VA. From 2000/2001−2003/2004 to 2013/2014−2016/2017, the crude odds ratios (CORs) of age increased from 1.251 (95% CI, 1.249−1.252) to 1.267 (95% CI, 1.265−1.268). (Supplementary Table S5) The ORs increased from 1.234 (95% CI, 1.232−1.237) to 1.330 (95% CI, 1.327−1.333) after adjusting for school type and sex and increased from 0.954 (95% CI, 0.950−0.958) to 1.095 (95% CI, 1.088−1.102) after adjusting for grade and sex. (Supplementary Table S6) In multivariate logistic analysis, the adjusted odds ratios (AORs) increased from 0.974 (95% CI, 0.969−0.979) to 1.097 (95% CI, 1.091−1.104) (Table 3).

3.4. School Type and Grade of Students

Relative to primary school, studying in secondary school had a higher risk for reduced VA with a decreasing trend of ORs from 2.898 (95% CI, 2.875−2.920) to 2.696 (95% CI, 2.676−2.716) before adjusting for age and sex, and a decreasing trend of ORs from 1.106 (95% CI, 1.094−1.119) to 0.707 (95% CI, 0.699−0.716) after adjustment from 2000/2001−2003/2004 to 2013/2014−2016/2017. The corresponding relative changes in OR were 162.03% and 281.33% in univariate analysis and were 117.48% and 191.47% in multivariate analysis. Age was a positive confounder and exaggerated the observed association between school type and reduced VA (Table 4).
Senior students had a higher risk for reduced VA with an increasing trend before adjusting for age and sex, and a decreasing trend after adjustment from 2000/2001−2003/2004 to 2013/2014−2016/2017. Relative to grade P1, for example, the ORs of grade S4 increased from 7.930 (95% CI, 7.732−8.134) to 8.864 (95% CI, 8.673−9.059) before adjusting for age and sex, and decreased from 11.923 (95% CI, 11.370 to 12.504) to 4.025 (95% CI, 3.802−4.262) after adjustment. The corresponding relative changes in OR were −33.49% and 120.22% in univariate analysis and were −21.52% and 126.47% in multivariate analysis. Age was a negative confounder in 2000/2001−2003/2004, and a positive confounder from 2004/2005−2007/2008 to 2013/2014−2016/2017.

3.5. Type of Housing

Relative to public rental housing, during 2000/2001−2003/2004, the CORs of living in subsidized Home Ownership Scheme (HOS) flats, private housing, villas and squatter were 1.133 (95% CI, 1.122−1.145), 1.270 (1.260−1.280), 0.717 (0.703−0.732) and 0.645 (0.626−0.664), respectively. Adjusting for age and sex, the corresponding ORs were 1.217 (95% CI, 1.204−1.230), 1.341 (1.330−1.352), 0.767 (0.751−0.782) and 0.561 (0.545−0.578). In multivariate analysis, the corresponding AORs were 1.153 (95% CI, 1.141−1.166), 1.203 (1.193−1.214), 0.792 (0.775−0.809) and 0.607 (0.589−0.626). During 2013/2014−2016/2017, the CORs of living in subsidized HOS flats, private housing, villas and squatter were 1.101 (95% CI, 1.087−1.115), 0.952 (0.944−0.960), 0.698 (0.685−0.710) and 0.772 (0.729−0.817), respectively. Adjusting for age and sex, the corresponding ORs were 1.078 (95% CI, 1.064−1.092), 0.990 (0.981−0.998), 0.720 (0.707−0.734) and 0.713 (0.672−0.757), respectively. In multivariate analysis, the corresponding AORs were 1.067 (95% CI, 1.053−1.082), 0.984 (0.975−0.993), 0.776 (0.761−0.791) and 0.745 (0.702−0.790).

3.6. School District and Home District

Throughout the study period, chi-square independence test (p-value, <0.01) showed that the presence of reduced VA and the 18 districts (school or home) were not independent and had a significant relationship. Kruskal–Wallis test (p-value, <0.01) showed that the prevalence of reduced VA was nonidentical among the 18 districts (school or home), and at least one of them was differentiated from the others in relation to the prevalence of reduced VA.
Figure 1 and Figure 2 displayed the AORs for reduced VA relative to Central and Western District by the student’s school district and home district, respectively on the map of HK. By school district, there were 3, 6, 4 and 4 districts having AORs of 0.85−0.94, 0.75−0.84, 0.65−0.74 and <0.65, respectively, during 2000/2001−2003/2004; and 1, 1, 5, 9 and 1 districts having AORs of ≥1.15, 1.05−1.14, 0.95−1.04, 0.85−0.94 and 0.75−0.84, respectively, during 2013/2014−2016/2017. By home district, there were 1, 12 and 4 districts having AORs of 1.05−1.14, 0.95−1.04 and 0.85−0.94, respectively, during 2000/2001−2003/2004, and 4, 6, 6 and 1 districts having AORs of 0.95−1.04, 0.85−0.94, 0.75−0.84 and 0.65−0.74, respectively, during 2013/2014−2016/2017.
Table 5 showed that in case of age-sex adjusted prevalence, the correlation coefficients of Mantel test were 0.341 and 0.426 for home district in 2008/2009−2012/2013 and 2013/2014−2016/2017, respectively, and 0.406 for school district in 2013/2014−2016/2017. In case of AORs, the correlation coefficients of Mantel test were 0.385, 0.683, 0.636 and 0.285 for home district, while Moran’s Index was 0.368 in 2004/2005−2007/2008 and 0.336 in 2008/2009−2012/2013 for home district. We showed that the spatial autocorrelation was positive between the distance from home district to city center (at the Central and Western District) and the prevalence (or AORs) of reduced VA, that is, there would be clustering of home districts with similar estimates of prevalence (or AORs).

