Abstract
Background
Childhood vision loss represents a significant public health burden worldwide, with the majority of these cases being treatable or preventable if identified early. This study aimed to investigate the contemporary prevalence, causes and refractive error-related risk factors of visual impairment among preschool children in Beijing, China.
Methods
In this cross-sectional study, preschool children aged 36 to 83 months were enrolled to undergo comprehensive ocular examinations, including visual acuity, autorefraction before and after cycloplegia (1% cyclopentolate), ocular biometry, anterior segment examination, and cover and uncover test. Visual impairment (VI) was classified according to the Pediatric Eye Evaluations Preferred Practice Pattern guidelines, which define it as presenting visual acuity (PVA) in either eye exceeding 0.4, 0.3, and 0.2 logMAR for respective age groups of 36–47, 48–59, and 60–83 months, and an interocular difference (IOD) of two or more lines in PVA.
Results
This study included 1,473 children with an average age of 4.89 ± 0.76 years; 52.5% were boys. The mean PVA for the worse-seeing eyes across the age groups 36–47, 48–59, 60–71, and 72–83 months were 0.29 ± 0.11, 0.22 ± 0.11, 0.18 ± 0.11, and 0.14 ± 0.11, respectively (ANOVA; P < 0.01). For the better-seeing eyes, the mean PVA were 0.24 ± 0.10, 0.17 ± 0.09, 0.13 ± 0.09, and 0.09 ± 0.10 (ANOVA; P < 0.01). The overall prevalence of VI was 17.99% in worse-seeing eyes, 8.83% in better-seeing eyes, and 1.77% for IOD. No significant differences were observed between boys and girls in mean PVA and the prevalence of VI. The primary causes of VI were refractive errors, including astigmatism (24.2%), hyperopia (19.6%), myopia (9.1%), and anisometropia (6.4%), followed by amblyopia (9.8%) and strabismus (2.3%). Factors associated with VI, as determined through both univariate and multivariate analyses, included older age (OR = 1.472, P < 0.01), greater absolute value of cylinder (OR = 5.691, P < 0.01), myopia (OR = 85.432, P < 0.01), and anisometropia (OR = 3.805, P = 0.02).
Conclusions
This extensive study provides contemporary insights into the prevalence and causes of VI in preschool children in Beijing, China. The findings reveal a higher prevalence of VI compared to previous reports from Western countries, highlighting the critical need for ocular screening in preschool children and support from local governments to promote early prevention strategies against refractive errors.
Keywords: Preschool children, Visual acuity, Visual impairment, Refractive error, Astigmatism, Amblyopia
Background
Childhood vision loss represents a significant public health burden worldwide [1–4], affecting 25.2 million children under 5 years in 2016 [2], with the majority of these cases being treatable or preventable if identified early. The first six years of life are critical for visual development, during which the visual system’s physiology and anatomy remain highly malleable [5, 6]. The onset and progression of visual impairment (VI) during childhood can profoundly impact developmental, educational, and social well-being [7–9]. Consequently, periodic vision screening is a critical component of preventative pediatric vision health care. Early screening and detection of visual impairment in preschool settings facilitate interventions prior to school entry, preventing further progression of ocular diseases. Additionally, contemporary data on visual impairment in children are essential for public health administrations to establish timely vision healthcare policies. Despite evidence indicating an upward trend in preterm birth rates for both singleton and multiple gestations in China from 2012 to 2018 [10], comprehensive epidemiological data remain limited [11]. Consequently, the importance of vision screening in preschool children has been endorsed by numerous healthcare professionals and governmental bodies [12, 13].
To date, comprehensive studies have illuminated the landscape of visual impairments among school-aged children and adolescents globally, revealing diverse trends and prevalence across different races and regions [11, 14, 15]. Unfortunately, a notable lack of rigorous of comparative and representative data regarding visual impairments in preschoolers, primarily aged 3 to 6 years, has limited early-stage interventions. Conducting studies within this demographic presents significant challenges, including limited health resources, poor cooperation from preschool children, time-intensive examination processes, and limited awareness among parents and educators [13]. The measurement of visual acuity has been shown to be both effective and practical for screening visual impairment in preschool children [16]. Visual impairment is categorized based on these measurements [17]. However, accurately pinpointing the underlying causes of visual impairment remains challenging. Most visual impairments are due to refractive errors, which are best assessed through cycloplegic refraction [18, 19]. Nevertheless, the side effects associated with cycloplegia, such as photophobia, blurred vision and the other potential for adverse reactions, can cause distress to both children and their parents [18].
