Many studies have shown that both genetic and environmental factors contribute to the risk of developing myopia, with differing views on the relative contributions of each.1 A putative environmental risk factor is near work, and various theories have been proposed to account for the association between near work and myopia2,3 or to downplay the link.4 Intensive near work is often invoked to account for the higher prevalence of myopia found in individuals with more years of education,5–8 although other factors such as intelligence, type of school, study habits, and outdoor play patterns may be involved. Near work also may be a factor in the higher prevalence of myopia found in individuals in certain white collar vs. blue collar occupations.9–11
The association between myopia in children and their parents is strong, with studies reporting increased odds of developing myopia with more myopic parents.12,13 Limited information is available relating myopia progression in children to parental myopia.14 As part of the Correction of Myopia Evaluation Trial (COMET), an ancillary study was conducted to explore the relationship between parental refractive error and myopia progression in their children.15,16 A main finding was that for children wearing conventional single vision lenses, the number of myopic parents was a statistically significant risk factor for the progression of myopia, with more progression in children who had two compared with zero or one myopic parent. Another result was a larger treatment benefit of progressive addition lenses vs. single vision lenses in those children with two myopic parents. In addition to parental refractive error, in the ancillary study, data also were collected on the education and occupations of the parents.
The purpose of these analyses was to investigate the refractive errors of parents of myopic children and their association with education and occupation. To our knowledge, this is the first investigation of refractive errors in the parents of a large group of myopic children. Our hypothesis is that higher education and white collar occupations are associated with myopia in parents of myopic children.
Data for the COMET ancillary study on parental refraction were obtained at all four clinical centers, at the New England College of Optometry (NECO), Boston, MA; University of Alabama at Birmingham School of Optometry (UAB), Birmingham, AL; University of Houston College of Optometry (UH), Houston TX; and the Pennsylvania College of Optometry (PCO) at Salus University, Philadelphia, PA. The institutional review boards at each participating center approved the research protocols. All COMET protocols and procedures conformed to the tenets of the Declaration of Helsinki. Informed consent from the parents was obtained after verbal and written explanation of the nature and possible consequences of the study.
Six hundred twenty-seven parents (n = 375 mothers and 252 fathers) of the 469 myopic 6- to <12-year-old children enrolled in COMET provided refraction data as well as answered questions about their education and occupation. Eighty-five percent of the refractions were obtained by non-cycloplegic autorefraction (Nidek ARK 700A) and 15% were obtained from the most recent spectacle prescription as long as it was <5 years old. The age range of the parents at the time of data collection was from 28.62 to 63.74 years, with a mean of 44.26 ± 5.81 years. The age range of the mothers was from 28.62 to 57.32 years, with a mean of 43.06 ± 5.35 years, and the age range of the fathers was from 32.42 to 63.74 years, with a mean of 46.03 ± 6.01 years.
At two clinical centers (UAB and NECO), the clinic coordinators asked parents to specify the “highest level of education completed” and then coded each parent's response into the most appropriate category from the list below. At the other two centers (UH and PCO), the categories were read aloud to the parents, who then chose the most appropriate response from the following options: (1) did not complete high school; (2) completed high school; (3); completed some college/technical school; (4) graduated from a 4-year college; or (5) completed graduate or professional school.
At three of the four clinical centers, the clinic coordinators collected occupation information by asking parents to indicate their “primary or lifetime occupation.” The type of work specified by the parents was then coded at the Coordinating Center into one of the nine major categories listed below, taken from the Occupational Outlook Handbook, 2000 to 2001 (U.S. Department of Labor, Bureau of Labor Statistics). At the fourth center (UH), a staff member read the categories aloud to the parents, who then chose the most appropriate category from the list. A 10th category (other) was created for occupations that did not fit into the other nine (mainly homemakers), or combined two or more categories.
1. Executive, administrative, managerial (e.g., accountant, education administrator)
2. Professional, technical (e.g., architect, lawyer)
3. Marketing, sales (e.g., real estate agent, travel agent)
4. Administrative support, including clerical (e.g., bank teller, secretary)
5. Service (e.g., janitor, cook)
6. Mechanic, installer, repairer (e.g., auto service technician, line installer)
7. Construction trade (e.g., bricklayer, carpenter)
8. Production (e.g., butcher, dental laboratory technician)
9. Transportation, material moving (e.g., bus driver, equipment operator)
10. Other (mainly homemakers)
For those refractions obtained by autorefraction, the mean of five spherical equivalent measurements was calculated for each eye. For those obtained from a prescription, the spherical equivalent of each eye was used. Because the two eyes were highly correlated (0.96 for mothers and 0.90 for fathers), the average of the spherical equivalent refraction (SER) of the two eyes from each parent was used for these analyses. Myopia was defined as an average SER ≤−0.75 D; hyperopia was defined as an average SER ≥1.0 D, and emmetropia included SERs between these two categories.
