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Figure 1.
Unadjusted myopia progression (diopters) at 1, 2, and 3 years by age (A), sex (B), and ethnicity (C).

Unadjusted myopia progression (diopters) at 1, 2, and 3 years by age (A), sex (B), and ethnicity (C).

Figure 2.
Unadjusted myopia (diopters) at each visit by age.

Unadjusted myopia (diopters) at each visit by age.

Figure 3.
Unadjusted change in axial length (mm) at 1, 2, and 3 years by age (A), sex (B), and ethnicity (C).

Unadjusted change in axial length (mm) at 1, 2, and 3 years by age (A), sex (B), and ethnicity (C).

Figure 4.
The relationship between myopia progression and change in axial length at 3 years. Three-year progression = −2.04 × (3-year change in axial length). Coefficient of determination, R 2 = 0.77.

The relationship between myopia progression and change in axial length at 3 years. Three-year progression = −2.04 × (3-year change in axial length). Coefficient of determination, R 2 = 0.77.

Table 1. 
Unadjusted 3-Year Myopia Progression by Baseline Age, Sex, Ethnicity, and Treatment Group*
Unadjusted 3-Year Myopia Progression by Baseline Age, Sex, Ethnicity, and Treatment Group*
Table 2. 
Unadjusted and Adjusted Differences in 3-Year Myopia Progression by Baseline Age, Sex, and Ethnicity*
Unadjusted and Adjusted Differences in 3-Year Myopia Progression by Baseline Age, Sex, and Ethnicity*
Table 3. 
Unadjusted 3-Year Change in Axial Length by Baseline Age, Sex, Ethnicity, and Treatment Group*
Unadjusted 3-Year Change in Axial Length by Baseline Age, Sex, Ethnicity, and Treatment Group*
Table 4. 
Unadjusted and Adjusted Differences in 3-Year Change in Axial Length* by Baseline Age, Sex, Ethnicity, and Baseline Corneal Curvature
Unadjusted and Adjusted Differences in 3-Year Change in Axial Length* by Baseline Age, Sex, Ethnicity, and Baseline Corneal Curvature
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July 01, 2005

Relationship of Age, Sex, and Ethnicity With Myopia Progression and Axial Elongation in the Correction of Myopia Evaluation Trial

Author Affiliations

Author Affiliations: Department of Preventive Medicine, School of Medicine, Stony Brook University, New York, NY (Drs Hyman, Wang; the COMET Group); New England College of Optometry, Boston, Mass (Dr Gwiazda; the COMET Group); Southwestern Medical School, Dallas, Tex (Dr Hussein); School of Optometry, University of Alabama at Birmingham, Birmingham, Ala (Drs Norton, Marsh-Tootle; the COMET Group); National Eye Institute, Bethesda, Md (Dr Everett), University of Houston, College of Optometry, Houston, Tex (the COMET Group); Pennsylvania College of Optometry, Philadelphia, Pa (the COMET Group).

Arch Ophthalmol. 2005;123(7):977-987. doi:10.1001/archopht.123.7.977
Abstract

Objective  To identify the baseline factors independently related to 3-year myopia progression and axial elongation in COMET.

Methods  A total of 469 children were enrolled, randomly assigned to progressive addition lenses with a + 2.00 diopter (D) addition or to single vision lenses and observed for 3 years. Eligible children were 6 to 11 years old, with spherical equivalent myopia of − 1.25 to − 4.50 D, bilaterally. The primary and secondary outcomes, myopia progression by cycloplegic autorefraction and axial elongation by A-scan ultrasonography, were measured annually. Multiple linear regression was used to adjust for covariates, including treatment.

Results  Younger baseline age (6-7 vs 11 years, 8 vs 11 years, and 9 vs 11 years, P<.001; 10 vs 11 years, P = .04), female sex (P = .01), and each ethnic group compared with African Americans (Asian, P = .02; Hispanic, P = .002; mixed, P = .002; white, P = .001) were independently associated with faster 3-year progression. Children aged 6 to 7 years had the fastest progression of all age groups, progressing by a mean (± SD) of 1.31 D ± 0.13 more than children aged 11 years. Females progressed 0.16 D more than the males. Children of mixed, Hispanic, Asian, and white ethnicity progressed more than African American children by 0.49 D ± 0.16, 0.33 D ± 0.11, 0.32 D ± 0.13, 0.27 D ± 0.08, respectively. Age and ethnicity, but not sex, were independently associated with axial elongation. Among these myopic children, a 0.5 mm increase in axial length was associated with 1 D of myopia progression.

Conclusions  Younger baseline age was the strongest factor independently associated with faster myopic progression and greater axial elongation at 3 years. African American children had less myopic progression and axial elongation than the other ethnic groups.

