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Systematic Review

Prevalence of Developmental Dyslexia in Primary School Children: A Systematic Review and Meta-Analysis

1
Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Guangzhou 510631, China
2
School of Psychology, South China Normal University, Guangzhou 510631, China
3
Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
4
CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Sciences, Shanghai 200031, China
5
Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai 200030, China
6
Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
7
Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou 311121, China
8
Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou 311121, China
*
Authors to whom correspondence should be addressed.
Brain Sci. 2022, 12(2), 240; https://doi.org/10.3390/brainsci12020240
Submission received: 18 December 2021 / Revised: 28 January 2022 / Accepted: 6 February 2022 / Published: 10 February 2022

Abstract

:
Background: Developmental dyslexia (DD) is a specific learning disorder concerning reading acquisition that may has a lifelong negative impact on individuals. A reliable estimate of the prevalence of DD serves as the basis for diagnosis, intervention, and evidence-based health resource allocation and policy-making. Hence, the present meta-analysis aims to generate a reliable prevalence estimate of DD worldwide in primary school children and explore the potential variables related to that prevalence. Methods: Studies from the 1950s to June 2021 were collated using a combination of search terms related to DD and prevalence. Study quality was assessed using the STROBE guidelines according to the study design, with study heterogeneity assessed using the I2 statistic, and random-effects meta-analyses were conducted. Variations in the prevalence of DD in different subgroups were assessed via subgroup meta-analysis and meta-regression. Results: The pooled prevalence of DD was 7.10% (95% CI: 6.27–7.97%). The prevalence in boys was significantly higher than that in girls (boys: 9.22%, 95%CI, 8.07–10.44%; girls: 4.66%, 95% CI, 3.84–5.54%; p < 0.001), but no significant difference was found in the prevalence across different writing systems (alphabetic scripts: 7.26%, 95%CI, 5.94–8.71%; logographic scripts: 6.97%, 95%CI, 5.86–8.16%; p > 0.05) or across different orthographic depths (shallow: 7.13%, 95% CI, 5.23–9.30%; deep: 7.55%, 95% CI, 4.66–11.04%; p > 0.05). It is worth noting that most articles had small sample sizes with diverse operational definitions, making comparisons challenging. Conclusions: This study provides an estimation of worldwide DD prevalence in primary school children. The prevalence was higher in boys than in girls but was not significantly different across different writing systems.

1. Introduction

Developmental dyslexia (DD) is a specific impairment characterized by severe and persistent problems in the acquisition of reading skills; these problems are not caused by mental age, visual acuity problems, or inadequate schooling [1,2]. DD, also referred to as specific reading disability or specific reading disorder, is by far the most common type of learning disability, accounting for approximately 80% of all learning disabilities [3]. Due to their frustration with reading, a great number of dyslexic children are also at increased risk of academic and social problems [4]. These children often have higher reading anxiety [5,6,7], lower positive well-being [8], and experience negative attitudes [6,9].
Typically, children begin to be formally taught to read after entering primary school, and their word-reading ability reaches adult-like levels by the end of primary school [10]. Diagnosis of DD is normally achieved after a child begins structured schooling [11]. The primary school is, thus, an important point at which early literacy screening and interventions can help to identify potential reading difficulties and address risk factors [12,13]. Therefore, the present study focuses on DD in primary school children.
Dyslexia is fairly widespread but demonstrates uncertain prevalence, ranging from 5% to 17.5% [14,15], and the variability of prevalence may be related to several factors. First, different operational definitions may result in a different prevalence. The common sets of the cut-off for reading achievement are 1 and 1.5 standard deviations (SD) below the mean for the same age [16,17,18]. Second, environmental variables (e.g., regions, socioeconomic status) and other factors (e.g., grade, sub-deficit) may also influence each child’s risk of dyslexia.
Finally, it is particularly interesting to ask whether and in what way orthographic depth influences the prevalence of DD. On the one hand, logographic scripts may yield different prevalence estimates relative to alphabetic scripts. In alphabetic scripts in which the letters represent phonemes, the prevalence of DD was reported to range from 2.28% to 12.70% [19,20], even as high as 15% and 19.90% [21,22]. Unlike alphabetic scripts, logographic scripts such as Chinese have special language characteristics: (1) the smallest written units are characters representing monosyllabic morphemes; and (2) grapheme to phoneme mappings are created in an arbitrary way [23,24,25]. As logographic scripts, such as Chinese, require the memorization of picture-like characters by rote, it was previously believed that the script presented little or no difficulty in reading [26] until 1982, when Stevenson et al. [27] reported for the first time that DD did exist among Chinese and Japanese readers. On the other hand, even within alphabetic writing systems, such systems differ in terms of orthographic depths. According to the orthographic depth hypothesis (ODH) [28], shallower orthographies are easier to learn than deeper ones. For children, it is easier to learn how to map letters onto phonological forms that are known from speech in the shallower orthographies, where in units in the written language reliably correspond to units in the spoken language. In contrast, the other two theories (the psycholinguistic grain size theory and the grapholinguistic equilibrium hypothesis) propose that the incidence of DD will be very similar across both consistent and inconsistent orthographies but that its manifestation might differ according to orthographic consistency [29,30].
In addition, the gender ratio of DD is the subject of an ongoing debate [31,32,33]. Most studies reported that more boys suffered from DD than girls, and the gender ratio of boys to girls was about 3:1 [34,35,36], but some studies found no differences in the prevalence of DD between boys and girls [18,31]. The latter interpreted the over-representation of boys in DD prevalence to be a result of bias in behavioral observation [37]. To address this problem, we conducted a subgroup analysis of gender prevalence.
Taken together, a large number of previous studies have assessed the prevalence of DD in primary school children, but the results are largely mixed. More importantly, the previous review articles did not thoroughly discuss the prevalence of Chinese DD [14,15], although the number of Chinese users is large and widely distributed. Therefore, it is necessary to include Chinese for meta-analysis.
The present study thus aimed to conduct a systematic and meta-analytical review of previous studies that reported the prevalence of DD in children in primary school. More specifically, the present study aimed to address two issues: (a) what is the prevalence of childhood DD worldwide; and (b) whether the prevalence of DD varies according to gender, writing system, and other variables.

2. Materials and Methods

2.1. Search Strategy and Selection Criteria

This systematic review and meta-analysis was conducted in accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) reporting guidelines [38]. The protocol of this study was registered in PROSPERO (registration number: CRD42021232958).
Looking at studies from the 1950s to 10 June 2021, two researchers (X.L. and M.Z.) independently conducted a literature search of the China National Knowledge Infrastructure, Wanfang, CQ-VIP, the China Hospital Knowledge Database, EBSCO host, ProQuest, PubMed, Web of Science, the OATD database, Cochrane, Springerlink and EMBASE, using a combination of search terms related to DD (dyslexia, reading disability, reading disorder, or learning disability), and prevalence (prevalence, detectable rate, incidence rate, or epidemiology). Then, a search of the reference lists of the studies included in the first step was performed to complement our database searches. No language or time restrictions were applied. The full search strategies for different bibliographic databases are presented in Table A1.
The study inclusion criteria were that: (i) participants consisted of primary school students (age range: 6–13 years; grade range: 1st–6th); (ii) subjects were recruited through probability sampling methods; (iii) studies included DD prevalence as a main or secondary outcome; (iv) measures with good psychometrics were used to assess the symptoms of DD; (v) no restrictions in terms of languages and published periods. For studies involving both adolescents and primary school children, the data of the primary group had to be able to be disaggregated. For multiple articles that used data from the same investigation (duplicates), only the articles with the most comprehensive results or the largest sample size were kept.
The following studies were excluded: (i) those including non-primary school students as participants; (ii) case-control studies, randomized clinical trials, review articles, and editorials; (iii) gray literature-material published by governments, organizations, and industrial or commercial entities for non-academic purposes, conference proceedings, and abstracts; (iv) no reports on DD prevalence were included in the articles; (v) studies were of specific sub-populations of participants (e.g., participants with acute or chronic disease); (vi) the articles could not be retrieved in full-text form through online databases, via library requests or email correspondence with the authors of the studies; (vii) the articles provided insufficient data regarding sample information.
After removing duplicates from different bibliographic databases, the two researchers (X.L. and M.Z.) independently screened the titles and abstracts of all retrieved records from the literature search. Then, the same two researchers assessed the eligibility of potentially relevant articles in the full text against the selection criteria. A consensus was reached for any disagreements through discussion, or the matter was decided by the other two researchers (L.Y. and J.Z.).

2.2. Data Extraction and Quality Assessment

Data were independently extracted from the included articles by two researchers (Q.A. and Y.Z.). The collected information included title, first author, year of publication, country, study design, sampling strategy, diagnostic materials, diagnostic criteria, sample size, the number of participants screened as DD, and prevalence estimate. The regions of study location were designated as African Region, Region of the Americas, Southeast Asia Region, European Region, Eastern Mediterranean Region, and Western Pacific Region according to the World Health Organization (WHO) criteria and as high-income countries and low- and middle-income countries according to the World Bank (WB) criteria.
We rated the quality of included articles according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline in several dimensions: sample population, sample size, participation rate, outcome assessment, and analytical methods (Table A2) [39].

2.3. Overall Pooled Prevalence of DD

Before pooling the prevalence estimates, the variance of raw prevalence from each included study was stabilized, using the Freeman–Tukey double arc-sine transformation [40]. All estimates were presented after back transformation. We assessed the heterogeneity of prevalence estimates among studies using the Cochran Q test and I2 index [41,42]. For the Cochran Q test, p < 0.05 represented significant heterogeneity. For the I2 index, values of 25% or lower corresponded to low degrees of heterogeneity, 26% to 50%, to moderate degrees of heterogeneity, and values greater than 50% to high degrees of heterogeneity [41,42].
Because of high heterogeneity (as expected and observed), a random-effect meta-analysis (following the DerSimonian and Laird method) was used to calculate the overall pooled prevalence of DD with 95% CIs throughout this study [40]. To examine whether single studies had a disproportionally excessive influence, we applied a “leave-1-out” sensitivity analysis for each meta-analysis [43]. Publication bias in the meta-analysis was detected qualitatively by a visual inspection of funnel plots and quantitatively by the Egger linear regression test and the Begg rank correlation test when more than 10 estimates were available in a single analysis [44,45,46].

