Prevalence and Incidence of Migraine in Pediatric and Adolescent Populations: Insights from a Targeted Literature Review
Bibliographic record
Abstract
Migraine affects 7.7% to 9.1% of children globally and impairs quality of life. However, pediatric migraine remains underdiagnosed and undertreated, with few approved preventive treatments. Understanding the epidemiological burden is essential to support regulatory expansion and market access of new preventive therapies. This study aimed to synthesize the evidence on migraine prevalence and incidence in children and adolescents aged 6-17 years in the 30 regions including the United States (US), Canada, Brazil, and the 27 European Union (EU) member states. A targeted literature review (TLR) was conducted using PubMed to identify relevant studies. Data were extracted on country, age group, migraine definition, prevalence and incidence. Supplementary data were obtained from Vizhub, an online tool that models disease burden using data from the Global Burden of Disease Study 2021. A gap analysis was conducted to identify evidence limitations. Of the 30 migraine studies included in the TLR, 28 reported on prevalence and 2 on incidence. Data were available for 13 of the 30 regions including 10 of 27 EU countries. Prevalence ranged from 0.15%-21.4% in the EU, 4.7%-32.2% in Brazil, 0.7%-9.4% in Canada, and 6.1%-12.3% in the US. Vizhub reported prevalence for ages 5-19: 7.1%-15.5% across the EU, 11% in Canada, 11.2% in the US, and 16.7% in Brazil. Incidence from the TLR was available only for Finland and the US. Evidence gaps included few studies, lack of studies matching the age range of interest, missing methodological details and variability in diagnostic criteria. These gaps highlight the need for well-designed, region-specific epidemiological studies on pediatric populations aged 6-17, with age stratification, and complete reporting to inform regulatory and reimbursement strategies for preventive therapies.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.028 | 0.023 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".