Some of the statistical analyses were performed using all the original categories, whereas other analyses used grouped categories. For example, the five education levels were grouped into two major categories: (1) some college and less education, and (2) 4-year college degree and more education; and the 10 occupation levels were grouped into three major categories: (1) white collar (categories 1 to 4); (2) blue collar (categories 5 to 9); and (3) other (category 10).
A one-way ANOVA was applied to test the difference across groups defined by individual education or occupation categories as well as the combined categories. Post hoc tests were conducted to identify which pairs were significantly different. A two-way ANOVA with interaction was used to test the overall effect of education/gender and their interaction. Odds ratios (OR) and 95% confidence intervals (CI) were computed to assess the association between education/occupation and myopia using univariate logistic regression, multivariate logistic regression adjusting for age and gender (if applicable) of the parents, and multivariate logistic regression with all covariates (age, education, occupation, and gender if applicable) included. The χ2 test was conducted to compare the proportions of white collar jobs for mothers vs. fathers at given education levels.
Overall 60.93% of the parents were myopic, 36.04% were emmetropic, and 3.03% were hyperopic. The distributions of spherical equivalent refractive errors in the mothers and fathers were similar, as shown in Fig. 1. Table 1 includes the numbers of mothers and fathers in the various education and occupation categories. More fathers than mothers had graduate education (p = 0.003), whereas the level of education for all other categories did not differ between mothers and fathers. The occupation distribution was similar for parents in some categories (e.g., executive, professional, marketing) but differed in others, with more mothers engaged in administrative support and in the other category (mainly homemakers) (p < 0.001).
The mean ± SD refraction of the parents of the myopic children enrolled in COMET was −2.34 ± 2.94 D. As shown in Fig. 2, mean myopia increased monotonically with increasing level of education, from −0.33 ± 1.30 D for parents without a high school diploma to −3.41 ± 3.05 D for those completing graduate education. Parents who did not finish high school were significantly less myopic compared with the other four education groups (ANOVA, p < 0.0001; Tukey adjustment, all p values <0.05). Parents with a graduate or professional degree had more myopia than those with some college or less education (ANOVA, p < 0.0001; Tukey adjustment, all p values <0.05).
Fig. 3 presents mean myopia in mothers and fathers in the same educational categories. The difference in average refractive error between genders varied by education levels (two-way ANOVA interaction, p = 0.04). Overall 50.56% of the parents had some college or less education (the first three categories), with 52.80% of the mothers and 47.22% of the fathers in these categories. In addition, 49.44% of the parents (47.20% of the mothers and 52.78% of the fathers) completed a 4-year college or graduate school. There was a significant gender difference in refraction in the three groups with less education, with mothers more myopic than fathers (−2.03 vs.−1.21 D; p = 0.01) but not for the two groups with more education (−2.91 vs. −3.04 D; p = 0.70).
The percentage of parents who were myopic by at least −0.75 D increased with education level, ranging from 23.91% in the group not completing high school to 79.31% in the most educated group. Looking at the extremes, the odds of being myopic were significantly higher for parents with a graduate education than for those not completing high school (OR = 12.20, 95% CI = 5.55 to 26.81).
After adjusting for age and gender and combining the five education categories into two, higher education (college degree or more education) vs. less education than a college degree was significantly associated with an approximately twofold greater odds of being myopic (OR = 2.77, 95% CI = 1.96 to 3.92) as shown in Table 2. In a multivariate analysis that included all covariates, the association between myopia and higher education remained statistically significant with a similar OR (OR = 2.12, 95% CI = 1.31 to 3.19). Mothers alone showed a similar trend to the overall results, but in the multivariate analysis including all covariates, the association between myopia and higher education in mothers was of borderline significance (OR = 1.60, 95% CI = 0.97 to 2.65). The ORs for fathers alone were higher than for mothers and were statistically significant in the multivariate analysis (OR = 3.80, 95% CI = 1.82 to 7.92).
