American Cancer Society’s Report on the Status of Cancer Disparities in the United States, 2025

Abstract

Since 2021, the American Cancer Society has published its biennial report on the status of cancer disparities in the United States. In this 2025 report, the authors provide updated data on disparities in cancer occurrence and outcomes by sex, race, ethnicity, socioeconomic status (SES [educational attainment as a proxy]), and geographic location (including urbanicity of county of residence and congressional district), along with contributors to these disparities, including major cancer risk factors, screening, and select social determinants of health (SDOH) and health‐related social needs. The authors found substantial disparities across the cancer continuum, including risk factors, incidence, stage at diagnosis, receipt of care, survival, and mortality for many cancers and in evaluated SDOH by race and ethnicity, educational attainment, and geographic location. During 2019 through 2023, Black and American Indian/Alaska Native populations had the highest cancer mortality rates, both overall and for the leading causes of cancer death. Cancer mortality rates were also consistently higher among adults with lower SES. However, differences in cancer mortality were substantially larger by education than by race, indicating that SES plays a major role in driving racial disparities in cancer mortality. Overall cancer mortality rates were higher in Black adults than in White adults with the same education level by 7%–28% among males and by 2%–43% among females. Within each race, however, overall cancer mortality rates were higher in adults with ≤12 years of education than in those with ≥16 years of education by 143%–192% among males and by 71%–140% among females. Mortality from all cancers combined was 21% higher in nonmetropolitan than in large metropolitan counties, with the greatest differences for lung (45%) and cervical (36%) cancers and the smallest for prostate, female breast, and pancreatic cancers (7%–8%). By congressional district, the highest cancer mortality rates both overall and for lung, colorectal, and breast cancers were largely found in the South and East North‐Central division of the Midwest; however, for prostate cancer, there was no distinct geographic pattern. Sociodemographic groups that had higher cancer mortality generally had higher exposure to risk factors, lower health insurance coverage, and limited access to cancer prevention, early detection, and treatment compared with groups that had lower cancer mortality, largely reflecting fundamental disparities in SDOH. Mitigating cancer disparities in the United States requires intersectoral stakeholder engagement, targeted funding, effective policies at the federal, state, and local levels, and broad implementation of evidence‐based interventions, such as expanding health insurance coverage, including through strengthening Marketplaces and protecting and expanding access to Medicaid.

INTRODUCTION

The American Cancer Society (ACS) first reported on cancer disparities by race and income in the United States in 1986, ¹ , ² followed by numerous publications in the following years. ³ , ⁴ , ⁵ , ⁶ , ⁷ , ⁸ , ⁹ The American Cancer Society’s Report on the Status of Cancer Disparities in the United States, started in 2021, is a biennial publication to examine disparities in cancer occurrence and outcomes and contributing factors to these disparities in the United States by sex, race, ethnicity, socioeconomic status (SES), and geographic location. ¹⁰ , ¹¹ This report also provides information on ACS programs targeting cancer disparities and policy recommendations.

MATERIALS AND METHODS

Data sources

Cancer occurrence

Data on cancer incidence and stage at diagnosis from 2018 through 2022 by race and ethnicity were obtained from the North American Association of Central Cancer Registries (NAACCR) ¹² and by urbanicity of county of residence from the National Cancer Institute’s (NCI’s) Surveillance, Epidemiology, and End Results (SEER) 21 registry program. ¹³ Data on cancer survival in patients with cancer aged 99 years and younger diagnosed in 2016 through 2022 were obtained from the SEER 21 cancer registries (excluding Illinois) ¹³ and on cancer mortality in 2019 through 2023 from the Centers for Disease Control and Prevention’s (CDC’s) National Center for Health Statistics (NCHS). ¹⁴ All cancer occurrence data were stratified by age group, sex, race, ethnicity, urbanicity of the county of residence, and cancer type. The latter included all cancers combined and four leading causes of cancer death in each sex, i.e., cancers of the lung and bronchus (lung), female breast (breast), prostate, colorectum, and pancreas, all of which except pancreatic cancer are recommended for screening in average‐risk populations, as well as uterine cervix (cervical) cancer—the other screenable cancer. Data on colorectal cancer incidence, stage at diagnosis, and survival excluded cancer of the appendix.

Consistent with our previous report, we stratified the analysis into two age groups: younger than 65 years and 65 years and older. This cutoff reflects the near universal eligibility for Medicare coverage at age 65 years and the finding that disparities in cancer occurrence are more pronounced among individuals younger than 65 years compared with older adults. ¹¹ Self‐identified race and ethnicity categories included non‐Hispanic White (White), non‐Hispanic Black (Black), non‐Hispanic American Indian and Alaska Native (AIAN), non‐Hispanic Asian and Pacific Islander (API), and Hispanic‐Latinx (Hispanic) populations. It should be noted that race and ethnicity are sociopolitical constructs and do not delineate distinct biologic categories. ¹⁵ Cancer incidence, stage at diagnosis, and survival data for the AIAN population were confined to individuals residing in Purchased/Referred Care Delivery Area counties, which cover approximately two thirds of the AIAN population, to minimize racial misclassification. ⁷ , ¹⁶ Mortality rates for the AIAN population (for the entire United States) were adjusted for racial misclassification on death certificates using classification ratios previously published by the NCHS. ¹⁷ Urbanicity was defined based on the 2013 Rural‐Urban Continuum Codes, ¹⁸ but using three consolidated categories: county of residence in a large metropolitan area (with a population of ≥1 million), a small‐medium metropolitan area (with a population of <1 million), and a nonmetropolitan area.

To estimate cancer mortality rates by education level in individuals aged 25–74 years, mortality data for 2019–2023 were obtained from the NCHS by sex, race and ethnicity (all, White, Black), cancer (all‐cancer, lung, colorectum, breast, and prostate), and highest education level attained (≤12, 13‐15, and ≥16 years of education). ¹⁹ The corresponding population data based on US census microdata were obtained through IPUMS USA. ²⁰ For records with missing information on education attainment and race and ethnicity (approximately 1.5% and 0.2% of mortality data, respectively), values were imputed according to the overall distribution of available categories within each variable.

To estimate sex‐specific and cancer‐specific cancer mortality rates in 2019–2023 by congressional district, we constructed cancer mortality and population data for 436 congressional districts corresponding to the 435 seats of the US House of Representatives among the 50 states plus the District of Columbia, which elects a nonvoting delegate to the House. District boundaries were defined according to the 119th Congress of the United States, in effect from January 2025 to January 2027. ²¹ We used state‐level data for seven congressional districts that comprised an entire state (Alaska, Delaware, North Dakota, South Dakota, Vermont, and Wyoming) or followed federal district boundaries (District of Columbia). Some other congressional districts comprise an entire county or a group of counties. For districts that encompassed part of one or several counties, we first estimated the sex‐specific population within each census block within the district using the 2020 US Census data. Then, the number of cancer deaths and the population from the NCHS in each county were weighted by the sex‐specific proportion of the block population to estimate the number of cancer deaths and population in the block. ²² The number of cancer deaths and population were aggregated over the blocks and counties that were included in the given congressional district.

Socioeconomic status and social determinants of health

Data on educational attainment of ≤12 years, income below the federal poverty level (FPL), delay or nonreceipt of medical care because of cost, measures of housing insecurity (inability to pay mortgage, rent, or utility bills) and food insecurity (inability to afford balanced meals), and health insurance coverage were obtained from self‐reported measures in the 2024 National Health Interview Survey (NHIS). ²³ Health insurance coverage was categorized into uninsured, Medicaid or other public only, and private for individuals younger than 65 years; and Medicare only (including Medicare Advantage), Medicare plus public only (Medicare dually eligible), and Medicare plus private (Medicare supplemental) for individuals aged 65 years and older. More information on insurance categories is available in the Supporting Methods.

Cancer risk factors and screening

Data on prevalence of ever and current cigarette smoking, obesity, heavy alcohol drinking, and physical inactivity in 2024 by age group, sex, race, ethnicity, and urbanicity of the county of residence in individuals aged 18 years and older were obtained from self‐reported measures in the NHIS. ²³ Ever cigarette smoking was defined as smoking at least 100 cigarettes in a lifetime. Current cigarette smoking was defined as smoking at least 100 cigarettes in a lifetime and currently smoking every day or some days. Obesity was defined as a body mass index (BMI) ≥30 kg/m². The prevalence of obesity in the United States generally is estimated based on BMI values measured on physical examination in the National Health and Nutrition Examination Survey ²⁴ but, because information on urbanicity is not available from this survey, we report obesity estimates from the NHIS. Heavy alcohol drinking was defined as greater than 14 drinks per week in the past year for men or greater than seven drinks per week in the past year for women. Alcohol consumption levels are usually adjusted for underreporting using a same adjustment factor for all subpopulations based on alcohol sales data. ²⁵ We defined heavy alcohol consumption based on unadjusted consumption levels only to compare the exposures across categories of race and ethnicity and urbanicity. Physical inactivity was defined as the absence of any aerobic leisure‐time physical activity.

Some surveys do not collect data on the same items in every cycle. Data on being up to date with the utilization of screening according to the ACS guidelines and US Preventive Services Task Force recommendations for cancers of the female breast (2023), colorectum (2023), cervix (2021), and prostate (2023) by age group, race, ethnicity, and urbanicity of the county of residence were based on data from the NHIS. ²³ We relied on a previous report and method to estimate lung cancer screening rates nationally and by state in 2022 based on the CDC’s Prevention Behavioral Risk Factor Surveillance System data ²⁶ because the NHIS has not collected detailed full‐year lung cancer screening data since 2015. ²⁷

Statistical methods

Age‐standardized incidence and mortality rates per 100,000 population and 5‐year relative survival were calculated using SEER*Stat software, version 9.0.41.4. Incidence and mortality rate ratios (RRs) and their 95% confidence intervals (CIs) were calculated using SEER*Stat software and Stata, version 15.1 (Stata Corporation), based on age‐adjusted rates and Tiwari and colleagues' method. ²⁸ For confidentiality reasons and to produce more stable results, rates were suppressed when there were <16 cancer cases or deaths in each group. ²⁹ Weighted prevalence estimates for measures of SES, social determinants of health (SDOH), risk factors, and cancer screening by corresponding stratifications were calculated using R, version 4.5.1 (R Core Team), and SAS‐callable SUDAAN, release 11.0.4 (SAS Institute Inc.), and accounted for the complex survey designs. To obtain stable results, prevalence estimates were suppressed when a denominator size was <50 or the relative standard error was ≥0.3. ²⁴

DISPARITIES IN CANCER OCCURRENCE

Cancer incidence

During 2018 through 2022, incidence rates were higher in Black males than in White males for all cancers combined and for each evaluated cancer type (Table 1). In addition, incidence rates were higher in Black females than in White females for cancers of the colorectum, pancreas, and cervix (Table 1). Incidence rates were also higher in AIAN males for lung and colorectal cancers and in AIAN females for all cancers combined and for lung, colorectal, pancreatic, and cervical cancers than in their White counterparts. In contrast, incidence rates for all cancers combined and for each evaluated cancer type in both sexes were lower in API and Hispanic populations than in the White population, except for a higher cervical cancer rate in Hispanic females relative to White females.

