July 2011
Volume 52, Issue 8
Free
Clinical and Epidemiologic Research  |   July 2011
Validating the Accuracy of a Model to Predict the Onset of Myopia in Children
Author Affiliations & Notes
  • Mingzhi Zhang
    From the Joint Shantou International Eye Center, Shantou, People's Republic of China (PRC);
  • Gus Gazzard
    the Department of Ophthalmology, King's College Hospital, London, United Kingdom;
  • Zhifu Fu
    the Mingren Eye Hospital, Anxi, Fujian, PRC;
  • Liping Li
    the Shantou University College of Medicine, Shantou, PRC;
  • Bin Chen
    From the Joint Shantou International Eye Center, Shantou, People's Republic of China (PRC);
  • Seang Mei Saw
    the Department of Epidemiology and Public Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore;
  • Nathan Congdon
    From the Joint Shantou International Eye Center, Shantou, People's Republic of China (PRC);
    the Department of Ophthalmology and Visual Sciences, Chinese University of Hong Kong, Hong Kong SAR, PRC; and
    the Zhongshan Ophthalmic Center, State Key Laboratory of Ophthalmology, Sun Yat Sen University, Guangzhou, PRC.
  • Corresponding author: Nathan Congdon, Zhongshan Ophthalmic Center, State Key Laboratory and Division of Preventive Ophthalmology, Sun Yat Sen University, Guangzhou, PRC; ncongdon1@gmail.com
Investigative Ophthalmology & Visual Science July 2011, Vol.52, 5836-5841. doi:10.1167/iovs.10-5592
  • Views
  • PDF
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Mingzhi Zhang, Gus Gazzard, Zhifu Fu, Liping Li, Bin Chen, Seang Mei Saw, Nathan Congdon; Validating the Accuracy of a Model to Predict the Onset of Myopia in Children. Invest. Ophthalmol. Vis. Sci. 2011;52(8):5836-5841. doi: 10.1167/iovs.10-5592.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

Purpose.: To assess the sensitivity and specificity of models predicting myopia onset among ethnically Chinese children.

Methods.: Visual acuity, height, weight, biometry (A-scan, keratometry), and refractive error were assessed at baseline and 3 years later using the same equipment and protocol in primary schools in Xiamen (China) and Singapore. A regression model predicting the onset of myopia < −0.75 diopters (D) after 3 years in either eye among Xiamen children was validated with Singapore data.

Results.: Baseline data were collected from 236 Xiamen children (mean age, 7.82 ± 0.63 years) and from 1979 predominantly Chinese children in Singapore (7.83 ± 0.84 years). Singapore children were significantly taller and heavier, and had more myopia (31.4% vs. 6.36% < −0.75 D in either eye, P < 0.001) and longer mean axial length. Three-year follow-up was available for 80.0% of Xiamen children and 83.1% in Singapore. For Xiamen, the area under the receiver-operator curve (AUC) in a model including ocular biometry, height, weight, and presenting visual acuity was 0.974 (95% confidence interval [CI], 0.945–0.997). In Singapore, the same model achieved sensitivity, specificity, and positive predictive value of 0.844, 0.650, and 0.669, with an AUC of 0.815 (95% CI, 0.791–0.839).

Conclusions.: Accuracy in predicting myopia onset based on simple measurements may be sufficient to make targeted early intervention practical in settings such as Singapore with high myopia prevalence. Models based on cohorts with a greater prevalence of high myopia than that in Xiamen could be used to assess accuracy of models predicting more severe forms of myopia.

