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. 2025 Apr 30;30(4):oyae373. doi: 10.1093/oncolo/oyae373

Examining the evolving landscape of kidney cancer mortality in the United States

Melis Guer 1,2,✉,2, Chinmay Jani 3,2, Georgina Hanbury 4, Shreya Arora 5, Ruchi Jani 6, Kristine Ly 7, Martin Schostak 8, Aditya Bagrodia 9, Brent Rose 10, Ithaar H Derweesh 11, Rana R McKay 12
PMCID: PMC12041913  PMID: 40304256

Abstract

Background

Kidney cancer (KC) remains a significant contributor to cancer-related mortality in the United States, with an alarming increase in disease burden. To address this critical health issue, this study aims to investigate trends in KC mortality.

Methods

We retrieved mortality data from the Center for Disease Control WONDER database using the International Classification of Diseases 10 code C64. Age Standardized Mortality Rates (ASMRs) per 100 000 population were divided by age/gender/race/ethnicity—American Indian or Alaskan Native (AIAN)/Asian/African American (AA)/White/Hispanic/non-Hispanic—from 1999 to 2020. Joinpoint regression is conducted to calculate Average Annual Percentage Changes (AAPCs) and compare trends.

Results

A total of 284 224 deaths were reported. In 2020, the greatest ASMR was in Whites (3.9/100.000), followed by AIANs (3.5), AA (3.3), and Asians (1.6). ASMRs were 3.2 for Hispanics and 3.5 for non-Hispanics, with decreases of 11.4% and 12.5%; 5.0 for males and 2.1 for females, with decreases of 13.8% and 22.2%, respectively. AIAN males experienced the greatest ASMR decrease (44.3%), White males the smallest (1.7%). AIAN (AAPC = −1.9%), and AA (AAPC = −1.3%) showed a single negative trend line, while ASMRs in Asian (AAPC = −0.6%) and White population (AAPC = −0.6%) initially increased then declined. Younger populations experienced greater decreases, whereas populations over 85 had increasing ASMRs.

Conclusions

Over 20 years, the greatest ASMR shifted from AIAN to White individuals, with a nationally decreasing trend. The elderly and male populations continue to experience greater ASMRs. Overall, our findings provide key insights for identifying at-risk populations, guiding the development of targeted strategies to reduce disparities.

Keywords: epidemiology, disparities, mortality, kidney cancer

Implications for Practice.

Kidney cancer (KC) represents a major global health issue. In this study, we investigated age, gender, racial, and ethnic differences in KC mortality utilizing CDC WONDER database, which offers high-quality public health data in the United States. We observed an overall decline in KC mortality over the past 2 decades; however, persistent disparities and significant changes across different demographic groups remain evident. Our findings provide crucial insights for identifying at-risk populations and developing targeted strategies to enhance KC care and reduce disparities in mortality.

Introduction

Kidney cancer (KC) is a significant global health concern, with incidence rates rising by about 1% annually, according to the American Cancer Society.1 This trend is noteworthy, contrasting with declining or stabilizing rates in many other cancers. Globally, KC ranks among the top 10 cancers in the United States (USA).2,3 In 2022, an estimated 434 419 new cases were reported worldwide, with 155 702 deaths.4 Histologically, renal cell carcinoma (RCC) accounts for the vast majority (90%) of KC cases, with clear cell RCC comprising the predominant subtype (70%).5,6 Key risk factors include obesity, hypertension, smoking, and chronic kidney disease.6

Investigating KC mortality can reveal disproportionately affected risk groups. As reported, KC affects males twice as often as females.4 The incidence rates increased for KC in almost all racial and ethnic groups, with more rapid increases among American Indian or Alaskan Native (AIAN) individuals in the USA.7 African American (AA) populations likely face a disproportionate burden, with greater disparities at younger ages.8 Hispanics experience lower incidence rates but are likely diagnosed at younger ages and with more advanced tumor stages.9,10 Although Asians show lower incidence rates, this broad classification may mask subgroup differences.10 RCC incidence is expected to rise with age.11 These disparities in incidence impact mortality outcomes, reflecting the complex interplay of genetic and environmental factors, and highlighting the unmet need for new strategies to address the KC burden.

