SEARCH

SEARCH BY CITATION

Abstract

  1. Top of page
  2. Abstract
  3. Patients and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References

A substantial baseline risk of liver cirrhosis exists for patients with chronic hepatitis C virus (HCV) infection. However, the extent to which this could be driven by heavy alcohol use is unclear. Therefore, our principal aim was to determine the fraction of cirrhosis attributable to heavy alcohol use among chronic HCV patients attending a liver clinic. The study population comprised chronic HCV patients who had attended one of five liver clinics in Scotland during 1996-2010 and had (1) remained in follow-up for at least 6 months, (2) acquired HCV through either injecting drugs or blood transfusion, and (3) an estimated date of acquiring infection. Predictors of cirrhosis were determined from multivariate logistic regression. Regression parameters were used to determine the fraction of cirrhosis attributable to heavy alcohol use. Among 1,620 patients, 9% were diagnosed with cirrhosis, and 34% had ever engaged in heavy alcohol use (>50 units/week for a sustained period). Significant predictors of cirrhosis were age, duration of infection, and ever heavy alcohol use. The fraction of cirrhosis attributable to ever heavy alcohol use was 36.1% (95% confidence interval [CI]: 24.4-47.4). Moreover, among patients who had ever engaged in heavy alcohol use specifically, this attributable fraction exceeded 50% (61.6%; 95% CI: 47.0-72.2). Conclusions: A substantial proportion of patients with chronic HCV develop liver cirrhosis as a consequence of heavy alcohol use. This has not been adequately acknowledged by cost utility analyses (CUAs). As such, estimates of cost-effectiveness may be exaggerated. Thus, these data are important to guide forthcoming CUAs in terms of taking better account of the factors leading to cirrhosis among patients with chronic HCV. (HEPATOLOGY 2013)

It is well established that chronic hepatitis C virus (HCV) infection frequently leads to liver cirrhosis and thereafter to decompensated cirrhosis and hepatocellular carcinoma {HCC}. Provision of antiviral therapy for treatment of chronic HCV infection has increased over the past decade,1 a trend expected to continue given the availability of more effective treatment regimens.2, 3 At the same time, recent work has indicated that HCV populations bear a considerable risk of liver cirrhosis, independent of chronic HCV infection.4-6 In particular, we previously demonstrated that the risk of a liver-related hospital episode among persons in Scotland who have spontaneously cleared HCV infection is up to 27 times that of the general population of Scotland.6 This finding is noteworthy, given that spontaneous resolvers are generally viremic for a short duration only7; thus, one can assume HCV-induced liver damage to be minimal, and that the excess risk is attributable to lifestyle factors instead.

When accounting for this baseline risk, alcohol should be foremost considered. This is because (1) excessive alcohol consumption is recognized as a major factor influencing the progression of liver disease8 and (2) persons with chronic HCV infection consume more alcohol than persons without.9, 10 For example, of participants tested for HCV markers in the U.S. National Health and Nutritional Examination Survey, HCV RNA–positive persons were approximately eight times more likely to consume more than three drinks per day (in the 12 months previous to survey) than other adults.10 By the time chronic HCV patients attend a specialist liver clinic to be considered for antiviral therapy, a substantial proportion (17%-51%11-16) have, at some point, engaged in heavy alcohol use for a sustained period of time.

It is well established that heavy alcohol use is associated with an increased risk of liver cirrhosis for patients with chronic HCV.17 However, the fraction of liver cirrhosis cases attributable to this exposure (i.e., the fraction of cases that would not have occurred had the patient not engaged in heavy alcohol use) is not known. This is in spite of the value such information would afford to healthcare policymakers. Therefore, using data on patients with chronic HCV attending liver clinics in Scotland, our principal aim was to determine the fraction of liver-cirrhosis cases attributable to heavy alcohol use.

Patients and Methods

  1. Top of page
  2. Abstract
  3. Patients and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References

The initial study population (N = 4,232) comprised treatment-naïve patients with chronic HCV infection (defined according to viral RNA positivity as well as an indication of chronic HCV by the presiding clinician) who had attended their first appointment between January 1996 and April 2010 at one of five specialist clinics in Scotland (one in Dundee: Ninewells Hospital; one in Edinburgh: Edinburgh Royal Infirmary; and three in Glasgow: Glasgow Royal Infirmary, Gartnavel General Hospital, and Southern General Hospital). These liver clinics are among the largest in Scotland and account for an estimated 40% of all patients with chronic HCV attending specialist services in Scotland over this period.

