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Hepatitis C in the post-interferon era: selected essays in health economics
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1
Hepatitis C in The Post-Interferon Era: Selected Essays in Health Economics
a dissertation by
Justin J. McGinnis
Faculty of the USC Graduate School
in fulfillment of the requirements for the degree of
Doctor of Philosophy
in the subject of
Pharmaceutical Economics & Policy
University of Southern California
Los Angeles, California
August 8, 2017
2
Table of Contents
Chapter 1. Introduction ................................................................................................................... 4
Chapter 2: Comparative Treatment Effectiveness of Direct Acting Antiviral Regimens for
Hepatitis C: Data from the Veterans Administration ...................................................................... 7
Background & Objectives ........................................................................................................... 7
Materials & Methods .................................................................................................................. 8
Results ....................................................................................................................................... 10
Discussion ................................................................................................................................. 13
Conclusions ............................................................................................................................... 18
Tables & Figures ....................................................................................................................... 20
Chapter 3: Analysis of the Impact of HIV Co-Infection on Hepatitis C Treatment Effectiveness
Among Patients Treated with All-Oral Direct-Acting Antivirals ................................................. 25
Background & Objectives ......................................................................................................... 25
Materials & Methods ................................................................................................................ 26
Results ....................................................................................................................................... 28
Discussion ................................................................................................................................. 29
Conclusions ............................................................................................................................... 32
Tables & Figures ....................................................................................................................... 33
Chapter 4: An Analysis of the Cascade of Care Associated with Hepatitis C Patients Treated in
the Veterans Health Administration .............................................................................................. 39
Background & Objectives ......................................................................................................... 39
Materials & Methods ................................................................................................................ 40
3
Results ....................................................................................................................................... 42
Discussion ................................................................................................................................. 44
Conclusions ............................................................................................................................... 48
Tables & Figures ....................................................................................................................... 50
Chapter 5. Summary & Future Research Directions .................................................................... 58
Summary ................................................................................................................................... 58
Future Research Directions ....................................................................................................... 59
Acknowledgements ....................................................................................................................... 62
References ..................................................................................................................................... 63
4
Chapter 1. Introduction
Hepatitis C is a blood-borne infectious disease that primarily affects the liver. Although it can be
a short-term illness, for the majority (70-85%) of patients it becomes a long-term, chronic
infection [1]. Those with chronic hepatitis C infection are at risk for the development of
cirrhosis, hepatocellular carcinoma (HCC), liver transplantation, extrahepatic manifestations, and
increased mortality [2]. It is estimated that 5-20% of those chronically infected will develop
cirrhosis, and 1-5% will die from liver-related complications [1]. In 2011 it was estimated that
the total healthcare cost associated with hepatitis C infection in the United States was $6.5 ($4.3-
$8.2) billion, and is expected to increase to $9.1 ($6.4-$13.3) billion by 2024 [3].
Prior to 2013, treatments for hepatitis C were interferon-based, which required weekly injections
up to 48 weeks in duration, had toxic side effect profiles, and achieved only modest rates of
sustained virologic response (SVR12) [4-6]. However, at the end of 2013 the first all-oral direct-
acting antiviral (DAA) regimen was approved, revolutionizing the treatment of hepatitis C [7].
All-oral DAAs have effectively reduced treatment duration (8-24 weeks), improved side effect
profiles, and significantly increased treatment efficacy. Currently, there are several all-oral DAA
combinations approved for the treatment of hepatitis C including sofosbuvir/simeprevir,
sofosbuvir/ledipasvir (Harvoni), ombitasvir/paritaprevir/ritonavir/dasabuvir (Viekira Pak),
sofosbuvir/daclatasvir, elbasvir/grazoprevir (Zepatier), and most recently the pangenotypic
regimen velpatasvir/sofosbuvir (Epclusa). These new all-oral DAA regimens have demonstrated
efficacy in the 80-100% range in their respective clinical trials [8-14].
Given how recent and rapid innovation has occurred in the hepatitis C treatment space, evidence
documenting the performance and impact of the new all-oral DAA regimens in real-world
5
settings is limited. Clinicians, health-systems, and policy makers rely on real-world evidence to
tailor treatment decisions and policies to their specific populations, and thus updated analyses are
need to inform the various healthcare stakeholders.
The studies presented in this dissertation utilize real-world data to address three important areas
of unmet research needs in the all-oral DAA era. The data used for all three studies are from the
Veterans Health Administration (VHA) Corporate Data Warehouse. The Corporate Data
Warehouse is a national database that contains the electronic health records of all patients treated
at VHA facilities across the country. The data sample included all patients with a documented
hepatitis C diagnosis. The major data domains utilized include demographics, diagnoses,
laboratory, and pharmacy.
Previous interferon-based treatments performed notably worse in real-world practice, than what
was demonstrated in their clinical trials. Consequently, there is some uncertainty as to whether
the new all-oral DAA regimens will perform as well in real-world settings as they did in clinical
trials. To inform this uncertainty, the first study presented in this dissertation evaluates the real-
world treatment effectiveness of three of the most widely prescribed all-oral DAA regimens, and
assesses the impact of various patient, disease, and treatment characteristics on treatment
effectiveness.
The second study presented focuses on an important subpopulation of hepatitis C patients, which
are those patients coinfected with HIV. Previous interferon-based treatments for hepatitis C had
limited use in coinfected populations due to the complex dosing regimens, poor tolerability, and
drug-to-drug interactions. Moreover, patients coinfected with HIV are at risk of increased rates
6
of liver fibrosis, cirrhosis, hepatocellular carcinoma, and mortality [15]. Thus, it is of particular
importance to successfully treat hepatitis C patients coinfected with HIV. The second study
presented in this dissertation evaluates the real-world treatment effectiveness of all-oral DAA
regimens among HIV coinfected patients, and explores the impact of the HIV coinfection on
treatment effectiveness.
Finally, given the significant differences in mode of administration, tolerability, and efficacy
between the previous interferon-based regimens and new all-oral DAA regimens, it is likely that
treatment uptake, barriers, and outcomes in the VHA have been impacted by the introduction of
all-oral DAAs. With several all-oral DAAs now available in the VHA, it is of interest to
document the performance of the hepatitis C care delivery system to identify potential gaps in
care and develop future strategies to optimize the care delivery system going forward. As such,
the third and final study presented in this dissertation analyzes the performance of the overall
hepatitis C care delivery system in the VHA during the all-oral DAA era.
7
Chapter 2: Comparative Treatment Effectiveness of Direct Acting Antiviral
Regimens for Hepatitis C: Data from the Veterans Administration
Full citation: Fox DS, McGinnis JJ, Tonnu-Mihara I, and McCombs JS. "Comparative Treatment Effectiveness of
Direct Acting Antiviral Regimens for Hepatitis C: Data from the Veterans Administration." Journal of
Gastroenterology and Hepatology. (2016)
Background & Objectives
Chronic infection with hepatitis C virus (HCV) in the United States has caused more than 19,000
deaths in 2014 alone [16]. Complications from HCV infection include cirrhosis, decompensated
cirrhosis, hepatocellular carcinoma (HCC), and liver transplantation [2, 17-20]. In the United
States, approximately 2.7 million persons have been estimated to have HCV infection, or
approximately 1% of the noninstitutionalized civilian population [21]. Among U.S. Veterans,
prevalence of HCV significantly exceeds that of the general population, and has been estimated
to be 5.4% [22]. Multiple highly effective direct acting antiviral (DAA) regimens have been
developed to treat HCV infection with three of the most widely prescribed being
simeprevir/sofosbuvir (SIM/SOF), ledipasvir/sofosbuvir (LDV/SOF), and
ombitasvir/paritaprevir/ritonavir/dasabuvir (3D). All three regimens have demonstrated high
rates of efficacy in clinical trials, achieving a sustained virologic response 12-weeks post-
treatment (SVR12) in 80-100% of patients. In the phase II COSMOS study, SIM/SOF±RBV was
randomly assigned to genotype 1 HCV patients with varying degrees of disease severity and
achieved SVR12 in 90-94% of treated patients [23]. Similarly, the efficacy of LDV/SOF±RBV
has been demonstrated in various clinical trials, most notably the phase III ION studies, which
yielded SVR12 in 82-100% patients [10, 24, 25]. Finally, in the SAPPHIRE and TURQUOISE
studies 3D±RBV achieved SVR12 in 87-100% of patients depending on disease severity,
treatment history, and treatment duration [11, 26, 27].
8
Although the efficacy of these regimens has proven impressively high in controlled studies,
research documenting their effectiveness in large real-world practices is only starting to emerge.
The earliest of the studies focused on SIM+SOF±RBV, the earliest all-oral DAA combination in
the market [28, 29]. Backus et al.
compared SVR12 rates for LDV/SOF±RBV and 3D±RBV for
genotype 1 patients treated within the Veterans Health Administration [30, 31]. Finally,
Ioannou, et al. compared SVR12 rates across a sample of VA patients treated with SOF,
LDV/SOF±RBV or 3D±RBV [32]. All of these studies have found that the new DAAs are
highly effective at achieving SVR12 but have been limited in scope or imputed values for
missing clinical data, including the patient’s HCV genotype and/or a final SVR12.
This study evaluates the real-world treatment effectiveness of these three most widely prescribed
DAA combination regimens used to treat HCV patients within the VHA using an intent-to-treat
analysis which included both initial and secondary treatment episodes. We control for the impact
of various patient, disease, and treatment characteristics on treatment effectiveness when
comparing effectiveness across the regimens.
Materials & Methods
Data used in this retrospective cohort study was from the Veterans Health Administration’s
Corporate Data Warehouse, and included nationwide patient demographics, disease diagnoses
(inpatient, outpatient, problem lists), pharmacy, and laboratory data. We included all HCV
infected patients who initiated treatment on any of the three study regimens, and stopped
treatment prior to July 1, 2015. This cutoff date allowed for sufficient post-treatment follow-up
to document treatment outcome, as measured by a sustained virologic response at 12 weeks post-
9
treatment. Potential study patients were then screened for availability of post-treatment HCV
viral load labs. Given the heterogeneity of viral load lab types in the VHA data, and result
reporting behavior, significant care was taken to accurately interpret viral load lab results. Viral
load lab results were defined as “not detected” if the result was reported either qualitatively as
“not detected”, or quantitatively as below that lab’s lower limit of quantification. Conversely,
viral load lab results were defined as “detected” if they were either reported qualitatively as
“detected”, or quantitatively as above that lab’s lower limit of quantification. Inconsistent results
were adjudicated manually. Finally, a patient was defined as having achieved SVR12 if all
available post-treatment viral load lab results were defined as “not detected”, with at least one
viral load lab tests occurring 12 or more weeks after the end-of-treatment. Patients with any
“detected” lab results post-treatment were classified as treatment failures. Patients with no post-
treatment viral load labs available were excluded from the analysis, as were patients who had
post-treatment viral load labs all with results of “not detected” but not having one occur 12 or
more weeks after end-of-treatment.