Myopia is a common ocular condition in the United States and Europe, affecting approximately 25% of adults,1 and is even more prevalent in Asian populations.2 Even low levels of myopia are associated with an increased risk of ocular complications such as retinal detachments, glaucoma and cataract,37 and progression to higher levels of myopia increases these risks further.4 Myopia progression is irreversible and there is no cure. Despite the high frequency of myopia and the risks associated with progression, understanding the factors associated with progression is limited.

The Correction of Myopia Evaluation Trial (COMET) was a randomized, double-masked, multicenter clinical trial that evaluated whether progressive addition lenses (PALs) vs single vision lenses (SVLs) slowed the rate of myopia progression in children with juvenile-onset myopia who were observed for at least 3 years. The study found that myopia in children assigned to PALs progressed less than in children with SVLs by a small, statistically significant amount (3-year adjusted difference [ ± SD], 0.20 ± 0.08 diopter [D]; P = .004).8 A similar treatment effect was seen for change in axial length (difference of 0.11 ± 0.03 mm; P<.001), a secondary outcome measure.8 In addition to determining the overall effect of treatment with progressive addition lenses,8 COMET provides an opportunity to evaluate factors related to myopia progression and axial elongation among this ethnically diverse cohort of children with mild to moderate juvenile-onset myopia.

Most studies of myopia have focused on factors associated with its onset or prevalence.915 Although results from studies investigating myopia progression have varied, reflecting differences in the definition of myopia, age range, duration of follow-up, and data analysis,1627 some consistent trends have been observed. Younger age,16,17,19,2125 initial myopia,1618,21,25 female sex,16,17,2426 and family history of myopia27 have been associated with myopia progression. Treatment with bifocal or PALs (vs SVLs) or with atropine has been identified by some recent clinical trials as slowing progression,8,2830 although not all studies have reported positive effects.31

Although axial elongation is the source of most myopia and is closely correlated with myopia progression,8,20,22 most studies report risk factors for progression only. Therefore, the risk factors for axial elongation and changes in vitreous chamber depth have been studied less frequently and mainly in cross-sectional studies.32 This variable is important because it is the elongation of the globe that appears to be responsible for the increased risks for ocular pathology.

The aim of this article is to identify baseline factors that are independently related to 3-year myopia progression and axial elongation in COMET children while adjusting for treatment with PALs vs SVLs. Factors related to myopia progression that interacted with treatment are examined in a separate report.33

METHODS

Details of the study design and the study population have been presented previously8,34,35 and are summarized here. Four clinical centers located at schools/colleges of optometry in Birmingham, Ala, Boston, Mass, Houston, Tex, and Philadelphia, Pa enrolled 469 children between September 1997 and September 1998, and observed them for at least 3 years, with outcome data collected annually. Eligible children were: (1) aged 6 to 11 years, inclusive at baseline, (2) had spherical equivalent myopia by cycloplegic autorefraction between − 1.25 D and − 4.50 D inclusive in both eyes, (3) astigmatism less than or equal to 1.50 D in either eye, (4) anisometropia less than or equal to 1.0 D (spherical equivalent between eyes), (5) visual acuity (with best distance correction) 0.20 logMAR units or better (Snellen equivalent of 20/32), and (5) birth weight greater than or equal to 1250 g. Children with strabismus by a cover test, any known ocular, systemic, or neurodevelopmental condition, use of medications effecting refractive development, or prior wear of progressive addition, bifocal, or contact lenses were excluded. Participants were assigned to wear either SVLs (the standard treatment) or PALs (Varilux comfort lenses; Essilor of America, St Petersburg, Fla) with a + 2.00 D addition. The COMET data were collected by masked examiners who were trained and certified according to a standard protocol.34 The Data and Safety Monitoring Committee reviewed overall study performance and child safety.

The COMET study and protocols conform to the tenets of the Declaration of Helsinki. The institutional review boards of each participating center approved the research protocols. Informed consent (parents) and assent (children) were obtained after verbal and written explanation of the nature and possible consequences of the study.

PROCEDURES

Progression of myopia, the primary outcome, was measured by cycloplegic autorefraction. An autorefractor/autokeratometer (ARK 700A; Nidek, Gamagori, Japan) was used to take 5 consecutive, reliable readings both before and after cycloplegia. The cycloplegic agent was 2 drops of 1% tropicamide, administered 4 to 6 minutes apart, after corneal anesthesia was obtained with proparacaine hydrochloride for all but 1 child for whom benoxinate was used. Cycloplegic autorefraction measurements were taken 30 minutes after administration of the second drop of 1% tropicamide.36

Following cycloplegic autorefraction, ocular component dimensions (anterior chamber depth, lens thickness, vitreous chamber depth, and overall axial length) were measured by A-scan ultrasonography (A-2500; Sonomed, Lake Success, NY). At least 3 measurements per eye, using either a slit-lamp or handheld technique, were necessary for study purposes, with 5 measurements obtained for 96% of eyes at all visits.