Patients were excluded from our analyses if (1) they did not remain in follow-up for at least 6 months after their first appointment (6 months generally being the amount of time required for a patient to undergo a full liver disease assessment) (N = 917), (2) diagnosed HIV positive or with active HBV infection (defined through HBV DNA, hepatitis B surface antigen, or hepatitis B e antigen positivity) (N = 104), (3) a probable date of acquiring HCV could not be inferred (N = 1,388, of which 688 were intravenous drug user [IVDU] or blood transfusion patients), and (4) the main risk group indicated for acquiring HCV was not IVDU or blood transfusion (N = 203). Therefore, statistical analysis was confined to 1,620 patients.

The decision to restrict to patients who acquired infection by IVDU or blood transfusion (e.g., excluding patients whose main risk factor was sexual or tattoo/body piercing, and so forth) was made because, for these risk groups, an invariable approach was adopted, when recording the likely date and year of acquiring HCV. For example, for IVDUs, the time of likely infection, recorded on the database, consistently relates to the year (and where available, month) of first IVDU episode. Similarly, for patients who acquired HCV through blood transfusion, the likely time of infection consistently relates to the date of this transfusion. This exclusion was necessary to ensure robust data on the duration of HCV infection, and such patients (i.e., blood transfusion and IVDU acquired) account for the vast majority (>85%) of patients on the clinical database with a known risk factor.

The primary outcome variable of interest was a first diagnosis of liver cirrhosis within 6 months of the patient's first appointment. A diagnosis of cirrhosis was made generally on the basis of one or more of the following (see Fig. 1): (1) clinical examination (in patients for whom decompensated liver cirrhosis was clinically evident); (2) radiology (i.e., ultrasound, FibroScan, computed tomography [CT] or magnetic resonance imaging [MRI] procedure); and (3) liver biopsy. All patients in our cohort were assessed for liver disease with equal rigor (regardless of, e.g., alcohol history disclosed, estimated duration of HCV infection, or age).

thumbnail image

Figure 1. Mode of diagnosis among 145 patients diagnosed with liver cirrhosis.

Download figure to PowerPoint

Exposure variables considered were as follows: gender; age group; high body weight; calendar period of first attendance; ever IVDU; duration of infection group; viral genotype; current heavy alcohol use; and ever heavy alcohol use. High body weight was defined as body weight at first appointment >92 kg for males and >78 kg for females (corresponding to an estimated body mass index [BMI] of 30, assuming an average height of 1.61 m for females and 1.75 m for males18). Current heavy alcohol use was defined as consumption ≥50 units/week in the week preceding first appointment. Ever heavy alcohol use was defined as consumption ≥50 units/week in most weeks for at least 6 months at any point previously. For patients who acquired HCV by IVDU, infection duration was estimated through subtracting the date of first clinic attendance from the midpoint of the reported year of first injection. We then further subtracted 3.4 years (the median time to seroconversion among a previously studied cohort of HCV-negative active IVUDs19), in recognition that it is fallacious to assume that all HCV-infected injectors acquire HCV at first injection. For blood transfusion patients, infection duration was defined as the time between blood transfusion and first clinic attendance.

Statistical Analysis.

Predictors of cirrhosis at first appointment

A binary logistic regression model was used to determine predictors of liver cirrhosis at time of first appointment in specialist care. Variables significantly associated with a diagnosis of cirrhosis at the univariate level (P < 0.1) were subsequently included in the multivariate model. Thereafter, variables were retained (in the final multivariate model) only if significant at the 5% threshold. We tested specifically for an interaction between duration of chronic HCV infection, and ever heavy alcohol use, at a uni- and multivariate level. Finally, Homer-Lemeshow's test was performed to assess the adequacy of our final regression model in fitting the observed outcome data.

Attributable fraction

Bruzi et al. previously described a regression-based approach to determine the attributable fraction (AF),20 as shown in Equation 1:

  • equation image(1)

where pj refers to the proportion of all cases that are in stratum j, RRj(derived from regression coefficients) is the relative risk for stratum j, compared with the baseline stratum, and j refers to mutually exclusive strata formed when cross-classifying all known risk factors. The odds ratio (OR) is often used as a substitute for the RR in Equation 1. However, when the prevalence of an outcome is not rare and/or the magnitude of association is not small, the OR can exceed the RR considerably.21 Thus, in this situation, substituting the RR for the OR will lead to inflated estimates of the AF.

