Global Burden and Drivers of Cutaneous Malignant Melanoma Among Women Aged 55 and Older

Abstract

Background

This study assessed the global burden of cutaneous malignant melanoma (CMM) among females aged ≥55 years from 1990 to 2021—a population that has been historically aggregated into broader age categories in previous GBD analyses, despite representing a high-risk group with distinct epidemiological patterns and clinical outcomes. By combining age-specific trend analysis with formal driver decomposition, this study provides novel insights into sex-specific and age-specific burden patterns that have been previously obscured.

Objective

To quantify temporal trends and geographic disparities in incidence and disability, and to identify key contributors to changes in CMM burden in older women through formal decomposition analysis.

Methods

Utilizing data from the Global Burden of Disease Study 2021, we analyzed age-standardized incidence and disability-adjusted life year (DALY) rates, estimated annual percentage changes (EAPCs), and performed decomposition analysis to quantify contributions from population growth, aging, and epidemiological change.

Results

Globally, age-standardized incidence rates increased (EAPC=0.90), while DALY rates declined (EAPC=−0.42). The highest EAPC for incidence rate was observed in Eastern Europe (EAPC=3.33) and at the national level in Côte d’Ivoire (EAPC=5.58). High-middle SDI regions recorded the most rapid rise in incidence numbers (EAPC=4.18). Decomposition analysis revealed population growth as the primary driver of absolute burden increases, contributing 75.89% to the rise in incident cases and 118.77% to the increase in DALYs, though epidemiological change substantially contributed to incidence growth in regions like Central Europe (62.09%).

Conclusion

The CMM burden in older women continues to rise, driven by demographic forces and increasing disease risk, with significant geographic heterogeneity. Targeted prevention and healthcare strategies are urgently needed, especially in regions experiencing rapid epidemiological transition.

Introduction

Cutaneous malignant melanoma (CMM) is among the most aggressive forms of skin cancer and represents an escalating global public health concern due to its steadily increasing disease burden worldwide.¹ CMM is the 12th most common cancer worldwide; globally, the annual number of incident cases has shown a sustained upward trend, with an average annual increase of 2.6%. However, substantial geographic disparities exist across continents: incidence rates are below 0.5 per 100,000 population in Asia, 13.2 per 100,000 in Europe, 21.6 per 100,000 in the United States, and 48.0 per 100,000 in Australia.² Robust epidemiological evidence consistently demonstrates a strong age-related gradient in CMM incidence, with rates rising markedly with advancing age. A pronounced clustering of cases is observed among individuals aged 55 years and older.^3,4 This age group constitutes a critical inflection point characterized by rapidly increasing incidence and the convergence of multiple long-term risk factors, including cumulative ultraviolet (UV) exposure, declining cellular DNA repair capacity, and immunosenescence. Moreover, CMM diagnosed in this population is more frequently associated with advanced disease stage at presentation and poorer clinical outcomes, leading to a disproportionate contribution to melanoma-related mortality and disability.⁵ Consequently, focusing on individuals aged 55 years and older provides a crucial entry point for elucidating the natural history of CMM, accurately estimating its public health impact, and informing targeted prevention and control strategies.

While CMM affects both sexes, we focused specifically on women aged ≥55 years for several evidence-based reasons. First, sex-specific differences in melanoma epidemiology are well documented: women generally have higher incidence rates in younger age groups but exhibit a more pronounced age-related increase in incidence after age 55 compared with men, resulting in a substantial and disproportionate burden among older women.⁶ Second, sex-specific UV exposure patterns differ significantly—women in many populations have distinct sun-exposure behaviors, including higher cumulative lifetime UV exposure from recreational sunbathing and indoor tanning practices, which warrant separate examination.⁷ Third, sex hormones and immune response differences may influence melanoma biology and prognosis, with 55 years serving as the threshold for menopause. Estrogen receptor expression and immune checkpoint profiles showing sex-specific patterns that could affect disease progression and treatment outcomes.⁷ Fourth, from a public health perspective, women aged ≥55 years represent a rapidly growing demographic segment globally and bear a disproportionate share of the melanoma burden in many high-income countries, making them a priority population for targeted prevention and healthcare planning. Importantly, this sex-specific focus is not intended to minimize the burden in men—who generally have higher melanoma mortality rates—but rather to provide granular evidence that can inform sex-stratified prevention and treatment strategies. Given the substantial biological, behavioral, and epidemiological differences between sexes, analyzing women and men separately is a necessary first step before meaningful sex-comparative analyses can be conducted.

Although numerous studies have documented overall trends in CMM incidence and mortality, comprehensive global burden of disease analyses specifically targeting the high-risk population aged 55 years and older remain limited.⁸ Existing investigations often aggregate older age groups or apply broad age stratifications, thereby obscuring heterogeneity in risk trajectories beyond midlife—particularly among the rapidly expanding and most heavily burdened subgroup aged 80 years and olde.⁹ In addition, prior research has largely emphasized incidence and mortality outcomes, with comparatively limited attention to the long-term dynamics of composite health loss indicators, such as DALYs, in this population. Notably, few studies have concurrently examined age-standardized rates, which reflect underlying risk changes, alongside absolute case numbers, which more directly represent pressure on healthcare systems.¹⁰ More importantly, although population aging is frequently cited as the principal explanation for increasing melanoma burden, there is a paucity of quantitative evidence disentangling the independent contributions of population growth, demographic aging, and true epidemiological change (ie, shifts in age-specific incidence rates) to the observed increases in disease burden among older adults.⁸ This lack of clarity regarding key driving forces substantially limits the development of targeted public health strategies, whether focused on risk reduction or healthcare system preparedness.⁹ Furthermore, global-level analyses often mask substantial regional heterogeneity, and the burden patterns, temporal trends, and driving mechanisms of CMM among older adults in low- and middle-income regions remain poorly characterized.

To address these critical knowledge gaps, the present study utilizes data from the Global Burden of Disease Study 2021 (GBD 2021), covering the period from 1990 to 2021, to conduct a comprehensive assessment of CMM burden among women aged 55 years and older. This study aims to perform a multi-level, driver-oriented analysis with the following objectives: (1) To characterize the magnitude and temporal trends of CMM burden among women aged ≥55 years by systematically estimating age-standardized incidence and DALY rates, absolute case numbers, and estimated annual percentage changes (EAPCs) from global to national levels over the 32-year period, with fine-grained age stratification (5-year age groups from 55 to ≥95 years) to capture heterogeneity previously obscured by broader categorizations. (2) To identify and quantify the drivers of burden growth through formal decomposition analysis, disentangling the relative contributions of population growth, population aging, and epidemiological change to increases in both CMM incidence and DALYs, and mapping these patterns across SDI regions, GBD regions, and individual countries to inform region-specific policy responses.

By focusing explicitly on individuals aged 55 years and older—a population that has been historically aggregated into broader age categories in previous GBD analyses— this study integrates long-term trend analysis, quantitative decomposition methods, and multidimensional assessments of geographic and age-related heterogeneity. It seeks not only to provide an updated and authoritative overview of the global burden of CMM in this high-risk population but also to elucidate the fundamental drivers underlying burden growth and regional inequality.¹⁰ This study has several novel contributions that distinguish it from recent GBD-based melanoma analyses. While Du et al (2025)¹¹ examined CMM burden in the elderly population (≥65 years) and Liu et al (2024)⁴ analyzed global CMM trends across all ages and both sexes, our study uniquely combines the following elements: (1) explicit focus on women aged ≥55 years—a population that has been historically aggregated into broader age categories in previous analyses, yet exhibits distinct epidemiological patterns and clinical outcomes; (2) systematic application of Das Gupta decomposition analysis to quantify the independent contributions of population growth, aging, and epidemiological change—a methodological approach not employed in prior GBD-based melanoma studies focusing on older adults; (3) comprehensive age-specific analyses across five-year age groups from 55 to ≥95 years, revealing burden patterns in the oldest-old that were previously obscured; and (4) integration of global, regional, and national perspectives with driver quantification to inform geographically tailored policy responses. This combination of approaches enables both an updated characterization of the burden and a clearer understanding of its fundamental drivers, providing evidence to inform sex-specific and age-targeted prevention and healthcare strategies. The findings are expected to offer valuable evidence to inform policy-making, optimize skin cancer prevention and early detection strategies, and guide resource allocation for aging populations in the context of accelerating global demographic transition.

