Introduction: This study aimed to investigate the relationship between the age of myopia onset and high myopia and to explore if age of onset mediated the associations of high myopia with parental myopia and time spent on electronics. Methods: This cross-sectional study enrolled 1,118 myopic patients aged 18–40. Information was obtained via a detailed questionnaire. Multivariable logistic regression and linear regression models were utilized to assess age of onset in relation to high myopia and spherical equivalent refractive error, respectively. Structural equation models examined the mediated effect of onset age on the association between parental myopia, time spent on electronics, and high myopia. Results: An early age at myopia onset was negatively correlated with spherical equivalent refractive power. Subjects who developed myopia before the age of 12 were more likely to suffer from high myopia than those who developed myopia after the age of 15. Age of myopia onset was the strongest predictor of high myopia, with an area under the curve in receiver operator characteristic analysis of 0.80. Additionally, the age of myopia onset served as a mediator in the relationships between parental myopia, electronic device usage duration, and the onset of high myopia in adulthood. Conclusions: Age of myopia onset might be the single best predictor for high myopia, and age at onset appeared to mediate the associations of high myopia with parental myopia and time spent on electronics.

Myopia has emerged as a significant global public health concern, with high myopia standing as a leading cause of irreversible blindness [1, 2]. The severity of high myopia is intricately linked to associated complications, including retinal detachment [3], macular degeneration [4], and glaucoma [5]. It is widely acknowledged that myopia arises from a complex interplay of genetic and environmental factors, encompassing activities such as close visual work and electronic device usage. Therefore, efforts aimed at preventing and controlling myopia must consider the interplay of genetics, environment, and lifestyle [3, 6, 7].

Numerous studies have illustrated a connection between the age of myopia onset and the eventual severity of myopia. A prospective population-based cohort study conducted over 12 years in China demonstrated that over half of individuals who developed myopia at the age of 7 or 8 subsequently progressed to high myopia in adulthood, and this risk significantly diminished with each year of delay in onset [8]. Similarly, a study in the UK revealed that wearing corrective spectacles for myopia prior to the age of 9 predicted high myopia in adulthood with 73% specificity and 80% sensitivity [9]. Nevertheless, these studies were conducted several years ago, and their relevance to the current surge in myopia rates is uncertain. While a recent investigation in East Asia [8] revealed a connection between an earlier onset of myopia and a heightened ultimate level of myopia, it is important to note that this study did not explore the potential mediating effects of onset age on the relationship between other risk factors and myopia. Given that other factors like parental myopia and the duration of time spent on electronic devices may influence the development of high myopia through the age of onset, a comprehensive understanding of these dynamics requires further investigation.

Furthermore, there are other studies comparing adult-onset myopia with school-onset myopia; these found that the myopia progressed more slowly when it happened after reaching adulthood [8]. Meanwhile, myopic adults of recent onset are at lower absolute risk of myopia-related ocular disease and visual impairment [9]. However, these studies have limitations, including recruiting teenagers with unstable refractive states and conducting only univariate analysis. In addition, the magnitude of the association between myopia onset age and high myopia in adulthood is still unclear. In this study, we examined the relationship between the age of myopia onset and high myopia in later adulthood and if the age of onset mediated the associations of high myopia with parental myopia and time spent on electronics.

Study Design and Study Populations

This cross-sectional study recruited myopic patients from the Tianjin Airport Medical Examination Center between June 2019 and December 2019. Eligible participants were myopic patients residing in Tianjin, China, and aged 18–40 years. Those who had ophthalmic diseases that might affect vision or refractive status were excluded. All participants completed a detailed epidemiological questionnaire and underwent health examinations at Tianjin Medical University General Hospital Airport Hospital.

The final analysis comprised 1,118 eyes, all belonging to the right eyes of 1,118 myopic participants. The study protocol was approved by the Ethics Board of Tianjin Medical University General Hospital, Tianjin, China. Written informed consent was obtained from all participants before data collection in accordance with the tenets of the Declaration of Helsinki. All questions and concerns were addressed before the consent forms were signed.

