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. Author manuscript; available in PMC: 2026 Aug 7.
Published before final editing as: Cancer Epidemiol Biomarkers Prev. 2023 May 24:OF1–OF10. doi: 10.1158/1055-9965.EPI-22-1358

Hepatocellular Carcinoma Surveillance Among Individuals with Cirrhosis: Trends by Payer, Etiology and Calendar Year, from a Statewide, Multi-Payer Dataset, 2010–2018

Christine D Hsu 1, Louise M Henderson 1,2,3, Bradford E Jackson 3, Christopher D Baggett 1,3, Michele Jonsson Funk 1, Andrew F Olshan 1, Parul Gupta 3, A Sidney Barritt 4th 4, Hanna K Sanoff 3,5, Stephanie B Wheeler 3,6, Andrew M Moon 3,4,*, Jennifer L Lund 1,3,*
PMCID: PMC13445533  NIHMSID: NIHMS2191993  PMID: 37222672

Abstract

Background.

Hepatocellular carcinoma surveillance is underutilized, with <25% of individuals with cirrhosis receiving surveillance exams as recommended. The epidemiology of cirrhosis and HCC in the US has also shifted in recent years, but little is known about recent trends in surveillance utilization. We characterized patterns of HCC surveillance by payer, cirrhosis etiology, and calendar year in insured individuals with cirrhosis.

Methods.

We conducted a retrospective cohort study of individuals with a diagnosis of cirrhosis using claims data from Medicare, Medicaid, and private insurance plans in North Carolina. We included individuals ≥18 years with a first occurrence of an ICD-9/10 code for cirrhosis between January 1, 2010, and June 30, 2018. The main outcome was HCC surveillance by abdominal ultrasound, computed tomography, or magnetic resonance imaging. We estimated 1- and 2-year cumulative incidences for HCC surveillance and assessed longitudinal adherence to surveillance by computing the proportion of time covered (PTC).

Results.

Among 46,052 individuals, 71% were enrolled through Medicare, 15% through Medicaid, and 14% through private insurance. The overall 1-year cumulative incidence of HCC surveillance was 49% and the 2-year cumulative incidence was 55%. For those with an initial screen in the first six months of their cirrhosis diagnosis, the median 2-year PTC was 67% (Q1, 38%; Q3, 100%).

Conclusion.

HCC surveillance initiation after cirrhosis diagnosis remains low, though it has improved slightly over time, particularly among individuals with Medicaid.

Impact.

This study provides insight into recent trends in HCC surveillance and highlights areas to target for future interventions, particularly among patients with non-viral etiologies.

Keywords: hepatocellular carcinoma, screening, surveillance, cirrhosis

Introduction.

Cirrhosis is the 11th leading cause of death1 and the 2nd leading cause of nonmalignant gastrointestinal mortality2 in the United States, with an age-adjusted mortality rate that has been on the rise over the past decade.3 Cirrhosis is also the primary risk factor for hepatocellular carcinoma (HCC),3 and annual incidence rates of HCC range from 1–7%, depending on cirrhosis etiology.4–9 Given the elevated risk of HCC in patients with cirrhosis, clinical guidelines from the American Association for the Study of Liver Disease and the National Comprehensive Cancer Network recommend that individuals with cirrhosis undergo HCC surveillance using abdominal ultrasound, with or without alpha-fetoprotein (AFP) test, every six months.10,11

Guideline-concordant HCC surveillance is associated with earlier stage at HCC diagnosis, increased receipt of curative treatment, and improved survival.12,13 Despite these benefits, prior reports indicate that HCC surveillance is underutilized among individuals with cirrhosis.14–17 In a recent meta-analysis, Wolf and colleagues assessed 29 studies on HCC surveillance published between January 2010 and August 2018 and found a pooled HCC surveillance estimate of only 24%.17

The epidemiology of cirrhosis and HCC in the U.S. have changed in recent years, especially with the increase in non-alcoholic fatty liver disease (NAFLD)18–20 and alcohol-related liver disease (ALD).21,22 Hepatitis C virus (HCV), the greatest contributor to cirrhosis and HCC in the U.S., is now curable and recent guidelines from the Centers from Disease Control and Prevention (CDC) recommend universal HCV screening.20,23–25 Universal hepatitis B virus (HBV) vaccination has also contributed to a decreased prevalence of chronic HBV in the U.S.26 Despite clear shifts in chronic liver disease epidemiology in the US, little is known about recent trends in HCC surveillance.

While there have been many studies on HCC surveillance in the U.S., few of these studies included multiple payers, and contemporary data are lacking. As such, the objective of this study was to describe patterns of HCC surveillance utilization by payer, cirrhosis etiology, and over time. We examined these trends using multi-payer claims data, allowing us to assess longitudinal trends in HCC surveillance among a large and diverse population of insured adults with cirrhosis in North Carolina.

Methods.

Data source.

We conducted a retrospective cohort study using claims data from Medicare, Medicaid, and private insurance plans in North Carolina.27 Combined, these data sources cover a majority of the population in North Carolina. The claims data capture information from healthcare encounters across both inpatient and outpatient settings, including International Classification of Diseases (ICD)-9-CM and ICD-10-CM codes, Current Procedural Terminology codes, patient demographics.

Study population.

We included all individuals 18 years or older with a first cirrhosis diagnosis between January 1, 2010, to June 30, 2018. We applied a modified validated algorithm to identify cirrhosis using ICD diagnosis codes for either cirrhosis or a cirrhosis-related complication (Supplementary Table 1).28,29

The index date was defined as the date of the first cirrhosis diagnosis. To be eligible for inclusion in the cohort, all individuals were required to have continuous enrollment in a covered health plan for at least twelve months before the index date. We excluded individuals with any history of HCC (ICD-9-CM: 155.0, ICD-10-CM: C22.0, C22.9) or liver transplant (ICD-9-CM: V42.7, ICD-10-CM: Z94.4). Individuals with an HCC diagnosis in the six months after the index date were also excluded.

