Incidence of cutaneous malignant melanoma by socioeconomic status in Canada: 1992–2006
Bibliographic record
Abstract
BACKGROUND: There are no nationwide studies documenting changes in cutaneous malignant melanoma incidence or association of incidence with socioeconomic status (SES) in Canada. We sought to determine whether melanoma incidence increased from 1992 to 2006 and if there was an association between SES and melanoma incidence. Additionally, we studied whether there was a correlation between province of residence and melanoma incidence. METHODS: Cases from the Canadian Cancer Registry were reviewed. Demographic and socioeconomic information were extracted from the Canadian Census of Population data. Cases were linked to income quintiles by postal code. A negative binomial regression was performed to identify relationships among these variables. RESULTS: Overall incidence of melanoma in Canada increased by 67 % from 1992 to 2006 (p < 0.0001). The increase in incidence was greater for melanoma in situ compared with invasive melanoma (136 % versus 52 % [p < 0.0001]). Incidence was positively correlated with higher income quintiles; the incidence rates among patients in the lowest income quintiles were 67 % of that for the highest income quintiles (p < 0.0001). DISCUSSION: A wide variety of explanations have been postulated for an increased incidence in melanoma among persons of higher SES, including access to and awareness of screening, more access to vacations in sunny climates, and increased leisure time. Variations in incidence of melanoma by urban vs. rural location and province may indicate differences in access to dermatologists across Canada. CONCLUSIONS: Melanoma incidence is increasing in Canada and is higher among people in high SES groups. This rise is likely due to a combination of factors including a true rise in incidence due to increases in sun exposure, and also an increased detection rate, particularly in those who are more aware of the disease and have access to resources for detection.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".