Materials and Methods

Data Sources and the GBD 2021 Research Framework

The data used in this study were obtained from the GBD 2021, a systematic and ongoing global health research initiative designed to quantify the burden of diseases, injuries, and risk factors worldwide and to generate comparable estimates across populations. GBD 2021 synthesizes extensive data from multiple sources, including epidemiological studies, vital registration systems, hospital records, population-based surveys, and published literature from diverse countries and regions. Standardized analytical models and uniform methodological frameworks are applied for data integration and estimation, ensuring consistency and comparability across time periods, geographic locations, and population groups. GBD 2021 provides comprehensive estimates for more than 350 diseases and injuries, including incidence, prevalence, mortality, and disability-adjusted life years (DALYs), spanning the period from 1990 to 2021. The data that support the findings of this study are openly available in the Global Burden of Disease Study 2021 (GBD 2021) at https://www.healthdata.org/research-analysis/gbd-data. The Institute for Health Metrics and Evaluation (IHME) maintains the GBD database and provides detailed documentation of data sources, estimation methods, and model specifications. In the present study, relevant data on CMM among women aged 55 years and older were extracted for analysis. In GBD research, CMM is defined as a malignant tumor originating from melanocytes in the skin. It is clearly classified as an independent disease under the category of malignant tumors. Its ICD-10 code is C43.0-C43.9.

Data extraction specifications:

Sex: Female

Age groups: 55–59, 60–64, 65–69, 70–74, 75–79, 80–84, 85–89, 90–94, 95+ years

Cause: Cutaneous malignant melanoma (CMM), ICD-10 codes C43.0-C43.9

Measures: Incidence and disability-adjusted life years (DALYs)

Metrics: Counts (number of cases) and age-standardized rates (per 100,000 population)

Locations: Global, 5 SDI regions, 21 GBD regions, and 204 countries and territories

Date of extraction: [August, 2025]

Key Indicators: Incidence Rate and Disability-Adjusted Life Years

The two primary health outcome indicators assessed in this study were incidence and DALYs. Incidence was defined as the number of newly diagnosed CMM cases occurring within a specified population during a given time period, typically one year, and was expressed per 100,000 population. This indicator reflects the underlying risk of disease onset within the population. DALYs were used as a comprehensive measure of disease burden, integrating both years of life lost due to premature mortality and years lived with disability attributable to CMM.¹² By capturing both fatal and non-fatal health outcomes, DALYs provide an overall estimate of total healthy life years lost due to the disease. To minimize the influence of differences in population age structures on comparisons, both incidence and DALY outcomes were reported as age-standardized rates. Age-standardized rates were calculated using the GBD standard population (a global age structure based on the WHO standard population), which allows for comparison across populations and time periods. The direct standardization method was applied using the standard population weights for the age groups of interest. Importantly, the age-standardized rates reported for the ≥55 years population were standardized using the full GBD standard population age distribution, not re-standardized within the ≥55 years age group alone, ensuring comparability with other GBD studies.

Socioeconomic Indicators

To examine the association between disease burden and levels of socioeconomic development, this study employed socioeconomic indicators defined within the GBD framework as stratification variables. The sociodemographic index (SDI) is a composite measure derived from per capita income, mean years of schooling, and total fertility rate, reflecting the overall level of socioeconomic development of a country or region. SDI values range from 0 to 1, with higher values indicating higher levels of development. Based on SDI scores, GBD regions are categorized into five groups: high SDI, high-middle SDI, middle SDI, low-middle SDI, and low SDI.¹³ Stratification by SDI enabled the assessment of distribution patterns and temporal variations in CMM burden across regions with differing socioeconomic contexts.

Trend Analysis: Estimated Annual Percentage Change

Temporal trends in disease burden indicators were quantified using the estimated annual percentage change (EAPC). EAPC was calculated by fitting a log-linear regression model to age-standardized rates over calendar years, assuming an exponential change over time. The model was specified as ln(rate) = α + β × year + ε, where β represents the annual rate of change. EAPC was derived using the formula [exp(β) − 1] × 100%. The corresponding 95% confidence intervals were calculated using the standard error of the regression coefficient (SE(β)): 95% CI=[exp(β±1.96×SE(β))−1]×100%. The corresponding 95% confidence intervals were used to assess the direction and statistical significance of temporal trends. A positive EAPC indicates an increasing trend, whereas a negative value indicates a decreasing trend over the study period. EAPC allows for direct comparison of trend magnitudes across regions and outcome indicators.¹¹ We used EAPC as the primary measure of temporal trend, calculated via log-linear regression of age-standardized rates over calendar years. This is the established standard method in GBD studies for quantifying long-term disease burden trends, enabling direct comparison with prior analyses. While the figures display year-to-year fluctuations reflecting inherent variability in GBD estimates from diverse data sources, these do not indicate statistically significant departures from a log-linear trend. GBD estimates are derived from sophisticated spatiotemporal Gaussian process regression and Bayesian meta-regression models that already account for non-linearities in the underlying data. Additionally, our decomposition analysis provides mechanistic insight into the drivers of burden changes beyond what joinpoint regression would offer.

Driver Decomposition Analysis

To further elucidate the factors driving changes in the absolute number of CMM incidence cases and DALYs, a decomposition analysis was conducted using the Das Gupta method (Das Gupta),¹⁴ a standard demographic technique for partitioning changes in aggregate outcomes into independent components. This method partitions the total change observed between 1990 and 2021 into the independent contributions of three components. First, the population growth component represents changes attributable solely to increases in total population size. Second, the population aging component reflects changes resulting from shifts in population age structure toward older age groups, specifically an increased proportion of individuals aged 55 years and older. Third, the epidemiological change component captures variations attributable to changes in age-specific incidence or DALY rates, representing shifts in disease risk after controlling for demographic factors. The relative contribution of each component was quantified as a percentage of the total change, enabling identification of the dominant drivers of increasing disease burden. This analysis provides critical insight for informing targeted public health strategies.

Mathematical framework: The Das Gupta method decomposes the change in total burden (ΔB) between two time points (t₁ and t₂) as: ΔB=Bt₂−B_t1=∑i[∏_j≠i (x_j,t2+x_j,t1)/2]×(x_i,t2−x_i,t1) where B=P×A×R (burden = population × age structure × rate), and x_i represents each of the three components (population size, proportion aged ≥55 years, and age-specific rate). This formulation provides a symmetric treatment of all components, avoiding order-dependence that would arise from sequential decomposition. The relative contribution of each component was quantified as a percentage of the total change: Contribution_i=Component_i/ΔB×100%, enabling identification of the dominant drivers of increasing disease burden. Uncertainty intervals for decomposition: The GBD study provides 1,000 posterior draws for each estimate. However, consistent with standard practice in GBD decomposition analyses, we report the decomposition results based on the mean estimates and do not incorporate uncertainty intervals directly into the decomposition percentages, as the Das Gupta method is deterministic given the input point estimates. The uncertainty in the underlying GBD rates is reflected in the 95% UIs presented in the main tables, which were derived from the 2.5th and 97.5th percentiles of the 1,000 posterior draws.

Statistical Analysis and Results Presentation

All data extraction and indicator calculations were based on standardized estimates provided by GBD 2021. Trend analyses and decomposition analyses were performed within the GBD analytical framework. Results are presented in both tabular and narrative formats, including age-standardized rates, EAPC values, and percentage contributions derived from decomposition analyses at the global, SDI regional, GBD regional, and national levels. In addition, cross-sectional analyses for 2021 were conducted across specific age groups to characterize age-related patterns in disease burden. The 95% uncertainty intervals (UIs) reported throughout the tables were derived from the GBD 2021 posterior simulation draws. The GBD study uses a Bayesian meta-regression modeling framework (MR-BRT) that generates 1,000 draws from the posterior distribution for each estimate; the 95% UIs represent the 2.5th and 97.5th percentiles of these draws. EAPC 95% confidence intervals were calculated using the standard error of the regression coefficient from the log-linear model. All statistical analyses were performed using R version 4.3.1 (R Core Team, 2024) with the following packages: “tidyverse” (v2.0.0) for data manipulation, “ggplot2” (v3.4.4) for visualization. The EAPC was calculated using the “lm” function from base R with log-linear regression. Decomposition analysis followed the Das Gupta (1993) method implemented using the “decompose” function from the “DemoDecomp” package (v0.1.0).