Recent developments across various countries have highlighted the essential need for conducting vision screenings in preschool children, advocating for enhanced efforts to prevent myopia in this younger demographic [20, 21]. In China, the government has issued comprehensive guidelines for children’s eye care and visual examinations (available at: http://www.nhc.gov.cn/jkj/s5899tg/202110/65a3a99c42a84e3f8a11f392d9fea91e.shtml), marking a significant advancement in vision screening protocols for this age group. However, the effectiveness of these initiatives is often limited by the lack of standardized diagnostic and visual acuity coding procedures, challenges associated with administering cycloplegia, and the voluntary nature of participation. Accurate data and a clear understanding of the causes of visual impairment are crucial for assessing the current state of vision health and for guiding public policy and resources toward addressing primary vision problems. Thus, it is critical to conduct extensive, school-based screening studies focused on preschool children, particularly within China.
This study was designed to assess the prevalence and distribution of VI among preschoolers in Beijing, northern China. It also aimed to explore the underlying causes of VI to understand how visual health parameters have changed in the post-COVID-19 era among this demographic.
Methods
Study population
This cross-sectional study derived from a kindergarten-based cohort began in November 2021 across nine kindergartens in the Tongzhou District, Beijing, with detailed methodology discussed in a previous publication [22]. We employed a stratified random cluster sampling strategy, initially selecting 1,917 children from nine schools, including two public and seven private preschool institutions. From this pool, 1732 participants were selected after excluding non-permanent residents of Tongzhou District.
Ocular examination
The study team, comprising ophthalmologists, optometrists, and nurses, underwent comprehensive training. The measurement of distant presenting visual acuity (PVA) was carried out both with and without the use of spectacles, as applicable, employing Lea Symbols ETDRS 3 m Set charts (250300, Goodlite, IL, USA) at a fixed distance of three meters [17, 23]. In cases where PVA exceeded 0.2 logMAR, best-corrected visual acuity (BCVA) was determined after a subjective refraction test [17]. Both PVA and BCVA were measured in logMAR units. Slit lamp biomicroscopy was performed to identify ocular abnormalities. An experienced pediatric ophthalmologist conducted the cover test, and Hirschberg test to assess the presence, type, and magnitude of strabismus [24]. This assessment required children to fixate on targets located at 33 cm (near) and 6 m (far), with and without spectacles, if worn. Refraction was done before and after cycloplegia using the autorefractor instrument (model RM-800; Topcon, Tokyo, Japan). For each eye, three measurements were obtained and averaged. If the spherical difference between any two measurements exceeded 0.5 D, additional measurements were conducted for accuracy [22, 25]. Ocular biometry was performed before cycloplegia using the Lenstar 900 (Haag-Streit, Koeniz, Switzerland) [22, 25]. Each eye was measured three times, and these measurements were automatically quality-checked by the instrument. Unsatisfactory measurements prompted further assessments, with the final data reflecting the average of three valid measurements.
Cycloplegia was achieved using 1% cyclopentolate [23]. Initially, each child received a drop of 0.5% proparacaine hydrochloride in each eye, followed by two drops of 1.0% cyclopentolate (Cyclogyl; Alcon, Fort Worth, TX, USA), administered five minutes apart [22]. Cycloplegia was considered adequate 30 min later if the pupil size was ≥ 6.0 mm with an absent light reflex. If these criteria were not met, an additional drop of cyclopentolate was applied. If the pupil size and light reflex still failed to meet the required standard after an additional 15 min, the cycloplegia was considered unsuccessful.
Definitions
Referral criteria were based on the American Academy of Ophthalmology’s (AAO) 2017 and 2022 Pediatric Eye Evaluations Preferred Practice Pattern guidelines, utilizing a monocular distance visual acuity test and interocular differences [17, 23]. Visual impairment (VI) was categorized as uncorrected or present visual acuity in either eye exceeding 0.4, 0.3, and 0.2 logMAR for age groups 36–47, 48–60, and 61–83 months, respectively, along with an interocular difference (IOD) of ≥ 0.2 logMAR [17, 23]. Better-seeing eyes were defined as those with superior distant PVA and worse-seeing eyes as those with worse distant PVA. In instances where visual acuity was identical between two eyes, the right eye was designated as the better-seeing eye.