Corneal curvature was measured using the keratometry setting of the Nidek autorefractor. Phoria was measured at near and far targets using the cover test. The accommodative response to near (33 cm) and far (4 m) targets was measured with a Canon R-1 (Canon USA, Lake Success, NY), an open field-of-view infrared autorefractor. These procedures have been described in detail elsewhere.8,34,35

STATISTICAL METHODS

Follow-up data were analyzed using the intent-to-treat principle37 according to the child’s assigned treatment group. Within 3 years, 2 children changed lens assignments, both from SVLs to PALs, due to binocular vision problems. For the 7 children lost to follow-up, progression information from their latest follow-up visit was used.

Progression of myopia, a continuous measurement, was defined as the change in spherical equivalent refractive (SER) error relative to baseline. For each eye, the mean of the 5 SER autorefraction measurements was computed for each visit. The analysis for progression of myopia in COMET was child-based, using the average of the 2 eyes to evaluate the magnitude of change in SER between follow-up and baseline (Pearson correlation coefficient for 3-year progression between the 2 eyes = 0.90; mean interocular difference <0.25 D with a 95% confidence interval that includes zero). Mean ( ± SE) progression values were determined for each visit overall, and then by age, sex, ethnicity, and treatment group. A similar approach was used for the change in axial length.

The analytic strategy followed a series of steps to identify those baseline factors that were significantly and independently associated with progression, while adjusting for the effects of other covariates, including the effect of the intervention with PALs vs SVLs. First, factors were selected for inclusion in multivariate analyses based on a univariate screen and scientific considerations. Age was classified into 1-year categories to evaluate the cross-sectional role of each year difference in age on myopia progression. The 2 youngest age groups (6- and 7-year-old children) were combined because of the low number of children in each category (6-year-olds, n = 10; 7-year-olds, n = 32).

Baseline myopia and baseline accommodative response were categorized based on a median split to allow for interpretation of their association with myopia progression based on higher and lower levels of these variables. Second, a multiple regression model used the selected factors to evaluate each covariate for its potential interaction with other covariates, using specific SAS macros (SAS Inc, Cary, NC). The final multiple linear regression model38 used to examine factors associated with myopia progression included baseline age, sex, ethnicity, treatment group, baseline myopia, accommodative response, and phoria (by cover test), as well as the interaction terms identified in previous multiple regression models. The final model for axial elongation was similar to that of myopia progression but included baseline axial length instead of myopia, baseline corneal curvature, and an interaction term between baseline accommodative response and treatment. Three-year adjusted differences within categories of each factor based on comparisons with a reference group (ie, 11-year-olds, males, and African Americans) and associated P values were obtained from these models to provide estimated differences based on the combined effects of the covariates in the model. Similar analyses were also conducted to evaluate factors independently associated with change in vitreous chamber depth.

Based on an update of the COMET database, the classification of age for this article is modified slightly from our previous publication8 reporting the study outcome, with 7 children reclassified as 1 year younger for these analyses. In addition, 3 children classified as “other” ethnicity in the article reporting baseline results35 were reclassified as Asian, white, and African American.

RESULTS
OVERVIEW

The 469 children enrolled in COMET had a mean (± SD) age of 9.3 ( ± 1.3) years and 52% were female. The COMET children were ethnically diverse (46% white, 26% African American, 15% Hispanic, 8% Asian, 5% mixed) with moderate myopia at baseline mean (± SD) spherical equivalent cycloplegic autorefraction of − 2.4 D (± 0.8). Baseline characteristics were balanced, with no statistically significant differences between treatment groups. Retention was excellent with 462 (98.5%) out of 469 children observed after 3 years.8

MYOPIA PROGRESSION

The amount of myopia in COMET children progressed an average (± SE) of 1.32 D (± 0.04 D) in 3 years with changes in SER ranging from + 0.60 D to − 4.13 D. Annual rates of myopia progression were 0.51 D, 0.46 D, and 0.36 D for years 1, 2, and 3, respectively, showing a general decrease in progression over time.

Because COMET data are derived from a clinical trial with 2 treatment groups and the trial results demonstrated slower progression in the PAL vs SVL group, Table 1 shows unadjusted progression for age, sex, and ethnicity by treatment group as well as for both treatment groups combined. Progression decreased with increasing age and females progressed more than males, overall and within each treatment group. However, progression patterns for ethnicity appeared to differ between the PAL and the SVL groups. For the PAL group, myopia in African Americans progressed the least and myopia in children of mixed ethnicity progressed the most. Among the children with SVLs, myopia in Hispanic and African American children progressed the least amount while Asian children had the highest progression. However, differences in progression between treatment groups were not statistically significant for any ethnic group and there was no interaction between treatment and ethnicity.8,33 Because of these findings and because of the general similarities in the relationships between age, sex, and progression within each treatment group, additional results are based on the treatment groups combined. A discussion of each of these factors follows.