2.4. Subgroup Meta-Analysis and Meta-Regression of DD Prevalence

We conducted subgroup meta-analyses to determine potential sources of heterogeneity. As a rule, at least three studies should be available per subgroup.
Multiple data points were generally reported in a single study. To assess the associations among various sample characteristics and the prevalence of DD, we first conducted a univariable meta-regression, if possible, followed by a multi-variable meta-regression [47]. As a rule, at least 10 data points should be available for each variable in univariable meta-regression, and 20 in multivariable meta-regression [48,49]. Data were analyzed using RStudio, version 2021.09.1-372 (R Foundation for Statistical Computing).

3. Results

3.1. Study Selection and Characteristics

As outlined in Figure 1, our initial literature search identified a total of 6564 records. After applying the eligibility criteria, a final set of 56 articles, featuring 58 studies, were included in our quantitative synthesis. A list of the 56 included articles is given in Table A3.
The detailed characteristics of the included articles can be found in Table A3. In all, 41 of the 58 studies (70.69%) reported prevalence data for both boys and girls. Of the 58 studies, 27 (46.55%) were conducted among children using alphabetic scripts, while 31 (53.45%) were conducted among children using alphabetic scripts. In addition, grade 3 was the most-studied grade (21, 36.21%) and random sampling was the most-used method (37, 63.79%), while only four studies (6.90%) had a sample size greater than 10,000. Moreover, more than half of the 58 studies (33, 56.90%) were conducted in the Western Pacific area and in middle-income countries (40, 68.97%).

3.2. Pooled Prevalence of DD

Table 1 illustrates the results of overall and subgroup meta-analyses. Regarding DD, the pooled prevalence was 7.10% (95% CI: 6.27–7.97%), as ascertained using random-effects meta-analysis (Figure 2).

3.3. Sensitivity Analysis and Publication Bias

The “leave-1-out” sensitivity analysis showed that the pooled prevalence of DD varied from 6.93% (95% CI: 6.13–7.78%) to 7.21% (95% CI: 6.38–8.09%) after removing a single study at one time (Figure A1), indicating that no individual study significantly influenced the overall pooled prevalence in the meta-analysis. Publication bias was established based on the funnel plot (Figure A2), Egger test (t = 6.25, p < 0.001), and Begg test (z = 1.96, p = 0.05).

3.4. Subgroup Meta-Analysis and Meta-Regression of DD

Table 1 and Figure 3 showed the prevalence of DD in different genders, writing systems, operational definitions, grades, sample sizes, sampling methods, sub-deficits, WHO regions, WB regions, and the forest plot for the difference in these factors.
There were significant differences in prevalence in terms of gender, operational definitions, and sample size. Specifically, the prevalence of DD was higher in boys (9.22%; 95% CI: 8.07–10.44%) than in girls (4.66%; 95% CI: 3.84–5.54%) (p < 0.001). In addition, a difference in DD prevalence was found among various operational definitions and sample sizes. The results of the post hoc analyses showed that DD prevalence was significantly lower when reporting 1.5 SD and 2SD as the cut-off values than without reporting the cut-off value (1.5 SD: 5.36%, 95% CI, 4.28–6.55%; 2 SD: 5.32%, 95% CI, 4.56–6.13%; without reporting SD: 9.10%, 95% CI, 7.18–11.21%; both p < 0.05, FDR-corrected). The prevalence in a large sample (more than 10,000) was significantly lower than that in smaller samples (500–1000 and 1000–1500) (10,000–: 3.13%, 95% CI, 2.32–4.06%; 500–1000: 8.43%, 95% CI, 6.83–10.18%; 1000–1500: 8.25%, 95% CI, 6.43–10.27%; both p = 0.09, FDR-corrected). However, there was no significant difference in the prevalence between the two smaller samples (p > 0.05). Univariate and multivariate regression results also showed that the subgroup of the largest sample size reported the lowest prevalence of DD.
Unexpectedly, the prevalence of DD did not differ significantly when it was stratified according to writing system (alphabetic scripts: 7.26%, 95% CI, 5.94–8.71%; logographic scripts: 6.97%, 95% CI, 5.86–8.16%; p > 0.05), or orthographic depth (shallow: 7.13%, 95% CI, 5.23–9.30%; deep: 7.55%, 95% CI, 4.66–11.04%; p > 0.05), or grade (grade 1: 7.59%, 95% CI, 2.65–14.72%; grade 2: 4.88%, 95% CI, 2.94–7.28%; grade 3: 6.35%, 95% CI, 4.78–8.13%; grade 4: 5.25%, 95% CI, 4.31–6.27%; grade 5: 7.44%, 95% CI, 4.59–10.90%; grade 6: 4.48%, 95% CI, 2.96–6.29%; p > 0.05). Similarly, there was no difference in the prevalence of DD among different subgroups of sub-deficits, sampling methods, WHO regions, and WB regions (p > 0.05).