Questionnaires

The survey contained questions pertaining to participants’ demographics (e.g., age, gender), socio-economic status (e.g., education attainment), individual behaviors (e.g., time spent in class, extra-curricular reading, time spent outdoors, time spent on electronics, sleeping time), and other myopia-related factors (parental myopia, age of myopia onset, degree of myopia onset). Although we employed questionnaires to elicit participants’ recollections of past exposures, previous research indicated that the initial acquisition of glasses is a highly emotive experience [10, 11]. It appears that patients’ responses are dependable when the questioning process is appropriately conducted [10, 11].

Ocular Examination

Ocular examinations were performed using standardized protocols by ophthalmologists. Myopia was defined as spherical equivalent (S.E.) of at least −0.50 diopters (D) in the right eye. Refraction error was measured by auto-refractometer (NIDEK, serial number: YM0021471; Gamagori, Japan). According to their refractive error, participants were categorized into three groups: high myopia (≤−6.00D), moderate myopia (>−6D and ≤−3D), and low hyperopia (<−3D and ≤0.75D).

Statistical Analysis

Independent sample t test was used for the comparison of continuous variables, while Pearson’s χ2 test was applied for the comparison of categorical variables. Multivariable linear regression and logistic regression models were used to calculate the regression coefficients (β), standardized odds ratios (OR), and corresponding 95% confidence intervals (95% CI) for the relationship between S.E./myopia and the risk factors of interest. We assessed a wide range of potential confounders (e.g., age, gender, and parental myopia), but only terms that entered the model at the level of p < 0.1 or improved the fit of the model were retained in the final model. Final covariates included gender, parental myopia (yes vs. no), education attainment (middle school, high school, university/college or higher), time spent on electronic devices during high school, time spent in class during high school, and time on private tutoring during middle school. We used a restricted cubic spline to graph the association between the age of onset and high myopia. We identified the optimal cutoffs that maximized joint sensitivity and specificity from receiver operator characteristic (ROC) curves.

Potential direct and indirect effects of myopia onset age and other potential risk factors on high myopia were assessed via conceptualized structural equation models. The goodness of model fit was evaluated by multiple indices, including (1) comparative fit index, for which a value exceeding 0.9 indicated acceptable model fit; (2) the root mean square error of approximation, where the acceptable level was less than 0.08 or 0.06; (3) Tucker Lewis index, with a value more than 0.9 or 0.95 indicating a good fit; (4) weighted root mean square residual for categorical data, where the value below 1.0 was considered acceptable; (5) standardized root mean square residual for continuous data. A value less than 0.08 was considered a good fit. All statistical analyses were performed using SAS 9.4 software (SAS Institute, Cary, NC, USA). p < 0.05 was considered statistically significant.

Of the 1,118 myopic subjects participated in this study, 118 had high myopia (10.6%), while 1,000 did not exhibit high myopia. Compared to participants with moderate or mild myopia, those with high myopia were younger and more likely to have parental myopia, higher education attainment, more frequent seat changes during primary school, and more time spent in class during middle/high school, but they appeared to spend less time on homework during high school (Table 1).

Table 1.

Demographic and lifestyle characteristics of participants by myopia status (N = 1,118)

