Association Between Alcohol Use Disorder and Receipt of Direct-Acting Antiviral Hepatitis C Virus Treatment

Lamia Y Haque; David A Fiellin; Janet P Tate; Denise Esserman; Debika Bhattacharya; Adeel A Butt; Stephen Crystal; E Jennifer Edelman; Adam J Gordon; Joseph K Lim; Jeanette M Tetrault; Emily C Williams; Kendall Bryant; Emily J Cartwright; Christopher T Rentsch; Amy C Justice; Vincent Lo Re, III; Kathleen A McGinnis

doi:10.1001/jamanetworkopen.2022.46604

. 2022 Dec 14;5(12):e2246604. doi: 10.1001/jamanetworkopen.2022.46604

Association Between Alcohol Use Disorder and Receipt of Direct-Acting Antiviral Hepatitis C Virus Treatment

Lamia Y Haque ^1,^2,^✉, David A Fiellin ^1,^2,³, Janet P Tate ^1,⁴, Denise Esserman ⁵, Debika Bhattacharya ^6,⁷, Adeel A Butt ^8,^9,¹⁰, Stephen Crystal ¹¹, E Jennifer Edelman ^1,^2,¹², Adam J Gordon ^13,¹⁴, Joseph K Lim ¹, Jeanette M Tetrault ^1,², Emily C Williams ^15,^16,¹⁷, Kendall Bryant ¹⁸, Emily J Cartwright ^19,²⁰, Christopher T Rentsch ^1,^4,²¹, Amy C Justice ^1,^3,⁴, Vincent Lo Re III ^22,^23,²⁴, Kathleen A McGinnis ⁴

¹Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut

²Yale Program in Addiction Medicine, Yale School of Medicine, New Haven, Connecticut

³Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut

⁴Veterans Affairs Connecticut Health Care System, West Haven

⁵Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut

⁶Department of Internal Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles

⁷Veterans Affairs Greater Los Angeles Health Care System, Los Angeles, California

⁸Department of Medicine, Weill Cornell Medicine, New York, New York

⁹Department of Population Health Sciences, Weill Cornell Medicine, New York, New York

¹⁰Veterans Affairs Pittsburgh Healthcare System, Pittsburgh, Pennsylvania

¹¹Center for Health Services Research, Rutgers University, New Brunswick, New Jersey

¹²Department of Social and Behavioral Sciences, Yale School of Public Health, New Haven, Connecticut

¹³Informatics, Decision-Enhancement, and Analytic Sciences Center, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah

¹⁴Program for Addiction Research, Clinical Care, Knowledge, and Advocacy, Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City

¹⁵Seattle-Denver Center of Innovation for Veteran-Centered and Value-Driven Care, Veterans Affairs Puget Sound Health Care System, Seattle, Washington

¹⁶Health Services Research and Development, Veterans Affairs Puget Sound Health Care System, Seattle, Washington

¹⁷Department of Health Systems and Population Health, University of Washington, Seattle

¹⁸HIV/AIDS and Alcohol Research Program, National Institute on Alcohol Abuse and Alcoholism, Bethesda, Maryland

¹⁹Department of Medicine, Emory School of Medicine, Atlanta, Georgia

²⁰Veterans Affairs Atlanta Health Care System, Atlanta, Georgia

²¹London School of Hygiene and Tropical Medicine, London, United Kingdom

²²Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, Philadelphia, Pennsylvania

²³Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, Philadelphia, Pennsylvania

²⁴Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Philadelphia, Pennsylvania

Accepted for Publication: October 19, 2022.

Published: December 14, 2022. doi:10.1001/jamanetworkopen.2022.46604

^✉

Corresponding Author: Lamia Y. Haque, MD, MPH, Yale School of Medicine, 40 Temple St, PO Box 208019, New Haven, CT 06520 (lamia.haque@yale.edu).

Author Contributions: Dr McGinnis had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Haque, Fiellin, Tate, Esserman, Bhattacharya, Crystal, Tetrault, Williams, Bryant, Cartwright, Rentsch, Lo Re, McGinnis.

Acquisition, analysis, or interpretation of data: Haque, Fiellin, Tate, Esserman, Butt, Crystal, Edelman, Gordon, Lim, Williams, Cartwright, Rentsch, Justice, McGinnis.

Drafting of the manuscript: Haque, Fiellin, Esserman.

Critical revision of the manuscript for important intellectual content: Haque, Fiellin, Tate, Bhattacharya, Butt, Crystal, Edelman, Gordon, Lim, Tetrault, Williams, Bryant, Cartwright, Rentsch, Justice, Lo Re, McGinnis.

Statistical analysis: Tate, Esserman, Lim, Rentsch, Justice, Lo Re, McGinnis.

Obtained funding: Justice.

Administrative, technical, or material support: Haque, Tetrault, Cartwright, Rentsch.

Supervision: Fiellin, Tate, Gordon, Lim, Tetrault, Bryant, Rentsch, Justice.

Conflict of Interest Disclosures: Dr Haque reported receiving grant support from the National Institutes of Health (NIH). Dr Fiellin reported that his wife is owner of Playbl, which distributes serious video games for risk prevention, including substance use, in youth. Dr Tate reported receiving grants from the NIH during the conduct of the study. Dr Esserman reported receiving grants from the NIH during the conduct of the study. Dr Bhattacharya reported receiving grants from Gilead Sciences outside the submitted work. Dr Gordon reported receiving grants from the US Department of Veterans Affairs to his institution, the NIH to his institution, and the US Department of Health and Human Services to his institution during the conduct of the study; receiving personal fees and honoraria from UpToDate; serving on the board of directors for the American Society of Addiction Medicine, the Association for Multidisciplinary Education and Research in Substance Use and Addiction, and the International Society of Addiction Journal Editors outside the submitted work. Dr Bryant reported receiving funding from the NIH during the conduct of the study and was assigned as the Scientific Collaborator under the cooperative agreement that generated these data and conceptualized this research in a broad program focused on the impact of alcohol on HIV prevention and treatment and other comorbidities, coinfections (including hepatitis), and complications. Dr Lo Re reported receiving personal fees from Takeda Consulting outside the submitted work. No other disclosures were reported.

Funding/Support: The COMpAAAS (Consortium to Improve Outcomes in HIV/AIDS, Alcohol, Aging, and Multi-Substance Use)/Veterans Aging Cohort Study, a CHAART (Consortia for HIV/AIDS and Alcohol-Related Research Trials) cooperative agreement, is supported by the NIH/National Institute on Alcohol Abuse and Alcoholism (grants U24-AA020794, U01-AA020790, U01-AA020795, U01-AA020799, and U10 AA013566 [completed]) and in kind by the US Department of Veterans Affairs. This work is also supported by the Yale Drug use, Addiction, and HIV Research Scholars program (grant K12 DA033312).

Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the US Department of Veterans Affairs.

Data Sharing Statement: See Supplement 2.

Additional Information: This work uses data provided by patients and collected by the Veterans Health Administration as part of their care and support.

^✉

Corresponding author.

PMCID: PMC9856353 PMID: 36515952

Key Points

Question

What is the association of alcohol use with receipt of direct-acting antiviral (DAA) treatment for hepatitis C virus (HCV) infection?

Findings

In this cohort study of 133 753 adults with HCV infection, those with alcohol use disorder, even if abstinent, were statistically significantly less likely to receive DAA treatment within 1 and 3 years after eligibility compared with those with lower-risk drinking after adjusting for demographic characteristics and comorbidities.

Meaning

This study suggests that, although guidelines recommend DAA treatment for patients with HCV regardless of alcohol use, patients with alcohol use disorder encounter barriers to HCV treatment.

Abstract

Importance

Direct-acting antiviral (DAA) treatment for hepatitis C virus (HCV) infection is associated with lower mortality and is effective in individuals with alcohol use disorder (AUD). However, despite recommendations, patients with AUD may be less likely to receive DAAs.

Objective

To assess the association between alcohol use and receipt of DAA treatment among patients with HCV within the Veterans Health Administration (VHA).

Design, Setting, and Participants

This cohort study included 133 753 patients with HCV born from 1945 to 1965 who had completed the Alcohol Use Disorders Identification Test–Consumption (AUDIT-C) questionnaire and had at least 1 outpatient visit in the VHA from January 1, 2014, through May 31, 2017, with maximal follow-up of 3 years until May 31, 2020; DAA receipt; or death, whichever occurred first.

Exposures

Alcohol use categories generated using responses to the AUDIT-C questionnaire and International Classification of Diseases, Ninth Revision and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision diagnoses: current AUD, abstinent with AUD history, at-risk drinking, lower-risk drinking, and abstinent without AUD history. Demographic, other clinical, and pharmacy data were also collected.

Main Outcomes and Measures

Associations between alcohol use categories and DAA receipt within 1 and 3 years estimated using Cox proportional hazards regression stratified by calendar year.

Results

Of 133 753 patients (130 103 men [97%]; mean [SD] age, 60.6 [4.5] years; and 73 493 White patients [55%]), 38% had current AUD, 12% were abstinent with a history of AUD, 6% reported at-risk drinking, 14% reported lower-risk drinking, and 30% were abstinent without a history of AUD. Receipt of DAA treatment within 1 year was 7%, 33%, 53%, and 56% for patients entering the cohort in 2014, 2015, 2016, and 2017, respectively. For patients entering in 2014, those with current AUD (hazard ratio [HR], 0.72 [95%, CI, 0.66-0.77]) or who were abstinent with an AUD history (HR, 0.91 [95% CI, 0.84-1.00]) were less likely to receive DAA treatment within 1 year compared with patients with lower-risk drinking. For those entering in 2015-2017, patients with current AUD (HR, 0.75 [95% CI, 0.70-0.81]) and those who were abstinent with an AUD history (HR, 0.76 [95% CI, 0.68-0.86]) were less likely to receive DAA treatment within 1 year compared with patients with lower-risk drinking.

Conclusions and Relevance

This cohort study suggests that individuals with AUD, regardless of abstinence, were less likely to receive DAA treatment. Improved access to DAA treatment for persons with AUD is needed.