TABLE 1.

Age‐standardized incidence and mortality rates for select cancers by sex, race, and ethnicity, and corresponding rate ratios for differences by race and ethnicity, United States, 2018–2023.

	NH White	NH Black		NH AIAN		NH API		Hispanic‐Latinx
Cancer site by sex	Rate a	Rate	RR (95% CI)	Rate	RR (95% CI)	Rate	RR (95% CI)	Rate	RR (95% CI)
Incidence: 2018–2022
All cancers	467.6	452.4	0.97 (0.97–0.97)	465.5	1.00 (0.99–1.01)	299.1	0.64 (0.64–0.64)	354.2	0.76 (0.76–0.76)
Males	501.9	522.9	1.04 (1.04–1.05)	484.8	0.97 (0.95–0.98)	292.7	0.58 (0.58–0.59)	368.4	0.73 (0.73–0.74)
Females	444.9	404.8	0.91 (0.91–0.91)	457.0	1.03 (1.01–1.04)	309.0	0.69 (0.69–0.70)	351.6	0.79 (0.79–0.79)
Lung and bronchus	56.9	54.0	0.95 (0.94–0.95)	60.2	1.06 (1.03–1.09)	32.5	0.57 (0.57–0.58)	27.0	0.47 (0.47–0.48)
Males	61.5	67.5	1.10 (1.09–1.11)	63.9	1.04 (1.00–1.09)	39.0	0.63 (0.62–0.64)	31.6	0.51 (0.51–0.52)
Females	53.5	44.6	0.83 (0.83–0.84)	57.7	1.08 (1.04–1.12)	27.7	0.52 (0.51–0.53)	23.7	0.44 (0.44–0.45)
Breast (female)	137.4	130.6	0.95 (0.95–0.96)	117.5	0.86 (0.83–0.88)	108.6	0.79 (0.78–0.80)	102.8	0.75 (0.74–0.75)
Prostate	111.2	185.6	1.67 (1.66–1.68)	89.7	0.81 (0.78–0.84)	61.8	0.56 (0.55–0.56)	91.2	0.82 (0.81–0.83)
Colorectum b	34.7	39.5	1.14 (1.13–1.15)	48.2	1.39 (1.34–1.43)	27.8	0.80 (0.79–0.81)	32.0	0.92 (0.91–0.93)
Males	39.6	47.1	1.19 (1.18–1.20)	55.1	1.39 (1.33–1.46)	32.7	0.83 (0.81–0.84)	37.6	0.95 (0.94–0.96)
Females	30.2	34.0	1.13 (1.11–1.14)	42.4	1.40 (1.34–1.47)	23.7	0.78 (0.77–0.80)	27.3	0.90 (0.89–0.91)
Pancreas	13.7	16.6	1.21 (1.19–1.22)	14.3	1.04 (0.98–1.11)	9.9	0.72 (0.71–0.74)	12.1	0.88 (0.87–0.89)
Males	15.8	18.0	1.14 (1.12–1.16)	15.2	0.96 (0.88–1.05)	10.7	0.68 (0.66–0.70)	12.9	0.82 (0.80–0.83)
Females	11.9	15.4	1.29 (1.27–1.31)	13.4	1.13 (1.03–1.22)	9.3	0.78 (0.76–0.80)	11.4	0.96 (0.94–0.98)
Uterine cervix	7.0	8.2	1.17 (1.15–1.20)	11.6	1.67 (1.52–1.83)	5.8	0.84 (0.81–0.87)	9.7	1.39 (1.36–1.42)
Mortality: 2019–2023
All cancers	151.2	166.5	1.10 (1.10–1.10)	176.0	1.16 (1.15–1.18)	93.1	0.62 (0.61–0.62)	106.0	0.70 (0.70–0.70)
Males	178.0	203.6	1.14 (1.14–1.15)	200.6	1.13 (1.11–1.15)	107.1	0.60 (0.60–0.61)	124.4	0.70 (0.69–0.70)
Females	131.2	143.7	1.10 (1.09–1.10)	159.9	1.22 (1.20–1.24)	83.1	0.63 (0.63–0.64)	93.2	0.71 (0.71–0.71)
Lung and bronchus	34.5	33.1	0.96 (0.95–0.97)	38.7	1.12 (1.09–1.15)	18.6	0.54 (0.53–0.55)	14.2	0.41 (0.41–0.42)
Males	39.6	44.5	1.12 (1.11–1.14)	43.2	1.09 (1.05–1.13)	23.4	0.59 (0.58–0.60)	18.7	0.47 (0.46–0.48)
Females	30.5	25.3	0.83 (0.82–0.84)	35.6	1.17 (1.12–1.21)	15.0	0.49 (0.48–0.50)	10.9	0.36 (0.35–0.37)
Breast (female)	19.3	26.5	1.37 (1.35–1.39)	20.8	1.08 (1.03–1.14)	11.8	0.61 (0.60–0.63)	13.6	0.71 (0.70–0.72)
Prostate	18.4	36.9	2.01 (1.98–2.04)	20.5	1.12 (1.05–1.19)	8.8	0.48 (0.46–0.50)	15.4	0.84 (0.82–0.86)
Colorectum	12.9	16.6	1.28 (1.26–1.30)	18.6	1.44 (1.38–1.49)	9.2	0.71 (0.69–0.72)	10.6	0.82 (0.81–0.83)
Males	15.2	21.0	1.38 (1.35–1.40)	21.9	1.44 (1.36–1.52)	10.9	0.72 (0.70–0.74)	13.2	0.86 (0.85–0.88)
Females	10.9	13.4	1.23 (1.21–1.25)	16.0	1.47 (1.38–1.56)	7.8	0.71 (0.69–0.73)	8.6	0.78 (0.77–0.80)
Pancreas	11.5	13.6	1.18 (1.16–1.19)	12.3	1.06 (1.01–1.12)	7.8	0.68 (0.66–0.69)	8.9	0.77 (0.76–0.78)
Males	13.3	15.1	1.13 (1.11–1.15)	13.1	0.98 (0.92–1.06)	8.4	0.63 (0.61–0.65)	9.6	0.72 (0.70–0.74)
Females	9.9	12.3	1.24 (1.22–1.26)	11.6	1.17 (1.09–1.25)	7.2	0.73 (0.71–0.75)	8.2	0.83 (0.81–0.85)
Uterine cervix	2.0	3.1	1.50 (1.45–1.56)	3.6	1.74 (1.52–1.98)	1.6	0.80 (0.74–0.85)	2.3	1.14 (1.09–1.19)

Note: Rates were per 100,000 population and age adjusted to the 2000 US standard population (20 age groups). Incidence rates for the AIAN population were based on Purchased/Referred Care Delivery Area counties. Mortality rates for the AIAN population (for the entire United States) were adjusted for racial misclassification on death certificates using classification factors from the National Center for Health Statistics.

Abbreviations: AIAN, American Indian and Alaska Native; API, Asian and Pacific Islander; CI, confidence interval; NH, non‐Hispanic.

^a The White population was the reference group for rate ratios.

^b Excluded appendix.

Source: North American Association of Central Cancer Registries (incidence) and National Center for Health Statistics (mortality) data.

Compared with counties in large metropolitan areas with a population ≥1 million, incidence rates in counties in nonmetropolitan areas during 2018 through 2022 were higher for cancers of the lung (by 46% in males and 38% in females), cervix (by 28%), and colorectum (by 19% in males and 18% in females; Table 2), cancer types that are largely attributable to potentially modifiable risk factors. ³⁰ In contrast, incidence rates of breast and prostate cancer were lower in nonmetropolitan than large metropolitan areas, whereas incidence rates of pancreatic cancer in these areas were similar. Cancer incidence rates in small‐medium metropolitan areas with a population <1 million were generally between the rates in large metropolitan areas and rates in nonmetropolitan areas (Table 2). Differences in cancer incidence rates by race and ethnicity (see Table S1) and urbanicity (Figure 1; see Table S2) were generally larger in people younger than 65 years than in older adults.

TABLE 2.

Age‐standardized incidence and mortality rates for select cancers by sex and urbanicity of county of residence and corresponding rate ratios for differences by urbanicity, United States, 2018–2023.