As shown in Fig. 4, mean myopia differed significantly across the 10 occupation categories (ANOVA, p < 0.0001), ranging from −3.40 ± 3.09 D in parents who were executives to −0.60 ± 1.10 D in parents who worked in construction. Parents in white collar jobs (−2.87 ± 3.10 D) were significantly more myopic than those in blue collar jobs (−1.21 ± 2.02 D) or the other category (−1.56 ± 2.70 D; ANOVA and Tukey adjustment, all p values < 0.001). The mean refraction of parents with blue collar jobs was similar to the mean refraction in the other category (Tukey adjustment; p = 0.54). After adjusting for age and gender, the white collar group was still significantly associated with more minus refractions (p < 0.001). Overall, the mean refractions of mothers and fathers were not significantly different for any occupation category (white collar, blue collar, or other), as shown in Fig. 5.
The percentage of myopes in the white collar group (68.87%) was significantly higher than in the blue collar group (44.60%; OR = 2.55, 95% CI = 1.70 to 3.84) when adjusting for age and gender, as shown in Table 2. The same pattern was shown for mothers and fathers. However, in the multivariate analysis including all covariates, the association between white collar vs. blue collar occupations and myopia became weaker. For all parents combined, the OR was reduced to 1.61 (95% CI = 0.999 to 2.60), which is of borderline significance. When data from mothers and fathers were analyzed separately, no significant association was observed between white collar vs. blue collar occupations and myopia.
Education, Occupation, and Gender
As shown in Fig. 3, mothers with some college or less education had a significantly more myopic mean spherical equivalent than fathers in the same education categories. Furthermore, as shown in Fig. 6A, for these education categories, mothers also were more likely to have white collar jobs compared with fathers (93/198 = 46.97% vs. 32/119 = 26.89%), and fathers were more likely to have blue collar jobs (83/119 = 69.75% vs. 48/198 = 24.24%), (χ2 test; p < 0.001). However, as shown in Fig. 6B, for parents who had a 4-year college degree or more education, a high percentage of both mothers and fathers had white collar jobs. The percentages of mothers and fathers with white collar jobs were not significantly different (157/177 = 88.70% vs. 126/133 = 94.74%, p = 0.10).
In this study, the parents of moderately myopic children had a mean spherical equivalent refractive error of −2.34 D and 60.93% of them were myopic, a prevalence almost twice as high as that found in American adults.17 These numbers may not be surprising given that all the parents had myopic children and considering the familial aspects of myopia. In addition, because COMET was a clinical trial, it is likely that the parents who responded to recruitment materials seeking myopic children for a study of a lens treatment to slow myopia progression were more likely to be myopic themselves and concerned about the future refractive state of their offspring.
Although the prevalence of myopia varies greatly in the different populations worldwide used to investigate risk factors for myopia, it appears that the association of myopia and higher education is robust. In our highly myopic sample, the overall association of more myopia with more years of education was similar to what has been reported in other adult populations, including those with less myopia and more limited education.1,5,18 For example, in the Los Angeles Latino Eye Study, a population-based study of 6357 adult Latinos, more education was one of the strongest risk factors for myopia.18 However, the overall prevalence of myopia ≤−1.0 D in that study was 16.7%, compared with 60.9% in our study using a cut-off of −0.75 D, and more than one-half the participants did not complete high school.18
Higher education could be related to myopia by a combination of extensive near work and high IQ. After reviewing the literature on risk factors, Morgan and Rose1 suggested that “school performance, performance on IQ tests, and near work be regarded as potentially linked variables in epidemiological analysis.” They further suggested that higher levels of education might be attained by individuals who were prepared for test taking, as evidenced by performance on IQ tests, and who also were involved in near work activities such as reading and studying. However, another study showed that non-verbal IQ was related to myopia independent of near work but the mechanism was not clear.19
In our study, more myopia overall was found to be associated with white collar vs. blue collar occupations. Previous investigations also have reported that individuals in white collar jobs had more myopia.20–22 In a population-based study of 3271 urban and 1473 rural adults living in Victoria, Australia, professionals and clerks had a prevalence of myopia of 30.3 and 27.7%, respectively, with more negative spherical equivalents than all other occupational groups.20 Similarly, in a population-based study of 2168 Japanese adults, myopia was associated with management occupations in the men and with clerical and sales occupations in the women.21 The prevalence of myopia in 1232 Chinese adults living in Singapore ranged from 52.4% for professionals to 30.7% for blue collar workers,22 lower than our findings of 68.9% for the white collar group and 44.6% for the blue collar group but with a similar difference in prevalence of ∼22% between white and blue collar workers. It is unclear whether these previous findings for the association of occupation and myopia were confounded by education.