The Cancer Information and Population Health Resource at the University of North Carolina in Chapel Hill has developed linkages between the Medicare, Medicaid, and private insurance datasets to identify individuals moving between payers and over time. Patients enrolled in more than one payer during their follow-up period were identified through a deterministic linkage using patient’s name, date of birth, social security number, and sex (N=4,136). For patients enrolled in multiple payers, we used available claims from all payers. However, for those with any gap in coverage between payers (N=38), only the claims from the initial payer were used.

Study outcome.

Guidelines recommend HCC surveillance for patients with cirrhosis and some other populations with chronic liver disease using abdominal ultrasound, but computed tomography scan (CT scan) and magnetic resonance imaging (MRI) are often used as well.11,30 Therefore, HCC surveillance was defined as utilization of abdominal ultrasound, CT scan, or MRI during the follow-up period. All surveillance outcomes were determined using CPT/HCPCS codes (Supplementary Table 2). We included surveillance tests regardless of indication, as they would serve surveillance purposes even if not the original intent.31

Covariates.

Patient-level demographic factors included age at index date, race/ethnicity (White non-Hispanic, Black non-Hispanic, Hispanic, Other), sex, rurality/urbanicity, and insurance type (private, Medicare, Medicaid). Rurality/urbanicity was defined using county-level Rural-Urban Continuum Codes.32,33 Clinical covariates included the Charlson Comorbidity Index,34 diagnoses for decompensated cirrhosis, and cirrhosis etiology. Decompensated cirrhosis was defined as the presence of at least one ICD-9-CM or ICD-10-CM code for esophageal varices, ascites, variceal hemorrhage, hepatic encephalopathy, spontaneous bacterial peritonitis, or hepatorenal syndrome on or before the index date.31 We also determined Charlson Comorbidity Index scores, with and without liver disease in the calculation.34

To determine cirrhosis etiology, we examined ICD-9-CM and ICD-10-CM codes in the twelve months before and in the six months after the index date. We applied a modification of validated algorithms to determine HCV,35 HBV,35 and ALD etiologies36 and used codes from an expert panel consensus statement on administrative coding to determine NAFLD etiology37 (Supplementary Table 3). Individuals without any of the aforementioned cirrhosis etiologies were categorized as “Other”. The “Other” group included individuals with autoimmune hepatitis, primary sclerosing cholangitis, primary biliary cirrhosis, among other cirrhosis etiologies. For individuals with more than one cirrhosis etiology, we used the hierarchical algorithm shown in Figure 1.

Figure 1. Hierarchical algorithm for determining cirrhosis etiology with ICD-9-CM and ICD-10-CM codes, using all available claims in the one year before the index date and six months after the index date.

Figure 1.

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For all study variables using the ICD-CM system, including the cirrhosis algorithm, we mapped ICD-9-CM codes to ICD-10-CM codes using the Centers for Medicare and Medicaid Services General Equivalence Mappings (GEMs),38 using forward-backward mapping, and modified the final list using manual review with clinical input from a practicing gastroenterologist (AM).39 Trends across the ICD-9 to ICD-10 transition were visualized as a metric for appropriateness of the mapping to assess consistency over time.40,41

Death information was available in the Medicare and Medicaid data but not in the private insurance data. Therefore, we implemented a simple imputation process for individuals with private insurance data alone, wherein disenrollment occurring in any month besides December was considered to be due to death. Because this imputation process may overestimate the number of deaths for those enrolled in private insurance, we also conducted sensitivity analysis without imputing deaths for those with private insurance.

Statistical analysis.

We calculated descriptive statistics for the study population overall and by cirrhosis etiology. For our main analysis, we determined 1- and 2-year cumulative incidences for a first HCC surveillance, or the proportion of individuals with an initial HCC surveillance test in the first one or two years after their index date. We used the Fine and Gray method in modeling the subdistribution hazard,42,43 with HCC diagnosis, liver transplant, and death as competing events.

We determined cumulative incidences overall, by the calendar year of the index date, and by cirrhosis etiology. We plotted the 1- and 2-year cumulative incidences of receipt of a first HCC surveillance by calendar year of the index date, overall and by insurance type. To allow for two years of follow-up, we only included individuals with index years up until 2016 for this portion of the analysis. We also depicted the 2-year cumulative incidence function for a first HCC surveillance stratified by primary cirrhosis etiology.

Additionally, for our secondary analysis, we measured the proportion of time covered (PTC) to assess longitudinal adherence to HCC surveillance exams during the follow-up time.44 The PTC was calculated as the percentage of time covered by a surveillance exam, in months, during the follow-up time. For the first PTC analysis, we determined the average PTC overall and by insurance type and reported results for the mean, standard deviation (std), median, and interquartile range. Because the PTC is sensitive to follow-up time, we also assessed the 1- and 2-year PTC among individuals with HCC surveillance in the first six months after their index date. This measure centers around the first surveillance and reflects adherence during the first and second years after the initial surveillance.

Results were reported for all surveillance modalities together (abdominal ultrasound, CT, MRI) and for abdominal ultrasound alone. For both PTC calculations, individuals who were diagnosed with HCC or had a liver transplant during the follow-up time were censored at the time of their HCC diagnosis or liver transplant. We included further details for the PTC measures as well as hypothetical examples for calculations in Supplementary Table 3.

In addition, we conducted sensitivity analysis excluding individuals with decompensated cirrhosis at the index date. These individuals are not recommended for HCC surveillance given their disease severity, but prior research studies have found that individuals in this group still commonly receive HCC surveillance exams.17 Lastly, we conducted sensitivity analysis without imputing deaths for those enrolled in private insurance.

Analyses were conducted using SAS statistical software (version 9.4; SAS Institute Inc, Cary, North Carolina). This study was approved by the Institutional Review Board at the University of North Carolina at Chapel Hill (IRB #21–1158).

Data availability.

Based on contractual data use considerations with data partners such as the Center for Medicare/Medicaid Services, data will not be publicly available. Investigators may propose to use the data in the UNC remote-access computing environment after project review and approval by the Institutional Review Board and with fully executed, project-specific, data sharing agreements in place.

Results.

Study population.