CharacteristicsHigh myopia, N (%)Moderate or mild myopia, N (%)pa value
(N = 118)(N = 1,000)
Age of myopia onset, years 12.05±2.33 15.02±2.75 0.00 
Age, years 26.48±5.42 28.07±5.57 0.00 
Degree of onset 224.35±93.56 162.76±73.81 0.00 
Parental myopia 
 None 86 (44.6) 642 (69.4) 0.00 
 One myopic 66 (34.2) 222 (24.0)  
 Both myopic 41 (21.2) 61 (6.6)  
Education attainment 
 High school or lower 154 (79.86) 798 (86.3) 0.01 
 University/college or higher 39 (20.2) 127 (13.7)  
Seat change during primary school 
 None 16 (8.4) 78 (8.5) 0.03 
 Every week 74 (38.7) 282 (30.6)  
 Every month 69 (36.1) 323 (35.0)  
 Every term 32 (16.8) 240 (26.0)  
Time spent in class during middle school 
 ≤7 h/day 35 (18.1) 251 (27.2) 0.01 
 7–8 h/day 98 (50.8) 368 (39.9)  
 >8 h/day 60 (31.1) 304 (32.9)  
Time spent in class during high school 
 ≤7 h/day 21 (10.88) 107 (11.7) 0.05 
 7–8 h/day 38 (19.69) 254 (27.7)  
 >8 h/day 134 (69.43) 556 (60.6)  
Time spent on homework during high school 
 <1 h/day 23 (11.9) 139 (15.2) 0.02 
 1–2 h/day 92 (47.7) 338 (36.9)  
 2–3 h/day 66 (34.2) 344 (37.5)  
 ≥4 h/day 12 (6.2) 96 (10.5)  
CharacteristicsHigh myopia, N (%)Moderate or mild myopia, N (%)pa value
(N = 118)(N = 1,000)
Age of myopia onset, years 12.05±2.33 15.02±2.75 0.00 
Age, years 26.48±5.42 28.07±5.57 0.00 
Degree of onset 224.35±93.56 162.76±73.81 0.00 
Parental myopia 
 None 86 (44.6) 642 (69.4) 0.00 
 One myopic 66 (34.2) 222 (24.0)  
 Both myopic 41 (21.2) 61 (6.6)  
Education attainment 
 High school or lower 154 (79.86) 798 (86.3) 0.01 
 University/college or higher 39 (20.2) 127 (13.7)  
Seat change during primary school 
 None 16 (8.4) 78 (8.5) 0.03 
 Every week 74 (38.7) 282 (30.6)  
 Every month 69 (36.1) 323 (35.0)  
 Every term 32 (16.8) 240 (26.0)  
Time spent in class during middle school 
 ≤7 h/day 35 (18.1) 251 (27.2) 0.01 
 7–8 h/day 98 (50.8) 368 (39.9)  
 >8 h/day 60 (31.1) 304 (32.9)  
Time spent in class during high school 
 ≤7 h/day 21 (10.88) 107 (11.7) 0.05 
 7–8 h/day 38 (19.69) 254 (27.7)  
 >8 h/day 134 (69.43) 556 (60.6)  
Time spent on homework during high school 
 <1 h/day 23 (11.9) 139 (15.2) 0.02 
 1–2 h/day 92 (47.7) 338 (36.9)  
 2–3 h/day 66 (34.2) 344 (37.5)  
 ≥4 h/day 12 (6.2) 96 (10.5)  

ap values are for the significance for variables, statistical significance present for values <0.05.

When we plotted the age of onset and final degree of myopia (Fig. 1), we found that individuals with a younger age of myopia onset seemed to have a higher final degree of myopia. Noteworthily, the final myopia degree decreased speedily when the onset age was between 12 and 15 years.

Fig. 1.

Age of myopia onset and current myopia severity in the past years.

Fig. 1.

Age of myopia onset and current myopia severity in the past years.

Close modal

After the adjustment of other potential risk factors, the age of myopia onset was negatively correlated with the final degree of myopia (β = −0.31, 95% CI: −0.34 to −0.28). Younger onset age was associated with a higher prevalence of high myopia (per year decrease: OR = 1.56, 95% CI: 1.42–1.72). Those who developed myopia before the age of 12 years were 15.69 times more likely to progress to high myopia than those who developed myopia after the age of 15 years (Table 2).

Table 2.