Each patient’s treatment initiation date was defined as their earliest recorded DAA prescription
release date, and end-of-treatment was calculated as the last recorded prescription release date
plus the days of supply released in the last recorded prescription. Patients who initiated treatment
on more than one of the three study regimens on the same date were excluded; however, patients
who were treated with more than one regimen sequentially were identified and included in the
SVR12 analysis. Treatment duration was defined as the sum of all days’ supply released across
the entire treatment episode.
10
Baseline control variables collected included demographics, disease severity, comorbidities,
previous treatment status, and ribavirin use. Disease severity measures included each of the
following diagnoses: cirrhosis, decompensated cirrhosis, hepatocellular carcinoma, and/or
history of liver transplantation prior to DAA treatment. Baseline FIB4 fibrosis score was also
used as a secondary measure of disease severity. Comorbidities included HIV, hepatitis B,
diabetes and obesity.
Logistic regression analysis was used to explore the impact of the control variables on the
likelihood of achieving SVR12, as well as to compare the likelihood of achieving SVR12 across
DAA regimens. Statistical analyses were carried out in SAS 9.2 [SAS Corporation, Cary N.C.].
This study was approved by the Institutional Review Board of the Veterans Administration
Healthcare System at Long Beach, California.
Results
In total, 3,549 SIM+SOF±RBV patients, 7,952 LDV/SOF±RBV patients, and 1,579 3D±RBV
patients were identified as having ended treatment before July 1, 2015 [Table 1]. A high
proportion of these patients had adequate follow-up laboratory data to determine SVR12: 92%
of SIM+SOF±RBV, 86% of LDV/SOF±RBV, and 88% of 3D±RBV patients. An additional six
patients who initiated treatment on two different regimens on the same date were excluded from
the analysis. The final study population was comprised of 3,263 SIM+SOF±RBV, 6,816
LDV/SOF±RBV, and 1,385 3D±RBV patients. This included patients who were treated,
sequentially, with more than one of the three DAA study regimens. For example, 398 patients
had a SIM+SOF±RBV episode followed by LDV/SOF±RBV. All initial treatment episodes for
11
these sequential DAA patients were defined as treatment failures. Their second episode of DAA
treatment was entered into the analysis if sufficient data were available to determine SVR12.
The distribution of treatment duration varied significantly across regimens. Over 70% of
SIM+SOF±RBV episodes were 12 weeks in duration, with an additional 17% being longer than
12 weeks. For LDV/SOF±RBV, approximately 61% of episodes were 12 weeks in duration and
nearly 26% were 8 weeks which may be indicated duration for certain patients [12].
Approximately 80% of 3D±RBV patients were treated for 12 weeks, while approximately 18%
completed less than 12 weeks of treatment.
Baseline patient characteristics are presented in Table 2. Several factors are consistent across
regimens, and reflective of the general VHA HCV population. For example, most patients were
male [96%] and over the age of 60 [64%]. Approximately 55% of the population was white,
35% black, and the remainder were of other or unknown race. Genotype 1 was the predominant
genotype, representing approximately 97% of all infections with a recorded genotype. Some
characteristics varied across DAA regimens, potentially reflecting triage of treatment to the most
severely ill patients, and the available VHA national guidelines provided to VA clinicians with
respect to the emergence of new evidence and the potential cost savings to the VA with drug
price negotiations. Specifically, patients treated with SIM+SOF±RBV exhibited significantly
higher rates of cirrhosis, decompensated cirrhosis, HCC and history of a liver transplant at the
time of treatment, compared with patients treated using the other two study regimens. Baseline
FIB4 showed a similar pattern, as did the rates of prior HCV treatment experience.
12
Table 3 presents data on the unadjusted SVR12 rates, categorized by patient characteristics and
regimen. Overall unadjusted SVR12 rates were 83.2% for SIM+SOF±RBV, 91.6% for
LDV/SOF±RBV, and 85.7% for 3D±RBV. Among patients who completed 12 weeks of
treatment the SVR12 rates were 85.8%, 93.0%, and 96.5%, respectively. The SVR12 rate for
those treated with 8 weeks of LDV/SOF±RBV was 93.0%.
For DAA regimens that included ribavirin, rates of SVR12 were 85.3% for SIM+SOF±RBV,
89.3% for LDV/SOF±RBV, and 84.2% for 3D±RBV. The SVR12 rates for patients treated
without ribavirin were 82.8%, 92.4%, and 90.2%, respectively. SVR12 rates were lower for
patients with cirrhosis, decompensated cirrhosis and HCC and, correspondingly, were also
significantly lower among those with a FIB4 score greater than 3.25 (78.2%, 87.6%, and 82.7%,
respectively).
Multivariable logistic regression results are reported in Table 4. They estimate the impact of
various patient, disease, and treatment characteristics on the likelihood of achieving SVR12. The
primary findings were:
1. Patients treated with LDV/SOFRBV (96.2%) and SIM+SOF±RBV (93.3%) were
significantly more likely to achieve SVR12 than patients treated with 3D±RBV (91.8%),
after controlling for other variables in the analysis by holding them at their reference
group levels.
2. Females (vs. males), and those with a history of liver transplantation were more likely to
achieve SVR12, while those who were less than 60 years of age (vs. 65+), black (vs.
white), non-genotype 1 (vs. genotype 1), or obese were less likely to achieve SVR12.
13
3. Cirrhosis, decompensated cirrhosis, HCC, and having a FIB4 score > 3.25 were
associated with a significantly lower likelihood of achieving SVR12.
4. Notable factors that did not significantly impact the likelihood of achieving SVR12 were:
coinfection with HIV, hepatitis B, or diabetes, as well as ribavirin use, and previous
treatment status.
Discussion
Our study augments the findings reported in previous studies that used data from the VHA [30,
31, 33] by applying clinically relevant differences in research methods. First, we include
SIM+SOF±RBV as one of our three treatment alternatives. While this regimen is not often used
today in the VHA system, this regimen is used in other settings which may face different pricing
than the VHA or have implemented different treatment guidelines. Second, we include patients
with multiple DAA episodes, and analyze all their episodes where sufficient data were available
to determine SVR12. Third, we do not impute values for missing data such as genotype or
SVR12 for patients with insufficient data.
This analysis of the three all-oral DAA regimens used by the VHA found overall unadjusted
SVR12 rates between 83% and 92%. These rates are lower than rates reported by other
researchers using the VHA data [30-32] for two reasons. First, we include patients who switched
therapy and imposed an assumption that the initial treatment episode was a treatment failure.
Ioannou, et al. argue that early switching could be related to factors other than drug
effectiveness, and dropped these patients from their analysis [32]. Second, we applied a strict
interpretation of SVR12 which required a confirmed undetectable viral load 12 or more weeks
14
post-treatment. Ioannou et al. relaxed this requirement and assumed that patients with
undetectable viral load tests after end of treatment but no test occurring beyond 12 weeks post-
treatment achieved SVR12 [32]. While concordance between SVR12 and SVR24 is quite high,
concordance between SVR4 and SVR12 appears somewhat lower: 78-98% (even in controlled
trials) [34-38]. We maintained the more conservative clinical trial standard of requiring
confirmed SVR12. The limited fraction of patients who lacked confirmed SVR12 (or a
confirmed failure sooner) were excluded from analysis, rather than imputed to be treatment
successes, since including them would inflate the observed SVR12 rate by some modest but
unknown percentage. Regardless of those differences in the methods and assumptions, these
studies using VHA data found real-world SVR12 rates for DAA therapy which approach those
found in clinical trials.
However, the statistically significant differences between SVR12 rates estimated here may not
be clinically significant. It is also no surprise that these observed rates are lower than those found
in controlled trials, most likely due to a less selected patient population, and lower treatment
adherence. Furthermore, while the VHA HCV population skews more male and less ethnically
diverse than the general U.S. HCV population, our results for non-white, non-black patients did
not differ radically from those for black patients. The very strong statistical difference in SVR12
for female patients suggests that it may also be clinically significant, albeit based on a total
sample of 405 female patients. Patients with specifically identified genotypes other than
genotype 1 represented a smaller proportion than in other published non-VHA studies,
prompting the decision to pool those patients rather than attempt a statistically suspect regression
on small sub-groups [28, 32]. We did not deem it reliable to impute genotype based on regimen.
However, it was clearly necessary to use “non-genotype 1” as a control factor, given that VHA
15
clinician’s choice of DAA was not evenly distributed across genotype and treatment failures in
non-genotype 1 patients could be due to inappropriate regimen selection. In general, the VHA
population spans the spectrum of comorbidities and the regression analysis results do allow the
SVR12 estimates to be adjusted for specific comorbidities prevalent in non-VHA populations.
This study estimated SVR12 for genotype 1 patients as: 83.3% for SIM+SOF±RBV; 85.8% for
3D±RBV; and 92.3% for LDV/SOF±RBV. These results are in line with other studies that used
VHA data. Backus et al. reported overall SVR12 rates for genotype 1 patients of 90.0-92.0% for
LDV/SOF±RBV, and 85.8-95.1% for 3D±RBV [30, 31]. Ioannou et al. reported an SVR12 rate
of 92.7% for LDV/SOF±RBV and 93.8% for 3D±RBV[32]. The apparent discrepancy between
our 3D SVR12 rate and that of Ioannou probably lies in our analytic approach, which assumed
switching to a second DAA regimen constituted treatment failure for the initial regimen; this
occurred in a significant proportion of 3D patients (93/1,385 = 6.7%). While some of those
switches may have been for financial reasons, others were likely ITT treatment failures, either
due to failure to suppress viral replication, or intolerable side effects. Finally, our study is the
only large study to date that evaluates the effectiveness of SIM+SOF±RBV for genotype 1
patients alongside of LDV/SOF±RBV and 3D±RBV in a real-world setting.
Since the unadjusted SVR12 rates reflected differences in average patient comorbidities between
treatment regimens, we also report adjusted SVR12 rates. These can be calculated by inverting
the regression coefficients to generate the estimated absolute SVR12 probabilities for patients
with specific comorbidities levels. We used the reference baseline comorbidities (e.g., male, >65
years, no cirrhosis, etc.), as noted in Table 4 for our adjustments.
