Analysis of global skin cancer epidemiology in 2022 and correlation with dermatologist density
Notice bibliographique
Résumé
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standard population, accounting for varying population age structures.4 Dermatologist density data was sourced from an unpublished L'Oreal survey covering 31 countries, with density calculated per 100,000 population.5 This survey compiled information from official registries, publications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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».