4. Discussion

4.1. Prevalence

Our results showed that the prevalence of reduced VA in 2010/2011 were 29.97% for age 6−7 and 58.06% for age 10−11. Inferring from previous reports in mainland China of refractive errors accounting for 85−97% cases of reduced VA [4,5,6,7,8,9,10], our estimated prevalence of refractive errors in Hong Kong is lower than the earlier report by Lam et al. of 34.2% and 66.6% for the respective comparable age groups [17]. In contrast to that study, the ethnicity of our subjects was not limited to Chinese, nor were the recruitment sites limited to just six specific primary schools located in Hong Kong.
Prevalence of myopia from a previous survey in Taiwan was 21% and 61% at age 7 and 12, respectively [32]. In our study, the prevalence of reduced VA was 26.08% and 61.48%, at age 6−7 and 12−13, respectively, suggesting a lower prevalence of myopia compared to Taiwan, after making similar inference from the relationship between reduced VA and myopia as mentioned.
Data from mainland China showed that the prevalence of reduced VA amongst schoolchildren aged 7 to 18 years were 38.5%, 49.5% and 56.8% in 2000, 2005 and 2010, respectively, compared to our local estimates of 49.23%, 53.02% and 56.17% for the same corresponding period, showing decreasing differences over time between HK and mainland China schoolchildren from 2000 to 2010 [21].
Overall prevalence of reduced VA was negatively associated with the proportion of primary to secondary schoolchildren. Although subgroup prevalence of each age group remains the same, differences in annual birth rate can cause fluctuation of age composition of study population and influence the overall prevalence, so that a smaller proportion of younger or junior students would raise the overall prevalence. Thus, we used a direct standardization method to adjust the age and sex composition from sampling weight to standardized weight for reviewing the prevalence trend of reduced VA, which was not considered in the two previous studies from mainland China.

4.2. Age and Sex

Although univariate and multivariate logistic analysis showed that girls were more susceptible than boys to having reduced VA, our subgroup analysis actually showed that being a girl was less susceptible at age 6−7 or grade P1-P2, and only became more susceptible when older. Significant interaction term showed that there would be multiplicative effect of combining age (or grade) with sex to have a joint effect greater than the product of their individual effects. This finding correlates with the higher incidence of myopic progression among girls compared to boys noted in most studies [3,33,34,35,36,37] and that this tends to occur as they reach adolescence [38]. Possible explanations included changes in lifestyle behavior influencing near vision usage, and the earlier onset of puberty among girls [39].

4.3. Student Health Service Centres

Since one SHSC served students coming from various school or home districts, we assumed that variations would exist among SHSCs. In the univariate analysis, SHSCs contributed to the variations on the prevalence of reduced VA. After adjusting for other variables, the risk effect of SHSCs reduced but still existed. We expected that the background of the students, for example, age, sex, home or school district, would partially but not fully explain the variations caused by SHSCs.

4.4. School Type and Grade of Students

The prevalence of reduced VA changed disproportionally among different age or grade subgroups. Throughout the study period, the magnitude of fluctuation of prevalence in younger age (6−9 years) or junior grade (P1–P4) was greater than that in older age (10–15 years) or senior grade (P5–S4). The ORs of secondary to primary school, and senior to junior grade, decreased from 2000/2001−2003/2004 to 2013/2014−2016/2017, reflecting a rising prevalence of reduced VA among primary school and junior grade students. Since the change of subgroup prevalence was uneven, more obvious at junior grade and less obvious at senior grade, we believed the onset of reduced VA is occurring earlier, and lessening the effect of schooling duration (or academic attainment) in the senior grade over the past 17 years.
Large relative changes in ORs of grade (and school type) on reduced VA indicated the presence of the confounding effect of age on influencing the observed association between grade (and school type) and reduced VA in univariate and multivariate analysis. The adjusted measure gave a better correlation and estimate than using the crude measure, since it disregarded the confounding effect of age. Without this adjustment, a negative confounding effect would result in an underestimation of the risk effect of grade during 2000/2001−2003/2004, and inversely, a positive confounding effect would overestimate the risk effect of grade from 2004/2005 to 2016/2017 and the risk effect of school type from 2000/2001 to 2016/2017. The rising relative change in ORs showed that the extent of overestimating the association between grade (and school type) and reduced VA had enlarged. Since the confounding effect reflected the natural relationships between lifestyle, habits and other characteristics, we expect age might possibly be associated with other potential risk factors for reduced VA which are not covered in our study.

4.5. Living Environment and Social Classes

Living in villas or squatter was associated with a lower risk for reduced VA relative to public or private apartment blocks. A possible explanation may be that villas and squatter are usually located in less developed areas with a lower population and building density, while apartment blocks are more likely to be located in urbanized areas with a higher population and building density. This finding was consistent with a previous study showing that a greater number of floors/levels of housings or higher frequency of seeing green were related to increasing prevalence of myopia, one major cause of reduced VA [27,28].
Relative to subsidized HOS flats and public housing (or squatter), living in private housing (whether apartments or villas) was associated with a higher risk for reduced VA. This is similar to previous report of positive association between myopia and socioeconomic status, since only households with higher than average income can afford private housing in HK [26]. Interestingly, the ORs of private housing relative to subsidized HOS flats and public rental housing (or villas relative to squatter) decreased from 2000/2001−2003/2004 to 2013/2014−2016/2017, indicating a lessening of the association between socioeconomic status and reduced VA, possibly from a significant increase in the use of mobile electronic devices among all social classes across HK during this period.

