Global Skin Cancer Burden From 1990 to 2023 and Projection to 2050 - Scorecard - MDSpire

Global Skin Cancer Burden From 1990 to 2023 and Projection to 2050

  • By

  • Youyou Zhou

  • Weiming Zhong

  • Xulei Liu

  • Jianglin Zhang

  • July 1, 2026

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Clinical Scorecard: Assessment of Global Skin Cancer Impact from 1990 to 2023 with Future Projections for 2050

At a Glance

CategoryDetail
ConditionMalignant Skin Cancers
Key MechanismsEpidemiology, prevalence, disability-adjusted life-years (DALYs), and projections using Bayesian models.
Target PopulationGlobal population with a focus on varying Sociodemographic Index (SDI) levels.
Care SettingGlobal health burden assessment and epidemiological analysis.

Key Highlights

  • Melanoma prevalence highest in Oceania, with significant increases in low- and middle-SDI regions.
  • Squamous cell carcinoma DALYs increased notably in low-SDI settings.
  • Basal cell carcinoma remains stable but shows increases in specific regions like East Asia.
  • Male prevalence rates higher across all skin cancers, with notable declines in melanoma prevalence from 2010 to 2023.
  • Projections indicate significant increases in DALYs for all three skin cancers by 2050.

Guideline-Based Recommendations

Diagnosis

  • Utilize epidemiological data to inform screening and diagnostic practices.

Management

  • Focus on enhancing health care access and screening in low- and middle-SDI regions.

Monitoring & Follow-up

  • Implement routine skin examinations and public awareness campaigns in high-SDI regions.

Risks

  • Underdiagnosis in low- and middle-SDI settings due to limited health care resources.

Patient & Prescribing Data

Individuals at risk for malignant skin cancers, particularly in low- and middle-SDI regions.

Increased burden in low- and middle-SDI regions necessitates improved health care infrastructure.

Clinical Best Practices

  • Encourage routine skin examinations in populations at higher risk.
  • Enhance public health policies to improve access to dermatological care.
  • Utilize data-driven approaches to tailor interventions based on regional epidemiology.

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