4. Discussion

This systematic review and meta-analysis estimated the worldwide prevalence of DD in primary school children, with a prevalence of 7.10% (95% CI: 6.27–7.97%). There was a significant gender difference, and the gender ratio of boys to girls was about 2:1. However, there was no language-specific difference in the prevalence of DD. In addition, the prevalence was influenced by operational definition and sample size, but not by sub-deficits, grade, sampling method, WHO region or WB region. To our best knowledge, this is the first synthesized analysis on the prevalence of DD.
The pooled prevalence of 7.10% (95% CI: 6.27–7.97%) that is estimated in the present study is within the range of previous selective reviews, which have suggested that the prevalence of DD was in the range of 5–17.5% [14,15]. This is likely due to the similar diagnostic criteria of DD in most of the previous studies, in which DD was mainly defined as the low end of a normal distribution of word-reading ability [50]. Many disorders do not represent categories but instead the extremes on a continuous distribution that ranges from optimal outcomes to poor outcomes, with the underlying causal mechanisms being similar across the whole distribution. Essentially, most behaviorally defined disorders, including DD, are continuous disorders. In the present study, we were able to pool the prevalence of DD in children based on the available evidence, which allowed our systematic review and meta-analysis to provide a more comprehensive estimate of the prevalence of DD.
Interestingly, our calculation of the gender ratio regarding DD of boys to girls is about 2:1 (boys: 9.22%; 95% CI: 8.07–10.44%; girls: 4.66%; 95% CI: 3.84–5.54%) (p < 0.001). This result is consistent with previous studies that reported a higher prevalence of DD for boys than for girls [31,35,51]. One explanation for this gender difference in DD prevalence is that some teachers are more likely to refer boys for assessment as having special problems because boys are often perceived as being more disruptive than girls [52]. However, focusing on large-scale epidemiological studies that were not based on school-referred samples, Rutter and his colleagues (2007) also found that boys were more likely than girls to have a reading disability, indicating that teacher bias cannot account entirely for gender difference [53]. A similar phenomenon is also found in logographic writing systems [54,55]. Other explanations come from biological and environmental hypotheses, including genetic causes [56,57], immunological factors, perinatal complications, differences in brain functioning due to differential exposure or sensitivity to androgens [58], and differential resilience to neural insult [59]. Our current study cannot provide enough evidence to support or reject any of the above hypotheses; therefore, more studies on DD in both boys and girls are needed in the future. At the same time, the current findings suggest that teachers may need to pay more attention to boys who exhibit reading difficulties or disorders.
Another important finding is that the prevalence of DD did not differ significantly when stratified by writing system (alphabetic scripts: 7.26%, 95% CI, 5.94–8.71%; logographic scripts: 6.97%, 95% CI: 5.86–8.16%; p = 0.74). This is an unexpected result since logographic scripts are very distinctive (such as arbitrary mapping between the graphic and sound forms of words) relative to alphabetic scripts from the perspective of language; therefore, some experts believe that DD may be absent or rare in logographic scripts [26]. Research on DD has been initially and mainly conducted among the users of alphabetic scripts. Until the 1980s, researchers examined large samples of fifth-grade children in Japan, Taiwan, and the United States using a reading test and a battery of 10 cognitive tasks. However, the results showed that the prevalence of DD in Japan, Taiwan, and the United States was 5.4%, 7.5%, and 6.3%, respectively, suggesting that there is no significant difference in the prevalence of DD among different writing systems [27]. One explanation for this and our current findings is that the similarity in DD prevalence across different writing systems may be related to cross-cultural universality in the neurobiological and neurocognitive underpinnings of DD [15]. Some Western researchers and writers believed that Chinese characters are derived from pictographs, but this is not true. Instead, Chinese orthography is not primarily pictographic [27].
In addition, we found that DD prevalence did not differ across languages with different orthographic depths (shallow: 7.13%, 95% CI, 5.23–9.30%; deep: 7.55%, 95% CI, 4.66–11.04%; p > 0.05). These findings support the psycholinguistic grain size theory rather than the orthographic depth hypothesis [28,29]. When the orthography of the language is relatively shallow, readers can focus exclusively on the small psycholinguistic grain size of the phoneme. Otherwise, they will learn additional correspondences for larger orthographic units, such as syllables, rhymes, or whole words. Therefore, the prevalence of DD is very similar in both consistent and inconsistent orthographies, but its manifestations may vary according to orthographic depth.
Remarkably, operational definitions significantly affected the prevalence of DD. The present study found that studies with stricter operational definitions reported lower prevalence. Specifically, DD prevalence was significantly lower when using 1.5 SD and 2SD as the cut-off values than when not reporting SD (1.5 SD: 5.36%, 95% CI, 4.28–6.55%; 2 SD: 5.32%, 95% CI, 4.56–6.13%; without reporting SD: 9.10%, 95% CI, 7.18–11.21%; both p < 0.05, FDR-corrected). This finding is consistent with a recent selective review, suggesting that the prevalence depends on the severity of the reading problem—with lower rates for more severe problems [16]. Although the recognition of DD dates back over a century, no consensus has been reached regarding its diagnostic criteria. Therefore, many studies even use scores below 20% [60], scores in the bottom 10% [61], using different materials, and many other cut-offs for convenience. Essentially, all behaviorally defined disorders, including DD, are continuous disorders, and their operational definitions are found to be confusing in the current study. Perhaps now is not the time for change, with the continuous development of theoretical and empirical research; perhaps there will be a more appropriate operational definition for DD in the future.
It is worth noting that studies with more than 10,000 subjects reported a lower average prevalence of DD when compared to studies with 500–1000 and 1000–1500 subjects. By reviewing these studies, we found that the large sample-size studies have a common feature: that is, the diagnostic criteria were relatively strict. Only students who scored 1.5 or even 2 SD below the average on diagnostic tests were diagnosed as having DD [35,62,63]. Because of their strict diagnostic criteria, the prevalence was significantly lower than that of other subgroups [18,20]. Interestingly, in studies on other disorders, such as Tourette’s syndrome, epidemiological investigations also demonstrated that studies with larger sample sizes tended to report a relatively lower prevalence [64,65], although the reason is not clear.
There was no grade difference in DD prevalence. In the literature, the association between grade and DD prevalence remains unclear. Some studies reported that DD prevalence was lower in higher grades than in lower grades [66], and explained this finding with the argument that DD symptoms improve through systematic learning [14]. Several studies, however, have shown a higher DD prevalence in higher grades, relative to that observed in lower grades [67]. In addition, most studies reported no difference in DD prevalence among different grades [68,69,70]. Studies have shown that the level of reading ability in the first few years of school will continue in the following years and that the DD prevalence during schooling does not change greatly [20,37]. Most previous studies only studied the prevalence of DD in specific grades, mainly in grades 3 to 5, which makes it difficult to directly and empirically address the above issue [55,70,71]. In order to examine whether and how DD prevalence changes with progression through grades, future studies need to include all grades of elementary school and make the sample sufficiently representative. There was also no difference in the prevalence of sub-deficits. This shows that different tests and different indicators have no effect on the prevalence rate. That is, when there is a problem with accuracy, there is usually a problem with fluency or comprehension, and dyslexia shows no obvious differentiation.
As expected, we found significant heterogeneity when pooling the prevalence rates of DD. Thus, we performed sensitivity analyses, subgroup analyses, and meta-regression on many variables. After omitting each study one at a time (leave-1-out forest), the pooled prevalence of DD was shown to be robust and consistent. That is, no one study in this meta-analysis exerted a very high influence on our overall results. Under this condition, we further explored the patterns of effect sizes and heterogeneity in our data through a graphic display of heterogeneity (GOSH) plots [72] and found that all included studies had a low effect size and high heterogeneity (Figure A3). This result was consistent with the results of subgroup analysis, i.e., each subgroup had high heterogeneity (Table 1). In meta-regression, only the p-value of the sample size reached a significant level, which could explain the 39.56% heterogeneity (R2 = 39.56%). This indicates that the large variations in sample size among different studies may be an important reason for their heterogeneity. Another reason for heterogeneity may be that children were drawn from studies performed in a wide variety of countries with differing cultural, ethnic, social, and economic characteristics. In conclusion, such high heterogeneity in epidemiological meta-analysis is not unexpected. However, the results of this study should be interpreted with caution.
The strengths of this study include the comprehensive search strategies, a double review process, and stringent selection criteria. In our systematic review, we included only studies that were conducted in standard primary schools so that the generalizability of our results could be fully guaranteed. Moreover, we were able to pool the prevalence of DD in the included children based on the available evidence, which allowed our systematic review and meta-analysis to cover a broad scope regarding the prevalence of childhood DD.
Several intrinsic limitations of this study should also be acknowledged. First, the pooled prevalence of DD in the studied children might be affected by publication bias. We tried to minimize publication bias by searching for non-English literature and conference abstracts. Unfortunately, we could not completely rule out publication bias because of the observational nature of our study. Second, there were inherent disadvantages in pooling prevalence reports from disparate studies. For DD, sufficient data were available to pool the prevalence estimates. However, our subgroup analysis on the prevalence of any DD according to grade group, region group, and income group were only based on a limited number of studies that provided corresponding prevalence numbers. Third, ten variables across the included studies were systematically assessed, and only those studies with a large sample size were identified as showing a lower prevalence of DD. Previous studies [73,74] have suggested that socioeconomic factors were likely to contribute to disparities in DD prevalence rates in different subgroups. However, only high- and middle-income countries were assessed in the current study. Future studies are needed to explain the heterogeneity. More high-quality epidemiologic investigations on DD appear to be necessary, especially regarding different grades and in low-income countries.

5. Conclusions

This systematic review and meta-analysis is the first study to estimate the worldwide prevalence of DD. The results suggested that DD represents a considerable public health challenge worldwide (with a prevalence of 7.10%, 95% CI: 6.27–7.97%) and boys seem to be more affected than girls. There was no significant difference in the prevalence of DD either between logographic and alphabetic writing systems or between alphabetic scripts with different orthographic depths. However, a clear operational definition is urgently needed for the diagnosis of DD.

Author Contributions

L.Y. and J.Z. conceived and designed the protocol. L.Y. drafted the protocol manuscript. C.L., X.W. and J.Z. critically revised the manuscript for methodological and intellectual content. X.L., M.Z., Q.A. and Y.Z. participated in the development of the search strategy and data analysis. All authors have read and agreed to the published version of the manuscript.

