Abstract
Background
Concerning data have revealed that viral hepatitis and hepatocellular carcinoma (HCC) disproportionally impact non-White patients and those from lower socioeconomic status. A recent study found that HCC clusters were more likely to be in high poverty areas in New York City.
Aims
We aim to investigate the impacts of neighborhood characteristics on those with viral hepatitis and cirrhosis, particularly with advanced HCC diagnosis.
Methods
Patients with cirrhosis and viral hepatitis admitted to a New York City health system between 2012 and 2019 were included. Those with prior liver transplants were excluded. Neighborhood characteristics were obtained from US Census. Our primary outcome was HCC and advanced HCC diagnosis.
Results
This study included 348 patients; 209 without history of HCC, 20 with early HCC, 98 with advanced HCC, and 21 patients with HCC but no staging information. Patients with advanced HCC were more likely to be older, male, Asian, history of HBV, and increased mortality. They were more likely to live in areas with more foreign-born, limited English speakers, and less than high school education. After adjusting for age, sex, and payor type, Asian race and low income were independent risk factors for advanced HCC. Neighborhood factors were not associated with mortality or readmissions.
Conclusion
We observed that in addition to age and sex, Asian race, lower household income, lower education, and lower English proficiency were associated with increased risk of advanced HCC. These disparities likely reflect suboptimal screening programs and linkage to care among vulnerable populations. Further efforts are crucial to validate and address these concerning disparities.
Keywords: social determinants of health, disparities, hepatocellular carcinoma, viral hepatitis
Chronic liver disease, including cirrhosis, is an important public health concern and plays a significant role in morbidity and mortality in the United States. The Center for Disease Control estimates that in 2020, chronic liver disease and cirrhosis represented the 11th leading cause of death in the United States and is responsible for over 50,000 deaths per year.1,2
Social determinants of health (SDOH) are increasingly recognized to play a critical role in health outcomes and patients’ quality of life. Two well-studied components of SDOH, race/ethnicity, and socioeconomic status (SES), are consistently associated with healthcare utilization and disease outcomes.3 Lower SES is linked to worse outcomes in many medical disciplines. For example, studies show that socioeconomic deprivation is an independent predictor of heart failure development and adverse outcomes.4 In addition, race/ethnicity is often connected to significant variation in healthcare access and management, which leads to significant disparities in medical care.5
Within the field of hepatology, higher household income is inversely associated with viral loads for hepatitis B (HBV) and hepatitis C (HCV).6 Additionally, socioeconomic factors such as unemployment and low household income are proven risk factors for both HCV infection and mortality after infection.7 Lower SES is also a negative prognostic factor after cirrhosis diagnosis and an independent predictor of hepatocellular carcinoma (HCC).8,9 Furthermore, race/ethnicity plays a large role in viral hepatitis infections. In the United States, HBV and HCV are disproportionally found in Asian and Black patients, respectively.10,11
A recent study identified New York City (NYC) zip codes with HCC clusters and found that poverty and uninsured status were associated with increased rates of HCC.12 However, each NYC zip code contains tens of thousands of people with diverse socioeconomic backgrounds. To better understand the impact of SDOH, analysis of census tract level data, rather than zip codes, is required to gather more granular neighborhood characteristics. In this study, we aim to investigate the impact of neighborhood SDOH on HCC diagnosis and mortality in patients with viral hepatitis and cirrhosis at a tertiary healthcare system. We hypothesize that lower socioeconomic characteristics such as low household income would be associated with higher rates of HCC and mortality after controlling for clinical characteristics. In addition, we expect that racial minorities experienced higher rates of HCC and mortality due to underlying social and healthcare disparities.
Methods
Study Population
We conducted a retrospective cohort study of adult patients with cirrhosis due to HBV or HCV who were admitted for any reason to New York-Presbyterian (NYP)/Weill Cornell Medicine (WCM), a quaternary referral center and 862-bed teaching hospital between January 1, 2012, and December 31, 2019. All patients diagnosed with viral hepatitis (HBV, HCV) as well as cirrhosis confirmed by pathologic or imaging were included in this study. Patients who were admitted on their index admission for liver transplantation were excluded from this study. Race is self-reported by the patient, and is categorized as White, Black, Asian, and Other.
