Global burden of cutaneous melanoma incidence attributable to ultraviolet radiation in 2022
Bibliographic record
Abstract
Cutaneous melanoma (CM) accounted for around 331,700 cancer cases globally in 2022. Ultraviolet radiation (UVR) is a major CM risk factor. In this study, we update and improve global estimates of UVR-attributable CM cases. Population attributable fractions (PAFs) were calculated by age, sex, and country using GLOBOCAN 2022 national incidence estimates comparing to a minimally exposed Nordic 1930 birth cohort reference population. Adjustments for acral lentiginous melanoma were made to exclude non-UVR-associated melanomas. In sensitivity analyses, PAFs were recalculated with a theoretical minimally exposed 1903 South Thames, England birth cohort and world region-specific reference populations. An estimated 267,353 (95% uncertainty intervals [UI]: 242,818, 278,638) CM cases were UVR attributable globally in 2022. Males contributed to a larger proportion (57%, 151,921 out of 267,353) of UVR-attributable CMs. We found significant regional variation with the highest PAF observed in Australia/ New Zealand, Northern Europe, and North America, all with more than 95% CM cases UVR-attributable. Attributable age-standardized rates were highest in regions with populations of lighter skin color such as Australia/New Zealand, Northern Europe, and North America, with 75.68 (95%UI: 74.50, 76.86), 36.82 (95%UI: 36.38, 37.26) and 33.69 (95%UI: 33.47, 33.91) attributable cases per 100,000 people. By age group, the burden increased with age, with PAF of 76.39% (95%UI: 66.24, 81.01) among people aged 30-49 versus 86.13% (95%UI: 80.04, 88.99) among 70+ years. Most of the global CM burden in 2022 was UVR-attributable. Primary prevention through increasing sun safety awareness and affordable sun protection provision options is key to reducing CM.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".