Occupation in our study appears not to be independently associated with myopia beyond its association with education, overall and separately for mothers and fathers. This is not surprising because myopia onset for most parents likely predated the choice of occupation, and the choice of occupation reflects in part the level of education attained. Because education and occupation were more closely related in fathers than mothers (Fig. 6A, B), the reduction in the OR between white collar occupations and myopia after adjusting for education appeared greater for fathers.
An interesting result was that among parents with lower educational levels, the mothers of COMET children had, on average, significantly more negative SERs than the fathers. They also were more likely to have white collar jobs. These results can be interpreted in different ways. The mothers in white collar jobs may have performed more near work, which could lead to more progression of existing myopia, and/or the fathers in blue collar jobs may have spent more time outdoors which could be protective. Another possibility is that factors related to gender differences other than choice of occupation, such as hours of near work in their youth or hours of indoor vs. outdoor activities, might have contributed to the difference in the amount of myopia between fathers and mothers. Such factors might also account for the higher prevalence of myopia for mothers compared with fathers within the lower education group.
There are some limitations to consider in interpreting the results of our study. Because COMET was a clinical trial, it is likely that in recruiting myopic children, we also recruited myopic parents. Therefore, the results of this study are not likely to be generalizable beyond this sample. Moreover, despite our best efforts to locate and refract the parents of COMET children, only 84% of the mothers and 57% of the fathers participated in this ancillary study. Moreover, the absence of refractive histories from the parents limits conclusions that can be drawn about the temporal association of refractive error, education, and occupation and the roles of near work and outdoor activity in myopia development and progression. Another study limitation is the lack of data, such as IQ, that might be related to myopia in the parents and also influence their level of education and choice of occupation. Finally, this study is cross-sectional, limiting conclusions that can be drawn about the etiology of myopia. Longitudinal studies of children and young adults in different educational and occupational situations provide more compelling evidence of a role for the visual environment, with individuals in more near-work intensive environments and/or with less outdoor activity showing more myopia development or progression of existing myopia.9–11
In summary, the parents of moderately myopic children participating in a clinical trial of progressive addition lenses vs. single vision lenses to slow the progression of myopia had a high prevalence of myopia that was associated with their level of education and to a lesser extent with their choice of occupation. To our knowledge, this is the first account of refractive errors and associated factors in the parents of a large group of myopic children.
This research was supported by National Eye Institute, National Institute of Health, NEI/NIH grants EY11756, EY11754, EY11805, EY11752, EY11740, and EY11755.
The New England College of Optometry
424 Beacon Street
Boston, Massachusetts 02115
Members of the COMET Study Group
Study Chair's Office
New England College of Optometry, Boston, Massachusetts: Jane Gwiazda (Study Chair/Principal Investigator); Kenneth Grice (Study Coordinator September 1996 to July 1999); Christine Fortunato (Study Coordinator August 1999 to September 2000); Cara Weber (Study Coordinator October 2000 to August 2003); Alexandra Beale (Study Coordinator November 2003 to July 2005); Rosanna Pacella (Research Assistant October 1996 to October 1998); Thomas Norton (Consultant, University of Alabama at Birmingham).