Patients with cirrhosis were initially identified by an algorithm of ICD-9 and ICD-10 codes (supplement 1), and were confirmed through chart review. Patients with history of HBV or HCV were identified within the cirrhosis cohort using a similar method.
Definitions
The presence of cirrhosis (by biopsy or laboratory/imaging data consistent with cirrhosis) and viral hepatitis were confirmed by chart review. We defined early-stage HCC as T1 (tumors without microvascular invasion) and advanced HCC as stage T2 and higher in the TNM staging system. Studies have shown that microvascular invasion is an independent risk factor for tumor recurrence and overall survival, and patients with T1 have significantly better survival compared to those with T2 or higher.13,14
Neighborhood socioeconomic characteristics were obtained by linking a patient's address with their Federal Information Processing Standard (FIPS) code. FIPS code is a Geographic Identifier that links to a census block group that can contain between 1200 and 8000 individuals.15 For example, a census block in NYC could contain an area of three street blocks and two avenue blocks. By utilizing FIPS codes, we have access to much more granular data compared to a zip code. This is especially critical in NYC, where each zip code can contain tens of thousands of residents with wide socioeconomic disparities.
Objectives
Our primary objectives are the diagnosis of HCC and the diagnosis of advanced HCC, which we used as a proxy for delayed diagnosis of HCC. Our secondary objectives are to identify an association of SDOH with overall mortality and hospital readmissions in order to evaluate the impact on SDOH with outcomes.
Statistical Analysis
Categorical variables are shown as frequencies, and continuous variables are presented as mean ± standard deviation (SD). Statistical analysis was performed using Stata version 17 (StataCorp. 2021. College Station, TX). For comparisons of categorical variables, the χ2 or Fisher's exact tests were used. For comparisons of continuous variables, Student's t-test was used. We created multivariable models using a priori covariates including age, sex, and payor type to identify independent variables associated with HCC and mortality. For patients with HCC, appropriate points (2 for localized and 6 for metastatic) were subtracted from Charlson Comorbidity Index.16 The study was approved by Weill Cornell Medicine Institutional Review Board.
Results
Study Population
A total of 359 unique patients with viral hepatitis and cirrhosis from 273 different neighborhoods were admitted, of which 11 had prior liver transplants and were excluded from this study. Median follow-up time was 2.7 years (IQR: 0.7–5.5 years). Of the 348 patients included, 85 (24.4%) had HBV, 245 (70.4%) had HCV, 18 (5.2%) had both; 139 (39.9%) patients developed HCC, and 139 (39.9%) died during the follow-up period or were discharged directly to hospice. Average age at the time of the first hospitalization was 61 years (IQR: 55.2–68.5 years), 121 (34.8%) patients were women, 75 (21.5%) were Black, and 44 (12.6%) were Asian. Despite accounting for 12.6% of the overall population, Asian patients accounted for 37.9% of the HBV cases (P < 0.001). Asian patients lived in neighborhoods with more foreign-born (46% vs. 32%, P < 0.001) and limited English speakers (28% vs. 14%, P < 0.001) compared to other race/ethnicities. A majority of the patients were covered under public insurance (Medicaid/Medicare, 85.9%). The most common comorbidities identified were diabetes (35.3%), chronic kidney disease (CKD) (24.7%), and chronic obstructive pulmonary disease (COPD) (23.6%). Table 1.
Table 1.
