Incidence and mortality rates of keratinocyte carcinoma from 1998–2017: a population-based study of sex differences in Ontario, Canada
Bibliographic record
Abstract
Background: Keratinocyte carcinoma is the most common malignant disease, but it is not captured in major registries. We aimed to describe differences by sex in the incidence and mortality rates of keratinocyte carcinoma in Ontario, Canada. Methods: We conducted a population-based retrospective study of adults residing in Ontario between Jan. 1, 1998, and Dec. 31, 2017, using linked health administrative databases. We identified the first diagnosis of keratinocyte carcinoma using a validated algorithm of health insurance claims, and deaths related to keratinocyte carcinoma from death certificates. We calculated the incidence and mortality rates of keratinocyte carcinoma, stratified by sex, age and income quintile. We evaluated trends using the average annual percentage change (AAPC) based on joinpoint regression. Results: After decreasing from 1998 to 2003, the incidence rate of keratinocyte carcinoma increased by 30% to 369 per 100 000 males and 345 per 100 000 females in 2017 (AAPC 1.9%, 95% confidence interval [CI] 1.7 to 2.1 from 2003 to 2017). The incidence rate was higher in females younger than 55 years, but higher in males aged 55 years or older. Between 2008 and 2017, the incidence rate rose faster in females than males aged 45–54 years (AAPC 1.2% v. 0.5%, p = 0.01) and 55–64 years (1.2% v. 0.1%, p < 0.01). The incidence was higher in males than females in the higher income quintiles. Between 1998 and 2017, the mortality rate of keratinocyte carcinoma was 1.8 times higher in males than females, on average, and rose 4.8-fold overall (AAPC 8.9%, 95% CI 6.4 to 11.4 in males; 8.0%, 95% CI 5.3–10.8 in females). Interpretation: The population burden of keratinocyte carcinoma is growing, and the incidence and mortality rates rose disproportionately among certain sex- and age-specific groups. This warrants further investigation into causal factors and renewed preventive public health measures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".