Abstract
Barriers remain in the hepatitis C virus (HCV) cascade of care (CoC), limiting the overall impact of direct acting antivirals. This study examines movement between the stages of the HCV CoC and identifies reasons why patients and specific patient populations fail to advance through care in a real world population. We performed a single-center, ambispective cohort study of patients receiving care in an outpatient infectious diseases clinic between October 2015 and September 2016. Patients were followed from treatment referral through sustained virologic response. Univariate and multivariate analyses were performed to identify factors related to completion of each step of the CoC. Of 187 patients meeting inclusion criteria, 120 (64%) completed an evaluation for HCV treatment, 119 (64%) were prescribed treatment, 114 (61%) were approved for treatment, 113 (60%) initiated treatment, 107 (57%) completed treatment, and 100 (53%) achieved a sustained virologic response. In univariate and multivariate analyses, patients with Medicaid insurance were less likely to complete an evaluation and were less likely to be approved for treatment. Treatment completion and SVR rates are much improved from historical CoC reports. However, linkage to care following referral continues to be a formidable challenge for the HCV CoC in the DAA era. Ongoing efforts should focus on linkage to care to capitalize on DAA treatment advances and improving access for patients with Medicaid insurance.
Citation: Zuckerman A, Douglas A, Nwosu S, Choi L, Chastain C (2018) Increasing success and evolving barriers in the hepatitis C cascade of care during the direct acting antiviral era. PLoS ONE 13(6): e0199174. doi:10.1371/journal.pone.0199174
Editor: Yury E. Khudyakov, Centers for Disease Control and Prevention, UNITED STATES
Received: March 26, 2018; Accepted: June 2, 2018; Published: June 18, 2018
Copyright: © 2018 Zuckerman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper.
Funding: No grant or commercial funding is associated with this research initiative.
Competing interests: The authors have declared that no competing interests exist.
‡ These authors also contributed equally to this work.
Introduction
The impact of direct-acting antivirals (DAA) on the hepatitis C virus (HCV) epidemic hinges on multiple steps from diagnosis, to referral, to evaluation, and finally treatment. With pharmaceutical advances in the field of HCV resulting in high cure rates once DAA treatment is completed, an increased emphasis is now being placed on care delivery and strategies to eliminate HCV.[1, 2] While new therapies provide ideal tools, both historical (pre-DAA HCV treatment) and modern barriers to care must be identified and addressed to eliminate HCV.
The majority of data evaluating the HCV Cascade of Care (CoC) was observed prior to DAA treatment becoming the standard of care. One of the most widely cited estimates of the HCV CoC in the United States (US) reported that as of July 2013, among 3.5 million people estimated to have chronic HCV, only 16% were prescribed treatment and 9% achieved sustained virologic response (SVR).[3] The impacts of DAA therapy on evaluation, access, and treatment have brought new advances and challenges within the CoC.[4–6]
This study examines the HCV CoC in one real-world clinic in the DAA era following referral to an HCV provider as well as identify barriers to successful CoC completion. Previous studies of the CoC in the DAA era have highlighted difficulty linking patients to care after diagnosis in specific populations, challenges accessing costly treatment, and high SVR rates in patients completing DAA therapy.[7–9] However, many studies have not identified specific case rationale for why individuals do not advance through the CoC. In this study, we sought to identify reasons why patients and specific patient populations did not advance through care. These findings provide meaningful insights to the HCV CoC in the DAA era and can drive appropriate allocation of resources to improve the care continuum.
Methods
A single-center, ambispective cohort study of patients receiving care at the Vanderbilt University Medical Center (VUMC) Infectious Diseases (ID) Clinic was performed. Data was retrospectively collected from October 2015 to July 2016, and prospectively collected from August to September 2016. Patients are referred to the VUMC ID Clinic from local community providers, by self-referral, or through internal VUMC referrals, including patients seen in internal medicine clinics, screened through the emergency department, and those receiving human immunodeficiency virus (HIV) care at the Vanderbilt Comprehensive Care Clinic, Following a referral to the ID Clinic, an appointment is scheduled and a reminder letter is sent to the address listed in the electronic medical record (EMR) or provided by the referring provider.
The VUMC ID Clinic employs an integrated model of care for patients with HCV consisting of three physicians, one clinical pharmacist, one pharmacy technician, and one nurse. Within this program, the physician team provides clinical evaluation and assessment in preparation for DAA therapy. The pharmacist delivers comprehensive medication management, including an evaluation for regimen appropriateness, drug interaction screening and mitigation, patient education, and DAA monitoring. The pharmacist and pharmacy technician ensure ongoing access to DAA treatment from prescription to completion of prescribed therapy either through insurers or patient assistance programs (PAP).