16
There were 398 patients who had a SIM+SOF±RBV treatment episode followed by an episode of
LDV/SOF±RBV. There were also 93 patients who had a 3D±RBV episode followed by
LDV/SOF±RBV. The reason(s) why these patients switched therapies is unknown. It is doubtful
that the second episode was initiated due to re-infection, given the short interval between
treatment episodes for the majority of patients. It is conceivable that some SIM+SOF±RBV
patients were switched to LDV/SOF±RBV to reduce cost – in which case defining these patients
as treatment failures would bias downward the reported SVR12 rate for SIM+SOF±RBV.
Conversely, restricting the analysis to single DAA episode patients upwardly biases SVR12 rates
- in this study that would result in notably higher SVR12 rates of 94.9% for SIM/SOF±RBV,
92.4% for LDV/SOF±RBV, and 91.9% for 3D±RBV. Our results thus allow the reader to better
interpret the SVR12 rates reported in prior studies [28-32], some of which specifically excluded
multiple regimen patients from analysis.
The reader should also note that both SIM-SOF and 3D are now contraindicated for patients with
decompensated cirrhosis. Our analysis included patients with decompensated cirrhosis treated
under those regimens prior to those indication changes.
Treatment with SIM+SOF±RBV appears to have been prioritized to sicker patients, as evidenced
by their higher rates of cirrhosis, decompensated cirrhosis and HCC [Table 2]. However, our
results also demonstrate that the likelihood of achieving SVR12 is inversely related liver-related
illness severity of illness. Those findings are consistent with our pre-DAA research using VHA
data which demonstrated that achieving viral load suppression after significant fibrosis has
occurred reduced the effectiveness of even successful treatment in reducing the risks of future
liver complications.
17
Limitations
This study was a retrospective cohort analysis, and thus has several potential limitations. The
selection of which patients received treatment, and the specific DAA regimen used, was at the
discretion of individual VHA physicians, subject to VHA policy. However, we controlled for any
differences in observed patient characteristics across the three alternative DAA regimens using
well established analytic methods, made conservative assumptions regarding missing data, and
also adjusted for observable potential risk factors. Post-treatment follow-up viral load lab data
was also incomplete, mostly due to limited time following the last recorded DAA prescription;
patients with a SVR at 4 to 11 weeks post-treatment often failed to re-visit the clinic for their
final viral load test. Nonetheless, more than 86% of patients in each group who initiated
treatment during the study period had sufficient laboratory data to identify the treatment
outcome.
A significant proportion of patients switched to a second regimen, usually before completing a
full course of the first regimen. The cause for these switches was not readily ascertainable. While
it may have been due to cost or other non-clinical considerations, we took the conservative
approach consistent with an intention to treat analysis and assumed that switching was due to
treatment failure. Other published analyses have either ignored the second treatment attempt for
patients switching regimens or excluded switchers from analysis altogether [30-32]. Data on
some relevant risk factors were incomplete (e.g., HCV genotype, and patient race). Our
regression analysis results found these factors, when known, were significantly correlated with
the likelihood of the patient achieving SVR12. However, given the frequency of incomplete data,
caution should be used when interpreting these results. For similar reasons, and to promote
18
appropriate parsimony, we also did not separately analyze second order cofactor interactions, for
example between the influence of cirrhosis and ribavirin use.
The definition of SVR12 used in this study conforms with the standard definition of a “cure‟ by
requiring all post-treatment viral load tests to be negative, including at least one performed 12 or
more weeks post-treatment. Patients with all negative post-treatment viral load tests, but without
a viral load at or beyond 12 weeks post-treatment (741 in total) were excluded from the analysis.
Conversely, Ioannou, et al. imputed a final SVR12 for patients with missing data based on a
confirmed SVR at 4 or more weeks which likely overstates the true SVR12 rate [32]. Both
approaches violate the strictest definition of an intention to treat analysis. Nevertheless, both
approaches report similar cure rates for the new DAA combination therapies. Excluding the
additional 861 patients with NO post treatment viral load is more problematic. Including those
patients as presumed treatment failures could be done, but would significantly depress observed
SVR12 rates. However, since many of those patients likely lacked VL follow-up labs due to
insufficient elapsed time, not treatment failure, this approach seems unreasonably pessimistic. It
seems plausible that many patients simply fail to return for scheduled testing once an SVR4 is
achieved.
Finally, there are other potential confounding risk factors not captured in our analysis including
the use of proton pump inhibitors, baseline viral load, and the presence of resistance associated
variants of HCV. Additional research into such factors is warranted.
Conclusions
19
Among genotype 1 patients, all three study regimens worked well in real world practice,
achieving SVR12 rates comparable to those observed in pre-approval randomized clinical trials.
The LDV/SOF±RBV regimen appears to have performed best, after adjusting for severity and
other risk factors. Some potential risk factors, including diabetes and concurrent HIV or HBV
infection, did not prove to have an impact on the likelihood of achieving SVR12. Conversely,
obesity, cirrhosis, decompensated cirrhosis, and HCC were found to significantly impact the
likelihood of achieving SVR12. The FIB4 results point to a similar conclusion: that initiating
treatment after significant fibrosis has occurred is associated with a lower likelihood of achieving
SVR12. Prior research also suggests that achieving viral load suppression later is associated with
significant progression to end stage liver disease and death [39]. Therefore, physicians should
not wait until HCV complications arise to treat patients’ infections [40]. It is not surprising that
other health economic studies project that treating all HCV+ patients is cost effective;
reinforcing the advantages of at least treating patients with an elevated FIB4 [41]. Conversely,
immediately treating all HCV+ patients would consume nearly the entire annual prescription
drug spending in the U.S., while ethical concerns dictate that the most severely ill patients are
treated first. The implication is that society should establish clinical parameters for HCV
treatment that allocate scare resources first to advanced HCV patients, but then adjust guidelines
over time to also treat less severely ill patients, and monitor completely asymptomatic patients
for any progression of the disease. Expanding treatment eligibility over time should prove
increasingly feasible as prices drop, especially following competition from newer, pan-genotypic
DAA regimens now entering the market.
20
Tables & Figures
Table 1. Selection of Treatment Episodes and Treatment Duration
† DAA = direct-acting antiviral, RBV = ribavirin, SIM = simeprevir, SOF = sofosbuvir, LDV = ledipasvir, 3D =
ombitasvir/paritaprevir/ritonavir+dasabuvir
Selection of Treatment Episodes SIM+SOF ±
RBV
LDV/SOF ±
RBV
3D ± RBV
Patient episodes terminating treatment
prior to July 1st, 2015
3549 7952 1579
Patient episodes with at least 1 post-
treatment viral load lab test
3405 7335 1479
Patient episodes with undetected viral
load post-treatment, but none occurring
≥ 12 weeks after treatment (excluded)
142 517 90
Patient episodes eligible for analysis 3263 6818 1389
Patients initiating episodes with 2
DAAs on the same date (excluded)
0 2 4
Total patient episodes included in the
analysis
3263 6816 1385
Patients with > 1 DAA Episode
SIM+SOF±RBV then LDV/SOF±RBV 398 116 -
LDV/SOF±RBV then SIM+SOF±RBV 2 29 -
SIM+SOF±RBV then 3D±RBV 4 - 1
3D±RBV then SIM+SOF±RBV 0 - 0
LDV/SOF±RBV then 3D±RBV - 12 2
3D±RBV then LDV/SOF±RBV - 17 93
SIM+SOF±RBV, LDV/SOF±RBV,
3D±RBV
1 2 1
Duration of Treatment N = 3263 N = 6816 N = 1385
< 8 weeks 124 (3.8%) 203 (3.0%) 152 (11.0%)
8 weeks 95 (2.9%) 1747 (25.6%) 52 (3.7%)
9 - 11 weeks 148 (4.5%) 131 (1.9%) 41 (3.0%)
12 weeks 2324 (71.2%) 4137 (60.7%) 1108 (80.0%)
> 12 weeks 572 (17.5%) 598 (8.8%) 32 (2.3%)
21
Table 2. Baseline Patient Characteristics
SIM+SOF ± RBV LDV/SOF ± RBV 3D ± RBV
N = 3263 N = 6816 N = 1385
(n) (%) (n) (%) (n) (%)
Male 3146 96.4% 6556 96.2% 1341 96.8%
Age
< 60 1179 36.1% 2431 35.7% 460 33.2%
60 - 64 1254 38.4% 2477 36.3% 522 37.7%
65+ 830 25.4% 1908 28.0% 403 29.1%
Race
Black 1040 31.9% 2342 34.4% 485 35.0%
White 1905 58.4% 3764 55.2% 770 55.6%
Other/Unknown 318 9.7% 710 10.4% 130 9.4%
Genotype
1 3146 96.4% 6324 92.8% 1355 97.8%
Other 50 1.5% 302 4.4% 13 1.0%
Unknown 67 2.1% 190 2.8% 17 1.2%
Disease Severity
Cirrhosis 2099 64.3% 2466 36.2% 434 31.3%
Decompensated 1167 35.8% 1299 19.1% 175 12.6%
HCC 286 8.8% 299 4.4% 28 2.0%
Liver Transplant 221 6.8% 221 3.2% 5 0.4%
FIB4
< 1.45 304 9.3% 1241 18.2% 287 20.7%
1.45 - 3.25 936 28.7% 2830 41.5% 635 45.8%
> 3.25 1708 52.3% 1957 28.7% 341 24.6%
Unknown 315 9.7% 788 11.6% 122 8.8%
Comorbidities
HIV 121 3.7% 343 5.0% 31 2.2%
HBV 328 10.1% 545 8.0% 107 7.7%
Diabetes 1298 39.8% 2245 32.9% 414 29.9%
Obesity 179 5.5% 347 5.1% 68 4.9%
Prior Treatment
Naïve 1950 59.8% 4771 70.0% 1025 74.0%
Experienced 1313 40.2% 2045 30.0% 360 26.0%
Ribavirin Use 517 15.8% 1726 25.3% 1047 75.6%
No 2746 84.2% 5090 74.7% 338 24.4%
Yes 517 15.8% 1726 25.3% 1047 75.6%