The final cohort included 46,052 unique individuals with an index date between January 1, 2010, and June 30, 2018, with 4,136 being dually enrolled (Figure 2). For individuals with dual Medicare/private or Medicare/Medicaid enrollment at the index date, Medicare was considered the primary payer (N=2,763). For individuals with dual Medicaid/private enrollment at the index date, private insurance was considered the primary payer (N=2).45,46 For individuals with more than one payer but without dual enrollment at the index date, the initial payer was considered the primary payer (N=1,371).

Figure 2. Flowchart of inclusion and exclusion criteria for study cohort.

Figure 2.

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In the final cohort, 14% were enrolled in private insurance (N=6,532), 15% in Medicaid (N=6,788), and 71% in Medicare (N=32,732) as the primary payer. For the Medicare and Medicaid populations, the majority of individuals (73%) were White Non-Hispanic, followed by Black Non-Hispanic (23%), and Other/Hispanic (4%); information on race/ethnicity was not available for those enrolled in private insurance. Over half of the cohort (54%) was male, and the mean age at the index date was 64 years (std, 14 years). Besides the non-specific “Other” category, the most common primary etiology was ALD (21%), followed by HCV (19%), NAFLD (16%), and HBV (1.2%). Over one-third of individuals had decompensated cirrhosis at the time of their index date (41%) (Table 1, Supplementary Tables 4-7). In the study period, 51% of those enrolled in private insurance died, 39% of those enrolled in Medicaid died, and 51% of those enrolled in Medicare died.

Table 1.

Baseline characteristics of individuals with cirrhosis eligible for HCC surveillance (N, %) (N=46,052).

Cirrhosis Etiology
Overall Alcohol Hepatitis B Hepatitis C NAFLD1 Other
Total (row %) 46,052 9,701 (21.1) 543 (1.2) 8,731 (19) 7,378 (16) 19,699 (42.8)
Age (mean, std) 63.8 (13.5) 61.6 (12.3) 58.8 (12.8) 57.1 (9.4) 63.2 (12.7) 68.1 (14.4)
Age Category
 18–39 years old 2,001 (4.3) 434 (4.5) 39 (7.2) 306 (3.5) 345 (4.7) 877 (4.5)
 40–64 years old 20,882 (45.3) 4,952 (51) 317 (58.4) 6,751 (77.3) 3,142 (42.6) 5,720 (29)
 65+ years old 23,169 (50.3) 4,315 (44.5) 187 (34.4) 1,674 (19.2) 3,891 (52.7) 13,102 (66.5)
Race/Ethnicity
 White Non-Hispanic 28,779 (72.7) 5,711 (70.9) 243 (55.5) 4,342 (58.8) 5,106 (85.3) 13,377 (75.4)
 Black Non-Hispanic 9,164 (23.1) 2,021 (25.1) 141 (32.2) 2,650 (35.9) 640 (10.7) 3,712 (20.9)
 Other/Hispanic2 1,664 (4.2) 326 (4) 54 (12.3) 389 (5.3) 240 (4) 655 (3.7)
 Missing3 6,445 1,643 105 1,350 1,392 1,955
Sex
 Male 24,655 (53.9) 6,814 (70.9) 334 (62.7) 5,448 (63) 2,841 (38.9) 9,218 (47)
 Female 21,048 (46.1) 2,796 (29.1) 199 (37.3) 3,203 (37) 4,461 (61.1) 10,389 (53)
 Missing 349 91 10 80 76 92
Insurance
 Private 6,532 (14.2) 1,666 (17.2) 107 (19.7) 1,371 (15.7) 1,413 (19.2) 1,975 (10)
 Medicaid 6,788 (14.7) 1,590 (16.4) 97 (17.9) 2,672 (30.6) 581 (7.9) 1,848 (9.4)
 Medicare 32,732 (71.1) 6,445 (66.4) 339 (62.4) 4,688 (53.7) 5,384 (73) 15,876 (80.6)
Charlson Comorbidity Score
 0 17,874 (38.8) 3,968 (40.9) 219 (40.3) 3,640 (41.7) 2,820 (38.2) 7,227 (36.7)
 1 11,266 (24.5) 2,456 (25.3) 115 (21.2) 2,303 (26.4) 2,009 (27.2) 4,383 (22.2)
 2+ 16,912 (36.7) 3,277 (33.8) 209 (38.5) 2,788 (31.9) 2,549 (34.5) 8,089 (41.1)
Charlson Comorbidity Score, without liver disease
 0 21,871 (47.5) 5,184 (53.4) 285 (52.5) 4,766 (54.6) 3,313 (44.9) 8,323 (42.3)
 1 11,386 (24.7) 2,392 (24.7) 98 (18) 2,105 (24.1) 2,075 (28.1) 4,716 (23.9)
 2+ 12,795 (27.8) 2,125 (21.9) 160 (29.5) 1,860 (21.3) 1,990 (27) 6,660 (33.8)
Decompensated
 Not decompensated at index 27,097 (58.8) 5,323 (54.9) 345 (63.5) 6,540 (74.9) 4,982 (67.5) 9,907 (50.3)
 Decompensated at index 18,955 (41.2) 4,378 (45.1) 198 (36.5) 2,191 (25.1) 2,396 (32.5) 9,792 (49.7)
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1

NAFLD: non-alcoholic fatty liver disease

2

To preserve confidentiality, cell sizes <11 were suppressed per the data use agreement, and the Hispanic race/ethnicity category was combined with the Other race/ethnicity category

3

Race/ethnicity not available in private insurance data

HCC surveillance utilization

The 1- and 2-year cumulative incidences for any HCC surveillance modality were 48.5% (95% confidence intervals (CI), 48.2–48.9%) and 54.7% (95% CI, 54.3–55.1%), respectively. When limiting to surveillance by abdominal ultrasound, the 1- and 2-year cumulative incidences were 42.6% (95% CI, 42.3–43.0%) and 49.3% (95% CI, 48.9–49.7%), respectively.

The overall 1- and 2-year cumulative incidences for any HCC surveillance trended upwards across calendar years of the index date from 2011 to 2016 (Figure 3A). For private insurance, the 1- and 2-year cumulative incidences decreased in 2012 but otherwise remained stable across calendar years of the index date (Figure 3B). The 1-year cumulative incidence for HCC surveillance in those with private insurance was 53.5% (95% CI, 50.7–56.6%) in 2010 and 54.5% (95% CI, 51.9–57.2%) in 2016. The 2-year cumulative incidence was 59.6% (95% CI, 57.2–62.1%) in 2010 and 60.6% (95% CI, 57.7–63.5%) in 2016.