Spherical equivalent (SE) was calculated as the algebraic sum of the sphere plus half the cylinder value (sphere + 0.5 × cylinder). Refractive errors were determined using the cycloplegic refraction of the corresponding eyes, including myopia, hyperopia, astigmatism, and anisometropia. Myopia was defined as SE ≤ -0.5D, hyperopia as SE ≥ + 2.0D, with other values indicating emmetropia [24]. Pseudomyopia was identified when the cycloplegic SE was > -0.5D while noncycloplegic SE was ≤ -0.5D. Anisometropia was defined as an SE difference ≥ 1.0D between eyes, and astigmatism as an average cylinder ≤ -1.0D [24]. Strabismus, whether constant or intermittent, was classified based on the primary direction of tropia, with or without spectacles [26]. Amblyopia followed the definition used in the 2022 Pediatric Eye Evaluations Preferred Practice Pattern guidelines, encompassing both unilateral and bilateral forms [23].
Statistical analysis
Rigorous quality control measures were implemented throughout the study. Data were collected on standardized forms and independently entered into a database using Epidata software 3.1 (The Epidata Association, Odense, Denmark) by two different individuals. Any discrepancies between the entries triggered a review of the raw data. Statistical analyses were performed using SAS software (V.9.4, SAS, Cary, North Carolina, USA).
Continuous variables were expressed as mean ± SD or as medians for distributions that were skewed. For comparing categorical variables, the chi-square test was employed. Continuous variables were analyzed using the independent t-test and analysis of variance (ANOVA). Spearman’s rank correlation analysis was used to determine the relationships between visual impairment and various factors. Multivariate logistic regression analysis was then performed to assess the association between visual impairment and those parameters which exhibited a value of P less than 0.1 in the univariate analysis. Statistical significance was established at a two-sided P value of less than 0.05, with 95% confidence intervals provided for all significant findings.
Results
Demographic characteristics
In this study, 1,515 preschool children were initially assessed. Thirty-six were excluded due to non-compliance with vision examinations, incomplete cycloplegic refraction and ocular biometry measurements, and an additional six for not meeting the age criteria of 3 to 6 years. This left 1,473 participants (97.22%) for analysis, with a mean age of 4.89 ± 0.76 years. Demographic data including gender and age for both included and excluded subjects are detailed in Table 1. Notably, while there was a significant statistical difference in age composition between the groups (P < 0.01), the mean ages were similar (P = 0.37). The ocular characteristics of the included children are outlined in Table 2.
Table 1.
Demographic comparison of preschool children included and excluded in the study by gender and age
| Variable | Included N (%) |
Excluded N (%) |
P value |
|---|---|---|---|
| Gender | 0.77 | ||
| Boys | 773 (52.5) | 23 (54.8) | |
| Girls | 700 (47.5) | 19 (45.2) | |
| Age (years) | < 0.01* | ||
| 2 | 1 (2.4) | ||
| 3 | 231 (15.7) | 12 (28.6) | |
| 4 | 523 (35.5) | 10 (23.8) | |
| 5 | 633 (43.0) | 12 (28.6) | |
| 6 | 86 (5.8) | 2 (4.8) | |
| 7 | 4 (9.5) | ||
| 8 | 1 (2.4) | ||
| Mean age (years) | 4.89 ± 0.76 | 4.82 ± 1.28 | 0.37 |
| All | 1473 | 42 |
* Statistical difference existed between groups.
Table 2.
