Prevalence of Atopic Dermatitis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Abstract: Background: Atopic dermatitis (AD) negatively affects quality of life and places a substantial financial burden on health care systems due to treatment costs and increased demand for services. Objective: To estimate the worldwide prevalence of AD, the proportion of severe cases worldwide and explore sources of heterogeneity. Methods: We searched MEDLINE, Embase, and Global Index Medicus from January 2012 up until August 30, 2022. We included primary prevalence studies published from 2012 onward. Study selection was conducted by two reviewers independently. One reviewer performed data extraction and assessed risk of bias using the JBI Critical Appraisal Checklist for Prevalence Studies, with independent checking by a second reviewer. Random-effects meta-analyses were conducted to pool results; subgroup analyses were conducted to evaluate potential modifiers. Certainty of evidence was rated using the Grading of Recommendations Assessment, Development, and Evaluation approach. Main outcomes were point prevalence and proportion of severe cases. Results: We identified 12,774 unique references and assessed 1029 full texts, ultimately resulting in the inclusion of 310 studies with 25.5 million individuals. Point prevalence was 11.1% (95% CI 9.4–13.1; 123 studies; 12,776,910 individuals; moderate certainty of evidence) in children and adolescents, and 6.3% (95% CI 5.0–7.8; 59 studies; 12,794,260 individuals; moderate certainty of evidence) in adults. Relatively similar results were observed for studies with low risk of bias. Proportion of severe cases varied from 1.9 to 7.2% in children and adolescents and 2.8% to 15.6% in adults. Conclusions: These findings may underpin effective health care policies, research initiatives, and clinical decision-making.
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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 | 0.023 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.046 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".