Department of Preventive Medicine, Stony Brook University Health Sciences Center; Stony Brook, New York: Leslie Hyman (Principal Investigator); M. Cristina Leske (Co-Principal Investigator until September 2003); Mohamed Hussein (Co-Investigator/Biostatistician until October 2003); Li Ming Dong (Biostatistician December 2003 to May 2010); Elinor Schoenfeld (Epidemiologist, until September 2005); Lynette Dias (Study Coordinator June 1998-present); Rachel Harrison (Study Coordinator April 1997 to March 1998); Jennifer Thomas (Assistant Study Coordinator December 2000 to April 2004); Marcela Wasserman (Assistant Study Coordinator May 2004 to July 2006); Cristi Rau (Assistant Study Coordinator February 1999 to November 2000); Elissa Schnall (Assistant Study Coordinator November 1997 to November 1998); Wen Zhu (Senior Programmer until December 2006); Ying Wang (Data Analyst January 2000 to December 2005); Ahmed Yassin (Data Analyst January 1998 to January 1999); Lauretta Passanant (Project Assistant February 1998 to December 2004); Maria Rodriguez (Project Assistant October 2000-present); Allison Schmertz (Project Assistant January 1998 to December 1998); Ann Park (Project Assistant January 1999 to April 2000); Phyllis Neuschwender (Administrative Assistant, until November 1999); Geeta Veeraraghavan (Administrative Assistant December 1999 to April 2001); and Angela Santomarco (Administrative Assistant July 2001 to August 2004).
National Eye Institute, Bethesda, Maryland
Donald Everett (Program Director, Collaborative Clinical Trials Branch).
University of Alabama at Birmingham School of Optometry, Birmingham, Alabama: Wendy Marsh-Tootle (Principal Investigator); Katherine Niemann (Optometrist September 1998-present); Kristine Becker (Ophthalmic Consultant July 1999 to March 2003); James Raley (Optician September 1997 to April 1999); Angela Rawden (Back-up Optician October 1997 to September 1998); Catherine Baldwin (Primary Optician & Clinic Coordinator October 1998-present); Nicholas Harris (Clinic Coordinator March 1998 to September 1999); Trana Mars (Back-up Clinic Coordinator October 1997 to March 2003); and Robert Rutstein (Consulting Optometrist until August 2003).
New England College of Optometry, Boston, Massachusetts
Daniel Kurtz (Principal Investigator until June 2007); Erik Weissberg (June 1999-present; Principal Investigator since June 2007); Bruce Moore (Optometrist until June 1999); Robert Owens (Primary Optician); Justin Smith (Clinic Coordinator January 2001 to August 2008); Sheila Martin (Clinic Coordinator until September 1998); Joanne Bolden (Coordinator October 1998 to September 2003); Benny Jaramillo (Back-up Optician March 2000 to June 2003); Stacy Hamlett (Back-up Optician June 1998-May 2000); Laura Vasilakos (Back-up Optician February 2002 to December 2005); Sarah Gladstone (Back-up Optician, June 2004 to March 2007); Patricia Kowalski (Consulting Optometrist until June 2001); and Jennifer Hazelwood (Consulting Optometrist July 2001 to August 2003).
University of Houston College of Optometry, Houston, Texas
Ruth Manny (Principal Investigator); Connie Cross-noe (Optometrist until May 2003); Sheila Deatherage (Optician until March 2007); Charles Dudonis (Optician until January 2007); Sally Henry (Clinic Coordinator until August 1998); Jennifer McLeod (Clinic Coordinator September 1998 to August 2004); Mamie Batres (Clinic Coordinator August 2004 to January 2006); Julio Quiralte (Back-up Coordinator January 1998 to July 2005); and Karen Fern (Consulting Optometrist until August 2003; Optometrist since September 2003).
Pennsylvania College of Optometry at Salus University, Philadelphia, Pennsylvania
Mitchell Scheiman (Principal Investigator); Kathleen Zinzer (Optometrist until April 2004); Timothy Lancaster (Optician until June 1999); Theresa Elliott (Optician until August 2001); Mark Bernhardt (Optician June 1999 to May 2000); Dan Ferrara (Optician July 2000 to July 2001); Jeff Miles (Optician August 2001 to December 2004); Scott Wilkins (Optician September 2001 to August 2003); Renee Wilkins (Optician January 2002 to August 2003); Jennifer Nicole Smith (Optician & Back-up Coordinator October 2003 to September 2005); Karen Pollack (Clinic Coordinator November 2003-present); Abby Grossman (Clinic Coordinator August 2001 to November 2003); Mariel Torres (Clinic Coordinator July 1997 to June 2000); Heather Jones (Clinic Coordinator August 00 to July 2001); Melissa Madigan-Carr (Coordinator July 2001 to March 2003); Theresa Sanogo (Back-up Coordinator July 1999 to March 2003); and JoAnn Bailey (Consulting Optometrist until August 2003).
1. Morgan I, Rose K. How genetic is school myopia? Prog Retin Eye Res 2005;24:1–38.
2. Goss DA. Clinical accommodation and heterophoria findings preceding juvenile onset of myopia. Optom Vis Sci 1991;68:110–6.