	Metro, ≥1 million pop	Metro, <1 million pop		Non‐metro
Cancer site by sex	Rate a	Rate	RR (95% CI)	Rate	RR (95% CI)
Incidence: 2018–2022
All cancers	436.6	458.9	1.05 (1.05–1.05)	470.7	1.08 (1.07–1.08)
Males	470.6	503.0	1.07 (1.07–1.07)	514.7	1.09 (1.09–1.10)
Females	416.0	428.4	1.03 (1.03–1.03)	438.5	1.05 (1.05–1.06)
Lung and bronchus	43.9	51.4	1.17 (1.16–1.18)	62.5	1.42 (1.41–1.44)
Males	48.2	57.5	1.19 (1.18–1.20)	70.2	1.46 (1.44–1.47)
Females	40.7	46.8	1.15 (1.14–1.16)	56.1	1.38 (1.36–1.39)
Breast (female)	132.3	129.7	0.98 (0.97–0.99)	122.1	0.92 (0.91–0.93)
Prostate	120.4	123.3	1.02 (1.02–1.03)	112.7	0.94 (0.93–0.94)
Colorectum b	34.2	35.7	1.05 (1.04–1.05)	40.6	1.19 (1.18–1.20)
Males	39.4	41.2	1.04 (1.03–1.06)	46.8	1.19 (1.17–1.20)
Females	29.7	30.9	1.04 (1.03–1.06)	34.9	1.18 (1.16–1.20)
Pancreas	13.8	13.8	1.00 (0.99–1.01)	13.9	1.01 (0.99–1.03)
Males	15.5	15.5	1.00 (0.98–1.01)	15.5	1.00 (0.97–1.02)
Females	12.3	12.3	1.00 (0.98–1.02)	12.3	1.00 (0.97–1.03)
Uterine cervix	7.4	8.1	1.09 (1.06–1.12)	9.4	1.28 (1.23–1.33)
Mortality: 2019–2023
All cancers	137.0	149.4	1.09 (1.09–1.09)	166.2	1.21 (1.21–1.22)
Males	160.5	177.0	1.10 (1.10–1.11)	196.8	1.23 (1.22–1.23)
Females	120.6	128.8	1.07 (1.06–1.07)	142.1	1.18 (1.17–1.18)
Lung and bronchus	27.8	33.3	1.20 (1.19–1.20)	40.2	1.45 (1.44–1.45)
Males	32.5	39.3	1.21 (1.20–1.22)	47.8	1.47 (1.46–1.48)
Females	24.3	28.5	1.17 (1.16–1.18)	33.9	1.40 (1.38–1.41)
Breast (female)	18.7	19.3	1.03 (1.02–1.04)	20.3	1.08 (1.07–1.10)
Prostate	18.8	19.2	1.02 (1.01–1.03)	20.2	1.07 (1.06–1.09)
Colorectum	12.1	12.8	1.06 (1.05–1.07)	15.5	1.28 (1.27–1.30)
Males	14.4	15.2	1.05 (1.04–1.07)	18.4	1.28 (1.26–1.29)
Females	10.2	10.8	1.05 (1.04–1.07)	13.0	1.27 (1.25–1.29)
Pancreas	11.1	11.3	1.02 (1.01–1.03)	11.9	1.08 (1.06–1.09)
Males	12.7	13.0	1.03 (1.01–1.04)	13.6	1.08 (1.06–1.10)
Females	9.8	9.9	1.02 (1.00–1.03)	10.3	1.06 (1.04–1.08)
Uterine cervix	2.0	2.3	1.15 (1.11–1.19)	2.7	1.36 (1.31–1.42)

Source: Surveillance, Epidemiology, and End Results 21 registries (incidence; covering 36.7% of the US population) and National Center for Health Statistics (mortality) data.

Note: Rates were per 100,000 population and age adjusted to the 2000 US standard population (20 age groups).

Abbreviations: CI, confidence interval; metro, metropolitan; pop, population; RR, rate ratio.

^a Large metropolitan areas with a population ≥1 million were the reference group for rate ratios.

^b Excluded appendix.

FIGURE 1.

Ratio of cancer incidence and mortality rates in nonmetropolitan versus large metropolitan (≥1 million population) areas overall and for select cancers by sex and age group, United States, 2019–2023. Rate ratios were based on rates age adjusted to the 2000 US standard population (20 age groups). Incidence rates for colorectal cancer excluded the appendix. CI indicates confidence interval; RR, rate ratio. Source: Surveillance, Epidemiology, and End Results 22 registries (incidence; covering 36.7% of the US population) and National Center for Health Statistics (mortality) data.

Cancer stage at diagnosis

Compared with the White population, the proportion of localized‐stage cancers during 2018 through 2022 was lower among all other racial and ethnic groups for cancers of the lung, breast, prostate (except the Black population), colorectum (except the API population), and cervix (Figure 2). For pancreatic cancer, however, the proportion of localized‐stage cancers was comparable, and low, across all evaluated racial and ethnic groups, including the White population. Currently, there are no accepted modalities for pancreatic cancer screening in average‐risk populations. Compared with individuals residing in counties in large metropolitan areas, individuals residing in nonmetropolitan areas had lower proportions of localized‐stage lung, breast, pancreatic, and cervical cancers during 2018 through 2022. Differences in the proportion of localized‐stage cancer diagnosis by race and ethnicity and urbanicity were generally similar in those younger than 65 years and aged 65 years and older, with a notable exception of cervical cancer, for which the observed differences were greater in females younger than 65 years (see Figure S1); currently, cervical cancer screening is recommended through age 65 years.

FIGURE 2.

Stage at diagnosis (%) for select cancers by race and ethnicity and urbanicity of county of residence, United States, 2018–2022. Estimates for colorectal cancer excluded the appendix. AIAN indicates American Indian and Alaska Native; API, Asian and Pacific Islander; Metro, metropolitan; NH, non‐Hispanic; pop, population. Source: North American Association of Central Cancer Registries (estimates by race and ethnicity) and Surveillance, Epidemiology, and End Results 21 registries (covering 36.7% of the US population; estimates by urbanicity of county of residence).

Cancer survival

The 5‐year age‐standardized relative survival during 2016 through 2022 for evaluated cancer types was generally lowest in Black and AIAN populations (Figure 3; see Figure S2). Similarly, stage‐specific survival was generally lower in Black and AIAN populations than in the White population, e.g., 36% and 30% versus 47%, respectively, for localized‐stage pancreatic cancer (see Table S3). When stratified by urbanicity of the county of residence, the age‐standardized 5‐year relative cancer survival for evaluated cancers during 2016 through 2022 was lower in nonmetropolitan areas than in large metropolitan areas, e.g., 59% versus 64% for cervical cancer (Figure 3), and for each stage at diagnosis, e.g., 57% versus 71% for localized‐stage lung cancer (see Table S3).

FIGURE 3.

Five‐year relative cancer survival for select cancers by race and ethnicity and urbanicity of county of residence, 2016–2022. Five‐year survival for cancer in patients younger than 99 years diagnosed in 2016–2022, excluding in situ carcinomas, age‐standardized to the International Cancer Survival Standard age standard 1 for all cancer types shown in this figure, except cancer of the uterine cervix, for which International Cancer Survival Standard age standard 2 was used. Age groups used for age standardization were 15–44, 45–54, 55–64, and 65–74 years and 75 years or older, except for prostate cancer (age groups 15–54, 55–64, 65–74, and 75–84 years and 85 years or older). Estimates for the AIAN population were based on Purchased/Referred Care Delivery Area counties. The estimate for cervical cancer in the NH AIAN population could not be calculated. Estimates for colorectal cancer excluded the appendix. AIAN indicates American Indian and Alaska Native; API, Asian and Pacific Islander; CI, confidence interval; NH, non‐Hispanic; SEER, Surveillance, Epidemiology, and End Results. Source: North American Association of Central Cancer Registries (estimates by race and ethnicity) and Surveillance, Epidemiology, and End Results 21 registries (excluding Illinois; estimates by urbanicity of county of residence).

Cancer mortality

The mortality rate for all cancers combined during 2019 through 2023 was higher in Black and AIAN males (by 14% and 13%, respectively) and females (by 10% and 22%, respectively) than in their White counterparts (Table 1). The 10% higher cancer mortality in Black females than in White females was despite a 9% lower overall cancer incidence rates in Black females. Mortality rates for each evaluated cancer were higher in Black and AIAN males than in White males, except pancreatic cancer, for which mortality rates were comparable in AIAN and White males. A similar pattern was observed for Black and AIAN females, except for a lower lung cancer mortality rate in Black females than in White females. With the exception of a 14% higher cervical cancer mortality rate among Hispanic females, mortality rates for each evaluated cancer type were lower in API and Hispanic males and females than in their White counterparts.

When evaluated by urbanicity of the county of residence, overall cancer mortality rates during 2019 through 2023 were higher in nonmetropolitan areas than in large metropolitan areas by 23% in males and 18% in females, about twice the magnitude of the incidence disparity (Table 2). Nonmetropolitan areas also had higher mortality rates for each evaluated cancer type, including breast and prostate cancers, despite lower incidence rates. The largest difference by urbanicity was observed for lung cancer, with mortality rates 47% higher in males and 40% higher in females in nonmetropolitan areas compared with large metropolitan areas; this was followed by cervical cancer (36% higher) and colorectal cancer (28% higher in males and 27% in females). When stratified jointly by urbanicity and race and ethnicity, the Black–White difference in overall cancer mortality rates in females was greater in large metropolitan areas than in nonmetropolitan areas, whereas the difference in males was similar across both settings (see Table S4).

Cancer mortality rates during 2019 through 2023 were higher among adults with lower SES (Table 3). The differences by education were substantially larger than by race, indicating that SES plays a major role in driving racial disparities in cancer mortality. Overall cancer mortality rates were higher in Black adults than in White adults with the same education level by 7%–28% among males and 2%–43% among females. Compared with their counterparts who had ≥16 years of education, however, the rate in adults who had ≤12 years of education was higher by 71% in Black females, by 2.4 times in Black males and White females, and by 2.9 times in White males. For the evaluated cancer types, mortality rates within each educational attainment category were higher in the Black population than in the White population, with the exception of lung cancer in males with ≤12 years of education and females with ≤16 years of education, among whom the rates were lower in the Black population. Similar to all cancers combined, differences in mortality were larger by education than by race for lung and colorectal cancers, whereas differences in prostate cancer mortality were larger by race than by education. Of note, the Black–White disparities in mortality from all evaluated cancers within each educational attainment category were generally largest in the highest educational attainment category.

TABLE 3.

Age‐standardized cancer mortality rates from common cancers among Black and White adults aged 25‐74 years by sex and educational attainment, and corresponding rate ratios, United States, 2019–2023.

