Significant Disparities in Hepatocellular Carcinoma Outcomes by Race/Ethnicity and Sociodemographic Factors

Abstract

Background:

Hepatocellular carcinoma (HCC) disproportionately affects racial/ethnic minorities. We evaluated the impact of income and geography on racial/ethnic disparities across the HCC care cascade in the U.S.

Methods:

Using National Cancer Institute registry data spanning 2000–2020, adults with HCC were evaluated to determine race/ethnicity-specific differences in tumor stage at diagnosis, delays and gaps in treatment, and survival. Adjusted regression models evaluated predictors of HCC outcomes.

Results:

Among 112,389 adults with HCC, cohort characteristics were as follows: 49.8% non-Hispanic White [NHW], 12.0% African American, 20.5% Hispanic, 16.5% Asian/Pacific Islander, 1.1% American Indian/Alaska Native. Compared to NHW patients, AA patients had lower odds of localized-stage HCC at diagnosis (aOR, 0.84), lower odds of HCC treatment receipt (aOR, 0.77), greater odds of treatment delays (aOR, 1.12), and significantly greater risk of death (aHR, 1.10). Compared to NHW from large-metro areas, AA from large metro areas had 8% higher mortality risk (aHR, 1.08) whereas AA from small-medium metro areas had 17% higher mortality risk (aHR, 1.17) (all P<0.05).

Conclusions:

Among a population-based cohort of U.S. adults with HCC, significant race/ethnicity-specific disparities across the HCC care continuum were observed. Lower household income and more rural geography among racial/ethnic minorities are also associated with disparities in HCC outcomes, particularly among AA patients.

Impact:

Our study shows that lower income and less urban/more rural geography among racial/ethnic minorities is also associated with disparities in HCC outcomes, particularly among AA patients with HCC. This contextualizes the complex relationship between sociodemographic factors and HCC outcomes through an intersectional lens.

Introduction

Hepatocellular carcinoma (HCC) is the sixth leading cause of cancer-related mortality in the United States¹ with a five-year survival rate of 21%.² HCC develops in patients with underlying chronic liver disease, especially those with cirrhosis.³ Viral hepatitis (hepatitis B and C) has historically been the major risk factor for the development of HCC.³ However, the burden of hepatitis C virus (HCV)-related HCC has plateaued since the introduction of highly effective direct-acting antivirals.^2,4 While HCV-related HCC has declined, the increasing prevalence of alcohol-related liver disease (ALD) and metabolic dysfunction-associated steatotic liver disease (MASLD) may continue to fuel the burden of HCC.

Vulnerable populations and racial/ethnic minorities are groups known to experience social and structural barriers to equitable healthcare are disproportionately affected by HCC. In fact, HCC incidence and mortality rates are significantly worse among African American patients compared to their White counterparts.⁵ Studies specifically among Asian patients have also demonstrated higher incidence of HCC as well as disparities in HCC outcomes, particularly among Southeast Asian subgroups.^6,7 Similarly, higher HCC incidence and mortality, along with decreased likelihood of early tumor stage at diagnosis due to delays and gaps in timely HCC surveillance, are observed among patients from low-income households.⁸ Furthermore, patients with HCC from rural areas are more likely to be diagnosed with late-stage HCC and have significantly higher mortality.⁸ These poor outcomes are likely attributable to several factors including disparities in timely access to HCC prevention,^9–11 HCC surveillance for early detection,^12–15 and HCC treatment once diagnosed.^16–18 Disparities in HCC burden may have been further amplified in recent years by limited access to hepatology care during the COVID-19 pandemic.^19,20 Although several studies have described the relationship between individual sociodemographic factors and HCC outcomes, these social and economic risk factors often coexist among vulnerable patient populations. Limited data have explored how the intersection of sociodemographic factors, especially race/ethnicity, annual household income, and geography, impact HCC outcomes.

Therefore, the objectives of our study were (1) to evaluate the current state of disparities across the HCC care cascade from tumor stage at diagnosis, receipt of HCC treatment, timely access to HCC treatment, and overall survival and (2) to assess the impact of intersecting sociodemographic factors, namely income and geographic-level variables across race/ethnicity, on HCC outcomes using the most recent data from the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) program.

Materials and Methods

Study Population

We performed a retrospective cohort study using the 2000–2020 cancer registry data from the national SEER program (RRID:SCR_006902). This database incorporates data from 17 U.S. geographic areas (the Alaska Native, Connecticut, Greater Georgia, Rural Georgia, Atlanta, Greater California, San Francisco-Oakland, Los Angeles, San Jose-Monterey, Hawaii, Iowa, Kentucky, Louisiana, New Mexico, New Jersey, Seattle-Puget Sound, and Utah) and represents 26.5% of the U.S. population. Adults with HCC (aged ≥20 years) were identified with anatomic site (liver, C22.0) and histology codes (hepatocellular carcinoma, 8170–8175) from the International Classification of Disease for Oncology, third edition.

Sociodemographic and Clinical Definitions

Race/ethnicity was categorized according to SEER definitions: non-Hispanic White (NHW), African American (AA), Hispanic, Asian/Pacific Islander (API), and American Indian/Alaskan Native (AI/AN). According to a classification system established by the US Department of Agriculture and Office of Management and Budget, geographic variables were defined by population size or degree of urbanization and proximity to metropolitan areas: large metro (regions with >1 million population), medium metro (250k to 1 million population), small metro (<250k population), and rural areas. Median annual household income (inflation adjusted to 2021 US dollars) was collected in a time-dependent manner using county-level data from the US Census American Community Survey. Income was categorized into four groups: ≥$70,000, $55,000-$69,999, $40,000-$54,999), and <$40,000.

Tumor stage at diagnosis was defined by SEER’s summary staging system, a schema unique to the SEER database to describe extent of cancer involvement from point of origin: localized (cancer limited to site of origin), regional (cancer extending to adjacent structures or lymph nodes), or distant (cancer extending to distant structures or lymph nodes). Patients with regional or distant stage tumors were combined and referred to as “advanced” stage. Utilizing SEER’s site-specific variables, HCC treatment included surgical (resection and transplantation) and non-surgical (chemotherapy, radiation, and other locoregional therapies) options. Additionally, the SEER database provided wait-times for treatment by calculating the difference between the month and year of treatment initiation from the month and year of diagnosis. Other clinically relevant information, including year of diagnosis and number of tumors at diagnosis was also queried from the SEER database.