We note that, equation image (from Equation 1), and equation image both refer to the total proportion of cases not attributable to exposure in stratum j (and an estimate of the latter can be derived from logistic regression parameters, without assuming the RR approximates the OR), where (1) Nj relates to the number of patients in stratum j, (2) equation image relates to the probability of the cirrhosis not attributable to exposure for stratum j, and (3) Cj relates to the number of cases in stratum j. Thus, we modified Equation 1 to enable determination of the AF in circumstances where the OR may not be a good approximation of the RR, as shown in Equation 2:

  • equation image(2)

Equations 1 and 2 describe the population AF, that being the number of cirrhosis cases attributable to ever heavy alcohol use, as a proportion of all cases with cirrhosis in our cohort. We further calculated the exposed AF (i.e., the number of cases with cirrhosis attributable to ever heavy alcohol use as a proportion of cases with cirrhosis with this exposure). The exposed AF was determined by restricting Equation 2 to stratum of j where ever heavy alcohol use was indicated.

Confidence intervals (CIs) for AFs were derived through bootstrapping.22 More specifically, we resampled with replacement from our cohort to yield 1,000 resampled datasets (or bootstrap samples) of size 1,620 and took the 2.5th, and 97.5th percentiles as the lower and upper CIs, respectively.

Sensitivity analyses

We assessed the variability of population and exposed AFs when calculated as follows: (1) according to the original Bruzi approach20 (i.e., when the OR was substituted for the RR in Equation 1); (2) after persons with unknown ever heavy alcohol use status were excluded (i.e., in our base case analysis, we collapsed never heavy alcohol use, with unknown ever heavy alcohol use); and (3) when the analysis was not confined to specific risk groups (as per exclusion criterion 4).

Results

  1. Top of page
  2. Abstract
  3. Patients and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References

Sample Characteristics.

In our final cohort (N = 1,620), the majority of patients were male (70.7%), had acquired HCV through IVDU (84.1%), and were not with cirrhosis (91.1%). The mean age and estimated duration of infection at first appointment were 39.6 (standard deviation [SD], 9.8), and 13.5 years (SD, 9.7), respectively. Genotypes 1 (46.8%) and 3 (46.7%) together accounted for >90% of all infections where genotype was known.

There was some evidence of noncomparability between our final cohort, and the 688 IVDU/blood transfusion patients excluded because of missing data on duration of infection (i.e., patients who would otherwise have been included in our final cohort, were they not missing these data). This was apparent with regard to age, ever IVDU, and current alcohol consumption (see Table 1). Furthermore, patients in our final cohort were better characterized than the excluded patients with respect to body weight (30.2% with unknown body weight in the final cohort versus 61.9% in the excluded cohort), genotype (10.5% versus 22.2%, respectively), current alcohol use (28.0% versus 52.6%, respectively), and ever heavy alcohol use (13.9% versus 33.1%, respectively).

Table 1. Characteristics of the Final Cohort (N = 1,620) and Patients Excluded From Final Analysis Because of No Information Available on Infection Duration
Exposure/CharacteristicFinal Cohort, (N = 1,620)Patients Excluded (N = 688)P Value (Excluding Unknowns)
Number of PatientsColumn %Column % (Excluding UnknownsNumber of PatientsColumn %Column % (Excluding Unknowns)
  1. N/A refers to “not available” as a result of no data on infection duration (i.e., the estimated time HCV was acquired, was not known).