	Mortality rate, %
Cancer site by sex	All races and ethnicities	Non‐Hispanic White	Non‐Hispanic Black	Black vs. White individuals: RR (95% CI)
Male
All cancers
≥16 years of education	79.2	81.6	104.8	1.28 (1.26–1.31)
13–15 years of education	109.2	115.3	126.8	1.10 (1.08–1.12)
≤12 years of education	206.8	238.6	254.8	1.07 (1.06–1.08)
≤12 vs. ≥16 years of education: RR (95% CI)	2.61 (2.60–2.63)	2.92 (2.90–2.94)	2.43 (2.39–2.48)
Lung and bronchus
≥16 years of education	10.6	10.6	13.5	1.27 (1.21–1.33)
13–15 years of education	21.9	23.9	24.8	1.04 (1.01–1.07)
≤12 years of education	55.6	69.2	64.8	0.94 (0.92–0.95)
≤12 vs. ≥16 years of education: RR (95% CI)	5.26 (5.19–5.33)	6.51 (6.40–6.61)	4.80 (4.57–5.05)
Colorectum
≥16 years of education	9.0	9.1	14.1	1.55 (1.48–1.63)
13–15 years of education	12.0	12.2	15.9	1.31 (1.25–1.36)
≤12 years of education	20.9	23.0	28.4	1.23 (1.20–1.26)
≤12 vs. ≥16 years of education: RR (95% CI)	2.34 (2.30–2.38)	2.53 (2.48–2.58)	2.01 (1.91–2.12)
Prostate
≥16 years of education	6.0	5.7	15.6	2.73 (2.60–2.87)
13–15 years of education	7.1	6.5	15.2	2.33 (2.24–2.43)
≤12 years of education	11.2	10.4	25.7	2.48 (2.41–2.55)
≤12 vs. ≥16 years of education: RR (95% CI)	1.88 (1.84–1.92)	1.82 (1.77–1.86)	1.65 (1.57–1.73)
Female
All cancers
≥16 years of education	79.2	79.7	114.1	1.43 (1.41–1.45)
13–15 years of education	95.5	98.1	124.3	1.27 (1.25–1.28)
≤12 years of education	160.8	191.0	194.5	1.02 (1.01–1.03)
≤12 vs. ≥16 years of education: RR (95% CI)	2.03 (2.02–2.04)	2.40 (2.38–2.41)	1.71 (1.68–1.73)
Lung and bronchus
≥16 years of education	10.0	10.3	12.1	1.17 (1.12–1.22)
13–15 years of education	18.1	20.4	18.7	0.92 (0.89–0.94)
≤12 years of education	39.4	53.8	36.2	0.67 (0.66–0.69)
≤12 vs. ≥16 years of education: RR (95% CI)	3.94 (3.89–4.00)	5.21 (5.12–5.29)	3.01 (2.87–3.14)
Colorectum
≥16 years of education	6.7	6.7	10.1	1.51 (1.44–1.59)
13–15 years of education	8.2	8.1	11.5	1.41 (1.35–1.47)
≤12 years of education	13.2	15.2	17.7	1.17 (1.13–1.20)
≤12 vs. ≥16 years of education: RR (95% CI)	1.98 (1.94–2.02)	2.27 (2.21–2.33)	1.75 (1.65–1.84)
Breast
≥16 years of education	17.0	16.9	28.0	1.66 (1.61–1.71)
13–15 years of education	17.9	17.4	28.5	1.64 (1.59–1.69)
≤12 years of education	24.5	26.9	38.0	1.41 (1.38–1.44)
≤12 vs. ≥16 years of education: RR (95% CI)	1.44 (1.42–1.46)	1.60 (1.57–1.62)	1.36 (1.31–1.41)

Source: National Center for Health Statistics (mortality) and US Census Bureau (population) data.

Note: Rates were per 100,000 population and age adjusted to the 2000 US standard population (20 age groups).

Abbreviations: CI, confidence interval; RR, rate ratio.

Congressional districts with the highest overall cancer mortality rates in both males and females usually were in the South and East North‐Central division of the Midwest (Figure 4). These areas include most states that did not expand Medicaid income eligibility under the Affordable Care Act (ACA) ³¹ and have historically had less restrictive tobacco‐control policies and high smoking prevalence. ³² A similar pattern existed for lung, female breast, and colorectal cancers. However, Alaska and several districts in the central part of the United States were also among districts with the highest colorectal cancer mortality rates among females. Of note, colorectal cancer mortality rates were also among the highest in several districts along the southern border in Texas, despite having relatively low mortality rates for all cancers combined and for the other evaluated cancer types. Congressional districts with the highest prostate cancer mortality rates were spread across the United States without a clear geographic pattern.

FIGURE 4.

Mortality rates for select cancers by sex and congressional district, United States, 2019–2023. Rates were per 100,000 population and age adjusted to the 2000 US standard population (20 age groups). Source: National Center for Health Statistics data.

When stratified by age group, disparities in cancer mortality rates between Black and AIAN populations versus White populations (see Table S1), between nonmetropolitan versus large metropolitan areas (Figure 1), between Black and White populations across categories of urbanicity (see Table S3), and by congressional district (see Figures S3 and S4) were generally larger in the group younger than 65 years than in the older age group, likely reflecting access to care through Medicare. For example, lung cancer mortality rates among females in nonmetropolitan versus large metropolitan areas were 95% higher in individuals younger than 65 years, but they were 26% higher in individuals aged 65 years and older (Figure 1).

FACTORS CONTRIBUTING TO CANCER DISPARITIES

Social determinants of health

Structural SDOH, including social, economic, and political mechanisms, result in a set of socioeconomic positions based on income, education, race, ethnicity, geographic location, and other factors. These positions, in turn, shape intermediary determinants of health, including living and working conditions, housing and food security, access to health care, and behavioral and psychosocial factors. ³³ , ³⁴ As such, inequalities in SDOH are major drivers for disparities in exposure to risk factors and receipt of preventive care, early detection (including screening), treatment, and long‐term survivorship care for cancer (Figure 5). ³⁴ , ³⁵ For example, redlining was a practice implemented in the 1930s and outlawed in 1968 that withheld financial services (including mortgages) from neighborhoods predominantly inhabited by people of racial and ethnic minoritized and lower SES groups. ³⁶ This practice limited opportunities for homeownership and wealth building in those marginalized communities, notably Black neighborhoods, and contributed to residential separatism, persistent underinvestment, and ongoing inequalities. ³⁶ , ³⁷ , ³⁸ , ³⁹ Of note, the Black–White wealth gap has largely remained unchanged since the 1950s. ⁴⁰ In 2022, the median wealth of Black households was $44,900: about 15% of the median wealth of White households at $285,000. ⁴¹ Redlining and residential separatism have been associated with poorer living conditions, ⁴² , ⁴³ , ⁴⁴ , ⁴⁵ , ⁴⁶ , ⁴⁷ higher exposure to risk factors of cancer, ⁴⁸ , ⁴⁹ higher risk of advanced‐stage cancer diagnosis, ³⁶ , ⁵⁰ , ⁵¹ , ⁵² lower receipt of treatment, ⁵³ and poorer cancer outcomes. ⁵⁴ , ⁵⁵ , ⁵⁶ , ⁵⁷ , ⁵⁸

FIGURE 5.

Social determinants of health and cancer disparities.

Inequalities in SDOH are disproportionately experienced by populations defined by race, ethnicity, SES, and place of residence. In 2024, racial and ethnic minoritized groups and people living in nonmetropolitan areas generally were more likely to have lower educational attainment and to experience poverty and food and housing insecurity (Figure 6). For example, the proportion of individuals aged 18–64 years with incomes below the FPL in 2024 was substantially higher in AIAN (21.4%), Black (16.0%), and Hispanic (15.7%) populations than in Asian (8.7%) and White (7.7%) populations and in nonmetropolitan (16.4%) than in large metropolitan (9.1%) areas.

FIGURE 6.

Select social determinants of health characteristics by age group, race and ethnicity, and urbanicity of county of residence, United States, 2024. Estimates were age‐adjusted prevalence using age groups 18–24, 25–34, 35–44, and 45–64 years and 65 years and older in individuals aged 18 years and older (25 years and older for education level). Estimates for housing insecurity in AIAN and Asian populations aged 65 years and older were unstable (denominator size <50 or relative standard error ≥0.30) and are not shown. The AIAN population comprised respondents of AIAN race alone or in combination with other any other group. AIAN indicates American Indian and Alaska Native; GED, General Educational Development (equivalent to high school diploma); Metro, metropolitan; NH, non‐Hispanic. Source: National Health Interview Survey data.

Health insurance coverage is a major determinant of access to and receipt of health care services and subsequent cancer outcomes in the United States. ⁵⁹ , ⁶⁰ Compared with insured individuals aged 18–64 years, uninsured individuals were more likely to delay or not receive needed medical care because of cost (30.8% vs. 8.3%–9.7% in 2024), or not to be up to date with colorectal (76.5% vs. 35.4%–45.4% in 2023) or female breast (65.2% vs. 26.3%–36.7% in 2023) cancer screening (Figure 7). In this age group, the Hispanic population (25.3%) had the highest proportion of individuals with no health insurance in 2024, followed by AIAN (13.3%), Black (10.5%), White (7.8%), and Asian (5.4%) populations (Table 4). The proportion of uninsured individuals was also higher in nonmetropolitan areas, in individuals with lower education or income levels, and in the South region. The largest disparity was by education level, with 4.3% of individuals who had ≥16 years of education uninsured compared with 31.8% among those who had <12 years of education. Most individuals aged 65 years and older are age‐eligible for Medicare coverage. As such, the proportion of individuals without health insurance coverage is negligible in this age group, although there are substantial differences in the prevalence of dual coverage (Medicare plus Medicaid or private insurance) across the evaluated populations. For example, the prevalence of Medicare plus private insurance was substantially higher in the White population and in individuals with higher education and income levels than in the other groups.

FIGURE 7.

Disparities in access to care by age group and health insurance coverage, United States, 2023–2024. Being up to date for colorectal cancer screening was defined as receipt of fecal occult blood test/fecal immunochemical test, sigmoidoscopy, colonoscopy, computed tomography colonography, or stool DNA test in the past 1, 5, 10, 5, and 3 years, respectively, in individuals aged 45 years and older. Being up to date for breast cancer screening was defined as receipt of mammogram within the past year (aged 45–54 years) or the past 2 years (aged 55 years and older). Both definitions were based on the American Cancer Society's guidelines for cancer screening. Source: National Health Interview Survey data.

TABLE 4.

Prevalence of insurance coverage (%) by age group, race and ethnicity, socioeconomic characteristics, urbanicity of county of residence, and region, United States, 2024.

	Aged 18–64 years			65 years and older
	Uninsured	Medicaid or other public only	Private (any)	Medicare only	Medicare + Medicaid or other public only	Medicare + private supplemental
Race/ethnicity
Non‐Hispanic White only	7.8	10.7	77.2	50.6	3.9	37.4
Non‐Hispanic Black only	10.5	24.7	59.0	49.7	16.7	22.1
Non‐Hispanic AIAN only or multiple	13.3	29.1	48.5	49.3	NA	NA
Non‐Hispanic Asian only	5.4	12.0	80.5	49.4	16.7	25.7
Hispanic‐Latinx	25.3	20.1	51.6	52.5	21.7	15.1
Urbanicity of county of residence
Metropolitan, ≥1 million population	10.9	13.7	71.9	52.5	8.3	31.1
Metropolitan, <1 million population	12.5	15.1	67.4	50.0	6.7	33.8
Nonmetropolitan	13.3	19.4	62.1	46.1	7.9	37.1
Educational attainment (aged 25 years and older)
Some high school or less	31.8	29.3	35.2	50.0	24.1	16.4
High school graduate	15.2	20.5	58.9	54.0	8.1	29.5
Some college	9.5	14.4	70.4	50.9	5.4	33.4
College graduate or higher	4.3	4.4	88.0	47.8	2.8	42.1
Income level
<100% federal poverty level (FPL)	21.7	47.6	26.1	45.0	37.7	9.0
100% to <200% FPL	22.3	31.9	40.0	57.4	14.4	19.1
≥200% FPL	7.8	6.2	82.3	49.5	2.4	39.5
US region
Northeast	7.7	18.2	70.5	45.1	9.6	38.7
Midwest	9.4	13.9	73.5	47.6	4.5	40.1
South	15.7	11.7	67.2	52.3	7.9	29.7
West	9.8	18.3	68.5	55.1	9.0	27.2
Total	11.7	14.8	69.3	50.7	7.7	32.9

Note: Percentages might not add up to 100% because a relatively small proportion of people did not fall into one of the categories shown in this table; for example, individuals aged 65 years and older who were uninsured or had military coverage with or without Medicare (see Supporting Methods). The AIAN population comprised respondents of AIAN race alone or in combination with other any other group.