Inclusion criteria were diagnosis of chronic HCV (ICD10 of B18.2) and a new referral to the clinic. Exclusion criteria included active hepatocellular carcinoma, cognitive impairment, life expectancy of less than 6 months, or patients who were in the midst of the CoC at the time of data analysis (September 2017). This study received approval and waiver of informed consent from the Vanderbilt University Institutional Review Board.
Outcomes and cascade of care definitions
The primary endpoint evaluated was sustained virologic response (SVR) at least 12 weeks after treatment completion. Secondary endpoints included achievement of each individual stage in the CoC as well as time to treatment approval after DAA prescription. For the purposes of this study, the CoC represented the progression from referral to the VUMC ID clinic through HCV evaluation, prescription, initiation, and completion of treatment, and achievement of a SVR at least 12 weeks after completing treatment. Definitions of each step within the CoC are found in Table 1.
ExpandTable 1. Cascade of care definitions.
doi:10.1371/journal.pone.0199174.t001
When individual patients did not proceed through the CoC, data was collected from the EMR on the reason(s) for lack of advancement. A patient was considered lost to follow-up if ≥ 5 attempts to contact the patient were made by phone as well as a letter sent to the patient’s most recent address with no response over at least three months. If a patient missed an initial appointment in the ID Clinic, one outbound call was made by pharmacist and a voicemail was left if there was an option to do so.
Data collection
Dates of clinic visits were obtained from the Epic scheduling system while all other outcomes were collected from the EMR. Patient characteristics assessed in the EMR prior to an evaluation included patient age, gender, ethnicity, insurer, and home zip code. When available in referring paperwork or the EMR, additional data points were collected including cirrhosis, active illicit substance use, HIV co-infection, insurance status, psychiatric history, and gender. Additional characteristics were confirmed at the time of evaluation, including: HCV genotype, HCV treatment history, fibrosis stage, HIV co-infection, history of and/or ongoing illicit substance or injection drug use (IDU), history of and/or ongoing alcohol abuse, and psychiatric disorder.
Medicaid recipients were identified as having Medicaid as a primary insurer for prescription coverage at the time of HCV prescription. Patients were considered co-infected with HIV if they were labelled with ICD10 code B20. Cirrhosis was defined as meeting any of the following criteria: anatomic ultrasound showing changes consistent with cirrhosis; ultrasound with acoustic radiation force impulse predicting F3-F4 or F4 fibrosis; FIB-4 score ≥3.25; Fibrosure of ≥0.72; or a liver biopsy with Metavir score F4. Diagnosed psychiatric disorder included patients labelled with an ICD10 including F01-F69 and F80-F99. Ongoing alcohol abuse was defined as >5 drinks on most days of the week as reported by the patient. Ongoing illicit substance or injection drug use (IDU) was defined as use within 3 months of evaluation as reported by the patient. The “baby boomer” age cohort was defined as any patient born between January 1, 1945 and December 31, 1965.
All patients were assessed for HCV treatment based on AASLD/IDSA Guidelines for the Treatment of Hepatitis C from the same group of providers working within the VUMC ID Clinic.
Statistical analysis
Categorical variables were described using number of patients and percentage, while continuous variables were described using, mean, median, standard deviation, and interquartile range. Demographic characteristics selected a priori included gender, ethnicity, insurance type, HIV co-infection, cirrhosis, psychiatric disorder, ongoing illicit substance use, and baby boomer age cohort. Outcomes were binary variables for retained status (indicating movement from one step in the cascade to the next), medication approval status, as well as the time to medication approval (measured in days).
To further investigate associations between patient demographic characteristics and outcome variables, analyses were performed using logistic regression for binary outcomes, a cox proportional hazard model for time to event (i.e., medication approval) outcomes, and multiple linear regression model for continuous outcomes initially fit without adjusting for covariates. This was followed by multivariable models that controlled for gender, insurance type, HIV co-infection, cirrhosis, psychiatric disorder, and illicit substance use. For the analysis of time to medication approval using a multiple linear regression model, logarithmic transformation was performed to reduce skewedness. To avoid case-wise deletion of records with missing covariates, we employed a multiple imputation method with 10 imputation samples using predictive mean matching. All statistical analyses were performed using the programming language R version 3.3.0.
Results
A total of 193 patients were referred to the VUMC ID Clinic for HCV infection that met inclusion criteria; six patients were actively progressing through the CoC and were excluded from this analysis. The majority were male (61%) and Caucasian (71%). Table 2 summarizes demographic information of those referred to clinic (n = 187) and those that completed HCV evaluation (n = 120). Most patients completing evaluation had genotype 1a infection (66%), were treatment naïve (90%), and did not have cirrhosis (77%).