Age

The youngest children at baseline (6-7 years old) had the most 3-year myopia progression (2.21 ± 0.16 D), regardless of treatment assignment, and their progression was over twice as high as the 11-year-olds (2.21 D vs 0.94 D). Overall myopia progression lessened with each successive year of age (Table 1), with larger differences in progression between the younger than the older age groups. The largest difference in progression was 0.56 D and occurred between the 6 to 7 and the 8-year-old children. The difference in progression between the 8- and 9-year-old children was 0.33 D. A smaller difference in progression of 0.19 D was observed between the 9- and 10-year-olds and between the 10- and 11-year-old children, showing the slowing of myopia progression at the older ages.

This pattern of decreasing 3-year myopia progression with increasing age was evident at the 1-year follow-up visit and was sustained for the next 2 years (Figure 1A). Annual myopia progression rates also were highest for the 6- to 7-year-old children at each follow-up visit, providing further support for the relationship between younger baseline age and faster myopia progression. For example, myopia progression between baseline and 1-year visit ranged from 0.87 D for the 6- to 7-year-olds to 0.65 D, 0.54 D, 0.38 D, and 0.38 D for the 8-, 9-, 10-, and 11-year-olds, respectively. Annual myopia progression also decreased with increasing age for the next 2 years within each group.

Based on unadjusted differences in myopia progression, the 11-year-old children progressed less than each of the other age groups (Table 2). Using multiple regression analyses adjusting for the covariates included in the final model, baseline age was found to be independently associated with 3-year progression (P<.001 for children aged 6-7 years vs 11 years, 8 years vs 11 years, and 9 years vs 11 years; P = .04 for ages 10 vs 11 years) (Table 2). The largest adjusted difference in myopia progression of − 1.31 D occurred between the 11-year-olds and the youngest children (6-7 years old) with the differences between 11-year-olds and the other age groups decreasing with each increasing year of age, further substantiating the observation of slower myopia progression with increasing baseline age.

Additional analyses also explored myopia at baseline and each follow-up visit as an alternative approach to evaluating the role of age (Figure 2). At baseline the 6- to 7-year-old children had similar amounts of myopia as the children who were 9, 10, and 11 years old and the 8-year-olds had significantly less myopia than the 9-, 10- and 11-year-olds (P<.05 based on analysis of variance). The unadjusted 3-year mean (± SE) SER ranged from − 4.53 D (± 0.21) for the 6- to 7-year-olds to − 3.78 D (± 0.13), − 3.87 D (± 0.11), − 3.53D (± 0.09), and − 3.38D ( ± 0.12) for the 8-, 9-, 10-, and 11-year-olds, respectively (Figure 2). The 6- to 7-year-old children had significantly more myopia at year 3 than all other age groups (8, 9, 10, and 11 years) (P<.05 based on analysis of variance) and also had the most myopia of all the age groups at 1 and 2 years.

Sex

On average, males progressed by 0.16 D less than females over 3 years (Table 1 and Figure 1B). Slower myopia progression in males was observed at each follow-up visit, beginning at 1 year with boys progressing less than girls by 0.07 D and at 2 years with boys progressing less by 0.09 D (Figure 1B). Sex was independently associated with myopia progression (P = .01) (Table 2) with the difference between males and females remaining unchanged after adjustment for other covariates.

Ethnicity

For all COMET children, among the 5 ethnic groups, African American children progressed the least at 3 years (mean [± SD] progression of 1.17 D ± 0.07) (Table 1 and Figure 1C). This pattern was also observed for the first 2 years of follow-up (Figure 1C). After adjustment for important covariates, 3-year myopia progression in African American children was slower than progression in children from each of the other ethnic groups (Table 2). Compared with African American children, the mean difference in 3-year myopia progression ranged from − 0.27 D (P = .001) for white children, which has the largest sample size, to − 0.49 D (P = .002) for mixed children.