This study aims to analyze KC mortality trends from 1999 to 2020 in the USA, focusing on identifying persistent disparities and tracking progress across age, gender, racial, and ethnic groups. This is crucial for highlighting areas needing targeted interventions and providing essential historical context for interpreting current KC mortality rates and projecting future patterns. Examining trends over a 20-year period provides a robust view of changes, minimizing the impact of short-term fluctuations. Therefore, we utilized the Centers for Disease Control (CDC) WONDER database, which offers a reliable and accessible data source for granular analysis across multiple demographic variables. Ultimately, this research aims to provide valuable insights to address health inequities and improve outcomes for all populations affected by KC.

Methods

We utilized the CDC WONDER database in collaboration with the National Center for Health Statistics and the Vital Statistics Cooperative Program to analyze KC mortality in the USA from 1999 to 2020. CDC WONDER database, developed and maintained by the CDC and its collaborating partners, provides access to a diverse spectrum of public health data and information, including mortality, morbidity, population demographics, and supporting public health practice and research.12,13 We extracted data on KC mortality from the CDC WONDER database, identifying all patients whose underlying cause of death was classified under the International Classification of Diseases, Tenth Revision (ICD-10) code C64.14 There were no exclusion criteria. Mortality rates are compared using age standardization statistical method of the CDC across gender-, race-, and ethnicity-based groups and reported by year. Institutional review board approval was not required since the collected data are readily available in the mentioned data repositories in a de-identified format. All data are reported per 100 000 population.

Statistical analysis

Joinpoint regression with annualized data was used to assess the overall trends in mortality, initially with no Joinpoints, and testing for changes in the model by sequential adding Joinpoints where there is a significant change in the slope of the line. Joinpoint software (Command Line Version 4.5.0.1) is provided by the USA National Cancer Institute Surveillance Research Program.15 The model computes an estimated annual percentage change (EAPC) for each trend by fitting a regression line to the natural logarithm of the rates. Joinpoint software requires continuous data throughout the observation period to be suitable for analysis. Mortality data were missing for a small subset of states in the database for one or more calendar years. In such cases, Age Standardized Mortality Rate (ASMR) data from the previous year were carried forward. Changes in ASMR over the observation period are calculated using the Average Annual Percentage Change (AAPC), which accounts for the rate of change over time between the earliest and most recent years available. For the subgroup analysis, we extracted the Crude Mortality Rate (CMR) based on 10-year age groups for overall KC mortality and analyzed age-stratified mortality across racial groups.

Results

Current KC mortality

Table 1 illustrates the overall ASMRs in KC. Over 20 years, a total of 284 224 deaths from KC were reported. In 2020, when comparing genders, ASMRs were 5.0 for males and 2.1 for females. Among racial and ethnic groups, the greatest ASMR in 2020 was observed in Whites (3.9), followed by AIANs (3.5), AA (3.3), and Asians (1.6); Hispanics (3.2), and non-Hispanics (3.5). White men had the highest ASMR (5.7), followed by AIAN (4.9) and AA men (4.9). The lowest ASMRs were in Asian males (2.3) and females (1.1).

Table 1.

Age-standardized mortality rates in kidney cancer from 1999 to 2020.

ASMR; 95% CI Percentage change
1999 2020
Overall 4.0 (3.9–4.1) 3.4 (3.4–3.5) −15.0
Gender
 Female 2.7 (2.6–2.7) 2.1 (2.0–2.1) −22.2
 Male 5.8 (5.7–5.9) 5.0 (4.9–5.1) −13.8
Race
 American Indians/Alaskan Natives 5.7 (4.4–7.2) 3.5 (2.9–4.1) − 38.6
 Asian 1.8 (1.4–2.1) 1.6 (1.5–1.8) −11.1
 African Americans 4.1 (3.8–4.4) 3.3 (3.1–3.4) −19.5
 White 4.0 (4.0–4.1) 3.9 (3.9–3.9) −2.5
Ethnicities
 Hispanic Whites 3.7 (3.3–4.0) 3.2 (3.1–3.4) −13.5
 Non-Hispanic Whites 4.0 (4.0–4.1) 3.5 (3.5–3.6) −12.5
Females
 American Indians/Alaskan Natives 3.5 (2.3–5.2) 2.3 (1.7–3.1) −34.3
 Asian 1.2 (0.8–1.6) 1.1 (0.9–1.2) −8.3
 African Americans 2.8 (2.5–3.1) 2.1 (1.9–2.3) −25.0
 White 2.7 (2.6–2.7) 2.1 (2.0–2.2) −22.2
Males
 American Indians/Alaskan Natives 8.8 (6.3–12.0) 4.9 (3.9–6.1) −44.3
 Asian 2.5 (2.0–3.2) 2.3 (2.0–2.6) −8.0
 African Americans 6.0 (5.5–6.5) 4.9 (4.5–5.2) −18.3
 White 5.8 (5.7–6.0) 5.7 (5.7–5.7) −1.7
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Changes in KC mortality