This cohort study of patients within the Veterans Health Administration assesses the association between alcohol use disorder and receipt of direct-acting antiviral treatment for hepatitis C virus (HCV) infection.

Introduction

Untreated hepatitis C virus (HCV) infection is associated with substantial morbidity and mortality due to cirrhosis, liver cancer, and related complications, especially for individuals with concomitant alcohol-associated liver disease.¹ Treatment for HCV infection has changed rapidly since 2014 with the introduction of highly efficacious direct-acting antivirals (DAAs).^1,2,3 Direct-acting antiviral treatment for HCV infection is associated with lower mortality and is effective in individuals with alcohol use disorder (AUD). Patients with HCV and AUD or other substance use disorders (SUDs) have faced barriers to DAA treatment that have limited access to these medications.⁴

There is no known safe level of alcohol use among patients with HCV.^1,5 Alcohol can accelerate fibrosis and increase the risk of liver-related complications, even after sustained virologic response (SVR) or HCV cure.^6,7 Despite this risk, unhealthy alcohol use—the range of drinking that includes at-risk use and AUD⁸—is common among patients with HCV.^9,10 Although clinical trials of DAAs typically excluded patients who drink alcohol,¹¹ observational studies have demonstrated high rates of SVR among patients consuming any level of alcohol during DAA treatment.¹² Clinical guidelines issued by the American Association for the Study of Liver Diseases and the Infectious Diseases Society of America (AASLD-IDSA) have recommended DAA treatment without consideration of alcohol use.¹

The Veterans Health Administration (VHA) is the largest integrated health care system and provider of HCV treatment in the United States.¹³ Treatment initiatives led by regional multidisciplinary Hepatitis C Innovation Teams support clinicians, including primary care clinicians, specialists, and pharmacists delivering DAA treatment.¹⁴ Similar to AASLD-IDSA guidelines, VHA guidelines published since 2014 have not excluded patients with AUD from DAA treatment but have emphasized that collaboration with mental health and addiction specialists be considered when offering DAAs.^15,16,17

Little is known regarding whether alcohol use is a barrier to DAA treatment in clinical practice. Therefore, we sought to evaluate the association between alcohol use and DAA treatment among patients in the VHA with HCV and to examine whether the association has changed over time.

Methods

Data Source

We conducted a cohort study using data from the VHA Birth Cohort, an observational study of all individuals born from 1945 to 1965 with at least 1 health care visit at any VHA location from January 1, 2000, to September 30, 2016.^18,19 We used this data source because persons born from 1945 to 1965 have a 6-fold higher prevalence of HCV compared with all other age groups and have been a major focus of HCV screening efforts by the Centers for Disease Control and Prevention and the VHA.^19,20,21 The VHA Birth Cohort consists of electronic health record data available through the national Corporate Data Warehouse, a data repository that includes demographic information, medical diagnoses (recorded by International Classification of Diseases, Ninth Revision [ICD-9] and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision [ICD-10] diagnoses), laboratory test results, procedures (recorded by Current Procedural Terminology codes), pharmacy data, and mortality status. The study was approved by the Human Investigations Committee at the VHA Connecticut Healthcare System and Yale University and was granted a waiver of informed consent because this research could not practicably be conducted without a waiver of consent. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.²²

Study Cohort

Patients were included if they had (1) at least 1 outpatient visit in the VHA from January 1, 2014 (date after which DAAs were available within the system), through May 31, 2017 (to allow for up to 3 years of follow-up)¹⁶; (2) positive HCV antibody and/or detectable HCV RNA; and (3) a documented alcohol screening. Patients were excluded if they had undetectable HCV RNA throughout the study period with or without positive HCV antibody. Patients with positive HCV antibody with unknown HCV RNA status were included because laboratory studies may have been completed outside the VHA or barriers to confirmatory HCV RNA testing may have existed.

The index date was the date of the first outpatient visit or first positive HCV RNA and/or antibody from January 1, 2014, to May 31, 2017. The year prior to the index date served as the baseline period during which alcohol use, comorbidities, and laboratory test results were ascertained. Patients were followed up for up to 3 years from their index date, until May 31, 2020. The 2 primary outcomes were time to receipt of the initial DAA regimen (1) within 1 year after the index date because treatment should start as soon as possible and (2) within 3 years after the index date because the initial large volume of eligible patients may have affected timeliness of treatment.

Data Collection

Alcohol Use Categories

Baseline alcohol use was categorized according to responses to the Alcohol Use Disorders Identification Test–Consumption (AUDIT-C) questionnaire^23,24,25,26 and ICD-9 or ICD-10 diagnoses.⁵ The AUDIT-C questionnaire is a 3-item screening test, with scores ranging from 0 to 12 (where unhealthy alcohol use is signified by a score of 4 or more among men and 3 or more among women), that assesses past-year alcohol consumption as well as frequency of heavy episodic drinking (≥6 drinks per occasion).²³ This instrument has good operating characteristics for the detection of at-risk drinking but is not sufficient to diagnose AUD.^27,28,29 We additionally defined AUD using ICD-9 or ICD-10 inpatient or outpatient codes including alcohol abuse, alcohol dependence, or related conditions, such as alcohol-associated cardiomyopathy, liver disease, pancreatitis, gastritis, myopathy, and psychosis, which have been shown to identify current AUD.^30,31,32,33

We created 5 mutually exclusive alcohol use categories: (1) current AUD (ICD-9 or ICD-10 AUD codes in the baseline year and nonzero AUDIT-C score); (2) at-risk drinking (AUDIT-C score ≥4 for men or ≥3 for women or reported consumption of ≥6 drinks on 1 occasion without the presence of ICD-9 or ICD-10 AUD codes); (3) lower-risk drinking (AUDIT-C score of 1-3 for men or 1-2 for women without the presence of ICD-9 or ICD-10 AUD codes); (4) abstinent with AUD history (presence of ICD-9 or ICD-10 AUD codes prior to the baseline year with an AUDIT-C score of 0); and (5) abstinent without AUD history (AUDIT-C score of 0 without the presence of ICD-9 or ICD-10 AUD codes). Among abstinent patients, we differentiated between those with and those without a history of AUD because the former may represent a distinct population consisting of those with AUD in remission, inability to tolerate alcohol owing to comorbidities, or inaccurate self-report.³²

Demographic or Clinical Variables

Demographic and clinical data collected from the electronic health record included age; sex; race and ethnicity; body mass index; tobacco use; ICD-9 or ICD-10 diagnoses for HIV, hepatitis B virus (HBV) coinfection, cirrhosis, hepatic decompensation,³⁴ hepatocellular carcinoma (HCC), liver transplant, diabetes, depression, severe mental illness (representing posttraumatic stress disorder, bipolar disorder, and/or schizophrenia), and history of drug use disorder (eTable in Supplement 1); laboratory test results including HCV genotype, alanine aminotransferase (ALT), aspartate aminotransferase (AST), and platelet count; and history of HCV treatment prior to 2014 with a non-DAA regimen. Platelet count, ALT level, and AST level were collected from dates closest to the index date within the window from up to 1 year prior to 30 days after. The Fibrosis-4 Index for Liver Fibrosis (FIB-4; based on 4 factors: age, AST level, ALT level, and platelet count), a noninvasive measure of hepatic fibrosis, was calculated.^35,36,37 A FIB-4 score of more than 3.25 is associated with advanced hepatic fibrosis³⁵ and may capture patients with compensated cirrhosis who may not otherwise be identified through diagnostic codes.^38,39 Finally, we identified dispensed fills for DAA regimens, including boceprevir (with peginterferon alfa and ribavirin), elbasvir-grazoprevir, glecaprevir-pibrentasvir, ombitasvir-paritaprevir-ritonavir (with or without dasabuvir and/or ribavirin), ledipasvir-sofosbuvir (with or without ribavirin), sofosbuvir (with peginterferon alfa and/or ribavirin), sofosbuvir-daclatasvir (with or without ribavirin), sofosbuvir-simeprevir (with or without ribavirin), simeprevir (with peginterferon alfa and ribavirin), sofosbuvir-velpatasvir (with or without ribavirin and/or voxilaprevir), and telaprevir (with peginterferon alfa and ribavirin).

Statistical Analysis

Demographic and clinical characteristics were described by alcohol use categories. We compared the percentage of patients with DAA treatment within 1 and 3 years of the index date by alcohol use for each calendar year using the χ² test for trend. We estimated the association of alcohol use and other potential covariates with DAA treatment using Cox proportional hazards regression models. Models were stratified by year (2014 vs 2015-2017) because the challenge of treating the large number of patients newly eligible for DAAs in 2014 may have influenced access compared with subsequent years when relatively fewer patients became eligible. Univariate models were used to examine whether alcohol use and other potential covariates were associated with DAA treatment within 1 and 3 years. Patients were censored at DAA treatment date, death, or 1 year (for the 1-year models) or 3 years (for the 3-year models), whichever came first. Variables evaluated in univariate models included age, sex, race (collapsed to Black, White, and all other races [American Indian, Asian, mixed race, Pacific Islander, and unknown race] because of the low numbers of patients in categories other than Black or White), ethnicity (Hispanic or Latinx or non-Hispanic or Latinx), severity of liver disease (FIB-4 score, cirrhosis, hepatic decompensation, HCC, or liver transplant), comorbidities (HBV, HIV, diabetes, drug use disorder, depression, or severe mental illness), prior HCV treatment, and year of cohort entry. These variables, including race and ethnicity, were included because of their potential to be associated with DAA treatment. Variables statistically significantly associated with DAA treatment in univariate models (P < .05) were included in multivariable models. Unadjusted and adjusted hazard ratios (HRs) with 95% CIs were estimated using lower-risk drinking as the reference category because patients in abstinent categories likely represent a heterogeneous group, including those with the inability to tolerate alcohol owing to comorbidities, inaccurate self-report, or AUD in remission.³² Analyses were run using Stata, version 14.0 (StataCorp LLC). All P values were from 2-sided tests, and results were deemed statistically significant at P < .05.

