The Determinants of Early Refractive Error on School-Going Chinese Children

Abstract

Refractive error is a common social issue in every walks of human life, and its prevalence recorded the highest among Chinese population, particularly among people living in southern China, Hong Kong, Thailand, Singapore, and Malaysia. Refractive error is the simplest disorder to treat and supposed to cost the effective health care intervention. The present study included 168 Chinese school-going children aged 10 to 12 years; they were selected from different schools of urban Malaysia. It was surprising to see that 112 (66.7%) children had the early onset of refractive error; refractive error was also detected late among the primary school or secondary school students. The findings revealed that the determinants of refractive error among Chinese children were personal achievements and machine dependence. The possible reasons for the above significant factors emerged could be attributed to the inbuilt culture and traditions of Chinese parents who insist that their children should be hardworking and focus on school subjects so that their parents allow them to use luxury electronic devices.

Keywords

refractive error Chinese children machine dependence genetic traits

Introduction

The World Health Organization has estimated that there are approximately 314 million people living with vision impairment. The geographical distribution of visual impairment is not uniform, and more than 90% people who are visually impaired are found in the developing and developed countries. The global initiative for the elimination of avoidable blindness (Vision 2020: The Right to Sight) has faced a major challenge that requires a significant increase in the provision and uptake of eye care services around the world (Lewallen & Kello, 2009). The shrinking economies of many countries in the world, especially some poor countries, have been severely over stretched with increasing pressure on health care budgets. Furthermore, the budget for eye health services has been pushed further down in the list of public health priorities competing with the demands of the life-threatening diseases (Kyndt, 2001). According to Thulasiraj, Aravind, and Pradhan (2003), the majority of people with refractive error may have their sight restored back through spectacles, but only a few of them have access to eye examinations because of cost constraints. A study on the prevalence of refractive error among school-going children in Malaysia found that more than half of those with refractive error needed corrective spectacles. There are basically four types of refractive errors with a need of spectacles. Three of them, namely, myopia, hyperopia, and astigmatism, affect children while presbyopia affects those who are above 40 years (Figure 1). Myopia occurs due to the excessive close use of the eyes and when people strain their eyes when looking at objects in the distance (Eulenberg, 1996). Myopia type of refractive errors generally appears at 8 to 14 years in the late primary school or the early secondary school. Furthermore, the progression of myopia may occur in the consecutive 10 to 15 years up to the age of about 30 years.

Figure 1.

Types of refractive errors.

In recent decades, the prevalence of myopia has dramatically increased, particularly in some Asian countries (Morgan & Rose, 2005; Saw, 2003). Myopia has exceeded 80% in some highly educated groups (Loman et al., 2002). It is the most common type of refractive error among children in Malaysia (Goh, Abqariyah, Pokharel, & Ellwein, 2005). A study in Hong Kong showed that the prevalence of myopia was 82.8% among Chinese students aged 13 to 15 years (C. S. Lam, Goldschmidt, & Edwards, 2004). Compared with Indians, 68.7%, and Malays, 65.0%, Singapore has been reported to be one of the highest areas of myopia (79.3%; H. M. Wu et al., 2001). Schneider (2004) argued that the optical correction of myopia in the school has significantly affected the social activities of millions of children during their productive age group. Myopia in Malaysia is prevalent among 9.2% Malays, 30.9% Chinese, and 12.5% Indians. As the Chinese have the highest prevalence of refractive error among all races in Malaysia, it is crucial to identify the possible underlying factors that contribute to refractive error. Hence, the present study was conducted to identify the factors that cause refractive error among Chinese students in Malaysia and to determine whether age and gender mediate the relationship between these factors and refractive errors.

Risk Factors Related to the Refractive Error

The progression of myopia is thought to be controlled by the retinal image quality, but its triggering factors have not been yet well known (Lundstrom, Mira-Agudelo, & Artal, 2009). In a large number of studies, children with myopic parents are more likely to have myopia than those without myopic parents (Guggenheim, Kirov, & Hodson, 2000; Mutti et al., 2002; Mutti & Zadnik, 1995; Saw, Nieto, et al., 2001; M. M. Wu & Edwards, 1999; Young, 2009). The relationship between parents and children might reflect the shared environments as well as the shared genes with respect to refractive error.