22
Table 3. Unadjusted SVR12 Rates
SIM+SOF ± RBV LDV/SOF ± RBV 3D ± RBV
N = 3263 N = 6816 N = 1385
(%) (n) (%) (n) (%) (n)
Overall 83.2% (2714/3263) 91.6% (6246/6816) 85.7% (1187/1385)
Gender
Female 91.5% (107/117) 94.6% (246/260) 95.5% (42/44)
Male 82.9% (2607/3146) 91.5% (6000/6556) 85.4% (1145/1341)
Age
< 60 80.8% (953/1179) 91.9% (2233/2431) 87.4% (402/460)
60 - 64 83.0% (1041/1254) 91.0% (2255/2477) 86.4% (451/522)
65+ 86.8% (720/830) 92.1% (1758/1908) 82.9% (334/403)
Race
Black 83.1% (864/1040) 91.6% (2146/2342) 83.7% (406/485)
White 83.3% (1587/1905) 92.0% (3462/3764) 87.3% (672/770)
Other/Unknown 82.7% (263/318) 89.9% (638/710) 83.9% (109/130)
Genotype
1 83.3% (2619/3146) 92.3% (5839/6324) 85.8% (1162/1355)
Other 78.0% (39/50) 75.2% (227/302) 76.9% (10/13)
Unknown 83.6% (56/67) 94.7% (180/190) 88.2% (15/17)
Disease Severity
Cirrhosis 79.1% (1660/2099) 89.0% (2192/2466) 85.0% (369/434)
Decompensated 76.4% (892/1167) 87.2% (1133/1299) 81.7% (143/175)
HCC 75.5% (216/286) 83.3% (249/299) 85.7% (24/28)
Liver Transplant 88.2% (195/221) 93.7% (207/221) 60.0% (3/5)
FIB4
< 1.45 94.4% (287/304) 93.9% (1165/1241) 88.5% (254/287)
1.45 - 3.25 90.0% (842/936) 93.3% (2641/2830) 86.5% (549/635)
> 3.25 78.2% (1335/1708) 87.6% (1714/1957) 82.7% (282/341)
Unknown 79.4% (250/315) 92.1% (726/788) 83.6% (102/122)
Comorbidities
HIV 84.3% (102/121) 90.7% (311/343) 87.1% (27/31)
HBV 84.2% (276/328) 90.6% (494/545) 82.2% (88/107)
Diabetes 83.4% (1083/1298) 90.8% (2039/2245) 83.8% (347/414)
Obesity 77.7% (139/179) 89.3% (310/347) 80.9% (55/68)
Prior Treatment
Naïve 84.9% (1656/1950) 91.8% (4378/4771) 86.2% (884/1025)
Experienced 80.6% (1058/1313) 91.3% (1868/2045) 84.2% (303/360)
Ribavirin Use
No 82.8% (2273/2746) 92.5% (4705/5090) 90.2% (305/338)
Yes 85.3% (441/517) 89.3% (1541/1726) 84.2% (882/1047)
23
Table 4. Logistic Regression Analysis of Likelihood of Achieving SVR12
Parameter Coefficient Odds Ratio Pr > Chi-
Square
Intercept 2.4149 - <0.0001
Treatment Type (ref. group: 3D ± RBV)
SIM+SOF ± RBV 0.2295 1.26 (1.01 - 1.56) 0.0373
LDV/SOF ± RBV 0.8235 2.28 (1.88 - 2.77) <0.0001
Gender (ref. group: male)
Female 0.7360 2.09 (1.39- 3.15) 0.0004
Age (ref. group: 65+)
< 60 -0.2196 0.80 (0.69 - 0.94) 0.0056
60 – 64 -0.1457 0.86 (0.74 - 1.01) 0.0591
Race (ref. group: white)
Black -0.1920 0.83 (0.72 - 0.94) 0.0047
Other/Unknown -0.1890 0.83 (0.68 - 1.01) 0.0576
Genotype (ref. group: 1)
Other -1.2616 0.28 (0.22 - 0.37) <0.0001
Unknown 0.2421 1.27 (0.82 - 1.98) 0.2829
Disease Severity
Cirrhosis -0.2359 0.79 (0.68 - 0.92) 0.0020
Decompensated -0.3596 0.70 (0.60 - 0.81) <0.0001
HCC -0.5115 0.60 (0.48 - 0.76) <0.0001
Liver Transplant 0.8391 2.31 (1.63 - 3.29) <0.0001
FIB4 (ref. group: < 1.45)
1.45 - 3.25 -0.1377 0.87 (0.70 - 1.08) 0.2091
> 3.25 -0.6694 0.51 (0.41 - 0.64) <0.0001
Unknown -0.4780 0.62 (0.48 - 0.80) 0.0003
Comorbidities
HIV 0.0154 1.01 (0.75 - 1.37) 0.9193
HBV -0.0200 0.98 (0.80 - 1.20) 0.8490
Diabetes -0.0121 0.99 (0.87 - 1.12) 0.8508
Obesity -0.2929 0.75 (0.59 - 0.95) 0.0171
Previous Treatment Status (ref. group:
naïve)
Experienced -0.1096 0.90 (0.79 - 1.02) 0.0903
Ribavirin Use (ref. group: no)
Yes 0.1083 1.11 (0.96 - 1.30) 0.1607
24
Table 5. Appendix/Supplementary Material
ICD9 Diagnosis ICD9 Procedure CPT4 Procedure
Hepatitis C 070.41, 070.44, 070.51,
070.54, 070.7, 070.71, or
V02.62
Cirrhosis 571.2, 571.5, 571.6
Decompensated
Cirrhosis
70.44, 70.71, 348.3x,
456.0, 456.1, 456.2x,
572.2, 572.3, 572.4,
572.8, 782.4, 789.59
42.91, 44.91, 54.9,
96.06
37140, 37160, 37180,
37181, 37182, 37183,
43204, 43205, 43243,
43244, 43400, 43401,
49080, 49081
HCC 155.x
Liver
Transplant
V42.7x, 50.5x V42.7x, 50.5x 47135, 47136
HIV 042.0, 042.1, 042.2,
042.9, 043.0, 043.1,
043.2, 043.3, 043.9,
044.0, 044.9
Hepatitis B 070.2x, 070.3x, V02.61
Diabetes 250.x
Obesity 278.0, 278.01
25
Chapter 3: Analysis of the Impact of HIV Co-Infection on Hepatitis C
Treatment Effectiveness Among Patients Treated with All-Oral Direct-Acting
Antivirals
Background & Objectives
An estimated 185 million individuals are infected with hepatitis C (HCV) worldwide, and
approximately 5 million are coinfected with human immunodeficiency virus (HIV) [42]. In the
United States, approximately one-third of all patients with HIV are coinfected with HCV [43].
HIV/HCV coinfection is associated with high rates of liver fibrosis, cirrhosis, hepatocellular
carcinoma, and mortality [15]. Coinfected patients who are cured of hepatitis C have been shown
to have improved clinical outcomes and survival [44].
Previous interferon-based treatments for hepatitis C had limited use in coinfected populations
due to complex dosing regimens, poor tolerability, and drug-to-drug interactions. Many patients
with HIV/HCV coinfection are ineligible for interferon-based hepatitis C treatments [45, 46].
However, new all-oral direct-acting antiviral (DAA) regimens have shown promise in
overcoming many of the barriers associated with interferon-based treatments. Indeed, clinical
trial studies of all-oral DAA regimens have reported rates of efficacy in excess of 90% among
coinfected patients, which are similar rates to those reported for hepatitis C monoinfected
patients [47, 48]. However, these clinical trials have been criticized for imposing inclusion
criteria that restrict study populations to primarily individuals with desirable patient and disease
characteristics (ie. non-cirrhotic patients with CD4 counts above 200 cells/mm
3
). To highlight
26
this point, a recent Canadian study by Saeed et al. compared clinical trial populations to a real
world coinfected population and found that only 5.9% to 9.8% of the real-world cohort would
have met clinical trial’s inclusion criteria for treatment eligibility [49]. Thus, there is concern that
the efficacy demonstrated in clinical trials may not be generalizable to real world populations,
where patient and disease characteristics are likely more heterogeneous.
Given the relatively recent introduction of the all-oral DAA regimens, and the uncertainty around
the generalizability of their clinical trial results, the documentation of real-world treatment
effectiveness of all-oral DAAs among large heterogeneous samples of coinfected patients is of
interest. The aim this study was to compare the real-world treatment effectiveness of all-oral
DAAs in coinfected patients vs. monoinfected patients, and analyze the impact of HIV
coinfection on the likelihood of achieving SVR12.
Materials & Methods
Data used for this study were from the Veterans Health Administration Corporate Data
Warehouse, and included demographics, diagnoses, pharmacy, and laboratory data. Eligible
study patients included patients with a documented hepatitis C ICD-9 code and who initiated
treatment on one of the four study regimens, and completed or stopped treatment prior to
November 1, 2016. This allowed for sufficient post-treatment follow-up to observe the primary
outcome, which was a sustained virologic response 12-weeks after treatment (SVR12). Eligible
study patients were then screened for availability of post-treatment HCV RNA viral load labs.
Viral load lab results were defined as either “detected” or “not detected”. Viral load lab results
were defined as “not detected” if the result was qualitatively reported as not detected, or
quantitatively as below the lower limit of quantification. Conversely, viral load lab results that
27
were reported as “detected”, or quantitatively as above the lower limit of quantification were
defined as “detected”. Finally, a patient was considered to have achieved SVR12 if all available
post-treatment viral load lab results were “not detected”, with at least one occurring 12 or more
weeks after end-of-treatment. Patients with any post-treatment lab defined as “detected” were
considered to have not achieved SVR12. Patients with no post-treatment viral load labs
available, or with all post-treatment viral load labs having results of “not detected” but not
having one occur at least 12-weeks after end-of-treatment, were excluded from the analysis.
Treatment episodes were created for each patient. Treatment initiation dates were defined as the
earliest recorded prescription release date, and end-of-treatment was calculated as the last
recorded prescription release date plus the remaining days’ supply released in the last recorded
prescription release. Treatment duration was defined as the sum of days’ supply released across
the entire treatment episode. Patients with more than one treatment episode were included in the
analysis, and the initial episodes were considered to be treatment failures.
Baseline control variables collected included demographics, disease characteristics, disease
severity, comorbidities, previous treatment status, and ribavirin use. Disease severity was
measured by the existence of a diagnosis of cirrhosis, decompensated cirrhosis, hepatocellular
carcinoma, and/or history of liver transplantation prior to DAA treatment. Baseline FIB4 was
also used as a secondary measure of disease severity. Comorbidities included hepatitis B,
diabetes and obesity. Patients were identified as having HIV coinfection if they had a
documented HIV ICD-9/ICD-10 code and either a positive HIV antibody lab, detectable HIV
viral load, or a prescription for a HIV antiretroviral at baseline. For patients who were coinfected
with HIV, baseline HIV viral load and CD4 counts were also collected.
28
Logistic regression analyses were used to explore the impact of HIV coinfection on HCV
treatment effectiveness, while controlling for other patient and disease characteristics. The
dependent variable in the regressions was SVR12, and independent variables in the model
included age, gender, genotype, liver disease severity, comorbidities, and HIV disease
characteristics. The independent variables of interest were HIV coinfection status, baseline HIV
viral load and CD4 count. Statistical analyses were carried out in SAS 9.2.