Results

Demographic and Clinical Characteristics

We identified 133 753 patients eligible for DAAs during 2014-2017. Patients had a mean (SD) age of 60.6 (4.5) years; were primarily male (97%), White (55%), or Black (40%); and had primarily HCV genotype 1 (67%) (Table 1). Twenty-two percent of patients had a FIB-4 score higher than 3.25, 12% had cirrhosis, and 4% had hepatic decompensation. Forty-seven percent of patients had a drug use disorder, 34% had depression, and 36% had severe mental illness. Thirty-eight percent of patients had current AUD, 6% had at-risk drinking, 14% had lower-risk drinking, 12% were abstinent with a history of AUD, and 30% were abstinent without a history of AUD. Most patients became eligible to receive DAA treatment in 2014 (86%), with 7% eligible in 2015, 5% eligible in 2016, and 2% eligible in 2017.

Table 1. Characteristics, by Alcohol Use Category, of Patients Eligible for HCV Treatment Within the Veterans Health Administration Birth Cohort From January 1, 2014, to May 31, 2017.

Baseline characteristic	Patients, No. (%)
Baseline characteristic	Overall (N = 133 753)	Current AUD (n = 51 372)	At-risk drinking (n = 7551)	Lower-risk drinking (n = 18 230)	Abstinent with AUD history (n = 16 063)	Abstinent without AUD history (n = 40 537)
Age, mean (SD), y	60.6 (4.5)	59.9 (4.5)	60.7 (4.5)	60.9 (4.5)	60.7 (4.4)	61.3 (4.4)
Sex
Female	3650 (3)	1051 (2)	224 (3)	629 (4)	363 (2)	1383 (3)
Male	130 103 (97)	50 321 (98)	7327 (97)	17 601 (97)	15 700 (98)	39 154 (97)
Race
American Indian	1055 (1)	411 (1)	70 (1)	123 (1)	121 (1)	330 (1)
Asian	246 (0.2)	60 (0.1)	10 (0.1)	39 (0.2)	22 (0.1)	115 (0.3)
Black	53 032 (40)	22 225 (43)	2301 (31)	6664 (37)	7219 (45)	14 623 (36)
Mixed race	857 (1)	332 (1)	39 (1)	118 (1)	100 (1)	268 (1)
Pacific Islander	516 (0.4)	159 (0.3)	47 (1)	77 (0.4)	40 (0.3)	193 (1)
White	73 493 (55)	26 878 (52)	4660 (62)	10 388 (57)	8251 (51)	23 316 (58)
Unknown	4554 (3)	1307 (3)	424 (6)	821 (5)	310 (2)	1692 (4)
Ethnicity
Hispanic or Latinx	7266 (5)	2655 (5)	436 (6)	912 (5)	867 (5)	2396 (6)
Non-Hispanic or Latinx	126 487 (95)	48 717 (95)	7114 (94)	17 318 (95)	15 196 (95)	38 141 (94)
FIB-4 score
<1.45	31 438 (24)	11 123 (22)	1762 (23)	4829 (27)	3876 (24)	9848 (24)
1.45-3.25	55 604 (42)	20 568 (40)	3126 (41)	7928 (44)	6562 (41)	17 420 (43)
>3.25	29 769 (22)	13 432 (26)	1666 (22)	3068 (17)	3262 (20)	8341 (21)
Unknown or missing	16 942 (13)	6249 (12)	997 (13)	2405 (13)	2363 (15)	4925 (12)
BMI category
Underweight	2546 (2)	1189 (2)	143 (2)	278 (2)	305 (2)	631 (2)
Normal	41 283 (31)	18 648 (36)	2470 (33)	5041 (28)	4582 (29)	10 542 (26)
Overweight	48 425 (36)	17 970 (35)	2677 (36)	6887 (38)	5783 (36)	15 077 (37)
Obesity Class I	26 078 (20)	8522 (17)	1444 (19)	3828 (21)	3375 (21)	8909 (22)
Obesity Class II	8844 (7)	2629 (5)	489 (7)	1359 (8)	1175 (7)	3192 (8)
Obesity Class III	3751 (3)	966 (2)	189 (3)	552 (3)	476 (3)	1571 (4)
Unknown or missing	2826 (2)	1448 (3)	108 (1)	289 (2)	367 (2)	615 (2)
HCV genotype
1	90 177 (67)	35 625 (69)	4429 (59)	11 560 (63)	11 632 (72)	26 931 (66)
2	10 116 (8)	3781 (7)	617 (8)	1407 (8)	1216 (8)	3095 (8)
3	6357 (5)	2580 (5)	348 (5)	745 (4)	835 (5)	1849 (5)
4	1039 (1)	419 (1)	46 (1)	133 (1)	124 (1)	317 (1)
5	8 (0.006)	3 (0.006)	0	0	1 (0.006)	4 (0.01)
6	12 (0.009)	4 (0.008)	0	2 (0.01)	1 (0.006)	5 (0.01)
Multiple	401 (0.3)	183 (0.4)	12 (0.2)	49 (0.3)	70 (0.4)	87 (0.2)
Unknown or missing	25 643 (19)	8777 (17)	2099 (28)	4334 (24)	2184 (14)	8249 (20)
Prior treatment	9939 (7)	3124 (6)	278 (4)	1006 (6)	1693 (11)	3838 (10)
Comorbidity
HIV	3651 (3)	1487 (3)	114 (2)	396 (2)	612 (4)	1042 (3)
HBV	7689 (6)	3801 (7)	200 (3)	566 (3)	1463 (9)	1659 (4)
Diabetes	35 707 (27)	11 983 (23)	1197 (16)	4436 (24)	5411 (34)	12 680 (31)
Drug use disorder	62 391 (47)	36 319 (71)	1023 (14)	3281 (18)	12 547 (78)	9221 (23)
Depression	45 340 (34)	23 254 (45)	811 (11)	3040 (17)	8492 (53)	9743 (24)
Severe mental illness	48 093 (36)	23 668 (46)	964 (13)	3345 (18)	8965 (56)	11 151 (28)
Hepatic decompensation	4968 (4)	2515 (5)	51 (1)	150 (1)	891 (6)	1361 (3)
HCC	2418 (2)	1073 (2)	32 (0.4)	107 (1)	417 (3)	789 (2)
Liver transplant	1143 (1)	175 (0.3)	6 (0.1)	43 (0.2)	254 (2)	665 (2)
Cirrhosis	15 988 (12)	7344 (14)	274 (4)	949 (5)	2574 (16)	4847 (12)

Open in a new tab

Abbreviations: AUD, alcohol use disorder; BMI, body mass index; FIB-4, fibrosis index based on 4 factors (age, aspartate aminotransferase level, alanine aminotransferase level, and platelet count); HBV, hepatitis B virus; HCC, hepatocellular carcinoma; HCV, hepatitis C virus.

Receipt of DAA Treatment by Year and Alcohol Use Category

For patients entering the cohort in 2014, 2015, 2016, and 2017, the receipt of DAA treatment within 1 year of eligibility was 7%, 33%, 53%, and 56%, respectively, and the receipt of DAA treatment within 3 years was 47%, 58%, 65%, and 65%, respectively (Figure). In 2014, 5% of those with current AUD received DAAs within 1 year compared with 8% to 10% of those in other alcohol use categories. In 2017, 50% of those with current AUD and 49% of those with past AUD received DAAs within 1 year compared with 57% to 60% of those in other alcohol use categories. The pattern was similar for receipt of DAA treatment within 3 years (Figure). The associations between DAA treatment receipt (within 1 and 3 years) and alcohol use were statistically significant based on the χ² test for trend (P < .001 for all associations except P = .005 for DAA treatment within 3 years among those eligible in 2017; for DAA treatment within 1 year, χ² tests for trend values were 625.9, 126.1, 53.6, and 12.2 for years 2014, 2015, 2016, and 2017, respectively; for DAA treatment within 3 years, χ² tests for trend values were 969.6, 94.7, 30.0, and 7.8 for years 2014, 2015, 2016, and 2017, respectively).

Figure. — AUD indicates alcohol use disorder.

Association Between Alcohol Use and Receipt of DAA Treatment, 2014

For patients eligible for DAAs in 2014, based on adjusted models, those who were abstinent without a history of AUD were more likely (HR, 1.10 [95% CI, 1.03-1.18]) to receive DAAs within 1 year compared with patients with lower-risk drinking (Table 2). Those with current AUD (HR, 0.72 [95% CI, 0.66-0.77]) or abstinent with an AUD history (HR, 0.91 [95% CI, 0.84-1.00]) were less likely to recieve DAAs within 1 year compared with patients with lower-risk drinking. Those with at-risk alcohol use (HR, 0.93 [95% CI, 0.89-0.98]) and current AUD (HR, 0.81 [95% CI, 0.79-0.83]) were less likely to receive DAAs within 3 years than those with lower-risk drinking. Additional information on the association of covariates with DAA treatment is shown in Table 2.

Table 2. Adjusted Hazard Ratios for Receipt of DAA Treatment Within 1 and 3 Years Among Patients Eligible for HCV Treatment Within the Veterans Health Administration Birth Cohort^a.