Funding

The work was supported by the Key-Area Research and Development Program of Guangdong Province (grant number 2019B030335001), the Science and Technology Project of Guangzhou City (grant number 201804020085), the National Social Science Foundation of China (grant number 20&ZD296), the National Science Foundation of China (grant number 32171063) and the Shanghai Clinical Research Center for Mental Health (grant number 19MC1911100).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data related to the research are presented in the article.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Search strategy.
Table A1. Search strategy.
DatabaseSearch Strategy
China National
Knowledge Infrastructure
TI = ‘阅读障碍’ + ’发展性阅读障碍’ + ’特异性阅读障碍’ + ’词盲’ + ’阅读困难’ + ’学习障碍’ AND AB = ‘流行病学’ + ’患病率’ + ’检出率’ + ’发生率’ + ’发病率’ (TI = ‘Dyslexia’ + ‘reading disabilit*’ + ‘reading disorder*’ + ‘word blindness’ + ‘specific reading retardation’ + ‘backward reading’ + ‘reading difficult*’ + ‘learning disabilit*’ AND AB = ‘prevalence’ + ‘detectable rate’ + ‘incidence rate’ + ‘epidemiology’)
Wanfang题名:(“阅读障碍” or “发展性阅读障碍” or “特异性阅读障碍” or “词盲” or “阅读困难” or “学习障碍”) and 摘要:(“患病率” or “检出率” or “发病率” or “流行病学” or “发生率”) [title: (“Dyslexia” or “reading disabilit*” or “reading disorder*” or “word blindness” or “specific reading retardation” or “backward reading” or “reading difficult*” or “learning disabilit*”) and abstract: (“prevalence” or “detectable rate” or “incidence rate” or “epidemiology”)]
CQ-VIP(R = 阅读障碍 + R = 发展性阅读障碍 + R = 特异性阅读障碍 + R = 词盲 + R = 阅读困难 + R = 学习障碍) AND (U = 患病率 + U = 检出率 + U = 发病率 + U = 流行病学 + U = 发生率) [(R = Dyslexia + R = reading disabilit* + R = reading disorder* + R = word blindness + R = specific reading retardation + R = backward reading + R = reading difficult* + R = learning disabilit*) AND (U = prevalence + U = detectable rate + U = incidence rate + U = epidemiology)]
China Hospital
Knowledge Database
TI = ‘阅读障碍’ + ’发展性阅读障碍’ + ’特异性阅读障碍’ + ’词盲’ + ’阅读困难’ + ’学习障碍’ AND TI = ‘流行病学’ + ’患病率’ + ’检出率’ + ’发生率’ + ’发病率’ (TI = ‘Dyslexia’ + ‘reading disabilit*’ + ‘reading disorder*’ + ‘word blindness’ + ‘specific reading retardation’ + ‘backward reading’ + ‘reading difficult*’ + ‘learning disabilit*’ AND TI = ‘prevalence’ + ‘detectable rate’ + ‘incidence rate’ + ‘epidemiology’)
EBSCO HostTI ((Dyslexia OR (reading disabilit*) OR (reading disorder*) OR (word blindness) OR (specific reading retardation) OR (backward reading) OR (reading difficult*) OR (learning disabilit*)) AND AB ((prevalence OR (detectable rate) OR (incidence rate) OR epidemiology))
Proquest((dyslexia) [SU] OR (reading disabilit*) [SU] OR (reading disorder*) [SU] OR (word blindness) [SU] OR (specific reading retardation) [SU] OR (backward reading) [SU] OR (reading difficult*) [SU] OR (learning disabilit*) [SU]) AND ((prevalence) [FT°] OR (detectable rate) [FT°] OR (incidence rate) [FT°] OR (epidemiology) [FT°])
PubMed(“dyslexia” [Title] OR “reading disabilit*” [Title] OR “reading disorder*” [Title] OR “word blindness” [Title] OR “specific reading retardation” [Title] OR “backward reading” [Title] OR “reading difficult*” [Title] OR “learning disabilit*” [Title]) AND (“prevalence” [Title/Abstract] OR “detectable rate” [Title/Abstract] OR “incidence rate” [Title/Abstract] OR “epidemiology” [Title/Abstract])
Web of ScienceTI = (Dyslexia OR (reading disabilit*) OR (reading disorder*) OR (word blindness) OR (specific reading retardation) OR (backward reading) OR (reading difficult*) OR (learning disabilit*)) AND AB = (prevalence OR (detectable rate) OR (incidence rate) OR epidemiology)
OATD databaseabstract:(dyslexia OR “reading disabilit*” OR “reading disorder*” OR “word blindness” OR “specific reading retardation” OR “backward reading” OR “reading difficult*” OR “learning disabilit*” OR “reading difficult*”) AND (prevalence OR “detectable rate” OR “incidence rate” OR epidemiology)
Cochrane(‘dyslexia’ OR ‘reading disabilit*’ OR ‘reading disorder*’ OR ‘word blindness’ OR ‘specific reading retardation’ OR ‘backward reading’ OR ‘reading difficult*’ OR ‘learning disabilit*’) in Title Abstract Keyword AND (‘prevalence’ OR ‘detectable rate’ OR ‘incidence rate’ OR ‘epidemiology’) in Abstract
SpringerlinkTI(“dyslexia” OR “reading disabilit*” OR “reading disorder*” OR “word blindness” OR “specific reading retardation” OR “backward reading” OR “reading difficult*” OR “learning disabilit*”) AND AB(“prevalence” OR “detectable rate” OR “incidence rate” OR “epidemiology”)
EMBASE((dyslexia OR ‘reading disabilit*’ OR ‘reading disorder*’ OR ‘word blindness’ OR ‘specific reading retardation’ OR ‘backward reading’ OR ‘reading difficult*’ OR ‘learning disabilit*’):ti) AND ((prevalence OR ‘detectable rate’ OR ‘incidence rate’ OR epidemiology):ab)
“*” was used to replace zero, single or multiple characters.
Table A2. Quality scores.
Table A2. Quality scores.
IDAuthorYear
Published
Quality Score
Sample PopulationSample SizeParticipationOutcome AssessmentAnalytical MethodsTotal Score
1Bruininks et al., 19711971212229
2Berger et al., 19751975212229
3Nathlie A. Badian, 19841984102227
4Lindgren et al., 19851985211228
5Farrag et al., 19881988212229
6Tonnessen et al., 19931993212229
7Lewis et al., 19941994212229
8Prior et al., 19951995212229
9Zhang et al., 19961996212229
10Miles et al., 19981998212229
11Nathlie A. Badian, 19991999112228
12Lv et al., 20002000102126
13Flannery et al., 20002000212229
14Bhakta et al., 20022002211228
15Yao et al., 20032003202127
16Han Juan, 20052005102227
17Pan et al., 20062006102126
18Song Ranran, 20062006202127
19Yu Yizhen, 20062006102126
20Chan et al., 20072007202228
21Lu Shan, 20072007202127
22Fluss et al., 200820082222210
23Wang Zhong, 20082008202127
24Zou Yuliang, 20082008202127
25Shaheen, H.A., 20102010102216
26Zou et al., 20102010102126
27Daseking et al., 20112011102216
28Jiménez et al., 20112011212229
29Pouretemad et al., 20112011202228
30Vale et al., 20112011212229
31Zhu Dongmei, 20112011202228
32Mogasale et al., 20122011212229
33Luo Yan, 20122012102126
34Zhao Xiaochen, 20132013102227
35Zuo et al., 20132013102126
36Liu et al., 20142014102227
37Irene Jepkoech Cheruiyot, 20152015112228
38Liu et al., 20162016102126
39Padhy et al., 20162016222118
40Sheikh et al., 20162016212229
41Song Yi, 20162016202127
42Zhang et al., 20162016202127
43Zhao et al., 20162016102126
44Cuadro et al., 20172017102227
45Qian Lizhu, 20172017102126
46Wang Rui, 20172017102216
47Yan Nairui, 20182018102126
48Yoo et al., 20182018102227
49Zhou et al., 20182018102126
50Barbiero et al., 20192019122218
51Fan et al., 20192019102216
52Gu et al., 20192019102126
53Zhu et al., 20192019102126
54Cai et al., 20202020122229
55Su et al., 20202020102126
56Yilizhati Maimaiti et al. 20202020102227
Table A3. Characteristics of included articles.
Table A3. Characteristics of included articles.
IDAuthor (Year) CountrySampling StrategyWritng SystemOzone (WHO) Income
(WB)
Diagnostic MaterialsDiagnostic CriteriaSample SizePrevalence NumberPrevalence Rate
1Bruininks et al., 1971USArandom samplingalphabetic scriptAmericasHIC(1) The Lorge-Thorndike intelligence tests;
(2) the reading comprehension and arithmetic computation subtest of the Iowa Tests of Basic Skills
(1) IQ ≥ 80;
(2) one grade or more below the expected achievement in a reading test
Total = 2486
boys = 1233
girls = 1253
3rd = 1303
6th = 1183
Total = 287
boys = 186
girls = 101
3rd = 202
6th = 85
Total = 11.54%
boys = 15.09%
girls = 8.06%
3rd = 15.50%
6th = 7.19%
2Berger et al., 1975Great Britainrandom samplingalphabetic scriptEuropeHIC(1) The NFER test NV5;
(2) the Watts-Vernon test SRI;
(3) the NFER test SRA;
(4) the short form of the WISC;
(5) the Neale Analysis of Reading Ability
(1) SRA ≤ 15 or SRI ≤ 10;
(2) scores on either the accuracy or comprehension scales on the Neale Test fell 30 months or more below those predicted
Total = 2802
boys = 1428
girls = 1374
Total = 209
boys = 156
girls = 53
Total = 7.46%
boys = 10.92%
girls = 3.86%
3Nathlie A. Badian, 1984USArandom samplingalphabetic scriptAmericasHIC(1) The Stanford achievement test, SAT;
(2) the Wechsler intelligence scale for children–revised, WISC-R
(1) Total reading score ≤ 20 percentile on SAT;
(2) IQ ≥ 85
Total = 550
boys = 284
girls = 266
Total = 22
boys = 16
girls = 6
Total = 4.00%
boys = 5.63%
girls = 2.26%
4Lindgren et al., 1985 (study1)USAcluster samplingalphabetic scriptAmericasHIC(1) The IEA reading test;
(2) the short form of the Wechsler intelligence scale for children
Reading score < 85 and either VIQ or PIQ ≥ 90Total = 895Total = 106Total = 11.84%
4Lindgren et al., 1985 (study2)Italystratified samplingalphabetic scriptEuropeHIC(1) The IEA reading test;
(2) the short form of the Wechsler intelligence scale for children
Reading score < 85 and either VIQ or PIQ ≥ 90Total = 448Total = 38Total = 8.48%
5Farrag et al., 1988Egyptstratified samplingalphabetic scriptEastern MediterraneanMIC(1) The modified Alaska letters identification test (ALIT);
(2) the Assiut dyslexia screening test (ADST);
(3) the Stanford–Binet IQ test
Reading scores of less than 142 and IQ levels of 90 or more. Total = 2878
boys = 1610
girls = 1268
Total = 84
boys = 57
girls = 27
Total = 2.92%
boys = 3.54%
girls = 2.13%
6Tønnessen et al., 1993Norwaycluster samplingalphabetic scriptEuropeHIC(1) The silent word recognition test;
(2) the phonological decoding test
Scored below 20% on two testsTotal = 734
boys = 394
girls = 340
Total = 75
boys = 50
girls = 25
Total = 10.22%
boys = 12.69%
girls = 7.35%
7Lewis et al., 1994Great Britaincluster samplingalphabetic scriptEuropeHIC(1) Young’s (1970) group mathematics test (GMT);
(2) Young’s (1976) SPAR (spelling and reading) test;
(3) Raven’s colored progressive matrices (CPM)
Scored above 90 on arithmetic and nonverbal intelligence tests, but scored below 85 on reading, have no sensory or perceptual handicap, no psychiatric disturbance history, and English is the first languageTotal = 1056
boys = 559
girls = 497
Total = 42
boys = 32
girls = 10
Total = 3.98%
boys = 5.72%
girls = 2.01%
8Prior et al., 1995Australiarandom samplingalphabetic scriptWestern PacificHIC(1) ACER word knowledge test;
(2) Rurrer child behavior scales A and B
Scored more than 1 SD below the grade-2 mean on the reading testTotal = 1219Total = 195Total = 16.00%