Results
The eligible study sample included 54,062 patient episodes. Of these, 44,945 (83%) had
adequate post-treatment viral load labs to determine treatment outcome, and thus included in the
study (Table 1). A total of 1,689 (3.8%) patients in the study sample were coinfected with HIV.
Baseline characteristics by coinfection status are presented in Table 2. Baseline demographics
varied across the study sample, with a higher proportion of coinfected patients being under the
age of 60, and of black race. The distribution of hepatitis C genotype, and liver disease severity
profiles also varied across the two groups. A significantly higher proportion of coinfected
patients had hepatitis B. Among coinfected patients, the majority had an undetectable HIV viral
load (83%), and a CD4 count > 200 at baseline (92%), indicating well controlled HIV for the
majority of coinfected patients.
The overall rate of SVR12 found across the entire study sample was 91.4%. Rates of SVR12 by
regimen and coinfection status are presented in Table 3. Among coinfected patients, the overall
rate of SVR12 was 90.8%, which was not significantly different than the rate found among
29
monoinfected patients (p = 0.38). By treatment regimen, there were no statistically significant
differences in rates of SVR12 between coinfected and monoinfected patients.
Multivariable logistic regression analysis was used to evaluate the impact of HIV coinfection on
the likelihood of achieving SVR12, after controlling for baseline characteristics. The full results
of this model are presented in Table 4. After adjusting for demographics, genotype, liver disease
severity, comorbidtites, use of ribavirin, and prior treatment status, HIV coinfection did not have
a statistically significant impact on SVR12 (p = 0.13). A second regression analysis was
conducted on only the coinfected sample to further explore the impact of baseline HIV
characteristics on the likelihood of achieving SVR12 (Table 5). The results of this analysis found
that having a detectable HIV viral load at baseline had a negative, but not statistically significant,
impact on the likelihood of achieving SVR12 relative to having an undetectable HIV viral load
(p = 0.08). Having a baseline CD4 count less than 200 also did not significantly impact the
likelihood of achieving SVR12, relative to a CD4 count greater than or equal to 200 (p= 0.86).
Discussion
This retrospective cohort study analyzed the impact of HIV coinfection on all-oral DAA
treatment effectiveness among a large real-world sample. The overall rate of SVR12 found
among coinfected patients was 90.8%, which was not significantly different from the rate found
among monoinfected patients (91.4%). The rates of SVR12 found in this study are similar to
those reported in other recent real-world studies of coinfected patients [50, 51].
This study adds to the existing literature by assessing the impact of HIV coinfection in a large
real-world sample, and across four of the most widely prescribed all-oral DAA regimens.
30
Previously published real-world studies have generally had small sample sizes and/or focused on
specific regimens [50, 51]. Analyzing SVR12 by regimen also found no statistically significant
differences by between coinfected vs. monoinfected patients. Additionally, this study analyzed
the impact of HIV coinfection after controlling for baseline characteristics including
demographics, hepatitis C genotype, liver disease severity, and other comorbidities. After
adjusting for these baseline characteristics, HIV coinfection was still found to not have a
significant impact on the likelihood of achieving SVR12.
Finally, to explore the impact of baseline HIV characteristics on SVR12, we performed an
additional regression analysis restricting the sample to only coinfected patients. The results from
this analysis found that after controlling for other baseline characteristics, having a detectable
HIV viral load at baseline did not significantly impact the likelihood of achieving SVR12.
Similarly, having a baseline CD4 count less than 200 did not have a significant impact, relative
to a CD4 count of 200 or greater. Although we find that having a detectable HIV viral load, or
CD4 count less than 200 did not significantly impact the likelihood of achieving SVR12, these
factors should still be considered before initiating treatment on a hepatitis C treatment regimen
for other clinical reasons.
Recent guidelines for the treatment of hepatitis C among HIV coinfected patients issued by the
American Association for the Study of Liver Diseases/Infectious Diseases Society of America
(AASLD/IDSA) state that “HIV/HCV co-infected persons should be treated and retreated the
same as persons without HIV infection, after recognizing and managing interactions with
antiretroviral medications”[52]. Similarly, the European Association for the Study of the Liver
(EASL) state “the same INF-free treatment regimens can be used in HIV co-infected patients as
31
in patients without co-infection because virological results of therapy are identical”[53]. These
statements were likely based on results from clinical trial data, which have been criticized for
strict inclusion/exclusion criteria imposed on coinfected patients. Our study overcomes the
limitation of the lack of generalizability of clinical trial results by analyzing a large real-world
cohort. The results from our study seem to corroborate those of clinical trials, in that we do not
find a significant impact of HIV coinfection on all-oral DAA treatment effectiveness. As such,
this study lends empirical support to the statements made regarding HIV/HCV coinfected
patients in the updated treatment guidelines.
As with all retrospective analyses, this study is not without limitations. The decision to initiate
treatment patients was at the discretion of physicians and/or VHA treatment policies. Therefore,
it is possible that the patients in our sample are not representative of the overall coinfected
population in the VHA. It is plausible that treatment was initiated in those patients who were
generally less severely ill (ie. well controlled HIV). For example, although some coinfected
patients in our study sample were found to have a detectable HIV viral load at baseline, it is
possible that the viral load was detected but at a very low level. Nonetheless, our analysis
included coinfected patients with varying degrees of liver disease severity, including patients
with cirrhosis, decompensated cirrhosis, and hepatocellular carcinoma, which are patients that
were generally not represented in clinical trial studies.
Post-treatment viral load lab data was incomplete for some eligible study patients, which resulted
in them being excluded from the analysis. However, we do not suspect any underlying reason for
the incomplete post-treatment lab data, and thus believe them to be missing at random.
Additionally, for lab data pertaining to baseline characteristics (ie. CD4 count, baseline HIV viral
32
load) we looked back one year from treatment initiation and collected the most recent lab value.
Thus, lab values that occurred earlier in the baseline look-back period may not accurately reflect
the true value at treatment initiation.
There are also potential risk factors that were not captured in our analysis, for example
antiretroviral regimens, chronic kidney disease, and resistance associated variants/substitutions
which may impact the likelihood of achieving SVR12. Future research into such risk factors is
warranted.
Finally, the VHA patient population differs in various demographic and patient characteristics
from the general United States population. Nevertheless, given that the VHA is the largest
provider hepatitis C care in the United States, and that veterans are often underrepresented in
clinical trials[54], studies specific to the VHA population are of particular importance.
Conclusions
This study found that the overall rate of SVR12 among coinfected patients was not significantly
different than that found among monoinfected patients. After adjusting for baseline
characteristics, HIV coinfection did not have a statistically significant impact on the likelihood of
achieving SVR12. These results suggest that HIV/HCV coinfected patients no longer need to be
treated as a “special” population, and that previous barriers to successful hepatitis C treatment in
coinfected populations have largely been overcome.
33
Tables & Figures
Table 1. Selection of Study Sample
n %
Patient episodes completed/stopped prior to Nov 1, 2016 54,062 -
No post-treatment viral load labs 4,914 9%
Undetected viral load on post-treatment labs, but not one
occurring >= 12 weeks post-treatment
4,203 8%
Patient episodes included in the analysis 44,945 83%
34
Table 2. Baseline Characteristics
Coinfected Monoinfected
(N = 1,689) (N = 43,256)
# % # %
Total 1,689 - 43,256 -
Gender*
Male 1,661 98.3% 41,765 96.6%
Female 28 1.7% 1,491 3.4%
Age*
< 60 676 40.0% 13,679 31.6%
60 - 64 561 33.2% 15,817 36.6%
65+ 452 26.8% 13,760 31.8%
Race*
Black 1138 67.4% 16,313 37.7%
White 423 25.0% 22,709 52.5%
Other/Unknown 128 7.6% 4,234 9.8%
Genotype*
1 1,626 96.3% 40,887 94.5%
Other (2,3,4,5, multi) 49 2.9% 1,678 3.9%
Unknown 14 0.8% 691 1.6%
Liver Disease Severity*
Cirrhosis 326 19.3% 9,153 21.2%
Decompensated 115 6.8% 3,980 9.2%
HCC 50 3.0% 1,237 2.9%
Liver Transplant 6 0.4% 961 2.2%
FIB4*
< 1.45 261 15.5% 7,969 18.4%
1.45 - 3.25 823 48.7% 17,730 41.0%
> 3.25 435 25.8% 11,688 27.0%
Unknown 170 10.1% 5,869 13.6%
Comorbidities*
HBV 333 19.7% 3,605 8.3%
Diabetes 530 31.4% 14,974 34.6%
Obesity 339 20.1% 14,360 33.2%
All-Oral DAA Regimen*
SIM+SOF 101 6.0% 3,269 7.6%
LDV/SOF 1,423 84.3% 31,140 72.0%
3D 119 7.0% 6,824 15.8%
ELB/GRZ 46 2.7% 2,023 4.7%
35
Coadministered*
Ribavirin 336 19.9% 13,346 30.9%
Previous HCV Treatment
Naïve 1,362 80.6% 34,403 79.5%
Experienced 327 19.4% 8,853 20.5%
Baseline HIV VL
Detected 272 16.1% - -
Not Detected 1,393 82.5% - -
Unknown 24 1.4% - -
Baseline CD4
< 200 71 4.2% - -
200+ 1,550 91.8% - -
Unknown 68 4.0% - -
* p < 0.05
36
Table 3. Unadjusted SVR12 Rates
SIM+SOF LDV/SOF 3D ELB/GRZ Overall
Coinfected 83.2%
(84/101)
91.7%
(1305/1423)
84.9%
(101/119)
93.5%
(43/46)
90.8%
(1533/1689)
Monoinfected 83.1%
(2715/3269)
92.5%