4.6. Geographic Variation and Spatial Autocorrelation

Despite the fact that HK has a total area of 1108 km2 only, the spatial location was not independent of the presence of reduced VA. There was geographic variation and positive spatial autocorrelation between the spatial location and presence of reduced VA on the map. In 2000/2001−2003/2004, school districts were not spatially clustered while home districts with the lowest AOR were located at the northern areas of HK (Tuen Mun, Yuen Long, North and Tai Po districts), where there is a lower population density and urbanization. During 2013/2014−2016/2017, school districts were also not spatially clustered, while home districts with the highest AOR were located at the southern areas of HK (Central and Western, Wan Chai, Eastern, Southern and Kowloon City districts), where there is a high population density and urbanization. Our findings are consistent with previous studies that showed living environment being a key factor for myopia prevalence, which is a major cause of reduced VA.
Compared to the student’s home district, most school districts had lower AOR for reduced VA in 2000/2001−2003/2004, but higher AOR by 2013/2014−2016/217, with 15 school districts having lower AOR for reduced VA than home districts in 2000/2001−2003/2004, with only one school district having lower AOR and seven school districts having higher AOR by 2013/2014−2016/2017. Between the periods of 2000/2001−2003/2004 and 2013/2014−2016/2017, 11 home districts had a decreasing trend of AORs and 13 school districts had an increasing trend of AORs. This may be related to the relatively static nature of most schools compared to homes in HK, as increasing urbanization occurs around schools initially established in less urbanized areas, while rising property prices encourage young families with children to move to less expensive and less urbanized areas, further away from the central areas of HK.
There were one school district and 13 home districts with insignificant AORs in 2000/2001−2003/2004, as well as eight school districts and four home districts with insignificant AORs in 2013/2014−2016/2017. AORs tended to be close to 1 and their mean on average by school district, but far from 1 and their mean on average by home district. We expected that the effect of geographic variation on AORs tended to be mitigated by school district but increased by home district.
The result of measuring spatial autocorrelation differed between school and home districts. Mantel test and Moran’s Index reported higher value of correlation coefficients for home district than school district. Home districts with similar AORs were likely to be clustered together, but this clustering did occur for school districts with similar AORs. Spatial location was significantly correlated to the presence of reduced VA by home district but not school district.
We deduced that the association of spatial location and attributes of the 18 districts contributed to the distinct results of AORs, geographic variation and spatial autocorrelation between school district and home district. The attributes of school districts (for example; culture, academic performance or teaching style) were less likely to be associated with the community distance from city center (located at the Central and Western District), in contrast to the attributes of home districts (for example; population density, urbanization and greenery ratio) which were more likely to be associated. Thus, home districts (but not school districts) with similar attributes were more likely to be clustered together in relation to the community distance from city center.

4.7. Cross-Boundary Students

Mainland China started to be an option of student’s home district from 2013/2014 onwards. The AORs of schoolchildren living in mainland China (but commuting daily to HK to attend school) relative to the Central and Western District in HK were 0.499 and 0.477 in 2013/2014 and 2016/2017, respectively, so that cross-boundary students were less susceptible to reduced VA than local HK students (Supplementary Table S7). Moreover, the fertility trend in HK reported that the proportion of live births to non-HK citizen parents increased from 1.29% in 2001 to 29.18% in 2012, when HK became a popular destination for birth tourism for mainland Chinese (Supplementary Table S8). It is possible that the decreasing prevalence of reduced VA among the younger age or junior grade students starting from 2012/2013 may be related to the increased proportion of such students who often resides in less densely populated areas across the border from HK.
Since 2013, birth tourism has decreased greatly after new restrictions on cross-border entry were adopted by the HK Government. As the school entrance age is generally around 6, we postulate that the prevalence of reduced VA among the newly admitted students would increase again from 2019/2020 onwards, to reflect the previous local trend before the large influx of cross-border students. We recommended conducting further study to compare the environmental association on the prevalence of reduced VA between children residing locally in HK with those residing in mainland China (but attending school in HK).

4.8. Strengths and Limitations

The overall prevalence of reduced VA was negatively associated with the proportion of primary to secondary schoolchildren. Assuming that the prevalence of reduced VA in each age or grade subgroup remained the same, fluctuation in the composition of study population would change the overall prevalence, that is, a smaller proportion of younger or junior students would raise the overall prevalence. Thus, we used direct standardization method to adjust the age and sex composition from sampling weight to standardized weight for reviewing the trend of prevalence of reduced VA, which was not considered in the two previous studies from mainland China.
Our study considered interaction effect, confounding, geographic features and dynamic changes over time, which were not reported previously. Significant interaction effect identified that the risk associated with being a girl relative to a boy was increasingly shifted to a later stage of development. Age was the common cause to the grade of students and presence of reduced VA. Geographic variation on the risk for reduced VA existed and spatial autocorrelation was positive. Cross-border students had a lower prevalence of reduced VA than local students, and may have been related to the decreasing prevalence of reduced VA among younger students since 2012/2013.
The limitation was that we considered personal-level factors but did not cover the district-level factors and measure their risk effect in our analysis. We recommended conducting a multilevel research to examine how the compositional and contextual factors affect the prevalence of reduced VA by combining the district-level data from other official departments such as median monthly household income, household size, population density and greenery scale [40,41].

5. Conclusions

Since the prevalence of reduced VA was negatively associated with the proportion of primary to secondary schoolchildren, it is important to adjust the sampling weight of age and sex composition to standardized weight in a serial cross-sectional study. In subgroup analysis, the interaction effect between age (or grade) and sex was statistically significant. Girls were less susceptible than boys to reduced VA at age 6−7 or grade P1–P2 but became more susceptible at older ages. There was an increasing prevalence of reduced VA in junior grade so that the onset of reduced VA has been advanced earlier, thus lessening the risk effect of higher grades over the past 17 years. Schoolchildren in HK had lower prevalence of myopia than those in Taiwan, whereas the difference of prevalence of reduced VA between HK and mainland China has lessened with time. The association of housing type with reduced VA is likely affected by both living environment and social classes. Although the area of HK is small, there are geographic variation on the risk for reduced VA and positive spatial autocorrelation. Cross-border students living in mainland China was associated with a lower risk for reduced VA and further study should be considered for investigating the environmental association between students living in and outside HK with the prevalence of reduced VA. We did not include district-level factors in our analysis and suggested to perform a multilevel research to examine how the compositional and contextual factors affect the prevalence of reduced VA.

Supplementary Materials

The following are available online at https://www.mdpi.com/1660-4601/17/3/1023/s1, Table S1: Demographic information of schoolchildren included in the study, Table S2: Missing data pattern of variables of schoolchildren included in the study, Table S3: Subgroup analysis of the prevalence of reduced visual acuity, stratified by age and grade, Table S4: Subgroup analysis of the odds ratios (girls relative to boys) of reduced visual acuity, stratified by age and grade, Table S5: Factors associated with reduced visual acuity in the schoolchildren of Hong Kong during 2000/01-03/04, 2004/05-07/08, 2008/09-12/13 and 2013/14-16/17: Univariate logistic regression before adjusting for age and sex, Table S 6: Factors associated with reduced visual acuity in the schoolchildren of Hong Kong during 2000/01-03/04, 2004/05-07/08, 2008/09-12/13 and 2013/14-16/17: Univariate logistic regression after adjusting for age and sex, Table S7: Odds ratios for reduced visual acuity on the schoolchildren living in China relative to Central & Western District in Hong Kong from 2013/14 to 2016/17, Table S8: The proportion of live births whose parents are not Hong Kong Permanent Residents to the total number of live births from 2001 to 2017.