Ethics Approval and Consent to Participate

This study was based exclusively on publicly available, aggregated, and de-identified data from the Global Burden of Disease Study 2021. No individual-level identifiable information was accessed, and no direct contact with human participants was involved. Therefore, this study was exempt from additional ethical review. The exemption was determined in accordance with Article 32, items (1) and (2), of the Measures for Ethical Review of Life Science and Medical Research Involving Human Subjects issued by the National Health Commission, Ministry of Education, Ministry of Science and Technology, and National Administration of Traditional Chinese Medicine of China on February 18, 2023, which states that studies using legally obtained public data or anonymized information data may be exempt from ethical review when they do not cause harm to the human body and do not involve sensitive personal information or commercial interests. The requirement for informed consent was also waived because the study used only publicly available, aggregated, and anonymized secondary data.

Results

At the global level, the burden of CMM among females aged 55 years and older demonstrated divergent trends between 1990 and 2021. The age-standardized DALY rate declined over the study period, with an EAPC of −0.42, whereas the absolute number of DALYs increased, corresponding to an EAPC of 2.16. In contrast, both the age-standardized incidence rate and the absolute number of incident cases increased, with EAPCs of 0.90 and 3.51, respectively (Table 1 and Figure 1). Decomposition analysis indicated that population growth was the predominant contributor to the increase in both DALYs (118.77%) and incident cases (75.89%). Epidemiological change contributed positively to the growth in incidence (21.04%) but contributed negatively to changes in DALYs (−22.07%) (Table 2). Age-specific analyses for 2021 showed a progressive increase in both incidence and DALY rates with advancing age, with the highest incidence rate (35.65 per 100,000) and DALY rate (165.58 per 100,000) observed in the ≥95-year age group (Tables S1 and S2 and Figure 2).

Table 1.

Global Burden of Cutaneous Malignant Melanoma in Females Aged 55 Years and Older and Its Trends from 1990 to 2021