Over 20 years, ASMR decreased nationally by 15% (females [−22.2%]/males [−13.8%]). ASMRs decreased among AIANs (−38.6%), AAs (−19.5%), Asians (−11.1%), and Whites (−2.5%); Hispanics (−13.5%) and non-Hispanics (−12.5%). The greatest drop in ASMR was in AIAN (females [−34.3%]/males [−44.3%]). White men observed the smallest decrease (−1.7%). ASMRs decreased in Asian women (−8.3%) and men (8.0%), as well as in AA women (−25.0%) and men (−18.3%) (Table 1).

Joinpoint regression analysis

Joinpoint analysis showed decreasing trends for both genders (Figure 1A/Table 2A). Females had a consistent decrease in ASMR (EAPC = −0.2%, 1999-2005; EAPC = −1.7%, 2005-2020). In males, ASMR initially increased from 1999 to 2001 (EAPC = +2.9%), then decreased from 2001 to 2018, with EAPCs of −1.9% (2001-2004), −0.6% (2004-2015), and −3.3% (2015-2018), followed by a subsequent increase (EAPC = +0.5%, 2018-2020). ASMRs decreased across all racial groups: AIANs (AAPC = −1.9%), AAs (AAPC = −1.3%), Asians (AAPC = −0.6%), and Whites (AAPC = −0.6%) (Figure 1B/Table 2B). AIAN and AA populations showed continuous decreasing trends. ASMRs for Asians transformed from an increasing (EAPC = +1.2%; 1999-2009) to decreasing trend (EAPC = −2.3%; 2009-2020). White individuals demonstrated an increase, followed by a subsequent decrease (EAPC = +4%, 1999-2001; EAPC = −1.1%, 2001-2020).

Figure 1.

A) Gender-based: The dark blue line represents females, the APCs are: -0.2 (1999–2005) and -1.7 (2005–2020). The light blue line represents males, the APCs are: +2.9 (1999–2001), -1.9 (2001–2004), -0.6 (2004–2015), -3.3 (2015–2018), and +0.5 (2018–2020). (B) Race-Based: The dark blue line represents the American Indian or Alaska Native (AIAN) population, APC = -1.91 (1999-2020). The yellow line represents Black or African American individuals, APC = -1.26 (1999-2020). The red line represents White individuals, the APCs are: +3.97 (1999–2001) and -1.07 (2001–2020). The light blue line represents the Asian or Pacific Islander population, the APCs are: +1.18 (1999-2009) and -2.27 (2009-2020). (C) Ethnicity-Based: The dark blue line represents Hispanic or Latino individuals, APC = -0.56 (1999-2020). The light blue line represents Non-Hispanic or Latino individuals, the APCs are: +3.93 (1999-2001) and -1.1 (2001-2020).

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Joinpoint trends in age-standardized mortality rates (ASMR) per 100 000 for KC in US countries between 1999 and 2020. APC (Annual Percentage Change).

Table 2.

Joinpoint analysis for ASMR in KC in the USA from 1999 to 2020 in (A) Gender Groups, (B) Racial Groups, and (C) Gender-based Racial Groups. EAPC indicates estimated annual percentage change, with 95% CIs in brackets. *Significantly different from 0 (P <.05). AAPC (Average annual percentage change) indicates a summary trend measure from 1999 to 2020.