Abbreviations: AIAN, American Indian and Alaska Native; NA, not available (sparse data, denominator size <50, or relative standard error ≥0.30).

Source: National Health Interview Survey data.

Disparities in exposure to major modifiable cancer risk factors

Modifiable risk factors account for nearly one half of all cancer deaths in the United States. ³⁰ The leading modifiable risk factor is cigarette smoking, accounting for nearly 30% of all cancer deaths, followed by obesity (7%) and alcohol consumption (4%) ³⁰ ; some other common risk factors, including dietary factors, physical inactivity, and carcinogenic infections, are listed in Table S5.

Disparities in cancer mortality by race, ethnicity, SES, and geographic location in part reflect differences in the prevalence of these modifiable risk factors. For example, the burden of lung cancer is higher in nonmetropolitan areas than in large metropolitan areas in both sexes and in Black males than in White males, whereas the burden is lower in Black females compared with White females. These patterns are consistent with the pattern of current cigarette smoking among those populations (Table 5). Of note, about 85% of lung cancer cases in the United States are attributable to cigarette smoking. ³⁰ In 2024, the prevalence of current cigarette smoking was higher in nonmetropolitan areas than in large metropolitan areas, particularly in younger age groups (18.5% vs. 10.2% among males and 15.3% vs. 6.4% among females aged 18–64 years; see Table S6). A substantial difference existed according to education level, with the prevalence of current cigarette smoking ranging from 5.3% among individuals with a college degree to 27.4% among individuals without a high school diploma in 2023 ²⁴ ; this difference was consistent with the 4‐fold to 5‐fold difference in the lung cancer mortality rate between the lowest and highest education groups in this study (Table 3). Of note, menthol cigarette smoking prevalence is higher in racial and ethnic minoritized groups, particularly the Black population. Of individuals who currently smoked in 2023, the prevalence of menthol cigarette smoking ranged from 27.9% in the White population to 35.9%–40.3% in AIAN, Asian, and Hispanic populations and to 75.6% in the Black population, ²⁴ suggesting that eliminating menthol as a characterizing flavor in cigarettes is likely to further reduce smoking, particularly among the racial and ethnic minoritized groups. ⁶¹ , ⁶² The greater use of menthol tobacco in Black communities is largely attributed to a history of targeted advertising. ⁶³

TABLE 5.

Age‐adjusted prevalence of major risk factors for cancer and being up to date with cancer screening (%) by sex (for risk factors), race and ethnicity, and urbanicity of county of residence, United States, 2021–2024. ^a

	Race and ethnicity					Urbanicity of county of residence
Risk factor and screening by age group	NH White only	NH Black only	NH AIAN only or multiple	NH Asian only	Hispanic‐Latinx	Metro, ≥1 million pop	Metro, <1 million pop	Nonmetropolitan
Risk factor b
Ever smoking, 18 years and older (2024)
Males	39.4	31.2	40.6	27.3	33.4	33.4	39.0	44.6
Females	32.9	19.3	47.5	8.8	14.7	22.6	28.9	36.5
Current smoking, 18 years and older (2024)
Males	12.5	13.4	18.5	5.7	10.2	9.7	13.4	17.3
Females	10.2	8.6	18.6	1.8	4.6	6.4	9.5	14.4
Obesity, 18 years and older (2024)
Males	33.6	36.4	44.2	13.6	36.3	30.3	36.0	41.9
Females	31.2	48.1	41.7	11.0	39.2	30.7	35.7	42.7
Heavy alcohol drinking, 18 years and older (2024)
Males	6.2	3.9	8.4	2.1	5.2	4.8	6.1	7.1
Females	7.5	3.7	7.8	1.9	2.9	5.8	6.0	5.3
Physical inactivity, 18 years and older (2024)
Males	21.0	26.4	19.2	18.0	31.3	20.0	26.1	30.5
Females	23.4	32.3	34.8	23.3	36.2	24.6	29.5	32.7
Screening
Breast cancer (2023) c
ACS, 45 years and older	69.1	75.4	59.4	70.5	64.3	70.3	68.2	65.2
USPSTF, 50–74 years	79.3	86.2	73.7	81.2	78.2	82.3	77.7	75.7
Colorectal cancer (2023) d
ACS, 45 years and older	67.5	65.7	58.9	58.4	56.0	65.5	64.9	62.3
USPSTF, 45–75 years	66.5	64.2	57.4	57.0	53.3	64.4	63.1	60.9
Cervical cancer (2021) e
ACS, 25–65 years	79.6	75.7	68.0	63.7	68.4	75.8	76.5	71.8
USPSTF, 21–65 years	77.9	72.4	64.8	61.7	66.4	73.5	74.2	71.0
Prostate cancer (2023) f
ACS, 50 years and older	40.9	33.5	23.2	26.3	26.6	38.7	36.6	33.2
USPSTF, 55–69 years	41.0	32.6	NA	23.2	27.4	38.4	37.4	30.7

Source: National Health Interview Survey data.

Abbreviations: AIAN, American Indian and Alaska Native; ACS, according to the American Cancer Society guidelines; Metro, metropolitan; NA, not available (sparse data, denominator size <50, or relative standard error ≥0.30); NH, non‐Hispanic; pop, population; USPSTF, US Preventive Services Task Force recommendations.

^a Lower and upper bounds for individuals younger than 65 years and aged 65 years and older were based on the age ranges shown in the first column; e.g., the age range for individuals younger than 65 years for ever smoking was from 18 to 64 years. The AIAN population comprised respondents of AIAN race alone or in combination with other any other group.

^b Estimates for risk factors were age‐adjusted prevalence using age groups 18–24, 25–34, 35–44, and 45–64 years and 65 years and older. Ever cigarette smoking was defined as smoking at least 100 cigarettes in a lifetime. Current cigarette smoking was defined as smoking at least 100 cigarettes in a lifetime and currently smoking every day or some days. Obesity was defined as a body mass index ≥30 kg/m². Heavy alcohol consumption was defined as >14 drinks per week in the past year (men) or greater than seven drinks per week in past year (women). Physical inactivity was defined as no aerobic leisure‐time physical activity.

^c Mammogram within the past year (aged 45–54 years) or the past 2 years (aged 55 years and older) based on ACS guidelines, or the past 2 years based on USPSTF recommendations (aged 50–74 years). Estimates for all ages and individuals younger than 65 years were age‐adjusted using the following age groups: 45–49 years, 50–64 years, and 65 years and older (ACS guidelines) and 50–64 and 65–74 years (USPSTF guidelines).

^d Fecal occult blood test/fecal immunochemical test, sigmoidoscopy, colonoscopy, computed tomography colonography, or stool DNA test in the past 1, 5, 10, 5, and 3 years, respectively. Estimates for all ages and individuals younger than 65 years were age‐adjusted using the following age groups: 45–49 years, 50–64 years, and 65 years and older (ACS guidelines) and 45–49, 50–64, and 65–75 years (USPSTF guidelines).

^e Papanicolaou test in the past 3 years (aged 25–65 years based on ACS guidelines and 21–65 years based on USPSTF recommendations) or Papanicolaou test and human papillomavirus test within the past 5 years (aged 30–65 years). Estimates were age‐adjusted using the following age groups: 25–29, 30–39, 40–49, and 50–65 years (ACS guidelines) and 21–29, 30–39, 40–49, and 50–65 years (USPSTF guidelines).

^f Serum prostate‐specific antigen testing in the past year (aged 50 years and older based on ACS guidelines and 55–69 years based on USPSTF recommendations) among men who have not been diagnosed with prostate cancer. Estimates were age‐adjusted using the following age groups: 50–64 years and 65 years and older (ACS guidelines) and 55–64 and 65–69 years (USPSTF guidelines). Both the ACS and the USPSTF recommend prostate cancer screening after shared decision making, for which data were not available from the National Health Interview Survey.

In 2024, the prevalence of obesity by race and ethnicity among males ranged from 13.6% in Asian males to 33.6%‐36.4% in White, Black, and Hispanic males and to 44.2% in AIAN males (Table 5; see Table S6). Obesity prevalence among females was highest in Black females (48.1%), followed by AIAN (41.7%), Hispanic (39.2%), White (31.2%) and Asian (11.0%) females. The prevalence of heavy alcohol drinking (>14 drinks per week for men, greater than seven drinks per week for women) in both males and females in 2024 was higher in AIAN (8.4% in males, 7.8% in females) and White (6.2% in males, 7.5% in females) populations than in other racial and ethnic groups (ranging from 2.1% to 5.2% in males and from 1.9% to 3.7% in females). The prevalence of physical inactivity in males ranged from 18.0%–21.0% in Asian, AIAN, and White males to 26.4% in Black males to 31.3% in Hispanic males; and, in females, the prevalence ranged from approximately 23% in Asian and White females to 32.3%–36.2% in Black, AIAN, and Hispanic females. The prevalence of obesity, heavy drinking (among younger males only), and physical inactivity was higher in nonmetropolitan than in large metropolitan areas. For example, the prevalence of obesity in 2024 was approximately 42% among males and females in nonmetropolitan areas compared with about 30% in large metropolitan areas.