Other Factors Associated With Progression

Baseline accommodative response and baseline myopia interacted with treatment findings that were reported previously8 and have been examined further in another report that also evaluates related factors including phoria, reading distance, and nearwork.33

AXIAL ELONGATION

The role of age, sex, and ethnicity was evaluated for axial elongation, the secondary outcome, using a similar approach to myopia progression. Additional analyses also evaluated the role of these factors separately for each anterior chamber depth, lens thickness, and vitreous chamber depth. The 3-year mean (± SE) change in axial length was 0.66 mm ( ± 0.02) (Table 3). Most of the eye growth was due to changes in the vitreous chamber which increased by 0.60 mm (± 0.02), with the anterior chamber increasing by 0.07 mm (± 0.01), and the lens thinning by − 0.01 mm (± 0.004) over 3 years. Annual increases in overall axial length were 0.28 mm (± 0.01) between baseline and 1-year visit; 0.21 mm (± 0.008) between 1 and 2 years and 0.17 mm (± 0.01) between 2 and 3 years, reflecting the slowing in eye growth over time.

Table 3 shows the 3-year unadjusted increase in axial length (in mm) for age, sex, and ethnicity by treatment group and overall. Similar to progression, increase in axial length was less for each year of baseline age in both treatment groups and the largest overall difference in axial elongation between 2 age groups was 0.26 mm between the 6 to 7 and 8-year-olds, with the differences between subsequent age groups decreasing to 0.14 mm (8-9 years), 0.11 mm (9-10 years) and 0.12 mm (10-11 years). African American children had the smallest increase in elongation among all the ethnic groups in both treatment groups. Three-year axial elongation was similar for males and females. Findings for age and ethnicity were also similar to those for progression at 1, 2, as well as 3 years (Figure 3A, 3B and 3C) with the youngest children having most eye growth and African American children having the least eye growth at each visit.

Table 4 shows the 3-year unadjusted and adjusted differences by age, sex, and ethnicity. In general, the findings for axial elongation parallel those for myopia progression. The unadjusted differences for age showed significant differences in axial elongation between the 11-year-olds and each of the younger age groups (P<.001), with the older children having less eye growth. For ethnicity, African American children had less increase in eye growth than children of Asian (P = .05) and mixed ethnicity (P = .04); eye growth between males and females was similar. Based on the results of the multiple regression model, age (P<.001 for 11-year-olds vs all the younger age groups) and ethnicity (African American vs Hispanic and mixed, P = .001; African American vs Asian, P = .01; African American vs white, P = .003), but not sex, were significantly associated with change in axial length. After accounting for the effect of covariates, the adjusted mean (± SE) difference in axial elongation between males and females was 0.05 mm (± 0.03), in favor of females having more elongation over 3 years. This difference was less than that observed for progression, but was not statistically significant (P = .09).

The relationship between sex and change in axial length was investigated further because of sex differences that were observed in baseline axial length and corneal curvature. At that time, based on the average of the 2 eyes, females had shorter eyes (23.91 mm vs 24.35 mm, P<.001) and steeper corneas in both meridians than males (7.68 mm vs 7.77 mm, P = .001 and 7.54 mm vs 7.64 mm, P<.001 for the horizontal and vertical meridians, respectively), yet had similar SER, − 2.41 D (± 0.05) for females and − 2.36 D (± 0.05) for males. Therefore, baseline corneal curvature was included as a covariate in the multiple regression model in addition to the other covariates mentioned previously, but was not associated with 3-year axial elongation. Further analyses examined changes in corneal curvature by sex during the follow-up period. Changes over 3 years were minimal and similar for males and females in both the horizontal and vertical meridians, with a change of 0.03 mm and 0.04 mm in the horizontal meridian for males and females, respectively, and − 0.01 mm in the vertical meridian for both sexes. Therefore, at the 3-year visit, females still had steeper corneas (7.72 mm vs 7.80 mm for males; P<.001) (horizontal meridian) and also had shorter eyes (24.59 mm vs 25.00 mm; P<.001), which was similar to the baseline observations.

Additional analyses were performed to evaluate change in vitreous chamber depth as an outcome in a model that included baseline vitreous chamber depth instead of baseline axial length along with the covariates described previously. Results were similar to those for axial length.

RELATIONSHIP BETWEEN MYOPIA PROGRESSION AND CHANGE IN AXIAL LENGTH

Three-year myopia progression was significantly correlated with a 3-year increase in axial length (Pearson correlation coefficient = − 0.88; P<.001) (Figure 4). On average, a 1-mm increase in axial length was associated with 2.04 D (± 0.05 SE) of myopia progression; alternatively, 1 D of myopia progression was associated with 0.5 mm of axial elongation. The correlation was also found to be similar and significant for each treatment group8 and for males (coefficient of correlation, r = − 0.90; P<.001) and females (r = − 0.87; P<.001). Since the increase in vitreous chamber depth accounted for most of the increase in overall axial length over 3 years, separate analyses were also conducted to quantify its independent relationship with myopia progression. The results were similar to those for overall axial length, showing a similar and significant correlation (r = − 0.88; P<.001).