In contrast to the limited evidence for the genetic determination of refractive error, there is considerable evidence of the importance of the environmental factors (Morgan & Rose, 2005). For example, urbanization has created a substantial change in children’s eating habits, including foods that are not fresh due to refrigeration, artificially grown foods, and canned and frozen foods. The genetic factors alone cannot be responsible for the high prevalence of refractive error. However, factors such as a visual experience, lifestyle, and diet may be the cause (Mutti, 2010). Concerning visual experience, Congdon et al. (2008) found that the fear of spectacles could be avoided with positive attitudes, practices, and self-confidence. Literature has revealed that eating habits could have some impact on children’s eye development. For example, Katz and Lambert (2011) reported that myopes were significantly eating less fresh fruits, vegetables, and whole grains in their daily diet. Besides, Edward (1996) also found that the children with myopia tended to have low food intake of protein, fat, cholesterol, vitamin B, vitamin C, phosphorus, and iron. Semba (2007) claimed that vitamin A deficiency caused the deterioration of the mucous-forming cells of the eyes, thereby resulting in conjunctiva necrosis; foamy gray spots which consist of the hardened epithelial cells; and the scarring and softening of the cornea which cause irreversible blindness. In another study, Eulenberg (1996) found that a diet with a high content of carbohydrates, starches or sugars, and low in proteins or fats favors the development of myopia.

Eating habits, which have been established during adolescence, may be difficult to change later. It is, therefore, necessary to create a healthy diet to prevent the development of refractive error or any other health problems during childhood. Saw, Wu, et al. (2001) commented that the hectic educational schedules such as close-up work activities in early childhood and tuition classes during the elementary school were positively related to myopia. Nowadays, busy parents send their children to attend many tuition classes hoping that their children may catch up their school lessons and achieve good results. These results underscore the strong influence of the environment on myopia pathogenesis. Hours of sleep are also related with myopia or myopia progression leading to a myopia risk factor among the young people (Loman et al., 2002). In an extensive study, Hartwig, Gowen, Charman, and Radhakrishnan (2011) found that there was no difference in mean head posture or reading distance between myopes and normals during reading tasks. In accord with the bulk of epidemiological evidence, Teasdale, Fuchs, and Goldschmidt (1988) commented that myopes in general, achieved higher intelligence test scores and higher educational levels than non-myopes. In addition, supporting the epidemiological evidence, Saw, Cheng, Fong, Tan, and Morgan (2007) found that those children with higher examination scores were 2.5 times more likely to have myopic complaints than the children with lower examination scores. According to Yingyong (2010), myopic children spend more time watching television and playing video and computer games. As computers have become an undeniable part of everyday life, more and more children are experiencing a variety of ocular symptoms, including eyestrain, tired eyes, irritation, redness, blurred vision, and double vision referred to as computer vision syndrome.

Model Conceptualization

Figure 2 shows the proposed framework for investigating the effects of eating habits, genetic traits, lifestyle, personal achievements, and machine dependence on reflective error among Chinese children in Malaysia.

Figure 2.

Research framework.

Hypothesis Development

Katz and Lambert (2011), Edward (1996), Semba (2007), and Eulenberg (1996) argued that the absence of nutrition such as vitamins A, B, C, protein, and food such as fruits and vegetables could have an impact on visual disabilities and might cause refractive error among children. Accordingly, this study hypothesized as follows:

Hypothesis 1 (H1): An unhealthy eating habit is positively related to the refractive error.

Young, Metlapally, and Shay (2007) and D. S. Lam et al. (2008) found that there was a positive relationship between parental myopia and probability of myopia development among children, thereby indicating a hereditary factor of refractive error. Accordingly, this study hypothesized as follows:

Hypothesis 2 (H2): Genetic traits are positively related to refractive error.

The different lifestyle factors such as sport activities, extra tuition classes, less hours of sleep, and too much of straining eyes may cause refractive error (Loman et al., 2002; Rose, Morgan, Ip, et al., 2008a; Saw, Wu, et al., 2001). Rose, Morgan, Smith, et al. (2008b) believed that the prevalence of myopia could be caused by reading books in close distance, watching TV in mid-working distance, and doing vigorous outdoor activities. Accordingly, this study hypothesized as follows:

Hypothesis 3 (H3): Unhealthy lifestyle is positively related to refractive error.

Teasdale et al. (1988), Saw et al. (2007), and Eulenberg (1996) showed that achieving high intelligence test scores and high educational levels and reading more books per week significantly influenced refractive error. Accordingly, this study hypothesized as follows:

Hypothesis 4 (H4): Good personal achievement is positively related to refractive error.

