Estimates of the Prevalence of Autism Spectrum Disorder in the Middle East and North Africa Region: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract Background and Objective Estimates of the prevalence of the autism spectrum disorder (ASD) in the Middle East and North Africa (MENA) region are not readily available, amid a lack of recent evidence. In this study, we estimated the prevalence of ASD in the MENA region by synthesising evidence from published studies in the region. Methods In this systematic review and meta-analysis, we searched PubMed, EMBASE, Scopus, and CINAHL databases for studies which assessed ASD prevalence in the MENA region. Risk of bias was assessed using the Newcastle Ottawa scale. A bias-adjusted inverse variance heterogeneity meta-analysis model was used to pool prevalence estimates from included studies. Cochran’s Q statistic and the I 2 statistic were used to assess heterogeneity, and publication bias assessed using funnel and Doi plots. Results A total of 3075 studies were identified, 16 studies of which met the inclusion criteria and involved 3,727,731 individuals. The studies were published during the period 2007-2022, and included individuals from Iran, Oman, Libya, Egypt, Kingdom of Saudi Arabia (KSA), Lebanon, United Arab Emirates (UAE), Bahrain and Qatar. Estimates of ASD prevalence ranged from 0.01% in Oman during the period June 2009-December 2009, to a high of 2.51% in the Kingdom of Saudi Arabia during the period December 2017-March 2018. The pooled prevalence of ASD was 0.13% (95% CI: 0.01% – 0.33%), with significant heterogeneity (I 2 = 99.8%). For Iran, the only country with multiple analysable studies, an overall prevalence of 0.06% (95% CI: 0.00 – 0.19, I2=97.5%, n= 6 studies) was found. A review of data from countries with repeated studies suggested that the prevalence of ASD is increasing. Conclusion Estimates of the prevalence of ASD vary widely across the MENA region, from 0.01% in Oman to 2.51% in Saudi, with an overall prevalence of 0.13%. Existing data suggests a trend towards increasing prevalence in the region. More and better-quality research is needed to provide up to date ASD prevalence estimates. Registration The protocol for this systematic review and meta-analysis was registered on the International Prospective Register of Systematic Reviews (PROSPERO) with registration ID CRD42024499837.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.069 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.040 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".