9Zhang et al., 1996Chinastratified samplinglogographic scriptWestern PacificMIC(1) A self-compiled reading achievement inventory;
(2) combined Raven’s test (city edition)
Children’s reading achievement was more than 2SD below the average for their gradeTotal = 967Total = 44Total = 4.55%
10Miles et al., 1998Great Britaincluster samplingalphabetic scriptEuropeHIC(1) The shortened Edinburgh reading test;
(2) the Bangor dyslexia test (left–right, months forward, and months reversed);
(3) the recall of digits subtest from the British ability scales (BAS)
(1) On the word recognition test, outliers beyond 1.5 standard deviations from the mean were excluded;
(2) those children whose residuals were ≥ 1.0 SD were described as “underachievers”
Total = 11,804
boys = 5995
girls = 5809
Total = 269
boys = 223
girls = 46
Total = 2.28%
boys = 3.72%
girls = 0.79%
11Nathlie A. Badian, 1999USAcluster samplingalphabetic scriptAmericasHIC(1) The Wechsler preschool and primary scale of intelligence (WPPSI);
(2) the Stanford achievement test (SAT);
(3) the Wechsler intelligence scale for children–revised (WISC-R)
(1) A reading comprehension score of less than the 25th percentile (< 90) on the SAT;
(2) scores were 1.5 SDs or more below the expected level, based on listening comprehension
Total = 5617
1st = 903
2nd = 919
3rd = 988
4th = 896
5th = 908
6th = 1003
Total = 162
1st = 28
2nd = 27
3rd = 28
4th = 33
5th = 32
6th = 14
Total = 2.88%
1st = 3.10%
2nd = 2.94%
3rd = 2.83%
4th = 3.68%
5th = 3.52%
6th = 1.40%
12Flannery et al., 2000USArandom samplingalphabetic scriptAmericasHIC(1) The Weschler intelligence scale for children (WISC);
(2) the wide range achievement test (WRAT);
(3) the NCPP behavioral checklist
(1) IQ ≥ 80 on WISC;
(2) reading scores < 1.5 SD on WRAT;
(3) in the first or second grade at the time of testing;
(4) English was the primary language;
(5) score was normal on the NCPP behavioral checklist
Total = 32,223
boys = 16,080
girls = 16,143
Total = 1410
boys = 947
girls = 463
Total = 4.38%
boys = 5.89%
girls = 2.87%
13Lv et al., 2000Chinarandom samplinglogographic scriptWestern PacificMIC(1) A self-compiled children’s family environment questionnaire;
(2) the Wechsler intelligence scale for children (WISC)
(1) IQ > 70;
(2) 1 SD below the average score of their peers in one or more subjects;
(3) equal learning opportunities with other children;
(4) no nervous system diseases, visual, auditory, or motor disorders
Total = 688
boys = 357
girls = 331
Total = 65
boys = 44
girls = 21
Total = 9.45%
boys = 12.32%
girls = 6.34%
14Bhakta et al., 2002Indiastratified random samplingalphabetic scriptSouth-East Asia MIC(1) The Malayalam translation of the Rutter A2 parent-completed scale;
(2) the Malayalam graded reading test (MGRT);
(3) the Malayalam vocabulary test (MVT);
(4) Raven’s colored progressive matrices, (CPM);
(5) the short-form Oseretsky test of motor proficiency;
6) the Rutter B2 teacher-completed scale (Malayalam version)
A GMRT score of less than 20Total = 119
boys = 604
girls = 566
Total = 98
boys = 71
girls = 27
Total = 8.22%
boys = 11.75%
girls = 4.77%
15Yao et al., 2003Chinarandom samplinglogographic scriptWestern PacificMIC(1) The pupil rating scale–revised screening for learning disabilities (PRS);
(2) Conners parent symptom questionnaire (PSQ);
(3) the YG personality scale;
(4) a self-compiled questionnaire on the general conditions of parents and children
(1) A score of PRS < 60
(2) IQ > 80;
(3) No history of congenital diseases and traumatic brain injury.
Total = 1151
boys = 605
girls = 546
Total = 118
boys = 79
girls = 39
Total = 10.25%
boys = 13.06%
girls = 7.14%
16Han Juan, 2005Chinarandom samplinglogographic scriptWestern PacificMIC(1) The pupil rating scale–revised screening for learning disabilities (PRS);
(2) general situation questionnaire;
(3) Conners parent symptom questionnaire (PSQ);
(4) revised children’s self-concept scale (PHCSS);
(5) Wechsler intelligence scale for children–Chinese revision (WISC-CR);
(6) Wechsler memory scale (WMS);
(7) digital cancellation, digital connection test A and word fluency test;
(8) children’s sensory integration development rating scale
(1) A score of PRS ≤ 60;
(2) the average score of the main course (Chinese, mathematics) was below the 10 percentile of the class, with LD lasting more than one year, and it was considered difficult to complete the class and homework independently;
(3) the reading test score was less than 1 SD of the mean of group test scores;
(4) IQ ≥ 85;
(5) no motivational problems, attention deficit hyperactivity disorder, emotional disorders and other psychological problems, no organic encephalopathy
Total = 800Total = 65Total = 8.13%
17Pan et al., 2006Chinarandom samplinglogographic scriptWestern PacificMIC(1) IQ self-test;
(2) learning disability behavior scale;
(3) the learning motivation diagnostic test (MAAT);
(4) the enhanced learning factor diagnostic test (FAT)
(1) The IQ score was between 85 and 140;
(2) there were one or more cases of I value ≥ 24, II value ≥ 18, III value ≥ 21, IV value ≥ 9, V value ≥ 18, VI value ≥ 12, VII value ≥ 12 in the LD behavior scale
Total = 332
boys = 169
girls = 161
3rd = 164
5th = 168
Total = 50
boys = 28
girls = 22
3rd = 27
5th = 23
Total = 15.06%
boys = 16.57%
girls = 13.66%
3rd = 16.46%
5th = 13.69%
18Song Ranran, 2006Chinarandom samplinglogographic scriptWestern PacificMIC(1) A family situation questionnaire compiled by the Shanghai Mental Health Center;
(2) the pupil rating scale–revised screening for learning disabilities (PRS);
(3) the dyslexia checklist for Chinese (DCCC);
(4) the Wechsler intelligence scale for children–Chinese revision (WISC-CR)
(1) A score of PRS ≤ 60;
(2) academic performance was in the bottom 10%;
(3) the DCCC score was less than 2 SD of students in the same grade;
(4) an IQ > 80 and no visual, auditory impairment, no organic lesions
Total = 1096
boys = 589
girls = 507
3rd = 533
4th = 370
5th = 193
Total = 69
boys = 49
girls = 20
3rd = 36
4th = 22
5th = 11
Total = 6.30%
boys = 8.32%
girls = 3.94%
3rd = 6.75%
4th = 5.95%
5th = 5.70%
19Yu Yizhen, 2006Chinarandom samplinglogographic scriptWestern PacificMIC(1) The pupil rating scale–revised screening for learning disabilities (PRS);
(2) Chinese classification and diagnostic criteria of mental disorders (2nd edition) (CCMD-2-R);
(3) the second revision of the Chinese combined Raven’s test (CRT-C2);
(4) a general situation questionnaire
(1) A score of PRS ≤ 60;
(2) meeting the standard of LD in CCMD-2-R;
(3) the average score of the main course (Chinese, Mathematics) was below the 10 percentile of the class, and it was difficult to complete the class and homework independently;
(4) IQ > 70;
(5) no visual or hearing impairment, no hyperactivity and organic lesions
Total = 903
boys = 496
girls = 407
Total = 90
boys = 58
girls = 32
Total = 9.97%
boys = 11.69%
girls = 7.86%
20Chan et al., 2007Chinastratified random samplinglogographic scriptWestern PacificHIC(1) The Hong Kong test of specific learning difficulties in reading and writing (HKT-SpLD);
(2) the Hong Kong Wechsler intelligence scale for children (HK-WISC)
(1) Scoring 7 or less on the literacy test domain and on one or more of the cognitive test domains;
(2) IQ ≥ 85
Total = 690
boys = 350
girls = 340
Total = 67
boys = 45
girls = 22
Total = 9.71%
boys = 12.86%
girls = 6.47%
21Lu Shan, 2007Chinarandom samplinglogographic scriptWestern PacificMIC(1) A general situation questionnaire;
(2) the pupil rating scale–revised screening for learning disabilities (PRS);
(3) the second revision of the Chinese combined Raven’s test (CRT-C2);
(4) the dyslexia checklist for Chinese (DCCC)
(1) A score of PRS < 65;
(2) the Chinese score lags behind the average score of the same class by more than 1 SD, with LD lasting more than one year, and it was difficult to complete the class and homework independently;
(3) the reading test score was less than 2 SD of the mean of group test scores;
(4) IQ > 70;
(5) excluding other disabilities and environmental factors
Total = 820
boys = 427
girls = 393
3rd = 332
4th = 213
5th = 275
Total = 55
boys = 43
girls = 12
3rd = 23
4th = 15
5th = 17
Total = 6.70%
boys = 10.07%
girls = 3.05%
3rd = 6.93%
4th = 7.04%
5th = 6.18%
22Fluss et al., 2008Francestratified samplingalphabetic scriptEuropeHIC(1) Reading comprehension;
(2) spelling skill;
(3) mathematical knowledge
On reading/spelling/mathematics (FL, FO, FM, respectively), children’ scores were below 1 SDTotal = 1020
boys = 544
girls = 476
Total = 130Total = 12.70%
23Wang Zhong, 2008Chinastratified samplinglogographic scriptWestern PacificMIC(1) The pupil rating scale–revised screening for learning disabilities (PRS);
(2) the combined Raven’s test (CRT)
According to ICD-10, the total score of PRS was less than 60, or the score of verbal type (factor A and B) was less than 20, or the score of non-verbal type (factor C, D and E) was less than 40Total = 3934
boys = 2321
girls = 1613
1st = 601
2nd = 617
3rd = 668
4th = 689
5th = 669
6th = 690
Total = 407
boys = 326
girls = 81
1st = 87
2nd = 63
3rd = 69
4th = 71
5th = 60
6th = 57
Total = 10.35%
boys = 14.05%
girls = 5.02%
1st = 14.48%
2nd = 10.21%
3rd = 10.33%
4th = 10.30%
5th = 8.97%
6th = 8.26%
24Zou Yuliang, 2008Chinarandom samplinglogographic scriptWestern PacificMIC(1) The dyslexia checklist for Chinese (DCCC);
(2) The second revision of the Chinese combined Raven’s test (CRT-C2);
(3) a students’ family situation questionnaire compiled by the research group
(1) T scores of each factor or the whole score of DCCC scale were above 98 percentile points;
(2) IQ > 80
Total = 255
boys = 123
girls = 132
Total = 25
boys = 19
girls = 6
Total = 9.80%
boys = 15.45%
girls = 4.55%
25Shaheen, H. A., 2010Egyptrandom samplingalphabetic scriptEastern MediterraneanMICArabic reading tests (ART)(1) With no visual, hearing problems, motor impairment, mental retardation (IQ less than 90%) or major psychological disorder;
(2) scored 40 or less in ART
Total = 206
boys = 117
girls = 89
Total = 22
boys = 12
girls = 10
Total = 10.68%
boys = 10.26%
girls = 11.24%
26Zou et al., 2010Chinarandom samplinglogographic scriptWestern PacificMIC(1) A family reading environment and reading ability questionnaire;