(A) Gender groups
Trend 1 Trend 2 Trend 3 Trend 4 Trend 5 AAPC
Years EAPC (95% CI) P-value Years EAPC (95% CI) P-value Years EAPC (95% CI) P-value Years EAPC (95% CI) P-value Years EAPC (95% CI) P-value
Female 1999–2005 −0.2 (−1.2–0.7) .6 2005–2020 −1.7 (−1.9 to −1.4) <.001 −1.3 (−1.6 to −1)
Male 1999–2001 2.9 (−0.2 to 6) .061 2001–2004 −1.9 (−4.7 to 1.1) .185 2004–2015 −0.6 (−0.8 to −0.3) <.001 2015–2018 −3.3 (−6.2 to −0.4) .031 2018–2020 0.5 (−2.4–3.6) .705 −0.7 (−1.3 to −0.1)
(B) Racial groups
Trend 1 Trend 2 AAPC
Years EAPC (95% CI) P-value Years EAPC (95% CI) P-value
American Indian or Alaskan Native 1999–2020 −1.9 (−2.6 to −1.2) <.001 −1.9 (−2.6 to −1.2)
Asia or Pacific Islander 1999–2009 1.2 (−0.9–3.3) .245 2009–2020 −2.3 (−4 to −0.5) .015 −0.6 (−1.9–0.6)
Black or African American 1999–2020 −1.3 (−1.5 to −1.1) <.001 −1.3 (−1.5 to −1.1)
White 1999–2001 4 (−1.8–10.1) .171 2001–2020 −1.1 (−1.2 to −0.9) <.001 −0.6 (−1.1 to −0.1)
(C) Gender-Based Racial Groups
Trend 1 Trend 2 Trend 3 AAPC
Years EAPC (95% CI) P-value Years EAPC (95% CI) P-value Years EAPC (95% CI) P-value
Female
American Indian or Alaskan Native 1999–2020 −1.8 (−2.9 to −0.8) .002 −1.8 (−2.9 to −0.8)
Asian or Pacific Islander 1999–2020 −0.9 (−1.6 to −0.2) .016 −0.9 (−1.6 to −0.2)
Black or African American 1999–2020 −1.7 (−2.1 to −1.3) <.001 −1.7 (−2.1 to −1.3)
White 1999–2005 0.1 (−1.1–1.3) .898 2005–2020 −1.6 (−1.9 to −1.3) <.001 −1.1 (−1.5 to −0.8)
Male
American Indian or Alaskan Native 1999–2020 −2.1 (−3.1 to −1.1) <.001 −2.1 (−3.1 to −1.1)
Asian or Pacific Islander 1999–2009 1.8 (−0.3–4) .084 2009–2020 −2.4 (−4.1 to −0.6) .012 −0.4 (−1.7–0.9)
Black or African American 1999–2020 −1.2 (−1.4 to −0.9) <.001 −1.2 (−1.4 to −0.9)
White 1999–2001 2.4 (−3.1–8.1) .378 2001–2014 −0.7 (−1.1 to −0.4) <.001 2014–2020 −1.6 (−2.6 to −0.7) .002 −0.7 (−1.3 to −0.1)
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Subgroup analysis for age group mortality

Joinpoint analysis for CMR in different age groups is demonstrated in Table 3/Figure 2. We observed a nonsignificant increasing trend of CMR in the 5-14 (EAPC = +1.1%) and 15-24 age groups (EAPC = +0.5%). In the 25-34 age group, CMR decreased (EAPC = −3.6%, 1999-2007; EAPC = −0.9%, 2007-2020). Similar decreasing trends were observed in the age groups of 35-44 (EAPC = −1.9%), 45-54 (EAPC = −1.9%), and 55-64 (EAPC = −1.8%). In the 65-74 age group, fluctuating trends were observed (EAPC = +1.9%, 1999-2001; EAPC = −1.2%, 2001-2015; EAPC = −4%, 2015-2018; EAPC = +0.9%, 2018-2020). The 75-84 age group demonstrated an increase, followed by a subsequent decrease (EAPC = +1.1%, 1999-2005; EAPC = −1%, 2005-2020). Populations over 85 years had a significant increase (EAPC = +2%; 1999-2006) and a slight decrease (EAPC = −0.1%; 2006-2020).

Table 3.

Subgroup analysis for age group-based CMR: joinpoint analysis for KC CMR in the USA from 1999 to 2020 in different age groups. EAPC indicates estimated annual percentage change, with 95% CIs in brackets. *Significantly different from 0 (P <.05). AAPC (Average annual percentage change) indicates a summary trend measure from 1999 to 2020.

Age group Trend 1 Trend 2 Trend 3 Trend 4 AAPC
Years EAPC (95% CI) P-value Years EAPC (95% CI) P-value Years EAPC (95% CI) P-value Years EAPC (95% CI) P-value
5–14 years 2000–2020 1.1 (−0.1–2.3) .074 1.1 (−0.1–2.3)
15–24 years 1999–2020 0.5 (−0.4–1.4) .276 0.5 (−0.4–1.4)
25–34 years 1999–2007 −3.6 (−5.6 to −1.6) .001 2007–2020 −0.9 (−1.9–0.1) .072 −1.9 (−2.8 to −1)
35–44 years 1999–2020 −1.9 (−2.2 to −1.7) <.001 −1.9 (−2.2 to −1.7)
45–54 years 1999–2020 −1.9 (−2.9 to −0.9) .001 −1.9 (−2.9 to −0.9)
55–64 years 1999–2020 −1.8 (−2 to −1.5) <.001 −1.8 (−2 to −1.5)
65–74 years 1999–2001 1.9 (−3.4–7.6) .445 2001–2015 −1.2 (−1.5 to −0.9) <.001 2015–2018 −4 (−9–1.3) .124 2018–2020 0.9 (−4.3–6.5) .706 −1.1 (−2 to −0.1)
75–84 years 1999–2005 1.1 (0–2.2) .042 2005–2020 −1 (−1.2 to −0.7) <.001 −0.4 (−0.7–0)
85+ years 1999–2006 2 (1.1–2.8) <.001 2006–2020 −0.1 (−0.4–0.2) .315 0.6 (0.2–0.9)
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Figure 2.