Summary of Demographic, Clinical, and Neighborhood Characteristics by Presence of HCC.
| Characteristics | Total (n = 348) | No HCC (n = 209) | HCC (n = 139) | P value |
|---|---|---|---|---|
| Age | 61.39 ± 11.0 | 59.87 ± 10.1 | 63.65 ± 11.9 | 0.001 |
| Female | 121 (34.7%) | 83 (39.7%) | 38 (27.3%) | 0.01 |
| Race | ||||
| White | 108 (31.0%) | 70 (33.4%) | 38 (27.3%) | Reference |
| Black | 75 (21.5%) | 47 (22.4%) | 28 (20.1%) | 0.77 |
| Asian | 44 (12.6%) | 16 (7.65%) | 28 (20.1%) | 0.001 |
| Other | 83 (23.8%) | 52 (24.8%) | 31 (22.3%) | 0.76 |
| Declined | 38 (10.9%) | 23 (11.0%) | 15 (10.7%) | 0.64 |
| Hepatitis | ||||
| B | 85 (24.4%) | 38 (18.1%) | 47 (33.8%) | Reference |
| C | 245 (70.4%) | 158 (75.5%) | 87 (62.5%) | 0.001 |
| Both | 15 (4.31%) | 12 (5.74%) | 3 (2.15%) | 0.01 |
| Payor | ||||
| Public | 299 (85.9%) | 180 (86.1%) | 119 (85.6%) | Reference |
| Private | 44 (12.6%) | 25 (11.9%) | 19 (13.6%) | 0.67 |
| Other | 5 (1.43%) | 3 (1.43%) | 2 (1.43%) | 0.99 |
| Comorbidities | ||||
| Diabetes | 123 (35.3%) | 69 (33.0%) | 54 (38.8%) | 0.302 |
| Alcohol use | 48 (13.7%) | 34 (16.2%) | 14 (10.0%) | 0.092 |
| CHF | 44 (12.6%) | 32 (15.3%) | 12 (8.63%) | 0.061 |
| COPD | 82 (23.5%) | 59 (28.2%) | 23 (16.5%) | 0.01 |
| MI | 5 (1.43%) | 0 (0%) | 5 (3.59%) | – |
| CVD/TIA | 36 (10.3%) | 26 (12.4%) | 10 (7.19%) | 0.108 |
| CKD | 86 (24.7%) | 56 (26.7%) | 30 (21.5%) | 0.244 |
| AKI | 92 (26.4%) | 56 (26.7%) | 36 (25.8%) | 0.802 |
| Dementia | 8 (2.29%) | 3 (1.43%) | 5 (3.59%) | 0.194 |
| Charleston comorbidity index | 3.71 | 3.7 | 3.67 | 0.93 |
| Hospitalization | ||||
| BMI (kg/m2) | 27.23 | 28.31 | 25.80 | 0.001 |
| ICU | 23 (6.60%) | 12 (5.74%) | 11 (7.91%) | 0.44 |
| Decompensation | 181 (52.0%) | 106 (50.7%) | 75 (53.9%) | 0.63 |
| Neighborhood factors | ||||
| Poverty rate | 0.22 | 0.22 | 0.24 | 0.21 |
| Median household income | 60,098 | 63,192 | 55,501 | 0.05 |
| SVI theme | 0.66 | 0.64 | 0.69 | 0.13 |
| Percent limited English | 0.16 | 0.14 | 0.19 | 0.01 |
| Percent foreign born | 0.34 | 0.32 | 0.36 | 0.005 |
| Percent less than high school | 0.06 | 0.05 | 0.07 | 0.002 |
| Percent less than college | 0.39 | 0.38 | 0.42 | 0.04 |
| Percent college | 0.67 | 0.67 | 0.68 | 0.09 |
| Walk score | 2.30 | 2.20 | 2.46 | 0.47 |
| Percent White | 0.46 | 0.48 | 0.42 | 0.04 |
| Unemployment rate | 0.10 | 0.10 | 0.09 | 0.87 |
| Percent uninsured | 0.11 | 0.10 | 0.11 | 0.39 |
| SVI flags | 3.17 | 3.11 | 3.26 | 0.58 |
| Felony | 118.78 | 123.74 | 111.40 | 0.15 |
| GINI | 0.49 | 0.50 | 0.49 | 0.20 |
| Readmission | ||||
| 7 Day | 12 (3.44%) | 9 (4.30%) | 3 (2.15%) | 0.27 |
| 30 Day | 55 (15.8%) | 31 (14.8%) | 24 (17.2%) | 0.57 |
| Mortality or discharged to hospice | 139 (39.9%) | 70 (33.4%) | 69 (49.6%) | 0.004 |
BMI, Body Mass Index; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease; HCC, hepatocellular carcinoma; ICU, Intensive care unit.