Ocular characteristics of preschool children analyzed in the study
| Variable | N (%) | Mean ± SD | Median (1stquartile, 3rdquartile) |
|---|---|---|---|
| SE of better-seeing eyes (D) | 1.27 ± 0.83 | 1.25 (0.88, 1.63) | |
| SE of worse-seeing eyes (D) | 1.26 ± 0.94 | 1.25 (0.88, 1.63) | |
| DC of better-seeing eyes (D) | -0.47 ± 0.44 | -0.25 (-0.50, -0.25) | |
| DC of worse-seeing eyes (D) | -0.54 ± 0.53 | -0.5 (-0.75, -0.25) | |
| DSE of better-seeing eyes (D) | 0.95 ± 0.65 | 0.88 (0.50, 1.38) | |
| DSE of worse-seeing eyes (D) | 0.95 ± 0.68 | 0.88 (0.50, 1.38) | |
| PVA of better-seeing eyes (LogMar) | 0.16 ± 0.10 | 0.14 (0.10, 0.20) | |
| PVA of worse-seeing eyes (LogMar) | 0.21 ± 0.12 | 0.18 (0.14, 0.26) | |
| AL of better-seeing eyes (mm) | 22.23 ± 0.69 | 22.21 (21.77, 22.7) | |
| AL of worse-seeing eyes (mm) | 22.23 ± 0.72 | 22.20 (21.75, 22.71) | |
| AL/CR of better-seeing eyes | 2.86 ± 0.07 | 2.86 (2.82, 2.90) | |
| AL/CR of worse-seeing eyes | 2.86 ± 0.08 | 2.86 (2.82, 2.91) | |
| Refractive status in better-seeing eyes | |||
| Hyperopia | 213 (14.5) | 2.62 ± 0.78 | 2.37 (2.13, 2.81) |
| Emmetropia | 1237 (84.0) | 1.07 ± 0.51 | 1.13 (0.75, 1.38) |
| Myopia | 23 (1.6) | -0.97 ± 0.53 | -0.75 (-1.25, -0.50) |
| Refractive status in worse-seeing eyes | |||
| Hyperopia | 225 (15.3) | 2.50 ± 0.85 | 2.38 (2.13, 2.88) |
| Emmetropia | 1221 (82.9) | 1.06 ± 0.51 | 1.13 (0.75, 1.38) |
| Myopia | 27 (1.8) | -1.48 ± 1.63 | -0.88 (-1.63, -0.63) |
| Pseudomyopia in better-seeing eyes | |||
| Yes | 110 (7.5) | 1.60 ± 0.79 | 1.63 (1.00, 2.16) |
| No | 1363 (92.5) | 0.89 ± 0.61 | 0.88 (0.50, 1.25) |
| Pseudomyopia in worse-seeing eyes | |||
| Yes | 120 (8.1) | 1.58 ± 0.92 | 1.50 (0.88, 2.13) |
| No | 1353 (91.9) | 0.89 ± 0.63 | 0.88 (0.50, 1.25) |
| Anisometropia | |||
| Yes | 26 (1.8) | ||
| No | 1447 (98.2) | ||
| Astigmatism in better-seeing eyes | |||
| Yes | 211 (14.3) | -1.18 ± 0.64 | -1.00 (-1.50, -0.75) |
| No | 1262 (85.7) | -0.35 ± 0.23 | -0.25 (-0.50, -0.25) |
| Astigmatism in worse-seeing eyes | |||
| Yes | 224 (15.2) | -1.52 ± 0.66 | -1.25 (-1.75, -1.00) |
| No | 1249 (84.8) | -0.36 ± 0.21 | -0.25 (-0.50, -0.25) |
SE, cycloplegic spherical equivalent; DC, cylinder in diopter; DSE, difference between noncycloplegic refraction and cycloplegic refraction; PVA, presenting visual acuity; AL, axial length; AL/CR, axial length to corneal radius ratio.
Distributions of presenting visual acuity (PVA) in preschool children
The distribution of PVA in the better-seeing and worse-seeing eyes is presented in Table 3. The mean PVA for worse-seeing eyes was 0.21 ± 0.12 for both boys and girls (t-test; t= -0.840, P = 0.40), and for better-seeing eyes, it was 0.16 ± 0.10 for both genders (t-test; t= -0.988, P = 0.32). Age-stratified PVA for worse-seeing eyes displayed mean values of 0.29 ± 0.11, 0.22 ± 0.11, 0.18 ± 0.11, and 0.14 ± 0.11 for the age groups 36–47, 48–59, 60–71, and 72–83 months, respectively (one-way ANOVA; F = 73.681, P < 0.01). The corresponding values for better-seeing eyes were 0.24 ± 0.10, 0.17 ± 0.09, 0.13 ± 0.09, and 0.09 ± 0.10 (one-way ANOVA; F = 97.196, P < 0.01). Figure 1 illustrates the detailed PVA distributions, stratified by age and gender for worse-seeing eyes, showing an age-correlated improvement in PVA across both genders.