Figure 3. One- and two-year cumulative incidence and 95% confidence interval bands for HCC surveillance across calendar years 2010–2016 for (A) the cohort overall, (B) private insurance, (C) Medicaid insurance, and (D) Medicare insurance.

Figure 3.

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For those enrolled in Medicaid, the 1- and 2-year cumulative incidences increased noticeably across calendar years of the index date, especially in 2013 through 2016 (Figure 3C). The 1-year cumulative incidence was 45.9% (95% CI, 43.1–48.8%) in 2010 and 57.1% (95% CI, 54.4–60.0%) in 2016. The 2-year cumulative incidence was 53.1% (95% CI, 50.1–56.4%) in 2010 and 64.9% (95% CI, 62.8–67.0%) in 2016.

For those with Medicare insurance, the 1- and 2-year cumulative incidences remained relatively stable across calendar years of the index date. The 1-year cumulative incidence was 51.1% (95% CI, 49.5–52.7%) in 2010 and 49.1% (95% CI, 47.7–50.5%) in 2016. The 2-year cumulative incidence was 57.5% (95% CI, 56.0–59.0%) in 2010 and 55.3% (95% CI, 53.9–56.8%) in 2016 (Figure 3D). The 1- and 2-year cumulative incidence results by payer and calendar year are reported in Supplementary Tables 8 and 9.

In the overall cohort, the 2-year cumulative incidence of HCC surveillance was highest for HBV (66.9%, 95% CI, 63.8–70.2%), followed by HCV (63.3%, 95% CI 62.4–64.2%), NAFLD (61.9%, 95% CI 60.9–62.9%), ALD (58.9%, 95% CI 58.1–59.8%), and Other etiology (45.7%, 95% CI 45.1–46.3%) (Figure 4A–4D, Supplementary Table 10).

Figure 4. Two-year cumulative incidence function for HCC surveillance by primary cirrhosis etiology for (A) the cohort overall, (B) private insurance, (C) Medicaid insurance, and (D) Medicare insurance.

Figure 4.

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Proportion of time covered by HCC surveillance

The median follow-up time, or length of continuous enrollment between the index date and either disenrollment, HCC diagnosis, liver transplant, or death, was 20 months for the overall cohort. The mean PTC for all surveillance modalities for the cohort was 24.9% (std, 34.4%), and the median was 15% (Q1, 0; Q3, 50%) (Figure 5, Supplementary Table 11). For all surveillance modalities, the mean PTC was highest among those with private insurance. For abdominal ultrasound alone, the mean PTC measure was highest among those with Medicaid.

Figure 5. Mean proportion of time covered for the entire cohort, by surveillance type1 and by payer.

Figure 5.

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1All surveillance modalities includes abdominal ultrasound, CT scan, and abdominal MRI.

Because the mean PTC is sensitive to follow-up time, we also determined the mean 1- and 2-year PTC among individuals with an initial surveillance in the first six months of their index date (N=20,268 for any surveillance modality; N=17,176 for abdominal ultrasound alone). The mean 1-year PTC was 75.9% (std, 23.0%), and the median was 75% (Q1, 50%; Q3, 100%). The mean 2-year PTC was 66.6% (std, 30.0%), and the median was 66.7% (Q1, 37.5%; Q3, 100%). The mean 2-year PTC for any surveillance modality was higher among individuals with private insurance and Medicare compared to those with Medicaid insurance. Similar trends were seen for surveillance by abdominal ultrasound alone (Table 2).

Table 2.

One- and two-year proportion of time covered among individuals with a surveillance test in the first six months of their cirrhosis diagnosis, overall and by insurance type (%).

N Mean (STD) Q1 Median Q3 IQR
Overall
Any surveillance modality
1-year PTC 20,268 75.9 (23.0) 50 75 100 50
2-year PTC 20,268 66.6 (30.0) 37.5 66.7 100 62.5
Abdominal ultrasound
1-year PTC 17,176 75.1 (23.2) 50 75 100 50
2-year PTC 17,176 65.7 (30.5) 33.3 66.7 100 66.7
Private insurance
Any surveillance modality
1-year PTC 3,097 76.8 (22.5) 50 83.3 100 50
2-year PTC 3,097 68.4 (29.1) 41.7 70.8 100 58.3
Abdominal ultrasound
1-year PTC 2,448 75.2 (22.6) 50 75 100 50
2-year PTC 2,448 66.6 (29.3) 37.5 66.7 100 62.5
Medicaid insurance
Any surveillance modality
1-year PTC 3,148 74.5 (22.9) 50 75 100 50
2-year PTC 3,148 64.3 (29.1) 37.5 62.5 100 62.5
Abdominal ultrasound
1-year PTC 2,707 73.8 (22.7) 50 72.7 100 50
2-year PTC 2,707 63.3 (29.5) 33.3 60 100 66.7
Medicare insurance
Any surveillance modality
1-year PTC 14,023 76.0 (23.1) 50 75 100 50
2-year PTC 14,023 66.7 (30.4) 35.3 66.7 100 64.7
Abdominal ultrasound
1-year PTC 12,021 75.4 (23.4) 50 75 100 50
2-year PTC 12,021 66.1 (30.9) 33.3 66.7 100 66.7
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STD: standard deviation; Q1: quartile 1; Q3: quartile 3; IQR: interquartile range.

Sensitivity analysis

In a sensitivity analysis with exclusion of individuals with decompensated cirrhosis at the index date, HCC surveillance utilization was lower than in the full cohort (Supplementary Tables 12-14). For HCC surveillance by all modalities, the overall 1- and 2-year cumulative incidences were 47.1% (95% CI, 46.6–47.6%) and 54.4% (95% CI, 53.9–55.0%), respectively. The mean 1-year PTC was 72.3% (std, 22.6%), and the median was 66.7% (Q1, 50%; Q3, 100%). The mean 2-year PTC was 61.5% (std, 29.3%), and the median was 57.1% (Q1, 30%; Q3, 100%).