Statistical Analysis

Descriptive analyses on the study cohort were performed using frequency and proportions. Adjusted multivariable logistic regression models were used to assess socio-demographic factors associated with localized (vs. advanced) tumor stage at diagnosis, receipt of any HCC treatment (vs. no treatment), receipt of surgical intervention (surgical vs. nonsurgical), and delays in treatment after diagnosis (≥3 months vs. <3 months). Our analysis additionally focused on those with localized HCC as these patients are more likely to qualify for potentially curative treatment.²¹ A threshold of 3 months to define treatment delay is based on tumor doubling times and consistent with prior literature examining treatment delays.^22,23Analyses of surgical treatment receipt and delays in care were performed among a subset of the population that received any treatment. Patients who received both surgical and nonsurgical interventions were grouped into the surgical category. All-cause mortality was evaluated using Kaplan-Meier methods and adjusted multivariable Cox proportional hazards models. Competing risks regressions were utilized to assess for HCC-specific mortality (competing event being death due to other causes). Predictors of interest were identified a priori and included sociodemographic (age, sex, race/ethnicity, geography, and annual household income) and relevant clinical factors (stage at diagnosis, receipt of treatment, year of diagnosis, and number of tumors at diagnosis). First, these predictors were analyzed in a model without the influence of interaction terms. Then, a combination of interaction terms across racial/ethnic, geographic, and income-level variables were created to assess the impact of geography and income on racial/ethnic disparities, respectively.

Statistical significance was met with a two-tailed p-value <0.05. Statistical analyses were performed using Stata (Version 17, StataCorp LLC, College Station, TX; RRID:SCR_012763). This study qualified for an institutional review board exemption since there was no involvement of human participants and the cancer registry data from SEER are available to the public without patient identifying information.

Data availability statement:

The data analyzed in this study were publicly available and obtained from the National Cancer Institute SEER database at: https://seer.cancer.gov/data-software/documentation/seerstat/ (RRID:SCR_006902).

Results

Cohort Characteristics

We identified 112,389 adults with HCC (66.2% aged ≥60 years, 76.2% men and 23.8% women, 49.8% NHW, 12.0% AA, 20.5% Hispanic, 16.5% API, 1.1% AI/AN) from 2000–2020 (Table 1). Majority of patients were from large metro areas (62.5% vs. 9.4% from rural areas). 52.3% of patients had an annual household income ≥$70,000 (vs. 13.7% <$55,000). Over half (52.2%) had localized stage, with 31.9% having regional stage and 15.9% distant stage at diagnosis. Only 62.2% of patients received any treatment for HCC; of those who received treatment, 22.6% received surgical therapy. Among patients who received HCC treatment, median time from diagnosis to receipt of first treatment was 2 (IQR 1–3) months, with 30.0% having treatment delay >3 months. Among all patients, the overall 5-year survival rate was 23.0%.

Table 1.

Cohort Characteristics: Adults with Hepatocellular Carcinoma, 2000–2020

Characteristic [N (%)]	Total Population (N=112,389)	Non-Hispanic White (N=44,689)	African American (N=11,071)	Hispanic (N=18,531)	Asian/Pacific Islander (N=14,382)	American Indian/Alaska Native (N=983)
Age  20-49 years  50-59 years  60-69 years  ≥70 years	8,914 (7.3) 29,836 (26.5) 38,084 (33.9) 36,275 (32.3)	2,037 (4.6) 11,707 (26.2) 16,114 (36.0) 14,831 (33.2)	749 (6.8) 3,550 (32.1) 4,764 (43.0) 2,008 (18.1)	1,542 (8.3) 5,609 (30.3) 6,162 (33.2) 5,218 (28.2)	1,415 (9.8) 3,185 (22.2) 4,342 (30.2) 5,440 (37.8)	68 (6.9) 324 (33.0) 371 (37.7) 220 (22.4)
Sex  Female  Male	26,701 (23.8) 85,688 (76.2)	9,620 (21.5) 35,069 (78.5)	2,497 (22.5) 8,574 (77.5)	4,770 (25.7) 13,761 (74.3)	3,884 (27.0) 10,498 (73.0)	274 (27.9) 709 (72.1)
Geography  Large metro  Medium metro  Small metro  Rural	70,228 (62.5) 23,629 (21.0) 7,957 (7.1) 10,575 (9.4)	24,463 (54.7) 9,775 (21.9) 4,161 (9.3) 6,290 (14.1)	7,129 (64.4) 2,453 (22.1) 758 (6.9) 731 (6.6)	12,526 (67.6) 4,221 (22.8) 1,030 (5.5) 754 (41)	11,348 (78.9) 2,415 (16.8) 310 (2.2) 309 (2.1)	403 (41.0) 210 (21.4) 138 (14.0) 232 (23.6)
Annual household income  ≥$70,000  $55,000-$69,999  $40,000-$54,999  <$40,000	58,810 (52.3) 38,210 (1.9) 13,270 (11.8) 2,099 (34.0)	22,860 (51.1) 13,925 (2.7) 6,704 (15.0) 1,200 (31.2)	4,287 (38.7) 4,195 (3.3) 2,228 (20.1) 4,287 (37.9)	9,143 (49.3) 7,708 (0.8) 1,537 (8.3) 143 (41.6)	10,349 (71.9) 3,699 (25.7) 328 (2.3) 6 (0.1)	413 (42.0) 323 (32.8) 206 (21.0) 41 (4.2)
Stage at Diagnosis  Localized  Regional  Distant	-- -- --	23,202 (51.9) 14,392 (32.2) 7,095 (15.9)	5,299 (47.9) 3,721 (33.6) 2,051 (18.5)	9,986 (53.9) 5,750 (31.0) 2,795 (15.1)	7,839 (54.5) 4,418 (30.7) 2,125 (14.8)	506 (51.5) 328 (33.4) 149 (15.1)

HCC Tumor Stage at Diagnosis Analyses

Of 89,656 patients diagnosed with HCC with available tumor staging data, lower rates of localized HCC at diagnosis were observed among AA patients, patients from small metro and rural areas, and patients with annual household incomes of $40,000-$54,999 and <$40,000 (Table 2).

Table 2.