Refractive error is the leading cause of poor visual acuity among school-aged children in China 1 4 and has been shown to be associated with significant decrements in visual function. 4 Underutilization of spectacles, 3,5 due in part to the false belief that their use will harm children's eyes, 6 together with widespread inaccuracy of presently available correction in rural areas, 7 have contributed to the significant burden of uncorrected and undercorrected refractive error there. Due to these practical limitations in the correction of refractive error with spectacles, and to reduce the risk of various serious ocular conditions associated with increased axial length, 8 11 investigators have examined various modalities to retard myopia progression, including the use of atropine, 12 14 pirenzepine, 15 17 corneal reshaping (“orthokeratology”), 18,19 contact lens wear, 20 22 progressive addition lenses, 23 25 and deliberate undercorrection of myopic refraction. 26,27  
Although some of these modalities, such as pirenzepine, have demonstrated clinically significant reductions in myopia progression with an acceptable side-effect profile, 15 17 these approaches raise a challenge to program planners: can noninvasive testing performed on emmetropic children at or before the age of highest incidence and most rapid progression of myopia (the early primary school years 28 ) accurately predict children who will later develop myopia? Risk factors for myopia onset and progression have been studied, 29 35 including younger age of myopia onset, parental education, and refractive error; various biometric factors, including longer axial length and higher corneal power; and female gender and genetic makeup. Although models to predict myopia onset have been reported for Caucasian populations, 36 39 the sensitivity, specificity, and positive predictive value of such models have not, to the best of our knowledge, been assessed previously among ethnically Chinese children. 
We report on data from two cohorts of primary school–age children followed prospectively with identical protocols for the development of myopia over 3 years. The purposes of the present report were:
  1.  
    To develop a model predicting the onset of myopia in the mixed urban–rural cohort from Xiamen, southern China.
  2.  
    To investigate the sensitivity and specificity of this model in predicting the onset of myopia < −0.75 diopters (D) in either eye of a child in the Xiamen cohort after 3 years, based on baseline demographic and biometric data.
  3.  
    To test the validity of the model in predicting development of a similar degree of myopia after 3 years in the second cohort of children from Singapore.
Methods
The methods of data collection in the Xiamen and Singapore cohorts have been reported elsewhere in detail 40,41 and are reviewed here for reference. Written informed consent was obtained from parents after the nature of the study was explained, and the conduct of the study followed the tenets of the Declaration of Helsinki. Approval was obtained from ethics committees at the Singapore National Eye Center and the Xiamen Eye Institute. 
Participants
In Xiamen, children in the second grade of elementary school at two urban and two rural schools were invited to participate, whereas similar-aged children were recruited from three schools in Singapore. Participation rates in Singapore and Xiamen were 62.8% and 91.0%, respectively, and the rates of myopia did not differ between participants and nonparticipants in Singapore. 40 All children recruited in Xiamen were Chinese; in Singapore, 72.5% of children were ethnically Chinese. 40 Children receiving treatment to prevent myopia and those with myopia associated with systemic syndromes or with serious ocular or systemic diseases were excluded. 
Measurement of Visual Acuity, Refractive Error, and Biometry
Children were examined at baseline under the identical protocol in Singapore in November 1999 and in Xiamen in April 2000, supervised by investigators from Singapore. Identical, calibrated equipment was used in both locations. 
Corrected and uncorrected distance visual acuity was measured in each eye separately using logarithm of minimal angle of resolution (logMAR) charts and a standard protocol. 42 Refractive error and corneal curvature (average of five separate measurements) (Canon RK5; Canon Inc. Ltd, Tochigiken, Japan) were measured 30 minutes after cycloplegia with 1% cyclopentolate hydrochloride (3 drops, 5 minutes apart). Ultrasound biometry (Nidek Echoscan US-800; Nidek Co. Ltd, Tokyo, Japan) was performed using the average of six values after anesthesia with 0.5% proparacaine hydrochloride. 
Measurement of visual acuity, refraction, and ocular biometry were carried out annually using the above-cited protocol over 3 years in both sites. 
Anthropometric Measurements
Height and weight were measured at the schools at baseline. Height was measured with the children standing and without shoes. Weight was measured using a single portable weighing machine calibrated at the beginning of the study. 
Statistical Methods
Statistical analysis software (Statistical Package for Social Sciences [SPSS], version 15.0 for Windows; SPSS, Inc., Chicago, IL) was used for analysis. χ2 and t-tests were used to analyze difference in visual acuity, height, weight, ocular biometry, and refractive error prevalence between children in the two cohorts. Generalized estimating equations (GEEs) and stepwise GEEs were used to build the model for prediction of the risk for developing spherical equivalent myopia < 0.75 D in either eye after 3 years using baseline data among the Xiamen cohort. The same level of myopia (< −0.75 D spherical equivalent in either eye) was used to exclude children with myopia at baseline and to define incident myopia based on observation among children without such myopia at baseline. All demographic, biometric, and vision parameters collected at baseline were considered, with only those variables, squared and cubic terms that were significant, being retained in the final model. Receiver-operating curves (ROCs) showed the sensitivity and specificity of predicting onset of myopia at different risk cutoffs in the model. All children with data at baseline and at 3 years who did not have myopia at baseline were entered into the models. Back substitution, 43 the sample test, 43 and Singapore data were used to test the effectiveness of the model, the latter being used to generate ROC curves for the prediction of myopia in the Singapore cohort at 3 years based on Singapore baseline data and the Xiamen model. Values of P < 0.05 were considered to be significant throughout. 
Results
Baseline data were collected from 236 children in Xiamen and from 1979 children in Singapore. Although the mean age of the children in the two settings did not differ (Xiamen 7.82 ± 0.63 years, Singapore 7.83 ± 0.84 years, P = 0.43), Singaporean children were significantly taller and heavier than children in Xiamen, had worse uncorrected logMAR visual acuity, and also had a number of biometric traits consistent with myopic refractive error, including longer axial length, and deeper anterior chamber and vitreous chamber depth (Table 1). 
Table 1.
 
Baseline Characteristics of Primary School–Age Children Participating in a Prospective Study of Myopia from Xiamen (China) and Singapore
Table 1.
 
Baseline Characteristics of Primary School–Age Children Participating in a Prospective Study of Myopia from Xiamen (China) and Singapore
Characteristic Xiamen, China (n = 236) Singapore (n = 1979) P
Boys Girls Total Boys Girls Total
Age, y 7.89 ± 0.59 7.74 ± 0.69 7.82 ± 0.63 7.86 ± 0.85 7.81 ± 0.84 7.83 ± 0.84 0.43
Weight, kg 23.60 ± 4.40 22.60 ± 3.30 23.20 ± 4.00 27.10 ± 6.80 25.70 ± 6.30 26.40 ± 6.60 <0.001
Height, cm 123.00 ± 5.50 122.00 ± 5.30 123.00 ± 5.40 127.00 ± 7.30 126.00 ± 7.70 127.00 ± 7.60 <0.001
Uncorrected vision, logMAR 0.04 ± 0.07 0.05 ± 0.07 0.045 ± 0.07 0.29 ± 0.35 0.27 ± 0.33 0.28 ± 0.34 <0.001
Axial length, mm 22.80 ± 0.78 22.10 ± 0.74 22.50 ± 0.83 23.60 ± 0.91 23.00 ± 0.88 23.30 ± 0.95 <0.001
Anterior chamber depth, mm 3.34 ± 0.27 3.23 ± 0.26 3.29 ± 0.28 3.65 ± 0.27 3.56 ± 0.27 3.60 ± 0.27 <0.001
Lens thickness, mm 3.54 ± 0.17 3.53 ± 0.18 3.53 ± 0.18 3.48 ± 0.19 3.47 ± 0.17 3.47 ± 0.18 <0.001
Vitreous chamber depth, mm 15.90 ± 0.72 15.30 ± 0.70 15.60 ± 0.76 16.50 ± 0.90 16.00 ± 0.85 16.20 ± 0.91 <0.001
Corneal curvature, mm 7.85 ± 0.27 7.67 ± 0.25 7.77 ± 0.27 7.81 ± 0.25 7.69 ± 0.24 7.75 ± 0.25 0.017
Myopia < −0.75 D in either eye was more prevalent in the Singapore cohort, increasing from 31.4% (621/1979) at baseline (Xiamen 15/236 = 6.36%, P < 0.001) to 61.7% (1014/1644) in the third year (Xiamen 41/189 = 21.7%, P < 0.001). Refractive information was available for 80.0% (189/236) of children in Xiamen and 83.1% (1644/1979) of children in Singapore at the end of 3 years (Table 2). Among 15 children with myopia at baseline in Xiamen, 11 (73.3%) had 3-year follow-up, as did 467/621 (75.2%) children with baseline myopia in Singapore. Children with myopia at baseline were removed from all analyses predicting the onset of myopia. Additionally, 2 children in Xiamen (both with myopia) and 23 children in Singapore (21 with myopia and 2 without) did not have complete data for modeling. Models predicting the onset of myopia were thus based on 176 children in Xiamen and 1154 in Singapore who were not myopic at baseline and had complete baseline data and refractive measurements at 3-year follow-up. 
Table 2.
 