Author Contributions

P.W.F.W. designed the study, conducted statistical analysis and manuscript preparation. J.S.M.L. directed its implementation and reviewed the manuscript. J.C.H.C. performed statistical analysis, reviewed the manuscript and made final approval of the version to be published. All authors confirmed that this manuscript has not been published, either in whole or in part, and is not in press or under review elsewhere. All authors approved the final manuscript and agreed with its submission to International Journal of Environmental Research and Public Health. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Acknowledgments

We would like to acknowledge the help from the Student Health Service, Department of Health, Hong Kong, for providing the dataset for our analysis. The Student Health Service of the Department of Health, Hong Kong, provided the original dataset free of charge, and had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript.

Conflicts of Interest

The authors have no competing interest to declare.

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Ijerph 17 01023 g001 550
Figure 1. Map of 18 administrative districts in Hong Kong showing the adjusted odds ratio for reduced visual acuity by student’s school district (17 districts relative to Central & Western District) during 2000/01–2003/04, 2004/05–2007/08, 2008/09–2012/13, and 2013/14–2016/17 (Modified map from original source: Wikimedia Commons (https://commons.wikimedia.org/wiki/File:Map_of_Hong_Kong_18_Districts_en.svg))
Figure 1. Map of 18 administrative districts in Hong Kong showing the adjusted odds ratio for reduced visual acuity by student’s school district (17 districts relative to Central & Western District) during 2000/01–2003/04, 2004/05–2007/08, 2008/09–2012/13, and 2013/14–2016/17 (Modified map from original source: Wikimedia Commons (https://commons.wikimedia.org/wiki/File:Map_of_Hong_Kong_18_Districts_en.svg))
Ijerph 17 01023 g001
Ijerph 17 01023 g002 550
Figure 2. Map of 18 administrative districts in Hong Kong showing the adjusted odds ratio for reduced visual acuity by student’ s home district (17 districts relative to Central & Western District) during 2000/01–2003/04, 2004/05–2007/08, 2008/09–2012/13, and 2013/14–2016/17 (Modified map from original source: Wikimedia Commons (https://commons.wikimedia.org/wiki/File:Map_of_Hong_Kong_18_Districts_en.svg)).
Figure 2. Map of 18 administrative districts in Hong Kong showing the adjusted odds ratio for reduced visual acuity by student’ s home district (17 districts relative to Central & Western District) during 2000/01–2003/04, 2004/05–2007/08, 2008/09–2012/13, and 2013/14–2016/17 (Modified map from original source: Wikimedia Commons (https://commons.wikimedia.org/wiki/File:Map_of_Hong_Kong_18_Districts_en.svg)).
Ijerph 17 01023 g002
Table
Table 1. Prevalence of reduced visual acuity in the schoolchildren of Hong Kong and its correlation with the proportion of primary to secondary schoolchildren from 2000/2001 to 2016/2017.
Table 1. Prevalence of reduced visual acuity in the schoolchildren of Hong Kong and its correlation with the proportion of primary to secondary schoolchildren from 2000/2001 to 2016/2017.
School YearCrude Prevalence RateAge-Sex Adjusted Prevalence RateStandardized Prevalence RateProportion of Primary to Secondary Schoolchildren
Estimate95% LB95% UBEstimate95% LB95% UBEstimate95% LB95% UBStudents Attending SHSEnrolled Students
2000/200145.4645.3145.6149.2349.0749.3949.2348.9949.474.261.54
2001/200246.0345.8946.1749.4249.2649.5849.4549.2249.683.881.53
2002/200345.9345.7846.0749.1048.9549.2548.9748.7449.193.581.5
2003/200447.5147.3747.6550.1450.0050.2949.7549.5449.973.211.44
2004/200549.4149.2749.5551.8751.7352.0250.9850.7651.192.981.36
2005/200651.0550.9151.1953.0252.8753.1751.7351.5151.942.681.29
2006/200751.7851.6451.9253.6253.4753.7752.0051.7852.212.451.23
2007/200852.8652.7153.0154.5154.3654.6652.6752.4552.892.261.16
2008/200953.9953.8454.1455.7255.5655.8753.6453.4253.872.11.1
2009/2010-----------
2010/201154.9354.7855.0956.1756.0156.3254.0853.8654.311.841.06
2011/201254.3654.2054.5255.9955.8356.1554.3454.1054.582.061.1
2012/201350.9850.8251.1452.8152.6452.9751.5951.3651.832.11.14
2013/201449.7449.5849.9151.6251.4651.7951.1850.9451.422.211.22
2014/201549.6449.4849.8051.2551.0851.4151.5851.3451.822.341.3
2015/201649.3549.1949.5150.8950.7351.0651.7051.4651.952.531.39
2016/201748.6548.4948.8150.2850.1150.4451.4251.1751.662.751.49
Mann-Kendall Trend Test
Time seriestaup-value taup-value taup-value
Overall
2000/2001 – 2016/20170.2670.163 0.2170.26 0.3830.043
Breakdown
2000/2001 – 2011/20120.927<0.001 0.891<0.001 0.927<0.001
2012/2013 – 2016/2017−10.027 −10.027 01