In this figure, from bottom to top, the blue line: 25-34 years, APC = +1.54 (1999-2020). The light blue line: 35-44 years, the APCs are -3.27 (1999-2008) and -0.62 (2008-2020). The yellow line: 45-54 years, APC = -1.80 (1999-2020). The red line: 55-64 years, APC = -1.53 (1999-2020). The orange line: 65-74 years, the APCs are +1.85 (1999-2001) and -1.32 (2001-2020). The pink line: 75-84 years, the APCs are +1.05 (1999-2004) and -0.74 (2004-2020). The dark blue line: 85+ years, APCs are +1.79 (1999-2006) and 0.0 (2006-2020).

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Multiple joinpoint models for KC CMR in different age groups in the USA between 1999 and 2020. APC (annual percentage change) indicates a summary trend measure from 1999 to 2020.

The race-based age group CMRs are shown in Figure 3 and Supplementary Table S1. Among AIAN individuals, analysis was started from the age group 45 years and older due to data availability. There was a decreasing trend with the highest decrease in the 75-84 age group (EAPC = −4%, 1999-2020). Among Asian populations, each age group demonstrated a decreasing trend. The highest decrease observed in the age group over 85 (EAPC = −2.7%), followed by the 65-74 (EAPC = −1.2%), and 45-54 age groups (EAPC = −1.2%). AA individuals demonstrated decreasing trends among all age groups starting from 25 years. Those over 85 had an initially increasing trend, then a decrease (EAPC = +1.8%, 1999-2009; EAPC = −2%, 2009-2020). Among White individuals, CMRs decreased continuously in the 35-44 (EAPCs = −1.6%) and 45-54 age groups (EAPCs = −1.8%). CMR in the 55-64 age group decreased, with the greatest drop from 2015 to 2018 (EAPC −4.6%). In the 65-74 and 75-84 age groups, initial increases were observed, followed by subsequent decreases. White individuals over 85 years had an increase throughout the study period (EAPC = +1.9%, 1999-2006; EAPC = +0.1%, 2006-2020).

Figure 3.

From bottom to top, A) Whites: The blue line: 25-34 years, APC = 0.0 (1999-2020). The light blue line: 35-44 years, APC = -1.6(1999-2020). The yellow line: 45-54 years, APC = -1.8 (1999-2020). The red line: 55-64 years, the APCs are -0.1 (1999-2003), -4.0 (2003-2006), -0.8 (2006-2015), -4.6 (2015-2018), and -0.2 (2018-2020). The orange line: 65-74 years, the APCs are +3.4 (1999-2001) and -1.4 (2001-2020). The pink line: 75-84 years, the APCs are +1.3 (1999-2005) and -0.8(2005-2020). The dark blue line: 85+ years, APCs are +1.9 (1999-2006) and +0.1 (2006-2020) B) African Americans: The blue line: 25-34 years, APC = -0.98 (1999-2020). The light blue line: 35-44 years, APC = -1.64 (1999-2020). The yellow line: 45-54 years, APC = -2.44 (1999-2020). The red line: 55-64 years, APC = -2.08 (1999-2020).The orange line: 65-74 years, APC = -1.15(1999-2020). The pink line: 75-84 years, APC = -1.2 (1999-2020). The dark blue line: 85+ years, APCs are +1.77 (1999-2009) and -2.01 (2009-2020). C) Asian or Pacific Islanders: The blue line: 45-54 years, APC = -1.20 (2001-2020). The light blue line: 55-64 years, APC = -0.58 (1999-2020). The yellow line: 65-74 years, APC = -1.18 (1999-2020). The red line: 75-84 years, APC = -0.66 (1999-2020).The orange line: 85+ years, APC = -2.71(2005-2020). D) American Indians or Alaskan Natives: The blue line: 45-54 years, APC = -1.8 (2007-2018). The light blue line: 55-64 years, APC = -2.17 (2001-2020). The yellow line: 65-74 years, APC = -3.60 (1999-2020). The red line: 75-84 years, APC = -4.0 (1999-2020).