Computers and televisions have become important devices in daily life. Blehm, Vishnu, Khattak, Mitra, and Yee (2005); Saw, Wu, et al. (2001); and Yingyong (2010) reported that close-up work activities such as video and computer games have contributed much to refractive error. Accordingly, this study hypothesized as follows:

Hypothesis 5 (H5): Machine dependence is positively related to refractive error.

Research Method

Data Collection

As mentioned above, the early refractive error was prevalent among Chinese primary school children in Malaysia; it was also found in the late primary school or early secondary school. Accordingly, the present study was conducted among Chinese primary school children aged 10 to 12 years. They were selected from different Chinese primary schools in the urban areas of Malaysia. In Malaysia, children go to the primary school only at the completed age of 7 when they are mature enough to respond independently. This schooling time is consistent with the onset of refractive error which starts from 8 years onward. There might be some complaints of wearing spectacles during this time. Data were collected from Chinese primary school students with the permission given by school headmasters to distribute the questionnaires among the students. They were selected based on a proportional stratified sampling design.

The primary data were collected by administering the questionnaires personally to each student in the school while the secondary data were collected based on the existing medical reports in Chinese schools. The detailed questionnaires were administered to each student individually to find the determinants of the early refractive error among the school-going Chinese children in Malaysia. The schoolteachers and the school authorities strongly supported the conduction of the study. They also provided the existing secondary data concerning the visual impairment reports of the children. A total of 168 Chinese children attending the Classes 3 to 5 participated in the study.

The power of a 168-respondent sample was measured using G*Power Version 3.1.9.2 (Faul, Erdfelder, Buchner, & Lang, 2009). A power of 0.873 was obtained at the statistical significance level (α level) of .05. This yield exceeded 0.80, indicating that the power of the current sample was satisfactory (Chin, 2001). This result indicated that the proposed sample size was strong enough to reject the null hypotheses (Faul et al., 2009).

Measure of the Constructs

The questionnaire consisted of five independent variables, namely, eating habits (7 dimensions), genetic traits (2 dimensions), lifestyle (3 dimensions), personal achievements (2 dimensions), and machine dependence (5 dimensions), based on the cite references (see the appendix). The independent variables were measured based on a 5-point Likert-type scale ranging from 1 being strongly disagree to 5 being strongly agree. The dependent variable included two groups, namely, Chinese children with refractive error and those without refractive error. In the present study, refractive error included myopia and astigmatism.

Analysis

Multivariate analysis was used to study the impact of independent variables on the dependent variable. The binary logistic regression was also used for the categorical dependent variable (Hair, Black, Babin, & Anderson, 2010). Prior to testing hypotheses, the present research used factor analysis and reliability tests to determine the validity and reliability of the measures. The exploratory factor analysis was applied through Varimax rotation (Hair et al., 2010).

Significant Findings and Results

Descriptive Statistics

Out of 168 children, 30 (17.8%) were 10 years old, 69 (41.1%) were 11 years old, and the remaining 69 (41.1%) were 12 years old. About 77 (45.8%) were males and 91 (54.2%) were females (Table 1). The sample included 98 (58.4%) children with myopia, 14 (8.3%) children with astigmatism, and 56 (33.3%) children without any refractive error. However, no cases of hyperopia were reported in the sample. It is clear that the prevalence of refractive error was high among the primary Chinese school children. Refractive error was more among female children, 73.6% (67 / 91), than male children, 58.4% (45 / 77).

Table 1.

Profile of the Chinese Children.

Variable	Description	No. of children	%
Age (in years)	10	30	17.8
	11	69	41.1
	12	69	41.1
Gender	Male	77	45.8
Gender	Female	91	54.2
Refractive error	Yes	112	66.7
Refractive error	No	56	33.3
Refractive error at different age	10	16	53.3
	11	50	72.5
	12	46	66.7
Refractive error of gender	Male	45	58.4
Refractive error of gender	Female	67	73.6
Onset of wearing spectacles (in years)	<5	4	3.6
	5-6	8	7.1
	7-8	36	32.1
	9-10	44	39.3
	11-12	17	15.2
	Not applicable	3	2.7

Table 2 shows some interesting findings about Chinese children who were suffering from either myopia or astigmatism. Out of 98 children with myopia, 96 (98%) wore spectacles whereas 10 out of 14 (71%) children with astigmatism wore spectacles. As many as 84 out of 100 felt comfortable and reported that wearing spectacles did not affect their face appearance. However, children with astigmatism complained of headache even with spectacles (5 / 10). Also, children with astigmatism said that they could not perform any activities without spectacles, and they had never lost or broken spectacles compared with myopes.