In sensitivity analysis without the imputation for deaths for those enrolled in private insurance, the overall 1- and 2-year cumulative incidences of HCC surveillance were 48.8% (95% CI, 48.5–49.3%) and 55.4% (95%CI, 54.9–55.8%), respectively (Supplementary Tables 15-17).

Discussion.

In this study, we found suboptimal HCC surveillance among patients with cirrhosis in North Carolina, with fewer than half of individuals in the cohort receiving an HCC surveillance exam in the year after their index date. HCC surveillance utilization trended slightly upwards over the study period, increasing most noticeably for those with Medicaid. Overall, individuals with HBV had the highest 2-year cumulative incidence of HCC surveillance, followed by individuals with HCV, NAFLD, ALD, and “Other” cirrhosis etiologies. Because NAFLD is underdiagnosed, a large proportion of the undefined, “Other” group may consist of individuals with NAFLD.

Our findings are consistent with other studies that found higher HCC surveillance rates among individuals with viral hepatitis compared to those with NAFLD or ALD etiologies.17,31,47–49 Several factors may contribute to the low surveillance uptake among the NAFLD group, including an under-recognition of NAFLD cirrhosis and disease severity (e.g., patients with NAFLD cirrhosis presenting with normal labs), providers’ failure to recommend HCC surveillance, and a lack of knowledge that HCC surveillance is recommended for these individuals.50

In our study, we also found PTC results suggesting poor longitudinal adherence, with the overall cohort only covered by an abdominal ultrasound for a quarter of their follow-up time. The mean PTC for those with private insurance was 25%, slightly lower than the mean PTC of 34% that Goldberg and colleagues found using MarketScan data from 2006–2010.57 This suggests that efforts to improve adherence to HCC surveillance should focus on ensuring that patients remain engaged in surveillance programs after the initial diagnosis of cirrhosis. Second, there are substantial differences in surveillance uptake by etiology, with increased surveillance utilization in patients with viral hepatitis compared to NAFLD and ALD.

Lastly, in a sensitivity analysis excluding individuals with decompensated cirrhosis at the index date, we found lower HCC surveillance utilization than in the full cohort. These findings may reflect the challenges providers face in identifying individuals with compensated cirrhosis who may not show symptoms of liver disease. A recently published study reported that inability to recognize cirrhosis was a common cause of HCC surveillance failure.58 Improved utilization of non-invasive tests to assist with the diagnosis of compensated cirrhosis or alternative HCC risk stratification tools may be needed to improve HCC surveillance among these patients. Efforts to improve HCC surveillance among those with compensated cirrhosis are especially important, as these individuals are more likely to be candidates for curative treatments if diagnosed with HCC.

This study has several strengths. First, a strength of the study is the use of multi-payer claims data from Medicare, Medicaid, and private insurers. Roughly 70% of the state’s population is enrolled through one of these payers, allowing us to capture a large and diverse population of individuals with cirrhosis in North Carolina. Compared to prior studies, which primarily used data from academic centers, large healthcare systems, and population-based registries, our use of data from several payers provided us with a cohort that more accurately reflects the clinically relevant population and allows for more generalizable knowledge about the population with cirrhosis in North Carolina.17

In addition, our study accounted for individuals with severe cirrhosis with our sensitivity analysis excluding those with decompensated cirrhosis.17 We also included non-ultrasound imaging using CT and MRI exams, accounted for loss to follow-up in our analyses, and included substantial length of follow-up months.17 Another strength is the use of several measures to characterize HCC surveillance utilization and assess surveillance adherence. Further, by including different PTC measures, we provide a more well-rounded description of longitudinal surveillance adherence that allows for comparison with other HCC surveillance studies.44

This study should be considered alongside some limitations. First, using claims data, we are unable to differentiate between diagnostic versus surveillance exams, as the same tests are used for both purposes. However, incidental HCC would most likely be identified even if the test were used for non-screening purposes. In addition, the ordering of CT or MRI tests for non-screening purposes would bias the findings towards an overestimation of HCC surveillance utilization, further emphasizing our study’s findings that HCC surveillance is underutilized.

Second, our study is not entirely representative of the population eligible for HCC surveillance in North Carolina. Individuals who are uninsured or insured through the Veterans Affairs, or other private insurers, are not represented in our study. Other studies have found lower rates of HCC surveillance among individuals who are uninsured or insured through the Veterans Affairs.48 However, the use a multi-payer data source that covers the majority of the state population is unique among studies examining HCC surveillance.51 Third, there remains a possibility of misclassification of cirrhosis or its underlying etiologies although we limited the likelihood of this by using well-validated ICD coding algorithms.28,35–37,59

Lastly, there is potential for misclassification of deaths with the imputation process that we used, resulting in an overestimation of deaths among those enrolled in private insurance and an underestimation of the cumulative incidence of HCC surveillance. However, in sensitivity analysis without imputing for deaths for those enrolled in private insurance, the overall 1- and 2-year cumulative incidences of HCC surveillance remained consistent with the cumulative incidences in the main analysis (1-year CI: 49%; 2-year CI: 55%). As expected, for those with private insurance, the 1- and 2-year cumulative incidences for surveillance were slightly higher in the sensitivity analysis without imputation for deaths compared to those in the main analysis (Supplementary Tables 15-17).

As the prevalence of non-viral cirrhosis etiologies continues to increase,3 it is important to identify and assess interventions to improve HCC surveillance. For example, interventions may include electronic medical record reminders and mailed outreach programs.51,52 In addition, interventions should engage diverse stakeholders, including primary care providers, gastroenterologists and hepatologists, public health professionals, and policy makers. Future research and interventions should also take into account the multilevel factors that contribute to disparities in HCC surveillance utilization and outcomes, including race/ethnicity, sex, rurality, insurance status, and provider specialty.53–56

In summary, our study adds more contemporary insights into HCC surveillance trends by cirrhosis etiology and insurance type among individuals with cirrhosis in North Carolina. We found that HCC surveillance rates have increased with time, although HCC surveillance initiation remains lower among individuals with non-viral cirrhosis etiologies. Given the continued rise in cirrhosis, there remains an urgent need for interventions focused on improving HCC surveillance uptake and longitudinal adherence.