Disparities in early detection and receipt of treatment

Disparities in early detection and treatment are other important factors contributing to differences in cancer survival and mortality, especially for cancers with disproportionally larger differences in mortality than incidence (Table 1, Table 2). ⁶⁴

Disparities in early detection (including cancer screening)

In 2023 (2021 for cervical cancer), the prevalence of being up to date with cancer screening was lower among racial and ethnic minoritized groups compared with the White population, with the exception of breast cancer screening, which was more prevalent among Black and Asian populations (Table 5; see Table S6). The prevalence for colorectal, cervical, and prostate cancer screening was particularly lower among AIAN, Asian, and Hispanic populations. For example, the prevalence of being up to date with colorectal cancer screening was lowest in the Hispanic population (53.3%), followed by Asian (57.0%), AIAN (57.4%), Black (64.2%), and White (66.5%) populations. The lower colorectal cancer screening prevalence in the Hispanic population may be a major factor contributing to relatively high colorectal cancer mortality rates along the southern border in Texas, an area with a high concentration of people of Mexican heritage and burdened by high poverty. ⁶⁵ Previous studies have reported lower prevalence of screening and higher proportions of advanced‐stage colorectal cancer in people of Mexican heritage than in other Hispanic populations. ⁶⁶ , ⁶⁷ , ⁶⁸ The prevalence of being up to date with the evaluated cancer screenings was about 3–7 percentage points lower in nonmetropolitan areas than in large metropolitan areas.

Receipt of cancer screening has been shown to vary by SES. ²⁴ In 2023, for example, the prevalence of being up to date with colorectal cancer screening in adults aged 45–75 years ranged from 47.6% among individuals without a high school diploma to 67.8% among individuals with a college degree. ²⁴ Moreover, timely follow‐up of abnormal screening results is critical for realizing the maximal benefits of screening, but it has been suboptimal, with disparities by sociodemographic factors. ⁶⁹ , ⁷⁰ , ⁷¹ , ⁷² Disparities in access to care can also delay the timely follow‐up of suspicious symptoms of cancers for which routine screening is not recommended in the general population ⁷³ , ⁷⁴ and receipt of surveillance testing or screening for other cancers among cancer survivors. ⁷⁵ , ⁷⁶

The prevalence of being up to date with lung cancer screening in 2022 among individuals who were eligible was 18.1% nationally but varied across states, ranging from 9.7% in Wyoming to 31.0% in Rhode Island. ²⁶ States with the highest prevalence of up‐to‐date lung cancer screening were located in the Northeast region. Individuals without health insurance had substantially lower prevalence of lung cancer screening nationally (3.7%) than individuals with insurance (14.4%–26.3%). ²⁶

Research is ongoing to develop simple blood tests capable of detecting many different types of cancer based on screening for fluidic biomarkers, including some cancer types with no existing recommended screening. ⁷⁷ Currently, only one in seven cancers is diagnosed through traditional screening modalities. ⁷⁸ However, none of these tests have proven their clinical utility to date, and prospective clinical trials are needed to address uncertainties and potential harms associated with these tests. ⁷⁹ , ⁸⁰

Disparities in receipt of treatment

It is well documented that racial and ethnic minoritized groups, people without health insurance coverage and/or of lower SES, and people residing in rural areas are less likely to receive treatment for many cancer types. ⁸¹ , ⁸² , ⁸³ , ⁸⁴ , ⁸⁵ , ⁸⁶ , ⁸⁷ , ⁸⁸ , ⁸⁹ , ⁹⁰ Even if these individuals receive cancer treatment, it is less likely to be guideline‐concordant or delivered in a timely manner. ⁹¹ , ⁹² , ⁹³ , ⁹⁴ , ⁹⁵ , ⁹⁶ , ⁹⁷ , ⁹⁸ , ⁹⁹ , ¹⁰⁰ , ¹⁰¹ , ¹⁰² , ¹⁰³ , ¹⁰⁴ , ¹⁰⁵ Multiple factors may contribute to these disparities, including affordability, health care system factors, and geographic accessibility.

Because of the high cost of treatment for many cancer types, many individuals may not be able to afford cancer treatments ¹⁰⁶ , ¹⁰⁷ , ¹⁰⁸ , ¹⁰⁹ , ¹¹⁰ , ¹¹¹ and may forgo or delay the recommended care. ⁶⁰ , ¹¹² , ¹¹³ , ¹¹⁴ Among those who receive care, the negative impact of medical expenses, or financial hardship, has been associated with higher prevalence of financial distress, asset depletion, medical debt, bankruptcy, limited transportation, food and housing insecurity, poorer quality of and adherence to care, poorer quality of life, and elevated mortality risk. ¹¹³ , ¹¹⁵ , ¹¹⁶ , ¹¹⁷ , ¹¹⁸ , ¹¹⁹ , ¹²⁰ , ¹²¹ , ¹²² , ¹²³ In addition to the high cost of medications, other barriers contribute to financial hardship, including income loss because of disrupted employment, absenteeism, or complete job loss for the patient and sometimes their informal caregivers, and costs associated with clinic and treatment visits, such as costs of transportation, meals, and accommodations. ⁶⁰ , ¹²⁴ , ¹²⁵ Although innovative, advanced medications and technologies have the potential to improve cancer outcomes, they are often accompanied by increased costs and thus may widen the gap in the receipt of guideline‐concordant cancer treatments and patient outcomes if affordability is not addressed effectively. ¹²⁶ , ¹²⁷ , ¹²⁸ , ¹²⁹ , ¹³⁰

Although the financial burden of cancer is likely to affect individuals without health insurance the most, many individuals with private or public health insurance coverage, particularly individuals with non‐ACA–compliant insurance (e.g., short‐term, limited‐duration plans) or limited incomes, may experience challenges in paying for their treatments because of high deductibles, copayments, or coinsurance. In the past decades, private health insurance plans cost‐sharing schemes have increased, resulting in a greater financial burden on patients. ¹³¹ , ¹³² Moreover, access to care may vary across different private or public insurance plans because of variations in utilization management restrictions and coverage of cancer treatments. For example, individuals enrolled in high‐deductible health plans can experience substantially higher out‐of‐pocket costs compared with their counterparts who have traditional private insurance ¹³³ , ¹³⁴ and are more likely to forgo subsequent diagnostic tests after abnormal cancer screening test results or to delay or forgo cancer care. ⁶⁹ , ¹³⁵ , ¹³⁶ Individuals with Medicare Advantage plans may experience more utilization management restrictions (e.g., prior authorization) for high‐cost medications than those with traditional Medicare. ¹³⁷ , ¹³⁸ Disruptions in health insurance coverage, which have historically been common among Medicaid enrollees, ¹³⁹ have also been associated with lower receipt of cancer prevention, screening, and treatment and higher mortality risk among individuals with public and private health insurance coverage. ¹⁴⁰ , ¹⁴¹ , ¹⁴² , ¹⁴³ , ¹⁴⁴ , ¹⁴⁵

Several health care system factors may contribute to disparities in cancer care, including provider networks, expertise and referral patterns, ¹⁴⁶ , ¹⁴⁷ , ¹⁴⁸ , ¹⁴⁹ population‐specific knowledge and skills, ¹⁵⁰ and geographic accessibility. ¹⁵¹ , ¹⁵² , ¹⁵³ , ¹⁵⁴ Rural hospital closures, which have been more common in states that did not expand Medicaid income eligibility, ¹⁵⁵ , ¹⁵⁶ can further limit the access to cancer care and specialists. Furthermore, many cancer survivors experience transportation barriers to care, ¹⁵⁷ which are adversely associated with care and patient outcomes. ¹⁵⁸ , ¹⁵⁹ The use of telemedicine may increase accessibility to some types of cancer care for people residing in remote areas and those with limited transportation resources. However, access to broadband and digital technologies presents challenges to those populations. ¹⁶⁰ , ¹⁶¹ Moreover, the Medicare telehealth flexibilities that made telehealth more accessible since the coronavirus disease 2019 public health emergency expired on October 1, 2025, restricting access to telehealth for many Medicare beneficiaries. ¹⁶²

Other factors contributing to disparities in receipt of care across the cancer continuum include time and administrative burden (e.g., treatment scheduling and wait time), ¹⁶³ lack of paid medical leave, ⁶⁰ , ¹⁶⁴ legal barriers (e.g., navigating claim denials and appeals), ¹⁶⁵ cultural factors (e.g., fatalism), ¹⁶⁶ and language barriers. ¹⁶⁷ Patient navigation, i.e., individualized support provided by trained personnel to help patients overcome barriers to accessing and completing recommended care, can alleviate some of these challenges. ¹⁶⁸ It has been demonstrated that navigation improves utilization of cancer prevention, early detection services, and treatment and enhances quality of life and patient satisfaction with care among historically marginalized populations. ¹⁶⁹ , ¹⁷⁰ , ¹⁷¹ , ¹⁷² However, patient navigation is still absent or limited in many cancer programs and hospital settings because of a lack of sustainable funding for these services, particularly in underresourced areas. ¹⁶⁹

Quality of care is another important contributor to disparities in cancer outcomes. People living in rural areas often have limited access to oncologists and hospitals with all cancer services, especially academic and high‐volume centers, which can result in worse cancer outcomes. ¹⁷³ , ¹⁷⁴ , ¹⁷⁵ , ¹⁷⁶ , ¹⁷⁷ , ¹⁷⁸ , ¹⁷⁹ In 2019, for example, about two thirds of all counties in the United States, largely in rural areas, did not have any oncologists with a primary practice location in that county. ¹⁸⁰ Similarly, people of racial and ethnic minoritized groups and of lower SES are more likely to receive cancer care at hospitals that have lower revenue, ¹⁸¹ lack adequate clinical resources, ¹⁸² and have lower surgical volumes, ¹⁴⁹ , ¹⁸³ which usually are associated with poorer outcomes. ¹⁸⁴ , ¹⁸⁵ , ¹⁸⁶ , ¹⁸⁷

ACS PROGRAMS TARGETING CANCER DISPARITIES

Cancer disparities research has been a major focus of the intramural research departments at the ACS, by publishing the American Cancer Society’s Report on the Status of Cancer Disparities in the United States, ¹⁰ Cancer Facts & Figures for African American/Black People, Cancer Facts & Figures for Hispanic/Latino People, ¹⁸⁸ and Cancer Facts & Figures for Asian American, Native Hawaiian, & Other Pacific Islander People, ¹⁸⁹ along with their accompanying scientific articles, ⁶ , ⁹ as well as multiple review articles ⁷ , ⁸ , ³⁵ , ¹²³ , ¹⁹⁰ , ¹⁹¹ , ¹⁹² and original research articles documenting cancer disparities and the effects of interventions to reduce those disparities. ²² , ⁵⁹ , ¹⁵³ , ¹⁹³ , ¹⁹⁴ , ¹⁹⁵ Some other examples of ongoing ACS research include examining the effects of SDOH on cancer disparities ⁵¹ , ¹¹⁹ , ¹⁶⁴ , ¹⁹⁶ , ¹⁹⁷ , ¹⁹⁸ , ¹⁹⁹ , ²⁰⁰ , ²⁰¹ and the effectiveness of insurance coverage expansion under the ACA in reducing disparities in cancer screening and cancer outcomes. ²⁰² , ²⁰³ , ²⁰⁴ , ²⁰⁵ , ²⁰⁶ The ACS has begun participant recruitment for a new cohort study entitled The VOICES of Black Women. This study aims to enroll at least 100,000 Black women aged 25–55 years in the United States to better understand cancer and how to improve overall health among Black women. ²⁰⁷ The ACS also leads Project 1350, an initiative to understand the factors contributing to elevated tobacco use in 13 high‐burden states across the Midwest and South; the goal is to provide critical evidence to inform and strengthen tobacco‐control policy advocacy efforts and address geographic disparities in cancer burden that disproportionately affect the region. ²⁰⁸