Presence of HCC
Compared to those without HCC, patients with HCC were more likely to be older (63.7 vs. 59.9 years), male (74.1% vs. 60.1%), Asian (20.1% vs. 7.7%), HBV (33.8% vs. 18.2%), lower BMI (25.8 vs. 28.3), and increased overall mortality (49.6% vs. 33.5%). For neighborhood characteristics, those with HCC were more likely to live in areas with a lower median household income ($55,000 vs. $63,200), higher percent of foreign-born (36% vs. 32%), higher limited English speakers (19% vs. 14%), and higher percent of schooling less than high school diploma (7% vs. 5%), all with P < 0.05. Public insurance was not significantly associated with presence of HCC (P = 0.67).
Primary Outcome—Diagnosis of HCC
In multivariable analysis, Asian race (adjusted OR 2.82, 95% CI: 1.31–6.11, P = 0.008) was an independent HCC risk factor after adjusting for age, sex, and payor status. Additionally, patients with cirrhosis and viral hepatitis living in neighborhoods with a lower proportion of patients who graduated high school (aOR 1.59, 95% CI: 1.18–2.14, P = 0.002), higher percent of foreign-born (aOR 1.21, 95% CI: 1.02–1.43, P = 0.03), and higher percent with limited English (aOR 1.23, 95% CI: 1.03–1.48, P = 0.02) were all more likely to have HCC (all numbers adjusted for every 10% change). Living in neighborhoods with higher median household income (aOR 0.91, 95% CI: 0.85–0.98, P = 0.01, for every additional $10,000 in income) was a protective factor against HCC. Table 2.
Table 2.
Multivariable Analysis for Predicting Presence of HCC by Neighborhood Characteristics∗.
| Odds Ratio | 95% Low | 95% High | P Value | |
|---|---|---|---|---|
| Median household income | 0.91 | 0.85 | 0.98 | 0.01 |
| Percent high school | 1.59 | 1.18 | 2.14 | 0.002 |
| Percent limited English | 1.23 | 1.03 | 1.48 | 0.02 |
| Percent foreign Born | 1.21 | 1.02 | 1.43 | 0.03 |
HCC, hepatocellular carcinoma.
Median household income was adjusted for every $10,000. All other categories were adjusted for every 10%.
Each model controlled the following covariates – age, sex, payor, and race.
Presence of Advanced HCC
Of 348 patients included in this study, 209 (60%) never had HCC, 20 (6%) had early HCC (T1 and earlier), 98 (28%) had advanced HCC (T2 and later), and 21 (6%) did not have staging information. Compared to those without HCC, patients with advanced HCC were more likely to be older (63 years vs. 60), male (77.8% vs. 60.1%), Asian (22.2% vs. 7.7%), have HBV (38.3% vs. 18.2%), and have a lower BMI (25.2 vs. 28.3). For neighborhood characteristics, those with advanced HCC were more likely to live in areas with higher percentage of foreign-born (37.0% vs. 32.0%), limited English speakers (19% vs. 14%), and achieve less than high school education (18.0% vs. 13.0%), all with P < 0.05. Table 3.
Table 3.