Myopia Prevalence over Time among Cohorts of Primary School-Age Children from Xiamen (China) and Singapore
Table 2.
 
Myopia Prevalence over Time among Cohorts of Primary School-Age Children from Xiamen (China) and Singapore
Year Xiamen, China Singapore P
Number of Participants Number (%) Myopic Number of Participants Number (%) Myopic
Baseline 236 15 (6.36) 1979 621 (31.4) <0.001
1 202 25 (12.4) 1869 826 (44.2) <0.001
2 205 32 (15.6) 1788 1012 (56.6) <0.001
3 189 41 (21.7) 1644 1014 (61.7) <0.001
Table 3 shows the characteristics of children in Xiamen and Singapore with complete data who were not myopic at baseline and did or did not go on to develop myopia. In general, children who were younger, had poorer visual acuity, longer axial length, and longer vitreous chamber depth at baseline were more likely to develop myopia, although differences were not always significant in the smaller Xiamen cohort. 
Table 3.
 
Characteristics of Children with Complete Data and without Myopia at Baseline in Xiamen and Singapore Who Did and Did Not Go on to Develop Myopia by 3-Year Follow-up
Table 3.
 
Characteristics of Children with Complete Data and without Myopia at Baseline in Xiamen and Singapore Who Did and Did Not Go on to Develop Myopia by 3-Year Follow-up
Factor Xiamen (n = 176) Singapore (n = 1154)
Developed Myopia (n = 28) Did Not Develop Myopia (n = 148) P Developed Myopia (n = 526) Did Not Develop Myopia (n = 628) P
Age, y 6.81 ± 0.65 6.88 ± 0.67 0.503 7.69 ± 0.85 7.80 ± 0.81 0.024
Weight, kg 24.07 ± 5.29 23.07 ± 3.68 0.102 25.80 ± 6.03 26.60 ± 6.84 0.046
Height, cm 123.00 ± 5.79 123.00 ± 5.24 0.995 126.00 ± 7.37 127.00 ± 6.84 0.332
Visual acuity, logMAR 0.06 ± 0.08 0.03 ± 0.05 0.003 0.12 ± 0.37 0.09 ± 0.14 0.001
Axial length, mm 22.70 ± 0.88 22.40 ± 0.80 0.059 23.10 ± 0.71 22.80 ± 0.72 <0.001
Anterior chamber depth, mm 3.31 ± 0.26 3.29 ± 0.27 0.577 3.58 ± 0.27 3.55 ± 0.26 0.069
Lens thickness, mm 3.52 ± 0.16 3.53 ± 0.18 0.818 3.49 ± 0.18 3.50 ± 0.18 0.386
Vitreous chamber depth, mm 15.80 ± 0.75 15.60 ± 0.75 0.058 16.10 ± 0.69 15.80 ± 0.69 <0.001
Corneal curvature, mm 7.75 ± 0.33 7.78 ± 0.27 0.520 7.45 ± 0.25 7.77 ± 0.25 0.075
Table 4 gives the final GEE model predicting the likelihood of developing myopia < −0.75 D in at least one eye by the end of 3 years among children in Xiamen, using baseline gender, height, presenting visual acuity, and biometric measurements obtained from A-scan ultrasonography and keratometry. Only easily measurable parameters were included in this model; information that was collected in the original studies but that might not be readily ascertained in most settings (such as children's birth weight, their time spent in near work) was excluded. 
Table 4.
 
Generalized Estimating Equation Model Predicting Myopia after 3 Years of Follow-up among 176 Primary School-Age Children in Xiamen, China
Table 4.
 