COMMENT

In this cohort study of 469 myopic children who were randomized to treatment with PALs or SVLs, younger baseline age, female sex, and mixed, Asian, Hispanic, and white (vs African American) ethnicity, were independently associated with faster 3-year myopia progression, whether children were treated with SVLs or PALs. The factors associated with change in axial length were similar to those for progression, except for sex, which showed a similar pattern between progression and axial elongation that did not reach statistical significance for axial elongation. Although the COMET children are a cohort of clinical trial participants who are not population-based, they are a large, well characterized, ethnically diverse group of children with mild to moderate myopia. The study data were collected using standard methods, with excellent reliability of the outcome measures and high retention (98.5% at 3 years) was maintained throughout the trial. As such, this cohort allows for quantification of the role for each of these factors in myopia progression and axial elongation. Thus, the results are likely to be generalizable to most children with juvenile onset myopia.

AGE

Baseline age was the strongest independent factor for myopia progression in this cohort, suggesting that younger children, particularly those 6 to 7 years old with at least −1.25 D of myopia, are at risk of faster progression than older children regardless of other baseline characteristics. The younger the age at baseline, the higher the myopia progression as well as the amount of myopia at 3 years despite the general similarity of baseline myopia across age groups (Figure 1A and Figure 2). Similarly, change in axial length was also associated with age, thus reinforcing the importance of age as a factor in myopia progression and related eye growth among COMET children. These observations suggest that children with significant myopia at young ages should be monitored closely for progression and the need for prescription changes.

One hypothesis for the faster progression at young ages is that the myopia that is already present in children by 6 or 7 years of age may be a different type from the myopia that occurs at ages 8 years and older (eg, more likely to have a genetic basis, more rapidly progressing, more likely to be at increased risk of high myopia). An alternative possibility was that the younger children simply may be at an early stage of myopization when rapid progression is most likely to occur, while the older children have reached a later phase in which their myopia is beginning to stabilize. The COMET data cannot address these analyses directly because the date of myopia onset is not available for COMET children, even though the study eligibility criteria were selected to capture children at the period when they were most likely to experience the greatest rate of progression. However, no interaction was found between age and amount of baseline myopia for progression. In additional multiple regression models, evaluating age and baseline myopia as continuous variables (instead of categorical), both remained as significant predictors of progression.

An issue to consider is the similarity of baseline myopia across baseline age groups despite the role of age in myopia progression. The fact that COMET children had similar levels of baseline myopia across age groups is likely because of the trial’s selection criteria which limited eligibility to myopia based on cycloplegic autorefraction between −1.25 D and − 4.50 D. As a result of the relationship between baseline myopia and age, it is difficult to disentangle their independent effects on myopia progression. The similarity of baseline myopia across age groups provides an opportunity to evaluate the independent effect of age while controlling for baseline myopia.

These findings support the importance of age as a risk factor for progression regardless of the level of baseline myopia. The role of age and amount of myopia at the time of stabilization will continue to be examined with additional follow-up of the COMET cohort. These analyses will extend the cross-sectional analyses based on baseline age to evaluate the relationship between change in age with progression over time. Such analyses will permit further evaluation of whether the effect of younger vs older age on progression persists as progression slows and the cohort grows older. Since myopia progression cannot be linear, future analyses will include nonlinear modeling techniques to determine the most appropriate fit of the data.

Most of the other studies that evaluated the role of age in myopia progression evaluated mean age or had different age groups making comparisons with COMET difficult.16,17,19,2125 One Singapore study by Saw et al,19 based on a subset of 153 out of 311 children recruited to participate in a randomized clinical trial, evaluated progression based on cycloplegic autorefraction for the same age categories as COMET. The Saw et al study estimated average annual progression, from a mean of 28 months of follow-up, limiting a direct comparison with COMET. Yet, it is interesting to note that the annual progression in these Singaporean children was similar to the 1-year progression observed in COMET for the 6- to 7-, 8-, 9-, and 10-year-old children and the annual progression for the 11-year-olds in Singapore was lower than in COMET at 1 year (0.20 D vs 0.38 D). This consistency between COMET and the Singapore study indicating more progression in the younger age groups lends further support to the conclusion that children with myopia at a younger age have faster progression.

SEX

The role of sex was more clear cut for myopia progression than for increase in axial length. Myopia in females progressed more than males at each year of follow-up with the 3-year difference of 0.16 D. However, for increase in axial length, this adjusted difference was lower (0.05 mm). Differences in progression between myopic males and females have been found in some,16,18,20 but not all studies.17,19 The absence of a significant association between sex and axial elongation in COMET may be because of the fact that the female eyes are shorter and have steeper corneas than those of the males throughout the follow-up period. However, the similar relationship between myopia progression and increase in axial length at 3 years for males (beta = − 2.07) and females (beta = − 2.02) indicates that the changes in these measurements occur at a similar rate for both sexes. Additional follow-up of this cohort will help to clarify the role of sex in progression and cessation of myopia.

