Global, Regional, and National Burden of Atopic Dermatitis: Insights From the Global Burden of Disease Study 2021
Bibliographic record
Abstract
Abstract: Background: The 2017 Global Burden of Disease (GBD) report identified atopic dermatitis (AD) as the skin disease with the highest disease burden, assessed in terms of disability-adjusted life-years (DALYs), and a modest increase in the number of AD-associated DALYs was observed in 2019 compared to 2017. Objective: This study aims to provide updated insights from the GBD 2021 database. Methods: Data on the prevalence and DALYs of AD in 2021 were extracted from the GBD database. Statistical analyses were performed by R software (version 4.4.1). Results: In 2021, AD still represented the highest age-standardized DALY rate (ASDR) among all skin disorders. Globally, the age-standardized prevalence rate (ASPR) and ASDR of AD were 1728.5 (95% UI: 1658.5–1798.6) and 75.5 (95% UI: 38.8–125.6) per 100,000 population, respectively. The ASPR and ASDR were highest in high-income Asia Pacific but lowest in Central Sub-Saharan Africa. The burden of AD was mainly in children, declining with age; however, a modest rise in ASPR and ASDR was noted among the elderly. Women generally experienced a higher burden than men. Conclusions: In 2021, AD remained the leading cause of DALYs among skin diseases, disproportionately affecting children and older adults, especially in urban areas.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".