P120 Climate change: impact on cutaneous carcinogenesis and dermatological practice
Bibliographic record
Abstract
Abstract Cases of skin cancer in the UK are projected to increase by 50% over the next 20 years, with stratospheric ozone depletion secondary to climate change a major contributory factor. ‘Our World in Data’ published data on skin cancer incidence in the USA and Northwest Europe comparing scenarios with no restriction on ozone-depleting substances (NROS) vs. the Montreal Protocol (MP) and the Copenhagen Amendment. By the year 2100, skin cancer is predicted to rise in the USA from 36 to 6530 cases per million with NROS (relative change +18 000%), and from 36 to 1958 cases per million with MP (relative change +5300%). In Northwest Europe it is projected to rise from 17 to 3468 cases per million with NROS (relative change +20 300%) and from 17 to 1051 cases per million with MP (relative change +6100%). A systematic literature review conducted in January 2025 across MEDLINE, Embase and the Cochrane Library yielded 11 relevant papers (10 reviews and one observational study) from 30 initial results. The review identified multiple factors linking climate change to increased skin cancer incidence, including stratospheric ozone depletion, increased air pollution and global warming. Despite MP regulation, current ozone levels remain 3% lower in the northern-mid latitudes and 5% lower in the southern-mid latitudes compared with historical levels. A new Arctic ozone hole has increased ultraviolet radiation (UVR) penetration by 60% above long-term averages. Epidemiological research indicates that for every 1% decrease in ozone concentration, melanoma incidence could increase by 1–2%, while nonmelanoma skin cancers might increase by 2.7–4.6%. Global warming affects human behaviour, leading to increased outdoor activity and UVR exposure. Air pollutants mirror UVR’s effects, causing keratinocyte proliferation and differentiation, melanogenesis and skin barrier dysfunction, elevating risks of skin cancer and inflammatory skin disease. Vulnerable populations include those in underprivileged areas, immunosuppressed individuals or transplant recipients, people in UVR-dense areas, light-skinned people and outdoor labourers. They require targeted interventions and heightened awareness of the risk of skin cancer. Current projections rely on computer-based models and animal studies, indicating a need for further original research. However, the evidence strongly links climate change to increased cutaneous carcinogenesis. Further outcomes depend heavily on compliance with the MP and Copenhagen Amendment. Education around climate change and skin health is paramount. Some studies have suggested a role for artificial intelligence to analyse environmental data and determine risk for patients. Dermatologists must recognize climate change’s profound implications for skin cancer burden. Focusing on preventative efforts in high-risk groups and supporting global climate mitigation strategies are crucial in addressing this escalating health challenge.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.002 |
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".