ETHNICITY

Progression varied among the different ethnic groups in COMET and, using African American children as the reference group in the multiple regression model, a significant difference was identified between African Americans and each ethnic group, which is a new finding. Even though the African American children had more myopia at baseline than the white children (− 2.45 D vs − 2.34 D), because of their slower progression their final level of myopia was lower ( − 3.62 D vs − 3.69 D). African American children in COMET had the least amount of myopia progression (−1.17 D) among all ethnic groups and mixed and Asian children progressed the most with 3-year progression of 1.57 D and 1.47 D, respectively. Annual progression for Asian children in COMET, predominantly of East Asian origin (30/36), was 0.62 D for year 1, 0.45 D for year 2, and 0.40 D for year 3. The 1-year progression observed in COMET is consistent with the findings from a school-based study of myopia conducted in Hong Kong that found an average 1-year change in SER of − 0.63 D.21 Additional studies in Hong Kong,22 Singapore,19 and the Shunyi district of China18 with follow-up periods of 2 years22 and 28 months18,19 determined average annual progression rates in their populations of myopic children from 0.46 D22 to 0.60 D19 and 0.35 diopters per year,18 which are similar or lower than progression observed in the COMET children. Despite differences in the study populations, definitions of myopia, and methods of data collection, the similarities in progression suggest that the Asian children in COMET progress at a similar rate to those in Asian countries. Studies have also identified Asian populations as having a greater prevalence of myopia39 and African Americans as having a lower prevalence.40 A recent report from the Collaborative Longitudinal Evaluation of Ethnicity and Refractive Error Study, a multicenter observational study of refractive error in 4 ethnic groups from 4 different locations in the United States, also found Asian children to have the highest prevalence, followed by Hispanics, while rates for whites and African Americans were similar.39 Therefore, with regard to sex, the factors associated with prevalence may differ from those for progression. The role of ethnicity in myopia progression and stabilization will be clarified with the additional follow-up of COMET children.

RELATIONSHIP BETWEEN MYOPIA PROGRESSION AND CHANGE IN AXIAL LENGTH

Among these myopic children, a 0.5 change in axial length was associated with 1 D of myopia progression, an observation that differs from the commonly used correlation of a 1-mm change corresponding to 3 D of progression,41 based on theoretical considerations including distance of the principal plane to the retina and the power of the adult eye. In children, some elongation occurs even in eyes that remain emmetropic, which contributes to the 1 to 2 relationship. Future analyses of axial component data will help clarify the relationship between baseline eye size, axial elongation, and myopia progression in COMET children.

In summary, these analyses have expanded on the primary results of COMET to investigate factors associated with 3-year progression and axial elongation. Progression is likely to be influenced by a combination of demographic, ocular, genetic, or environmental factors, including intervention with PALs. The potential role of nearwork in myopia progression, an issue frequently raised in literature, was evaluated in a separate article that investigated factors that interacted with treatment.33 While nearwork was not found to be independently associated with myopia progression, an adjusted treatment effect of PALs of 0.42 D was found at 3 years for children with larger accommodative lags who performed more than 104.5 diopter per hour of nearwork per week. This effect was not statistically significant, perhaps owing to the smaller number of subjects with this information, but was higher for children with smaller accommodative lags or those who performed less nearwork,33 thus suggesting that intervention with PALs was more effective for those children who are most likely to experience blurred vision.

The risk factors for 3-year myopia progression identified in this article are those that are independent of treatment (PAL vs SVL) group to which the children were randomly assigned. These results confirm previous reports regarding age, sex, and initial myopia, provide a new observation concerning ethnic differences that should be explored in other populations, and offer guidance for identification of children at higher risk of progression to be targeted for closer monitoring. The continued follow-up of the COMET cohort for an additional 5 years will further clarify the role of these factors on progression and stabilization of myopia.

Box Section Ref ID

COMET Study Group

Study Chair's Office, New England College of Optometry, Boston, Mass

Jane Gwiazda (study chair/principal investigator); Kenneth Grice (study coordinator, ending 1999); Christine Fortunato (study coordinator, 1999-2000); Cara Weber (study coordinator, 2000-2003); Rosanna Pacella (research assistant, 1996-1998); Thomas Norton (consultant, University of Alabama at Birmingham).

