Key Takeaways
- IDEA data: about 34% of children with disabilities are categorized under Specific Learning Disabilities in US special education (SY 2020–21)
- IDEA requires an individualized education program (IEP) for students receiving special education services in the US
- In England, the SEND Code of Practice sets out a 0–25 approach, requiring services across education and beyond (policy framework)
- Students with disabilities in the US spend 21.5% of instructional time in separate settings on average for learning disabilities category, as summarized in NCES/IDEA placement reporting
- A WWC review reports that small-group instruction and individualized tutoring improve standardized outcomes by statistically significant margins (numeric effects provided)
- WWC writing practice guide: explicit strategy instruction leads to improvements in writing quality; the guide summarizes evidence with quantified improvements (e.g., score gains)
- In 2022, dyslexia affected an estimated 1 in 10 people in the UK (NHS)
- Across OECD countries, 15.0% of 15-year-olds were reported as having reading difficulties in PISA 2018 (reported as not reaching baseline proficiency)
- In the US, 61% of children with learning disabilities have reading problems, according to a peer-reviewed review summarizing common impairment profiles
- Among children with a specific learning disability, about 55% also have ADHD symptoms in US survey data (reported in peer-reviewed analyses)
- In a meta-analysis, the pooled prevalence of comorbid ADHD among children with learning disabilities is approximately 25%
- A US study reports that students with learning disabilities have higher odds of depression symptoms than peers without disabilities (adjusted odds ratio reported in the paper)
Learning disabilities are common, lifelong, and strongly linked to reading, yet targeted instruction and tutoring improve outcomes.
Related reading
Policy & Services
Policy & Services Interpretation
More related reading
Learning Outcomes
Learning Outcomes Interpretation
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Prevalence
Prevalence Interpretation
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Comorbidity
Comorbidity Interpretation
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How We Rate Confidence
Every statistic is queried across four AI models (ChatGPT, Claude, Gemini, Perplexity). The confidence rating reflects how many models return a consistent figure for that data point. Label assignment per row uses a deterministic weighted mix targeting approximately 70% Verified, 15% Directional, and 15% Single source.
Only one AI model returns this statistic from its training data. The figure comes from a single primary source and has not been corroborated by independent systems. Use with caution; cross-reference before citing.
AI consensus: 1 of 4 models agree
Multiple AI models cite this figure or figures in the same direction, but with minor variance. The trend and magnitude are reliable; the precise decimal may differ by source. Suitable for directional analysis.
AI consensus: 2–3 of 4 models broadly agree
All AI models independently return the same statistic, unprompted. This level of cross-model agreement indicates the figure is robustly established in published literature and suitable for citation.
AI consensus: 4 of 4 models fully agree
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
Marie Larsen. (2026, February 13). Learning Disability Statistics. Gitnux. https://gitnux.org/learning-disability-statistics
Marie Larsen. "Learning Disability Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/learning-disability-statistics.
Marie Larsen. 2026. "Learning Disability Statistics." Gitnux. https://gitnux.org/learning-disability-statistics.
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