The Global Burden of Atopic Dermatitis in Elderly Populations: Trends, Disparities, and Future Projections
Bibliographic record
Abstract
Background: Atopic dermatitis (AD) is a common chronic inflammatory skin disease often affecting infants. However, its significance in adult populations is increasingly recognized. Notably, its prevalence and impact among elderly individuals remain poorly understood, highlighting the need for a deeper understanding of its global burden. This study aims to evaluate the prevalence, incidence, and Disability-Adjusted Life Years (DALYs) of AD in individuals aged 60 and older from 1990 to 2021, with projections to 2045. Methods: Data from the Global Burden of Disease (GBD) Study were used to analyze trends in the global burden of AD by region and sex. Key metrics were calculated using annual average percentage changes (AAPC). Based on historical trends, projections for 2022–2045 were developed. Results: In 2021, the prevalence of AD in the elderly exhibited substantial regional variation, with the highest rates observed in Northern Europe and North America. Although global prevalence slightly declined from 1990 to 2021, females consistently demonstrated a higher burden than males. Projections indicate a substantial increase in AD cases by 2045, particularly among elderly females, with the 60–64 age group expected to exceed 4 million cases. The disease burden correlated with Universal Health Coverage (UHC) indices, suggesting healthcare access impacts disease reporting and management. Conclusions: The increasing burden of AD, especially in elderly females, highlights the urgent need for targeted healthcare strategies to manage AD in aging populations. Further research is required to address regional and gender disparities in AD care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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 | 0.000 |
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".