Pearson correlation coefficient (correlation of prevalence of reduced VA with proportion of primary to secondary students attending SHS and enrolled students)
Students attending SHS−0.891 −0.809 −0.875
Enrolled students −0.947 −0.940 −0.885
LB, lower bound; UB, upper bound; SHS, Student Health Service; VA, visual acuity; Bolded figures were referred in Section 3.2; School year 2009/2010 was excluded because SHS limited the annual appointments in that year for taking part in the Human Swine Influenza Vaccination Program.
Table
Table 2. Interaction effect of age (or grade) on the association between sex and reduced visual acuity.
Table 2. Interaction effect of age (or grade) on the association between sex and reduced visual acuity.
(a) Effect Modifier: Age
2000/20012001/20022002/20032003/20042004/20052005/20062006/20072007/2008
ORp-valueORp-valueORp-valueORp-valueORp-valueORp-valueORp-valueORp-value
Intercept 0.336<0.00010.326<0.00010.289<0.00010.317<0.00010.305<0.00010.314<0.00010.322<0.00010.329<0.0001
age7 1.163<0.00011.263<0.00011.335<0.00011.241<0.00011.351<0.00011.375<0.00011.352<0.00011.375<0.0001
age8 1.613<0.00011.66<0.00011.813<0.00011.762<0.00011.977<0.00011.941<0.00011.965<0.00011.958<0.0001
age9 2.145<0.00012.158<0.00012.475<0.00012.27<0.00012.623<0.00012.632<0.00012.594<0.00012.733<0.0001
age10 2.825<0.00012.817<0.00013.079<0.00012.95<0.00013.283<0.00013.342<0.00013.296<0.00013.322<0.0001
age11 3.376<0.00013.586<0.00013.938<0.00013.598<0.00014.023<0.00014.112<0.00014.052<0.00014.143<0.0001
age12 4.054<0.00014.234<0.00014.861<0.00014.42<0.00014.738<0.00014.833<0.00014.721<0.00014.819<0.0001
age13 4.652<0.00014.849<0.00015.509<0.00015.319<0.00015.569<0.00015.364<0.00015.533<0.00015.469<0.0001
age14 5.264<0.00015.568<0.00016.228<0.00015.771<0.00016.59<0.00016.207<0.00015.867<0.00015.976<0.0001
age15 5.525<0.00015.731<0.00017.068<0.00016.641<0.00016.949<0.00017.001<0.00016.635<0.00016.451<0.0001
sexF 0.893<0.00010.96<0.00010.958<0.00010.912<0.00010.943<0.00010.946<0.00010.913<0.00010.946<0.0001
age*sex7F1.095<0.00010.966<0.00010.9850.02781.062<0.00011.033<0.00011.0290.00011.04<0.00011.038<0.0001
age*sex8F1.151<0.00011.057<0.00011.042<0.00011.081<0.00011.056<0.00011.094<0.00011.11<0.00011.05<0.0001
age*sex9F1.164<0.00011.142<0.00011.074<0.00011.191<0.00011.102<0.00011.139<0.00011.216<0.00011.112<0.0001
age*sex10F1.207<0.00011.149<0.00011.186<0.00011.214<0.00011.191<0.00011.186<0.00011.264<0.00011.227<0.0001
age*sex11F1.314<0.00011.186<0.00011.2<0.00011.339<0.00011.287<0.00011.256<0.00011.291<0.00011.269<0.0001
age*sex12F1.337<0.00011.246<0.00011.195<0.00011.308<0.00011.332<0.00011.309<0.00011.357<0.00011.264<0.0001
age*sex13F1.367<0.00011.272<0.00011.241<0.00011.247<0.00011.275<0.00011.348<0.00011.331<0.00011.28<0.0001
age*sex14F1.274<0.00011.24<0.00011.215<0.00011.334<0.00011.194<0.00011.235<0.00011.333<0.00011.279<0.0001
age*sex15F1.361<0.00011.275<0.00011.214<0.00011.205<0.00011.256<0.00011.186<0.00011.297<0.00011.257<0.0001
2008/20092010/20112011/20122012/20132013/20142014/20152015/20162016/2017
ORp-valueORp-valueORp-valueORp-valueORp-valueORp-valueORp-valueORp-value
Intercept 0.36<0.00010.376<0.00010.404<0.00010.252<0.00010.256<0.00010.264<0.00010.275<0.00010.268<0.0001
age7 1.349<0.00011.367<0.00011.341<0.00011.662<0.00011.535<0.00011.529<0.00011.45<0.00011.504<0.0001
age8 1.883<0.00011.978<0.00011.898<0.00012.702<0.00012.46<0.00012.356<0.00012.23<0.00012.165<0.0001
age9 2.539<0.00012.563<0.00012.459<0.00013.685<0.00013.495<0.00013.403<0.00013.094<0.00013.057<0.0001
age10 3.199<0.00013.129<0.00013.001<0.00014.492<0.00014.502<0.00014.314<0.00014.134<0.00013.91<0.0001
age11 3.939<0.00013.838<0.00013.566<0.00015.392<0.00015.322<0.00015.33<0.00015.179<0.00015.079<0.0001
age12 4.677<0.00014.439<0.00014.076<0.00016.257<0.00016.185<0.00016.14<0.00015.852<0.00015.887<0.0001
age13 5.196<0.00014.927<0.00014.599<0.00016.818<0.00016.761<0.00016.466<0.00016.432<0.00016.791<0.0001
age14 5.623<0.00015.41<0.00014.886<0.00017.534<0.00017.261<0.00017.201<0.00016.941<0.00017.103<0.0001
age15 6.215<0.00015.932<0.00015.365<0.00018.042<0.00018.194<0.00017.489<0.00017.419<0.00017.616<0.0001
sexF 0.917<0.00010.91<0.00010.916<0.00010.875<0.00010.897<0.00010.868<0.00010.931<0.00010.953<0.0001
age*sex7F1.036<0.00011.036<0.00010.9960.58811.038<0.00011.0230.00351.102<0.00011.0220.00520.97<0.0001
age*sex8F1.113<0.00011.065<0.00011.037<0.00011.074<0.00011.086<0.00011.109<0.00011.045<0.00011.058<0.0001
age*sex9F1.145<0.00011.161<0.00011.155<0.00011.124<0.00011.119<0.00011.188<0.00011.122<0.00011.079<0.0001
age*sex10F1.235<0.00011.236<0.00011.19<0.00011.256<0.00011.135<0.00011.238<0.00011.17<0.00011.184<0.0001
age*sex11F1.303<0.00011.239<0.00011.246<0.00011.306<0.00011.254<0.00011.255<0.00011.225<0.00011.226<0.0001