Table 3.
Distribution of presenting visual acuity in both Worse-seeing and Better-seeing eyes of preschool children
| Variable | Age (months) | |||
|---|---|---|---|---|
| 36–47 N = 231 |
48–59 N = 523 |
60–83 N = 719 |
All N = 1473 |
|
| PVA (worse-seeing eyes) N (% of age group) | ||||
| ≤ 0 | 0 (0) | 1 (0.19) | 22 (3.06) | 23 (1.56) |
| 0.02–0.1 | 6 (2.60) | 61 (11.66) | 188 (26.15) | 255 (17.31) |
| 0.12–0.2 | 61 (26.41) | 219 (41.87) | 333 (46.31) | 613 (41.62) |
| 0.22–0.3 | 96 (41.56) | 176 (33.65) | 122 (16.97) | 394 (26.75) |
| 0.32–0.4 | 45 (19.48) | 39 (7.46) | 30 (4.17) | 114 (7.74) |
| > 0.4 | 23 (9.96) | 27 (5.16) | 24 (3.34) | 74 (5.02) |
| All | 231 (100) | 523 (100) | 719 (100) | 1473 (100) |
| PVA (better-seeing eyes) N (% of age group) | ||||
| ≤ 0 | 0 (0) | 8 (1.53) | 56 (7.79) | 64 (4.34) |
| 0.02–0.1 | 17 (7.36) | 133 (25.43) | 306 (42.56) | 456 (30.96) |
| 0.12–0.2 | 95 (41.13) | 244 (46.65) | 271 (37.69) | 610 (41.41) |
| 0.22–0.3 | 79 (34.20) | 106 (20.27) | 63 (8.76) | 248 (16.84) |
| 0.32–0.4 | 28 (12.12) | 23 (4.40) | 14 (1.95) | 65 (4.41) |
| > 0.4 | 12 (5.19) | 9 (1.72) | 9 (1.25) | 30 (2.04) |
| All | 231 (100) | 523 (100) | 719 (100) | 1473 (100) |
PVA, presenting visual acuity.
Fig. 1.
Distribution of Presenting visual acuity (PVA) in the worse-seeing eyes in preschool children stratified by sex. (A) Children aged 36–47 months. (B) Children aged 48–59 months. (C) Children aged 60–83 months
Prevalence of visual impairment (VI) and its associated risk factors
The prevalence of VI was 17.99% (95%CI: 16.2-19.8%) for worse-seeing eyes, 8.83% (95%CI: 7.4-10.4%) for better-seeing eyes, and 1.77% (95%CI: 1.1-2.6%) for interocular difference (IOD). The age and gender-varied distributions of VI in worse-seeing eyes and IOD are shown in Table 4, indicating a progressive increase in VI prevalence with age, without significant gender differences.
Table 4.
Age and Gender-Stratified prevalence of visual impairment in Worse-seeing eyes and interocular difference
| Variable | Age (months) | |||||
|---|---|---|---|---|---|---|
| 36–47 N = 231 |
48–59 N = 523 |
60–83 N = 719 |
All N = 1473 |
F value | P value | |
| Visual impairment in the worse-seeing eyes | ||||||
| Gender | N (% of age group) | |||||
| Boys | 12 (9.76) | 31 (11.36) | 94 (24.93) | 137 (17.72) | 22.489 | < 0.01* |
| Girls | 11 (10.19) | 35 (14.00) | 82 (23.98) | 128 (18.29) | 14.307 | < 0.01* |
| F value | 0.012 | 0.828 | 0.089 | 0.079 | --- | |
| P value | 0.913 | 0.363 | 0.766 | 0.779 | --- | |
| All | 23 (9.96) | 66 (12.62) | 176 (24.48) | 265 (17.99) | 36.532 | < 0.01* |
| Visual impairment defined by interocular difference | ||||||
| Gender | N (% of age group) | |||||
| Boys | 0 (0) | 8 (2.93) | 6 (1.59) | 14 (1.81) | 0.264 | 0.61 |
| Girls | 2 (1.85) | 5 (2.00) | 5 (1.46) | 12 (1.71) | 0.163 | 0.69 |
| F value | --- | 0.466 | 0.02 | 0.02 | --- | |
| P value | 0.22 | 0.50 | 0.89 | 0.89 | --- | |
| All | 2 (0.87) | 13 (2.49) | 11 (1.53) | 26 (1.77) | 0.011 | 0.92 |
* Statistical difference existed between groups.