Supplementary Material

Supplementary Materials

Disclosure:

This work was funded by the Lineberger Development Funding Program.

This work was supported by the Cancer Information and Population Health Resource at the UNC Lineberger Comprehensive Cancer Center, with funding provided by the University Cancer Research Fund via the State of North Carolina. The findings and conclusions in this manuscript are those of the authors and do not necessarily represent the view of the NC Department of Health and Human Services, Division of Public Health.

Christine D. Hsu was supported by the National Cancer Institute’s National Research Service Award sponsored by the Lineberger Comprehensive Cancer Center at the University of North Carolina (grant number: T32 CA116339) for a portion of the project.

Footnotes

Conflicts of interest:

Andrew Moon is a consultant for TARGET RWE.

References

  • 1.Xu JQ, Murphy SL, Kochanek KD, and Arias. Deaths: Final data for 2019. National Vital Statistics Reports. Natl Cent Health Stat. 2021;vol 70 no 08.:87. [Google Scholar]
  • 2.Peery AF, Crockett SD, Murphy CC, et al. Burden and Cost of Gastrointestinal, Liver, and Pancreatic Diseases in the United States: Update 2021. Gastroenterology. 2022;162(2):621–644. doi: 10.1053/j.gastro.2021.10.017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Moon AM, Singal AG, Tapper EB. Contemporary Epidemiology of Chronic Liver Disease and Cirrhosis. Clin Gastroenterol Hepatol. 2020;18(12):2650–2666. doi: 10.1016/j.cgh.2019.07.060 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Beasley RP, Hwang LY, Lin CC, Chien CS. Hepatocellular carcinoma and hepatitis B virus. A prospective study of 22 707 men in Taiwan. Lancet Lond Engl. 1981;2(8256):1129–1133. doi: 10.1016/s0140-6736(81)90585-7 [DOI] [PubMed] [Google Scholar]
  • 5.Degos F, Christidis C, Ganne-Carrie N, et al. Hepatitis C virus related cirrhosis: time to occurrence of hepatocellular carcinoma and death. Gut. 2000;47(1):131–136. doi: 10.1136/gut.47.1.131 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Fattovich G, Giustina G, Degos F, et al. Morbidity and mortality in compensated cirrhosis type C: a retrospective follow-up study of 384 patients. Gastroenterology. 1997;112(2):463–472. doi: 10.1053/gast.1997.v112.pm9024300 [DOI] [PubMed] [Google Scholar]
  • 7.Niederau C, Heintges T, Lange S, et al. Long-term follow-up of HBeAg-positive patients treated with interferon alfa for chronic hepatitis B. N Engl J Med. 1996;334(22):1422–1427. doi: 10.1056/NEJM199605303342202 [DOI] [PubMed] [Google Scholar]
  • 8.Orci LA, Sanduzzi-Zamparelli M, Caballol B, et al. Incidence of Hepatocellular Carcinoma in Patients With Nonalcoholic Fatty Liver Disease: A Systematic Review, Meta-analysis, and Meta-regression. Clin Gastroenterol Hepatol Off Clin Pract J Am Gastroenterol Assoc. 2022;20(2):283–292.e10. doi: 10.1016/j.cgh.2021.05.002 [DOI] [PubMed] [Google Scholar]
  • 9.Jepsen P, Kraglund F, West J, Villadsen GE, Sørensen HT, Vilstrup H. Risk of hepatocellular carcinoma in Danish outpatients with alcohol-related cirrhosis. J Hepatol. 2020;73(5):1030–1036. doi: 10.1016/j.jhep.2020.05.043 [DOI] [PubMed] [Google Scholar]
  • 10.Kanwal F, Singal AG. Surveillance for Hepatocellular Carcinoma: Current Best Practice and Future Direction. Gastroenterology. 2019;157(1):54–64. doi: 10.1053/j.gastro.2019.02.049 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Marrero JA, Kulik LM, Sirlin CB, et al. Diagnosis, Staging, and Management of Hepatocellular Carcinoma: 2018 Practice Guidance by the American Association for the Study of Liver Diseases: Marrero et al. Hepatology. 2018;68(2):723–750. doi: 10.1002/hep.29913 [DOI] [PubMed] [Google Scholar]
  • 12.Choi DT, Kum HC, Park S, et al. Hepatocellular Carcinoma Screening Is Associated With Increased Survival of Patients With Cirrhosis. Clin Gastroenterol Hepatol Off Clin Pract J Am Gastroenterol Assoc. 2019;17(5):976–987.e4. doi: 10.1016/j.cgh.2018.10.031 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Singal AG, Zhang E, Narasimman M, et al. HCC Surveillance Improves Early Detection, Curative Treatment Receipt, and Survival in Patients with Cirrhosis: A Systematic Review and Meta-Analysis. J Hepatol. Published online February 6, 2022:S0168–8278(22)00068-X. doi: 10.1016/j.jhep.2022.01.023 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Goldberg DS, Taddei TH, Serper M, et al. Identifying barriers to hepatocellular carcinoma surveillance in a national sample of patients with cirrhosis. Hepatol Baltim Md. 2017;65(3):864–874. doi: 10.1002/hep.28765 [DOI] [PubMed] [Google Scholar]
  • 15.Davila JA, Morgan RO, Richardson PA, Du XL, McGlynn KA, El-Serag HB. Utilization of Surveillance for Hepatocellular Carcinoma among Patients with Cirrhosis in the United States. Hepatol Baltim Md. 2010;52(1). doi: 10.1002/hep.23615 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Singal AG, Yopp A, S Skinner C, Packer M, Lee WM, Tiro JA. Utilization of hepatocellular carcinoma surveillance among American patients: a systematic review. J Gen Intern Med. 2012;27(7):861–867. doi: 10.1007/s11606-011-1952-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Wolf E, Rich NE, Marrero JA, Parikh ND, Singal AG. Use of Hepatocellular Carcinoma Surveillance in Patients With Cirrhosis: A Systematic Review and Meta-Analysis. Hepatology. 2021;73(2):713–725. doi: 10.1002/hep.31309 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.National Institute of Diabetes and Digestive and Kidney Diseases. Definition & Facts of NAFLD & NASH. National Institute of Diabetes and Digestive and Kidney Diseases. Accessed November 11, 2020. https://www.niddk.nih.gov/health-information/liver-disease/nafld-nash/definition-facts [Google Scholar]