	DALYs (Disability-Adjusted Life Years)						Incidence
	1990 Rate	1990 Number	2021 Rate	2021 Number	EAPC Rate	EAPC Number	1990 Rate	1990 Number	2021 Rate	2021 Number	EAPC Rate	EAPC Number
Global	63.88(58.39,69.64)	229,939.37(210,175.18,250,658.75)	56.11(48.79,63.66)	441,310.91(383,714.97,500,675.80)	−0.42(−0.55,-0.30)	2.16(2.09,2.22)	9.16(8.56,9.59)	32,962.64(30,794.62,34,520.40)	11.82(10.62,12.77)	92,952.33(83,499.86,100,471.10)	0.90(0.61,1.18)	3.51(3.30,3.73)
Age groups
55–59 years	48.96(43.07,54.64)	45,196.18(39,757.54,50,435.91)	37.87(31.13,44.02)	76,125.97(62,572.66,88,481.22)	−0.74(−0.94,-0.53)	1.95(1.74,2.16)	5.62(5.28,5.92)	5189.57(4877.66,5461.67)	6.50(5.95,7.03)	13,072.54(11,955.75,14,137.27)	0.71(0.27,1.15)	3.43(3.04,3.83)
60–64 years	55.43(50.08,61.37)	45,484.88(41,098.49,50,360.12)	45.98(39.17,52.43)	75,641.09(64,440.71,86,261.10)	−0.55(−0.69,-0.41)	1.93(1.76,2.10)	6.83(6.43,7.22)	5602.85(5274.12,5926.59)	8.45(7.79,9.11)	13,908.01(12,813.49,14,987.06)	0.88(0.53,1.22)	3.40(3.15,3.64)
65–69 years	64.70(59.38,70.32)	42,884.87(39,356.39,46,608.10)	52.27(44.73,59.16)	75,277.85(64,415.23,85,197.94)	−0.66(−0.76,-0.56)	1.66(1.50,1.81)	9.10(8.56,9.56)	6029.39(5676.35,6337.37)	10.46(9.57,11.27)	15,066.10(13,783.83,16,236.58)	0.73(0.46,1.00)	3.08(2.94,3.21)
70–74 years	68.38(62.11,75.27)	32,166.51(29,220.18,35,409.31)	62.63(54.68,71.22)	68,544.68(59,841.94,77,953.12)	−0.57(−0.71,-0.43)	1.73(1.49,1.97)	10.76(10.02,11.42)	5059.74(4714.72,5370.83)	14.02(12.73,15.23)	15,347.04(13,935.34,16,672.69)	0.65(0.43,0.86)	2.97(2.70,3.25)
75–79 years	82.20(74.87,88.37)	29,858.20(27,193.83,32,099.10)	71.91(62.23,81.21)	51,843.77(44,868.18,58,547.78)	−0.43(−0.56,-0.30)	2.11(1.87,2.34)	13.31(12.19,14.12)	4833.07(4428.21,5127.19)	16.87(14.63,18.49)	12,159.41(10,547.43,13,327.40)	0.64(0.40,0.88)	3.21(2.89,3.53)
80–84 years	89.61(79.17,97.58)	19,796.52(17,491.30,21,557.16)	86.10(72.81,97.96)	43,850.42(37,085.12,49,894.29)	−0.18(−0.33,-0.04)	2.73(2.51,2.95)	15.84(13.79,17.20)	3498.49(3045.73,3800.75)	20.96(17.12,23.53)	10,674.93(8717.22,11,984.84)	0.82(0.53,1.10)	3.76(3.46,4.06)
85–89 years	98.34(82.97,108.01)	9880.53(8336.19,10,851.59)	99.08(77.53,113.75)	28,205.87(22,071.13,32,383.34)	0.21(0.03,0.39)	3.66(3.40,3.92)	19.54(15.86,21.56)	1963.39(1593.16,2166.13)	27.55(20.87,31.49)	7844.30(5941.00,8963.72)	1.37(1.03,1.71)	4.86(4.50,5.23)
90–94 years	119.09(95.12,132.44)	3604.04(2878.57,4008.07)	126.86(96.24,144.25)	15,300.44(11,607.20,17,398.17)	0.42(0.28,0.56)	4.82(4.58,5.06)	20.28(15.89,22.74)	613.77(480.80,688.03)	28.82(21.50,33.07)	3475.95(2593.59,3988.37)	1.51(1.25,1.77)	5.96(5.65,6.27)
95+ years	140.88(106.16,161.35)	1067.63(804.56,1222.84)	165.58(118.21,191.28)	6520.81(4655.41,7532.93)	0.54(0.45,0.62)	6.10(5.95,6.25)	22.75(16.99,26.13)	172.38(128.79,198.04)	35.65(25.27,41.77)	1404.04(995.13,1644.85)	1.58(1.40,1.75)	7.20(6.95,7.45)
SDI regions
High SDI	119.15(111.81,124.79)	125,790.14(118,050.00,131,744.51)	107.33(95.98,115.29)	197,920.40(176,992.65,212,607.42)	−0.28(−0.45,-0.10)	1.64(1.49,1.78)	23.07(21.38,24.07)	24,356.70(22,576.43,25,411.80)	33.26(29.53,35.35)	61,333.36(54,454.54,65,185.35)	1.24(0.90,1.57)	3.18(2.88,3.48)
High-middle SDI	65.06(59.41,70.14)	63,298.71(57,803.08,68,241.10)	68.49(58.83,76.94)	128,685.84(110,547.09,144,565.90)	0.07(−0.06,0.21)	2.22(2.15,2.30)	6.61(6.13,7.09)	6430.61(5965.26,6898.90)	11.89(10.42,13.09)	22,348.73(19,579.40,24,600.71)	1.99(1.83,2.15)	4.18(4.05,4.31)
Middle SDI	26.64(19.49,35.96)	23,703.55(17,346.31,31,998.66)	28.81(18.84,36.07)	70,907.27(46,357.91,88,763.91)	0.11(0.03,0.19)	3.48(3.35,3.61)	1.50(1.09,1.95)	1337.57(974.32,1733.90)	2.66(1.72,3.31)	6546.45(4239.14,8143.57)	1.74(1.66,1.83)	5.17(5.02,5.31)
Low-middle SDI	18.90(13.25,28.02)	9355.45(6559.65,13,873.77)	21.32(13.97,30.13)	26,753.02(17,527.62,37,808.03)	0.37(0.33,0.41)	3.45(3.37,3.53)	0.95(0.65,1.34)	470.26(322.98,664.65)	1.40(0.90,1.88)	1751.88(1132.82,2360.20)	1.21(1.14,1.28)	4.31(4.19,4.44)
Low SDI	40.87(24.30,58.86)	7417.74(4410.64,10,683.71)	39.17(21.20,58.44)	16,387.69(8870.62,24,451.41)	−0.27(−0.33,-0.20)	2.48(2.35,2.61)	1.83(1.09,2.62)	332.26(198.23,475.05)	2.05(1.10,2.98)	859.70(461.12,1246.25)	0.25(0.17,0.33)	3.01(2.85,3.17)
GBD regions
Andean Latin America	66.52(46.91,91.04)	1145.51(807.86,1567.90)	69.38(50.29,95.42)	3588.93(2601.49,4935.79)	0.15(0.01,0.28)	3.86(3.71,4.02)	3.62(2.57,4.83)	62.35(44.22,83.22)	6.05(4.33,8.11)	313.15(223.72,419.25)	1.78(1.61,1.95)	5.56(5.37,5.75)
Australasia	466.10(415.45,524.97)	9939.03(8859.01,11,194.27)	251.99(217.29,282.19)	11,674.37(10,066.79,13,073.74)	−1.98(−2.42,-1.54)	0.60(0.19,1.01)	75.87(67.74,85.82)	1617.94(1444.51,1830.09)	82.37(69.37,93.86)	3816.03(3213.91,4348.43)	0.49(−0.06,1.06)	3.14(2.61,3.67)
Caribbean	29.26(25.04,36.38)	650.93(557.01,809.38)	33.85(28.46,41.51)	1661.97(1397.39,2038.12)	−0.07(−0.35,0.20)	2.49(2.21,2.77)	2.10(1.86,2.42)	46.72(41.46,53.88)	3.58(3.07,4.18)	175.99(150.62,205.06)	1.13(0.83,1.43)	3.73(3.42,4.03)
Central Asia	56.72(50.13,63.65)	2698.41(2385.30,3028.17)	46.09(39.94,52.96)	3762.09(3260.16,4322.69)	−1.09(−1.38,-0.79)	0.46(−0.09,1.02)	3.99(3.52,4.46)	190.07(167.41,212.32)	4.14(3.59,4.77)	338.28(292.73,389.61)	−0.25(−0.63,0.13)	1.31(0.63,1.99)
Central Europe	117.48(109.37,128.79)	17,698.39(16,475.99,19,402.58)	155.35(138.75,173.22)	32,384.24(28,923.24,36,107.84)	0.97(0.85,1.08)	2.19(2.08,2.31)	9.83(9.13,10.92)	1481.02(1374.75,1644.97)	24.01(21.25,26.97)	5004.72(4428.83,5621.20)	3.07(2.87,3.26)	4.32(4.12,4.51)
Central Latin America	36.80(35.11,38.54)	2588.29(2469.53,2710.21)	52.33(46.29,58.59)	12,093.88(10,696.44,13,539.01)	0.76(0.58,0.94)	4.79(4.63,4.96)	2.22(2.10,2.33)	155.93(147.71,163.82)	4.75(4.16,5.34)	1097.49(961.35,1234.75)	2.08(1.86,2.30)	6.17(5.97,6.37)
Central Sub-Saharan Africa	38.79(26.63,71.37)	779.44(535.13,1434.14)	42.45(25.01,81.37)	2080.73(1225.97,3988.29)	0.38(0.30,0.45)	3.18(2.99,3.36)	1.67(1.14,3.01)	33.48(22.97,60.39)	2.10(1.25,3.95)	102.93(61.14,193.79)	0.81(0.72,0.90)	3.62(3.42,3.83)
East Asia	23.88(15.28,37.15)	18,103.81(11,583.05,28,157.52)	23.76(9.81,34.78)	48,089.69(19,864.61,70,407.05)	−0.09(−0.23,0.06)	3.18(2.92,3.45)	1.15(0.75,1.78)	869.32(571.08,1347.01)	2.17(0.89,3.16)	4393.25(1796.37,6394.50)	2.17(1.98,2.36)	5.52(5.18,5.86)
Eastern Europe	81.42(75.84,89.39)	25,697.91(23,934.13,28,213.23)	142.83(128.49,158.65)	54,504.68(49,032.93,60,540.83)	1.54(1.34,1.75)	2.12(1.97,2.28)	7.42(6.93,8.16)	2342.65(2187.43,2574.22)	21.02(18.96,23.04)	8020.21(7233.63,8791.88)	3.33(3.14,3.52)	3.92(3.64,4.20)
Eastern Sub-Saharan Africa	77.24(45.05,107.02)	4676.52(2727.60,6479.00)	72.97(38.46,115.43)	10,330.99(5444.91,16,343.15)	−0.30(−0.35,-0.25)	2.50(2.34,2.66)	3.38(1.97,4.69)	204.57(119.49,284.20)	3.66(1.93,5.72)	518.47(272.89,809.50)	0.13(0.06,0.21)	2.94(2.75,3.13)
High-income Asia Pacific	16.76(15.19,18.50)	3280.50(2973.15,3620.13)	21.17(16.79,24.92)	8073.92(6405.04,9507.09)	0.71(0.50,0.91)	2.94(2.64,3.25)	2.61(2.32,2.89)	510.96(454.72,566.12)	6.13(4.76,7.29)	2337.43(1815.72,2781.47)	2.79(2.42,3.16)	5.08(4.59,5.57)
High-income North America	148.43(137.98,156.33)	48,753.03(45,322.93,51,348.07)	120.74(109.18,130.19)	72,737.26(65,777.03,78,433.61)	−0.70(−0.95,-0.45)	1.45(1.28,1.62)	39.23(36.03,40.99)	12,885.18(11,834.36,13,464.68)	43.84(39.29,46.55)	26,410.02(23,670.35,28,042.56)	0.19(−0.22,0.60)	2.35(2.04,2.67)
North Africa and Middle East	24.60(10.99,36.35)	3412.68(1524.28,5042.60)	22.75(9.25,28.60)	8565.28(3482.37,10,769.41)	−0.27(−0.36,-0.19)	2.98(2.81,3.16)	3.07(1.40,4.39)	425.33(194.49,608.56)	7.77(3.22,9.97)	2924.05(1212.74,3754.38)	3.21(3.08,3.33)	6.58(6.43,6.73)
Oceania	5.78(3.84,10.60)	13.35(8.87,24.49)	5.79(3.81,10.32)	34.04(22.42,60.67)	0.11(−0.05,0.28)	3.19(2.98,3.39)	0.28(0.19,0.51)	0.65(0.44,1.18)	0.29(0.19,0.51)	1.72(1.13,3.01)	0.24(0.07,0.41)	3.31(3.11,3.52)
South Asia	13.85(8.61,21.23)	6240.53(3876.14,9561.54)	14.65(8.74,24.22)	18,543.46(11,066.85,30,665.47)	0.01(−0.13,0.14)	3.45(3.29,3.61)	0.63(0.39,0.96)	284.79(175.27,431.91)	0.92(0.54,1.50)	1170.85(689.25,1896.04)	1.07(0.88,1.26)	4.56(4.33,4.78)
Southeast Asia	12.39(8.60,20.81)	2790.54(1937.42,4689.02)	14.00(8.40,21.08)	8582.69(5153.15,12,924.44)	0.34(0.26,0.42)	3.58(3.51,3.65)	0.56(0.39,0.93)	125.85(87.60,210.43)	0.73(0.44,1.13)	448.40(270.21,695.72)	0.80(0.74,0.87)	4.06(3.98,4.14)
Southern Latin America	61.82(57.39,65.78)	2710.81(2516.85,2884.79)	79.08(70.96,86.25)	6435.36(5774.65,7018.77)	0.70(0.38,1.02)	2.76(2.47,3.05)	4.41(4.04,4.72)	193.41(177.20,206.98)	9.63(8.52,10.57)	783.90(693.60,860.51)	2.38(2.02,2.74)	4.47(4.14,4.80)
Southern Sub-Saharan Africa	74.50(43.75,115.73)	1876.89(1102.17,2915.60)	95.74(48.61,123.76)	5451.91(2768.32,7047.64)	0.99(0.76,1.22)	3.59(3.39,3.79)	4.13(2.38,6.34)	104.04(60.01,159.69)	5.98(2.97,7.65)	340.61(169.10,435.90)	1.32(1.18,1.47)	3.93(3.81,4.05)
Tropical Latin America	61.78(57.63,65.28)	4983.69(4648.74,5265.36)	67.14(60.31,71.84)	16,334.21(14,672.39,17,478.02)	0.14(−0.02,0.31)	3.84(3.69,3.98)	3.59(3.31,3.80)	289.31(267.23,306.81)	5.83(5.16,6.27)	1419.18(1254.44,1525.02)	1.46(1.29,1.64)	5.20(5.05,5.36)
Western Europe	125.45(117.42,131.09)	69,706.64(65,248.73,72,843.15)	137.43(121.74,149.02)	110,198.40(97,615.82,119,486.66)	0.45(0.31,0.60)	1.67(1.55,1.79)	19.86(18.45,20.86)	11,034.97(10,250.74,11,590.80)	41.16(35.92,44.25)	33,006.03(28,805.08,35,478.86)	2.60(2.29,2.92)	3.85(3.56,4.14)
Western Sub-Saharan Africa	31.51(16.12,41.80)	2192.47(1121.47,2908.59)	36.19(13.69,50.96)	6182.80(2338.57,8706.55)	0.52(0.48,0.57)	3.46(3.25,3.66)	1.50(0.79,1.96)	104.10(55.02,136.53)	1.93(0.76,2.69)	329.63(129.45,460.14)	0.90(0.84,0.96)	3.84(3.61,4.07)

