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Record W4412755233 · doi:10.1111/jdv.20883

Analysis of global skin cancer epidemiology in 2022 and correlation with dermatologist density

2025· letter· en· W4412755233 on OpenAlexaffabout
Samir Salah, Delphine Kérob, Khaled Ezzedine, Puneet Khurana, Deepthi Balan, Thierry Passeron

Bibliographic record

VenueJournal of the European Academy of Dermatology and Venereology · 2025
Typeletter
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsFuture Earth
Fundersnot available
KeywordsMedicineDermatologyEpidemiologySkin cancerCancerPathologyInternal medicine

Abstract

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Skin cancer represents a significant health burden, with rising incidence worldwide.1 We analysed melanoma and non-melanoma skin cancer rates across 185 countries, examining the relationship with dermatologist density to understand healthcare system effectiveness. The International Agency for Research on Cancer (IARC) GLOBOCAN 2022 estimates provided data for melanoma (ICD-10 code C43) and non-melanoma skin cancer (NMSC, ICD-10 code C44), with NMSC incidence excluding basal cell carcinoma while mortality includes it.2 CDC data contributed melanoma incidence by ethnicity in the United States.3 Age-standardized rates per 100,000 person-years were calculated using the World standard population, accounting for varying population age structures.4 Dermatologist density data was sourced from an unpublished L'Oreal survey covering 31 countries, with density calculated per 100,000 population.5 This survey compiled information from official registries, publications and professional associations.5 In 2022, there were 331,722 estimated melanoma cases (58,667 deaths) and 1.2 million NMSC cases (69,416 deaths) globally. The highest age-standardized incidence rates (ASR) for melanoma were found in Oceania (29.78/100,000), North America (16.3) and Europe (10.43). Notably, mortality-to-incidence ratios were highest in Africa (0.35) and Asia (0.30), compared to North America and Oceania (both 0.02), suggesting potentially worse prognosis. NMSC, while generally less lethal, resulted in more estimated deaths globally than melanoma (69,416 vs. 58,667) due to its much higher incidence, highlighting the importance of including NMSC in skin cancer prevention strategies (Table 1). Certain populations demonstrate increased susceptibility to skin cancers due to physiological conditions, genetic predispositions and lifestyle choices, which partly explain regional disparities.6 Gender analysis revealed equal melanoma incidence between males and females until age 54, after which male incidence rates continuously increased relative to females, reaching a male-to-female incidence rate ratio of 2.14 by age 85. Ethnicity data from the United States showed that non-Hispanic whites had an ASR of 30.2, which was 34 times higher than non-Hispanic blacks, 25 times higher than non-Hispanic Asian or Pacific Islanders and 6 times higher than Hispanic. These disparities suggest the need for targeted screening and prevention strategies, particularly in resource-limited settings. Dermatologist density varied from 1.05 to 6.08/100,000 between African and European countries. We examined whether high-incidence countries had higher dermatologist density and whether this density was associated with mortality-to-incidence ratios. High incidence is not correlated with high density and countries like Australia, the United Kingdom and Canada achieved low mortality-to-incidence ratios despite low dermatologist densities (Figure 1). This suggests successful outcomes depend on factors beyond specialist availability, including public health initiatives, efficient referral systems and integrated pathways.7, 8 Some countries with higher dermatologist densities showed less favourable outcomes. Brazil exhibited high dermatologist density despite low melanoma incidence while maintaining high mortality rates. The IARC estimates precision varies across regions for melanoma and NMSC. Countries with high-quality registries generally provide accurate melanoma data, but NMSC is often underreported even in these nations. In many low- and middle-income countries, estimates for both cancers rely on neighbouring countries' data or mortality-to-incidence ratios, affecting accuracy.9 NMSC estimation faces additional challenges due to issues in histological confirmation, multiple NMSCs per patient and outpatient management, leading to significant global underestimation.10 Regarding dermatologist density, the absence of official registries in many countries introduces a layer of uncertainty and dermatologists face competing demands from various skin conditions (such as tropical diseases in low- and middle-income countries). While specialist access is important, successful outcomes depend on healthcare systems that integrate multiple care levels, public health initiatives and efficient resource use, with solutions adapted to local contexts. Future efforts should focus on system-wide strategies involving GPs, specialized nurses and AI-powered apps for patient triage/education. Study limitations involve key unmeasured confounders like skin colour population proportion, income among others. The study was sponsored by La Roche-Posay laboratoire dermatologique. Dr Kerob and Mr Salah are employees of La Roche-Posay. Pr Ezzedine has served as a consultant for La Roche-Posay, L'Oréal, Incyte, MSD, BMS, Abbvie, Pfizer and Pierre Fabre. Pr Passeron has served as a consultant for La Roche-Posay, L'Oréal, SVR, Symrise, Isis Pharma, Bioderma, Beiersdorf, ISDIN, Pierre Fabre and Hyphen. Dr Khurana and Mr Balan are employees of FutureBridge and have served as consultants for La Roche-Posay. Not applicable. This study analysed publicly available aggregated data without direct human subject involvement. Not applicable. The data that support the findings of this study are openly available in https://gco.iarc.fr/today/en.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.521
Threshold uncertainty score0.853

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.315
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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