Among the 265 children with VI in worse-seeing eyes, 46.1% had astigmatism at least 0.5D or higher, 24.2% had astigmatism at least 1.0D or higher, and 14.3% had astigmatism of no less than 2.0D, as determined by cycloplegic refraction. Additionally, 9.1% presented with myopia, 12.5% children exhibited with pseudomyopia and 19.6% with hyperopia, based on cycloplegic spherical equivalent. Other ocular disorders included amblyopia in 26 children, anisometropia of 1.0D or more in 17 children, along with six cases of manifest strabismus, and one case each of ptosis, corneal opacity, intraocular lens implant, congenital cataract, and nystagmus. Among 130 children with visual impairment in the better-seeing eyes, the number of children with myopia, hyperopia, and astigmatism (value of cylinder ≤ -1.0D) was 17 (13.1%), 24 (18.5%) and 49 (37.7%), respectively. In the group of 1,343 children without visual impairment of better-seeing eyes, the number of children with myopia, hyperopia, and astigmatism (value of cylinder ≤ -1.0D) was 6 (0.4%), 189 (14.1%) and 120 (8.9%), respectively.
Spearman’s correlation analysis and Chi-square tests identified several demographic factors and ocular characteristics of worse-seeing eyes statistically associated with VI, including age, cylinder value, presence of myopia, hyperopia, pseudomyopia, astigmatism (with a cylinder value of ≤ -1.0D), and anisometropia, detailed in Table 5. In the multivariate logistic regression, significant risk factors for VI in the worse-seeing eyes included older age (Odds Ratio [OR] = 1.472, P < 0.01), a higher absolute value of cylinder (OR = 5.691, P < 0.01), myopia (OR = 85.432, P < 0.01), and anisometropia (OR = 3.805, P = 0.02), as presented comprehensively in Table 5.
Table 5.
Factors associated with visual impairment in worse-seeing eyes, assessed both in univariate analysis and multivariate analysis
| Variables | Spearman’s Correlation Analysis | Chi-square Test | Multivariate Logistic Regression (R2 = 0.282) | ||||||
|---|---|---|---|---|---|---|---|---|---|
| r | P value | OR | P value | Β | OR (95% CI) | P value | |||
| Age | 0.147 | < 0.01 | 0.386 | 1.472 (1.238–2.749) | < 0.01 | ||||
| SE | -0.043 | 0.10 | 0.271 | 1.311 (0.996–1.726) | 0.054 | ||||
| DC | 0.308 | < 0.01 | 1.739 | 5.691 (3.419–9.474) | < 0.01 | ||||
| DSE | 0.011 | 0.68 | |||||||
| AL | 0.011 | 0.68 | |||||||
| AL/CR | 0.051 | 0.05 | 0.025 | 1.026 (0.838–1.255) | 0.81 | ||||
| Gender (boy) | 1.039 | 0.79 | |||||||
| Myopia | 40 | < 0.01 | 4.448 | 85.432 (21.307–342.550) | < 0.01 | ||||
| Hyperopia | 1.461 | 0.04 | 0.090 | 1.094(0.613–1.955) | 0.76 | ||||
| Pseudomyopia | 1.833 | 0.01 | 0.329 | 1.389 (0.809–2.385) | 0.23 | ||||
| Anisometropia | 10.931 | < 0.01 | 1.336 | 3.805 (1.260-11.494) | 0.02 | ||||
| Astigmatism | 5.570 | < 0.01 | -0.139 | 0.870 (0.465–1.629) | 0.87 | ||||
SE, cycloplegic spherical equivalent; DC, absolute value of cylinder in diopter; DSE, difference between noncycloplegic refraction and cycloplegic refraction; AL, axial length; AL/CR, axial length to corneal radius ratio; Myopia, cycloplegic SE≤ -0.5D; Hyperopia, cycloplegic SE ≥ + 2.0D; Pseudomyopia, cycloplegic SE > -0.5D while noncycloplegic SE ≤ -0.5D; Anisometropia, difference of SE between the two eyes ≥ 1.0D; Astigmatism, value of cylinder ≤ -1.0D.
r, non-parametric rank-based correlation coefficient by Spearman’s correlation analysis; OR of Chi-square Test, Odds ratio from cross-tabulation analysis; B of multivariate logistic regression, represents the change in log odds of the outcome per one-unit increase in the predictor variable; OR of multivariate logistic regression, adjusted odds ratio (95% CI) accounting for covariates.