Figure 1.

Global burden of cutaneous malignant melanoma in females aged 55 years and older and its trends from 1990 to 2021. (A) DALYs (B) Incidence disability-adjusted life year (DALY).

Table 2.

Decomposition Analysis Results of Cutaneous Malignant Melanoma in Females Aged 55 Years and Older at Global, SDI, and Regional Levels from 1990 to 2021

Measure	Location	Overall Difference	Population (Contribution %)	Aging (Contribution %)	Epidemiological Change (Contribution %)
DALYs	Global	21137153713.15	25,105,564,234.90 (118.77%)	695,853,132.01 (3.29%)	−4,664,263,653.76 (−22.07%)
DALYs	High SDI	7213025537.15	8,753,900,017.47 (121.36%)	300,242,449.37 (4.16%)	−1,841,116,929.69 (−25.52%)
DALYs	High-middle SDI	6538713322.52	6,045,967,549.62 (92.46%)	197,197,554.97 (3.02%)	295,548,217.93 (4.52%)
DALYs	Middle SDI	4720371891.81	4,376,517,245.56 (92.72%)	125,676,974.36 (2.66%)	218,177,671.89 (4.62%)
DALYs	Low-middle SDI	1739757459.49	1,541,360,280.21 (88.60%)	22,431,303.85 (1.29%)	175,965,875.43 (10.11%)
DALYs	Low SDI	896995126.28	944,099,281.26 (105.25%)	1,391,228.60 (0.16%)	−48,495,383.57 (−5.41%)
DALYs	East Asia	2998588118.62	2,992,649,151.30 (99.80%)	104,756,404.69 (3.49%)	−98,817,437.37 (−3.30%)
DALYs	Southeast Asia	579215503.38	515,916,873.68 (89.07%)	5,136,001.37 (0.89%)	58,162,628.32 (10.04%)
DALYs	Oceania	2069619.96	2,060,284.34 (99.55%)	48,069.00 (2.32%)	−38,733.39 (−1.87%)
DALYs	Central Asia	106367009.64	181,570,606.67 (170.70%)	−14,679,294.07 (−13.80%)	−60,524,302.97 (−56.90%)
DALYs	Central Europe	1468585313.47	842,589,402.06 (57.37%)	141,788,784.80 (9.65%)	484,207,126.61 (32.97%)
DALYs	Eastern Europe	2880676691.58	740,227,471.61 (25.70%)	23,183,504.78 (0.80%)	2,117,265,715.19 (73.50%)
DALYs	High-income Asia Pacific	479341694.71	365,947,957.05 (76.34%)	74,936,659.70 (15.63%)	38,457,077.96 (8.02%)
DALYs	Australasia	173534405.66	786,877,141.29 (453.44%)	11,341,485.05 (6.54%)	−624,684,220.67 (−359.98%)
DALYs	Western Europe	4049175503.07	3,257,751,688.77 (80.45%)	195,919,035.65 (4.84%)	595,504,778.65 (14.71%)
DALYs	Southern Latin America	372454165.92	268,405,414.85 (72.06%)	11,252,776.89 (3.02%)	92,795,974.19 (24.91%)
DALYs	High-income North America	2398423780.85	3,649,828,551.12 (152.18%)	−47,463,216.44 (−1.98%)	−1,203,941,553.83 (−50.20%)
DALYs	Caribbean	101104050.23	85,449,959.14 (84.52%)	2,556,378.30 (2.53%)	13,097,712.79 (12.95%)
DALYs	Andean Latin America	244342118.74	235,165,897.04 (96.24%)	5,606,393.70 (2.29%)	3,569,828.00 (1.46%)
DALYs	Central Latin America	950559048.37	735,951,248.57 (77.42%)	19,576,669.35 (2.06%)	195,031,130.46 (20.52%)
DALYs	Tropical Latin America	1135051772.83	1,052,204,245.05 (92.70%)	31,197,448.53 (2.75%)	51,650,079.25 (4.55%)
DALYs	North Africa and Middle East	515260115.32	556,571,511.99 (108.02%)	8,409,970.69 (1.63%)	−49,721,367.35 (−9.65%)
DALYs	South Asia	1230292772.06	1,165,227,488.08 (94.71%)	5,833,684.37 (0.47%)	59,231,599.61 (4.81%)
DALYs	Central Sub-Saharan Africa	130129644.46	117,963,800.06 (90.65%)	1,278,420.09 (0.98%)	10,887,424.31 (8.37%)
DALYs	Eastern Sub-Saharan Africa	565446557.68	605,813,618.65 (107.14%)	−1,529,670.45 (−0.27%)	−38,837,390.52 (−6.87%)
DALYs	Southern Sub-Saharan Africa	357502272.19	272,622,250.71 (76.26%)	−2,757,693.85 (−0.77%)	87,637,715.32 (24.51%)
DALYs	Western Sub-Saharan Africa	399033554.41	343,147,620.82 (85.99%)	−15,954,804.06 (−4.00%)	71,840,737.65 (18.00%)
Incidence	Global	5998968931.29	4,552,769,074.31 (75.89%)	183,942,396.78 (3.07%)	1,262,257,460.20 (21.04%)
Incidence	High SDI	3697666264.87	2,258,321,104.09 (61.07%)	86,122,630.98 (2.33%)	1,353,222,529.80 (36.60%)
Incidence	High-middle SDI	1591812026.15	857,920,713.26 (53.90%)	43,937,758.13 (2.76%)	689,953,554.76 (43.34%)
Incidence	Middle SDI	520887858.38	339,117,103.30 (65.10%)	16,945,997.17 (3.25%)	164,824,757.91 (31.64%)
Incidence	Low-middle SDI	128162104.07	91,511,733.28 (71.40%)	3,140,349.64 (2.45%)	33,510,021.15 (26.15%)
Incidence	Low SDI	52744867.36	46,365,995.00 (87.91%)	619,933.59 (1.18%)	5,758,938.77 (10.92%)
Incidence	East Asia	352393214.19	218,290,222.08 (61.95%)	14,322,328.56 (4.06%)	119,780,663.55 (33.99%)
Incidence	Southeast Asia	32254537.59	25,494,999.41 (79.04%)	531,008.15 (1.65%)	6,228,530.03 (19.31%)
Incidence	Oceania	107233.85	102,776.12 (95.84%)	6646.41 (6.20%)	−2188.68 (−2.04%)
Incidence	Central Asia	14820647.67	13,731,297.67 (92.65%)	−1,752,824.91 (−11.83%)	2,842,174.91 (19.18%)
Incidence	Central Europe	352370249.09	110,158,329.92 (31.26%)	23,428,746.15 (6.65%)	218,783,173.01 (62.09%)
Incidence	Eastern Europe	567756572.48	93,594,225.87 (16.48%)	7,655,308.52 (1.35%)	466,507,038.09 (82.17%)
Incidence	High-income Asia Pacific	182647063.89	90,078,584.45 (49.32%)	20,218,313.51 (11.07%)	72,350,165.93 (39.61%)
Incidence	Australasia	219808749.34	197,860,919.65 (90.02%)	2,648,666.09 (1.20%)	19,299,163.60 (8.78%)
Incidence	Western Europe	2197105758.51	763,989,241.00 (34.77%)	48,245,712.20 (2.20%)	1,384,870,805.31 (63.03%)
Incidence	Southern Latin America	59048690.58	27,043,783.63 (45.80%)	1,831,626.68 (3.10%)	30,173,280.26 (51.10%)
Incidence	High-income North America	1352484031.65	1,134,695,585.96 (83.90%)	−32,947,764.63 (−2.44%)	250,736,210.32 (18.54%)
Incidence	Caribbean	12927016.48	7,828,558.62 (60.56%)	353,805.58 (2.74%)	4,744,652.28 (36.70%)
Incidence	Andean Latin America	25080219.49	17,282,550.42 (68.91%)	627,376.55 (2.50%)	7,170,292.52 (28.59%)
Incidence	Central Latin America	94155471.13	58,164,381.21 (61.77%)	2,318,312.04 (2.46%)	33,672,777.87 (35.76%)
Incidence	Tropical Latin America	112986278.46	79,395,998.00 (70.27%)	4,124,007.30 (3.65%)	29,466,273.16 (26.08%)
Incidence	North Africa and Middle East	249871322.40	130,209,045.01 (52.11%)	2,470,685.45 (0.99%)	117,191,591.94 (46.90%)
Incidence	South Asia	88605352.87	65,150,095.15 (73.53%)	1,774,700.28 (2.00%)	21,680,557.44 (24.47%)
Incidence	Central Sub-Saharan Africa	6945102.34	5,536,136.04 (79.71%)	198,744.96 (2.86%)	1,210,221.34 (17.43%)
Incidence	Eastern Sub-Saharan Africa	31390777.28	28,673,611.44 (91.34%)	195,407.08 (0.62%)	2,521,758.77 (8.03%)
Incidence	Southern Sub-Saharan Africa	23656862.89	16,139,841.89 (68.22%)	−362,080.38 (−1.53%)	7,879,101.37 (33.31%)
Incidence	Western Sub-Saharan Africa	22553779.12	17,240,545.29 (76.44%)	−1,384,229.58 (−6.14%)	6,697,463.41 (29.70%)