(2) the dyslexia checklist for Chinese (DCCC);
(3) the pupil rating scale–revised screening for learning disabilities (PRS);
(4) the second revision of the Chinese combined Raven’s test (CRT-C2)
(1) The total score of DCCC was more than 2 SD higher than the mean score;
(2) a score of PRS < 65;
(3) academic achievement was at the bottom 10% of the class;
(4) IQ > 80;
(5) no visual, auditory impairment, no organic lesions
Total = 587
boys = 305
girls = 282
Total = 23
boys = 18
girls = 5
Total = 3.92%
boys = 5.90%
girls = 1.77%
27Daseking et al., 2011Germanyrandom samplingalphabetic scriptEuropeHICThe social–paediatric screening of developmental status for school entry (SOPESS)A PR of no more than 10 on the SOPESSTotal = 372Total = 11Total = 2.96%
28Jiménez et al., 2011 (study 1)Spainrandom samplingalphabetic scriptEuropeHIC(1) Culture-fair (or -free) intelligence tests;
(2) reading comprehension test;
(3) fluency task;
(4) working memory test
(1) No absence of sensory, acquired neurological and other problems;
(2) a percentile score below 25 on accuracy on pseudoword reading from the naming task, or a percentile above 75 on reading time on pseudoword or word reading from the naming task;
(3) IQ > 75
Total = 1048
boys = 630
girls = 418
Total = 164
boys = 98
girls = 66
Total = 15.65%
boys = 15.56%
girls = 15.79%
28Jiménez et al., 2011 (study 2)Guatemalanrandom samplingalphabetic scriptAmericasMIC(1) Culture-fair (or -free) intelligence tests;
(2) reading comprehension test;
(3) fluency task;
(4) working memory test
(1) No absence of sensory, acquired neurological and other problems;
(2) a percentile score below 25 on accuracy on pseudoword reading from the naming task, or a percentile above 75 on reading time on pseudoword or word reading from the naming task;
(3) IQ > 75
Total = 557
boys = 316
girls = 241
Total = 110
boys = 65
girls = 45
Total = 19.90%
boys = 20.57%
girls = 18.67%
29Pouretemad et al., 2011Iranrandom samplingalphabetic scriptEastern MediterraneanMIC(1) An analysis of Persian reading ability (APRA);
(2) Wechsler intelligence scale for children–third edition (WISC-III)
(1) IQ ≥ 85;
(2) reading scores in three trimesters of one academic year were more than 1.5 SD below that expected from their math scores;
(3) no history of brain damage, hearing or visual problems
Total = 1562
boys = 773
girls = 789
1st = 298
2nd = 271
3rd = 309
4th = 330
5th = 354
Total = 82
boys = 59
girls = 23
1st = 11
2nd = 9
3rd = 22
4th = 20
5th = 20
Total = 5.20%
boys = 7.63%
girls = 2.92%
1st = 3.69%
2nd = 3.32%
3rd = 7.12%
4th = 6.06%
5th = 5.65%
30Vale et al., 2011Portugalrandom samplingalphabetic scriptEuropeHIC(1) The TIL-reading age test;
(2) the PRP–word recognition test;
(3) the MPC Raven;
(4) the phonological awareness tests of the ALEPE battery
(1) Achieved a result equal to or less than the percentage 5 in the TIL;
(2) a result below the PRP mastery criteria;
(3) normal IQ;
(4) the phonological awareness score was significantly lower than those presented by control groups
Total = 1360
2nd = 493
3rd = 445
4th = 422
Total = 74
boys = 45
girls = 29
2nd = 38
3rd = 15
4th = 21
Total = 5.44%
2nd = 7.70%
3rd = 3.37%
4th = 4.98%
31Zhu Dongmei, 2011Chinarandom samplinglogographic scriptWestern PacificMIC(1) A general situation questionnaire;
(2) the pupil rating scale–revised screening for learning disabilities (PRS);
(3) the dyslexia checklist for Chinese (DCCC);
(4) Chinese reading ability test;
(5) the second revision of the Chinese combined Raven’s test (CRT-C2)
(1) A score of PRS < 65;
(2) Chinese scores were in the bottom 10 of the class. According to the head teacher’s evaluation, they had learning difficulties lasting more than one year, and had difficulties in completing the classroom and homework independently;
(3) IQ > 80;
(4) the converted T-score of DCCC was lower than the mean plus 2 SD;
(5) scores 2 SD below the standard score on Chinese reading ability test;
6) no other diseases and environmental factors
Total = 1048
boys = 513
girls = 535
3rd = 425
4th = 426
5th = 197
Total = 74
Boy = 44
girls = 30
3rd = 37
4th = 20
5th = 17
Total = 7.10%
boys = 8.6%
girls = 5.6%
3rd = 8.7%
4th = 4.7%
5th = 8.6%
32Mogasale et al., 2012Indiastratified random samplingalphabetic scriptSouth-East Asia MIC(1) Rutter‘s proforma A;
(2) Seguin form board test;
(3) the specific learning disabilities (SpLD) battery test
(1) Poor grades (C or C+) of academic record in two consecutive examinations;
(2) no visual, hearing disorders or severe physical conditions;
(3) IQ ≥ 90
Total = 1079Total = 121Total = 11.21%
33Luo Yan, 2012Chinarandom samplinglogographic scriptWestern PacificMIC(1) The dyslexia checklist for Chinese (DCCC);
(2) The pupil rating scale–revised screening for learning disabilities (PRS);
(3) the second revision of the Chinese combined Raven’s test (CRT-C2)
(1) The transformed T-scord of DCCC > 70;
(2) a score of PRS < 65;
(3) Chinese score ranked in the bottom 10 of the class, with LD lasting more than one year, and it was difficult to complete the class and homework independently;
(4) IQ ≥ 80;
(5) no visual, auditory impairment, no organic lesions
Total = 435
boys = 221
girls = 214
3rd = 136
4th = 159
5th = 140
Total = 33
boys = 23
girls = 10
3rd = 12
4th = 10
5th = 11
Total = 7.59%
boys = 10.41%
girls = 4.68%
3rd = 8.82%
4th = 6.29%
5th = 7.86%
34Zhao Xiaochen, 2013Chinarandom samplinglogographic scriptWestern PacificMIC(1) The Hong Kong behavior checklist of specific learning difficulties in reading and writing for primary school students (second edition) (BCL-P(II));
(2) Conners’ teacher rating scale;
(3) Raven’s test;
(4) the Hong Kong-specific learning difficulties behavior checklist (HKSLDBC);
(5) the Hong Kong test of specific learning difficulties in reading and writing (HKT-SpLD)
(1) The students in the bottom 25% of each grade were selected according to their most recent grade scores in Chinese and math;
(2) the score on the BCL scale was greater than or equal to 18;
(3) IQ ≥85;
(4) subjects performed 1 SD lower than the average level of the same grade in one-minute word reading task, Chinese word reading task, literacy task, and fast naming task;
(5) no brain injury, emotional or behavioral problems
Total = 1069Total = 49Total = 4.58%
35Zuo et al., 2013Chinarandom samplinglogographic scriptWestern PacificMIC(1) The pupil rating scale–revised screening for learning disabilities (PRS);
(2) the dyslexia checklist for Chinese, (DCCC);
(3) the Wechsler intelligence scale for children–Chinese revision (WISC-CR)
(1) A score of PRS < 65;
(2) the DCCC score was lower than the standard score by 2 SD;
(3) IQ > 70;
(4) no visual or auditory impairment, no organic lesions
Total = 1206
boys = 621
girls = 585
3rd = 401
4th = 398
5th = 409
Total = 82
boys = 55
girls = 27
3rd = 27
4th = 26
5th = 31
Total = 6.80%
boys = 8.86%
girls = 4.62%
3rd = 6.73%
4th = 6.53%
5th = 7.58%
36Liu et al., 2014Chinarandom samplinglogographic scriptWestern PacificMIC(1) The one-minute Chinese word reading test;
(2) Raven’s standard progressive matrices (SPM)
(1) The Chinese teachers in the bilingual classes of each grade selected the bottom 10 students in the class, based on the children’s Chinese test scores;
(2) the 10 students tested the self-compiled “One-minute Chinese Word Reading Test”, and then selected children whose scores were lower than the percentile grade corresponding to 1.5 SD from the average score of the grade norm;
(3) no obvious physiological injury, behavioral and emotional disorders;
(4) Raven percentile level above 25% on SPM
Total = 1397
3rd = 458
4th = 418
5th = 521
Total = 46
3rd = 15
4th = 11
5th = 20
Total = 3.29%
3rd = 3.28%
4th = 2.63%
5th = 3.84%
37Irene Jepkoech Cheruiyot, 2015The Republic of Kenyarandom samplingalphabetic scriptAfricaMIC(1) The Burt reading test (1974) revised;
(2) the Pearson dyslexia screening test for juniors (DST-J);
(3) a socio-demographic questionnaire
(1) Reading age was way below chronological age (by 9 months or more) on the Burt reading test (1974)–revised;
(2) an at-risk quotient of 0.6 or greater on the DST-J
Total = 120
boys = 63
girls = 57
Total = 9
boys = 6
girls = 3
Total = 7.50%
boys = 9.52%
girls = 5.26%
38Liu et al., 2016Chinarandom samplinglogographic scriptWestern PacificMIC(1) The dyslexia checklist for Chinese children (DCCC);
(2) the pupil rating scale–revised screening for learning disabilities (PRS)
(1) The score of DCCC was 2 SD higher than the mean score of all the students in the same grade;
(2) a score of PRS < 65;
(3) the Chinese language exam was below the 10% of all children in the same grade;
(4) no intellectual disability, brain injury, visual and auditory disorders, epilepsy, or other neurological disorders.
Total = 34,748
boys = 16,752
girls = 16,645
3rd = 7901
4th = 8387
5th = 8591
6th = 8669
Total = 1200
boys = 893
girls = 301
3rd = 316
4th = 332
5th = 297
6th = 255
Total = 3.45%
boys = 5.06%
girls = 1.78%
3rd = 3.85%
4th = 3.81%
5th = 3.34%
6th = 2.86%
39Padhy et al., 2016Indiastratified random samplingalphabetic scriptSouth-East Asia MIC(1) The specific learning disability screening questionnaire (SLD-SQ);
(2) Brigance diagnostic inventory (BDI)—part of NIMHANS index of specific learning disabilities
(1) Being considered by the teacher to have some form of learning difficulty;
(2) scored above 4 on the SLD-SQ
Total = 3600Total = 108Total = 3.08%
40Sheikh et al., 2016Egyptstratified random samplingalphabetic scriptEastern MediterraneanMIC(1) The reading disability test (RDT);
(2) the Wechsler intelligence scale for children (WISC);
(3) the “kiddie“ schedule for affective disorders and schizophrenia, present and lifetime versions (k-SADSPL)
Students whose reading scores were below the cut-off score (57 for fifth grade, 49 for fourth grade) of RDT and IQ levels of 90 or moreTotal = 567
boys = 305
girls = 262
Total = 64
boys = 37
girls = 27
Total = 11.30%
boys = 12.13%
girls = 10.31%
41Song Yi, 2016Chinarandom samplinglogographic scriptWestern PacificMIC(1) The pupil rating scale–revised screening for learning disabilities (PRS);
(2) the second revision of the Chinese combined Raven’s test (CRT-C2);
(3) the dyslexia checklist for Chinese (DCCC)