age*sex12F1.309<0.00011.308<0.00011.241<0.00011.31<0.00011.285<0.00011.34<0.00011.242<0.00011.29<0.0001
age*sex13F1.298<0.00011.33<0.00011.264<0.00011.346<0.00011.304<0.00011.417<0.00011.321<0.00011.299<0.0001
age*sex14F1.287<0.00011.299<0.00011.336<0.00011.335<0.00011.296<0.00011.363<0.00011.327<0.00011.323<0.0001
age*sex15F1.289<0.00011.24<0.00011.322<0.00011.427<0.00011.273<0.00011.462<0.00011.339<0.00011.346<0.0001
(b) Effect Modifier: Grade
2000/20012001/20022002/20032003/20042004/20052005/20062006/20072007/2008
ORp-valueORp-valueORp-valueORp-valueORp-valueORp-valueORp-valueORp-value
Intercept 0.331<0.00010.333<0.00010.297<0.00010.321<0.00010.311<0.00010.333<0.00010.335<0.00010.349<0.0001
GRADEP2 1.292<0.00011.343<0.00011.45<0.00011.4<0.00011.561<0.00011.468<0.00011.503<0.00011.486<0.0001
GRADEP3 1.805<0.00011.763<0.00011.955<0.00011.893<0.00012.141<0.00012.051<0.00012.086<0.00012.114<0.0001
GRADEP4 2.437<0.00012.351<0.00012.622<0.00012.491<0.00012.81<0.00012.777<0.00012.776<0.00012.821<0.0001
GRADEP5 3.148<0.00013.063<0.00013.345<0.00013.203<0.00013.483<0.00013.462<0.00013.514<0.00013.443<0.0001
GRADEP6 3.896<0.00013.982<0.00014.298<0.00013.994<0.00014.32<0.00014.208<0.00014.259<0.00014.308<0.0001
GRADES1 5.01<0.00014.733<0.00015.411<0.00014.937<0.00015.159<0.00014.931<0.00014.994<0.00014.949<0.0001
GRADES2 5.596<0.00015.633<0.00016.14<0.00015.882<0.00016.116<0.00015.477<0.00015.56<0.00015.54<0.0001
GRADES3 6.198<0.00016.525<0.00017.175<0.00016.477<0.00017.136<0.00016.382<0.00016.296<0.00015.995<0.0001
GRADES4 6.827<0.00016.578<0.00018.182<0.00017.408<0.00017.293<0.00017.417<0.00016.874<0.00016.565<0.0001
sexF 0.909<0.00010.929<0.00010.938<0.00010.922<0.00010.945<0.00010.944<0.00010.934<0.00010.958<0.0001
GRADE*sexP2F1.085<0.00011.038<0.00011.03<0.00011.06<0.00011.032<0.00011.029<0.00011.028<0.00011.0080.2174
GRADE*sexP3F1.137<0.00011.114<0.00011.066<0.00011.105<0.00011.082<0.00011.136<0.00011.102<0.00011.045<0.0001
GRADE*sexP4F1.165<0.00011.179<0.00011.127<0.00011.17<0.00011.116<0.00011.142<0.00011.223<0.00011.146<0.0001
GRADE*sexP5F1.235<0.00011.218<0.00011.231<0.00011.237<0.00011.223<0.00011.206<0.00011.241<0.00011.243<0.0001
GRADE*sexP6F1.316<0.00011.237<0.00011.218<0.00011.294<0.00011.29<0.00011.274<0.00011.279<0.00011.249<0.0001
GRADE*sexS1F1.302<0.00011.324<0.00011.244<0.00011.292<0.00011.309<0.00011.331<0.00011.303<0.00011.232<0.0001
GRADE*sexS2F1.293<0.00011.267<0.00011.244<0.00011.26<0.00011.238<0.00011.32<0.00011.32<0.00011.233<0.0001
GRADE*sexS3F1.198<0.00011.263<0.00011.197<0.00011.235<0.00011.186<0.00011.203<0.00011.255<0.00011.256<0.0001
GRADE*sexS4F1.273<0.00011.197<0.00011.183<0.00011.177<0.00011.254<0.00011.117<0.00011.256<0.00011.231<0.0001
2008/20092010/20112011/20122012/20132013/20142014/20152015/20162016/2017
ORp-valueORp-valueORp-valueORp-valueORp-valueORp-valueORp-valueORp-value
Intercept 0.38<0.00010.399<0.00010.414<0.00010.272<0.00010.275<0.00010.284<0.00010.291<0.00010.291<0.0001
GRADEP2 1.441<0.00011.459<0.00011.503<0.00011.827<0.00011.666<0.00011.695<0.00011.614<0.00011.574<0.0001
GRADEP3 1.964<0.00012.041<0.00012.034<0.00012.781<0.00012.616<0.00012.485<0.00012.362<0.00012.253<0.0001
GRADEP4 2.71<0.00012.647<0.00012.667<0.00013.748<0.00013.618<0.00013.538<0.00013.308<0.00013.182<0.0001
GRADEP5 3.248<0.00013.179<0.00013.093<0.00014.444<0.00014.489<0.00014.345<0.00014.266<0.00013.972<0.0001
GRADEP6 4.094<0.00013.956<0.00013.793<0.00015.394<0.00015.388<0.00015.498<0.00015.271<0.00015.164<0.0001
GRADES1 4.749<0.00014.342<0.00014.132<0.00015.961<0.00015.9<0.00015.742<0.00015.662<0.00015.641<0.0001
GRADES2 5.243<0.00014.989<0.00014.741<0.00016.716<0.00016.568<0.00016.376<0.00016.461<0.00016.604<0.0001
GRADES3 5.883<0.00015.254<0.00014.88<0.00017.235<0.00017.019<0.00016.762<0.00016.644<0.00016.673<0.0001
GRADES4 6.103<0.00016.004<0.00015.913<0.00018.1<0.00018.354<0.00017.45<0.00017.502<0.00017.346<0.0001
sexF 0.918<0.00010.903<0.00010.916<0.00010.875<0.00010.903<0.00010.889<0.00010.937<0.00010.919<0.0001
GRADE*sexP2F1.057<0.00011.058<0.00011.020.00321.051<0.00011.0150.03581.065<0.00010.9980.76941.073<0.0001
GRADE*sexP3F1.117<0.00011.103<0.00011.067<0.00011.115<0.00011.073<0.00011.093<0.00011.069<0.00011.083<0.0001
GRADE*sexP4F1.148<0.00011.185<0.00011.135<0.00011.149<0.00011.147<0.00011.159<0.00011.114<0.00011.135<0.0001
GRADE*sexP5F1.264<0.00011.23<0.00011.224<0.00011.259<0.00011.136<0.00011.247<0.00011.172<0.00011.241<0.0001
GRADE*sexP6F1.318<0.00011.276<0.00011.23<0.00011.313<0.00011.253<0.00011.202<0.00011.238<0.00011.274<0.0001
GRADE*sexS1F1.316<0.00011.326<0.00011.258<0.00011.334<0.00011.32<0.00011.38<0.00011.27<0.00011.394<0.0001