Summary of Demographic, Clinical, and Neighborhood Characteristics by Staging of HCC.
| Characteristics | No HCC (n = 209) | Early HCC (n = 20) | Advanced HCC (n = 98) | No HCC vs. Early |
No HCC vs. Advanced |
|---|---|---|---|---|---|
| P value | P value | ||||
| Age | 59.87 ± 10.1 | 61.82 ± 7.4 | 63.23 ± 12.5 | 0.29 | 0.009 |
| Female | 83 (39.9%) | 9 (45%) | 22 (22.2%) | 0.66 | 0.002 |
| Race | |||||
| White | 70 (33.6%) | 4 (20%) | 27 (27.2%) | Reference | Reference |
| Black | 47 (22.5%) | 6 (30%) | 17 (17.1%) | 0.22 | 0.86 |
| Asian | 16 (7.69%) | 2 (10%) | 22 (22.2%) | 0.38 | 0.001 |
| Other | 52 (25%) | 7 (35%) | 20 (20.2%) | 0.18 | 0.99 |
| Declined | 23 (11.0%) | 1 (5%) | 13 (13.1%) | 0.81 | 0.36 |
| Hepatitis | |||||
| B | 38 (18.2%) | 4 (20%) | 38 (38.3%) | Reference | Reference |
| C | 158 (75.9%) | 16 (80%) | 56 (56.5%) | 0.95 | <0.001 |
| Both | 12 (5.76%) | 0 (0%) | 5 (5.05%) | – | 0.12 |
| Payor | |||||
| Public | 180 (86.5%) | 17 (85%) | 86 (86.8%) | Reference | Reference |
| Private | 25 (12.0%) | 3 (15%) | 12 (12.1%) | 0.72 | 0.99 |
| Other | 3 (1.44%) | 0 (0%) | 1 (1.01%) | – | 0.76 |
| Comorbidities | |||||
| Diabetes | 69 (33.1%) | 8 (40%) | 36 (36.3%) | 0.54 | 0.58 |
| Alcohol use | 34 (16.3%) | 2 (10%) | 10 (10.1%) | 0.46 | 0.14 |
| CHF | 32 (15.3%) | 2 (10%) | 7 (7.07%) | 0.52 | 0.04 |
| COPD | 59 (28.3%) | 4 (20%) | 16 (16.1%) | 0.42 | 0.02 |
| MI | 0 (0%) | 1 (5%) | 3 (3.03%) | – | – |
| CVD/TIA | 26 (12.5%) | 2 (10%) | 8 (8.08%) | 0.75 | 0.25 |
| CKD | 56 (26.9%) | 6 (30%) | 19 (19.1%) | 0.77 | 0.14 |
| AKI | 56 (26.9%) | 7 (35%) | 24 (24.2%) | 0.44 | 0.62 |
| Dementia | 3 (1.44%) | 0 (0%) | 5 (5.05%) | – | 0.06 |
| Charleston comorbidity index | 3.7 | 4.26 | 3.67 | 0.44 | 0.88 |
| Hospitalization | |||||
| BMI kg/m2 | 28.3 | 26.2 | 25.2 | 0.09 | <0.001 |
| ICU | 12 (5.76%) | 1 (5%) | 5 (5.05%) | 0.89 | 0.80 |
| Decompensation | 106 (50.9%) | 14 (70%) | 50 (50.5%) | 0.10 | 0.94 |
| Readmission | |||||
| 7 Day | 9 (4.32%) | 1 (5%) | 2 (2.02%) | 0.89 | 0.31 |
| 30 Day | 31 (14.9%) | 5 (25%) | 15 (15.1%) | 0.24 | 0.96 |
| Neighborhood factors | |||||
| % Poverty | 22% | 28% | 23% | 0.06 | 0.35 |
| Median household income | 63,192 | 40,885 | 56,102 | 0.003 | 0.10 |
| SVI theme | 0.64 | 0.79 | 0.68 | 0.005 | 0.25 |
| Percent limited English | 14% | 19% | 19% | 0.08 | 0.01 |
| Percent foreign born | 32% | 37% | 37% | 0.07 | 0.01 |
| Percent less than high school | 5% | 9% | 6% | <0.001 | 0.04 |
| Percent less than college | 38% | 51% | 40% | <0.001 | 0.24 |
| Percent college | 67% | 72% | 68% | 0.01 | 0.41 |
| Walk score | 2.20 | 3.10 | 2.29 | 0.26 | 0.83 |
| Percent White | 48% | 32% | 42% | 0.02 | 0.08 |
| Percent unemployed | 10% | 10% | 10% | 0.73 | 0.91 |
| Percent uninsured | 10% | 12% | 11% | 0.32 | 0.22 |
| SVI flags | 3.10 | 3.65 | 3.33 | 0.36 | 0.50 |
| Felony | 124 | 136 | 109 | 0.63 | 0.09 |
| GINI | 0.50 | 0.48 | 0.49 | 0.31 | 0.21 |
| Mortality or discharged to hospice | 70 (33.6%) | 4 (20%) | 55 (55.5%) | 0.21 | <0.001 |
BMI, Body Mass Index; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease; HCC, hepatocellular carcinoma; ICU, Intensive care unit.