Characteristic	Hazard ratio (95% CI)
	DAA treatment within 1 y		DAA treatment within 3 y
	2014 (n = 115 272)	2015-2017 (n = 18 481)	2014 (n = 115 272)	2015-2017 (n = 18 481)
Alcohol use
Lower-risk drinking	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]
Abstinent without AUD history	1.10 (1.03-1.18)	0.98 (0.93-1.04)	1.01 (0.99-1.04)	0.97 (0.93-1.02)
At-risk drinking	0.96 (0.86-1.08)	0.94 (0.87-1.02)	0.93 (0.89-0.98)	0.96 (0.90-1.03)
Abstinent with AUD history	0.91 (0.84-1.00)	0.76 (0.68-0.86)	0.99 (0.95-1.02)	0.81 (0.73-0.89)
Current AUD	0.72 (0.66-0.77)	0.75 (0.70-0.81)	0.81 (0.79-0.83)	0.80 (0.76-0.85)
Sex
Female	1.22 (1.08-1.37)	1.16 (1.02-1.32)	1.05 (1.00-1.11)	1.14 (1.02-1.27)
Male	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]
Age, y	0.98 (0.98-0.99)	1.00 (1.00-1.01)	1.00 (1.00-1.01)	1.00 (1.00-1.00)
Race and ethnicity
Black	0.69 (0.66-0.73)	0.81 (0.77-0.85)	0.89 (0.87-0.91)	0.88 (0.85-0.92)
Hispanic or Latinx	0.70 (0.61-0.81)	0.88 (0.71-1.10)	0.92 (0.87-0.97)	0.90 (0.75-1.08)
White	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]
Other	0.88 (0.80-0.97)	0.84 (0.77-0.92)	0.90 (0.86-0.93)	0.86 (0.80-0.92)
Prior HCV treatment	3.69 (3.51-3.87)	0.68 (0.54-0.88)	1.72 (1.67-1.76)	0.72 (0.59-0.87)
Diabetes	0.75 (0.71-0.79)	0.94 (0.88-1.01)	0.88 (0.86-0.90)	0.94 (0.89-0.99)
BMI category
Normal	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]
Underweight	0.46 (0.35-0.62)	0.80 (0.67-0.95)	0.74 (0.68-0.81)	0.79 (0.69-0.91)
Overweight or obesity	1.31 (1.25-1.38)	1.18 (1.12-1.24)	1.24 (1.22-1.27)	1.14 (1.09-1.18)
Missing	0.56 (0.42-0.75)	0.73 (0.61-0.86)	0.75 (0.69-0.82)	0.71 (0.62-0.82)
HIV	1.07 (0.94-1.21)	0.61 (0.47-0.79)	1.19 (1.14-1.25)	0.58 (0.47-0.72)
HBV	0.85 (0.77-0.94)	0.98 (0.86-1.13)	0.93 (0.90-0.97)	0.88 (0.55-1.11)
Drug use disorder	0.71 (0.67-0.75)	0.76 (0.72-0.82)	0.78 (0.76-0.80)	0.81 (0.77-0.86)
Depression	0.81 (0.68-0.75)	1.00 (0.92-1.08)	0.99 (0.97-1.01)	0.99 (0.93-1.05)
Severe mental illness	0.87 (0.77-0.85)	0.87 (0.81-0.94)	0.94 (0.92-0.96)	0.91 (0.86-0.97)
Liver transplant	1.43 (1.25-1.63)	1.59 (1.09-2.33)	1.31 (1.21-1.42)	1.47 (1.03-2.11)
Cirrhosis	1.51 (1.43-1.59)	0.96 (0.82-1.12)	1.14 (1.11-1.17)	0.83 (0.73-0.95)
HCV genotype
1	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]
2	1.65 (1.54-1.76)	0.95 (0.88-1.03)	0.85 (0.83-0.88)	0.98 (0.91-1.05)
3	1.08 (0.99-1.18)	0.70 (0.63-0.78)	0.78 (0.75-0.81)	0.74 (0.68-0.81)
4	0.71 (0.55-0.93)	0.91 (0.68-1.20)	0.82 (0.75-0.90)	0.89 (0.70-1.14)
Other or multiple	1.27 (0.93-1.74)	0.84 (0.45-1.57)	1.12 (0.99-1.28)	0.98 (0.60-1.61)
Unknown	0.17 (0.15-0.20)	0.20 (0.19-0.22)	0.08 (0.07-0.08)	0.24 (0.23-0.25)
FIB-4 score
1.45-3.25	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]
<1.45	0.71 (0.66-0.76)	0.89 (0.84-0.94)	0.88 (0.86-0.90)	0.88 (0.84-0.92)
>3.25	1.81 (1.72-1.90)	0.98 (0.92-1.04)	1.19 (1.16-1.22)	0.97 (0.92-1.02)
Unknown	0.65 (0.59-0.71)	0.59 (0.54-0.64)	0.78 (0.75-0.80)	0.66 (0.62-0.71)
Index year
2015	1 [Reference]	1 [Reference]	1 [Reference]	1 [Reference]
2016	NA	2.31 (2.20-2.43)	NA	1.68 (1.62-1.75)
2017	NA	6.49 (6.00-7.01)	NA	3.66 (3.43-3.90)

Open in a new tab

Abbreviations: AUD, alcohol use disorder; BMI, body mass index; DAA, direct-acting antiviral; FIB-4, fibrosis index based on 4 factors (age, aspartate aminotransferase level, alanine aminotransferase level, and platelet count); HBV, hepatitis B virus; HCC, hepatocellular carcinoma; HCV, hepatitis C virus; NA, not applicable.

^{^a}

Adjusted for age, sex, race, ethnicity, diabetes, HIV, HBV, drug use disorder, depression, severe mental illness, liver transplant, hepatic decompensation, HCC, HCV genotype, FIB-4 score, prior treatment for HCV, and time entering cohort (index time).

Association Between Alcohol Use and Receipt of DAA Treatment, 2015-2017

For patients eligible for DAAs in 2015-2017, in adjusted models, those with current AUD (HR, 0.75 [95% CI, 0.70-0.81]) or abstinent with an AUD history (HR, 0.76 [95% CI, 0.68-0.86]) were less likely to receive DAAs within 1 year compared with those with lower-risk drinking (Table 2). Similarly, those with current AUD (HR, 0.80 [95% CI, 0.76-0.85]) or abstinent with an AUD history (HR, 0.81 [95% CI, 0.73-0.89]) were less likely to recieve DAAs within 3 years compared with those with lower-risk drinking. Additional information on the association of covariates with DAA treatment is shown in Table 2.

Discussion

Among patients with HCV receiving care in the VHA during the DAA era, individuals with AUD were less likely to receive DAAs compared with those with lower-risk drinking. This association persisted within both the 2014 and 2015-2017 periods and held true for DAA treatment at 1 and 3 years.

There are several possible mechanisms that may explain the lower likelihood of DAA treatment among patients with HCV and AUD. Despite guideline recommendations, health care professionals may assume that patients with AUD or other SUDs will be nonadherent to DAA treatment, which may influence their decision to place a referral or initiate DAA treatment.⁴⁰ For instance, 1 study⁴¹ indicated that less than 60% of clinicians were willing to prescribe DAAs for patients currently drinking alcohol. Clinicians were also reluctant to refer patients for HCV treatment if there were mental health concerns or substance use.⁴⁰ In addition, patient-level factors, such as competing priorities, fear of adverse effects, stigma, and negative interactions with health care professionals, may be associated with acceptance of DAAs or referrals for specialty care.^42,43 Finally, systemwide factors associated with the lower likelihood of DAA treatment among patients with HCV and AUD may also exist. Outside the VHA, the high cost associated with DAA treatment has led to payer-associated barriers in the form of prior authorizations and policies requiring abstinence from alcohol and other substances to be eligible for insurance coverage.^44,45 Although insurance coverage is not a barrier for patients receiving care within the VHA, it took several years after the availability of DAAs for sufficient funds to be secured to treat all patients with HCV¹³; it is possible that clinicians may have initially adopted similar practices with regard to alcohol and other substances when triaging patients for DAA treatment. Consistent with AASLD-IDSA recommendations, VHA HCV treatment guidelines also indicate that alcohol use, substance use, and mental illness are not absolute contraindications to DAA treatment; however, clinicians are advised to consider treating such patients on a case-by-case basis and coordinate care with mental health and addiction specialists.^15,16,17 Although facilitating such treatment is an important aspect of providing holistic care for patients with HCV and AUD or other SUDs, an unintended consequence may have been additional delays affecting the HCV cascade of care for this group.^4,46

Disparities in the receipt of DAA treatment for patients with AUD persisted for several years after the availability of DAAs despite extensive efforts to eradicate HCV within the VHA.¹³ This disparity is concerning because of the importance of timely DAA treatment for those at increased risk of hepatic fibrosis¹ and because patients with unhealthy alcohol use and HCV can achieve high rates of SVR.¹² Furthermore, there seem to be disparities in the receipt of DAA treatment even among those with self-reported abstinence but with a history of AUD. Finally, lower levels of DAA treatment among patients with severe mental illness and other drug use disorders highlight the need for further research to evaluate the factors associated with HCV treatment as well as interventions to improve access in these subgroups.

The associations of race other than White and Hispanic or Latinx ethnicity with lower receipt of DAA treatment are consistent with prior literature on racial and ethnic disparities in HCV treatment and emphasize the need for culturally appropriate and antiracist efforts to improve equity in care.^47,48,49 Black patients and patients from other minoritized racial and ethnic groups were less likely to receive DAAs at 1 and 3 years throughout the study period. Patients of Hispanic or Latinx ethnicity who entered the cohort in 2014 were less likely to receive DAAs within 1 and 3 years; however, this trend did not persist in 2015-2017. Racial and ethnic disparities in DAA treatment are particularly troubling because Black and Hispanic or Latinx patients are at greater risk of presenting with HCC and are less likely to receive a liver transplant for end-stage liver disease.^50,51 Further research to explore the structural determinants of HCV treatment for patients of minoritized racial and ethnic groups is needed.

Strengths and Limitations

This study has several strengths. The population consisted of a large national cohort that was screened for HCV in a system providing universal DAA coverage. Furthermore, adjustment for covariates, including psychiatric conditions, drug use disorders, and liver fibrosis, demonstrated that the association between AUD and lower receipt of DAA treatment was independent of these factors.