GRADE*sexS2F1.241<0.00011.357<0.00011.279<0.00011.316<0.00011.271<0.00011.359<0.00011.282<0.00011.295<0.0001
GRADE*sexS3F1.246<0.00011.281<0.00011.374<0.00011.345<0.00011.273<0.00011.35<0.00011.323<0.00011.39<0.0001
GRADE*sexS4F1.325<0.00011.204<0.00011.248<0.00011.42<0.00011.228<0.00011.376<0.00011.267<0.00011.38<0.0001
Bolded figures were referred in Section 3.3; age*sex, interaction term between age and sex; GRADE*sex, interaction term between grade and sex; School year 2009/2010 was excluded because SHS limited the annual appointments in that year for taking part in the Human Swine Influenza Vaccination Program.
Table
Table 3. Factors associated with reduced visual acuity in the schoolchildren of Hong Kong during 2000/2001−2003/2004, 2004/2005−2007/2008, 2008/2009−2012/2013 and 2013/2014−2016/2017: Multivariate logistic regression.
Table 3. Factors associated with reduced visual acuity in the schoolchildren of Hong Kong during 2000/2001−2003/2004, 2004/2005−2007/2008, 2008/2009−2012/2013 and 2013/2014−2016/2017: Multivariate logistic regression.
2000/2001–2003/20042004/2005–2007/20082008/2009–2012/20132013/2014–2016/2017
OR95% LB95% UBOR95% LB95% UBOR95% LB95% UBOR95% LB95% UB
age 0.9740.9690.9791.0351.0301.0401.0621.0561.0681.0971.0911.104
SexBoys (ref)
Girls1.1021.0951.1091.1141.1081.1211.0781.0711.0851.0871.0791.094
School DistrictCentral and Western (ref)
Wan Chai0.9090.8840.9340.9240.8980.9510.9320.9040.9600.9390.9100.968
Eastern0.8470.8160.8800.9110.8780.9440.9920.9551.0301.0080.9691.049
Southern0.8480.8240.8740.8230.7980.8470.8260.8020.8520.8250.7990.851
Yau Tsim Mong0.8240.7650.8860.8410.7980.8860.9630.9161.0121.0060.9581.057
Sham Shui Po0.7370.6840.7940.7900.7490.8330.8960.8530.9410.9290.8850.976
Kowloon City0.8650.8040.9310.8480.8050.8930.9160.8720.9620.9420.8980.989
Wong Tai Sin0.6780.6290.7310.7120.6750.7520.8960.8510.9430.9330.8870.983
Kwun Tong0.5880.5460.6330.6560.6230.6920.7680.7310.8070.8770.8350.921
Tsuen Wan0.5760.5380.6180.6390.6030.6760.8930.8440.9451.0360.9781.098
Tuen Mun0.8670.7331.0270.9110.8031.0321.0220.9031.1561.0210.9031.154
Yuen Long0.8050.6980.9280.9330.8351.0421.1491.0381.2721.2551.1301.394
North0.6640.5980.7360.7380.6670.8160.9690.8501.1050.8930.7821.021
Tai Po0.7650.6960.8420.6610.6070.7191.0490.9511.1561.1060.9981.226
Sai Kung0.6500.6020.7010.6760.6400.7140.8400.7980.8850.9260.8800.975
Sha Tin0.7700.7070.8390.7850.7350.8380.9390.8811.0010.9430.8841.007
Kwai Tsing0.6160.5750.6590.6730.6370.7110.8770.8310.9260.9700.9171.026
Islands0.6090.5740.6450.7420.6960.7920.9120.8610.9660.8870.8350.942
Grade of StudentP1 (ref)
P21.1721.1491.1961.1611.1341.1881.2301.2011.2601.2931.2631.324
P31.7241.6891.7611.6481.6081.6881.6761.6341.7191.7701.7241.816
P42.3772.3192.4382.1502.0912.2112.1382.0772.2012.3102.2422.379
P53.3033.2123.3972.6902.6092.7742.4932.4142.5752.7002.6102.793
P64.2964.1594.4373.2253.1143.3402.9162.8103.0263.0952.9763.219
S15.5505.3525.7563.7043.5623.8513.0942.9683.2253.1533.0173.296
S26.4296.1706.6984.0083.8374.1863.2543.1053.4103.1753.0193.338
S37.3907.0587.7374.3414.1374.5543.3333.1663.5093.0882.9223.265
S48.1097.7018.5394.6214.3844.8713.5893.3953.7953.2683.0753.472
Student Health Service CentreChai Wan (ref)
Kowloon Bay1.1931.1061.2871.1491.0901.2101.0000.9521.0501.0330.9841.084
Kowloon City LC1.1431.0591.2331.1071.0501.1680.9870.9381.0391.0561.0041.110
Lam Tin1.3071.2111.4101.2321.1671.3001.1701.1121.2311.1791.1221.239
South Kwai Chung1.4101.3161.5111.3401.2741.4101.1681.1091.2301.1451.0841.209
Sha Tin1.0830.9931.1811.0560.9871.1290.8940.8370.9550.9630.9001.030
Tai Po0.9890.9021.0851.1531.0611.2540.7310.6600.8100.8860.7950.986
Shek Wu Hui1.2511.1291.3861.0970.9911.2140.8420.7360.9631.1491.0021.318
Tuen Mun0.9410.7971.1120.9310.8181.0580.8010.7050.9100.8980.7901.020
Western1.0931.0591.1291.0581.0291.0880.9980.9691.0281.0010.9701.034
TWS Wu York Yu1.1301.0461.2211.0581.0021.1170.9590.9111.0101.1071.0521.165
Yuen Long1.0500.9111.2101.1150.9951.2500.9250.8321.0290.7960.7130.888
Type of HousingPublic rental housing (ref)
Subsidized home ownership flats1.1531.1411.1661.1701.1591.1831.1041.0911.1171.0671.0531.082
Private housing1.2031.1931.2141.1901.1811.2001.0441.0351.0530.9840.9750.993
Villas/Bungalows/Modern Village Houses0.7920.7750.8090.7860.7720.8010.7810.7670.7960.7760.7610.791
Squatter/Temp. Housing Area/Stone Hut0.6070.5890.6260.6840.6610.7090.7160.6840.7500.7450.7020.790
Home DistrictCentral and Western (ref)
Wan Chai1.0310.9871.0760.9670.9321.0040.9670.9291.0060.9700.9311.012
Eastern1.0841.0501.1181.0861.0541.1191.0711.0381.1051.0220.9891.057
Southern1.0160.9871.0460.9900.9621.0191.0080.9781.0391.0200.9871.055
Yau Tsim Mong1.0020.9561.0510.9810.9411.0230.9400.9030.9780.8850.8500.921
Sham Shui Po1.0040.9571.0540.9510.9120.9920.9960.9571.0370.9120.8760.949