  • 19.Knudsen ES, Gopal P, Singal AG. The Changing Landscape of Hepatocellular Carcinoma: Etiology, Genetics, and Therapy. Am J Pathol. 2014;184(3):574–583. doi: 10.1016/j.ajpath.2013.10.028 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Younossi ZM, Otgonsuren M, Henry L, et al. Association of nonalcoholic fatty liver disease (NAFLD) with hepatocellular carcinoma (HCC) in the United States from 2004 to 2009. Hepatol Baltim Md. 2015;62(6):1723–1730. doi: 10.1002/hep.28123 [DOI] [PubMed] [Google Scholar]
  • 21.Pollard MS, Tucker JS, Green HD. Changes in Adult Alcohol Use and Consequences During the COVID-19 Pandemic in the US. JAMA Netw Open. 2020;3(9):e2022942. doi: 10.1001/jamanetworkopen.2020.22942 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Spillane S, Shiels MS, Best AF, et al. Trends in Alcohol-Induced Deaths in the United States, 2000–2016. JAMA Netw Open. 2020;3(2):e1921451. doi: 10.1001/jamanetworkopen.2019.21451 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Alexander M, Loomis AK, van der Lei J, et al. Risks and clinical predictors of cirrhosis and hepatocellular carcinoma diagnoses in adults with diagnosed NAFLD: real-world study of 18 million patients in four European cohorts. BMC Med. 2019;17. doi: 10.1186/s12916-019-1321-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Ioannou GN, Green PK, Berry K. HCV eradication induced by direct-acting antiviral agents reduces the risk of hepatocellular carcinoma. J Hepatol. Published online September 5, 2017. doi: 10.1016/j.jhep.2017.08.030 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Testing Recommendations for Hepatitis C Virus Infection | CDC. Published June 14, 2021. Accessed April 19, 2022. https://www.cdc.gov/hepatitis/hcv/guidelinesc.htm [Google Scholar]
  • 26.Kruszon-Moran D, Paulose-Ram R, Martin CB, Barker L, McQuillan G. Prevalence and Trends in Hepatitis B Virus Infection in the United States, 2015–2018; 2020. Accessed April 19, 2022. https://www.cdc.gov/nchs/products/databriefs/db361.htm [PubMed] [Google Scholar]
  • 27.Meyer AM, Olshan AF, Green L, et al. Big data for population-based cancer research: the integrated cancer information and surveillance system. N C Med J. 2014;75(4):265–269. doi: 10.18043/ncm.75.4.265 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Nehra MS, Ma Y, Clark C, Amarasingham R, Rockey DC, Singal AG. Use of administrative claims data for identifying patients with cirrhosis. J Clin Gastroenterol. 2013;47(5):e50–54. doi: 10.1097/MCG.0b013e3182688d2f [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Mapakshi S, Kramer JR, Richardson P, El-Serag HB, Kanwal F. Positive Predictive Value of International Classification of Diseases, 10th Revision, Codes for Cirrhosis and Its Related Complications. Clin Gastroenterol Hepatol Off Clin Pract J Am Gastroenterol Assoc. 2018;16(10):1677–1678. doi: 10.1016/j.cgh.2018.01.042 [DOI] [PubMed] [Google Scholar]
  • 30.Loomba R, Lim JK, Patton H, El-Serag HB. AGA Clinical Practice Update on Screening and Surveillance for Hepatocellular Carcinoma in Patients With Nonalcoholic Fatty Liver Disease: Expert Review. Gastroenterology. 2020;158(6):1822–1830. doi: 10.1053/j.gastro.2019.12.053 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Goldberg DS, Valderrama A, Kamalakar R, Sansgiry SS, Babajanyan S, Lewis JD. Hepatocellular Carcinoma Surveillance Rates in Commercially Insured Patients with Non-Cirrhotic Chronic Hepatitis B. J Viral Hepat. 2015;22(9):727–736. doi: 10.1111/jvh.12381 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Talbot JA, Burgess AR, Thayer D, Parenteau L, Paluso N, Coburn AF. Patterns of Telehealth Use Among Rural Medicaid Beneficiaries. J Rural Health. 2019;35(3):298–307. doi: 10.1111/jrh.12324 [DOI] [PubMed] [Google Scholar]
  • 33.USDA ERS - Rural-Urban Continuum Codes. Accessed February 7, 2021. https://www.ers.usda.gov/data-products/rural-urban-continuum-codes.aspx [Google Scholar]
  • 34.Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613–619. doi: 10.1016/0895-4356(92)90133-8 [DOI] [PubMed] [Google Scholar]
  • 35.Niu B, Forde KA, Goldberg DS. Coding algorithms for identifying patients with cirrhosis and hepatitis B or C virus using administrative data. Pharmacoepidemiol Drug Saf. 2015;24(1):107–111. doi: 10.1002/pds.3721 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Philip G, Djerboua M, Carlone D, Flemming JA. Validation of a hierarchical algorithm to define chronic liver disease and cirrhosis etiology in administrative healthcare data. PLoS ONE. 2020;15(2):e0229218. doi: 10.1371/journal.pone.0229218 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Hagström H, Adams LA, Allen AM, et al. Administrative Coding in Electronic Health Care Record-Based Research of NAFLD: An Expert Panel Consensus Statement. Hepatology. 2021;74(1):474–482. doi: 10.1002/hep.31726 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Agency for Healthcare Research and Quality. Data Innovations - ICD-10-CM/PCS Resources: Tools. Accessed October 8, 2021. https://www.hcup-us.ahrq.gov/datainnovations/icd10_tools.jsp [Google Scholar]
  • 39.Kinlaw A Easy ICD9-to-10 GEMs mapping. Published online May 10, 2020. Accessed January 13, 2022. https://github.com/alankinlaw/Easy_ICD9-to-10_GEMs_mapping [Google Scholar]