This study also has some limitations. First, alcohol use categories were based on self-report and ICD-9 or ICD-10 diagnoses, which may be subject to bias or misclassification.^52,53 For instance, individuals with high AUDIT-C scores without ICD-9 or ICD-10 codes for AUD may have had current AUD, although a small number of patients in our study fit this profile. Social desirability bias might also result in decreased self-report of alcohol consumption among patients with HCV. Self-report of alcohol use has been validated and remains the standard for research and medication approvals, although biomarkers may increase accuracy.^54,55,56,57 Second, we could not determine the reason a patient did not receive DAA treatment and whether the prescriber did not offer it or the patient refused treatment. Because we measured DAA treatment in terms of filled prescriptions, we did not include prescriptions written but not filled. Patients may also have received DAA treatment outside of the VHA. In addition, factors such as estimated life expectancy, which could be associated with DAA prescribing, were not examined. Third, we did not examine rates of SVR, although prior research demonstrated that patients with unhealthy alcohol use can achieve similar SVR rates compared with those who are abstinent or consume lower levels of alcohol.¹² Fourth, practice changes that may have occurred after 2020 with regard to facilitating DAA treatment for groups experiencing barriers, such as patients with AUD, would not be reflected in this analysis. Fifth, our results may not be fully generalizable outside the VHA system.

Conclusions

The findings of this cohort study should be considered in light of prior work⁵ demonstrating a high prevalence of advanced hepatic fibrosis and cirrhosis among individuals who drink alcohol at levels thought to be “safe” and literature⁷ indicating poorer outcomes among patients with unhealthy alcohol use after DAA treatment. Given the higher risk of liver-related complications, DAA treatment should be prioritized for patients with HCV and unhealthy alcohol use. The VHA has used implementation strategies to support HCV treatment using clinical teams and patient outreach.^14,58 Similar efforts should be made to address disparities in DAA treatment using integrated care models that address unhealthy alcohol use and HCV treatment simultaneously. In the context of clinical trials, analogous stepped treatment models have been implemented in HIV clinics to reach individuals with HIV and AUD.⁵⁹ Providing alcohol treatment in liver clinics can decrease alcohol consumption among patients with HCV as well.^60,61,62 Further research to determine barriers to and facilitators of DAA treatment for patients with HCV and AUD and to develop interventions to ensure access to DAAs are needed.

Supplement 1.

eTable. ICD Codes Used to Define Study Variables

Click here for additional data file.^{(57.1KB, pdf)}

Supplement 2.

