Global, Regional, and National Burden of Dermatitis from 1990 to 2021, and Forecasts to 2050: A Systematic Analysis of the Global Burden of Disease Study 2021
Bibliographic record
Abstract
Abstract: Background: Dermatitis significantly impacts global health, affecting both physical discomfort and mental health. However, its full scope remains under-appreciated, necessitating further analysis to inform effective public health strategies. Objective: This research aims to provide an updated assessment of the global, regional, and national burden of dermatitis using data from Global Burden of Disease 2021 database. Methods: We analyzed incidence cases, age-standardized incidence rates (ASIR), disability-adjusted life years (DALYs), and age-standardized DALYs rates (ASDR) by region, sex, age, and disease type. Temporal trends were examined using percentage change and estimated annual percentage change. Results: In 2021, dermatitis caused 405 million incidence cases and 8.2 million DALYs, reflecting a 63% and 32% increase from 1990, respectively. ASIR and ASDR were stable overall but varied significantly by demographic factors. Females exhibited higher ASIR and ASDR than males. A negative correlation was found between socio-demographic index (SDI) and ASIR (r = −0.62, P < 0.001), while ASDR correlated positively with SDI (r = 0.69, P < 0.001). Conclusions: Dermatitis burden continues to rise, driven primarily by population growth. Significant disparities persist across regions, ages, and sexes and targeted interventions are urgently needed.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.008 |
| 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) | 0.001 | 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".