Association of myopia onset age with high myopia and spherical equivalent

Event/risk, n/nHigh myopia (≤−6.0D) versus mild or moderate myopia (−0.50 to −5.9D)Spherical equivalent refraction (D)
OR (95% CI)regression coefficient β
Age of myopia onset (per year decrease) 
 Crude OR 118/1,118 1.60 (1.46–1.75) −0.33 (−0.36 to −0.30) 
 Multivariable OR 118/1,118 1.56 (1.42–1.72) −0.31 (−0.34 to −0.28) 
Age of myopia onset (years) 
 ≥15 4/352 1.00 1.00 
 12–15 36/484 4.01 (2.27–7.10) −1.23 (−1.42 to −1.04) 
 <12 78/282 15.69 (8.31–29.64) −2.34 (−2.65 to −2.03) 
Event/risk, n/nHigh myopia (≤−6.0D) versus mild or moderate myopia (−0.50 to −5.9D)Spherical equivalent refraction (D)
OR (95% CI)regression coefficient β
Age of myopia onset (per year decrease) 
 Crude OR 118/1,118 1.60 (1.46–1.75) −0.33 (−0.36 to −0.30) 
 Multivariable OR 118/1,118 1.56 (1.42–1.72) −0.31 (−0.34 to −0.28) 
Age of myopia onset (years) 
 ≥15 4/352 1.00 1.00 
 12–15 36/484 4.01 (2.27–7.10) −1.23 (−1.42 to −1.04) 
 <12 78/282 15.69 (8.31–29.64) −2.34 (−2.65 to −2.03) 

Multivariable models adjusted for gender, parental myopia, education attainment, time spent on electronic devices during high school, time spent in class during high school, and private tutoring during middle school.

OR, odds ratio; 95% CI, 95% confidence interval.

The ROC curves of age of myopia onset alone and in combination with other variables are shown in Figure 2. Of these single predictors of high myopia, the age of myopia onset was the strongest predictor of high myopia (area under the curve (AUC): 0.80), with an optimal cutoff point of 14 years of age. The presence of parental myopia and education attainment had an AUC of 0.65 and 0.55, respectively. The AUC derived from a multivariable model incorporating age of onset, gender, parental myopia, education attainment, time spent on electronic devices during high school, time spent in class during high school, and private tutoring during middle school was not significantly higher than using age of onset alone (AUC = 0.85). The optimal cutoff point in the multivariable model was 15 years of age.

Fig. 2.

ROC curves of age of myopia onset alone and in combination with other variables for the prediction of high myopia onset. Area under the curve (AUC) for multivariable model based on the combination of age of onset, gender, parental myopia, education attainment, time spent on electronic devices during high school, time spent in class during high school, and private tutoring during middle school.

Fig. 2.

ROC curves of age of myopia onset alone and in combination with other variables for the prediction of high myopia onset. Area under the curve (AUC) for multivariable model based on the combination of age of onset, gender, parental myopia, education attainment, time spent on electronic devices during high school, time spent in class during high school, and private tutoring during middle school.

Close modal

The associations between relevant covariates (e.g., sex, parental myopia, education attainment) and high myopia are shown in Table 3. When these variables were mutually adjusted, sex, parental myopia, education attainment, time spent on electronic devices, and time spent in class were associated with an increased risk of high myopia. When the model was additionally adjusted for age at myopia onset, the positive relationship among parental myopia, electronics time, time spent in class, and high myopia becomes not significant, suggesting that onset age might mediate the associations of high myopia with parental myopia, electronics time, and class time.

Table 3.