  • 40.Nam YH, Mendelsohn AB, Panozzo CA, Maro JC, Brown JS. Health outcomes coding trends in the US Food and Drug Administration’s Sentinel System during transition to International Classification of Diseases‐10 coding system: A brief review. Pharmacoepidemiol Drug Saf. 2021;30(7):838–842. doi: 10.1002/pds.5216 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Panozzo CA, Woodworth TS, Welch EC, et al. Early impact of the ICD-10-CM transition on selected health outcomes in 13 electronic health care databases in the United States. Pharmacoepidemiol Drug Saf. 2018;27(8):839–847. doi: 10.1002/pds.4563 [DOI] [PubMed] [Google Scholar]
  • 42.Fine JP, Gray RJ. A Proportional Hazards Model for the Subdistribution of a Competing Risk. J Am Stat Assoc. 1999;94(446):496–509. doi: 10.2307/2670170 [DOI] [Google Scholar]
  • 43.Guo C, So Y. Cause-Specific Analysis of Competing Risks Using the PHREG Procedure. :18. [Google Scholar]
  • 44.Chubak J, Anderson ML, Cook AJ, Murphy CC, Jackson ML, Green BB. Methodologic Considerations in Calculating and Analyzing Proportion of Time Covered as a Measure of Longitudinal Cancer Screening Adherence. Cancer Epidemiol Biomark Prev Publ Am Assoc Cancer Res Cosponsored Am Soc Prev Oncol. 2020;29(8):1549–1556. doi: 10.1158/1055-9965.EPI-20-0388 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Amin Shilpa. Primary and Secondary Payers: How Do They Work With Medicare? Healthline. Accessed May 23, 2022. https://www.healthline.com/health/medicare/medicare-secondary-payers [Google Scholar]
  • 46.Worstell Christian. Medicaid as Secondary Insurance | How Medicaid Works With Medicare and Other Coverage. Medicare Advantage. Accessed May 23, 2022. https://www.medicareadvantage.com/medicaid/medicaid-as-secondary-insurance [Google Scholar]
  • 47.Palmer LB, Kappelman MD, Sandler RS, Hayashi PH. Surveillance for hepatocellular carcinoma in a Medicaid cirrhotic population. J Clin Gastroenterol. 2013;47(8):713–718. doi: 10.1097/MCG.0b013e318286fd97 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Singal AG, Li X, Tiro J, et al. Racial, social, and clinical determinants of hepatocellular carcinoma surveillance. Am J Med. 2015;128(1):90.e1–7. doi: 10.1016/j.amjmed.2014.07.027 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Singal AG, Yopp A, Gupta S, et al. Failure Rates in the Hepatocellular Carcinoma Surveillance Process. Cancer Prev Res Phila Pa. 2012;5(9):1124–1130. doi: 10.1158/1940-6207.CAPR-12-0046 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Singal AG, El-Serag HB. Rational HCC screening approaches for patients with NAFLD. J Hepatol. 2022;76(1):195–201. doi: 10.1016/j.jhep.2021.08.028 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Wolf E, Rich NE, Marrero JA, Parikh N, Singal AG. Utilization of hepatocellular carcinoma surveillance in patients with cirrhosis: A systematic review and meta-analysis. Hepatol Baltim Md. 2021;73(2):713–725. doi: 10.1002/hep.31309 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Singal AG, Reddy S, Radadiya Aka Patel H, et al. Multicenter Randomized Clinical Trial of a Mailed Outreach Strategy for Hepatocellular Carcinoma Surveillance. Clin Gastroenterol Hepatol Off Clin Pract J Am Gastroenterol Assoc. 2022;20(12):2818–2825.e1. doi: 10.1016/j.cgh.2021.12.014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Yeo YH, Hwang J, Jeong D, et al. Surveillance of patients with cirrhosis remains suboptimal in the United States. J Hepatol. Published online May 7, 2021:S0168–8278(21)00307-X. doi: 10.1016/j.jhep.2021.04.042 [DOI] [PubMed] [Google Scholar]
  • 54.Ladhani S, Ohri A, Wong RJ. Disparities in Hepatocellular Carcinoma Surveillance: Dissecting the Roles of Patient, Provider, and Health System Factors. J Clin Gastroenterol. 2020;54(3):218–226. doi: 10.1097/MCG.0000000000001313 [DOI] [PubMed] [Google Scholar]
  • 55.Ajayi F, Jan J, Singal AG, Rich NE. Racial and Sex Disparities in Hepatocellular Carcinoma in the USA. Curr Hepatol Rep. 2020;19(4):462–469. doi: 10.1007/s11901-020-00554-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Moon AM, Sanoff HK, Chang Y, et al. Medicare/Medicaid Insurance, Rurality, and Black Race Associated With Provision of Hepatocellular Carcinoma Treatment and Survival. J Natl Compr Canc Netw. 2021;19(3):285–293. doi: 10.6004/jnccn.2020.7616 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Goldberg DS, Valderrama A, Kamalakar R, Sansgiry SS, Babajanyan S, Lewis JD. Hepatocellular Carcinoma Surveillance Among Cirrhotic Patients With Commercial Health Insurance. J Clin Gastroenterol. 2016;50(3):258–265. doi: 10.1097/MCG.0000000000000411 [DOI] [PubMed] [Google Scholar]
  • 58.Parikh ND, Tayob N, Al-Jarrah T, et al. Barriers to Surveillance for Hepatocellular Carcinoma in a Multicenter Cohort. JAMA Netw Open. 2022;5(7):e2223504. doi: 10.1001/jamanetworkopen.2022.23504 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Goldberg DS, Lewis JD, Halpern SD, Weiner MG, Lo Re V. Validation of a coding algorithm to identify patients with hepatocellular carcinoma in an administrative database. Pharmacoepidemiol Drug Saf. 2013;22(1):103–107. doi: 10.1002/pds.3367 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

Supplementary Materials
NIHMS2191993-supplement-Supplementary_Materials.docx (78.7KB, docx)

Data Availability Statement

Based on contractual data use considerations with data partners such as the Center for Medicare/Medicaid Services, data will not be publicly available. Investigators may propose to use the data in the UNC remote-access computing environment after project review and approval by the Institutional Review Board and with fully executed, project-specific, data sharing agreements in place.

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