Generalized Estimating Equation Model Predicting Myopia after 3 Years of Follow-up among 176 Primary School-Age Children in Xiamen, China
Predictor Beta SE P
Female gender −1.49 0.175 <0.001
Height, cm −0.076 0.014 <0.001
Visual, logMAR
    Vision2 12.2 2.61 <0.001
    Vision3
Axial length (AL), mm
    AL2 1.95 0.593 <0.001
    AL3 −0.05 0.0199 <0.001
Anterior chamber depth (ACD), mm
    ACD −64.2 14.6 <0.001
    ACD3 0.368 0.0867 <0.001
Lens thickness (LT), mm
    LT −544.0 134.0 <0.001
    LT2 138.0 37.9 <0.001
    LT3 −12.7 3.60 <0.001
Vitreous chamber depth (VCD), mm
    VCD2 −2.26 0.820 0.006
    VCD3 0.069 0.0214 0.001
Corneal curvature (CC), mm
    CC2 −3.88 1.18 0.001
    CC3 0.279 0.101 0.006
ACD × LT × VCD 0.656 0.135 <0.001
Figure 1 depicts the ROC curve for predicting onset of myopia in either eye after 3 years in Xiamen using this model. For example, at a 0.4 probability of developing myopia in the eye, the sensitivity was 0.821 and the specificity was 0.973. The area under the ROC curve (AUC) was 0.974 (95% confidence interval [CI], 0.945–0.997). Table 5 depicts the effect of removing each of the parameters included in the model on the AUC, indicating that the accuracy of the model was decreased by any such removal. Each variable was removed singly and all other terms were retained in the model. Terms are listed in order of the size of residual AUC after removal of the term. 
Figure 1.
 
ROC curve for prediction of myopia < −0.75 D spherical equivalent after 3 years of follow-up among primary school–age children in Xiamen China (area under the ROC curve [AUC], 0.974; 95% CI, 0.945–0.997).
Figure 1.
 
ROC curve for prediction of myopia < −0.75 D spherical equivalent after 3 years of follow-up among primary school–age children in Xiamen China (area under the ROC curve [AUC], 0.974; 95% CI, 0.945–0.997).
Table 5.
 
Impact on AUC of Removing Each of the Variables Included in the Model Predicting Onset of Myopia Based on Children in Xiamen without Myopia at Baseline
Table 5.
 
Impact on AUC of Removing Each of the Variables Included in the Model Predicting Onset of Myopia Based on Children in Xiamen without Myopia at Baseline
Factor Removed AUC (95% CI)
Original model* 0.974 (0.945–0.997)
Gender removed 0.797 (0.701–0.892)
Visual acuity removed 0.786 (0.672–0.900)
Height removed 0.503 (0.387–0.620)
Anterior chamber depth removed 0.500 (0.371–0.629)
Vitreous chamber depth removed 0.500 (0.371–0.629)
Lens thickness removed 0.500 (0.371–0.629)
Corneal curvature removed 0.500 (0.371–0.629)
Axial depth removed 0.372 (0.241–0.503)
Figure 2 shows the ROC curve for predicting development of myopia < −0.75 D after 3 years in Singapore using the Xiamen model. At the furthest distance from the line of identity on the ROC curve, sensitivity was 0.844 and specificity 0.650, yielding a positive predictive value of 0.669. The AUC was 0.815 (0.791–0.839). 
Figure 2.
 
ROC curve for prediction of myopia < −0.75 D spherical equivalent after 3 years of follow-up among primary school–age children in Singapore (AUC, 0.815; 95% CI, 0.791–0.839).
Figure 2.
 