(28813/31140)
89.7%
(6120/6824)
92.9%
(1879/2023)
91.4%
(39527/43256)
Overall 83.1%
(2799/3370)
92.5%
(30118/32563)
89.6%
(6221/6943)
92.9%
(1922/2069)
91.4%
(41060/44945)
37
Table 4. Regression Analysis of Likelihood of Achieving SVR12 (N = 44,495)
Parameter
Odds
Ratio
95% Confidence
Limits
Pr > Chi-
Square
HIV 0.87 (0.73 - 1.04) 0.133
HCV Treament Type (ref. group = LDV/SOF)
SIM+SOF 0.47 (0.43 - 0.52) <0.000
3D 0.67 (0.61 - 0.73) <0.000
ELB/GRZ 0.99 (0.83 - 1.18) 0.929
Gender (ref. group = male)
Female 1.59 (1.26- 2.00) <0.000
Age (reg. group = 65+)
<60 0.81 (0.75 - 0.89) <0.000
60 - 64 0.88 (0.81 - 0.95) 0.002
Race (ref. group = white)
Black 0.86 (0.80 - 0.93) <0.000
Other/Unknown 0.93 (0.83 - 1.04) 0.208
Genotype (ref. group = 1)
Other 0.35 (0.31 - 0.40) <0.000
Unknown 1.48 (1.07 - 2.03) 0.016
Disease Severity
Cirrhosis 0.89 (0.82 - 0.98) 0.014
Decompensated 0.62 (0.56 - 0.69) <0.000
Hepatocellular Carcinoma 0.32 (0.27 - 0.37) <0.000
Liver Transplant 1.22 (0.94 - 1.58) 0.141
FIB-4 (ref. group = < 1.45)
1.45 - 3.25 0.95 (0.85 - 1.06) 0.340
> 3.25 0.60 (0.54 - 0.68) <0.000
Unknown 0.83 (0.73 - 0.94) 0.004
Comorbidities
HBV 0.98 (0.87 - 1.10) 0.693
Diabetes 0.89 (0.83 - 0.96) 0.002
Obesity 0.89 (0.82 - 0.95) 0.002
Co-Administered (ref. group = no ribavirin)
Ribavirin 0.91 (0.84 - 0.99) 0.023
Prior Treatment (ref. group = naive)
Experienced 0.99 (0.92 - 1.08) 0.889
38
Table 5. Regression Analysis of Likelihood of Achieving SVR12 – Coinfected Only (N = 1,689)
Parameter
Odds
Ratio
95% Confidence
Limits
Pr > Chi-
Square
Baseline HIV VL (ref. group = undetectable)
Detected 0.69 (0.45 - 1.05) 0.082
Unknown 0.35 (0.11 - 1.15) 0.083
Baseline CD4 Count (ref. group 200+)
< 200 0.93 (0.39 - 2.19) 0.860
Unknown 1.92 (0.77 - 4.77) 0.161
HCV Treatment Type (ref. group = LDV/SOF)
SIM+SOF 0.62 (0.36 - 1.11) 0.088
3D 0.45 (0.25 - 0.85) 0.009
ELB/GRZ 1.44 (0.39 - 4.91) 0.568
Gender (ref. group = male)
Female 2.76 (0.43 - 16.56) 0.306
Age (reg. group = 65+)
<60 0.91 (0.60 - 1.47) 0.685
60 - 64 1.01 (0.69 - 1.66) 0.963
Race (ref. group = white)
Black 0.71 (0.47 - 1.14) 0.113
Other/Unknown 0.55 (0.29 - 1.12) 0.077
Genotype (ref. group = 1)
Other/Unknown 0.70 (0.32 - 1.62) 0.390
Disease Severity
Cirrhosis 1.19 (0.67 - 2.02) 0.500
Decompensated 0.49 (0.51 - 1.35) 0.017
Hepatocellular Carcinoma 0.22 (0.13 - 0.44) <0.000
Liver Transplant 0.61 (0.05 - 5.52) 0.671
FIB-4 (ref. group = < 1.45)
1.45 - 3.25 0.64 (0.35 - 1.23) 0.167
> 3.25 0.44 (0.21 - 0.80) 0.019
Unknown 0.43 (0.20 - 0.89) 0.025
Comorbidities
HBV 0.96 (0.64 - 1.50) 0.854
Diabetes 0.69 (0.47 - 0.99) 0.041
Obesity 1.53 (0.31 - 2.74) 0.080
Co-Administered (ref. group = no ribavirin)
Ribavirin 0.95 (0.59 - 1.49) 0.822
Prior Treatment (ref. group = naive)
Experienced 0.81 (0.53 - 1.19) 0.309
39
Chapter 4: An Analysis of the Cascade of Care Associated with Hepatitis C
Patients Treated in the Veterans Health Administration
Background & Objectives
The introduction of all-oral direct acting antiviral (DAA) regimens has significantly changed the
hepatitis C treatment landscape. Prior to their introduction, treatments were interferon-based and
required weekly injections for up to 48 weeks in duration, had poor side-effect profiles, and
achieved only modest rates of sustained virologic response (SVR) [55-59]. Because of these
clinical limitations, uptake of interferon-based treatments was low [60-63]. However, at the end
of 2013 the first all-oral DAA regimen was approved, ushering in a new era in the treatment of
hepatitis C. This new generation of all-oral DAAs have improved tolerability, reduced treatment
duration and have efficacy in the 90-100% range [9, 23, 64-67]. As a result of these significant
clinical advances, it is likely that treatment barriers, uptake, and outcomes associated with
hepatitis C have changed.
The Veterans Health Administration (VHA) is the largest provider of hepatitis C care in the U.S.,
with an estimated 233,898 patients with hepatitis C [68]. Prior to 2014, it was estimated that only
17% of these patients had been treated, and of those treated, only 41% achieved SVR [68]. With
several highly effective all-oral DAAs on the market, the VHA has made it a priority to treat the
large cohort of untreated patients. Indeed, the VHA was provided with $696 million in 2015 and
approximately one billion dollars in 2016 for the treatment of hepatitis C [69]. The latest
injection of funding allowed for the VHA to expand treatment to all patients, regardless of
disease severity. Given the high patient burden of hepatitis C, and the magnitude of resources
40
being allocated to treating patients, it is of interest to document and analyze the performance of
the overall hepatitis C care delivery system within the VHA.
A population health approach that is often used to assess the performance a care delivery system
is the “cascade of care” methodology, which specifies sequential steps in care associated with a
disease and estimates the number of patients in, and progressing to, each step along the care
continuum. This methodology has previously been used as a tool to inform health care decision
makers on the performance of the care delivery systems associated with HIV, HBV, and more
recently HCV [68, 70-73]. The cascade of care approach provides a way to analyze the
performance of the delivery system at specific steps along the care continuum, thus allowing for
the identification of potential gaps in care, which is necessary to optimizing future resource
utilization and patient outcomes. The primary objective of this study is to document and analyze
the cascade of care associated with hepatitis C patients in the VHA during the all-oral DAA era.
Materials & Methods
The data used for the study were from the Veterans Health Administration Corporate Data
Warehouse. Patients were eligible to be included in the study if they had a documented hepatitis
C ICD-9/ICD-10 at the time of the analysis, and their first documented positive hepatitis C
antibody occurred between January 1, 2014 and December 31, 2015. These dates were chosen so
as to identify patients whose hepatitis C infection first became apparent to the VHA during the
all-oral DAA era. Patients whose first documented positive antibody fell between these dates, but
who had an earlier documented detectable hepatitis C viral load, genotype, or ICD-9/ICD-10
were excluded. The patients included in the study were followed from the date of their first
positive hepatitis C antibody until December 31, 2016.
41
The cascade of care was defined using the following sequential steps: 1) positive antibody 2)
confirmatory testing 3) treatment initiation 4) post-treatment follow-up 5) sustained virologic
response (SVR12).
Positive Antibody
Positive antibody was defined as having a documented hepatitis C antibody lab with a qualitative
result of positive or reactive, or a signal-to-cutoff ratio greater than or equal to 1.
Confirmatory Testing
Confirmatory testing was defined as having a documented hepatitis C RNA test (quantitative,
qualitative, or genotype). Patients were considered to have a detectable hepatitis C viral load if
they had a documented genotype, or a hepatitis C RNA viral load test with a qualitative result of
“detected” or a quantitative result greater than the lower limit of quantification.
Treatment Initiation
Treatment initiation was defined as having a documented prescription release of any of the
following DAA regimens: sofsobuvir (SOF), simeprevir+sofosbuvir (SIM+SOF),
daclatasvir+sofosbuvir (DCL+SOF), ledipasvir/sofosbuvir (LDV/SOF),
ombitasvir/paritaprevir/ritonavir/dasabuvir (3D), elbasvir/grazoprevir (ELB/GRZ), or
velpatasvir/sofosbuvir (VEL/SOF).
Post-Treatment Follow-Up
42
Post-treatment follow-up was defined as having adequate post-treatment viral load lab(s) to
determine the treatment outcome, which was a sustained virologic response 12 weeks after
treatment.
Sustained Virologic Response (SVR12)
Sustained virologic response was defined as having an undetectable viral load on all available
post-treatment viral load labs, with at least one lab occurring 12 or more weeks after the end-of-
treatment.
Baseline patient characteristics were collected and included age, gender, race, liver disease
severity profile, and comorbidities. Time-to-event analysis was used to analyze the duration
between steps along the cascade of care, and Cox proportional hazard models were used to assess
the impact of baseline characteristics on various cascade steps (events).
Results
A total of 12,531 patients were identified as having their first documented positive antibody
occur between January 1, 2014 and December 31, 2015, with no previously documented hepatitis
C viral load, genotype, or ICD-9/ICD-10, and thus included in the study. The full results of the
cascade of care are presented in Figure 1. Of the 12,531 patients with a positive hepatitis C
antibody, 12,249 (98%) received a confirmatory lab. The median duration between
documentation of positive antibody and receiving a confirmatory lab was 0 days, and the mean
was 34 days (Figure 2).
43
Among the 12,249 patients who received a confirmatory test, 10,158 (83%) had confirmed
hepatitis C. The average age of patients with confirmed hepatitis C was 56 years old, and the
majority were male (95%) and of white race (62%). The predominant genotype was genotype 1
(78%), 311 (3%) patients had a diagnosis of cirrhosis or decompensated cirrhosis at baseline, and
1,538 (15%) had diabetes (Table 1).
Of the patients with confirmed hepatitis C, 5,464 (54%) had initiated treatment by the end of the
follow-up period. The median duration from confirmatory lab to treatment initiation was 487
days, and the mean was 512 days (Figure 3). The majority of patients (64%) were treated with
LDV/SOF (Table 2). There were 117 patients who had more than one treatment episode on
different DAA regimens.
Cox-proportional hazard analysis was used analyze the impact of baseline characteristics on
treatment initiation. Male gender, black, other/unknown race, genotype 3, and unknown
genotype were found to have a significant and negative effect on treatment initiation, while age,
and obesity were found to have a significant and positive effect. The results of the full model are
presented in Table 3.