Figure 2.

Temporal trends of cutaneous malignant melanoma in females aged 55 years and older from 1990 to 2021 by age groups. (A) DALYs (B) Incidence disability-adjusted life year (DALY).

Across sociodemographic index (SDI) regions, high-SDI regions exhibited the highest age-standardized DALY rate (107.33 per 100,000) and incidence rate (33.26 per 100,000) in 2021, with an EAPC of 3.18 for the number of incident cases (Table 1). High-middle SDI regions showed the most rapid increases in incidence, with the highest EAPCs for both incidence rate (1.99) and incidence number (4.18). Middle SDI regions had the largest contribution from epidemiological change to increases in DALYs (4.62%) and incidence (31.64%) (Table 1 and Table 2). Low-SDI regions recorded the lowest incidence rate (2.05 per 100,000) but exhibited a positive EAPC for incidence number (3.01). Decomposition analysis further demonstrated that population aging had the greatest impact on DALYs in high-SDI regions (4.16%) and on incidence in high-middle SDI regions (2.76%) (Table 2). Age-specific data for 2021 indicated that high-SDI regions consistently had the highest incidence rates across all age groups, particularly among those aged 85–89 years (53.92 per 100,000), whereas low-middle SDI regions exhibited the lowest incidence rates in most age groups (Table S1). A similar pattern was observed for DALYs, with high-SDI regions showing the highest rates in older age groups, reaching 214.19 per 100,000 among individuals aged ≥95 years (Table S2).

Several decomposition contributions exceeded 100% in magnitude or were negative, reflecting offsetting components that act in opposite directions. These values are mathematically valid within the Das Gupta decomposition framework and arise when one component’s positive contribution is partially or fully counterbalanced by negative contributions from other components. For example, in Australasia, the population growth component (453.44%) was substantially offset by a negative epidemiological change component (−359.98%). This indicates that while demographic expansion dramatically increased absolute DALY numbers, reductions in age-specific DALY rates—likely reflecting improved survival outcomes—more than compensated for this demographic pressure. The net result, after accounting for aging (6.54%), is a 100% total change. Similarly, in Central Asia, population growth (170.70%) was partially offset by negative contributions from aging (−13.80%) and epidemiological change (−56.90%), resulting in a net positive change. These offsetting patterns are not artefacts; rather, they reflect the countervailing forces of demographic expansion and improving age-specific health outcomes that shape disease burden trends across regions.

At the Global Burden of Disease (GBD) regional level, Australasia recorded the highest age-standardized incidence rate in 2021 (82.37 per 100,000) and the highest age-standardized DALY rate (251.99 per 100,000), but the lowest EAPC for DALY rate (−1.98). Central Europe ranked second in DALY rate (155.35 per 100,000) and also showed a high EAPC for incidence rate (3.07). Eastern Europe had the third-highest age-standardized DALY rate (142.83 per 100,000) and the highest EAPC for incidence rate (3.33) (Table S3 and 3). Central Europe ranked second in DALY rate (155.35 per 100,000) and also showed a high EAPC for incidence rate (3.07), with epidemiological change accounting for 32.97% of DALY growth and 62.09% of incidence growth (Table 2 and Table 3). High-income North America exhibited a relatively high incidence rate (43.84 per 100,000) but a low EAPC for incidence rate (0.19), whereas Central Latin America experienced the largest increase in incidence number, rising by 7.04-fold compared with 1990 (Table 3). Age-specific analyses for 2021 demonstrated that Australasia had the highest incidence rate among individuals aged 85–89 years (152.99 per 100,000), while Eastern Europe recorded the highest DALY rate in the 55–59-year age group (124.63 per 100,000) (Tables S1 and S2 and Figure 2). Oceania consistently showed the lowest incidence rate (0.29 per 100,000) and DALY rate (5.79 per 100,000) in 2021 (Table 3).

Table 3.

Top Three and Bottom Three Regions/Countries of Cutaneous Malignant Melanoma in Females Aged 55 Years and Older

Measure	Top Three Regions/Countries (Value)			Bottom Three Regions/Countries (Value)
	1st	2nd	3rd	3rd	2nd	1st
2021 Age-Standardized Rate (per 100,000)
DALY (Region)	Eastern Europe (142.83)	Central Europe (155.35)	Australasia (251.99)	South Asia (14.65)	Southeast Asia (14.00)	Oceania (5.79)
Incidence (Region)	Western Europe (41.16)	High-income North America (43.84)	Australasia (82.37)	South Asia (0.92)	Southeast Asia (0.73)	Oceania (0.29)
DALY (Country)	Australia (243.97)	Norway (250.90)	New Zealand (293.64)	Maldives (2.18)	Northern Mariana Islands (1.97)	Guam (1.45)
Incidence (Country)	Australia (76.00)	Norway (76.49)	New Zealand (115.47)	Northern Mariana Islands (0.12)	Kiribati (0.12)	Guam (0.10)
Number (increase times, 2021 vs 1990)
DALY (Region)	Andean Latin America (3.13)	Tropical Latin America (3.28)	Central Latin America (4.67)	High-income North America (1.49)	Central Asia (1.39)	Australasia (1.17)
Incidence (Region)	East Asia (5.05)	North Africa and Middle East (6.87)	Central Latin America (7.04)	Australasia (2.36)	High-income North America (2.05)	Central Asia (1.78)
DALY (Country)	Northern Mariana Islands (6.47)	Côte d’Ivoire (9.91)	Mauritius (21.67)	Niue (0.78)	Georgia (0.76)	Kyrgyzstan (0.72)
Incidence (Country)	United Arab Emirates (11.09)	Côte d’Ivoire (13.20)	Mauritius (24.41)	Kyrgyzstan (1.03)	Georgia (0.91)	Niue (0.78)
EAPC from 1990 to 2021
DALY (Region)	Central Europe (0.97)	Southern sub-Saharan Africa (0.99)	Eastern Europe (1.54)	High-income North America (−0.70)	Central Asia (−1.09)	Australasia (−1.98)
Incidence (Region)	Central Europe (3.07)	North Africa and Middle East (3.21)	Eastern Europe (3.33)	High-income North America (0.19)	Eastern Sub-Saharan Africa (0.13)	Central Asia (−0.25)
DALY (Country)	Belarus (2.82)	Mauritius (4.39)	Côte d’Ivoire (4.43)	Guam (−2.79)	Kyrgyzstan (−2.88)	Qatar (−3.13)
Incidence(Country)	Republic of Korea (4.87)	Belarus (5.12)	Côte d’Ivoire (5.58)	Guam (−1.88)	Kuwait (−1.98)	Turkmenistan (−2.06)