ROC curve for prediction of myopia < −0.75 D spherical equivalent after 3 years of follow-up among primary school–age children in Singapore (AUC, 0.815; 95% CI, 0.791–0.839).
Discussion
The goal of the present study was to determine whether it is possible to predict the future development of myopia in young children accurately enough that targeted interventions could practically be used to prevent or slow myopic axial elongation of the globe. The answer to this depends on several factors, including the accuracy, cost, and difficulty of the screening tests, and the prevalence of myopia, an important determinant of positive predictive value. Our results suggest that a predictive algorithm based on results of inexpensive, simple, and noninvasive testing (gender, A-scan ultrasonography, keratometry, and measurements of height and visual acuity) can achieve an acceptable positive predictive value in the range of 75%, at least in a setting such as Singapore, where myopia prevalence is high. It seems likely that such screening and prevention programs would generally be undertaken in settings with a similarly high burden of refractive error. In view of this fact, we chose to test the model in the Singapore cohort, rather than develop it based on Singapore data and test it in the Xiamen cohort, which had lower myopia prevalence. 
The practicality of strategies for myopia prevention in children rests in part on the efficacy and safety of available treatments. Present modalities have clear limitations, including rebound progression after cessation of medical treatments such as atropine 13 and limited success of refractive strategies such as the use of progressive addition lenses, 23 25 contact lenses, 20 22 and deferred prescription of corrective spectacles. 26,27 Nonetheless, research is ongoing on the efficacy of compounds such as selective γ-aminobutyric acid type C antagonists in retarding axial elongation in animal models 44,45 ; the question of whether screening accuracy will limit the practical applications of such advances in the prevention of human myopia remains relevant. 
The values for sensitivity, specificity, and AUC in the Singapore test cohort are somewhat lower than those reported by Zadnik et al. 39 for a model using baseline refractive error to predict onset of myopia ≤ −0.75 D within the Orinda Longitudinal Study of Myopia (sensitivity, 0.867; specificity, 0.733; AUC, 0.880). Jones-Jordan et al. 38 reported values comparable to ours (sensitivity, 0.625; specificity, 0.819) for a model using myopia at baseline and parental history of myopia to predict the onset of myopia ≤ −0.75 D in the Collaborative Longitudinal Evaluation of Ethnicity and Refractive Error Study. Unlike these studies, the present report used one cohort (Xiamen) to develop a model and another (Singapore) to test it. Although our model was somewhat more complex than that reported by Zadnik and colleagues, 39 our analyses (Table 5) suggest that removal of any of the variables from the model reduced its performance as measured by the AUC. Applying Zadnik's model to the Xiamen data set yielded an AUC of 0.711, considerably reduced from a value of 0.974 for our original model. Unlike the Zadnik model, the baseline spherical equivalent refractive error was not an important predictor in the present study. Our results indicating that female gender is associated with myopia risk are consistent with other studies in Chinese populations. 30,31  
Significantly increased risk for development of glaucoma, 10 cataract, 9 and retinal detachment 8 appears most pronounced at higher powers of myopic refractive error. Thus, in practice, there will likely be the greatest interest in early prediction and prevention of high myopia. The model in the present study was constrained by the absence of myopia in this range in the Xiamen cohort on which calculations were based, and thus focused on the ascertainment of myopia < −0.75 D. Children reaching this level of myopia by 11 years of age might be expected to subsequently progress to higher, more visually significant levels. Still, future prospective studies using comparable protocols in matched cohorts with greater prevalence of high myopia are needed to develop and test models predicting the onset of high myopia. Such models will likely benefit from higher sensitivity and specificity, in that there will be less overlap of baseline biometric characteristics between normal children and those who will later develop high myopia. However, the positive predictive value will likely be reduced by the lower prevalence of high compared with low myopia. It is unknown at present how the balance of these two influences will affect predictive value, and thus the practical utility, of models for high myopia. 
Parental myopia appears in other studies 30,33,38 to be an important determinant of children's myopia progression, and thus might improve the accuracy of predictive models; this would, however, come at the cost of added complexity in the screening process, which might render the collection of such data impractical in many settings. In many parts of China, for example, a history of glasses wear, as has been used as a simple proxy for parental myopia in some studies, will not be useful in view of low rates of spectacle wear among myopic persons. The same may be true for detailed assessment of near work, 40,46 48 outdoor activity, 49 and genetic makeup, 34 also previously reported as risk factors for myopia and myopia progression. 
The strengths of this study include the validation of our model by replication in a Singapore cohort, in which data were gathered using the identical protocol and equipment as those used in the original Xiamen children. In each of these two quite different locations, a high degree of follow-up was achieved over 3 years. Both the settings and the screening modalities used are relevant to the practical problem of myopia screening: Singapore and China have very high burdens of myopic refractive error and all the tests used are widely available, inexpensive, and noninvasive. The high rate of success in data collection within the original studies indicates that screening of all children at entry to elementary school, for example, could be practical in a setting with high myopia prevalence such as Singapore or urban China. 
Weaknesses of the study include the complexity of the model. The use of squared and cubed terms and interaction variables means that risk of myopia does not vary in an intuitively clear way with biometric factors such as axial length and corneal curvature. However, the purpose of this model was to predict future myopia in an eye as accurately as possible, and not to better understand risk factors for myopia. An additional limitation noted earlier is that a model predicting onset of higher degrees of myopia, not practical with the Xiamen cohort, would have been more relevant to the screening problems facing program planners hoping to decrease the visual burden of myopia. Finally, generalizability of the model is limited by the relatively narrow range of ages at baseline in the two samples. 
Nonetheless, despite its limitations, this report is among the first of which we are aware to explore the potentially important question of whether myopia prevention strategies, as they improve, can be delivered accurately in a targeted fashion in China. It is hoped that further work on such models will be joined with a better understanding of the biochemistry of axial elongation to yield practical and cost-effective strategies for the prevention of myopia in severely affected populations. 
Footnotes
 Supported in part by The Joint Shantou International Eye Center of Shantou University and The Chinese University of Hong Kong.
Footnotes
 Disclosure: M. Zhang, None; G. Gazzard, None; Z. Fu, None; L. Li, None; B. Chen, None; S.M. Saw, None; N. Congdon, None
References
Zhao J Pan X Sui R Munoz SR Sperduto RD Ellwein LB . Refractive error study in children: results from Shunyi District, China. Am J Ophthalmol. 2000;129:427–435. [CrossRef] [PubMed]
He M Zeng J Liu Y Xu J Pokharel GP Ellwein LB . Refractive error and visual impairment in urban children in southern China. Invest Ophthalmol Vis Sci. 2004;45:793–799. [CrossRef] [PubMed]
He M Huang W Zheng Y Huang L Ellwein LB . Refractive error and visual impairment in school children in rural southern China. Ophthalmology. 2007;114:374–382. [CrossRef] [PubMed]