Kowloon City1.0260.9791.0751.0521.0101.0961.0421.0031.0830.9960.9581.035
Wong Tai Sin1.0370.9881.0871.0030.9621.0450.9770.9381.0180.9350.8980.974
Kwun Tong1.0220.9761.0700.9930.9541.0330.9750.9391.0130.8790.8460.913
Tsuen Wan1.0080.9591.0600.9450.9040.9880.8800.8430.9180.8390.8030.876
Tuen Mun0.8660.8050.9310.8250.7750.8770.8840.8330.9370.8420.7970.889
Yuen Long0.8760.8180.9390.7780.7340.8250.8250.7810.8710.8140.7740.856
North0.9410.8841.0000.9030.8540.9540.9170.8720.9650.7970.7590.838
Tai Po0.8990.8470.9540.9340.8850.9870.9650.9151.0180.7750.7360.816
Sai Kung1.0410.9921.0921.0130.9721.0560.9580.9210.9970.9020.8680.938
Sha Tin1.0310.9811.0840.9950.9531.0400.9760.9361.0180.9240.8870.964
Kwai Tsing0.9930.9451.0430.9150.8760.9550.8500.8160.8860.8120.7790.846
Islands0.9910.9341.0520.8400.7930.8890.7390.7050.7750.7120.6780.747
LB, lower bound; UB, upper bound; SHS, Student Health Service; Bolded figures were referred in Section 3.3, 3.4 and 3.5; School year 2009/2010 was excluded because SHS limited the annual appointments in that year for taking part in the Human Swine Influenza Vaccination Program.
Table
Table 4. Relative change in odds ratio of school type (and grade) on reduced visual acuity before and after adjusting for age in logistic analysis.
Table 4. Relative change in odds ratio of school type (and grade) on reduced visual acuity before and after adjusting for age in logistic analysis.
(a) Univariate(b) Multivariate
Relative change in odds ratio (%)Relative change in odds ratio (%)
2000/2001−2003/20042004/2005−2007/20082008/2009−2012/20132012/2013−2016/20172000/2001−2003/20042004/2005−2007/20082008/2009−2012/20132012/2013−2016/2017
(i) School type
 Primary School (ref)
 Secondary School162.03213.89222.92281.33117.48131.72137.09191.47
(ii) Grade
 P1 (ref)
 P2−4.951.935.7610.08−3.924.136.8311.52
 P3−9.623.8111.6320.94−6.387.8913.8422.82
 P4−14.185.7317.9332.64−9.311.5821.1934.85
 P5−18.487.7424.5245.48−11.915.6528.9248.3
 P6−22.439.6631.3759.16−14.3919.7537.0762.88
 S1−25.9711.7638.8474.97−16.5424.2446.2579.67
 S2−29.113.6946.3491.76−18.5928.4254.9896.76
 S3−31.815.553.44108.65−20.3832.1463.4114.67
 S4−33.4916.6658.19120.22−21.5234.9368.4126.47
Bolded figures were referred in Section 3.4; School year 2009/2010 was excluded because SHS limited the annual appointments in that year for taking part in the Human Swine Influenza Vaccination Program.
Table
Table 5. Correlation coefficients of Mantel test and Moran’s Index by home district and school district.
Table 5. Correlation coefficients of Mantel test and Moran’s Index by home district and school district.
2000/2001−2003/20042004/2005−2007/20082008/2009−2012/20132013/2014−2016/2017
Estimatep-ValueEstimatep-ValueEstimatep-ValueEstimatep-Value
(a) Age-Sex Adjusted Prevalence
(i) Home district
 Mantel Test0.1960.0770.2490.0540.3410.0350.4260.008
 Moran’s Index0.0670.4630.0480.5290.1030.3220.2140.107
(ii) School district
 Mantel Test0.1430.1130.0990.2010.2490.0740.4060.007
 Moran’s Index0.0530.518−0.020.8240.0180.6440.130.27
(b) Adjusted Odds Ratio in Multivariate Logistic Analysis
(i) Home district
 Mantel Test0.3850.0220.683<0.0010.636<0.0010.2850.024
 Moran’s Index0.1320.2490.3680.010.3360.0160.0840.406
(ii) School district
 Mantel Test−0.0280.522−0.0420.6040.1130.2270.0830.249
 Moran’s Index−0.1040.793−0.140.641−0.1820.451−0.130.641
Bolded figures were referred in Section 3.6; School year 2009/2010 was excluded because SHS limited the annual appointments in that year for taking part in the Human Swine Influenza Vaccination Program.

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Wong, P.W.F.; Lai, J.S.M.; Chan, J.C.H. A Serial Cross-Sectional Analysis of the Prevalence, Risk Factors and Geographic Variations of Reduced Visual Acuity in Primary and Secondary Students from 2000 to 2017 in Hong Kong. Int. J. Environ. Res. Public Health 2020, 17, 1023. https://doi.org/10.3390/ijerph17031023

AMA Style

Wong PWF, Lai JSM, Chan JCH. A Serial Cross-Sectional Analysis of the Prevalence, Risk Factors and Geographic Variations of Reduced Visual Acuity in Primary and Secondary Students from 2000 to 2017 in Hong Kong. International Journal of Environmental Research and Public Health. 2020; 17(3):1023. https://doi.org/10.3390/ijerph17031023

Chicago/Turabian Style

Wong, Perseus W.F., Jimmy S.M. Lai, and Jonathan C.H. Chan. 2020. "A Serial Cross-Sectional Analysis of the Prevalence, Risk Factors and Geographic Variations of Reduced Visual Acuity in Primary and Secondary Students from 2000 to 2017 in Hong Kong" International Journal of Environmental Research and Public Health 17, no. 3: 1023. https://doi.org/10.3390/ijerph17031023

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