Multivariable Logistic Regression Models for Tumor Stage at Diagnosis (Localized vs. Advanced, N=89,656)

	Stage at Diagnosis [N (%)]		Multivariable Analyses
Characteristics	Localized	Advanced	OR	95% Cl	p-value
Age  20–49 years  50–59 years  60–69 years  ≥70 years	2,928 (50.4) 12,537 (51.4) 16,686 (52.6) 14,681 (53.0) 12,238 (58.1) 34,594 (50.4) 23,202 (51.9) 5,299 (47.9) 9,986 (53.9) 7,839 (54.5) 506 (51.5) 29,732 (53.2) 9,846 (51.6) 3,172 (49.6) 4,082 (49.1) 24,828 (52.8) 15,685 (52.6) 5,456 (49.6) 863 (49.3)	2,883 (49.6) 11,838 (48.6) 15,067 (47.4) 13,036 (47.0)	-- 1.08 1.13 1.02	Ref 1.02–1.15 1.07–1.20 0.97–1.09	-- 0.007 <0.001 0.406
Sex  Female  Male		8,807 (41.9) 34,017 (49.6)	-- 0.74	Ref 0.71–0.76	-- <0.001
Race/ethnicity  Non-Hispanic White  African American  Hispanic  Asian/Pacific Islander  American Indian/Alaska Native		21,487 (48.1) 5,772 (52.1) 8,545 (46.1) 6,543 (45.5) 477 (48.5)	-- 0.84 1.09 1.09 1.01	Ref 0.81–0.88 1.05–1.13 1.05–1.14 0.89–1.15	-- <0.001 <0.001 <0.001 0.850
Geography  Large metro  Medium metro  Small metro  Rural		26,137 (46.8) 9,228 (48.4) 3,225 (50.4) 4,234 (50.9)	-- 0.95 0.89 0.87	Ref 0.92–0.99 0.84–0.94 0.82–0.92	-- 0.008 <0.001 <0.001
Annual household income  ≥$70,000  $55,000-$69,999  $40,000-$54,999  <$40,000		22,224 (47.2) 14,165 (47.4) 5,547 (50.4) 888 (50.7)	-- 1.01 1.00 1.05	Ref 0.98–1.05 0.95–1.06 0.94–1.17	-- 0.391 0.912 0.386
Interaction Analyses
Race/Income*  High-income Non-Hispanic White  Middle-income Non-Hispanic White  Low-income Non-Hispanic White  High-income African American  Middle-income African American  Low-income African American  High-income Hispanic  Middle-income Hispanic  Low-income Hispanic  High-income Asian/Pacific Islander  Middle-income Asian/Pacific Islander  Low-income Asian/Pacific Islander  High-income American Indian/Alaska Native  Middle-income American Indian/Alaska Native  Low-income American Indian/Alaska Native			-- 1.02 0.99 0.83 0.86 0.89 1.08 1.11 1.74 1.12 1.05 5.97 0.93 1.06 1.58	Ref 0.98–1.06 0.87–1.13 0.77–0.88 0.82–0.91 0.71–1.10 1.03–1.13 1.06–1.17 1.24–2.45 1.07–1.17 0.98–1.12 0.70–51.24 0.77–1.14 0.89–1.26 0.84–2.98	-- 0.427 0.910 <0.001 <0.001 0.268 0.003 <0.001 0.002 <0.001 0.201 0.103 0.495 0.504 0.155
Race/Geography†  Large metro Non-Hispanic White  Small-medium metro Non-Hispanic White  Rural Non-Hispanic White  Large metro African American  Small-medium metro African American  Rural African American  Large metro Hispanic  Small-medium metro Hispanic  Rural Hispanic  Large metro Asian/Pacific Islander  Small-medium metro Asian/Pacific Islander  Rural Asian/Pacific Islander  Large metro American Indian/Alaska Native  Small-medium American Indian/Alaska Native  Rural American Indian/Alaska Native			-- 0.95 0.86 0.86 0.77 0.75 1.06 1.08 1.24 1.13 0.96 0.72 0.96 0.95 0.98	Ref 0.91–0.99 0.81–0.92 0.82–0.91 0.71–0.83 0.64–0.88 1.02–1.11 1.01–1.14 1.07–1.45 1.08–1.18 0.88–1.04 0.57–0.90 0.78–1.16 0.77–1.12 0.76–1.28	-- 0.027 <0.001 <0.001 <0.001 <0.001 0.007 0.019 0.005 <0.001 0.278 0.005 0.650 0.643 0.906

“Advanced” category includes regional and distant tumors

^*Adjusted for age, sex, geography, year of diagnosis, and number of tumors

^†Adjusted for age, sex, income, year of diagnosis, and number of tumors

On multivariable logistic regression analysis, compared to NHW patients, AA patients (aOR, 0.84; 95% CI, 0.81–0.88; P<0.001) had lower odds of localized-stage HCC at diagnosis, whereas Hispanic (aOR, 1.09; 95% CI, 1.05–1.13; P<0.001) and API patients (aOR, 1.09; 95% CI, 1.05–1.14; P<0.001) had higher odds of being diagnosed with localized-stage HCC. Compared with patients from large metro areas, patients from medium metro (aOR, 0.95; 95% CI, 0.92–0.99; P=0.008), small metro (aOR, 0.89; 95% CI, 0.84–0.94; P<0.001), and rural areas (aOR, 0.87; 95% CI, 0.82–0.92; P<0.001) had lower odds of localized-stage HCC at diagnosis (Table 2).

Racial/ethnic differences in HCC tumor stage were exacerbated when including geographical factors. For example, compared to NHW patients from large metro areas, AA patients from large metro areas had 14% lower odds of having localized-stage HCC (aOR 0.86; 95% CI 0.82–0.91, P<0.001), but AA patients from small-medium metro (aOR, 0.77; 95% CI, 0.71–0.83; P<0.001) and rural areas (aOR, 0.75; 95% CI, 0.64–0.88; P<0.001) had 23% and 25% lower odds of having localized-stage HCC at diagnosis, respectively (Table 2). Supplementary Table 1 shows the results of adjusted multivariable logistic regression analyses evaluating for predictors of tumor stage at diagnosis among patients with HCC, stratified by race/ethnicity.

HCC Receipt of Treatment Analyses

Among 89,349 patients with available treatment data, the highest proportions receiving treatment were seen in NHW and API individuals, and the lowest proportions receiving treatment were seen in AA, AI/AN, and Hispanic individuals. Individuals from more metropolitan areas vs. rural areas had higher proportions of receiving HCC treatment (Table 3).

Table 3.