ExpandTable 2. Baseline characteristics of patients referred and completing an evaluation.
doi:10.1371/journal.pone.0199174.t002
Of the 187 patients referred to the ID clinic, 120 patients (64%) completed evaluation for treatment, 119 (64%) were prescribed treatment, 114 (61%) were approved for treatment, 113 (60%) initiated treatment, 107 (57%) completed treatment, and 100 (53%) achieved an SVR. The largest drop between CoC stages occurred from referral to completing an evaluation (36%), including 51 (27%) patients who missed their scheduled appointment (i.e. were not linked to care) and 16 (9%) who never completed necessary work-up for treatment prescription and were lost to follow-up. One patient was not prescribed treatment due to other medical priorities. Of those that were prescribed treatment (n = 119), only five were never approved by insurance or through PAP (4%). Overall, 93% of all patients completing treatment achieved SVR; of those with available HCV RNA results at time of SVR evaluation (at least 12 weeks after treatment completion), 97% achieved SVR. The majority of patients that fell out of the CoC following an evaluation were lost to follow-up (9%). Reasons for lack of movement through the CoC at each stage are depicted in Fig 1.
ExpandFig 1. Cascade of care and reasons for lack of progression.
Of 187 patients meeting inclusion criteria, 120 (64%) completed an evaluation for HCV treatment, 119 (64%) were prescribed treatment, 114 (61%) were approved for treatment, 113 (60%) initiated treatment, 107 (57%) completed treatment, and 100 (53%) achieved a SVR. The largest lack of progression was seen from a referral to an evaluation with 51 patients never attending a scheduled clinic appointment. After an evaluation was completed, the most common reason for lack of progression was losing a patient to follow-up, defined as ≥5 attempts to contact the patient were made by phone as well as a letter sent to the patient’s most recent address with no response over at least three months.
doi:10.1371/journal.pone.0199174.g001
In both univariate and multivariable logistic regression models, gender and insurance type were significantly associated with completing a clinic evaluation. After controlling for other factors, male patients were approximately 3 times more likely to complete an evaluation when compared to female patients (OR = 3.13, 95% CI = 1.50 to 6.55, p = 0.002). Additionally, after controlling for other factors, the odds of completing an evaluation decreased by 79% (OR = 0.21, 95% CI = 0.10 to 0.45, p<0.001) in patients with Medicaid (Table 3).
ExpandTable 3. Characteristics associated with evaluation completion.
doi:10.1371/journal.pone.0199174.t003
Univariate analyses for investigating baseline patient characteristics and factors that may be associated with movement through the CoC found that patients with Medicaid were less likely to have treatment approved (p <0.001). No other baseline characteristics, including gender, HIV co-infection, cirrhosis, psychiatric disorder, and active illicit substance use were found to be significant when compared at any stage beyond evaluation within the CoC. All five patients never approved for treatment had Medicaid.
The median days to approval of treatment among patients who ultimately received treatment approval (n = 114) was five days (IQR 3–14). The time-to-event analysis indicated that insurance type and psychiatric disorder were important predictors associated with time to treatment approval after controlling for other factors. The rate in days to approval decreased by 73% in patients with Medicaid compared with non-Medicaid (HR = 0.27, 95% CI = 0.15 to 0.48, p<0.001), reflecting a longer time to treatment approval in this population (Fig 2). The median time to approval for patients with Medicaid was 30 days (SD 54 ± 73) compared to 4 days in non-Medicaid patients (SD 9±16). Conversely, the approval rate in days for patients with a psychiatric disorder increased relative to those without a psychiatric disorder, reflecting a shorter time to treatment approval in this population; however, this was not statistically significant (HR = 1.43, 95% CI = 0.95 to 2.16, p = 0.089). The multivariable linear regression analysis also showed that insurance type was associated with days to approval, indicating that patients with Medicaid had the geometric mean (GM) of 4.6 days longer time to approval when compared to non-Medicaid patients (GM = 4.6, 95% CI = 2.7 to 7.9, p<0.001).
ExpandFig 2. Time-to-approval analysis.
Insurance type was a significant predictor of the rate in days to approval of direct acting antiviral therapy. The rate in days to approval decreased by 73% in patients with Medicaid compared with non-Medicaid (HR = 0.27, 95% CI = 0.15 to 0.48, p<0.001), reflecting a longer time to treatment approval in this population.
doi:10.1371/journal.pone.0199174.g002