For patients who initiated treatment, 3,325 (61%) had adequate post-treatment follow up labs to
determine treatment outcome, of which 94% achieved SVR12. By regimen, the rate of SVR12
was 91% (373/408) for SOF, 95% (61/64) for SIM+SOF, 95% (2039/2153) for LDV/SOF, 96%
(496/474) for 3D, 90% (65/72) for DCL+SOF, and 98% (93/95) for ELB/GRZ. Only one patient
in the study sample was treated with VEL/SOF and had adequate post-treatment viral load labs,
and they did not achieve SVR12. Cox-proportional hazard analysis found genotype 3, cirrhosis,
44
decompensated cirrhosis, and hepatocellular carcinoma to have a significant and negative effect
on achieving SVR12 (Table 4). “Treatment delay”, as measured by the number of days between
confirmation of hepatitis C and treatment initiation, also had a negative effect on achieving
SVR12, though the estimate was not statistically significant (p = 0.07).
Discussion
The clinical advances that have occurred in the hepatitis C treatment space in recent years have
significantly altered the treatment landscape. With several highly effectively all-oral DAAs now
available, many patients who were previously ineligible for treatment can now be treated.
Treatment duration of all-oral DAAs is significantly shorter than previous interferon-based
treatments, and tolerability is better, which has likely improved adherence and ultimately patient
outcomes. Given these changes, and the magnitude of resources being allocated to the treatment
of hepatitis C in the VHA, assessing the performance of the overall hepatitis C care delivery
system is of interest. This study analyzed the care delivery system by identifying patients who
had their first documented positive hepatitis C antibody occur during the all-oral DAA era, and
following them over time to observe how they progressed along the cascade of care.
Of the 12,531 patients included in the study, 98% received a confirmatory test. This important
step in care is essential to identifying those patients with active infection, versus those who have
previously cleared the virus or were a false positive. The rate of confirmatory testing found in
this study is in line with results from a previous study, which analyzed patients in the VHA up
until December 31, 2011 and found an overall confirmatory testing rate of 94.7%[54]. Thus, our
findings may indicate an improvement in confirmatory testing rates in the all-oral DAA era. Of
the patients who received a confirmatory test, 17% were found to not have active hepatitis C
45
infection. These are likely patients who had a false-positive antibody, spontaneously cleared the
virus, or potentially were previously treated outside of the VHA.
There were ultimately 10,158 patients with confirmed hepatitis C in this study. The
demographics of these patients were reflective of the general hepatitis C population in the VHA,
with the majority being white males between the ages of 50 and 70 years old. However, the
disease severity profile of our study sample is different than that of the general VHA hepatitis C
population. According to the 2014 State of Care for Veterans with Hepatitis C report, 17% of
hepatitis C patients have cirrhosis[74], whereas only 2% of our study sample had a diagnosis of
cirrhosis. This difference can be explained by the fact that our study includes only patients whose
first documented positive antibody lab occurred between 2014 and 2015, and therefore are likely
to be patients who were more recently infected, thus earlier in disease stage relative to the
general VHA hepatitis C population. For the same reason, our study sample had lower rates of
decompensated cirrhosis, hepatocellular carcinoma, and history of liver transplantation than the
general VHA hepatitis C population.
Approximately 54% of patients in our study sample initiated treatment by the end of the study
period. These findings show a significant increase in the rate of treatment initiation compared to
the rate reported in an earlier study, which found that as of December 31, 2013 only 17% of the
overall VHA hepatitis C population had initiated treatment[68]. Moreover, the treatment protocol
when all-oral DAAs first became available was generally to treat the more severely ill patients
(i.e. cirrhotics) first, and therefore the rate found in our study sample of relatively healthy
patients may be an underestimate of the overall treatment initiation rate in the VHA. The rate of
treatment initiation appeared to consistently increase over the study period (Figure 4), which is
46
likely due to the introduction of additional treatment regimens, increases in funding, and/or
increases in personnel. Nonetheless, with 46% of patients in the study sample having not
initiated treatment by the end of the study period, treatment uptake remains an area of
opportunity for the VHA, particularly as more treatment options and additional resources
become available.
Cox-proportional hazard analysis found genotype 3 to have a significant and negative impact on
treatment initiation. This is likely due to the limited availability of treatments indicated for this
genotype during the study period. As pangenotypic regimens become available, it is expected
that this effect will dissipate. Finally, the analysis also found evidence of racial disparities in
treatment uptake, with black and other/unknown race having a significant and negative effect on
treatment initiation. Although it is possible that race is picking up the effect of risk factors that
we did not capture in our analysis, the existence of a significant effect warrants further research
into this area.
Overall, we found that 61% of patients who initiated treatment had adequate post-treatment
follow-up labs to determine treatment outcome. Of the 2,139 patients that did not have adequate
post-treatment labs to determine SVR12, 1,422 (66%) had no post-treatment viral load lab
available, and 717 (34%) had post-treatment viral load labs indicating an undetectable viral load
but not one occurring 12 or more weeks’ post-treatment. Much of this lack of follow-up is likely
reflecting patients who initiated treatment late in the study period, and thus should not be
expected to have complete post-treatment follow-up. For example, restricting the sample to just
those patients who initiated treatment prior to January 1, 2016, the rate of complete post-
treatment follow-up is 85%. Additionally, in this study we use SVR12 as the measure of
47
treatment outcome, thus requiring patients to have a post-treatment lab occur 12 or more weeks’
post-treatment to be considered to have achieved SVR12. It is possible that in practice post-
treatment viral load labs are not scheduled exactly 12 or more weeks after treatment, but rather at
some earlier point in time post-treatment, in which case our estimate of post-treatment follow-up
may be an under estimate. Regardless of the measure utilized, ensuring complete post-treatment
follow-up is an essential step in the care delivery system, as it allows for the identification of
patients whose virus may have rebounded/relapsed after treatment completion.
Finally, this study found that 94% of patients with adequate post-treatment follow-up achieved
SVR12, which is in line with other recent studies of the real-world treatment effectiveness of all-
oral DAA regimens in VHA[31, 75, 76]. Our Cox regression analysis of SVR12 included a
measure of “treatment delay”, which was defined as the duration (in days) from confirmed
hepatitis C to treatment initiation, and found it to have a negative but not statistically significant
effect on SVR12. However, given the relatively short time period covered in this analysis, this
finding should be interpreted with caution. Additionally, the analysis found cirrhosis,
decompensated cirrhosis, and hepatocellular carcinoma to have a significant and negative impact
on SVR12, potentially lending evidence to the idea that patients should be treated earlier in the
disease stage to maximize outcomes.
As with all retrospective analyses, this study is not without limitation. The inclusion criteria
utilized in this study rely heavily on accurate identification of labs, lab results, and ICD-9/ICD-
10 codes, which have well known limitations. It is possible that we were not able to capture
every antibody lab and corresponding result, and thus there may have been some patients
included in the analysis whose first documented positive antibody was actually prior to 2014.
48
However, we attempted to minimize this possibility by excluding any patient who had a hepatitis
C ICD-9/ICD-10 or detectable viral load documented prior to their first positive antibody.
Similarly, it is possible that we did not capture all available post-treatment viral load labs
available in the data, in which case our estimates of post-treatment follow-up may be
underestimates of the true rate. This could also affect our estimated rates of SVR12. Significant
care was taken to identify viral load labs and accurately interpret their results, including manual
adjudication when necessary.
There are potential risk factors that are not captured in our regression analyses, including
socioeconomic factors, geographic region, resistance associated variants (RAVs), and other
comorbidities, which may impact the outcomes analyzed. Additional research into these and
other such factors is warranted.
Lastly, this study only used data from the Veterans Health Administration, and therefore the
results may not be generalizable to other health systems and/or populations. Nonetheless, with
the VHA being the largest provider of hepatitis C care in the United States, documenting and
analyzing the performance of its hepatitis C care delivery system is of unique importance.
Additionally, the results from this study could serve as a reference measure for other health
systems to use when assessing the performance of their hepatitis C care delivery systems.
Conclusions
Overall the hepatitis C care delivery system in the VHA appears to be performing well in the all-
oral DAA era. The step along the cascade of care with the greatest opportunity for improvement
is treatment initiation, which will likely improve as more resources (both financial and
49
personnel) become available. Finally, additional care should also be taken to ensure patients are
being adequately followed up with post-treatment.
50
Tables & Figures
Table 1. Baseline Characteristics of Patients with Confirmed Hepatitis C
n (%)
Total 10,158
Gender
Male 9,648 95%
Female 510 5%
Age
< 60 5,641 56%
60 - 64 2,907 29%
65+ 1,610 16%
Race
White 6,293 62%
Black 2,905 29%
Other/Unknown 960 9%
Genotype
1 6,567 65%
2 972 10%
3 756 7%
4 72 1%
5 2 0%
6 5 0%
Multiple 28 0%
Unknown 1,756 17%
Disease Severity
Cirrhosis 186 2%
Decompensated 125 1%
HCC 50 0%
Liver Transplant 12 0%
Comorbidities
HIV 136 1%
HBV 67 1%
Diabetes 1,538 15%
Obesity 1,241 12%
Note: Patients in the study sample with confirmed hepatitis C, as defined by having a detectable viral load or
genotype.
51
Table 2. Number of patients initiating treatment, by regimen
n %
SOF 539 11%
SIM+SOF 74 2%
LDV/SOF 3,485 63%
3D 578 14%
DCL+SOF 131 2%
ELB/GRZ 302 4%
VEL/SOF 238 2%
Multiple 117 2%
Total 5,464 -
Note: Patients in the study sample who initiated treatment by the end of the study period.
SOF = sofosbuvir, SIM+SOF = simeprevir+sofosbuvir, LDV/SOF = ledipasvir/sofosbuvir, 3D =
ombitasvir/paritaprevir/ritonavir/dasabuvir, DCL+SOF = daclatasvir+sofosbuvir, ELB/GRZ = elbasvir/grazoprevir,
VEL/SOF = velpatasvir/sofosbuvir
52
Table 3. Impact of baseline characteristics on treatment initiation
Parameter
Hazard
Ratio
95%
Confidence Interval
p-value
Age 1.01 (1.01 - 1.02) 0.000
Gender (ref. group = female)
Male 0.88 (0.78 - 0.99) 0.043
Race (ref. group = white)
Black 0.82 (0.77 - 0.88) 0.000
Other/Unknown 0.89 (0.81 - 0.98) 0.015
Genotype (ref. group = 1)
2 0.96 (0.88 - 1.04) 0.316
3 0.72 (0.65 - 0.81) 0.000
4 0.83 (0.61 - 1.13) 0.223
5 0.66 (0.09 - 4.68) 0.676
6 0.61 (0.20 - 1.90) 0.394
Multiple 1.09 (0.69 - 1.71) 0.716
Unknown 0.07 (0.06 - 0.09) 0.000
Disease Severity
Cirrhosis 1.03 (0.84 - 1.26) 0.770
Decompensated 0.92 (0.70 - 1.21) 0.563
HCC 0.58 (0.26 - 1.29) 0.182
Liver Transplant 0.53 (0.20 - 1.42) 0.208
Comorbidities
HIV 0.86 (0.67 - 1.09) 0.204
HBV 0.93 (0.66 - 1.31) 0.678
Diabetes 1.05 (0.97 - 1.13) 0.217
Obesity 1.13 (1.04 - 1.22) 0.003
Note: Analysis conducted on the 10,158 patients in the study sample with confirmed hepatitis C, as defined by
having a detectable viral load or genotype.