Data Sharing Statement

Click here for additional data file.^{(17.4KB, pdf)}

References

1.American Association for the Study of Liver Diseases–Infectious Diseases Society of America . HCV guidance: recommendations for testing, managing, and treating hepatitis C. Accessed January 5, 2022. https://www.hcvguidelines.org/ [DOI] [PMC free article] [PubMed]
2.Ward JW, Mermin JH. Simple, effective, but out of reach? public health implications of HCV drugs. N Engl J Med. 2015;373(27):2678-2680. doi: 10.1056/NEJMe1513245 [DOI] [PubMed] [Google Scholar]
3.Strader DB, Seeff LB. A brief history of the treatment of viral hepatitis C. Clin Liver Dis (Hoboken). 2012;1(1):6-11. doi: 10.1002/cld.1 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Grebely J, Haire B, Taylor LE, et al. ; International Network for Hepatitis in Substance Users . Excluding people who use drugs or alcohol from access to hepatitis C treatments—is this fair, given the available data? J Hepatol. 2015;63(4):779-782. doi: 10.1016/j.jhep.2015.06.014 [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Lim JK, Tate JP, Fultz SL, et al. Relationship between alcohol use categories and noninvasive markers of advanced hepatic fibrosis in HIV-infected, chronic hepatitis C virus–infected, and uninfected patients. Clin Infect Dis. 2014;58(10):1449-1458. doi: 10.1093/cid/ciu097 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Llamosas-Falcón L, Shield KD, Gelovany M, et al. Impact of alcohol on the progression of HCV-related liver disease: a systematic review and meta-analysis. J Hepatol. 2021;75(3):536-546. doi: 10.1016/j.jhep.2021.04.018 [DOI] [PubMed] [Google Scholar]
7.Kim NJ, Pearson M, Vutien P, et al. Alcohol use and long-term outcomes among U.S. veterans who received direct-acting antivirals for hepatitis C treatment. Hepatol Commun. 2020;4(2):314-324. doi: 10.1002/hep4.1464 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Saitz R. Clinical practice: unhealthy alcohol use. N Engl J Med. 2005;352(6):596-607. doi: 10.1056/NEJMcp042262 [DOI] [PubMed] [Google Scholar]
9.Alavi M, Janjua NZ, Chong M, et al. The contribution of alcohol use disorder to decompensated cirrhosis among people with hepatitis C: an international study. J Hepatol. 2018;68(3):393-401. doi: 10.1016/j.jhep.2017.10.019 [DOI] [PubMed] [Google Scholar]
10.Taylor AL, Denniston MM, Klevens RM, McKnight-Eily LR, Jiles RB. Association of hepatitis C virus with alcohol use among U.S. adults: NHANES 2003-2010. Am J Prev Med. 2016;51(2):206-215. doi: 10.1016/j.amepre.2016.02.033 [DOI] [PubMed] [Google Scholar]
11.Maughan A, Sadigh K, Angulo-Diaz V, et al. Contemporary HCV pangenotypic DAA treatment protocols are exclusionary to real world HIV-HCV co-infected patients. BMC Infect Dis. 2019;19(1):378. doi: 10.1186/s12879-019-3974-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Tsui JI, Williams EC, Green PK, Berry K, Su F, Ioannou GN. Alcohol use and hepatitis C virus treatment outcomes among patients receiving direct antiviral agents. Drug Alcohol Depend. 2016;169:101-109. doi: 10.1016/j.drugalcdep.2016.10.021 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Belperio PS, Chartier M, Ross DB, Alaigh P, Shulkin D. Curing hepatitis C virus infection: best practices from the U.S. Department of Veterans Affairs. Ann Intern Med. 2017;167(7):499-504. doi: 10.7326/M17-1073 [DOI] [PubMed] [Google Scholar]
14.Gonzalez R, Park A, Yakovchenko V, et al. HCV elimination in the US Department of Veterans Affairs. Clin Liver Dis (Hoboken). 2021;18(1):1-6. doi: 10.1002/cld.1150 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.U.S. Department of Veterans Affairs . Chronic hepatitis C virus (HCV) infection: treatment considerations. Updated August 27, 2018. Accessed February 15, 2022. https://www.hepatitis.va.gov/pdf/treatment-considerations-2018-08-27.pdf
16.U.S. Department of Veterans Affairs . New VA HCV treatment guidelines March 2014. Accessed February 15, 2022. https://natap.org/2014/HCV/051314_03.htm
17.U.S. Department of Veterans Affairs . Chronic hepatitis C virus (HCV) infection: treatment considerations. Updated March 18, 2021. Accessed February 15, 2022. https://www.hepatitis.va.gov/hcv/treatment/hcv-treatment-considerations.asp
18.Njei B, Esserman D, Krishnan S, et al. Regional and rural-urban differences in the use of direct-acting antiviral agents for hepatitis C virus: the Veteran Birth Cohort. Med Care. 2019;57(4):279-285. doi: 10.1097/MLR.0000000000001071 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Sarkar S, Esserman DA, Skanderson M, Levin FL, Justice AC, Lim JK. Disparities in hepatitis C testing in U.S. veterans born 1945-1965. J Hepatol. 2016;65(2):259-265. doi: 10.1016/j.jhep.2016.04.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Denniston MM, Jiles RB, Drobeniuc J, et al. Chronic hepatitis C virus infection in the United States, National Health and Nutrition Examination Survey 2003 to 2010. Ann Intern Med. 2014;160(5):293-300. doi: 10.7326/M13-1133 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Backus LI, Belperio PS, Loomis TP, Yip GH, Mole LA. Hepatitis C virus screening and prevalence among US veterans in Department of Veterans Affairs care. JAMA Intern Med. 2013;173(16):1549-1552. doi: 10.1001/jamainternmed.2013.8133 [DOI] [PubMed] [Google Scholar]
22.von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP; STROBE Initiative . The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370(9596):1453-1457. doi: 10.1016/S0140-6736(07)61602-X [DOI] [PubMed] [Google Scholar]
23.Bradley KA, DeBenedetti AF, Volk RJ, Williams EC, Frank D, Kivlahan DR. AUDIT-C as a brief screen for alcohol misuse in primary care. Alcohol Clin Exp Res. 2007;31(7):1208-1217. doi: 10.1111/j.1530-0277.2007.00403.x [DOI] [PubMed] [Google Scholar]
24.Rubinsky AD, Dawson DA, Williams EC, Kivlahan DR, Bradley KA. AUDIT-C scores as a scaled marker of mean daily drinking, alcohol use disorder severity, and probability of alcohol dependence in a U.S. general population sample of drinkers. Alcohol Clin Exp Res. 2013;37(8):1380-1390. doi: 10.1111/acer.12092 [DOI] [PubMed] [Google Scholar]
25.Fiellin DA, Reid MC, O’Connor PG. Screening for alcohol problems in primary care: a systematic review. Arch Intern Med. 2000;160(13):1977-1989. doi: 10.1001/archinte.160.13.1977 [DOI] [PubMed] [Google Scholar]
26.Mulvaney-Day N, Marshall T, Downey Piscopo K, et al. Screening for behavioral health conditions in primary care settings: a systematic review of the literature. J Gen Intern Med. 2018;33(3):335-346. doi: 10.1007/s11606-017-4181-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
27.McGinnis KA, Justice AC, Kraemer KL, Saitz R, Bryant KJ, Fiellin DA. Comparing alcohol screening measures among HIV-infected and -uninfected men. Alcohol Clin Exp Res. 2013;37(3):435-442. doi: 10.1111/j.1530-0277.2012.01937.x [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Hasin D, Hatzenbuehler ML, Keyes K, Ogburn E. Substance use disorders: Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) and International Classification of Diseases, Tenth Edition (ICD-10). Addiction. 2006;101(suppl 1):59-75. doi: 10.1111/j.1360-0443.2006.01584.x [DOI] [PubMed] [Google Scholar]
29.Hasin DS, O’Brien CP, Auriacombe M, et al. DSM-5 criteria for substance use disorders: recommendations and rationale. Am J Psychiatry. 2013;170(8):834-851. doi: 10.1176/appi.ajp.2013.12060782 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Justice AC, Lasky E, McGinnis KA, et al. ; VACS 3 Project Team . Medical disease and alcohol use among veterans with human immunodeficiency infection: a comparison of disease measurement strategies. Med Care. 2006;44(8)(suppl 2):S52-S60. doi: 10.1097/01.mlr.0000228003.08925.8c [DOI] [PubMed] [Google Scholar]
31.Justice AC, Smith RV, Tate JP, et al. ; VA Million Veteran Program . AUDIT-C and ICD codes as phenotypes for harmful alcohol use: association with ADH1B polymorphisms in two US populations. Addiction. 2018;113(12):2214-2224. doi: 10.1111/add.14374 [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Gordon KS, McGinnis K, Dao C, et al. Differentiating types of self-reported alcohol abstinence. AIDS Behav. 2020;24(2):655-665. doi: 10.1007/s10461-019-02638-x [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Williams EC, Rubinsky AD, Lapham GT, et al. Prevalence of clinically recognized alcohol and other substance use disorders among VA outpatients with unhealthy alcohol use identified by routine alcohol screening. Drug Alcohol Depend. 2014;135:95-103. doi: 10.1016/j.drugalcdep.2013.11.016 [DOI] [PubMed] [Google Scholar]
34.Lo Re V III, Lim JK, Goetz MB, et al. Validity of diagnostic codes and liver-related laboratory abnormalities to identify hepatic decompensation events in the Veterans Aging Cohort Study. Pharmacoepidemiol Drug Saf. 2011;20(7):689-699. doi: 10.1002/pds.2148 [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Adler M, Gulbis B, Moreno C, et al. The predictive value of FIB-4 versus FibroTest, APRI, FibroIndex and Forns index to noninvasively estimate fibrosis in hepatitis C and nonhepatitis C liver diseases. Hepatology. 2008;47(2):762-763. doi: 10.1002/hep.22085 [DOI] [PubMed] [Google Scholar]
36.Vallet-Pichard A, Mallet V, Pol S. FIB-4: a simple, inexpensive and accurate marker of fibrosis in HCV-infected patients. Hepatology. 2006;44(3):769. doi: 10.1002/hep.21334 [DOI] [PubMed] [Google Scholar]
37.Sterling RK, Lissen E, Clumeck N, et al. ; APRICOT Clinical Investigators . Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology. 2006;43(6):1317-1325. doi: 10.1002/hep.21178 [DOI] [PubMed] [Google Scholar]
38.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-e54. doi: 10.1097/MCG.0b013e3182688d2f [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Ramrakhiani NS, Le MH, Yeo YH, Le AK, Maeda M, Nguyen MH. Validity of International Classification of Diseases, Tenth Revision, codes for cirrhosis. Dig Dis. 2021;39(3):243-246. doi: 10.1159/000510981 [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Rogal SS, McCarthy R, Reid A, et al. Primary care and hepatology provider–perceived barriers to and facilitators of hepatitis C treatment candidacy and adherence. Dig Dis Sci. 2017;62(8):1933-1943. doi: 10.1007/s10620-017-4608-9 [DOI] [PubMed] [Google Scholar]
41.Zhang G, Patel K, Moghe A, et al. Provider perceptions of hepatitis C treatment adherence and initiation. Dig Dis Sci. 2020;65(5):1324-1333. doi: 10.1007/s10620-019-05877-z [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Patel K, Zickmund SL, Jones H, et al. Determinants of hepatitis C treatment adherence and treatment completion among veterans in the direct acting antiviral era. Dig Dis Sci. 2019;64(10):3001-3012. doi: 10.1007/s10620-019-05590-x [DOI] [PubMed] [Google Scholar]
43.Rogal SS, Arnold RM, Chapko M, et al. The patient-provider relationship is associated with hepatitis C treatment eligibility: a prospective mixed-methods cohort study. PLoS One. 2016;11(2):e0148596. doi: 10.1371/journal.pone.0148596 [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Canary LA, Klevens RM, Holmberg SD. Limited access to new hepatitis C virus treatment under state Medicaid programs. Ann Intern Med. 2015;163(3):226-228. doi: 10.7326/M15-0320 [DOI] [PubMed] [Google Scholar]
45.Lo Re V III, Gowda C, Urick PN, et al. Disparities in absolute denial of modern hepatitis C therapy by type of insurance. Clin Gastroenterol Hepatol. 2016;14(7):1035-1043. doi: 10.1016/j.cgh.2016.03.040 [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Thomas DL. State of the hepatitis C virus care cascade. Clin Liver Dis (Hoboken). 2020;16(1):8-11. doi: 10.1002/cld.915 [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Kim NJ, Locke CJ, Park H, Magee C, Bacchetti P, Khalili M. Race and hepatitis C care continuum in an underserved birth cohort. J Gen Intern Med. 2019;34(10):2005-2013. doi: 10.1007/s11606-018-4649-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Wong RJ, Jain MK, Therapondos G, et al. Race/ethnicity and insurance status disparities in access to direct acting antivirals for hepatitis C virus treatment. Am J Gastroenterol. 2018;113(9):1329-1338. doi: 10.1038/s41395-018-0033-8 [DOI] [PubMed] [Google Scholar]
49.Ansell DA, James B, De Maio FG. A call for antiracist action. N Engl J Med. 2022;387(1):e1. doi: 10.1056/NEJMp2201950 [DOI] [PubMed] [Google Scholar]
50.Nguyen GC, Segev DL, Thuluvath PJ. Racial disparities in the management of hospitalized patients with cirrhosis and complications of portal hypertension: a national study. Hepatology. 2007;45(5):1282-1289. doi: 10.1002/hep.21580 [DOI] [PubMed] [Google Scholar]
51.Nephew LD, Serper M. Racial, gender, and socioeconomic disparities in liver transplantation. Liver Transpl. 2021;27(6):900-912. doi: 10.1002/lt.25996 [DOI] [PubMed] [Google Scholar]
52.McGinnis KA, Tate JP, Williams EC, et al. Comparison of AUDIT-C collected via electronic medical record and self-administered research survey in HIV infected and uninfected patients. Drug Alcohol Depend. 2016;168:196-202. doi: 10.1016/j.drugalcdep.2016.09.015 [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Williams EC, Achtmeyer CE, Thomas RM, et al. Factors underlying quality problems with alcohol screening prompted by a clinical reminder in primary care: a multi-site qualitative study. J Gen Intern Med. 2015;30(8):1125-1132. doi: 10.1007/s11606-015-3248-z [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Falk D, Wang XQ, Liu L, et al. Percentage of subjects with no heavy drinking days: evaluation as an efficacy endpoint for alcohol clinical trials. Alcohol Clin Exp Res. 2010;34(12):2022-2034. doi: 10.1111/j.1530-0277.2010.01290.x [DOI] [PubMed] [Google Scholar]
55.Witkiewitz K, Finney JW, Harris AH, Kivlahan DR, Kranzler HR. Recommendations for the design and analysis of treatment trials for alcohol use disorders. Alcohol Clin Exp Res. 2015;39(9):1557-1570. doi: 10.1111/acer.12800 [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Schröck A, Wurst FM, Thon N, Weinmann W. Assessing phosphatidylethanol (PEth) levels reflecting different drinking habits in comparison to the Alcohol Use Disorders Identification Test–C (AUDIT-C). Drug Alcohol Depend. 2017;178:80-86. doi: 10.1016/j.drugalcdep.2017.04.026 [DOI] [PubMed] [Google Scholar]
57.Stewart SH, Koch DG, Willner IR, Anton RF, Reuben A. Validation of blood phosphatidylethanol as an alcohol consumption biomarker in patients with chronic liver disease. Alcohol Clin Exp Res. 2014;38(6):1706-1711. doi: 10.1111/acer.12442 [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Yakovchenko V, Miech EJ, Chinman MJ, et al. Strategy configurations directly linked to higher hepatitis C virus treatment starts: an applied use of configurational comparative methods. Med Care. 2020;58(5):e31-e38. doi: 10.1097/MLR.0000000000001319 [DOI] [PMC free article] [PubMed] [Google Scholar]
59.Edelman EJ, Maisto SA, Hansen NB, et al. Integrated stepped alcohol treatment for patients with HIV and alcohol use disorder: a randomised controlled trial. Lancet HIV. 2019;6(8):e509-e517. doi: 10.1016/S2352-3018(19)30076-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
60.Dieperink E, Ho SB, Heit S, Durfee JM, Thuras P, Willenbring ML. Significant reductions in drinking following brief alcohol treatment provided in a hepatitis C clinic. Psychosomatics. 2010;51(2):149-156. doi: 10.1016/S0033-3182(10)70675-7 [DOI] [PubMed] [Google Scholar]
61.Dieperink E, Fuller B, Isenhart C, et al. Efficacy of motivational enhancement therapy on alcohol use disorders in patients with chronic hepatitis C: a randomized controlled trial. Addiction. 2014;109(11):1869-1877. doi: 10.1111/add.12679 [DOI] [PubMed] [Google Scholar]
62.Proeschold-Bell RJ, Patkar AA, Naggie S, et al. An integrated alcohol abuse and medical treatment model for patients with hepatitis C. Dig Dis Sci. 2012;57(4):1083-1091. doi: 10.1007/s10620-011-1976-4 [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

Supplement 1.

eTable. ICD Codes Used to Define Study Variables

Click here for additional data file.^{(57.1KB, pdf)}

Supplement 2.