3. Gwiazda J, Thorn F, Bauer J, Held R. Myopic children show insufficient accommodative response to blur. Invest Ophthalmol Vis Sci 1993;34:690–4.
4. Mutti DO, Zadnik K. Has near work's star fallen? Optom Vis Sci 2009;86:76–8.
5. Richler A, Bear JC. Refraction, nearwork and education. A population study in Newfoundland. Acta Ophthalmol (Copenh) 1980;58:468–78.
6. Kinge B, Midelfart A, Jacobsen G, Rystad J. The influence of near-work on development of myopia among university students. A three-year longitudinal study among engineering students in Norway. Acta Ophthalmol Scand 2000;78:26–9.
7. Zylbermann R, Landau D, Berson D. The influence of study habits on myopia in Jewish teenagers. J Pediatr Ophthalmol Strabismus 1993;30:319–22.
8. Konstantopoulos A, Yadegarfar G, Elgohary M. Near work, education, family history, and myopia in Greek conscripts. Eye (Lond) 2008;22:542–6.
9. Hepsen IF, Evereklioglu C, Bayramlar H. The effect of reading and near-work on the development of myopia in emmetropic boys: a prospective, controlled, three-year follow-up study. Vision Res 2001;41:2511–20.
10. McBrien NA, Adams DW. A longitudinal investigation of adult-onset and adult-progression of myopia in an occupational group. Refractive and biometric findings. Invest Ophthalmol Vis Sci 1997;38:321–33.
11. Cortinez MF, Chiappe JP, Iribarren R. Prevalence of refractive errors in a population of office-workers in Buenos Aires, Argentina. Ophthalmic Epidemiol 2008;15:10–6.
12. Zadnik K. The Glenn A. Fry Award Lecture 1995. Myopia development in childhood. Optom Vis Sci 1997;74:603–8.
13. Pacella R, McLellan J, Grice K, Del Bono EA, Wiggs JL, Gwiazda JE. Role of genetic factors in the etiology of juvenile-onset myopia based on a longitudinal study of refractive error. Optom Vis Sci 1999;76:381–6.
14. Saw SM, Nieto FJ, Katz J, Schein OD, Levy B, Chew SJ. Familial clustering and myopia progression in Singapore school children. Ophthalmic Epidemiol 2001;8:227–36.
15. Gwiazda J, Hyman L, Hussein M, Everett D, Norton TT, Kurtz D, Leske MC, Manny R, Marsh-Tootle W, Scheiman M. A randomized clinical trial of progressive addition lenses versus single vision lenses on the progression of myopia in children. The COMET Study Group. Invest Ophthalmol Vis Sci 2003;44:1492–500.
16. Kurtz D, Hyman L, Gwiazda JE, Manny R, Dong LM, Wang Y, Scheiman M. Role of parental myopia in the progression of myopia and its interaction with treatment in COMET children. Invest Ophthalmol Vis Sci 2007;48:562–70.
17. Vitale S, Ellwein L, Cotch MF, Ferris FL III, Sperduto R. Prevalence of refractive error in the United States, 1999–2004. Arch Ophthalmol 2008;126:1111–9.
18. Tarczy-Hornoch K, Ying-Lai M, Varma R. Myopic refractive error in adult Latinos: the Los Angeles Latino Eye Study. Invest Ophthalmol Vis Sci 2006;47:1845–52.
19. Saw SM, Tan SB, Fung D, Chia KS, Koh D, Tan DT, Stone RA. IQ and the association with myopia in children. Invest Ophthalmol Vis Sci 2004;45:2943–8.
20. Wensor M, McCarty CA, Taylor HR. Prevalence and risk factors of myopia in Victoria, Australia. Arch Ophthalmol 1999;117:658–63.
21. Shimizu N, Nomura H, Ando F, Niino N, Miyake Y, Shimokata H. Refractive errors and factors associated with myopia in an adult Japanese population. Jpn J Ophthalmol 2003;47:6–12.
22. Wong TY, Foster PJ, Hee J, Ng TP, Tielsch JM, Chew SJ, Johnson GJ, Seah SK. Prevalence and risk factors for refractive errors in adult Chinese in Singapore. Invest Ophthalmol Vis Sci 2000;41:2486–94.