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Joinpoint trends in CMR per 100 000 for KC in USA countries between 1999 and 2020.

Discussion

We present the most up-to-date, comprehensive analysis of age, gender, racial, and ethnic differences in KC mortality, providing several key insights utilizing the CDC WONDER database, which offers high-quality public health data. First, we observed an overall decreasing trend in ASMR over 20 years. Second, gender-based mortality rates continue to be around 2 times higher for men. Third, across racial groups, the highest ASMR in 1999 was observed in AIAN males (8.8), whereas White males demonstrated the highest ASMR in 2020 (5.7). Over 20 years, non-Hispanic individuals continued to demonstrate higher ASMR. Fourth, age-based joinpoint analysis revealed decreases in ASMRs for younger individuals, whereas the elderly population showed a notable rise in ASMR. These findings, derived from the robust CDC WONDER database, provide crucial information for identifying at-risk populations for the future development of targeted strategies to enhance KC care and reduce disparities in mortality.

One of the most notable findings of our study was the overall decrease in ASMR for KC over 20 years. This decrease is encouraging and likely reflects improved earlier detection, advancements in surgical techniques, and improved systemic therapy strategies for advanced disease. However, despite this decreasing trend, KC remains a significant health concern. Importantly, our analysis revealed granular differences across age, gender, race, and ethnic groups, highlighting the complex nature of KC epidemiology and the persistent disparities in outcomes. Overall, ASMR for KC decreased by 15% nationally, from 4.0 to 3.4 (1999-2020). Since 1999, novel developments in cross-sectional imaging and treatment modalities have revolutionized the landscape of KC management by enabling earlier detection and treatment at the early tumor stage, resulting in improved outcomes.16 Another explanation of the decrease in ASMRs would be the development of novel drugs including targeted therapy and immunotherapies for advanced KC.17 Multimodal approaches that combine systemic and metastasis-targeted therapies have significantly improved survival outcomes.18 Smoking rates reached a historic low of 14.2% in 2019, with 61.7% of smoking cessation in the US population, significantly contributing to the decline in ASMRs.19 Promoting healthy lifestyle changes could help accelerate further progress in addressing the KC burden. The substantial reduction in ASMRs for KC from 1999 to 2020 demonstrates significant progress in disease management, highlighting the cumulative impact of improved detection methods, surgical techniques, targeted therapies, and public health initiatives in decreasing KC mortality.

Research into health disparities in KC incorporates multiple domains. One of the most important disparities in KC is gender-based. Our findings revealed that ASMRs remain higher in males than females (5.0 vs 2.1 in 2020). This persistent gender-based disparity in KC is likely the result of multifaceted interactions between biological and environmental factors. Focusing on biological differences, previous studies have reported Y chromosome loss in 47% of 1252 male renal tumors,20 suggesting that significant downregulation of chromosome Y may represent a signature of KC risk in males.21,22 Additionally, biallelic expression of tumor-suppressor genes escaping X-inactivation may reduce cancer incidence and mortality in women.23,24 Indeed, genomic sex differences may cause variations in tumor biology, highlighting the need for sex-specific future research in KC.25-27 Differences in environmental risk factors such as obesity, smoking, hypertension, and comorbidities may influence ASMRs, with higher rates of these factors in men likely contributing to greater KC mortality compared with women.28,29 Our findings underscore the complex interplay of biological and environmental factors in gender-based disparities in KC mortality, highlighting the need to focus on lifestyle changes for at-risk populations. Further research is needed to refine risk assessments and address these disparities.