Data Sharing Statement

Click here for additional data file.^{(17.4KB, pdf)}

[zoi221315r1] 1.American Association for the Study of Liver Diseases–Infectious Diseases Society of America . HCV guidance: recommendations for testing, managing, and treating hepatitis C. Accessed January 5, 2022. https://www.hcvguidelines.org/ [DOI] [PMC free article] [PubMed]

[zoi221315r2] 2.Ward JW, Mermin JH. Simple, effective, but out of reach? public health implications of HCV drugs. N Engl J Med. 2015;373(27):2678-2680. doi: 10.1056/NEJMe1513245 [DOI] [PubMed] [Google Scholar]

[zoi221315r3] 3.Strader DB, Seeff LB. A brief history of the treatment of viral hepatitis C. Clin Liver Dis (Hoboken). 2012;1(1):6-11. doi: 10.1002/cld.1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi221315r4] 4.Grebely J, Haire B, Taylor LE, et al. ; International Network for Hepatitis in Substance Users . Excluding people who use drugs or alcohol from access to hepatitis C treatments—is this fair, given the available data? J Hepatol. 2015;63(4):779-782. doi: 10.1016/j.jhep.2015.06.014 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi221315r5] 5.Lim JK, Tate JP, Fultz SL, et al. Relationship between alcohol use categories and noninvasive markers of advanced hepatic fibrosis in HIV-infected, chronic hepatitis C virus–infected, and uninfected patients. Clin Infect Dis. 2014;58(10):1449-1458. doi: 10.1093/cid/ciu097 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi221315r6] 6.Llamosas-Falcón L, Shield KD, Gelovany M, et al. Impact of alcohol on the progression of HCV-related liver disease: a systematic review and meta-analysis. J Hepatol. 2021;75(3):536-546. doi: 10.1016/j.jhep.2021.04.018 [DOI] [PubMed] [Google Scholar]

[zoi221315r7] 7.Kim NJ, Pearson M, Vutien P, et al. Alcohol use and long-term outcomes among U.S. veterans who received direct-acting antivirals for hepatitis C treatment. Hepatol Commun. 2020;4(2):314-324. doi: 10.1002/hep4.1464 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi221315r8] 8.Saitz R. Clinical practice: unhealthy alcohol use. N Engl J Med. 2005;352(6):596-607. doi: 10.1056/NEJMcp042262 [DOI] [PubMed] [Google Scholar]

[zoi221315r9] 9.Alavi M, Janjua NZ, Chong M, et al. The contribution of alcohol use disorder to decompensated cirrhosis among people with hepatitis C: an international study. J Hepatol. 2018;68(3):393-401. doi: 10.1016/j.jhep.2017.10.019 [DOI] [PubMed] [Google Scholar]

[zoi221315r10] 10.Taylor AL, Denniston MM, Klevens RM, McKnight-Eily LR, Jiles RB. Association of hepatitis C virus with alcohol use among U.S. adults: NHANES 2003-2010. Am J Prev Med. 2016;51(2):206-215. doi: 10.1016/j.amepre.2016.02.033 [DOI] [PubMed] [Google Scholar]

[zoi221315r11] 11.Maughan A, Sadigh K, Angulo-Diaz V, et al. Contemporary HCV pangenotypic DAA treatment protocols are exclusionary to real world HIV-HCV co-infected patients. BMC Infect Dis. 2019;19(1):378. doi: 10.1186/s12879-019-3974-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi221315r12] 12.Tsui JI, Williams EC, Green PK, Berry K, Su F, Ioannou GN. Alcohol use and hepatitis C virus treatment outcomes among patients receiving direct antiviral agents. Drug Alcohol Depend. 2016;169:101-109. doi: 10.1016/j.drugalcdep.2016.10.021 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi221315r13] 13.Belperio PS, Chartier M, Ross DB, Alaigh P, Shulkin D. Curing hepatitis C virus infection: best practices from the U.S. Department of Veterans Affairs. Ann Intern Med. 2017;167(7):499-504. doi: 10.7326/M17-1073 [DOI] [PubMed] [Google Scholar]

[zoi221315r14] 14.Gonzalez R, Park A, Yakovchenko V, et al. HCV elimination in the US Department of Veterans Affairs. Clin Liver Dis (Hoboken). 2021;18(1):1-6. doi: 10.1002/cld.1150 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi221315r15] 15.U.S. Department of Veterans Affairs . Chronic hepatitis C virus (HCV) infection: treatment considerations. Updated August 27, 2018. Accessed February 15, 2022. https://www.hepatitis.va.gov/pdf/treatment-considerations-2018-08-27.pdf

[zoi221315r16] 16.U.S. Department of Veterans Affairs . New VA HCV treatment guidelines March 2014. Accessed February 15, 2022. https://natap.org/2014/HCV/051314_03.htm

[zoi221315r17] 17.U.S. Department of Veterans Affairs . Chronic hepatitis C virus (HCV) infection: treatment considerations. Updated March 18, 2021. Accessed February 15, 2022. https://www.hepatitis.va.gov/hcv/treatment/hcv-treatment-considerations.asp

[zoi221315r18] 18.Njei B, Esserman D, Krishnan S, et al. Regional and rural-urban differences in the use of direct-acting antiviral agents for hepatitis C virus: the Veteran Birth Cohort. Med Care. 2019;57(4):279-285. doi: 10.1097/MLR.0000000000001071 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi221315r19] 19.Sarkar S, Esserman DA, Skanderson M, Levin FL, Justice AC, Lim JK. Disparities in hepatitis C testing in U.S. veterans born 1945-1965. J Hepatol. 2016;65(2):259-265. doi: 10.1016/j.jhep.2016.04.012 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi221315r20] 20.Denniston MM, Jiles RB, Drobeniuc J, et al. Chronic hepatitis C virus infection in the United States, National Health and Nutrition Examination Survey 2003 to 2010. Ann Intern Med. 2014;160(5):293-300. doi: 10.7326/M13-1133 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi221315r21] 21.Backus LI, Belperio PS, Loomis TP, Yip GH, Mole LA. Hepatitis C virus screening and prevalence among US veterans in Department of Veterans Affairs care. JAMA Intern Med. 2013;173(16):1549-1552. doi: 10.1001/jamainternmed.2013.8133 [DOI] [PubMed] [Google Scholar]

[zoi221315r22] 22.von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP; STROBE Initiative . The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370(9596):1453-1457. doi: 10.1016/S0140-6736(07)61602-X [DOI] [PubMed] [Google Scholar]

[zoi221315r23] 23.Bradley KA, DeBenedetti AF, Volk RJ, Williams EC, Frank D, Kivlahan DR. AUDIT-C as a brief screen for alcohol misuse in primary care. Alcohol Clin Exp Res. 2007;31(7):1208-1217. doi: 10.1111/j.1530-0277.2007.00403.x [DOI] [PubMed] [Google Scholar]

[zoi221315r24] 24.Rubinsky AD, Dawson DA, Williams EC, Kivlahan DR, Bradley KA. AUDIT-C scores as a scaled marker of mean daily drinking, alcohol use disorder severity, and probability of alcohol dependence in a U.S. general population sample of drinkers. Alcohol Clin Exp Res. 2013;37(8):1380-1390. doi: 10.1111/acer.12092 [DOI] [PubMed] [Google Scholar]

[zoi221315r25] 25.Fiellin DA, Reid MC, O’Connor PG. Screening for alcohol problems in primary care: a systematic review. Arch Intern Med. 2000;160(13):1977-1989. doi: 10.1001/archinte.160.13.1977 [DOI] [PubMed] [Google Scholar]

[zoi221315r26] 26.Mulvaney-Day N, Marshall T, Downey Piscopo K, et al. Screening for behavioral health conditions in primary care settings: a systematic review of the literature. J Gen Intern Med. 2018;33(3):335-346. doi: 10.1007/s11606-017-4181-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi221315r27] 27.McGinnis KA, Justice AC, Kraemer KL, Saitz R, Bryant KJ, Fiellin DA. Comparing alcohol screening measures among HIV-infected and -uninfected men. Alcohol Clin Exp Res. 2013;37(3):435-442. doi: 10.1111/j.1530-0277.2012.01937.x [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi221315r28] 28.Hasin D, Hatzenbuehler ML, Keyes K, Ogburn E. Substance use disorders: Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) and International Classification of Diseases, Tenth Edition (ICD-10). Addiction. 2006;101(suppl 1):59-75. doi: 10.1111/j.1360-0443.2006.01584.x [DOI] [PubMed] [Google Scholar]

[zoi221315r29] 29.Hasin DS, O’Brien CP, Auriacombe M, et al. DSM-5 criteria for substance use disorders: recommendations and rationale. Am J Psychiatry. 2013;170(8):834-851. doi: 10.1176/appi.ajp.2013.12060782 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi221315r30] 30.Justice AC, Lasky E, McGinnis KA, et al. ; VACS 3 Project Team . Medical disease and alcohol use among veterans with human immunodeficiency infection: a comparison of disease measurement strategies. Med Care. 2006;44(8)(suppl 2):S52-S60. doi: 10.1097/01.mlr.0000228003.08925.8c [DOI] [PubMed] [Google Scholar]

[zoi221315r31] 31.Justice AC, Smith RV, Tate JP, et al. ; VA Million Veteran Program . AUDIT-C and ICD codes as phenotypes for harmful alcohol use: association with ADH1B polymorphisms in two US populations. Addiction. 2018;113(12):2214-2224. doi: 10.1111/add.14374 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi221315r32] 32.Gordon KS, McGinnis K, Dao C, et al. Differentiating types of self-reported alcohol abstinence. AIDS Behav. 2020;24(2):655-665. doi: 10.1007/s10461-019-02638-x [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi221315r33] 33.Williams EC, Rubinsky AD, Lapham GT, et al. Prevalence of clinically recognized alcohol and other substance use disorders among VA outpatients with unhealthy alcohol use identified by routine alcohol screening. Drug Alcohol Depend. 2014;135:95-103. doi: 10.1016/j.drugalcdep.2013.11.016 [DOI] [PubMed] [Google Scholar]

[zoi221315r34] 34.Lo Re V III, Lim JK, Goetz MB, et al. Validity of diagnostic codes and liver-related laboratory abnormalities to identify hepatic decompensation events in the Veterans Aging Cohort Study. Pharmacoepidemiol Drug Saf. 2011;20(7):689-699. doi: 10.1002/pds.2148 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi221315r35] 35.Adler M, Gulbis B, Moreno C, et al. The predictive value of FIB-4 versus FibroTest, APRI, FibroIndex and Forns index to noninvasively estimate fibrosis in hepatitis C and nonhepatitis C liver diseases. Hepatology. 2008;47(2):762-763. doi: 10.1002/hep.22085 [DOI] [PubMed] [Google Scholar]