At the national level, New Zealand had the highest age-standardized incidence rate in 2021 (115.47 per 100,000), followed by Norway (76.49 per 100,000) and Australia (76.00 per 100,000), whereas Guam had the lowest incidence rate (0.10 per 100,000) (Table 3 and S1). For DALYs, New Zealand (293.64 per 100,000), Norway (250.90 per 100,000), and Australia (243.97 per 100,000) ranked highest, while Guam again recorded the lowest rate (1.45 per 100,000) (Table 3 and S1). Côte d’Ivoire exhibited the highest EAPCs for both incidence rate (5.58) and DALY rate (4.43), whereas Turkmenistan (−2.06) and Qatar (−3.13) showed the largest declines in incidence and DALY rates, respectively (Table 3 and S1). Mauritius experienced the greatest relative increases in disease burden, with DALYs and incidence numbers increasing by 21.67-fold and 24.41-fold, respectively, compared with 1990 (Table 3 and S1). Age-specific analyses for 2021 revealed exceptionally high incidence rates among individuals aged ≥95 years in Palau (496.81 per 100,000), while Poland had the highest DALY rate in the same age group (886.22 per 100,000) (Tables S1 and S2 and Figure 2). Decomposition analyses at the country level indicated that epidemiological change was the dominant contributor to incidence growth in Belarus and other Central European countries (Table 2).

Discussion

This study systematically evaluated the global burden of CMM among women aged 55 years and older from 1990 to 2021 using data from the GBD 2021, with a particular focus on underlying drivers of burden change. Several key findings emerged, reflecting the complex interplay of public health, sociodemographic, and clinical practice–related factors influencing CMM burden in older women. It is important to emphasize that the “epidemiological change” component in decomposition analysis represents a residual category—the change in age-specific rates not explained by demographic shifts. While this component captures the net effect of all non-demographic factors, including changes in disease risk, detection practices, and healthcare access, the decomposition method itself does not identify which specific factors are responsible. The following interpretations should therefore be read as hypotheses consistent with the observed data, not as causal conclusions.

First, our analysis identified a modest global decline in age-standardized disability-adjusted life year (DALY) rates alongside a concurrent increase in incidence rates, a pattern that appears paradoxical at first glance. The reduction in age-standardized DALY rates is likely attributable to improvements in early detection and clinical management over the past three decades, particularly in high-income settings. Public education initiatives promoting skin self-examination, targeted screening of high-risk populations, and advances in systemic therapies—including targeted treatments and immunotherapy—have collectively contributed to reductions in melanoma-related mortality and disability, thereby lowering health loss per capita. For example, Australia provides a compelling case study. The country’s long-standing “Slip, Slop, Slap, Seek, Slide” campaign—launched in the 1980s and continuously updated—combined with a national skin cancer prevention program and subsidized dermatology services, has contributed to earlier diagnosis and improved survival outcomes. Recent targeted campaigns, including “End the Trend” for young Australians and “Save Your Skin” for outdoor workers, further demonstrate sustained investment in primary prevention. Between 1990 and 2021, Australia’s age-standardized DALY rate declined substantially (EAPC=−1.98), despite having the highest incidence rates globally. Similarly, in Western Europe, the European Code Against Cancer—endorsed by the European Commission—provides evidence-based sun protection recommendations, and countries with strong implementation have shown improved melanoma survival.¹⁵ These examples demonstrate that the decline in DALY rates—observed globally but most pronounced in high-SDI regions—is associated with investment in integrated prevention, early detection, and treatment infrastructure. Beyond epidemiological surveillance, recent molecular studies have identified actionable transcriptional networks driving melanoma metastasis. For example, FRA1 has been characterized as a potent driver of an actionable transcriptional network that promotes melanoma progression.¹⁶ Similarly, translational control mechanisms, such as eIF4G1-dependent translation, have been implicated in melanoma resistance to BRAF inhibitors, offering new therapeutic modalities.¹⁷ These findings highlight the potential for integrating population-level burden data with molecular prognostic models to guide personalized therapeutic approaches. In contrast, the continued rise in age-standardized incidence rates suggests that underlying disease risk remains inadequately controlled at the global level. Several mechanisms may contribute to this pattern. Persistently high cumulative UV exposure, especially among older populations, together with ongoing ozone depletion in certain regions, is widely recognized as a major risk factor for melanoma and may contribute to the sustained incidence increases observed in our study.¹⁸ Ozone depletion has been most pronounced in the Antarctic stratosphere, where the annual “ozone hole” reached 26.1 million km² in 2023.¹⁹ Thanks to the Montreal Protocol banning ozone-depleting substances, the 2025 ozone hole was the smallest in five years, with a maximum single-day area during the peak depletion period. However, ozone-depleting substances such as chlorofluorocarbons (CFCs) persist in the atmosphere for decades-12. While full recovery to pre-1980 levels is projected for the middle of this century-6, ozone levels over Antarctica and parts of the Arctic remain below historical norms, resulting in elevated UV-B radiation reaching the Earth’s surface.^20,21 Stratospheric ozone losses have also been documented over mid-latitude regions, particularly southern South America (Patagonia, Chile, Argentina), Australia, New Zealand, and southern Africa. Notably, Australia and New Zealand—which experience high UV radiation due to both latitude and residual ozone depletion—have the highest CMM incidence rates globally, consistent with the geographic pattern of elevated UV exposure. In addition, increased public awareness and widespread adoption of advanced diagnostic tools, such as dermoscopy, have facilitated the detection of asymptomatic and early-stage lesions, which may have contributed to an overdiagnosis effect that is particularly evident in high-SDI regions. Moreover, increased population mobility and convergence of lifestyle patterns are plausible contributors to the observed trends, although the decomposition approach cannot establish a causal link between specific behavioral changes and the epidemiological component of burden growth.²² Although the World Health Organization has classified UV radiation as a carcinogen and promotes initiatives such as the Global Sun Protection Initiative, the findings of this study indicate that current global efforts to reduce UV exposure and promote protective behaviors vary substantially across regions and remain insufficient to reverse the overall upward trend in CMM incidence.²³

Second, decomposition analysis demonstrated that population growth was the dominant driver of the absolute increase in both CMM incidence and DALYs, underscoring the substantial pressure exerted on health systems by expanding population size. Importantly, epidemiological transition—reflected by rising age-specific incidence rates—also made a significant positive contribution to the growth in incidence. This finding indicates that increasing disease burden cannot be explained solely by demographic expansion and aging but also reflects a genuine increase in disease risk in certain settings. Such effects were particularly pronounced in upper-middle and middle SDI regions, where epidemiological change accounted for a substantial proportion of burden growth. These findings are consistent with the hypothesis that rapid socioeconomic transition—potentially involving lifestyle modifications associated with increased outdoor leisure activities, changes in cosmetic practices that reduce sun avoidance, evolving environmental exposures, and improved access to healthcare services that facilitates diagnosis—may have contributed to the observed increases in age-specific incidence rates.²⁴ However, the decomposition analysis cannot directly attribute these changes to any specific behavioral or environmental factor. At the same time, population aging contributed most prominently to DALY growth in high-SDI regions, consistent with their advanced age structures and high proportions of elderly individuals.²⁵ These findings align closely with the United Nations 2030 Agenda for Sustainable Development, particularly targets addressing noncommunicable disease burden and the promotion of healthy aging. Together, they highlight the need for a dual-track response to CMM: strengthening healthcare systems to manage increasing caseloads driven by demographic forces, while simultaneously intensifying primary prevention efforts—especially in regions where epidemiological change plays a major role—through measures such as sun protection and avoidance of intentional sun exposure. Complementary to primary prevention through UV protection, emerging evidence suggests that nutraceuticals and dietary bioactive compounds may offer chemopreventive potential. Natural terpenoids found in fruits and herbs have demonstrated anticancer activities in both melanoma and non-melanoma skin cancer models.²⁶ For instance, ligstroside aglycone, a compound derived from extra-virgin olive oil, has shown potent antitumor effects against BRAF V600E mutant melanoma by targeting the BRAF signaling pathway.²⁷