The ACS has also funded over 800 research grants to address heath disparities since 1999, with an investment of over $390M across 695 unique investigators at 312 institutions. These projects include research on practice‐based tactics for overcoming barriers to accessing care, inequities resulting from public policy and new cancer treatments, and the effects of structural and intermediary determinants of health based on factors such as age, sex, gender identity, race, ethnicity, literacy, geography, and SES. ²⁰⁹ , ²¹⁰ In 2022, the ACS created a new grant mechanism, Cancer Health Research Centers, to support research projects poised to make an impact on their local community within the framework of a coordinated center approach focused on demonstrated health inequities. ²¹¹ In addition, recognizing that representation of all identities in the cancer research workforce is critical for addressing cancer disparities, the ACS invested over $35M in 2022 to support several new initiatives to increase diversity in the cancer workforce. In 2023, a new center focused on developing the next generation of cancer research and cancer care professionals was launched. Initial programs funded by the Center for Innovation in Cancer Research Training for All include a program to support cancer research training for high school students at 10 institutions, an internship program for undergraduate college students at 32 institutions, and a postbaccalaureate fellow program at eight institutions. ²¹²

The ACS, individually or in collaboration with other institutions or companies, has invested in multiple cancer‐control programs targeting cancer disparities. These programs aim to increase cancer information, cancer screening and early detection, human papillomavirus vaccination, and access to care in populations that have been historically marginalized, generally administered through health care providers or research institutions. Some of these programs include the CHANGE (Community Health Advocates Implementing Nationwide Grants for Empowerment and Equity) grant program, ¹⁶⁸ the National Human Papillomavirus Vaccination Roundtable, ²¹³ the National Colorectal Cancer Roundtable, ²¹⁴ , ²¹⁵ the National Lung Cancer Roundtable, ²¹⁶ the National Breast Cancer Roundtable, ²¹⁷ the National Roundtable on Cervical Cancer, ²¹⁸ the National Prostate Cancer Roundtable, ²¹⁹ and the National Navigation Roundtable. ²²⁰ Moreover, the ACS provides free services directly to individuals in active treatment for cancer, including the National Cancer Information Center, ²²¹ Hope Lodge, ²²² and Road to Recovery, ²²³ providing trustworthy and up‐to‐date cancer information (including on insurance and obtaining assistance in paying for costs of treatment), accommodation while receiving treatment away from home, and transportation to and from treatment, respectively. Furthermore, the ACS recently launched ACS ACTS (Access to Clinical Trials and Support), a clinical trials information and matching service. This service combines personalized counseling and education, standardized screening for health‐related social needs, and an artificial information‐driven match between patients and clinical trials, and then connects matched patients to other services to reduce barriers to accessing clinical trials. ²²⁴

The ACS Cancer Action Network (ACS CAN), the advocacy affiliate of the ACS, has been actively advocating for policies to address cancer disparities and protect cancer research and prevention funding. For example, ACS CAN advocates for protecting and expanding Medicaid and maintaining patient protections that ensure individuals who have cancer can enroll in and maintain health insurance coverage. Some other activities of ACS CAN are mentioned below (see Future Directions).

FUTURE DIRECTIONS

Important next steps to reduce cancer disparities in the United States are listed in Table 6 and are briefly described here.

TABLE 6.

Future directions to reduce cancer disparities in the United States.

Protecting and expanding access to Medicaid Strengthening health insurance Marketplaces Promoting cancer prevention, screening, and early detection Implementing/enforcing evidence‐based tobacco control programs and policies Addressing food and nutrition insecurity Increasing funding for cancer screening programs Improving data infrastructure and ensuring complete and timely collection and publication of data Increasing diverse representation in and access to clinical trials Increasing opportunities to join the cancer care and research workforce for everyone

Protecting and expanding access to Medicaid

The ACA was signed into law in March 2010 and went fully into effect in January 2014 to improve access to affordable care in the United States. ¹⁹⁰ Among provisions of this law was the expansion of Medicaid eligibility to broader groups of people with lower income (≤138% of FPL). After the Supreme Court’s ruling in National Federation of Independent Business v. Sebelius, however, Medicaid expansion was not mandatory but, rather, an option for states to consider. ²²⁵ The ACA has resulted in lower uninsured rates; and the expansion of Medicaid coverage in applicable states, in particular, increased insurance coverage among individuals with limited income. ¹⁹⁰ , ²⁰³ , ²²⁶ The ACA has been associated with improvements in cancer screening, early stage diagnosis, receipt of care, and survival, especially among individuals with limited income in Medicaid expansion states. ¹⁹⁰ , ²⁰² , ²⁰⁴ , ²²⁷ , ²²⁸ , ²²⁹ , ²³⁰ , ²³¹ , ²³²

The 2025 Budget Reconciliation Bill, enacted in July 2025, however, contains the largest reduction in federal Medicaid spending in history, with more than a $1 trillion projected reduction during 2025 through 2034. ²³³ The legislation also contains provisions that make changes to Marketplace plans, making it more difficult for people to enroll in coverage. Consequently, 10 million individuals are estimated to lose the only affordable health insurance coverage available to them and to become uninsured, according to the nonpartisan Congressional Budget Office. ²³³ Many of the coverage losses from the 2025 Budget Reconciliation Bill are predicted to come from the Medicaid expansion population. Several provisions in the law place new limits—including work requirements, more frequent eligibility checks, and reduction in retroactive coverage—specifically on the Medicaid expansion population as well as penalizing or discouraging states from adopting or maintaining Medicaid expansion. These losses of coverage are projected to result in substantial increases in people forgoing care, and consequently, the number of deaths. ²³⁴

As of August 2025, 10 states had not adopted Medicaid expansion, including Alabama, Florida, Georgia, Kansas, Mississippi, South Carolina, Tennessee, Texas, Wisconsin, and Wyoming. ³¹ Most of these states have the highest proportions of lower income individuals with no health insurance; and, in six states (Alabama, Florida, Georgia, Mississippi, South Carolina, and Texas), racial and ethnic minoritized groups constitute greater than one third of the population. ²³⁵ Of 13 states with the highest uninsured rates among individuals aged 19–64 years who had incomes below the FPL in 2023 (≥23.9%), nine states have not yet adopted Medicaid expansion, and three states adopted the expansion more recently (2021 or later; Figure 8). ²³⁶ ACS CAN advocates for protecting access to care through Medicaid and urges all states to expand Medicaid or maintain their expansion, as applicable.

FIGURE 8.

Proportion of Individuals aged 19–64 years with incomes below the federal poverty level and no health insurance by state and Medicaid expansion status, 2023. Categories were based on quartiles. Years indicate Medicaid expansion implementation year. “(Non)” represents states that have not expanded Medicaid eligibility (10 states, as of May 2025). Coverage in South Dakota started July 1, 2023; and coverage in North Carolina started December 1, 2023. Coverage in Maine was retroactive to July 2, 2018. Source: KFF, Status of State Medicaid Expansion Decisions and Health insurance coverage of adults 19–64 living in poverty (under 100% FPL). ²³⁶

Strengthening Marketplaces

The ACA also allowed for the creation of Marketplaces, where consumers can purchase comprehensive health insurance and where tax credits are available to people, depending on their income. The ACA Marketplaces were associated with a significant decline in the uninsured rate and increased access to care among previously uninsured individuals. ²³⁷ , ²³⁸ ACS CAN advocates for policies that support healthy Marketplaces, including, but not limited to, special enrollment periods to facilitate enrollment for individuals who meet certain requirements, an extensive open enrollment period to allow people sufficient time to review their plan options and enroll in coverage that best meets their needs, and the elimination of barriers to enrollment and the encouragement of auto‐enrollment to ensure individuals maintain continuous coverage.

Provisions in the American Rescue Plan Act of 2021 expanded health care tax credits to improve the affordability of comprehensive coverage in the Marketplaces, ²³⁹ and the Inflation Reduction Act of 2022 extended these tax credits for a limited time (until December 31, 2025). ²⁴⁰ The health care tax credits make premiums more affordable for millions of individuals and help them obtain comprehensive health coverage on the ACA Marketplaces. ²⁴⁰ However, these provisions are not permanent; and, without further congressional action, an estimated 4.2 million individuals will lose their coverage after these provisions expire. ²⁴¹ ACS CAN strongly urges Congress to extend the health care tax credits before they expire to prevent millions of individuals enrolled in Marketplace plans from facing higher premiums or losing coverage.

Promoting cancer prevention, screening, and early detection

Implementing evidence‐based tobacco‐control programs and policies

Although tobacco control has substantially reduced the burden of smoking‐related cancers in United States, ²⁴² cigarette smoking still accounts for about 30% of all cancer deaths in the country ³⁰ ; and, in 2019 alone, it was associated with about 2.2 million years of life lost because of premature cancer deaths among individuals aged 25–79 years. ³² This burden is substantially greater in lower SES groups, as indicated by wide variations in lung cancer mortality rates by education level (Table 3). Tobacco excise taxes can benefit people with limited incomes more and reduce tobacco‐related health disparities ²⁴³ , ²⁴⁴ , ²⁴⁵ because those individuals are generally more sensitive to price than higher income individuals and quit tobacco at greater rates after a tax increase. ²⁴⁶ , ²⁴⁷ ACS CAN continues to advocate for evidence‐based tobacco prevention and cessation programs and policies at the federal, state, and local levels that aim to reduce disparities and improve health outcomes for all individuals.

