Global burden of bacterial skin diseases from 1990 to 2045: an analysis based on global burden disease data
Bibliographic record
Abstract
Abstract Background The global burden of bacterial skin diseases has not been well evaluated.Objective We aimed to describe the burden and trend of bacterial skin diseases, to explore potential associated factors, and to predict the burden up to 2045.Methods Data on incidence and disability-adjusted life years (DALYs) of bacterial skin diseases were obtained from Global Burden of Disease 2021. We used average annual percent change (AAPC) by Joinpoint Regression to quantify the temporal trends. We conducted decomposition analysis to understand the contribution of aging, epidemiological changes, and population growth. Bayesian Age-Period-Cohort model was used to predict burden up to 2045.Results Global incidence rate of bacterial skin diseases increased from 8,988.74 per 100,000 in 1990 to 10,823.88 per 100,000, with AAPC of 0.62% (0.61 ~ 0.63%). The highest incidence rate was in low Socio-demographic Index (SDI) region and population aged < 35. The DALY rate increased from 20.82 per 100,000 in 1990 to 25.45 per 100,000 in 2021, with AAPC of -0.11% (-0.34 ~ 0.13%). The highest increase of DALY was in high SDI region and population aged > 85. The major drivers of incident case rise were population growth, followed by epidemiological changes; the major drivers of DALY case rise were population growth, followed by aging. Increasing trends were seen in prediction of incidence rate, incident cases and DALY cases; decreasing trend of DALY rate prediction was seen.Conclusion The incidence of bacterial skin diseases increased and varied considerably. The targeted prevention and treatment are needed to reduce burden of bacterial skin disease.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".