Discussion
This kindergarten-based study represents one of the initial efforts to provide contemporary data on VI in preschool children in China, adhering to the criteria set by the AAO and utilizing cycloplegic parameters. The principal findings include: (1) a VI prevalence of 17.99% in worse-seeing eyes, 8.83% in better-seeing eyes, and 1.77% for interocular difference (IOD); (2) significant risk factors for VI in this demographic included older age, greater amounts of astigmatism, myopia, and anisometropia; (3) other prevalent ocular disorders influencing VI were amblyopia and strabismus.
This study also demonstrated that overall PVA in preschool children improved with age, supporting previous research findings [16, 27]. Specifically, the mean PVA for worse-seeing eyes ranged from 0.29 ± 0.11 to 0.14 ± 0.11 logMAR, and for better-seeing eyes from 0.24 ± 0.10 to 0.09 ± 0.10 logMAR, across the age spectrum from 3 to 6 years. This annual change in PVA was − 0.05 logMAR for both eyes, which is less than the change specified in AAO referral criteria [17, 23], potentially explaining the observed increase in prevalence of reduced VA from age 3 to age 6. This aligns closely with the − 0.048 logMAR reported by Wang et al. [27], and − 0.057 logMAR found in a multi-ethnic population including African-American and Hispanic children aged 30 to 72 months [28]. When the analysis was limited to preschoolers without clinically significant ocular abnormalities (spherical equivalent refraction greater than − 0.50D to less than + 2.00D, astigmatism less than 0.75 D, and anisometropia less than 2.00 D), the annual change of visual acuity was − 0.061 logMAR per year in Chinese children. This suggests a need for longitudinal studies with extended follow-up periods in this demographic.
Sub-group analysis revealed no significant differences between boys and girls regarding the mean PVA and the prevalence of VI, nor was there a statistical relationship between gender and VI in univariate analysis. These findings are in line with those of Wang et al., who reported marginally better age-adjusted uncorrected visual acuity in boys compared to girls for the worse eyes (0.161 ± 0.002 vs. 0.168 ± 0.002, P = 0.03), albeit without clinical significance [27]. Moreover, the prevalence of reduced visual acuity did not differ significantly between boys and girls across various age groups, with P-values of 0.86, 0.16, 0.348, and 0.618 respectively, as determined by chi-square analysis. Consistent with these observations, other studies have also reported no significant correlation between gender and visual acuity or VI, both in Australian and American preschool children, and no discernible difference in the global health burden of pediatric vision impairment due to gender [3, 29–31]. These findings collectively indicate that gender does not inherently influence the development of visual acuity or VI. However, these studies hint at potential differing risks for refractive errors based on gender, with girls more likely to develop myopia, especially as they grow older [32].
The observed prevalence of VI in the worse-seeing eyes of our study cohort, at 17.99%, was comparable to that found in a similar age group (36–83 months) in Southern China, which was 13.1% [27]. However, this rate is significantly higher compared to the prevalence reported in various other countries and regions. For instance, the prevalence ranged from 1.21 to 5.3% among 30-72-month-old children in the United States [30, 33, 34], was 6.4% in the same age group in Australia [29], and 7% among 2-7-year-old children in Taiwan [35]. In 2015, the overall prevalence of VI in preschool children aged 36 to 72 months was estimated at 1.5% (95% Confidence Interval: 1.2–1.8%) in the United States, with an expected 26% increase by 2060 [36]. The considerable variance in VI prevalence across different regions and countries can be attributed not only to methodological and definitional differences in these studies but also to racial and regional disparities, as analyzed and established in the United States [36]. Furthermore, the greater prevalence of VI in China, compared to other regions, may be linked to a higher incidence of refractive errors which are known initial risk factors for VI, such as myopia and astigmatism [16, 27, 36, 37]. This suggests a distinct epidemiological pattern of visual impairments in Chinese children compared to their global counterparts.