Congdon NG Wang Y Song Y . Visual disability, visual function and myopia among rural Chinese secondary school children: the Xichang Pediatric Refractive Error Study (X-PRES) Report #1. Invest Ophthalmol Vis Sci. 2008;49:2888–2894. [CrossRef] [PubMed]
Congdon N Zheng MW Sharma A . Prevalence and determinants of spectacle nonwear among rural Chinese secondary schoolchildren: the Xichang Pediatric Refractive Error Study Report 3. Arch Ophthalmol. 2008;126:1717–1723. [CrossRef] [PubMed]
Li LP Lam J Lu YG . Attitudes of students, parents, and teachers toward glasses use in rural China. Arch Ophthalmol. 2010;128:759–765. [CrossRef] [PubMed]
Zhang MZ Lv H Gao Y . Visual morbidity due to inaccurate spectacles among school children in rural China: the See Well to Learn Well Project, Report #1. Invest Ophthalmol Vis Sci. 2009;50:2011–2017. [CrossRef] [PubMed]
Sheu SJ Ger LP Ho WL . Late increased risk of retinal detachment after cataract extraction. Am J Ophthalmol. 2010;149:113–119. [CrossRef] [PubMed]
Chang MA Congdon NG Bykhovskaya I Munoz B West SK . The association between myopia and various subtypes of lens opacity: SEE (Salisbury Eye Evaluation) project. Ophthalmology. 2005;112:1395–1401. [CrossRef] [PubMed]
Mitchell P Hourihan F Sandbach J Wang JJ . The relationship between glaucoma and myopia: the Blue Mountains Eye Study. Ophthalmology. 1999;106:2010–2015. [CrossRef] [PubMed]
Wong TY Klein BE Klein R Knudston M Lee KE . Refractive errors, intraocular pressure, and glaucoma in a white population. Ophthalmology. 2003;110:211–217. [CrossRef] [PubMed]
Chua WH Balakrishnan V Chan YH . Atropine for the treatment of childhood myopia. Ophthalmology. 2006;113:2285–2291. [CrossRef] [PubMed]
Tong L Huang XL Koh ALT Zhang X Tan DTH Chua W-H . Atropine for the treatment of childhood myopia: effect on myopia progression after cessation of atropine. Ophthalmology. 2009;116:572–579. [CrossRef] [PubMed]
Fan DS Lam DS Chan CK Fan AH Cheung EY Rao SK . Topical atropine in retarding myopic progression and axial length growth in children with moderate to severe myopia: a pilot study. Jpn J Ophthalmol. 2007;51:27–33. [CrossRef] [PubMed]
Tan DT Lam DS Chua WH Shu-ping DF , Crockett RS for the Asian Pirenzepine Study Group. One-year multi-center, double-masked, placebo-controlled, parallel safety and efficacy study of 2% pirenzepine ophthalmic gel in children with myopia. Ophthalmology. 2005;112:84–91. [CrossRef] [PubMed]
Siatkowski RM Cotter S Miller JM Scher CA Crockett RS Novack GD for the U.S. Pirenzepine Study Group. Safety and efficacy of 2% pirenzepine ophthalmic gel in children with myopia: a 1-year, multicenter, double-masked, placebo-controlled parallel study. Arch Ophthalmol. 2004;122:1667–1674. [CrossRef] [PubMed]
Siatkowski RM Cotter SA Crockett RS Miller JM Novack GD Zadnik K for the U.S. Pirenzepine Study Group. Two-year multicenter, randomized, double-masked, placebo-controlled, parallel safety and efficacy study of 2% pirenzepine ophthalmic gel in children with myopia. J AAPOS. 2008;12:332–339. [CrossRef] [PubMed]
Walline JJ Jones LA Sinott LT . Corneal re-shaping and myopia progression. Br J Ophthalmol. 2009;93:1181–1185. [CrossRef] [PubMed]
Van Meter WS Musch DC Jacobs DS Kaufman SC Reinhart WJ Udell IJ for the American Academy of Ophthalmology. Safety of overnight orthokeratology for myopia: a report by the American Academy of Ophthalmology. Ophthalmology. 2008;115:2301–2313. [CrossRef] [PubMed]
Walline RJ Jones LA Mutti DO Zadnik K . A randomized trial of the effects of rigid contact lenses on myopia progression. Arch Ophthalmol. 2004;122:1760–1766. [CrossRef] [PubMed]
Gwiazda J . Treatment options for myopia. Optom Vis Sci. 2009;86:624–628. [CrossRef] [PubMed]
Walline JJ Jones LA Sinnott L . for the ACHIEVE Study Group. A randomized trial of the effect of soft contact lenses on myopia progression in children. Invest Ophthalmol Vis Sci. 2008;49:4702–4706. [CrossRef] [PubMed]
Gwiazda J Hyman L Hussein M . A randomized clinical trial of progressive addition lenses versus single vision lenses on the progression of myopia in children. Invest Ophthalmol Vis Sci. 2003;44:1492–1500. [CrossRef] [PubMed]
Hasebe S Ohtsuki H Nonaka T . Effect of progressive addition lenses on myopia progression in Japanese children: a prospective, randomized, double-masked, crossover trial. Invest Ophthalmol Vis Sci. 2008;49:2781–2789. [CrossRef] [PubMed]
Yang Z Lan W Ge J . The effectiveness of progressive addition lenses on the progression of myopia in Chinese children. Ophthalmic Physiol Opt. 2009;29:41–48. [CrossRef] [PubMed]
Adler D Millodot M . The possible effect of undercorrection on myopic progression in children. Clin Exp Optom. 2006;89:315–321. [CrossRef] [PubMed]
Chung K Mohidin N O'Leary DJ . Undercorrection of myopia enhances rather than inhibits myopia progression. Vision Res. 2002;42:2555–2559. [CrossRef] [PubMed]
Hyman L Gwiazda J Hussein M . Relationship of age, sex, and ethnicity with myopia progression and axial elongation in the correction of myopia evaluation trial. Arch Ophthalmol. 2005;123:977–987. [CrossRef] [PubMed]
Luu CD Foulds WS Tan DTH . Features of the multifocal electroretinogram may predict the rate of myopia progression in children. Ophthalmology. 2007;114:1433–1438. [CrossRef] [PubMed]
Saw SM Chua WH Gazzard G Koh D Tan DT Stone RA . Eye growth changes in myopic children in Singapore. Br J Ophthalmol. 2005;89:1489–1494. [CrossRef] [PubMed]
Zhao JL Mao J Luo R Li FR Munoz SR Ellwein LB . The progression of refractive error in school-age children: Shunyi District, China. Am J Ophthalmol. 2002;134:735–743. [CrossRef] [PubMed]
Gwiazda J Hyman L Dong LM . for the COMET Group. Factors associated with high myopia after 7 years of follow-up in the Correction of Myopia Evaluation Trial (COMET) Cohort. Ophthalmic Epidemiol. 2007;14:230–237. [CrossRef] [PubMed]
Kurtz D Hyman L Gwiazda JE . for the COMET Group. Role of parental myopia in the progression of myopia and its interaction with treatment in COMET children. Invest Ophthalmol Vis Sci. 2007;48:562–570. [CrossRef] [PubMed]
Khor CC Grignani R Ng DPK . cMET and refractive error progression in children. Ophthalmology. 2009;116:1469–1474. [CrossRef] [PubMed]
Khandekar R Kurup P Mohammed AJ . Determinants of the progress of myopia among Omani school children: a historical cohort study. Eur J Ophthalmol. 2007;17:110–116. [PubMed]
Thorn F Gwiazda J Held R . Myopia progression is specified by a double exponential growth function. Optom Vis Sci. 2005;82:286–297. [CrossRef] [PubMed]
Mutti DO Zadnik K . The utility of three predictors of childhood myopia: a Bayesian analysis. Vision Res. 1995;35:1345–1352. [CrossRef] [PubMed]
Jones-Jordan LA Sinnott LT Manny RE . for the CLEERE Study Group. Early childhood refractive error and parental history of myopia as predictors of myopia. Invest Ophthalmol Vis Sci. 2010;51:115–121. [CrossRef] [PubMed]
Zadnik K Mutti DO Friedman NE . Ocular predictors of the onset of juvenile myopia. Invest Ophthalmol Vis Sci. 1999;40:1936–1943. [PubMed]
Saw SM Zhang MZ Hong RZ Fu ZF Pang MH Tan DTH . Near-work activity, night lights and myopia in the Singapore-China Study. Arch Ophthalmol. 2002;120:620–627. [CrossRef] [PubMed]
Saw SM Chua WH Hong CY . Nearwork in early-onset myopia. Invest Ophthalmol Vis Sci. 2002;43:332–339. [PubMed]
Ferris FLIII Kassoff A Bresnick GH Bailey I . New visual acuity charts for clinical research. Am J Ophthalmol. 1982;94:91–96. [CrossRef] [PubMed]
Jin PH . Statistical Methods in Medicine. Shanghai: Fudan University Press; 2003.
Stone RA Liu J Sugimoto R Capehart C Zhu X Pendrak K . GABA, experimental myopia, and ocular growth in chick. Invest Ophthalmol Vis Sci. 2003;44:3933–3946. [CrossRef] [PubMed]
Chebib M Hinton T Schmid KL . Novel, potent, and selective GABAC antagonists inhibit myopia development and facilitate learning and memory. J Pharmacol Exp Ther. 2009;328:448–457. [CrossRef] [PubMed]
Mutti DO Mitchell GL Moeschberger ML Jones LA Zadnik K . Parental myopia, near work, school achievement, and children's refractive error. Invest Ophthalmol Vis Sci. 2002;43:3633–3640. [PubMed]
Saw SM Chua WH Hong CY . Near-work in early-onset myopia. Invest Ophthalmol Vis Sci. 2002;43:332–339. [PubMed]
Wong L Coggon D Cruddas M Hwang CH . Education, reading, and familial tendency as risk factors for myopia in Hong Kong fishermen. J Epidemiol Community Health. 1993;47:50–53. [CrossRef] [PubMed]
Rose KA Morgan IG Ip J . Outdoor activity reduces the prevalence of myopia in children. Ophthalmology. 2008;115:1279–1285. [CrossRef] [PubMed]
Figure 1.
 