Table 2.

Remarks From Chinese Children.

Remarks from Chinese children	Myopia (90)		Astigmatism (10)
Remarks from Chinese children	Yes	No	Yes	No
Wearing Spectacles do not give negative impact to my appearance	77 (85.6%)	13 (14.4%)	7 (70.0%)	3 (30.0%)
I do not feel headache when wearing spectacles	81 (90.0%)	9 (10.0%)	5 (50.0%)	5 (50.0%)
Spectacles are important in my daily activities	75 (83.3%)	15 (16.7%)	10 (100.0%)	0 (0.0%)
I agree that spectacles are must for children having refractive error	81 (90.0%)	9 (10.0%)	8 (80.0%)	2 (20.0%)
I seldom lost or break my spectacles	83 (92.2%)	7 (7.8%)	10 (100.0%)	0 (0.0%)

Note. Eight children with myopia and four children with astigmatism were excluded. Chi-square test has not applied because of small sample under astigmatism.

The descriptive statistics were calculated for mean and standard deviation among the 168 Chinese primary school children with respect to each independent variable, including eating habits, genetic traits, lifestyle, personal achievements, and machine dependence. Table 3 shows that students disagreed that they had unhealthy eating habits (M = 2.346). The mean of genetic traits (M = 2.920) shows that approximately half of the students’ family members had refractive error. In addition, the students also disagreed that they lived unhealthy lifestyle (M = 2.268). Furthermore, the mean of personal achievements indicates that students averagely had good personal achievements in their academic life (M = 3.581). Finally, the mean of machine dependence (M = 3.417) shows that the students were spending much time using electronic devices in their daily activities, thus, leading to high machine dependence.

Table 3.

Descriptive Statistic of Independent Variables.

Score of independent variables	M	SD
Eating habit	2.346	0.595
Genetic traits	2.920	0.061
Lifestyle	2.268	0.867
Personal achievement	3.581	0.746
Machine dependence	3.417	0.022

Factor Analysis

Regarding the questions addressing the five independent variables, factor analysis was used to find whether the subjective measurements in the Likert-type scale and their respective variables converged. The main loadings of factor analysis were above 0.5 for 19 dimensions, which were used for further data analysis. The reliability analysis determined whether the measurements of the five variables were consistent. Estimates of construct reliability were measured for each construct. Cronbach’s alpha values were all greater than .5 ranging from .625 to .793, indicating the consistency of the data.

Binary Logistic Analysis

The dependent variable is dichotomous, namely, the children with refractive error (1) and the children without refractive error (2). In the model, all independent variables influenced refractive error with Nagelkerke’s R² of .225, p < .05, and the overall probability of correct classification was .702 (Table 4).

Table 4.

Result of Binary Logistic Regression.

Variable	Exp(B)	Wald	p value	Decision
Eating habits	0.996	0.000	.990	Not supported
Genetic traits	0.939	0.077	.781	Not supported
Lifestyle	0.718	2.225	.136	Not supported
Personal achievements	0.595	4.183	.041*	Supported
Machine dependence	0.413	15.432	.000**	Supported
Nagelkerke’s R²	.225
Classification results	70.20%

Note. Dependent Variable: Refractive error (Yes: 112; No: 56).

p < .05. **p < .01.

It is interesting to see that personal achievements and machine dependence were found to be statistically significant at 5% level. Therefore, H4 and H5 were supported. Accordingly, it could be claimed that positive relationship between personal achievements and the prevalence of refractive error exists (H4). It should be noted that the concept of personal achievements is viewed in terms of children’s good class grade due to their hardworking quality and high intelligence. Similarly, the variable machine dependence was found to have a positive relationship with the prevalence of refractive error (H5). Nowadays, the technology of the information communication and the features of television are so high-tech and appealing that children spend most of their time using electronic devices for doing their homework, surfing the Internet for information, and playing video or computer games during leisure time. It seems that parents should monitor their children periodically and should not allow them to use electronic devices excessively.