Multivariable Logistic Regression Models for Receipt of Treatment (Any Treatment vs. No Treatment, N=89,349)

Characteristics	Receipt of Treatment [N (%)]		Multivariable Analyses
	Any Treatment	No Treatment	OR	95% Cl	p-value
Age  20–49 years  50–59 years  60–69 years  ≥70 years	3,591 (62.1) 15,241 (62.7) 21,116 (66.7) 15,674 (56.8) 13,113 (62.5) 42,509 (62.2) 28,042 (63.0) 6,315 (57.3) 11,100 (60.0) 9,579 (66.7) 586 (59.7) 34,861 (62.5) 12,173 (64.0) 3,784 (59.5) 4,804 (58.3) 30,274 (64.5) 18,057 (60.7) 6,347 (58.1) 944 (64.6)	2,194 (37.9) 9,064 (37.3) 10,533 (33.3) 11,936 (43.2)	-- 0.98 1.05 0.64	Ref 0.93–1.05 0.99–1.12 0.61–0.68	-- 0.610 0.087 <0.001
Sex  Female  Male		7,861 (37.5) 25,866 (37.8)	-- 1.00	Ref 0.96–1.03	-- 0.864
Race/ethnicity  Non-Hispanic White  African American  Hispanic  Asian/Pacific Islander  American Indian/Alaska Native		16,453 (37.0) 4,708 (42.7) 7,390 (40.0) 4,780 (33.3) 396 (40.3)	-- 0.77 0.84 1.19 0.85	Ref 0.74–0.81 0.81–0.87 1.14–1.24 0.75–0.98	-- <0.001 <0.001 <0.001 0.020
Geography  Large metro  Medium metro  Small metro  Rural		20,868 (37.5) 6,843 (36.0) 2,575 (40.5) 3,441 (41.7)	-- 1.10 0.98 0.98	Ref 1.06–1.14 0.92–1.04 0.91–1.02	-- <0.001 0.482 0.238
Annual household income  ≥$70,000  $55,000-$69,999  $40,000-$54,999  <$40,000		16,690 (35.5) 11,682 (39.3) 4,570 (41.9) 785 (45.4)	-- 0.90 0.81 0.72	Ref 0.88–0.93 0.77–0.85 0.64–0.81	-- <0.001 <0.001 <0.001
Interaction Analyses
Race/Income*  High-income Non-Hispanic White  Middle-income Non-Hispanic White  Low-income Non-Hispanic White  High-income African American  Middle-income African American  Low-income African American  High-income Hispanic  Middle-income Hispanic  Low-income Hispanic  High-income Asian/Pacific Islander  Middle-income Asian/Pacific Islander  Low-income Asian/Pacific Islander  High-income American Indian/Alaska Native  Middle-income American Indian/Alaska Native  Low-income American Indian/Alaska Native			-- 0.92 0.75 0.77 0.69 0.59 0.87 0.75 0.84 1.23 1.04 2.07 0.94 0.72 0.71	Ref 0.88–0.96 0.65–0.85 0.72–0.82 0.65–0.73 0.47–0.73 0.82–0.91 0.71–0.79 0.59–1.19 1.17–1.29 0.97–1.12 0.24–18.15 0.76–1.16 0.60–0.87 0.37–1.34	-- <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 0.330 <0.001 0.259 0.513 0.560 <0.001 0.287
Race/Geography†  Large metro Non-Hispanic White  Small-medium metro Non-Hispanic White  Rural Non-Hispanic White  Large metro African American  Small-medium metro African American  Rural African American  Large metro Hispanic  Small-medium metro Hispanic  Rural Hispanic  Large metro Asian/Pacific Islander  Small-medium metro Asian/Pacific Islander  Rural Asian/Pacific Islander  Large metro American Indian/Alaska Native  Small-medium American Indian/Alaska Native  Rural American Indian/Alaska Native			-- 1.06 1.02 0.75 0.93 0.67 0.83 0.95 0.90 1.25 1.10 0.83 0.91 0.89 0.78	Ref 1.01–1.11 0.95–1.09 0.71–0.80 0.86–1.01 0.57–0.79 0.79–0.87 0.89–1.01 0.77–1.05 1.19–1.32 1.01–1.20 0.66–1.05 0.74–1.12 0.71–1.12 0.59–1.02	-- 0.012 0.577 <0.001 0.068 <0.001 <0.001 0.110 0.169 <0.001 0.022 0.127 0.377 0.323 0.073

^*Adjusted for age, sex, geography, year of diagnosis, number of tumors, and stage at diagnosis

^†Adjusted for age, sex, income, year of diagnosis, number of tumors, and stage at diagnosis

In comparison to NHW patients, significantly lower odds of treatment receipt were observed among AA (aOR, 0.77; 95% CI, 0.74–0.81; P<0.001), AI/AN (aOR, 0.85; 95% CI, 0.75–0.98; P<0.001), and Hispanic patients (aOR, 0.84; 95% CI, 0.81–0.87; P<0.001), whereas API patients (aOR, 1.19; 95% CI, 1.14–1.24; P<0.001) had greater odds of receiving treatment for HCC. Compared to individuals with an annual household income ≥$70,000, significantly lower odds of treatment receipt was observed in those with $55,000-$69,999 (aOR, 0.90; 95% CI, 0.88–0.93; P<0.001), $40,000-$54,999 (aOR, 0.81; 95% CI, 0.77–0.85; P<0.001), and <$40,000 (aOR, 0.72; 95% CI, 0.64–0.81; P<0.001) (Table 3). Sensitivity analyses excluding patients who died within 1-month and 3-months after HCC diagnosis demonstrated similar findings.

Racial/ethnic differences in HCC treatment receipt were further exacerbated by income level. Compared to high-income NHW patients, high-income AA patients (aOR, 0.77; 95% CI, 0.72–0.82; P<0.001) had 23% lower odds of receiving treatment for HCC while middle-income (aOR, 0.69; 95% CI, 0.65–0.73; P<0.001) and low-income AA patients (aOR, 0.59; 95% CI, 0.47–0.73; P<0.001) had 31% and 41% lower odds of receiving HCC treatment, respectively. Similar findings were observed among high-income (aOR, 0.87; 95% CI, 0.82–0.91; P<0.001) and middle-income Hispanic patients (aOR, 0.75; 95% CI, 0.71–0.79; P<0.001), who had 13% and 25% lower odds of receiving HCC treatment compared to high-income NHW patients. When compared to NHW patients from large metro areas, AA patients from large metro areas were 25% less likely to receive HCC treatment (aOR, 0.75; 95% CI, 0.71–0.80; P<0.001), and AA patients living in rural areas were 33% less likely to receive HCC treatment (aOR, 0.67; 95% CI, 0.57–0.79; P<0.001) (Table 3). Supplementary Table 2 shows the results from the adjusted multivariable logistic regression analyses evaluating for predictors of receipt of HCC treatment, stratified by race/ethnicity.

HCC Receipt of Surgical Intervention Analyses

Among 55,622 patients that received HCC treatment, the proportion of patients receiving surgical HCC treatment were higher among NHW and API individuals and lower among AA, AI/AN, and Hispanic individuals. Individuals from rural areas had lower proportions receiving surgical HCC treatment compared to individuals from more metro areas. Individuals with annual income <$40,000 had the lowest proportion receiving surgical HCC treatment (Table 4).

Table 4.