Similar to HCC diagnosis, Asian race (aOR 2.64, 95% CI: 1.22–5.74, P = 0.03) remained an independent risk factor for presence of advanced HCC, after adjusting for age, sex, and payor type. Living in neighborhoods with higher median household income served as a protective factor against presence of advanced HCC (aOR = 0.92, 95% CI: 0.85–0.99, P = 0.03, for every additional $10,000 in income). Neighborhood characteristics such as foreign-born, limited English speakers, and less than high school education failed to remain statistically significant after adjusting for age, sex, race, and payor type.
Mortality and Readmission
Of 139 patients (39.9%) who died or were discharged to hospice, 70 (50.4%) had no history of HCC, 4 (2.9%) were diagnosed with early-stage HCC, 55 (39.6%) with advanced HCC, and 10 (7.2%) did not have staging information. Patients with advanced HCC had the highest mortality rate (55.6%) compared to those without HCC (35.5%, P < 0.001) and those with early-stage HCC (20.0%, P = 0.004). Male sex was associated with mortality (41.7% vs. 29.8%, P = 0.005), whereas neighborhood characteristics were not. Seven- and 30-day readmissions rates were 3.4% and 15.8%, respectively. Presence and staging of HCC and neighborhood characteristics were not found to be associated with 7- and 30-day readmissions.
Discussion
NYC is one of the most diverse cities in the world, with immigrants from over 150 countries and comprising of 40% of the city's population.17 While this uniquely positions NYC as a microcosm of the global population, it has also been linked to the rising prevalence of viral hepatitis in NYC, particularly HBV.18 It is crucial to understand how residents of NYC are impacted by viral hepatitis, and how SDOH plays a role in their ability to access and receive quality medical care. Additionally, resident demographics and their SES can change drastically just within a few street blocks. This further necessitates the need to evaluate the impact their environment has on our patients at a granular level rather than a borough or city wide level. We hypothesize that neighborhoods in which our patients live have an impact on their rates of HCC diagnosis, advanced HCC staging at time of diagnosis, and mortality.
This retrospective study assessed the associated impacts of patient and neighborhood characteristics, as functions of SDOH, on the presence of HCC as well as presence of advanced HCC among those with cirrhosis and viral hepatitis. Prior studies have demonstrated that residents of low-income neighborhoods tend to have worse outcomes for a wide range of conditions, including heart failure, pneumonia, and COVID-19.19,20 These patients are also less likely to receive preventative care such as colorectal cancer screening.21,22 Using detailed, neighborhood-level socioeconomic characteristics in a large, diverse NYC cohort, we found that patients living in lower income neighborhoods were more likely to have HCC and advanced HCC. Potential causes for the increases in HCC include lack of access to viral hepatitis screening and therapies. These patients also likely experience significant barriers to regular HCC screening. This could lead to delayed HCC diagnosis, which has been shown to have worse quality of life and survival outcomes.23,24 HCC screening continues to be underutilized despite its ability to detect HCC at an early and curable stage. One study found that less than half of all at-risk patients received any screening over a 3-year period.25
Historically, HCC incidence is highest among Asians and is more than double that of Whites. However, while incidence has been declining for Asians, rates of HCC have increased for American Indians and Hispanics since 2001.26 Our study showed that in patients with viral hepatitis, after controlling for age, sex, and payor status, Asian race had almost 3-fold higher odds to have HCC as well as advanced HCC. Additionally, we found that Asians are more likely to have HBV and are more likely to live in neighborhoods with more foreign-born individuals and limited English speakers. Importantly, these SDOH factors are all associated with increased incidence of HCC. While the increased incidence of HCC is likely to be multifactorial, further studies are warranted to better understand causality for this higher incidence in HCC. While there have been significant advancements in disease modifying therapies for HBV and HCV, immigrants and non-English speakers are less likely to seek and receive care due to factors such as lack of knowledge, language barrier, and fear of legal implications.27 In addition, even when individuals are diagnosed, there are barriers to linkage of care, ultimately leading to lower treatment rates.28 However, we can utilize this information to pinpoint neighborhoods within NYC to dedicate additional resources to improve education and access to effective treatment and screening programs. Various outreach programs have been developed in NYC to educate, screen, vaccinate, and treat migrant populations at risk for viral hepatis.29,30 Additional studies on cohorts outside of NYC must also be performed to evaluate on the generalizability of these findings.