Furthermore, pronounced geographic disparities in CMM burden were observed. High-SDI regions and countries such as Australia and New Zealand bore a disproportionately high burden, whereas low-SDI regions experienced comparatively low incidence and DALY rates. These disparities extend beyond differences in healthcare capacity and reflect a constellation of underlying factors, including genetic susceptibility related to skin phototype distribution, historical and cultural preferences for tanned skin, latitudinal variation in UV radiation intensity, and inequalities in access to preventive and diagnostic services. Notably, several low-SDI regions exhibited low current incidence rates but positive EAPCs, suggesting a potential rise in future burden as socioeconomic development progresses. This transitional phase represents a critical opportunity for early, context-specific prevention strategies. Consistent with the World Health Organization’s Global Strategy for Cancer Control, which advocates tailored national cancer control plans, the country-level burden and trend data generated in this study provide actionable evidence to support the integration and refinement of skin cancer prevention strategies within national frameworks. Australia’s long-standing “Slip, Slop, Slap, Seek, Slide” campaign serves as a notable example of effective population-level intervention in high-burden settings and offers transferable insights for regions with similar risk profiles but less developed prevention infrastructure.

The decomposition analysis provides a framework for region-specific policy responses tailored to the dominant drivers of burden growth: High-SDI regions (eg, Australia, Western Europe, North America), where population aging contributes significantly to DALY growth (4.16%) and epidemiological change plays a major role in incidence increases (36.60%), should prioritize screening optimization and survivorship care. Australia’s “Slip, Slop, Slap, Seek, Slide” campaign and subsidized dermatology services serve as model examples. Given the overdiagnosis concerns in these settings, efforts should focus on risk-stratified screening protocols and improved diagnostic specificity through dermoscopy and artificial intelligence-assisted triage. Additionally, the growing population of older melanoma survivors requires integrated survivorship care addressing psychosocial needs and late effects of treatment. High-middle and middle SDI regions, where epidemiological change contributes substantially to incidence growth (43.34% and 31.64%, respectively), should prioritize strengthening primary prevention and early detection programs. These regions are undergoing rapid socioeconomic transition, with changing lifestyle patterns and increasing UV exposure. Public education campaigns targeting sun protection behaviors, regulation of indoor tanning, and training of primary care providers in melanoma recognition should be accelerated before disease burden escalates further. The European Code Against Cancer provides a useful framework for coordinated prevention efforts. Low-SDI regions, where current incidence rates remain low but EAPCs are positive (incidence number EAPC=3.01), should focus on improving cancer registration and diagnostic capacity. Building robust cancer surveillance systems is essential for monitoring emerging trends. Training healthcare workers in basic skin examination and establishing referral pathways to dermatology services can improve early detection. These regions are in a critical window of opportunity for implementing prevention strategies before melanoma burden becomes entrenched. As demonstrated in low-resource settings, community-based education and task-shifting to primary care can be cost-effective approaches.

The overdiagnosis hypothesis, while plausible—particularly in high-SDI regions with intensive skin surveillance—does not undermine the case for early detection; rather, it refines the approach required. Several considerations reconcile these seemingly contradictory positions. First, overdiagnosis is primarily a concern for opportunistic screening (ie, incidental detection during routine care) and whole-population campaigns, whereas risk-stratified screening targeting individuals with established high-risk factors (eg, high cumulative UV exposure, family history of melanoma, fair skin phenotype, numerous atypical nevi) can maximize benefit while minimizing harm from detection of indolent lesions. Second, overdiagnosis does not negate the value of early detection for clinically significant melanomas—those with higher Breslow thickness, ulceration, or mitotic activity—which are more likely to progress and metastasize if undetected. Third, diagnostic advances such as dermoscopy and sequential digital monitoring can improve specificity, reducing unnecessary biopsies while still enabling timely identification of concerning lesions. Fourth, the incidence-DALY paradox observed in our data—rising incidence alongside declining DALY rates—suggests that improved survival is occurring alongside overdiagnosis, indicating that the net population-level effect remains beneficial. Therefore, the appropriate policy response is not to abandon early detection, but to optimize it: implement targeted screening protocols, enhance clinician training in dermoscopic assessment, integrate artificial intelligence-assisted triage to improve diagnostic accuracy, and ensure that biopsy decisions are guided by objective risk assessment tools. This nuanced approach—’smarter screening, not more screening’—can preserve the survival benefits of early diagnosis while mitigating the harms of overdiagnosis.

The strengths of this study include its comprehensive scope and methodological rigor. Utilizing GBD 2021, the most authoritative and comprehensive source of global disease burden estimates, enabled a 32-year longitudinal analysis across more than 200 countries and regions, ensuring broad representativeness and comparability. The application of decomposition analysis extended beyond descriptive epidemiology by quantitatively disentangling the relative contributions of demographic and epidemiological drivers, thereby providing more policy-relevant insights. In addition, the explicit focus on women aged 55 years and older—a high-risk population frequently aggregated into broader age categories—enhances the clinical and public health relevance of the findings.

Several limitations should also be acknowledged. First, GBD estimates are derived from statistical modeling and are dependent on the availability and quality of input data. In regions with limited cancer registration systems, underdeveloped healthcare infrastructure, or incomplete population data, particularly in some low-SDI settings, uncertainty in estimates may be substantial. Second, although decomposition analysis distinguishes between demographic and epidemiological contributions, it cannot isolate the effects of specific underlying risk factors, such as UV exposure intensity, sun-protective behaviors, or screening practices. Third, the focus on women aged 55 years and older limits the generalizability of findings to men and younger populations, who may exhibit different burden patterns and etiological drivers. Fourth, this study use of EAPC as the sole trend metric. While joinpoint regression might identify specific years of trend change, the EAPC provides a robust summary of overall 32-year trends consistent with the standard GBD methodology. Future studies may employ joinpoint or time series approaches to complement these findings. Future studies incorporating more granular environmental, behavioral, and genetic data are warranted to clarify causal pathways and to explore sex- and age-specific disparities in CMM burden.

Conclusion

In summary, this study indicates a sustained increase in the global burden of CMM among women aged 55 years and older, associated with the combined effects of population growth, population aging, and epidemiological change, with substantial geographic inequality. These findings suggest that effective mitigation of CMM burden requires more than expansion of clinical services alone. Instead, a comprehensive public health approach is needed, integrating robust primary prevention, risk-stratified screening, equitable access to effective treatment, and continuous surveillance. Such coordinated, multi-level strategies may help curb the growing global burden of CMM, a malignancy for which primary prevention strategies have demonstrated potential in high-income settings. Importantly, population-level burden studies such as this one complement rather than replace molecular and translational melanoma research. While epidemiological surveillance quantifies disease distribution, identifies high-risk populations, and informs public health policy, molecular prognostic models and immune-related risk stratification provide insights into individual-level disease mechanisms, treatment response, and therapeutic targets. The integration of these complementary approaches offers the most promising pathway for reducing the global burden of CMM.

Funding Statement

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data Sharing Statement

The data that support the findings of this study are openly available in GBD 2021 at https://www.healthdata.org/research-analysis/gbd-data.

Ethics Approval

The GBD study was approved by University of Washington. This study employed publicly available data that did not include confidential or personally identifiable patient information. This study exclusively utilized de-identified, aggregated public data from the Global Burden of Disease 2021 database, without access to any individual identifiable personal information and no direct contact with human participants. In accordance with Item (1) and Item (2) of Article 32 of the Measures for Ethical Review of Life Science and Medical Research Involving Human Subjects issued by four Chinese national authorities on February 18, 2023, research that adopts legally obtained anonymized public population data without harm to human subjects is exempted from institutional ethics review by our hospital’s Institutional Review Board (IRB, Ethics Committee of Yuyao People’s Hospital). Therefore, formal ethical approval and written informed consent were not required for the present analysis. The original GBD 2021 database received independent ethical clearance from the Institute for Health Metrics and Evaluation (IHME), University of Washington, United States.

Author Contributions

The author made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The author reports there are no competing interests to declare.