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

Hepatocellular carcinoma (HCC) is the sixth leading cause of cancer-related mortality in the United States (1), with a 5-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–related HCC has plateaued since the introduction of highly effective direct-acting antivirals (2, 4). Although hepatitis C virus–related HCC has declined, the increasing prevalence of alcohol-related liver disease and metabolic dysfunction–associated steatotic liver disease 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 health care and are disproportionately affected by HCC. In fact, HCC incidence and mortality rates are significantly worse among African American (AA) patients compared with 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, affects HCC outcomes.

Therefore, the objectives of our study were (i) 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 (ii) assess the impact of intersecting sociodemographic factors, namely, income- and geography-level variables across race/ethnicity, on HCC outcomes using the most recent data from the NCI Surveillance, Epidemiology, and End Results (SEER) program.

We performed a retrospective cohort study using the 2000 to 2020 cancer registry data from the national SEER program (RRID: SCR_006902). This database incorporates data from 17 US geographic areas (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 US population. Adults with HCC (ages ≥20 years) were identified with anatomic sites (liver, C22.0) and histology codes (HCC, 8170–8175) from the International Classification of Disease for Oncology, third edition.

Race/ethnicity was categorized according to SEER definitions: non-Hispanic White (NHW), AA, Hispanic, Asian/Pacific Islander (API), and American Indian/Alaska 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 the 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. The 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 to $69,999, $40,000 to $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 the extent of cancer involvement from the point of origin: localized (cancer limited to the 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 nonsurgical (chemotherapy, radiotherapy, 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 the year of diagnosis and number of tumors at diagnosis, was also queried from the SEER database.

Descriptive analyses on the study cohort were performed using frequency and proportions. Adjusted multivariable logistic regression models were used to assess sociodemographic 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, 23). Analyses 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 risk 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 was 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; RRID: SCR_012763). This study qualified for an institutional review board exemption as there was no involvement of human participants, and the cancer registry data from SEER are available to the public without patient-identifying information.

The data analyzed in this study are publicly available and can be obtained from the NCI SEER database at: https://seer.cancer.gov/data-software/documentation/seerstat/ (RRID: SCR_006902).

We identified 112,389 adults with HCC (66.2% ages ≥60 years; 76.2% men and 23.8% women; and 49.8% NHW, 12.0% AA, 20.5% Hispanic, 16.5% API, and 1.1% AI/AN patients) from 2000 to 2020 (Table 1). The majority of patients were from large metro areas (62.5% vs. 9.4% from rural areas). An annual household income of ≥$70,000 was reported in 52.3% of patients. More than half (52.2%) had localized stage, 31.9% had regional stage, and 15.9% had 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, the 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%.

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 to $54,999 and <$40,000 (Table 2).

On multivariable logistic regression analysis, compared with NHW patients, AA [aOR, 0.84; 95% confidence interval (CI), 0.81–0.88; P < 0.001] patients 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 (aOR, 1.09; 95% CI, 1.05–1.14; P < 0.001) patients 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 geographic factors. For example, compared with 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 S1 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.

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. The proportion of individuals receiving HCC treatment from more metropolitan areas was higher than that from rural areas (Table 3).

In comparison with 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 (aOR, 0.84; 95% CI, 0.81–0.87; P < 0.001) patients, whereas API (aOR, 1.19; 95% CI, 1.14–1.24; P < 0.001) patients had greater odds of receiving treatment for HCC. Compared with individuals with an annual household income ≥$70,000, significantly lower odds of treatment receipt were observed in those with $55,000 to $69,999 (aOR, 0.90; 95% CI, 0.88–0.93; P < 0.001), $40,000 to $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 with 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, whereas middle-income (aOR, 0.69; 95% CI, 0.65–0.73; P < 0.001) and low-income (aOR, 0.59; 95% CI, 0.47–0.73; P < 0.001) AA patients 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 with high-income NHW patients. When compared with 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 S2 shows the results from the adjusted multivariable logistic regression analyses evaluating for predictors of receipt of HCC treatment, stratified by race/ethnicity.

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

Even after adjusting for tumor stage at diagnosis, compared with 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 (aOR, 0.71; 95% CI, 0.66–0.75; P < 0.001) patients had significantly lower odds of receiving surgical treatment, whereas API (aOR, 1.43; 95% CI, 1.36–1.51; P < 0.001) patients 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 with 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 the receipt of surgical treatment among Hispanics. For example, compared with 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 areas 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 S3 shows the results from the adjusted multivariable logistic regression analyses evaluating for predictors of receiving HCC surgical treatment, stratified by race/ethnicity.