[zoi221315r36] 36.Vallet-Pichard A, Mallet V, Pol S. FIB-4: a simple, inexpensive and accurate marker of fibrosis in HCV-infected patients. Hepatology. 2006;44(3):769. doi: 10.1002/hep.21334 [DOI] [PubMed] [Google Scholar]

[zoi221315r37] 37.Sterling RK, Lissen E, Clumeck N, et al. ; APRICOT Clinical Investigators . Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology. 2006;43(6):1317-1325. doi: 10.1002/hep.21178 [DOI] [PubMed] [Google Scholar]

[zoi221315r38] 38.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-e54. doi: 10.1097/MCG.0b013e3182688d2f [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi221315r39] 39.Ramrakhiani NS, Le MH, Yeo YH, Le AK, Maeda M, Nguyen MH. Validity of International Classification of Diseases, Tenth Revision, codes for cirrhosis. Dig Dis. 2021;39(3):243-246. doi: 10.1159/000510981 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi221315r40] 40.Rogal SS, McCarthy R, Reid A, et al. Primary care and hepatology provider–perceived barriers to and facilitators of hepatitis C treatment candidacy and adherence. Dig Dis Sci. 2017;62(8):1933-1943. doi: 10.1007/s10620-017-4608-9 [DOI] [PubMed] [Google Scholar]

[zoi221315r41] 41.Zhang G, Patel K, Moghe A, et al. Provider perceptions of hepatitis C treatment adherence and initiation. Dig Dis Sci. 2020;65(5):1324-1333. doi: 10.1007/s10620-019-05877-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi221315r42] 42.Patel K, Zickmund SL, Jones H, et al. Determinants of hepatitis C treatment adherence and treatment completion among veterans in the direct acting antiviral era. Dig Dis Sci. 2019;64(10):3001-3012. doi: 10.1007/s10620-019-05590-x [DOI] [PubMed] [Google Scholar]

[zoi221315r43] 43.Rogal SS, Arnold RM, Chapko M, et al. The patient-provider relationship is associated with hepatitis C treatment eligibility: a prospective mixed-methods cohort study. PLoS One. 2016;11(2):e0148596. doi: 10.1371/journal.pone.0148596 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi221315r44] 44.Canary LA, Klevens RM, Holmberg SD. Limited access to new hepatitis C virus treatment under state Medicaid programs. Ann Intern Med. 2015;163(3):226-228. doi: 10.7326/M15-0320 [DOI] [PubMed] [Google Scholar]

[zoi221315r45] 45.Lo Re V III, Gowda C, Urick PN, et al. Disparities in absolute denial of modern hepatitis C therapy by type of insurance. Clin Gastroenterol Hepatol. 2016;14(7):1035-1043. doi: 10.1016/j.cgh.2016.03.040 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi221315r46] 46.Thomas DL. State of the hepatitis C virus care cascade. Clin Liver Dis (Hoboken). 2020;16(1):8-11. doi: 10.1002/cld.915 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi221315r47] 47.Kim NJ, Locke CJ, Park H, Magee C, Bacchetti P, Khalili M. Race and hepatitis C care continuum in an underserved birth cohort. J Gen Intern Med. 2019;34(10):2005-2013. doi: 10.1007/s11606-018-4649-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi221315r48] 48.Wong RJ, Jain MK, Therapondos G, et al. Race/ethnicity and insurance status disparities in access to direct acting antivirals for hepatitis C virus treatment. Am J Gastroenterol. 2018;113(9):1329-1338. doi: 10.1038/s41395-018-0033-8 [DOI] [PubMed] [Google Scholar]

[zoi221315r49] 49.Ansell DA, James B, De Maio FG. A call for antiracist action. N Engl J Med. 2022;387(1):e1. doi: 10.1056/NEJMp2201950 [DOI] [PubMed] [Google Scholar]

[zoi221315r50] 50.Nguyen GC, Segev DL, Thuluvath PJ. Racial disparities in the management of hospitalized patients with cirrhosis and complications of portal hypertension: a national study. Hepatology. 2007;45(5):1282-1289. doi: 10.1002/hep.21580 [DOI] [PubMed] [Google Scholar]

[zoi221315r51] 51.Nephew LD, Serper M. Racial, gender, and socioeconomic disparities in liver transplantation. Liver Transpl. 2021;27(6):900-912. doi: 10.1002/lt.25996 [DOI] [PubMed] [Google Scholar]

[zoi221315r52] 52.McGinnis KA, Tate JP, Williams EC, et al. Comparison of AUDIT-C collected via electronic medical record and self-administered research survey in HIV infected and uninfected patients. Drug Alcohol Depend. 2016;168:196-202. doi: 10.1016/j.drugalcdep.2016.09.015 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi221315r53] 53.Williams EC, Achtmeyer CE, Thomas RM, et al. Factors underlying quality problems with alcohol screening prompted by a clinical reminder in primary care: a multi-site qualitative study. J Gen Intern Med. 2015;30(8):1125-1132. doi: 10.1007/s11606-015-3248-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi221315r54] 54.Falk D, Wang XQ, Liu L, et al. Percentage of subjects with no heavy drinking days: evaluation as an efficacy endpoint for alcohol clinical trials. Alcohol Clin Exp Res. 2010;34(12):2022-2034. doi: 10.1111/j.1530-0277.2010.01290.x [DOI] [PubMed] [Google Scholar]

[zoi221315r55] 55.Witkiewitz K, Finney JW, Harris AH, Kivlahan DR, Kranzler HR. Recommendations for the design and analysis of treatment trials for alcohol use disorders. Alcohol Clin Exp Res. 2015;39(9):1557-1570. doi: 10.1111/acer.12800 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi221315r56] 56.Schröck A, Wurst FM, Thon N, Weinmann W. Assessing phosphatidylethanol (PEth) levels reflecting different drinking habits in comparison to the Alcohol Use Disorders Identification Test–C (AUDIT-C). Drug Alcohol Depend. 2017;178:80-86. doi: 10.1016/j.drugalcdep.2017.04.026 [DOI] [PubMed] [Google Scholar]

[zoi221315r57] 57.Stewart SH, Koch DG, Willner IR, Anton RF, Reuben A. Validation of blood phosphatidylethanol as an alcohol consumption biomarker in patients with chronic liver disease. Alcohol Clin Exp Res. 2014;38(6):1706-1711. doi: 10.1111/acer.12442 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi221315r58] 58.Yakovchenko V, Miech EJ, Chinman MJ, et al. Strategy configurations directly linked to higher hepatitis C virus treatment starts: an applied use of configurational comparative methods. Med Care. 2020;58(5):e31-e38. doi: 10.1097/MLR.0000000000001319 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi221315r59] 59.Edelman EJ, Maisto SA, Hansen NB, et al. Integrated stepped alcohol treatment for patients with HIV and alcohol use disorder: a randomised controlled trial. Lancet HIV. 2019;6(8):e509-e517. doi: 10.1016/S2352-3018(19)30076-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[zoi221315r60] 60.Dieperink E, Ho SB, Heit S, Durfee JM, Thuras P, Willenbring ML. Significant reductions in drinking following brief alcohol treatment provided in a hepatitis C clinic. Psychosomatics. 2010;51(2):149-156. doi: 10.1016/S0033-3182(10)70675-7 [DOI] [PubMed] [Google Scholar]

[zoi221315r61] 61.Dieperink E, Fuller B, Isenhart C, et al. Efficacy of motivational enhancement therapy on alcohol use disorders in patients with chronic hepatitis C: a randomized controlled trial. Addiction. 2014;109(11):1869-1877. doi: 10.1111/add.12679 [DOI] [PubMed] [Google Scholar]

[zoi221315r62] 62.Proeschold-Bell RJ, Patkar AA, Naggie S, et al. An integrated alcohol abuse and medical treatment model for patients with hepatitis C. Dig Dis Sci. 2012;57(4):1083-1091. doi: 10.1007/s10620-011-1976-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Association Between Alcohol Use Disorder and Receipt of Direct-Acting Antiviral Hepatitis C Virus Treatment

Lamia Y Haque, MD, MPH

David A Fiellin, MD

Janet P Tate, PhD

Denise Esserman, PhD

Debika Bhattacharya, MD, MSc

Adeel A Butt, MBBS, MS

Stephen Crystal, PhD

E Jennifer Edelman, MD, MHS

Adam J Gordon, MD, MPH

Joseph K Lim, MD

Jeanette M Tetrault, MD

Emily C Williams, PhD, MPH

Kendall Bryant, PhD

Emily J Cartwright, MD

Christopher T Rentsch, PhD

Amy C Justice, MD, PhD

Vincent Lo Re III, MD, MSCE

Kathleen A McGinnis, DrPh, MS

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Exposures

Main Outcomes and Measures

Results

Conclusions and Relevance

Introduction

Methods

Data Source

Study Cohort

Data Collection

Alcohol Use Categories

Demographic or Clinical Variables

Statistical Analysis

Results

Demographic and Clinical Characteristics

Table 1. Characteristics, by Alcohol Use Category, of Patients Eligible for HCV Treatment Within the Veterans Health Administration Birth Cohort From January 1, 2014, to May 31, 2017.

Receipt of DAA Treatment by Year and Alcohol Use Category

Figure. Percentage of Patients With Hepatitis C Virus Within Veterans Health Administration Birth Cohort Receiving Direct-Acting Antiviral (DAA) Treatment Within 1 and 3 Years by Alcohol Use Category and Index Year.

Association Between Alcohol Use and Receipt of DAA Treatment, 2014

Table 2. Adjusted Hazard Ratios for Receipt of DAA Treatment Within 1 and 3 Years Among Patients Eligible for HCV Treatment Within the Veterans Health Administration Birth Cohorta.

Association Between Alcohol Use and Receipt of DAA Treatment, 2015-2017

Discussion

Strengths and Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases

Table 2. Adjusted Hazard Ratios for Receipt of DAA Treatment Within 1 and 3 Years Among Patients Eligible for HCV Treatment Within the Veterans Health Administration Birth Cohort^a.