Odds ratios for the associations between covariates and high myopia

CovariatesModel 1a OR (95% CI)Model 2b OR (95% CI)
Sex (women vs. men) 1.16 (0.76–1.75) 1.20 (0.77–1.87) 
Parental myopia 
 None 1.00 1.00 
 One myopic 2.19 (1.39–3.43) 1.74 (1.08–2.80) 
 Both myopic 4.64 (2.71–7.97) 2.92 (1.62–5.25) 
Education attainment (university/college or higher vs. high school or lower) 1.81 (1.10–2.96) 2.07 (1.21–3.52) 
Time spent on electronic devices during high school 
 0 h/day 1.00 1.00 
 ≤1 h/day 1.26 (0.75–2.13) 1.03 (0.60–1.79) 
 1–2 h/day 1.95 (1.09–3.46) 1.45 (0.78–2.69) 
 >2 h/day 3.05 (1.45–6.42) 2.20 (0.99–4.90) 
Time spent in class during high school 
 ≤7 h/day 1.00 1.00 
 7–8 h/day 0.79 (0.35–1.79) 0.61 (0.25–1.49) 
 >8 h/day 3.05 (1.45–6.42) 1.25 (0.57–2.75) 
Sleep time during high school (h) 0.69 (0.55–0.87) 0.69 (0.54–0.88) 
CovariatesModel 1a OR (95% CI)Model 2b OR (95% CI)
Sex (women vs. men) 1.16 (0.76–1.75) 1.20 (0.77–1.87) 
Parental myopia 
 None 1.00 1.00 
 One myopic 2.19 (1.39–3.43) 1.74 (1.08–2.80) 
 Both myopic 4.64 (2.71–7.97) 2.92 (1.62–5.25) 
Education attainment (university/college or higher vs. high school or lower) 1.81 (1.10–2.96) 2.07 (1.21–3.52) 
Time spent on electronic devices during high school 
 0 h/day 1.00 1.00 
 ≤1 h/day 1.26 (0.75–2.13) 1.03 (0.60–1.79) 
 1–2 h/day 1.95 (1.09–3.46) 1.45 (0.78–2.69) 
 >2 h/day 3.05 (1.45–6.42) 2.20 (0.99–4.90) 
Time spent in class during high school 
 ≤7 h/day 1.00 1.00 
 7–8 h/day 0.79 (0.35–1.79) 0.61 (0.25–1.49) 
 >8 h/day 3.05 (1.45–6.42) 1.25 (0.57–2.75) 
Sleep time during high school (h) 0.69 (0.55–0.87) 0.69 (0.54–0.88) 

aModel 1 mutually adjusted.

bModel 2 additionally adjusted for age at myopia onset.

OR, odds ratio; 95% CI, 95% confidence interval.

To better discover the interrelationships among risk factors in the etiology of high myopia, the conceptualized structural equation model was conceptualized (Fig. 3). We found that parental myopia (β = −0.23) and time spent on electronics (β = −0.09) were associated with the age of myopia onset, while age of myopia onset (β = −0.28), parental myopia (β = 0.13), and time spent on electronics (β = 0.06) were linked to the risk of high myopia. Parental myopia and the duration of time spent on electronic devices may impact the likelihood of developing high myopia either directly or indirectly through the age of myopia onset. However, sleep duration and educational attainment only exert a direct influence on high myopia. Further details about the fit degree of conceptualized structural equation model for influencing factors are provided in online supplementary Table 1 (for all online suppl. material, see https://doi.org/10.1159/000538442).

Fig. 3.

Conceptualized structural equation model of the etiology of high myopia *Numbers are path coefficients (β). Only significant path coefficients (p < 0.05) are presented.

Fig. 3.

Conceptualized structural equation model of the etiology of high myopia *Numbers are path coefficients (β). Only significant path coefficients (p < 0.05) are presented.

Close modal

The present study demonstrated that age of early-onset myopia was the most significant predictor of high myopia in adulthood, with an AUC of 0.80. The addition of other factors including parental myopia and education attention did not improve the prediction model seriously, with an AUC of 0.85. The age of myopia onset is the single best predictor for high myopia.

The typical trajectory of ocular refractive development follows a sequence, starting with hyperopia, transitioning into an emmetropic phase during early school life [12]. It is crucial to preserve hyperopia during the emmetropization process, as this helps delay the onset of myopia [13, 14]. If emmetropization concludes at an early age, it can be followed by myopia development, characterized by a myopic shift that tends to stabilize during the mid to late teenage years. Therefore, future trials for the prevention of myopia should target the child with low hyperopia as the child at risk [15]. Our study corroborated that a younger age at myopia onset is associated with a higher prevalence of high myopia, a pattern consistently observed in several investigations involving both children and adults.