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

Compared with 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 (aOR, 1.59; 95% CI, 1.34–1.89; P < 0.001) patients had greater odds of experiencing delays in receipt of HCC treatment following diagnosis, whereas 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 with high-income NHW patients, high-income (aOR, 1.35; 95% CI, 1.26–1.44; P < 0.001) Hispanic patients had 35% higher odds of waiting ≥3 months (vs. <3 months) for HCC treatment, whereas middle-income (aOR, 1.65; 95% CI, 1.54–1.76; P < 0.001) and low-income (aOR, 1.79; 95% CI, 1.15–2.79; P = 0.010) Hispanic patients 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 with 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 with NHW individuals from large metro areas (Table 5). Supplementary Table S4 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.

Supplementary Figure S1A–S1E shows all-cause 5-year survival stratified by age, sex, race/ethnicity, geography, and annual household income. Overall, all-cause 5-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 with NHW patients, the overall mortality risk was higher among AA (aHR, 1.10; 95% CI, 1.07–1.13; P < 0.001) patients but lower among Hispanic (aHR, 0.97; 95% CI, 0.95–0.99; P < 0.001) and API (aHR, 0.83; 95% CI, 0.81–0.85; P < 0.001) patients. Compared with patients with HCC 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 with patients with HCC with ≥$70,000 in annual household income, significantly higher risk of mortality was observed in those with $55,000 to $69,999 (aHR, 1.06; 95% CI, 1.04–1.08; P < 0.001), $40,000 to $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).

When evaluating overall mortality in patients with HCC, low household income exacerbated existing disparities by race/ethnicity as well. For example, compared with 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 with 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 patients 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 S5 shows the results from the adjusted multivariable Cox proportional hazards model evaluating for predictors of overall mortality, stratified by race/ethnicity. Supplementary Table S6 shows the results from the adjusted multivariable competing risk analyses evaluating for predictors of HCC-specific mortality. Supplementary Table S7 shows the results from the adjusted multivariable competing risk analyses evaluating for predictors of HCC-specific mortality, stratified by race/ethnicity.

Among a large US 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 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 tumor 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 individuals 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 healthcare 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 postpandemic 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 Hispanic 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; ref. 31), provider- (e.g., implicit and explicit biases), and system-level factors (e.g., insurance coverage and accessibility to liver transplantation services; refs. 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, among 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 transplantation (34), 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 patients 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 healthcare services and healthcare 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 affect 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). Although increasing access to care for patients is a significant priority, further efforts should also focus on improving provider knowledge of HCC surveillance guidelines. Wong and colleagues (43) have identified significant gaps in knowledge and barriers to HCC screening among both primary care physicians and gastroenterologists/hepatologists. Improving delivery of education with regard to 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. Data on etiology of liver disease contributing to HCC as well as the presence of cirrhosis were 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 affected by certain chronic liver diseases. For example, metabolic dysfunction–associated steatotic liver disease predominantly affects Hispanic populations in the United States (44). Although 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 affects 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 tumor 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 radiotherapy 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 insights into the current state of disparities across the HCC care continuum in the United States. 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. A better understanding of the multifactorial drivers of these disparities and identification of modifiable determinants of health are needed to mitigate the disparities that affect vulnerable and safety-net populations with HCC.

M. Khalili reports grants and other support from Gilead Sciences Inc., grants from Intercept Pharmaceuticals, and other support from Resolution Therapeutics and GSK Pharmaceuticals outside the submitted work. A.G. Singal reports personal fees from Genentech, AstraZeneca, Eisai, Bayer, Exelixis, Elevar, Merck, Boston Scientific, Sirtex, HistoSonics, Exact Sciences, Fujifilm Medical Sciences, Abbott, Roche, Helio Genomics, DELFI, and Glycotest outside the submitted work. P.D. Jones reports grants from the NIH during the conduct of the study. R.J. Wong reports grants from Gilead Sciences, Exact Sciences, Theratechnologies, and Durect Corporation outside the submitted work. No disclosures were reported by the other authors.

S. Patel: Conceptualization, data curation, formal analysis, investigation, methodology, writing–original draft, writing–review and editing. M. Khalili: Writing–review and editing. A.G. Singal: Writing–review and editing. P.S. Pinheiro: Writing–review and editing. P.D. Jones: Writing–review and editing. R.G. Kim: Writing–review and editing. V. Kode: Writing–review and editing. A. Thiemann: Writing–review and editing. W. Zhang: Writing–review and editing. R. Cheung: Writing–review and editing. R.J. Wong: Conceptualization, formal analysis, supervision, methodology, writing–review and editing.

R.J. Wong’s research is supported by National Institute on Minority Health and Health Disparities 5R01MD017063. M. Khalili is partly supported by National Institute on Alcohol Abuse and Alcoholism K24AA022523 and National Institute on Minority Health and Health Disparities U24MD017250. A.G. Singal’s research is supported by NIH grants R01MD012565 and R01CA256977.