Multivariable Logistic Regression Models for Receipt of Surgical Intervention (Surgical vs. Non-surgical Intervention, N=55,622)

Characteristics	Receipt of Curative Treatment [N (%)]		Multivariable Analyses
	Surgical	Non-surgical	OR	95% CI	p-value
Age  20–49 years  50–59 years  60–69 years  ≥70 years	1,245 (34.7) 3,658 (24.0) 4,797 (22.7) 2,884 (18.4)	2,346 (65.3) 11,583 (76.0) 16,319 (77.3) 12,790 (81.6)	-- 0.62 0.59 0.40	Ref 0.57–0.67 0.55–0.64 0.36–0.43	-- <0.001 <0.001 <0.001
Sex  Female  Male	3,277 (25.0) 9,307 (21.9)	9,836 (75.0) 33,202 (78.1)	-- 0.84	Ref 0.80–0.88	-- <0.001
Race/ethnicity  Non-Hispanic White  African American  Hispanic  Asian/Pacific Islander  American Indian/Alaska Native	6,350 (22.6) 1.274 (20.2) 1,966 (17.7) 2,903 (30.3) 91 (15.5)	21,692 (77.4) 5,041 (79.8) 9,134 (82.3) 6,676 (69.7) 495 (84.5)	-- 0.84 0.71 1.43 0.61	Ref 0.78–0.90 0.66–0.75 1.36–1.51 0.48–0.77	-- <0.001 <0.001 <0.001 <0.001
Geography  Large metro  Medium metro  Small metro  Rural	8,085 (23.2) 2,634 (21.6) 842 (22.3) 1,023 (21.3)	26,776 (76.8) 9,539 (78.4) 2942 (77.7) 3,781 (78.7)	-- 0.98 1.03 0.96	Ref 0.93–1.04 0.94–1.13 0.87–1.05	-- 0.504 0.558 0.380
Annual household income  ≥$70,000  $55,000-$69,999  $40,000-$54,999  <$40,000	6,959 (23.0) 4,084 (22.4) 1,377 (21.7) 200 (21.2)	23,315 (77.0) 14,009 (77.6) 4,970 (78.3) 744 (78.8)	-- 0.98 1.04 1.06	Ref 0.94–1.03 0.96–1.13 0.88–1.28	-- 0.513 0.330 0.518
Interaction Analyses
Race/Income*  High-income Non-Hispanic White  Middle-income Non-Hispanic White  Low-income Non-Hispanic White  High-income African American  Middle-income African American  Low-income African American  High-income Hispanic  Middle-income Hispanic  Low-income Hispanic  High-income Asian/Pacific Islander  Middle-income Asian/Pacific Islander  Low-income Asian/Pacific Islander  High-income American Indian/Alaska Native  Middle-income American Indian/Alaska Native  Low-income American Indian/Alaska Native			-- 1.05 1.17 0.85 0.89 0.67 0.76 0.68 0.72 1.50 1.38 1.92 0.78 0.51 0.48	Ref 0.99–1.12 0.96–1.44 0.76–0.95 0.81–0.97 0.44–1.03 0.70–0.82 0.63–0.74 0.40–1.27 1.40–1.60 1.25–1.52 0.30–12.12 0.57–1.08 0.36–0.73 0.14–1.63	-- 0.103 0.126 0.003 0.011 0.067 <0.001 <0.001 0.254 <0.001 <0.001 0.488 0.129 <0.001 0.237
Race/Geography†  Large metro Non-Hispanic White  Small-medium metro Non-Hispanic White  Rural Non-Hispanic White  Large metro African American  Small-medium metro African American  Rural African American  Large metro Hispanic  Small-medium metro Hispanic  Rural Hispanic  Large metro Asian/Pacific Islander  Small-medium metro Asian/Pacific Islander  Rural Asian/Pacific Islander  Large metro American Indian/Alaska Native  Small-medium American Indian/Alaska Native  Rural American Indian/Alaska Native			-- 1.00 0.98 0.82 0.90 0.71 0.73 0.67 0.59 1.44 1.43 1.31 0.70 0.54 0.54	Ref 0.93–1.07 0.87–1.09 0.75–0.90 0.80–1.02 0.53–0.96 0.68–0.78 0.60–0.74 0.45–0.77 1.35–1.53 1.28–1.60 0.93–1.85 0.50–0.97 0.36–0.81 0.32–0.90	-- 0.942 0.746 <0.001 0.108 0.024 <0.001 <0.001 <0.001 <0.001 <0.001 0.122 0.034 0.003 0.018

Analyses were performed among a subset of patients who received treatment for HCC

^*Adjusted for age, sex, geography, year of diagnosis, number of tumors, and stage at diagnosis

^†Adjusted for age, sex, income, year of diagnosis, number of tumors, and stage at diagnosis

Even after adjusting for tumor stage at diagnosis, compared to NHW patients, AA (aOR, 0.84; 95% CI, 0.78–0.90; P<0.001), AI/AN (aOR, 0.61; 95% CI, 0.48–0.77; P<0.001), and Hispanic patients (aOR, 0.71; 95% CI, 0.66–0.75; P<0.001) had significantly lower odds of receiving surgical treatment while API patients (aOR, 1.43; 95% CI, 1.36–1.51; P<0.001) had higher odds of undergoing surgical treatment (Table 4).

Existing racial/ethnic differences in surgical treatment were exacerbated by low household income. For example, compared to high-income NHW patients, high-income Hispanic (aOR, 0.76; 95% CI, 0.70–0.82; P<0.001) and middle-income Hispanic patients (aOR, 0.68; 95% CI, 0.63–0.74; P<0.001) had lower odds of surgical treatment receipt. When evaluating the impact of geography on race/ethnicity-specific disparities, living in less urban and more rural areas worsened receipt of surgical treatment among Hispanics. For example, compared to NHW patients from large metro areas, Hispanic patients from large metro areas had 27% lower odds of receiving surgical treatment (aOR, 0.73; 95% CI, 0.68–0.78; P<0.001), whereas Hispanic patients from small-medium metro areas and rural had 33% (aOR, 0.67; 95% CI, 0.60–0.74; P<0.001) and 41% (aOR, 0.59; 95% CI, 0.45–0.77; P<0.001) lower odds of receiving surgical intervention, respectively (Table 4). Supplementary Table 3 shows results from the adjusted multivariable logistic regression analyses evaluating for predictors of receiving HCC surgical treatment, stratified by race/ethnicity.

HCC Delays in Care Analyses

A greater proportion of AA, AI/AN, and Hispanic individuals experienced delays in HCC treatment compared to NHW or API individuals. Those from rural areas also had higher proportions experiencing delays in HCC treatment compared to those from metropolitan areas. Individuals with an annual income of 55,000-$69,999 had the highest proportions of experiencing delays in HCC treatment (Table 5).

Table 5.