(1) The Chinese score was ranked in the bottom 15% of the grade;
(2) the language part of the PRS scale scored less than 20 points;
(3) normal IQ;
(4) the transformed T-score of DCCC > 70;
(5) no visual, auditory and other sensory disorders, no nervous system diseases
Total = 395
boys = 200
girls = 195
Total = 23
boys = 16
girls = 7
Total = 5.80%
boys = 8.00%
girls = 3.59%
42Zhang et al., 2016Chinastratified samplinglogographic scriptWestern PacificMIC(1) A family economic environment and reading ability questionnaire;
(2) the dyslexia checklist for Uygur children (DCUC);
(3) the Wechsler intelligence scale for children–Chinese revision (WISC-CR)
(1) The transformed T-scored of DCUC > 70;
(2) IQ > 80;
(3) no visual, auditory impairment, no organic lesions
Total = 3508
boys = 1837
girls = 1671
3rd = 1281
4th = 1210
5th = 1017
Total = 207
boys = 144
girls = 63
3rd = 85
4th = 75
5th = 47
Total = 5.90%
boys = 7.84%
girls = 3.78%
3rd = 6.63%
4th = 6.20%
5th = 4.62%
43Zhao et al., 2016Chinastratified samplinglogographic scriptWestern PacificMIC(1) The pupil rating scale–revised screening for learning disabilities (PRS);
(2) the dyslexia checklist for Chinese children (DCCC);
(3) the dyslexia checklist for Uyghur children (DCUC);
(4) the home literacy environment and reading ability survey scale (HLE-RA);
(5) the China–Wechsler intelligence scale for children (C-WISC)
(1) A score of PRS < 65;
(2) the score of DCCC was 2 SD higher than the mean scores of Han Chinese children; DCUC score was 2 SD higher than the mean scores of Uyghur children;
(3) IQ > 80;
(4) no visual and/or auditory disorders or psychiatric diseases
Total = 2348
boys = 1163
girls = 1185
3rd = 623
4th = 719
5th = 798
6th = 208
Total = 129
boys = 86
girls = 43
3rd = 39
4th = 48
5th = 39
6th = 3
Total = 5.49%
boys = 7.39%
girls = 3.63%
3rd = 6.26%
4th = 6.68%
5th = 4.89%
6th = 1.44%
44Cuadro et al., 2017Spainstratified samplingalphabetic scriptEuropeHIC(1) Reading efficiency test;
(2) orthographic level test
A cut-off point of 1.5 SD below the mean of each school year in the reading efficiency testTotal = 1408
boys = 718
girls = 690
2nd = 308
3rd = 305
4th = 273
5th = 271
6th = 251
Total = 75
boys = 47
girls = 28
2nd = 10
3rd = 12
4th = 12
5th = 22
6th = 19
Total = 5.32%
boys = 6.55%
girls = 4.06%
2nd = 3.20%
3rd = 3.90%
4th = 4.40%
5th = 8.10%
6th = 7.60%
45Qian Lizhu, 2017Chinarandom samplingChineseWestern PacificMICThe dyslexia checklist for Chinese children (DCCC) T score of any factor or full scale ≥ 70Total = 325
boys = 179
girls = 146
5th = 221
6th = 104
Total = 38
boys = 29
girls = 9
5th = 26
6th = 12
Total = 11.69%
boys = 16.20%
girls = 6.16%
5th = 11.76%
6th = 11.54%
46Wang Rui, 2017Chinarandom samplinglogographic scriptWestern PacificMIC(1) Chinese character literacy test for primary school students;
(2) the pupil rating scale–revised screening for learning disabilities (PRS);
(3) Raven’s standard progressive matrices (SPM);
(4) the grade of Chinese
(1) The literacy level was 1.5 SD below the grade average, according to the Chinese character literacy test for primary school students;
(2) a score of PRS < 65;
(3) normal IQ;
(4) The students’ Chinese score was lower than the grade average level in the past half a year
Total = 847Total = 66Total = 7.79%
47Yan Nairui, 2018Chinarandom samplinglogographic scriptWestern PacificMIC(1) A parental rearing style assessment scale (EMBU);
(2) the family environment scale (EFS);
(3) a self-compiled specific learning disability screening questionnaire;
(4) a self-compiled children’s mental development assessment questionnaire;
(5) a self-compiled questionnaire on the risk factors of pregnancy, lactation and early childhood
(1) The students in the bottom 25% of each grade were selected according to their most recent grade scores in Chinese and math;
(2) a score of the specific learning disability screening questionnaire ≥ 34
Total = 1179
boys = 642
girls = 537
1st = 382
3rd = 465
5th = 332
Total = 139
boys = 92
girls = 47
1st = 46
3rd = 55
5th = 38
Total = 11.79%
boys = 14.33%
girls = 8.75%
1st = 12.04%
5th = 11.45%
3rd = 11.83%
48Yoo et al., 2018South Korearandom samplingalphabetic scriptWestern PacificMIC(1) The dyslexia screening checklist (DySC);
(2) Korean–Wechsler intelligence scale for children—fourth edition (K-WISC-IV);
(3) the comprehensive learning test–reading (CLT-R);
(4) the comprehensive learning test–math (CLT-M);
(5) the comprehensive attention test (CAT)
Being in the bottom 15% on DySC and CLT-R, and having no intelligence or attention problemsTotal = 659
boys = 340
girls = 319
Total = 37
boys = 22
girls = 15
Total = 5.61%
boys = 6.473%
girls = 4.70%
49Zhou et al., 2018Chinarandom samplinglogographic scriptWestern PacificMIC(1) The dyslexia checklist for Chinese (DCCC);
(2) the second revision of the Chinese combined Raven’s test (CRT-C2);
(3) the pupil rating scale–revised screening for learning disabilities (PRS)
(1) The transformed T-scored of DCCC > 70;
(2) the Chinese score ranked in the bottom 10 of the class, with LD lasting more than one year, and it was difficult to complete the class and homework independently;
(3) a score of PRS > 65;
(4) IQ ≥ 80;
(5) no visual, auditory and other sensory disorders, no nervous system diseases
Total = 369
boys = 188
girls = 181
Total = 15
boys = 13
girls = 2
Total = 4.07%
boys = 6.9%
girls = 1.1%
50Barbiero et al., 2019Italyrandom samplingalphabetic scriptEuropeHIC(1) A questionnaire derived from the validated questionnaire “RSR-DSA”;
(2) a 4th-grade dictation task;
(3) the DDE-2 battery (battery for the assessment of developmental dyslexia and dysorthographia-2);
(4) the Wechsler intelligence scale for children (WISC-III);
(5) battery for the evaluation of developmental dyslexia and dysorthography-2 (DDE-2);
(6) the MT battery (prove di lettura MT per la scuola elementare-2);
(7) Raven’s progressive matrices (PM47);
(8) a strengths and difficulties questionnaire (SDQ)
(1) The total score was > 85% or the score on two subgroups of questions specifically addressing dyslexia > 90%;
(2) children scoring ≥ 90% in the dictation task;
(3) children failed in at least one of four scores in DDE-2;
(4) WISC-III weighted score > 7;
(5) Z-score ≤ −1.8 (speed) or percentile ≤ 5 (accuracy) in the DDE-2 non-word test
Total = 9964Total = 350Total = 3.51%
51Fan et al., 2019Chinarandom samplingChineseWestern PacificMICMultiple achievement tests (MATs)(1) The scores of the last three Chinese mid-term and final exams were lower than the grade average level and the math scores were normal;
(2) the evaluation results of Chinese teachers on students’ Chinese reading performance;
(3) no brain damage or intellectual, visual or hearing impairment;
(4) students scored 1.5 SD below the norm on standardized reading tests
Total = 834
boys = 444
girls = 390
Total = 62
4th = 35
5th = 27
Total = 7.43%
52Gu et al. 2019ChinaStratified cluster samplingChineseWestern PacificMIC(1) The dyslexia checklist for Chinese children (DCCC);
(2) the pupil rating scale–revised screening for learning disabilities (PRS);
(1) No brain diseases such as visual and hearing impairment, brain trauma, epilepsy, etc.;
(2) the Chinese score was in the last 10% of the class;
(3) one subscale or total score in the DCCC was 2 SD higher than that of children of the same age;
(4) the score of the PRS was < 65
Total = 11,668
boys = 6289
girls = 5369
2nd = 2916
3rd = 2743
4th = 2254
5th = 2537
6th = 1218
Total = 302
boys = 233
girls = 69
2nd = 79
3rd = 66
4th = 58
5th = 665
6th = 33
Total = 2.59%
boys = 3.7%
girls = 1.29%
2nd = 2.71%
3rd = 2.41%
4th = 2.57%
5th = 2.60%
6th = 2.71%
53Zhu et al., 2019ChinaStratified cluster samplingChineseWestern PacificMIC(1) The dyslexia checklist for Chinese children (DCCC);
(2) the pupil rating scale–revised screening for learning disabilities (PRS);
(1) No brain diseases such as visual and hearing impairment, brain trauma, epilepsy, etc.;
(2) the Chinese score was in the last 10% of the class;
(3) one subscale or total score in the DCCC was 2 SD higher than that of children of the same age;
(4) score of the PRS < 65
Total= 3673
boys= 2118
girls= 1555
3rd= 838
4th= 924
5th = 946
6th = 965
Total= 119
boys= 95
girls= 24
3rd= 13
4th= 29
5th = 36
6th = 41
Total= 3.24%
boys= 4.49%
girls= 1.54%
3rd= 1.55%
4th= 3.14%
5th= 3.81%
6th= 4.25%
54Cai et al., 2020ChinaStratified cluster samplingChineseWestern PacificMIC(1) The pupil rating scale–revised screening for learning disabilities (PRS);
(2) the Chinese character recognition measure and assessment scale for primary school children;
(3) a combined Raven’s test
(1) PRS score below 65;
(2) at least 1 SD below the average level of actual grade in Chinese character recognition;
(3) IQ > 80;
(4) according to the head-teachers’ reports, there was no suspected brain damage, uncorrected sensory impairment, or other external factors
Total = 1661
boys = 882
girls = 779
2nd = 452
3rd = 407
4th = 432
5th = 370
Total = 81
boys = 66
girls = 15
2nd = 28
3rd = 13
4th = 24
5th = 16
Total = 4.88%
boys = 7.48%
girls = 1.93%
2nd = 6.19%
3rd = 3.19%
4th = 5.56%
5th = 4.32%
55Su et al., 2020ChinaRandom samplingChineseWestern PacificMICRaven’s standard progressive matrices (SPM)(1) The Chinese score was at the bottom 10% of the class;
(2) an IQ score of above 25 percent on the SPM test;
(3) no hearing impairment, attention deficit, hyperactivity disorder, autism or mood disorders
Total = 624
3rd = 217
4th = 224
5th = 183
Total = 62
3rd = 22
4th = 22
5th = 18
Total= 9.94%
3rd = 10.14%
4th = 9.82%
5th = 9.84%
56YILIZHATI et al., 2020ChinaRandom samplingChineseWestern PacificMIC(1) One-minute word reading test;
(2) Raven’s intelligence test
(1) Students whose reading level was considered by the teacher to be at the bottom 25% of the class;
(2) the score of “one-minute word reading test” was 1 SD lower than the grade average;
(3) no obvious physical injury, behavioral and emotional disorders;
(4) an IQ score of above 25 percent on the SPM test
Total = 1233Total = 119Total = 9.65%
Figure A1. Leave-1-out forest.
Figure A1. Leave-1-out forest.
Brainsci 12 00240 g0a1
Figure A2. Funnel plot.
Figure A2. Funnel plot.
Brainsci 12 00240 g0a2
Figure A3. GOSH plot.
Figure A3. GOSH plot.
Brainsci 12 00240 g0a3