Conclusion

The prevalence of refractive error among Malaysian citizens, particularly Chinese race, is alarming. Chinese children are suffering from refractive error, in particular myopia. The primary data were collected by administering the questionnaires personally to each student in the school while the secondary data were collected based on the existing medical reports in Chinese schools. A total of 168 Chinese children attending the Classes 3 to 5 participated in the study. The early onset of refractive error was among Chinese children in Malaysia; however, it was detected late among the primary school or early secondary school students. Concerning the three types of refractive errors (myopia, hyperopia, astigmatism) prevalent among children, myopia was found to be the most common type of refractive error among Chinese children followed by astigmatism; however, no case with hyperopia was found in the sample. Binary logistic regression was used to measure the impact of the independent variables on the dependent variable. Considering the five independent variables (eating habits, genetic traits, lifestyle, personal achievements, and machine dependence) in the management model, two variables, namely, personal achievements and machine dependence, influenced refractive error and were found to be statistically significant. Because the Chinese are hardworking and workaholic, passing down the same culture to their children results in hectic activities. This could be the reason why Chinese children strain their eyes more than their counterpart races in Malaysia (Malays and Indians). Chinese children generally have a greater focus and a better concentration level than other children. The personal achievements of Chinese children are clearly evident from school records. According to these records, Chinese children are intelligent and get a good class grade due to their hardworking quality. Playing video games, watching TV, and participating more in activities put a strain on Chinese children’s eyes. Therefore, it was not surprising to find machine dependence a significant factor in the development of refractive error. This may be avoided if parents monitor their children periodically and keep them away from the excessive use of electronic devices. Considering the role of gender, male children are more dependent on the machine-oriented activities than female children. Concerning eating habits, because the Chinese are well adapted to a variety of foods, eating habits were not found to be a significant factor in the development of refractive error. The factor genetic traits did not influence refractive error.

Footnotes

Appendix

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research and/or authorship of this article.

Author Biographies

K. Jayaraman is associate professor of Management at Graduate School of Business, Universiti Sains Malaysia. His research interest include: operations management, service innovation and quality management.

Mohammad Iranmanesh is a research assistant at the Faculty of Business and Accountancy, University of Malaya. He received his MBA from Graduate School of Business (USM).

Chuan Chin Liang received his MBA from Graduate School of Business (USM).

Mahboobeh Iranmanesh is resident of oral and maxilofacial radiology at Tehran University of Medical Sciences.

References

Blehm

Vishnu

Khattak

Mitra

Yee

R. W.

(2005). Computer vision syndrome: A review. Survey of Ophthalmology, 50, 253-262.

Chin

D. N.

(2001). Empirical evaluation of user models and user-adapted systems. User Modeling and User-Adapted Interaction, 18, 181-194.

Congdon

Wang

Song

Choi

Zhang

Zhou

. . . Lam

D. S. C.

(2008). Visual disability, visual function, and myopia among rural Chinese secondary school children: The Xichang Pediatric Refractive Error Study (X-PRES). Investigative Ophthalmology & Visual Science, 97, 2888-2894.

Edward

M. H.

(1996). Do variations in normal nutrition play a role in the development of myopia. Optometry and Vision Science, 73, 638-643.

Eulenberg

(1996). The case for the preventability of myopia. Bloomington, IN. Retrieved from http://www.i-see.org/prevent_myopia.html

Faul

Erdfelder

Buchner

Lang

A. G.

(2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41, 1149-1160.

Goh

P. P.

Abqariyah

Pokharel

G. P.

Ellwein

L. B.

(2005). Refractive error and visual impairment in school-age children in Gombak District, Malaysia. American Academy of Ophthalmology, 112, 678-685.

Goss

D. A.

(2000). Nearwork and myopia. The Lancet, 356, 1456-1457.

Guggenheim

J. A.

Kirov

Hodson

S. A.

(2000). The heritability of high myopia: A reanalysis of Goldschmidt’s data. Journal of Medical Genetics, 37, 227-231.

10.

Hair

J. F.

Black

W. C.

Babin

B. J.

Anderson

R. E.

(2010). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Prentice Hall.

11.

Hartwig

Gowen

Charman

W. N.

Radhakrishnan

(2011). Analysis of head position used by myopes and emmetropes when performing a near-vision reading task. Vision Research, 51, 1712-1717.

12.

Katz

Lambert

(2011). A new look at myopia development: Possible links with childhood stress and diet. Journal of Behavioral Optometry, 22(3), 69-73.

13.

Kyndt

(2001). Importance of affordable eye care. Community Eye Health Journal, 14(37), 1-3.

14.

Lam

C. S.

Goldschmidt

Edwards

M. H.

(2004). Prevalence of myopia in local and international schools in Hong Kong. Optometry and Vision Science, 81, 317-322.

15.

Lam

D. S.

Fan

D. S.

Lam

R. F.

Rao

S. K.

Chong

K. S.

Lau

J. T.

. . . Cheung

E. Y.

(2008). The effect of parental history of myopia on children’s eye size and growth: Results of a longitudinal study. Investigative Ophthalmology & Visual Science, 49, 873-876.

