Analysis of Geographic and Environmental Factors and Their Association with Cutaneous Melanoma Incidence in Canada
Bibliographic record
Abstract
BACKGROUND: Over 90% of skin cancers including cutaneous melanoma (CM) are related directly to sun exposure. Despite extensive knowledge on ultraviolet radiation's (UVR) detrimental impact, many still fail to implement sun protection/sun avoidance. Human behavior, attitudes, and cultural norms of individuals and communities heavily depend on the surrounding climate/environment. In many instances, the climate shapes the culture/norms of the society. Canada has vast geographic/environmental differences. METHODS: In the current ecological study, we sought to examine the relationship between various geographic and environmental factors and the distribution of CM incidence by Forward Sortation Area (FSA) postal code across Canada. CM incidence data were extracted from the Canadian Cancer Registry, while environmental data were extracted from the Canadian Urban Environmental Health Research Consortium (greenspace, as measured by the normalized difference vegetation index; annual highest temperature; absolute number and average length of yearly heat events; annual total precipitation [rain and snow]; absolute number and average length of events with precipitation [rain and snow]; and summer UVR index). The above geographic/environmental data by FSA were correlated with the respective CM incidence employing negative binomial regression model. RESULTS: Our analysis highlights that increases in annual average temperature, summer UVR, and greenspace were associated with higher expected incidence of CM cases, while higher number of annual heat events together with highest annual temperature and higher average number of annual rain events were associated with a decrease in CM incidence rate. This study also highlights regional variation in environmental CM risk factors in Canada. CONCLUSIONS: This national population-based study presents clinically relevant conclusions on weather/geographic variations associated with CM incidence in Canada and will help refine targeted CM prevention campaigns by understanding unique weather/geographic variations in high-risk regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".