Multivariable Logistic Regression Models for Delays in Treatment (≥3 months vs. <3 months, N=55,001)

Characteristics	Delays in Treatment [N (%)]		Multivariable Analyses
	≥3 months	<3 months	OR	95% Cl	p-value
Age  20–49 years  50–59 years  60–69 years  ≥70 years	886 (25.1) 4,656 (30.9) 6,658 (31.8) 4,292 (27.7)	2,650 (74.9) 10,395 (69.1) 14,255 (68.2) 11,209 (72.3)	-- 1.31 1.33 1.11	Ref 1.21–1.43 1.22–1.44 1.02–1.21	-- <0.001 <0.001 0.013
Sex  Female  Male	3,884 (30.0) 12,608 (30.0)	9,085 (70.0) 29,424 (70.0)	-- 1.03	Ref 0.98–1.07	-- 0.260
Race/ethnicity  Non-Hispanic White  African American  Hispanic  Asian/Pacific Islander  American Indian/Alaska Native	7,800 (28.1) 1,925 (30.8) 4,049 (37.0) 2,496 (26.4) 222 (38.3)	19,972 (71.9) 4,327 (69.2) 6,907 (63.0) 6,946 (73.6) 357 (61.7)	-- 1.12 1.45 0.93 1.59	Ref 1.05–1.19 1.38–1.52 0.88–0.99 1.34–1.89	-- <0.001 <0.001 0.013 <0.001
Geography  Large metro  Medium metro  Small metro  Rural	10,448 (30.4) 3,598 (29.8) 1,133 (30.2) 1,313 (27.5)	23,971 (69.6) 8,461 (70.2) 2,619 (69.8) 3,458 (72.5)	-- 0.96 0.99 0.92	Ref 0.91–1.00 0.92–1.08 0.84–1.00	-- 0.057 0.897 0.047
Annual household income  ≥$70,000  $55,000-$69,999  $40,000-$54,999  <$40,000	8,741 (29.2) 5,754 (32.3) 1,733 (27.5) 264 (28.1)	21,229 (70.8) 12,044 (67.7) 2,560 (72.5) 676 (71.9)	-- 1.15 0.95 1.02	Ref 1.10–1.20 0.88–1.02 0.87–1.21	-- <0.001 0.172 0.771
Interaction Analyses
Race/Income*  High-income Non-Hispanic White  Middle-income Non-Hispanic White  Low-income Non-Hispanic White  High-income African American  Middle-income African American  Low-income African American  High-income Hispanic  Middle-income Hispanic  Low-income Hispanic  High-income Asian/Pacific Islander  Middle-income Asian/Pacific Islander  Low-income Asian/Pacific Islander  High-income American Indian/Alaska Native  Middle-income American Indian/Alaska Native  Low-income American Indian/Alaska Native			-- 1.03 1.05 1.07 1.19 1.01 1.35 1.65 1.79 0.88 1.10 1.77 1.32 1.90 2.15	Ref 0.97–1.09 0.87–1.27 0.98–1.18 1.10–1.29 0.72–1.42 1.26–1.44 1.54–1.76 1.15–2.79 0.82–0.94 1.00–1.21 0.29–10.69 1.02–1.71 1.50–2.42 0.93–4.95	-- 0.311 0.611 0.151 <0.001 0.949 <0.001 <0.001 0.010 <0.001 0.043 0.536 0.032 <0.001 0.072
Race/Geography†  Large metro Non-Hispanic White  Small-medium metro Non-Hispanic White  Rural Non-Hispanic White  Large metro African American  Small-medium metro African American  Rural African American  Large metro Hispanic  Small-medium metro Hispanic  Rural Hispanic  Large metro Asian/Pacific Islander  Small-medium metro Asian/Pacific Islander  Rural Asian/Pacific Islander  Large metro American Indian/Alaska Native  Small-medium American Indian/Alaska Native  Rural American Indian/Alaska Native			-- 1.01 0.94 1.14 1.09 1.08 1.50 1.41 1.12 0.97 0.83 0.87 1.55 1.36 2.03	Ref 0.95–1.07 0.85–1.03 1.05–1.23 0.98–1.21 0.84–1.39 1.41–1.59 1.30–1.53 0.90–1.39 0.91–1.03 0.74–0.93 0.61–1.23 1.20–2.01 1.02–1.82 1.41–2.93	-- 0.778 0.195 0.001 0.132 0.540 <0.001 <0.001 0.309 0.336 0.002 0.423 0.001 0.038 <0.001

Analyses were performed among a subset of patients who received treatment for HCC

^*Adjusted for age, sex, geography, year of diagnosis, number of tumors, and stage at diagnosis

^†Adjusted for age, sex, income, year of diagnosis, number of tumors, and stage at diagnosis

Compared to NHW patients, AA (aOR, 1.12; 95% CI, 1.05–1.19; P<0.001), Hispanic (aOR, 1.45; 95% CI, 1.38–1.52; P<0.001), and AI/AN patients (aOR, 1.59; 95% CI, 1.34–1.89; P<0.001) had greater odds of experiencing delays in receipt of HCC treatment following diagnosis, while lower odds were observed among API patients (aOR, 0.93; 95% CI, 0.88–0.98; P=0.013) (Table 5).

Racial/ethnic disparities in wait-times for treatment were exacerbated across income levels. For instance, compared to high-income NHW patients, high-income Hispanic patients (aOR, 1.35; 95% CI, 1.26–1.44; P<0.001) had 35% higher odds of waiting ≥3 months (vs. <3 months) for HCC treatment while middle-income (aOR, 1.65; 95% CI, 1.54–1.76; P<0.001) and low-income Hispanic patients (aOR, 1.79; 95% CI, 1.15–2.79; P=0.010) had 65% and 79% higher odds of experiencing delays in care, respectively. Similar trends were observed among high-income and middle-income AI/AN patients. When evaluating the effect of geography on racial/ethnic outcomes, compared to NHW patients from large metro areas, Hispanic patients from large (aOR, 1.50; 95% CI, 1.41–1.59; P<0.001) and small-medium (aOR, 1.41; 95% CI, 1.30–1.53; P<0.001) metro areas had higher odds of experiencing delayed time-to-treatment. Similarly, AI/AN individuals from large metro (aOR, 1.55; 95% CI, 1.20–2.01; P=0.001), small-medium metro (aOR, 1.36; 95% CI, 1.02–1.82; P=0.038), and rural areas (aOR, 2.03; 95% CI, 1.41–2.92; P<0.001) all had significantly greater odds of experiencing delays in care compared to NHW individuals from large metro areas (Table 5). Supplementary Table 4 shows the results from the adjusted multivariable logistic regression analyses evaluating for predictors of delays in HCC treatment (≥3 months vs. <3 months), stratified by race/ethnicity.