In our study, the predominant causes of VI were identified as refractive errors, including myopia, high hyperopia, astigmatism, and anisometropia, along with amblyopia and strabismus. This finding aligns with those of previous research [30, 34, 38]. Among these, refractive errors emerged as the most significant contributors to VI in preschool children, a trend that is consistent with observations in school-aged children and young adults [39, 40]. Given these results, it is recommended that routine vision screenings be integrated into the healthcare protocol for preschool children, especially considering the increased diagnosis of refractive errors in this age group, as evidenced by cycloplegic refraction findings. This prevalence surpasses figures reported in earlier studies and those observed in Western countries. Considering these findings, it is crucial to initiate targeted educational and awareness campaigns for parents and preschool children. These initiatives should not only focus on increasing awareness about ocular diseases such as amblyopia and strabismus but also on the various types of refractive errors. By fostering early recognition of visual problems, these interventions aim to contribute significantly to the overall ocular health and well-being of preschool children.
There were still several limitations in our study. Our cohort protocol focused on key assessments such as cycloplegic refraction, axial length, strabismus and amblyopia evaluation. And the primary objective of this study was to explore the current prevalence and underlying refractive-related causes of VI. However, fundus imaging and perinatal factors for these preschool children were absent, potentially confounding amblyopia diagnosis in this preschool cohort. Additionally, cerebral visual impairment (CVI)—a condition often coexisting with normal visual acuity—was not explicitly evaluated, despite its clinical relevance. Although the American Academy of Ophthalmology (AAO) guidelines incorporate select neurodevelopmental indicators, comprehensive neurodevelopmental screening (e.g., cognitive/behavioral assessments) remains important in certain conditions. Future studies should integrate multidisciplinary evaluations to address these gaps. Moreover, a more cost-effective yet precise approach for these screenings that ensures both high sensitivity and specificity were encouraged, especially for broader application and effectiveness in detecting visual impairments.
Conclusions
In summary, our study offers up-to-date data on the prevalence of VI in a large cohort of preschool children from Northern China, revealing higher rates than those previously reported in both Western nations and earlier studies. The primary cause of VI in this demographic was identified as refractive errors, a consistent finding across different age groups, including school-aged children and young adults, followed by amblyopia and strabismus. Future research should focus on collecting up-to-date data on preschool populations and exploring cost-effective methods for screening. This focus will help enhance early detection and intervention strategies, improving outcomes for affected children.
Author contributions
All authors have read and approved the manuscript. JF and YYS designed the study. YYS, BDZ, HJL, YQ, SNW, XQ, JTC and WBC joined the data collection. LL, MXJ and YBL were involved in data cleaning and verification. LL analyzed the data. YYS drafted the manuscript. JF contributed to the interpretation of the results and critical revision of the manuscript for important intellectual content and approved the final version of the manuscript.
Funding
This study was supported by the Talent Development Plan for Beijing High-level Public Health Technical Project (Discipline Leader-02-10); National Natural Science Foundation of China (82070998); National Natural Science Foundation of China (82301250); Science and Technology Incubation Project of Beijing Hospital Management Center (PX2024007).
Data availability
The raw data for this study are not publicly available due to ethical restrictions and privacy concerns related to participant data. However, these data may be available from the corresponding author Jing Fu (email: fu_jing@126.com) or the first author Yunyun Sun (email: 2008sunshinesyy@163.com) upon reasonable request and with appropriate institutional review board approval.
Declarations
Ethics approval and consent to participate
The study protocol adhered to the Declaration of Helsinki and received ethical approval from the Ethics Committee of Beijing Tongren Hospital, affiliated with Capital Medical University (Ethics Approval Number: TRECKY2020-152). Before examinations commenced, online meetings were held with parents to discuss the study’s aims, methods, and the precautions associated with cycloplegia. Informed consent was obtained from all participants’ parents or legal guardians.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The raw data for this study are not publicly available due to ethical restrictions and privacy concerns related to participant data. However, these data may be available from the corresponding author Jing Fu (email: fu_jing@126.com) or the first author Yunyun Sun (email: 2008sunshinesyy@163.com) upon reasonable request and with appropriate institutional review board approval.