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Figure 1. PRISMA flow diagram of literature search and study selection.
Figure 1. PRISMA flow diagram of literature search and study selection.
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Figure 2. Forest plot for the prevalence of DD using random-effects meta-analysis.
Figure 2. Forest plot for the prevalence of DD using random-effects meta-analysis.
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Figure 3. Forest plot for the subgroup meta-analysis of the prevalence of DD.
Figure 3. Forest plot for the subgroup meta-analysis of the prevalence of DD.
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Table 1. Prevalence of DD using random-effects meta-analysis and subgroup meta-analysis.
Table 1. Prevalence of DD using random-effects meta-analysis and subgroup meta-analysis.
VariableNo. of
Studies
Prevalence
(95% CI)
I2, %p-Value
Q TestEgger TestBegg TestSubgroup
Difference
Global Analysis for DD
 DD567.10 [6.27; 7.97]97.60 <0.001<0.0010.05 NA
Gender
 boy419.22 [8.07; 10.44]95.80 <0.001<0.0010.35 <0.001
 girl414.66 [3.84; 5.54]95.20<0.001<0.0010.17
Writing system
 alphabetic scripts277.26 [5.94; 8.71]98.10<0.001<0.050.060.74
 logographic writing system316.97 [5.86; 8.16]96.90<0.001<0.0010.27
Orthography depth
 shallow orthography177.13 [5.23; 9.30]98.30<0.001<0.050.190.83
 deep orthography107.55 [4.66; 11.04]97.80<0.001<0.050.24
Operational definition
 1 SD117.10 [4.51; 10.22]98.40<0.001<0.010.14<0.01
 1.5 SD65.36 [4.28; 6.55]87.70<0.001NANA
 2 SD185.32 [4.56; 6.13]93.70<0.001<0.010.18
 Without reporting SD239.10 [7.18; 11.21]97.20<0.0010.030.58
Grade
 147.59 [2.65; 14.72]96.40<0.001NANA0.40
 274.88 [2.94; 7.28]92.00<0.001NANA
 3216.35 [4.78; 8.13]95.20<0.0010.060.15
 4185.25 [4.31; 6.27]85.00<0.0010.030.12
 5207.44 [4.59; 10.90]98.20<0.0010.470.01
 694.48 [2.96; 6.29]93.20<0.001NANA
Sample size <0.001
 <500107.97 [5.75; 10.51]84.00<0.0010.500.53
 500–1000168.43 [6.83; 10.18]90.90<0.0010.590.72
 1000–1500168.25 [6.43; 10.27]95.80<0.0010.150.22
 1500–300066.01 [3.84; 8.63]97.20<0.001NANA
 3000–10,00064.53 [2.81; 6.63]98.40<0.001NANA
 10,000–43.13 [2.32; 4.06]98.10<0.001NANA
Sampling method
 cluster sampling55.55 [3.13; 8.60]98.10<0.001NANA0.25
 random sampling377.66 [6.60; 8.80]97.20<0.001<0.0010.80
 stratified sampling166.43 [4.84; 8.21]97.80<0.001<0.050.05
Sub-deficits
 accuracy85.43 [3.91; 7.18]97.80<0.001NANA0.50
 accuracy or comprehension47.60 [5.46; 10.06]88.00<0.001NANA
 accuracy or fluency59.71 [4.29; 16.99]98.80<0.001NANA
 comprehension87.97 [4.60; 12.15]98.30<0.001NANA
 fluency66.64 [4.34; 9.37]92.40<0.001NANA
 Unclassified276.97 [5.77; 8.27]97.30<0.001<0.0010.44
WHO region
 Americas68.11 [4.97; 11.93]98.80<0.001NANA0.97
 Eastern Mediterranean46.88 [3.50; 11.27]95.90<0.001NANA
 Europe116.55 [4.49; 8.97]98.20<0.001<0.050.31
 South-East Asia and Africa47.11 [3.04; 12.66]97.50<0.001NANA
 Western Pacific337.16 [6.01; 8.41]97.30<0.001<0.0010.44
WB region
 HIC187.09 [5.54; 8.82]98.40<0.001<0.010.430.97
 MIC407.11 [6.08; 8.20]97.00<0.001<0.0010.07
Abbreviations: WHO, World Health Organization; WB, World Bank; HIC, high-income countries; MIC, middle-income countries; NA, not applicable.
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Yang, L.; Li, C.; Li, X.; Zhai, M.; An, Q.; Zhang, Y.; Zhao, J.; Weng, X. Prevalence of Developmental Dyslexia in Primary School Children: A Systematic Review and Meta-Analysis. Brain Sci. 2022, 12, 240. https://doi.org/10.3390/brainsci12020240

AMA Style

Yang L, Li C, Li X, Zhai M, An Q, Zhang Y, Zhao J, Weng X. Prevalence of Developmental Dyslexia in Primary School Children: A Systematic Review and Meta-Analysis. Brain Sciences. 2022; 12(2):240. https://doi.org/10.3390/brainsci12020240

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Yang, Liping, Chunbo Li, Xiumei Li, Manman Zhai, Qingqing An, You Zhang, Jing Zhao, and Xuchu Weng. 2022. "Prevalence of Developmental Dyslexia in Primary School Children: A Systematic Review and Meta-Analysis" Brain Sciences 12, no. 2: 240. https://doi.org/10.3390/brainsci12020240

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