Racial disparities in KC ASMRs have significantly shifted over 20 years. In 1999, the greatest ASMR for KC was observed in AIAN individuals. This aligns with prior studies showing a high KC incidence and mortality in AIAN populations. Contributing factors likely include differences in health care access, socioeconomic variations affecting health-seeking behaviors, and lifestyle choices such as higher rates of tobacco use, alcohol consumption, and diets high in processed foods.19,30,31 However, since 1999, the AIAN population experienced a continuous and steep decline in ASMRs with a percentage change of −38.6% compared with −2.5% in White individuals. By 2020, White individuals exhibited the greatest mortality rates in the USA (Figure 1B). The significant decline in ASMRs among AIAN individuals likely reflects increased health care access through the development of financial assistance and social services for AIAN population and may underscore improvements in health characteristics, including lifestyle behaviors and socioeconomic factors, among AIANs in the USA.31 A consistent decreasing trend in ASMRs was also observed in Black individuals. This decline can be attributed to multiple factors, including a significant decrease in smoking prevalence among Black individuals from 24.3% to 14.9% (1999-2019), improved access to early detection and treatment, and modifications of other KC risk factors.30,32,33 The significant shift in mortality rates by 2020 between White and Black populations can likely be explained by improved management of comorbidities, greater access to surgical treatments, and higher treatment rates at earlier tumor stages. Over 20 years, Asian individuals have consistently exhibited the lowest ASMRs for KC. This trend may be attributed to a combination of genetic, lifestyle, and environmental factors, including lower rates of obesity and smoking, and cultural emphasis on preventive health.30,32 Collectively, these findings highlight significant shifts in race-based KC mortality trends, emphasizing the need for continued efforts to address racial disparities and tailor prevention and treatment strategies to specific racial groups.

In the USA, Hispanic individuals represent the second largest ethnic minority group after non-Hispanic Whites, comprising people with heritage from Central and South America, the Caribbean, and Spain. This group encompasses a diverse array of cultures, nationalities, and ethnic backgrounds, and health considerations.34 Hence, we compared the mortality trends between Hispanics and non-Hispanic Whites. In 2020, ASMR was 3.2 for Hispanic population and 3.5 for non-Hispanic Whites, with similar decreases of 13.5% and 12.5%, respectively. Despite higher incidence and mortality rates in non-Hispanic Whites, Hispanics continue to bear a disproportionately high KC burden, likely influenced by diagnoses at younger ages and more frequent advanced tumor stages.10,35 Additionally, several risk factors for KC, including obesity, smoking, and diabetes mellitus, have been observed to be more prevalent in Hispanics.35 Another key factor is social determinants of health, including lower household income, living in underserved areas, and greater distance to cancer care centers, which are most likely to affect healthcare access in Hispanic populations.34,36 Indeed, recent studies demonstrated reduced disparities in survival outcomes between Hispanics and non-Hispanics when treated surgically or with systemic therapy at tertiary cancer centers.37,38 Although, both ethnic groups showed a notable decrease in mortality, studies evaluating the underlying complex interplay between social determinants and tumor biology across ethnic groups have been limited. Hispanic population continue to be underrepresented in precision oncology studies and clinical trials.39 Further studies with inclusion of ethnic minority groups may help address ethnic-based disparities in KC mortality.

Over 20 years, there was an increasing trend in CMRs among individuals over 85 and a more pronounced decrease in younger individuals. As the population ages and KC risk factors increases, the KC burden is expected to rise among the elderly. Moreover, this increase among elderly White populations likely reflects higher comorbidities and vulnerability, leading to conservative treatment decisions, including nonsurgical strategies such as tumor ablation or watchful-waiting to manage the disease.40 Across racial groups, AA individuals had the greatest decrease in CMRs in the 45-54 age group and smaller decreases in older age groups, which could be explained by higher multimorbidity in elderly Black population affecting mortality.41 AIANs demonstrated greatest decreases in the 65-74 and 75-84 age groups, likely associated with an increase in insured patients, which grew from 64% to 78% (2013-2018) in elderly AIAN populations.42 A noteworthy aspect of our results is the clear demonstration of differences in CMRs across racial age groups. Overall, these notable changes in age-group based CMR might be associated with improved health coverage, including the availability of early detection and surgical treatments in middle age to older age groups. This may highlight the importance of age-based, individualized treatment strategies to address the age-group based racial disparities.

This study demonstrates a comprehensive examination of population-level trends using annual mortality data from the CDC national surveillance over an extended period. Its administrative design significantly impacts its limitations. The main limitation is the lack of tumor-specific data, which affects our understanding of stage migration in mortality trends. Additionally, the absence of individual-level data on comorbidities and confounding variables must be considered when interpreting our results. Additionally, unmeasured risk factors could have influenced our results. Given the considerable diversity among ethnic groups, our analysis was limited by the absence of granular data on country of origin or birth. This study is designed to understand changes in KC mortality in the USA. Therefore, substantial differences in the prevalence rate of KC between gender, race, and age groups are not possible to elucidate in this report. While acknowledging the limitations of this study, we have observed significant changes in KC mortality rates in the USA. Gaining a more detailed understanding of KC trends is the first step toward addressing the care needs of the most at-risk groups.