In a study involving school-age children, Chua observed that if myopia manifested before the age of 7, it could progress to −5.48D by the age of 11, whereas if it appeared at age 10, the myopia would only reach −1.53D [16]. Therefore, early-onset myopia allows for a more extended period for myopia to progress, placing individuals at a heightened risk of developing high myopia. Similarly, research conducted by He indicated that children with myopia onset in the early school years (7 or 8 years of age) faced a relatively high risk of myopia development (>50%), which notably decreased with each year of delayed onset. Additionally, Hu found that individuals with an earlier onset experienced a swifter rate of myopia progression [17]. In the SPAN cohort, it was observed that for each year, a participant delayed the commencement of wearing spectacles, their myopia decreased by −0.16D, and early-onset myopia was notably more severe compared to late-onset myopia [18]. These findings help elucidate why individuals with early-onset myopia are at an elevated risk of high myopia. However, it is worth noting that these studies did not encompass critical factors such as genetic background, lifestyle, and educational level, which could also play a significant role in shaping the myopia trajectory.

In our study, we observed a compelling trend: individuals who developed myopia before the age of 12, typically during primary school or earlier, were at a staggering 15.69 times higher risk of developing high myopia compared to those whose myopia onset occurred after the age of 15. If these findings are replicated by future research, they hold significant implications for the prevention and management of myopia. Commencing early screening and implementing measures to mitigate myopia risk factors before the age of 12 could substantially reduce the likelihood of developing high myopia.

Myopia is widely recognized as a condition influenced by both genetic and environmental factors [19]. Our study identified several risk factors for the eventual development of high myopia, including parental myopia, educational background, frequency of seat changes, and daily classroom hours. Apart from genetic factors, it is noteworthy that lifestyle changes have resulted in an elevated prevalence of myopia [20, 21]. For instance, recent studies have shown a significant annual increase in the incidence of new myopia, largely attributed to extended reading and electronic device usage [22]. In our investigation, the time spent in class and reading showed positive associations with the risk of high myopia. Higher levels of education also appeared to increase the incidence of high myopia, possibly attributed to prolonged study hours or insufficient time spent outdoors. But it is a retrospective study, we have not included outdoor activities, and we will explore this in future studies. Moreover, myopic parents influenced the age of myopia onset, emphasizing the primary role of heredity.

Remarkably, our study revealed for the first time that parental myopia and time spent on electronic devices directly or indirectly impacted the risk of high myopia by influencing the age of myopia onset. In essence, the age of onset appeared to mediate the associations between high myopia, parental myopia, and electronic device use. Therefore, delaying myopia onset could effectively reduce the likelihood of developing high myopia in the future, particularly among adolescents with a family history of myopia [23].

The results of our study underscore the critical importance of implementing myopia prevention strategies, including reducing indoor and electronic device use, to curtail or delay early myopia onset. These findings can serve as valuable reference data for developing effective approaches to alleviate the burden of high myopia.

There are some limitations in our study. First, although our questionnaire assessed a comprehensive list of potential risk factors, we required subjects to recall the age of myopia onset that may have occurred more than 20 years ago, which may introduce recall bias. However, according to existing literature, the initial prescription of myopia glasses elicits a profound experience that is distinctly etched in memory, and participants could substantiate their information by detailed accounts of the specifics surrounding the acquisition, including the manner and timing of the event. Consequently, the data offered by patients in this regard is likely to be deemed dependable with proper questioning [10, 11]. In addition, the current refractions were obtained without cycloplegia, potentially increasing the prevalence of myopia in these non-presbyopic participants. Moreover, this study merely indicated an association between parental myopia, time spent on electronic devices, and the early onset of myopia, without confirming causality. Further investigations employing a longitudinal approach are warranted to explore causal relationships.

In conclusion, the age of myopia onset might be the single best predictor for high myopia, and age at onset appeared to mediate the associations of high myopia with parental myopia and time spent on electronics. Future prospective studies with larger sample sizes are needed to replicate our findings. While existing treatments for early myopia progression, such as low-concentration atropine, lifestyle adjustments (e.g., increased outdoor activities), and the use of corrective eyewear, are in place, there remains a compelling need for research into innovative interventions aimed at preventing the advancement of early myopia.

The study protocol was approved by the Ethics Board of Tianjin Medical University General Hospital, Tianjin, China, approval number (IRB2019-129-01). Written informed consent was obtained from all participants before data collection in accordance with the tenets of the Declaration of Helsinki.

The authors have no conflicts of interest to declare.