ROC curve for prediction of myopia < −0.75 D spherical equivalent after 3 years of follow-up among primary school–age children in Xiamen China (area under the ROC curve [AUC], 0.974; 95% CI, 0.945–0.997).
Figure 1.
 
ROC curve for prediction of myopia < −0.75 D spherical equivalent after 3 years of follow-up among primary school–age children in Xiamen China (area under the ROC curve [AUC], 0.974; 95% CI, 0.945–0.997).
Figure 2.
 
ROC curve for prediction of myopia < −0.75 D spherical equivalent after 3 years of follow-up among primary school–age children in Singapore (AUC, 0.815; 95% CI, 0.791–0.839).
Figure 2.
 
ROC curve for prediction of myopia < −0.75 D spherical equivalent after 3 years of follow-up among primary school–age children in Singapore (AUC, 0.815; 95% CI, 0.791–0.839).
Table 1.
 
Baseline Characteristics of Primary School–Age Children Participating in a Prospective Study of Myopia from Xiamen (China) and Singapore
Table 1.
 
Baseline Characteristics of Primary School–Age Children Participating in a Prospective Study of Myopia from Xiamen (China) and Singapore
Characteristic Xiamen, China (n = 236) Singapore (n = 1979) P
Boys Girls Total Boys Girls Total
Age, y 7.89 ± 0.59 7.74 ± 0.69 7.82 ± 0.63 7.86 ± 0.85 7.81 ± 0.84 7.83 ± 0.84 0.43
Weight, kg 23.60 ± 4.40 22.60 ± 3.30 23.20 ± 4.00 27.10 ± 6.80 25.70 ± 6.30 26.40 ± 6.60 <0.001
Height, cm 123.00 ± 5.50 122.00 ± 5.30 123.00 ± 5.40 127.00 ± 7.30 126.00 ± 7.70 127.00 ± 7.60 <0.001
Uncorrected vision, logMAR 0.04 ± 0.07 0.05 ± 0.07 0.045 ± 0.07 0.29 ± 0.35 0.27 ± 0.33 0.28 ± 0.34 <0.001
Axial length, mm 22.80 ± 0.78 22.10 ± 0.74 22.50 ± 0.83 23.60 ± 0.91 23.00 ± 0.88 23.30 ± 0.95 <0.001
Anterior chamber depth, mm 3.34 ± 0.27 3.23 ± 0.26 3.29 ± 0.28 3.65 ± 0.27 3.56 ± 0.27 3.60 ± 0.27 <0.001
Lens thickness, mm 3.54 ± 0.17 3.53 ± 0.18 3.53 ± 0.18 3.48 ± 0.19 3.47 ± 0.17 3.47 ± 0.18 <0.001
Vitreous chamber depth, mm 15.90 ± 0.72 15.30 ± 0.70 15.60 ± 0.76 16.50 ± 0.90 16.00 ± 0.85 16.20 ± 0.91 <0.001
Corneal curvature, mm 7.85 ± 0.27 7.67 ± 0.25 7.77 ± 0.27 7.81 ± 0.25 7.69 ± 0.24 7.75 ± 0.25 0.017
Table 2.
 