53
Table 4. Impact of baseline characteristics on SVR12
Parameter
Hazard
Ratio
95%
Confidence Interval
p-value
Age 1.00 (0.99 - 1.00) 0.357
Gender (ref. group = female)
Male 1.09 (0.93 - 1.28) 0.297
Race (ref. group = white)
Black 0.96 (0.88 - 1.04) 0.341
Other/Unknown 1.01 (0.88 - 1.15) 0.919
Genotype (ref. group = 1)
2 1.08 (0.85 - 1.37) 0.517
3 0.67 (0.55 - 0.81) 0.000
4 0.75 (0.50 - 1.11) 0.154
6 0.52 (0.13 - 2.10) 0.360
Multiple 0.63 (0.36 - 1.09) 0.097
Unknown 1.12 (0.87 - 1.45) 0.375
Disease Severity
Cirrhosis 0.76 (0.68 - 0.84) 0.000
Decompensated 0.63 (0.52 - 0.76) 0.000
HCC 0.44 (0.29 - 0.67) 0.000
Liver Transplant 0.70 (0.33 - 1.47) 0.343
Comorbidities
HIV 0.84 (0.63 - 1.12) 0.223
HBV 0.82 (0.61 - 1.11) 0.198
Diabetes 0.93 (0.85 - 1.02) 0.142
Obesity 0.99 (0.90 - 1.09) 0.891
Treatment Type (ref. group = LDV/SOF)
SOF 0.52 (0.41 - 0.65) 0.000
SIM+SOF 0.58 (0.45 - 0.75) 0.000
3D 0.71 (0.64 - 0.79) 0.000
DCL+SOF 0.71 (0.53 - 0.94) 0.018
ELB/GRZ 0.79 (0.64 - 0.97) 0.028
Multiple 0.30 (0.21 - 0.42) 0.000
Treatment Delay 0.99 (0.99 - 1.00) 0.070
Note: Analysis conducted on the 3,325 patients in the study sample that had adequate post-treatment viral load labs
to determine treatment outcome (SVR12). There were no patients with adequate post-treatment viral load labs who
had genotype 5, thus it was dropped as an independent variable. There was only 1 patient treated with VEL/SOF
who had adequate post-treatment viral load, and was thus excluded from the analysis.
54
Figure 1. Cascade of Care Results
Inclusion/exclusion criteria: Patients with a documented hepatitis C ICD-9/ICD-10 at the time of the analysis, with
their first documented positive hepatitis C antibody occurring between January 1, 2014 and December 31, 2015.
Patients whose first documented positive antibody fell between these dates, but who had an earlier documented
detectable hepatitis C viral load, genotype, or ICD-9/ICD-10 were excluded.
*Of the patients who received confirmatory testing, 2,081 were found to not have active hepatitis C infection.
55
Figure 2. Time-to-event analysis: positive antibody to confirmatory test
Note: Analysis conducted using all 12,531 patients in the study sample.
56
Figure 3. Time-to-event analysis: confirmatory lab to treatment initiation
Note: Analysis conducted on the 10,158 patients in the study sample with confirmed hepatitis C, as defined by
having a detectable viral load or genotype.
57
Figure 4. Time-to-event analysis: confirmatory lab to treatment initiation, by time-period
Note: Analysis conducted on the 10,158 patients in the study sample with confirmed hepatitis C, as defined by
having a detectable viral load or genotype. Time-period was based on the date the confirmatory lab was conducted.
58
Chapter 5. Summary & Future Research Directions
Summary
The introduction of all-oral DAAs revolutionized the hepatitis C treatment landscape. Relative
to previous interferon-based treatments, all-oral DAAs have reduced duration, improved
tolerability, and significantly increased efficacy. When new treatments are introduced into the
market, clinicians, health systems, and policy makers rely on evidence from real-world studies to
tailor treatment decisions to their specific populations. The studies presented in this dissertation
utilized a large real-world dataset to address three important areas of unmet research need in the
all-oral DAA era.
We analyzed the real-world treatment effectiveness of three of the most widely prescribed all-
oral DAAs, and found that all-oral DAAs are performing well in real-world practice, achieving
rates of SVR12 that are similar to those reported in clinical trials. This is particularly
encouraging, as previous interferon-based treatments performed notably worse in real-world
practice than what was demonstrated in clinical trials. It is likely that the improvement in
tolerability of all-oral DAAs have allowed clinicians and patients to overcome many of the
treatment barriers that were associated with interferon-based treatments. Overall, we found that
LDV/SOF performed better than the other two all-oral DAAs analyzed. However, although these
findings were statistically significant, it may be that the small differences in treatment
effectiveness are not clinically significant in practice.
We found that existence of advanced liver disease (cirrhosis, decompensated cirrhosis,
hepatocellular carcinoma) was associated with a lower likelihood of achieving SVR12. This
59
result lends evidence to the notion that patients should be treated earlier in the disease
progression cycle to maximize treatment outcomes. We found that HIV coinfection did not have
a significant impact on all-oral DAA treatment effectiveness. This is also an encouraging
finding, as the effectiveness of previous interferon-based treatments was severely limited among
HIV coinfected patients due to low uptake and low rates of SVR12[46].
Overall, we found that the Veterans Health Administration is performing well in the delivery of
hepatitis C care in the all-oral DAA era. In particular, we found that the VHA has very high rates
of confirmatory testing among patients who have a positive hepatitis C antibody, at an
approximately 97% completion rate. This is an important step in care in that it not only confirms
the existence of active hepatitis C infection for the clinician, but also reengages the patient in the
health system which may increase the likelihood of patients initiating treatment.
The two aspects of the care delivery system with the greatest opportunities for improvement
were treatment initiation, and post-treatment follow-up. We found that approximately 54% of
patients with confirmed hepatitis C had initiated treatment over the study period. With several
highly effective all-oral DAAs now available in the VHA, it should be made a priority to allocate
the necessary resources to successfully increase treatment uptake. Finally, although the majority
of patients who complete treatment go on to achieve SVR12, further efforts should be made to
ensure that patients are adequately followed-up with after treatment to identify any patients
whose viral load has rebounded/relapsed.
Future Research Directions
60
The results from the research presented in this dissertation demonstrate that all-oral DAA
regimens are highly effective at achieving SVR12 in real-world practice. However, what is yet to
be evidenced, are the long-term outcomes associated with patients treated with all-oral DAAs.
Understanding the impact of achieving SVR12 with all-oral DAAs on the long-term health
outcomes is high-priority research that has yet to be comprehensively addressed. Such research is
not only needed to inform current treatment decisions (i.e. can we safely delay treatment?), but is
also critical to projecting the health care utilization and costs associated with patients who have
been successfully treated. Early adopters of all-oral DAAs (i.e. the VHA), now have patients
with over three years of post-treatment follow-up data to study, making it a rich source of data to
execute such research.
Given the high price of the all-oral DAA regimens, investigation into patients who fail all-oral
DAA treatment failures is also needed. Research to better understand the patient, disease, and
treatment characteristics associated with treatment failures would be of high value to public
health systems as well as private payers. Such analysis could be used to develop a model to
determine the ideal all-oral DAA regimen for a given patient based on their individual patient
and disease characteristics.
Finally, given the enormous amount of financial and personnel resources that are being allocated
to the treatment of hepatitis C in the VHA, research documenting the value that all-oral DAAs
have brought to the health system is warranted. For such research, value should be measured in
dollars (i.e. costs saved, future costs avoided), as well as health-related measures (ie. QALYs
gained/DALYs avoided, hepatocellular carcinoma cases and/or liver transplants avoided, etc.).
Estimating and communicating the value of all-oral DAAs is essential to demonstrating the
61
importance of focusing on value rather than price when making treatment decisions, which is
crucial to securing the development of future breakthrough medical innovations.
62
Acknowledgements
I would like to extend my sincerest gratitude to my advisor Dr. Jeffrey McCombs, for the
immeasurable support and guidance he has provided me. It is through his dedication, and
unfaltering generosity in both his time and resources, that I have been able to achieve my
academic and professional goals. For this, I am forever indebted.
I am infinitely grateful for my committee members, Dr. Neeraj Sood and Dr. Jeffrey Kahn, for
the knowledge, advice, and encouragement they have afforded me. Their passion and expertise in
their fields has, and forever will, embolden me to be better.
I would also like to acknowledge Dr. Steven Fox and Dr. Ivy Tonnu-Mihara for their
collaboration and support, as well as Dr. Joel Hay for his teachings and encouraging me to
pursue this area of research.
Finally, my deepest gratitude goes to my mother, Kelly, and my two brothers, Jeremy and
Joshua, for always believing in me. It is they who are my daily inspiration, and without their
unwavering love, support, and sacrifices, I would not be where I am today.
63
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Abstract (if available)
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Asset Metadata
Creator
McGinnis, Justin J.
(author)
Core Title
Hepatitis C in the post-interferon era: selected essays in health economics
School
School of Pharmacy
Degree
Doctor of Philosophy
Degree Program
Pharmaceutical Economics and Policy
Publication Date
07/18/2017
Defense Date
03/30/2017
Publisher
University of Southern California
(original),
University of Southern California. Libraries
(digital)
Tag
Health Economics,hepatitis C,OAI-PMH Harvest
Language
English
Contributor
Electronically uploaded by the author
(provenance)
Advisor
McCombs, Jeffrey (
committee chair
), Kahn, Jeffrey (
committee member
), Sood, Neeraj (
committee member
)
Creator Email
justin.mcginnis@me.com,justin.mcginnis@usc.edu
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https://doi.org/10.25549/usctheses-c40-402355
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UC11264794
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etd-McGinnisJu-5539.pdf (filename),usctheses-c40-402355 (legacy record id)
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etd-McGinnisJu-5539.pdf
Dmrecord
402355
Document Type
Dissertation
Rights
McGinnis, Justin J.
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texts
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University of Southern California
(contributing entity),
University of Southern California Dissertations and Theses
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The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...
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Tags
hepatitis C