This is supported by the Science and Technology Plan Projects of Tianjin (Grant No. 20JCZXJC00180).

Hua Yan and Yun Zhu participated in designing the study and supervised the study. Chunjie Mao and Xiaodan Zhang participated in preparing figures and tables and authoring drafts of the paper. Mengyu Liao, Xinlei Zhu, Tian Wang, Ruotian Xie, Haokun Zhang, Tiantian Yang, Kai He, Miao Guo, Yanfang Zhu, Yi Lei, YimingLi, Ling Yao, Bohao Cui, Yuyang Miao, Han Han, Xiao Zhao, Yinting Song, Zhiyong Sun, Jinguo Yu, and Wei Zhou participated in the collection and analysis of the data. Fengqi Zhou made contributions to both data interpretation and the critical revision of the manuscript. All authors reviewed the paper and approved the final draft.

Additional Information

Chunjie Mao and Xiaodan Zhang contributed equally to this work.

The data that support the findings of this study are not publicly available. Because in some statistics, sensitive personal information may be included, such as name, address, social security number, etc. If this information is made public, it may lead to the disclosure and abuse of personal privacy. But the data are available from the corresponding author upon reasonable request. A preprint version of this article is available on Research Square [24].

1.
Bourne
RR
,
Stevens
GA
,
White
RA
,
Smith
JL
,
Flaxman
SR
,
Price
H
, et al
.
Causes of vision loss worldwide, 1990-2010: a systematic analysis
.
Lancet Glob Health
.
2013
;
1
(
6
):
e339
49
.
2.
Baird
PN
,
Saw
SM
,
Lanca
C
,
Guggenheim
JA
,
Smith Iii
EL
,
Zhou
X
, et al
.
Myopia
.
Nat Rev Dis Primers
.
2020
;
6
(
1
):
99
.
3.
Wu
PC
,
Huang
HM
,
Yu
HJ
,
Fang
PC
,
Chen
CT
.
Epidemiology of myopia
.
Asia Pac J Ophthalmol
.
2016
;
5
(
6
):
386
93
.
4.
Lavanya
R
,
Kawasaki
R
,
Tay
WT
,
Cheung
GC
,
Mitchell
P
,
Saw
SM
, et al
.
Hyperopic refractive error and shorter axial length are associated with age-related macular degeneration: the Singapore Malay Eye Study
.
Invest Ophthalmol Vis Sci
.
2010
;
51
(
12
):
6247
52
.
5.
Marcus
MW
,
de Vries
MM
,
Junoy Montolio
FG
,
Jansonius
NM
.
Myopia as a risk factor for open-angle glaucoma: a systematic review and meta-analysis
.
Ophthalmology
.
2011
;
118
(
10
):
1989
94.e2
.
6.
Pan
CW
,
Ramamurthy
D
,
Saw
SM
.
Worldwide prevalence and risk factors for myopia
.
Ophthalmic Physiol Opt
.
2012
;
32
(
1
):
3
16
.
7.
Wang
SK
,
Guo
Y
,
Liao
C
,
Chen
Y
,
Su
G
,
Zhang
G
, et al
.
Incidence of and factors associated with myopia and high myopia in Chinese children, based on refraction without cycloplegia
.
JAMA Ophthalmol
.
2018
;
136
(
9
):
1017
24
.
8.
Williams
KM
,
Hysi
PG
,
Nag
A
,
Yonova-Doing
E
,
Venturini
C
,
Hammond
CJ
.
Age of myopia onset in a British population-based twin cohort
.
Ophthalmic Physiol Opt
.
2013
;
33
(
3
):
339
45
.
9.
Bullimore
MA
,
Lee
SS
,
Schmid
KL
,
Rozema
JJ
,
Leveziel
N
,
Mallen
EAH
, et al
.
IMI-onset and progression of myopia in young adults
.
Invest Ophthalmol Vis Sci
.
2023
;
64