16.

Lewallen

Kello

A. B.

(2009). The need for management capacity to achieve VISION 2020 in sub-Saharan Africa. PLoS Medicine, 6(12), e1000184.

17.

Loman

Quinn

G. E.

Kamoun

Ying

G. S.

Maguire

M. G.

Hudesman

Stone

R. A.

(2002). Darkness and near work: Myopia and its progression in third-year law students. Ophthalmology, 109, 1032-1038.

18.

Lundstrom

Mira-Agudelo

Artal

(2009). Peripheral optical errors and their change with accommodation differ between emmetropic and myopic eyes. Journal of Vision, 9(6), 1-11.

19.

Morgan

Rose

(2005). How genetic is school myopia? Progress in Retinal and Eye Research, 24(1), 1-38.

20.

Mutti

D. O.

(2010). Hereditary and environmental contributions to emmetropization and myopia. Optometry and Vision Science, 87, 255-259.

21.

Mutti

D. O.

Semina

Marazita

Cooper

Murray

J. C.

Zadnik

(2002). Genetic loci for pathological myopia are not associated with juvenile myopia. American Journal of Medical Genetics, 112, 355-360.

22.

Mutti

D. O.

Zadnik

(1995). The utility of three predictors of childhood myopia: A Bayesian analysis. Vision Research, 35, 1345-1352.

23.

Rose

K. A.

Morgan

I. G.

Kifley

Huynh

Smith

Mitchell

(2008a). Outdoor activity reduces the prevalence of myopia in children. Ophthalmology, 115, 1279-1285.

24.

Rose

K. A.

Morgan

I. G.

Smith

Burlutsky

Mitchell

Saw

S. M.

(2008b). Myopia, lifestyle and schooling in students of Chinese ethnicity in Singapore and Sydney. Archives of Ophthalmology, 126, 527-530.

25.

Saw

S. M.

(2003). A synopsis of the prevalence rates and environmental risk factors for myopia. Clinical and Experimental Optometry, 86, 289-294.

26.

Saw

S. M.

Cheng

Fong

Tan

Morgan

(2007). School grades and myopia. Ophthalmic Physiological Optics, 27, 126-129.

27.

Saw

S. M.

Nieto

F. J.

Katz

Schein

O. D.

Levy

Chew

S. J.

(2001). Familial clustering and myopia progression in Singapore school children. Ophthalmic Epidemiology, 8, 227-236.

28.

Saw

S. M.

H. M.

Hong

C. Y.

Chua

W. H.

Chia

K. S.

Tan

(2001). Myopia and night lighting in children in Singapore. British Journal of Ophthalmology, 85, 527-528.

29.

Schneider

(2004). How to broaden the availability of eyeglasses worldwide? Retrieved from http://www.cfr.org/content/meetings/global_health_rt/eyeglasses_rapporteur.pdf

30.

Semba

R. D.

(2007). Handbook of nutrition and ophthalmology. Totowa, New Jersey: Human Press.

31.

Teasdale

T. W.

Fuchs

Goldschmidt

(1988). Degree of myopia in relation to intelligence and education level. The Lancet, 332, 1351-1354.

32.

Thulasiraj

R. D.

Aravind

Pradhan

K. B.

(2003). Spectacles for the millions addressing a priority of “VISION 2020-The Right to Sight.” Journal of Arrived Eye Care System, 3(4), 19-21.

33.

H. M.

Seet

Yap

E. P.

Saw

S. M.

Lim

T. H.

Chia

K. S.

(2001). Does education explain ethnic differences in myopia prevalence? A population-based study of young adult males in Singapore. Optometry and Vision Science, 78, 234-239.

34.

M. M.

Edwards

M. H.

(1999). The effect of having myopic parents: An analysis of myopia in three generations. Optometry and Vision Science, 76, 387-392.

35.

Yingyong

(2010). Risk factors for refractive errors in primary school children (6-12 years old) in Nakhon Pathom Province. Journal of the Medical Association of Thailand = Chotmaihet Thangphaet, 93, 1288-1293.

36.

Young

T. L.

(2009). Molecular genetics of human myopia: An update. Optometry and Vision Science, 86(1), E8-E22.

37.

Young

T. L.

Metlapally

Shay

A. E.

(2007). Complex trait genetics of refractive error. Archives of Ophthalmology, 125(1), 38-48.