Canadian Burden of Skin Disease From 1990 to 2017: Results From the Global Burden of Disease 2017 Study
Bibliographic record
Abstract
BACKGROUND: Skin diseases can have high morbidity that can be costly to society and individuals. To date, there has been no comprehensive assessment of the burden of skin disease in Canada. OBJECTIVES: To evaluate the burden of 18 skin and subcutaneous diseases from 1990 to 2017 in Canada using the Global Burden of Disease (GBD) data. METHODS: The 2017 GBD study measures health loss from 359 diseases and injuries in 195 countries; we evaluated trends in population health in Canada from 1990 to 2017 using incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life years (DALYs). Data are presented as rates (per 100 000), counts, or percent change with the uncertainty interval in brackets. RESULTS: From 1990 to 2017 for all skin diseases, DALY rates increased by 8% to 971 per 100 000 (674-1319), YLD rates increased by 8% to 897 per 100 000 (616-1235), YLL rates increased by 4% to 74 per 100 000 (53-89), and death rates increased by 18% to 5 per 100 000 (3-6). DALY rates for melanoma increased by 2% to 54 per 100 000 (39-68), for keratinocyte carcinoma by 14% to 17 per 100 000 (16-19), and for skin and subcutaneous disease by 8% to 900 per 100 000 (619-1233). The observed over expected ratios were higher for skin and subcutaneous disease (1.37) and keratinocyte carcinoma (1.17) and were lower for melanoma (0.73). CONCLUSIONS: The burden of skin disease has increased in Canada since 1990. These results can be used to guide health policy regarding skin disease in Canada.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".