In addition to neighborhood SDOH, we observed that older age, men, Asian race, and low household income were associated with increased risk of HCC and advanced HCC, all of which are known risk factors for HCC.9,31 Our study found that older age and men continue to remain as risk factors in all of our multivariable analyses that included race and neighborhood characteristics. Furthermore, male sex and presence of advanced HCC were associated with increased mortality, while neighborhood characteristics did not appear to impact mortality.
In this study, we showed that despite advancements in treatment and surveillance guidelines, viral hepatitis and HCC continue to disproportionally impact patients living in neighborhoods with lower SES, who likely have lower access to quality medical care. Interventions through community involvement, technological improvements, and insurance expansions must be incorporated for these populations. Studies have shown that certain neighborhoods have significantly improved HBV screening by incorporating screening and discussions at community centers and churches.32,33 Multiple studies have shown the increase in screening and treatment retention when individuals are paired with providers who speak their native language.34,35 In addition, screening programs must provide adequate resources to offer continuity of care for patients, making sure patients receive treatment after their diagnosis.28,33, 34 Lastly, studies showed that Medicaid expansion is associated with a reduction in end-stage liver disease related transplants and deaths, and this is particularly true for states with lenient eligibility criteria for HCV medications.,36, 37
Limitations of this investigation relate to the retrospective nature of this study and the focus on one tertiary care hospital system within one urban center. The included cohort is also likely to be of higher risk due to inclusion of those who had hospital admission. Lastly, it is likely that neighborhood characteristics are not always representative of the SES of individual patients and the lack of ethnicity category may have led to missed recognition of specific healthcare disparities. However, these findings further validate and strengthen the concerns of identifying those who are vulnerable to high disease burden and experience poorer outcomes among those suffering with advanced liver disease with viral hepatitis and liver cancer.
In conclusion, we observed that in addition to age and sex, Asian race, and low household income are associated with increased risk of HCC and advanced HCC. While the explanations for these findings are likely multifactorial, these disparities likely reflect suboptimal treatment and screening for viral hepatitis and HCC among specific vulnerable populations. Further efforts are crucial to determine hotspots across NYC and the United States to address these concerning and ongoing disparities.
Credit authorship contribution statement
XY: conceptualization; methodology; formal analysis; data curation; writing – original draft; YP: data curation; writing – review and editing; RR: methodology; formal analysis; writing – review and editing; CN: data curation; software; ES: data curation; software; KF: writing – review and editing; AJ: writing – review and editing; BF: conceptualization; methodology; writing – review and editing; supervision.
Conflicts of interest
AJ received consulting and speaking fees for Salix Pharmaceuticals and consulting fees for Dynavax Therapeutics. BEF received speaker fees from W.L. Gore & Associates and Cook Medical. All remaining authors disclose no conflicts.
Funding
None.
Footnotes
Supplementary data to this article can be found online at https://doi.org/10.1016/j.jceh.2023.01.005.
Appendix A. Supplementary data
The following are the supplementary data to this article.
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