Addressing food and nutrition insecurity

Consistent access to affordable nutritious food directly affects health and can help prevent and manage cancer and other chronic diseases. ²⁴⁸ , ²⁴⁹ Sociodemographic factors, such as race, ethnicity, health insurance status, income, and where an individual lives, strongly affect regular access to healthy food. ¹¹ The proportion of individuals with a cancer diagnosis who experience food insecurity in the United States is estimated to range between 17% and 55%. ²⁵⁰ Food security programs, such as SNAP (the Supplemental Nutrition Assistance Program), the National School Lunch Program, the Food Distribution Program on Indian Reservations, the Nutrition Assistance Program, and WIC (the Special Supplemental Nutrition Program for Women, Infants, and Children) help people, children, and families with limited incomes and disabilities access quality food. ²⁴⁸ , ²⁵¹ , ²⁵² Increased access to free school meals, including universal free school meal policies and expanding access to the Community Eligibility Provision, allows schools located in high‐poverty areas to offer all students free school meals and has been associated with increased meal participation and decreased obesity prevalence. ²⁵³ , ²⁵⁴ ACS CAN advocates for policies at the federal, state, and local levels aimed at addressing food and nutrition insecurity and reducing health disparities. ACS CAN also strongly supports ensuring that the federal dietary guidelines for Americans (currently being updated) ²⁵⁵ reflect the current science regarding diet and cancer risk. These guidelines aim to help Americans lead a healthy lifestyle, including lowering their risk of cancer, ²⁵⁶ and form the basis of all federal policies and programs. ²⁵⁷

Increasing funding for cancer screening programs

Beyond the ACA, there are several other federal and state policies and programs aimed at reducing disparities in cancer prevention and screening and increasing access to care for individuals with limited income. For example, in 2023 the National Breast and Cervical Cancer Early Detection Program (NBCCEDP) provided breast and cervical cancer screening to about 280,000 and 130,000 women with limited income and no or suboptimal health insurance, respectively. ²⁵⁸ The NBCCEDP has been associated with more favorable disease stage at diagnosis, a reduction in years of life lost because of breast and cervical cancers among participants, and has been identified as cost‐effective. ²⁵⁹ , ²⁶⁰ , ²⁶¹ Despite the NBCCEDP’s success, federal and state funding is inadequate to reach all individuals who are eligible to receive its services, as reflected in the relatively small proportions of eligible women who receive breast and cervical cancer screening services (13.5% and 5.9%, respectively). ²⁶² In addition, the federal Colorectal Cancer Control Program provides grant funding to 20 state health departments, eight universities, two tribal organizations, and five other organizations to increase colorectal cancer screening rates among high‐need groups ²⁶³ and has shown substantial potential health gains. ²⁶⁴ , ²⁶⁵ ACS CAN advocates for increased federal and state funding for cancer prevention and control programs, including the NBCCEDP and the Colorectal Cancer Control Program.

Improving data infrastructure and ensuring complete and timely collection and publication of data

It is important that federal, state, and local agencies standardize data‐collection methods and reporting in surveys, registries, administrative data, and data linkages and include data on demographic and socioeconomic factors, sexual orientation, gender identity, disability status, and geospatial information. This information is necessary to inform policy and help stakeholders at all levels, including health care providers, invest in and direct resources to address health care needs of the population. ³³ , ²⁶⁶ Moreover, differences in social needs, including housing, food insecurity, and transportation, among individuals diagnosed with cancer are associated with disparities in cancer care delivery and survival. ²⁶⁷ , ²⁶⁸ Information on these social needs or how to address those that contribute to cancer disparities (e.g., by housing assistance, food programs, and improving the built environment) could inform and result in interventions with a much greater population‐level effect. ¹⁹⁹ , ²⁶⁹ , ²⁷⁰ , ²⁷¹ , ²⁷² , ²⁷³

Data collection has been affected by the current political environment. For instance, certain widely used government data sets have been permanently removed or removed and later restored with redacted data. ²⁷⁴ Furthermore, reductions in the federal workforce, including individuals and entire groups responsible for collecting, curating, and maintaining these data, may adversely affect future data collection and dissemination. The scope of data removal and longer term data‐collection changes remain unclear and leaves researchers and effected communities uncertain about the potential effect of these changes. Consistent, ongoing, and comprehensive data collection and dissemination are instrumental to ensure that relevant and timely data inform interventions to prevent, detect, and treat chronic diseases, including cancer. It is crucial that adequate funding is maintained for cancer registries and nationally representative population‐based surveys, which help identify emerging trends and measure progress in early detection and survival rates for cancer. ACS CAN supports funding and policies to promote the timely collection and publication of cancer occurrence and survey data that include a wide range of sociodemographic factors and information about health care access, including cancer prevention and screening, across the US population to inform policies to reduce cancer disparities.

Increasing diverse representation in and access to clinical trials

Clinical trials are vital to advancing new and improved standards of care and offering patients the opportunity to participate in research and development of new treatments. In addition, for many patients who do not respond to standard‐of‐care cancer treatments, cancer treatment clinical trials offer another option. Although overall patient willingness to enroll in clinical trials is high, access to and participation in clinical trials differs across populations. Some patients decline to participate because of costs as they are often responsible for nonmedical costs, such as transportation and lodging associated with trial enrollment, ²⁷⁵ , ²⁷⁶ as well as lost household income because of time away from work. These costs can be substantial when no local trials are available and patients must travel to distant trial sites or when there is a need for more frequent clinic visits for additional trial‐related treatment or monitoring. ²⁷⁷ To address this issue, the Clinical Trials Modernization Act would allow clinical trial sponsors to provide financial support to trial participants and the technology needed to participate in trials remotely, which would particularly benefit rural communities. ²⁷⁸ Offering to reimburse patients for nonmedical costs associated with trials can reduce the cost of participation and increase overall enrollment. ²⁷⁹ ACS CAN advocates for policies like the Clinical Trials Modernization Act, which can increase diversity in clinical trials and make it easier for all people with cancer to participate in clinical trials by reducing barriers to enrollment.

Increasing opportunities to join the cancer care and research workforce for everyone

Racial and ethnic minoritized groups are underrepresented in the medical care and research workforce. ²⁸⁰ , ²⁸¹ , ²⁸² In addition to implementation of known effective interventions, ²⁸³ , ²⁸⁴ more research is needed to identify and implement interventions that can guarantee opportunities to join the cancer care workforce regardless of racial, ethnic, or other factors associated with disparities because it can improve patient care, satisfaction, trust, and outcomes; a diverse research workforce can also result in better science. ²⁸⁴ , ²⁸⁵ , ²⁸⁶

LIMITATIONS

Although this biennial report provides robust data across multiple domains related to cancer disparities, it has several limitations. First, we did not examine disparities in several SDOH that can affect cancer disparities, such as built environment. ²⁸⁷ , ²⁸⁸ Second, we did not include environmental or occupational risk factors of cancer, ²⁸⁹ notably air pollution, because of generally sparse nationally representative data at the individual level and stratified by age group, race, and ethnicity, although these factors are more likely to affect historically marginalized populations. ²⁹⁰ , ²⁹¹ , ²⁹² Finally, for the same reason, we were not able to provide information on disparities in cancer occurrence in some patient subgroups within broad categories of race and ethnicity or by nativity. ⁷ , ²⁹³ , ²⁹⁴ , ²⁹⁵ Compared with the White population, for example, the odds of being diagnosed with advanced‐stage nonsmall cell lung cancer in Florida during 2005 through 2018 was substantially higher in the Hispanic population of Central American background than in the population with Cuban background. ²⁹⁴ Other studies have also documented limited access to cancer screening and treatment by sociodemographic factors other than race, ethnicity, SES, and geographic location, e.g., in individuals with disabilities ²⁹⁶ , ²⁹⁷ and LGBTQ+ (lesbian, gay, bisexual, transgender, queer, questioning, or another diverse gender identity) communities. ⁸ , ²⁹⁸ , ²⁹⁹

CONCLUSIONS

Substantial disparities by sociodemographic factors exist across the cancer continuum, from prevention, screening, diagnosis, treatment, and survivorship to mortality, largely reflecting fundamental disparities in SDOH. Mitigating cancer disparities in the United States requires intersectoral stakeholder engagement, targeted funding, and effective policies at all levels of government as well as broad implementation of evidence‐based interventions, such as increasing health insurance coverage through strengthening Marketplaces and protecting and expanding access to Medicaid. Further research is needed to identify contributing factors and effective interventions to reduce cancer disparities, particularly in populations that have been underrepresented in research studies.

CONFLICT OF INTEREST STATEMENT

K. Robin Yabroff reports honoraria from the National Comprehensive Cancer Network; grants from the National Cancer Institute and the Leukemia & Lymphoma Society; and support for other professional activities from Flatiron Health outside the submitted work. Arif H. Kamal is the chief executive officer of Prepped Health. Kirsten Sloan reports service as an unpaid member of the Alliance for Health Policy board outside the submitted work. Carmen E. Guerra reports honoraria from the National Comprehensive Cancer Network; grants from the National Cancer Institute, the Colon Cancer Alliance, the Breast Cancer Research Foundation, and Genentech; personal/consulting or advisory fees from Guardant Health, Janssen Pharmaceuticals, Natera, and Roche; and owns stock in Beam Therapeutics, CRISPR, Editas, and Intellia Therapeutics outside the submitted work. Otis W. Brawley reports service as a consultant for Agilent Technologies, Grail, Illumina Inc., Incyte Corporation, PDS Biotechnology, Lydell Immunotherapy, Lyle Immunopharma, Genentech, and AstraZeneca outside the submitted work. Robert A. Winn reports personal/consulting fees from Genentech, Merck, and Phillips; and support for other professional activities from the American Cancer Society Board of Directors, the Bristol Myers Squibb Foundation, and the LUNGevity Foundation outside the submitted work.

Gladys Arias, Kirsten Sloan, and Lisa A. Lacasse are employed by the American Cancer Society Cancer Action Network, and Farhad Islami, Dongjun Lee, Daniel Wiese, Jordan Baeker Bispo, K. Robin Yabroff, Rebecca L. Siegel, Priti Bandi, Nigar Nargis, Alpa V. Patel, Paul P. Thienprayoon, Arif H. Kamal, Elvan C. Daniels, Christina M. Annunziata, William L. Dahut, and Ahmedin Jemal are employed by the American Cancer Society, which receives grants from private and corporate foundations, including foundations associated with companies in the health sector for research outside the submitted work. All authors have nothing else to disclose.

Supporting information

Supporting Information S1

Islami F, Arias G, Lee D, et al. American Cancer Society’s Report on the Status of Cancer Disparities in the United States, 2025. CA Cancer J Clin. 2026;e70045. doi: 10.3322/caac.70045