Myopia Prevalence over Time among Cohorts of Primary School-Age Children from Xiamen (China) and Singapore
Table 2.
 
Myopia Prevalence over Time among Cohorts of Primary School-Age Children from Xiamen (China) and Singapore
Year Xiamen, China Singapore P
Number of Participants Number (%) Myopic Number of Participants Number (%) Myopic
Baseline 236 15 (6.36) 1979 621 (31.4) <0.001
1 202 25 (12.4) 1869 826 (44.2) <0.001
2 205 32 (15.6) 1788 1012 (56.6) <0.001
3 189 41 (21.7) 1644 1014 (61.7) <0.001
Table 3.
 
Characteristics of Children with Complete Data and without Myopia at Baseline in Xiamen and Singapore Who Did and Did Not Go on to Develop Myopia by 3-Year Follow-up
Table 3.
 
Characteristics of Children with Complete Data and without Myopia at Baseline in Xiamen and Singapore Who Did and Did Not Go on to Develop Myopia by 3-Year Follow-up
Factor Xiamen (n = 176) Singapore (n = 1154)
Developed Myopia (n = 28) Did Not Develop Myopia (n = 148) P Developed Myopia (n = 526) Did Not Develop Myopia (n = 628) P
Age, y 6.81 ± 0.65 6.88 ± 0.67 0.503 7.69 ± 0.85 7.80 ± 0.81 0.024
Weight, kg 24.07 ± 5.29 23.07 ± 3.68 0.102 25.80 ± 6.03 26.60 ± 6.84 0.046
Height, cm 123.00 ± 5.79 123.00 ± 5.24 0.995 126.00 ± 7.37 127.00 ± 6.84 0.332
Visual acuity, logMAR 0.06 ± 0.08 0.03 ± 0.05 0.003 0.12 ± 0.37 0.09 ± 0.14 0.001
Axial length, mm 22.70 ± 0.88 22.40 ± 0.80 0.059 23.10 ± 0.71 22.80 ± 0.72 <0.001
Anterior chamber depth, mm 3.31 ± 0.26 3.29 ± 0.27 0.577 3.58 ± 0.27 3.55 ± 0.26 0.069
Lens thickness, mm 3.52 ± 0.16 3.53 ± 0.18 0.818 3.49 ± 0.18 3.50 ± 0.18 0.386
Vitreous chamber depth, mm 15.80 ± 0.75 15.60 ± 0.75 0.058 16.10 ± 0.69 15.80 ± 0.69 <0.001
Corneal curvature, mm 7.75 ± 0.33 7.78 ± 0.27 0.520 7.45 ± 0.25 7.77 ± 0.25 0.075
Table 4.
 
Generalized Estimating Equation Model Predicting Myopia after 3 Years of Follow-up among 176 Primary School-Age Children in Xiamen, China
Table 4.
 
Generalized Estimating Equation Model Predicting Myopia after 3 Years of Follow-up among 176 Primary School-Age Children in Xiamen, China
Predictor Beta SE P
Female gender −1.49 0.175 <0.001
Height, cm −0.076 0.014 <0.001
Visual, logMAR
    Vision2 12.2 2.61 <0.001
    Vision3
Axial length (AL), mm
    AL2 1.95 0.593 <0.001
    AL3 −0.05 0.0199 <0.001
Anterior chamber depth (ACD), mm
    ACD −64.2 14.6 <0.001
    ACD3 0.368 0.0867 <0.001
Lens thickness (LT), mm
    LT −544.0 134.0 <0.001
    LT2 138.0 37.9 <0.001
    LT3 −12.7 3.60 <0.001
Vitreous chamber depth (VCD), mm
    VCD2 −2.26 0.820 0.006
    VCD3 0.069 0.0214 0.001
Corneal curvature (CC), mm
    CC2 −3.88 1.18 0.001
    CC3 0.279 0.101 0.006
ACD × LT × VCD 0.656 0.135 <0.001
Table 5.
 
Impact on AUC of Removing Each of the Variables Included in the Model Predicting Onset of Myopia Based on Children in Xiamen without Myopia at Baseline
Table 5.
 
Impact on AUC of Removing Each of the Variables Included in the Model Predicting Onset of Myopia Based on Children in Xiamen without Myopia at Baseline
Factor Removed AUC (95% CI)
Original model* 0.974 (0.945–0.997)
Gender removed 0.797 (0.701–0.892)
Visual acuity removed 0.786 (0.672–0.900)
Height removed 0.503 (0.387–0.620)
Anterior chamber depth removed 0.500 (0.371–0.629)
Vitreous chamber depth removed 0.500 (0.371–0.629)
Lens thickness removed 0.500 (0.371–0.629)
Corneal curvature removed 0.500 (0.371–0.629)
Axial depth removed 0.372 (0.241–0.503)
×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×