(
6
):
2
.
10.
Fledelius
HC
.
Myopia and diabetes mellitus with special reference to adult-onset myopia
.
Acta Ophthalmol
.
1986
;
64
(
1
):
33
8
.
11.
Fledelius
HC
.
Myopia of adult onset. Can analyses be based on patient memory
.
Acta Ophthalmol Scand
.
1995
;
73
(
5
):
394
6
.
12.
Thorn
F
,
Gwiazda
J
,
Held
R
.
Myopia progression is specified by a double exponential growth function
.
Optom Vis Sci
.
2005
;
82
(
4
):
286
97
.
13.
Kinge
B
,
Midelfart
A
.
Refractive changes among Norwegian university students--a three-year longitudinal study
.
Acta Ophthalmol Scand
.
1999
;
77
(
3
):
302
5
.
14.
Lv
L
,
Zhang
Z
.
Pattern of myopia progression in Chinese medical students: a two-year follow-up study
.
Graefe’s archive Clin Exp Ophthalmol
.
2013
;
251
(
1
):
163
8
.
15.
Zadnik
K
,
Sinnott
LT
,
Cotter
SA
,
Jones-Jordan
LA
,
Kleinstein
RN
,
Manny
RE
, et al
.
Prediction of juvenile-onset myopia
.
JAMA Ophthalmol
.
2015
;
133
(
6
):
683
9
.
16.
Chua
SY
,
Sabanayagam
C
,
Cheung
YB
,
Chia
A
,
Valenzuela
RK
,
Tan
D
, et al
.
Age of onset of myopia predicts risk of high myopia in later childhood in myopic Singapore children
.
Ophthalmic Physiol Opt
.
2016
;
36
(
4
):
388
94
.
17.
Hu
Y
,
Ding
X
,
Guo
X
,
Chen
Y
,
Zhang
J
,
He
M
.
Association of age at myopia onset with risk of high myopia in adulthood in a 12-year follow-up of a Chinese cohort
.
JAMA Ophthalmol
.
2020
;
138
(
11
):
1129
34
.
18.
Bullimore
MA
,
Reuter
KS
,
Jones
LA
,
Mitchell
GL
,
Zoz
J
,
Rah
MJ
.
The study of progression of adult nearsightedness (SPAN): design and baseline characteristics
.
Optom Vis Sci
.
2006
;
83
(
8
):
594
604
.
19.
Kurtz
D
,
Hyman
L
,
Gwiazda
JE
,
Manny
R
,
Dong
LM
,
Wang
Y
, et al
.
Role of parental myopia in the progression of myopia and its interaction with treatment in COMET children
.
Invest Ophthalmol Vis Sci
.
2007
;
48
(
2
):
562
70
.
20.
Fan
Q
,
Guo
X
,
Tideman
JW
,
Williams
KM
,
Yazar
S
,
Hosseini
SM
, et al
.
Childhood gene-environment interactions and age-dependent effects of genetic variants associated with refractive error and myopia: the CREAM Consortium
.
Sci Rep
.
2016
;
6
:
25853
.
21.
Tideman
JW
,
Fan
Q
,
Polling
JR
,
Guo
X
,
Yazar
S
,
Khawaja
A
, et al
.
When do myopia genes have their effect? Comparison of genetic risks between children and adults
.
Genet Epidemiol
.
2016
;
40
(
8
):
756
66
.
22.
Foreman
J
,
Salim
AT
,
Praveen
A
,
Fonseka
D
,
Ting
DSW
,
Guang He
M
, et al
.
Association between digital smart device use and myopia: a systematic review and meta-analysis
.
Lancet Digit Health
.
2021
;
3
(
12
):
e806
18
.
23.
Yam
JC
,
Zhang
XJ
,
Zhang
Y
,
Yip
BHK
,
Tang
F
,
Wong
ES
, et al
.
Effect of low-concentration atropine eyedrops vs placebo on myopia incidence in children: the LAMP2 randomized clinical trial
.
JAMA
.
2023
;
329
(
6
):
472
81
.
24.
Mao
C
,
Zhang
X
,
Liao
M
,
Zhou
F
,
Zhu
X
,
Wang
T
, et al
.
Recalled age of myopia onset may predict risk of high adult myopia in China
.
Res Square
.
2023
. [preprint].