Coordinating Center, Department of Preventive Medicine, Stony Brook University Health Sciences Center, Stony Brook, NY

Leslie Hyman (principal investigator); M. Cristina Leske (co-principal investigator, ending 2003); Mohamed Hussein (co-investigator/biostatistician, ending 2003); Elinor Schoenfeld (epidemiologist); Lynette Dias (study coordinator, 1998-present); Rachel Harrison (study coordinator, 1997-1998); Jennifer Thomas (assistant study coordinator, 2000-2004); Cristi Rau (assistant study coordinator, 1999-2000); Elissa Schnall (assistant study coordinator, 1997-1998); Wen Zhu (senior programmer); Ying Wang (data analyst, 2000-present); Ahmed Yassin (data analyst, 1998-1999); Lauretta Passanant (project assistant, 1998-present); Maria Rodriguez (project assistant, 2000-present); Allison Schmertz (project assistant, 1998-1998); Ann Park (project assistant, 1999-2000); Phyllis Neuschwender (administrative assistant, ending 1999); Geeta Veeraraghavan (administrative assistant, 1999-2001); Angela Santomarco (administrative assistant, 2001-2004); Amy Radford (administrative assistant, 2004-present).

National Eye Institute, Bethesda, Md

Donald Everett, program director, Collaborative Clinical Trials.

Clinical Centers

University of Alabama at Birmingham School of Optometry, Birmingham, Ala: Wendy Marsh-Tootle (principal investigator); Katherine Niemann (optometrist, 1998-present); Kristine Becker (ophthalmic consultant, 1999-2003); James Raley (optician, ending 1999); Angela Rawden (back-up optician, ending 1998); Catherine Baldwin (primary optician and clinic coordinator, 1998-present); Nicholas Harris (clinic coordinator, 1998-1999); Trana Mars (back-up clinic coordinator, 1997-2003); Robert Rutstein (consulting optometrist).

New England College of Optometry, Boston, Mass: Daniel Kurtz (principal investigator); Erik Weissberg (optometrist, 1999-present); Bruce Moore (optometrist, ending 1999); Robert Owens (primary optician); Justin Smith (clinic coordinator, 2001-present); Sheila Martin (clinic coordinator, ending 1998); Joanne Bolden (coordinator, 1998-2003); Benny Jaramillo (back-up optician, 2000- 2003); Stacy Hamlett (back-up optician, 1998-2000); Patricia Kowalski (consulting optometrist, ending 2001); Jennifer Hazelwood (consulting optometrist, beginning 2001).

University of Houston College of Optometry, Houston, Tex: Ruth Manny (principal investigator); Connie Crossnoe (optometrist, ending 2003); Sheila Deatherage (optician); Charles Dudonis (optician); Sally Henry (clinic coordinator, ending 1998); Jennifer McLeod (clinic coordinator, 1998-present); Julio Quiralte (backup coordinator, 1998-present); Karen Fern (consulting optometrist).

Pennsylvania College of Optometry, Philadelphia, Pa: Mitchell Scheiman (principal investigator); Kathleen Zinzer (optometrist, ending 2004); Timothy Lancaster (optician, ending 1999); Theresa Elliott (optician, ending 2001); Mark Bernhardt (optician, 1999-May 2000); Dan Ferrara (optician, 2000-2001); Jeff Miles (optician, 2001-present); Abby Grossman (clinic coordinator, 2001-2003); Mariel Torres (clinic coordinator, 1997-2000); Heather Jones (clinic coordinator, 2000-2001); Melissa Madigan-Carr (coordinator, 2001-2003); Theresa Sanogo (back-up coordinator, 1999-2003); JoAnn Bailey (consulting optometrist).

Committees

Data and Safety Monitoring Committee: Robert Hardy (chair); Argye Hillis; Don Mutti; Richard Stone; Sr Carol Taylor.

Executive Committee: Jane Gwiazda (chair); Donald Everett; Leslie Hyman; Wendy Marsh-Tootle.

Steering Committee: Jane Gwiazda (chair); Donald Everett; Mohamed Hussein; Leslie Hyman; M. Cristina Leske; Daniel Kurtz; Ruth Manny; Wendy Marsh-Tootle; Mitchell Scheiman; Thomas Norton.

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Article Information

Correspondence: Leslie Hyman, PhD, Department of Preventive Medicine, School of Medicine Stony Brook University, Health Sciences Center, L3 086, Stony Brook, NY 11794 (lhyman@notes.cc.sunysb.edu).

Submitted for Publication: July 14, 2004; final revision received November 10, 2004; accepted November 13, 2004.

Financial Disclosure: None.

Funding/Support: This study was supported by the National Eye Institute, grants EY11805, EY11756, EY11754, EY11740, EY11752, and EY11755, and by Essilor of America, Marchon Eyewear, Marco Technologies, and Welch Allyn.

Acknowledgment: We thank Li Ming Dong, PhD, for her valuable comments on the manuscript.

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