HCC Mortality Analyses

Supplementary Figure 1A-E shows all cause five-year survival age, sex, race/ethnicity, geography, and annual household income. Overall, all-cause five-year survival was highest among API individuals and lowest among AA individuals. When stratified by geography and annual household income, survival was lower among those who lived in less urban/more rural areas and had lower household income.

Compared to NHW patients, overall mortality risk was higher among AA patients (aHR, 1.10; 95% CI, 1.07–1.13; P<0.001), but lower among Hispanic (aHR, 0.97; 95% CI, 0.95–0.99; P<0.001) and API patients (aHR, 0.83; 95% CI, 0.81–0.85; P<0.001). Compared with HCC patients from large metro regions, patients in medium metro (aHR, 1.09; 95% CI, 1.07–1.11; P<0.001), small metro (aHR, 1.08; 95% CI, 1.04–1.12; P<0.001), and rural regions (aHR, 1.06; 95% CI, 1.02–1.09; P=0.004) all had significantly higher risk of mortality. Compared to HCC patients with ≥$70,000 in annual household income, significantly higher risk of mortality was observed in those with $55,000-$69,999 (aHR, 1.06; 95% CI, 1.04–1.08; P<0.001), $40,000-$54,999 (aHR, 1.15; 95% CI, 1.11–1.18; P<0.001), and <$40,000 (aHR, 1.25; 95% CI, 1.17–1.33; P<0.001) (Table 6).

Table 6.

Multivariable Cox Proportional Hazards Model for Overall Hepatocellular Carcinoma Mortality (N=78,471)

Characteristics	HR	95% Cl	p-value
Age  20–49 years  50–59 years  60–69 years  ≥70 years	-- 1.16 1.22 1.66	Ref 1.12–1.20 1.17–1.26 1.60–1.72	-- <0.001 <0.001 <0.001
Sex  Female  Male	-- 1.11	Ref 1.09–1.14	-- <0.001
Race/ethnicity  Non-Hispanic White  African American  Hispanic  Asian/Pacific Islander  American Indian/Alaska Native	-- 1.10 0.97 0.83 1.00	Ref 1.07–1.13 0.95–0.99 0.81–0.85 0.93–1.09	-- <0.001 0.008 <0.001 0.908
Geography  Large metro  Medium metro  Small metro  Rural	-- 1.09 1.08 1.06	Ref 1.07–1.11 1.04–1.12 1.02–1.09	-- <0.001 <0.001 0.004
Annual household income  ≥$70,000  $55,000-$69,999  $40,000-$54,999  <$40,000	-- 1.06 1.15 1.25	Ref 1.04–1.08 1.11–1.18 1.17–1.33	-- <0.001 <0.001 <0.001
Interaction Analyses
Race/Income*  High-income Non-Hispanic White  Middle-income Non-Hispanic White  Low-income Non-Hispanic White  High-income African American  Middle-income African American  Low-income African American  High-income Hispanic  Middle-income Hispanic  Low-income Hispanic  High-income Asian/Pacific Islander  Middle-income Asian/Pacific Islander  Low-income Asian/Pacific Islander  High-income American Indian/Alaska Native  Middle-income American Indian/Alaska Native  Low-income American Indian/Alaska Native	-- 1.08 1.22 1.09 1.20 1.44 0.99 1.03 1.05 0.84 0.87 0.52 0.93 1.17	Ref 1.06–1.11 1.13–1.31 1.04–1.13 1.16–1.24 1.27–1.64 0.96–1.02 1.00–1.06 0.86–1.29 0.81–0.86 0.84–0.86 0.17–1.62 0.83–1.05 1.05–1.30	-- <0.001 <0.001 <0.001 <0.001 <0.001 0.487 0.035 0.618 <0.001 <0.001 0.262 0.268 0.004
Race/Geography†  Large metro Non-Hispanic White  Small-medium metro Non-Hispanic White  Rural Non-Hispanic White  Large metro African American  Small-medium metro African American  Rural African American  Large metro Hispanic  Small-medium metro Hispanic  Rural Hispanic  Large metro Asian/Pacific Islander  Small-medium metro Asian/Pacific Islander  Rural Asian/Pacific Islander  Large metro American Indian/Alaska Native  Small-medium American Indian/Alaska Native  Rural American Indian/Alaska Native	-- 1.05 1.03 1.08 1.17 1.16 0.96 1.04 0.94 0.79 1.02 1.07 0.99 1.03 1.11	Ref 1.02–1.08 0.99–1.07 1.05–1.12 1.12–1.23 1.05–1.27 0.94–0.99 1.00–1.08 0.85–1.03 0.77–0.81 0.97–1.07 0.93–1.24 0.88–1.12 0.91–1.18 0.95–1.30	-- <0.001 0.148 <0.001 <0.001 0.003 0.007 0.060 0.179 <0.001 0.523 0.325 0.867 0.635 0.192

^*Adjusted for age, sex, geography, year of diagnosis, number of tumors, stage at diagnosis, and receipt of treatment

^†Adjusted for age, sex, income, year of diagnosis, number of tumors, and stage at diagnosis, and receipt of treatment

When evaluating overall mortality in patients with HCC, low household income exacerbated existing disparities by race/ethnicity as well. For example, compared to high-income NHW patients, middle-income AA patients had 20% higher risk of mortality (aHR, 1.20; 95% CI, 1.16–1.24; P<0.001) and low-income AA patients had 44% higher risk of mortality (aHR, 1.44; 95% CI, 1.27–1.64; P<0.001). Geography was also observed to exacerbate existing racial/ethnic disparities in survival. For example, compared to NHW patients from large-metro areas, AA patients from large metro areas had 8% higher mortality risk (aHR, 1.08; 95% CI, 1.05–1.12; P=0.001) whereas AA from small-medium metro areas had 17% higher mortality risk (aHR, 1.17; 95% CI, 1.12–1.23; P<0.001) (Table 6). Supplementary Table 5 shows results from the adjusted multivariable Cox proportional hazards model evaluating for predictors of overall mortality, stratified by race/ethnicity. Supplementary Table 6 shows results from the adjusted multivariable competing risks analyses evaluating for predictors of hepatocellular carcinoma-specific mortality. Supplementary Table 7 shows results from the adjusted multivariable competing risks analyses evaluating for predictors of HCC-specific mortality, stratified by race/ethnicity.

Discussion

Among a large U.S. population-based cancer registry, we observed significant disparities in the HCC care cascade by race/ethnicity and sociodemographic factors. Racial/ethnic minorities, especially AA, had higher odds of advanced tumor stage at diagnosis, lower odds of receiving HCC treatment, higher odds of experiencing delays in treatment, and had overall greater risk of mortality. Interestingly, we observed that lower annual household income and living in less urban and more rural areas exacerbate existing race/ethnicity-specific disparities in HCC care and outcomes, particularly for AA with HCC. These findings may reflect social and structural barriers in access to HCC prevention, surveillance, and treatment that disproportionately affect vulnerable and safety-net populations.