Conclusion

This comprehensive analysis of KC mortality trends in the USA over the past 2 decades provides crucial insights into the KC burden. By leveraging the high-quality, up-to-date data from the CDC WONDER database, we identified significant trends and disparities across gender, age, race, and ethnic groups. While the overall decreasing trend in ASMR is encouraging, persistent disparities and notable changes in KC mortality among different demographic groups remain evident. By identifying at-risk populations and highlighting areas of persistent disparity, our work paves the way toward reducing disparities and improving outcomes for all individuals affected by KC.

Supplementary Material

oyae373_suppl_Supplementary_Tables_1
oyae373_suppl_supplementary_tables_1.docx (1MB, docx)

Contributor Information

Melis Guer, Moores Cancer Center, University of California San Diego, San Diego, CA 92037, United States; Clinic of Urology, Uro-Oncology, Robot-Assisted and Focal Therapy, University Hospital Magdeburg, 39120 Magdeburg, Germany.

Chinmay Jani, Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, United States.

Georgina Hanbury, Guy’s and St Thomas’ Hospital, London SE1 7EH, United Kingdom.

Shreya Arora, Harvard Medical School, Mount Auburn Hospital, Boston, MA 02138, United States.

Ruchi Jani, Smt. NHL Municipal Medical College, Gujarat University, Gujarat 380006, India.

Kristine Ly, Moores Cancer Center, University of California San Diego, San Diego, CA 92037, United States.

Martin Schostak, Clinic of Urology, Uro-Oncology, Robot-Assisted and Focal Therapy, University Hospital Magdeburg, 39120 Magdeburg, Germany.

Aditya Bagrodia, Moores Cancer Center, University of California San Diego, San Diego, CA 92037, United States.

Brent Rose, Moores Cancer Center, University of California San Diego, San Diego, CA 92037, United States.

Ithaar H Derweesh, Moores Cancer Center, University of California San Diego, San Diego, CA 92037, United States.

Rana R McKay, Moores Cancer Center, University of California San Diego, San Diego, CA 92037, United States.

Author contributions

Melis Guer (Conceptualization, Project administration, Writing—original draft, Writing—review & editing), Chinmay Jani (Conceptualization, Data curation, Resources, Visualization, Writing—original draft, Writing—review & editing), Georgina Hanbury (Writing—original draft, Writing—review & editing), Shreya Arora (Formal analysis, Methodology, Software, Writing—review & editing), Ruchi Jani (Formal analysis, Methodology, Software, Writing—review & editing), Kristine Ly (Writing—original draft, Writing—review & editing), Martin Schostak (Writing—review & editing), Aditya Bagrodia (Writing—review & editing), Brent Rose (Writing—review & editing), Ithaar Derweesh (Writing—review & editing), and Rana R. McKay (Conceptualization, Data curation, Investigation, Project administration, Methodology, Resources, Supervision, Validation, Writing—original draft, Writing—review & editing)

Funding

This research received no external funding.

Conflict of interest

Melis Guer: None; Chinmay Jani: None; Georgina Hanbury: None; Shreya Arora: None; Ruchi Jani: None; Kristine Ly: None; Martin Schostak: Consulting and advisory work: Amgen, Astellas, AstraZeneca, Bayer-Vital, Bristol-Myers-Squibb, Ipsen, Janssen, Merck, MSD, Novartis; Aditya Bagrodia: Consulting and advisory work: Veracyte, Ferring Pharmaceuticals, UroGen; Brent Rose: None; Ithaar H. Derweesh: None; Rana R. McKay: Consulting and advisory work: Ambrx, Arcus, AstraZeneca, Aveo, Bayer, Blue Earth Diagnostics, Bristol–Myers Squibb, Calithera, Caris, Dendreon, Eli Lilly, Esiai, Exelixis, Janssen, Merck, Myovant, Novartis, Pfizer, Sanofi, SeaGen, Sorrento Therapeutics, Telix, Tempus, Institutional Research Support: AstraZeneca, Artera, Bayer, Bristol-Myers Squibb, Exelixis, Oncternal, Tempus.

Data Availability

The mortality data used in this article are accessible through the publicly available Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (CDC-WONDER). You can access the data at: https://wonder.cdc.gov/.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

oyae373_suppl_Supplementary_Tables_1
oyae373_suppl_supplementary_tables_1.docx (1MB, docx)

Data Availability Statement

The mortality data used in this article are accessible through the publicly available Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (CDC-WONDER). You can access the data at: https://wonder.cdc.gov/.

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