Best chance for curative therapy of HCC is early detection of localized HCC. Advanced-stage HCC is unlikely to be curative and portends worse overall survival. Our study revealed significant disparities in stage at diagnosis, especially across racial/ethnic and geographic lines. These findings are consistent with previous SEER-based studies that also have identified striking AA-NHW disparities in HCC stage at the time of diagnosis.^8,24 Our analysis further contributes to the literature by showing the impact of geography on racial/ethnic disparities, identifying AA in less urban/more rural areas as groups unlikely to be diagnosed with early-stage HCC. These observed differences are most likely indicative of disparities in timely access to HCC screening. These disparities are partly mediated by patient and provider-reported barriers to accessing routine medical care and HCC surveillance, which are more pronounced in rural health care settings.^25–28 Moreover, as HCC screening rates declined during the COVID-19 pandemic, we may potentially observe a widening gap in tumor stage at diagnosis among vulnerable populations.²⁹ Further efforts should focus on improving HCC surveillance rates in the post-pandemic era and addressing barriers to HCC surveillance, especially among AA in less urban/more rural areas.

Our study also revealed significant differences in access to HCC treatment among at-risk populations. For example, AA and Hispanic individuals had lower odds of HCC treatment, including surgical options, and were more likely to experience delays in HCC treatment. These findings are consistent with several population-based studies that have demonstrated significant disparities in receipt of curative treatment among AA and Hispanics individuals^16,30 and higher rates of treatment delays among AA individuals.¹⁸ This study further adds to the literature in showing the potential negative impact that geographic and income-based variables can have on racial/ethnic disparities in timely access to HCC treatment. In line with the National Institute on Minority Health and Health Disparities research framework, the factors driving these disparities are multifactorial and complex, encompassing a combination of patient (e.g., medical mistrust, knowledge, stigma, and beliefs about HCC and underlying liver conditions),³¹ provider (e.g., implicit and explicit biases), and system-level factors (e.g., insurance coverage and accessibility to liver transplantation services).^32,33 For example, a survey study among patients with HCC demonstrated marked differences in several patient-level factors including health literacy, medical mistrust, and barriers to care between AA, Hispanic, and NHW patients.³¹ Among system-level factors, insurance status is associated with access to liver transplantation services. Hence, patients who are uninsured are less likely to receive a referral for transplantation³⁴ and those who lack commercial insurance have lower odds of being considered for transplantation.³⁵ Geography also plays a role in resource utilization of liver transplantation services as lack of proximity, transportation barriers, and geographic variability in donor supply can limit capability to pursue curative options.³⁶

Finally, our results showed that HCC survival rates differed significantly by race/ethnicity, geography, and annual household income. Our findings are consistent with a recent meta-analysis of 35 articles that also demonstrated worse HCC survival among racial/ethnic minorities.⁵ Interestingly, our analysis additionally demonstrated that lower income and less urban geography worsened prognosis among AA with HCC. These analyses further contextualize the complex relationship between sociodemographic factors and the HCC care cascade through the lens of intersectionality, a framework that recognizes how overlapping identities and statuses can shape an individual’s access to health care services and health care outcomes.^37,38 Future studies will need to explore the mechanisms by which concurrent sociodemographic factors influence racial/ethnic disparities among patients with HCC.

In light of these significant disparities across racial/ethnic, geographic, and socioeconomic lines, our authors join the recent call to action to bridge disparities in HCC.³⁹ As early HCC detection can impact treatment options and survival, efforts should focus on enhancing care across the HCC screening continuum, including risk assessment, screening initiation, result follow-up, diagnostic evaluation, and treatment evaluation.⁴⁰ These interventions should particularly target vulnerable and high-risk populations to close existing gaps in care. Early models of targeted interventions are well documented in the viral hepatitis literature. Key initiatives, such as Project ECHO and Specialty Care Access Network-ECHO, are examples of programs that increased access to hepatitis C treatment among geographically dispersed individuals.^41,42 While increasing access to care for patients is a significant priority, further efforts should also focus on improving provider knowledge of HCC surveillance guidelines. Wong et al. has identified significant gaps in knowledge and barriers to HCC screening among both primary care physicians and gastroenterologists/hepatologists.⁴³ Improving delivery of education regarding HCC surveillance is essential to improve screening practices.

The major strength of this study was the large sample size and broad geographic representation of cancer registries across the United States. Our study also had limitations that are inherent to observational studies and the SEER database. SEER’s cancer staging system for HCC is unique to SEER and is not the typical staging system for HCC used in clinical practice. However, the tumor stage classification used in this study still provides important information about disparities. Etiology of liver disease contributing to HCC as well as presence of cirrhosis were data not readily available, and along the same lines, whether patients received specific therapies for underlying liver disease (e.g., antivirals for viral hepatitis) was also not included in this dataset. We recognize the importance of this limitation, especially as progression to HCC varies across etiologies and specific populations are differentially impacted by certain chronic liver diseases. For example, MASLD predominantly affects Hispanic populations in the U.S.⁴⁴ While it can directly lead to HCC, the risk for HCC progression is much higher among those with concomitant cirrhosis.⁴⁵ On the other hand, hepatitis B virus infection disproportionately impacts Asian communities and carries a significant risk for progression to HCC even in patients without cirrhosis.⁴⁶ Moreover, the SEER database aggregates Asian and Pacific Islander patients under one racial/ethnic category. As prevalence of hepatitis B is higher among foreign-born Southeast Asian populations,⁴⁷ it is possible that disparities in stage at diagnosis and HCC-related care are underestimated in this particular group. Future studies should focus on disaggregated analyses of Asian subpopulations to further elucidate HCC disparities among different Asian subgroups. The SEER database only included information on radiation therapy and chemotherapy if they were administered as part of the first course of treatment, and hence there may be some misclassification bias with respect to HCC treatment assessment. Additionally, there are several factors that influence approach to care and management of HCC including patient preferences, provider recommendations, and other coexisting comorbidities that are not available for assessment in this database.

In summary, this study provides further insight into the current state of disparities across the HCC care continuum in the U.S. Notably, our comprehensive analysis of SEER’s cancer registry database demonstrated the negative impact that lower income and less urban/more rural geography can have on racial/ethnic disparities, especially among AA individuals with HCC. Better understanding the multi-factorial drivers of these disparities and identifying modifiable determinants of health is needed to mitigate the disparities that affect vulnerable and safety-net populations with HCC.