GenderFemale47529.329.319229.929.90.49
Male1,14570.770.749672.172.1
Age group at attendance, years<4089554.854.845666.366.3<0.001
40-5968242.142.121931.831.8
60+513.23.2131.91.9
Mean age (SD) 39.6 (9.8)37.3 (9.2)<0.001
High body weightNo96659.685.421531.382.10.17
Yes16510.214.6476.817.9
Unknown48930.242661.9
Calendar period of first attendance1996-200048429.929.916423.823.80.001
2001-200555234.134.128541.441.4
2006-201058436.136.123934.734.7
Mean year of first attendance (SD) 2003 (4.3)2004 (4.0)0.99
Ever IVDUNo25815.915.9659.59.5<0.001
Yes1,36284.184.162390.690.6
Duration of infection group, years0-960637.437.4N/AN/A
10-2060737.537.5N/A
>=2040725.125.1N/A
Viral genotype167841.946.824535.645.80.07
2825.15.7233.34.3
367741.846.726738.849.9
4, 5, 6130.80.900.00.0
Unknown17010.515322.2
Current heavy alcohol use, >=50 units/weekNo1,05264.990.227139.483.1<0.001
Yes1157.19.9558.016.9
Unknown45328.036252.6
Ever heavy alcohol use (>=50 units/week for sustained period)No85152.561.026839.058.30.31
Yes54433.639.019227.941.7
Unknown22513.922833.1
Diagnosed liver cirrhosisNo1,47591.191.160988.588.50.06
Yes1459.09.07911.511.5
Total1,620100.0100.0688100.0100.0 

The prevalence of ever heavy alcohol use was approximately 2-3 times as high among males (39.8%, compared to 18.5% among females), ever IVDUs (37.4%, compared to 13.2% among blood transfusion patients), and patients diagnosed with liver cirrhosis (58.6%, compared to 31.1% among those not diagnosed) (see Table 2). Almost all patients with current alcohol consumption ≥50 units/week (n = 115) had maintained this level of consumption at some point previously for a sustained period (95.7%). However, among patients whose current alcohol consumption at first appointment was known to be <50 units/week (n = 1,052), 30.8% had ever engaged in heavy alcohol use.

Table 2. Prevalence of Ever Heavy Alcohol Use (>=50 units/week for Sustained Period) Among the Final Cohort (N = 1,620) According to Demographic/Exposure Factors
Exposure/CharacteristicAll PatientsHistory of Heavy Alcohol UseRow %P Value (Excluding Unknowns)
GenderFemale4758818.5<0.001
Male1,14545639.8
Age group at attendance, years<4088726730.1<0.001
40-5968227340.0
60+5147.8
High body weightNo96630131.20.48
Yes1655633.9
Unknown48918738.2
Calendar period of first attendance1996-200048415532.00.52
2001-200555218333.2
2006-201058420635.3
Ever IVDUNo2583413.2<0.001
Yes1,36251037.4
Duration of infection group, years0-960618129.90.002
10-2060719832.6
>=2040716540.5
Viral genotype167822633.30.41
2823239.0
367721832.2
4, 5, 613323.1
Unknown1706538.2
Current heavy alcohol use, >=50 units/weekNo/unknown1,50543428.8<0.001
Yes11511095.7
Diagnosed liver cirrhosisNo1,47545931.1<0.001
Yes1458558.6

Statistical Analyses

Logistic regression

In univariate analysis, all factors, with the exception of gender, calendar period, and genotype, were associated with cirrhosis (see Table 3). In multivariate analysis, age group (adjusted odds ratio [AOR], compared with <40 and 40-59 years: 2.39; 95% CI: 1.45-3.95; AOR 60+ yrs: 6.92; 95% CI: 3.04-15.71), duration of infection (AOR, compared with <10 and 10-19 years: 2.68; 95% CI: 1.43-5.03; AOR ≥20 years: 5.58; 95% CI: 2.90-10.76), and ever heavy alcohol use (AOR, 3.16; 95% CI: 2.17-4.61) remained significant, whereas ever IVDU, high body weight and current alcohol consumption did not. We found no evidence of an interaction between duration of infection and ever heavy alcohol use at either the uni- (P = 0.27) or multivariate level (P = 0.26). Homer-Lemeshow's test, performed on our final model, indicated no significant difference between predicted and observed outcomes (P = 0.38).

Table 3. Factors Associated With a Diagnosis of Liver Cirrhosis Among 1,620 Patients With Chronic HCV Attending Specialist Clinics in Scotland: Results of Logistic Regression Analyses
Exposure/Characteristic  Univariate AnalysisMultivariate Analysis
No. of Patients (%)No. of Cirrhotic Patients (% of N)OR95% CIOR95% CI
  1. Abbreviation: NS, not significant.

GenderFemale475 (29.3)39 (8.2)RefRef  
Male1,145 (70.7)106 (9.3)1.140.78-1.67  
Age group at attendance, years<40887 (54.8)29 (3.3)RefRefRefRef
40-59682 (42.1)103 (15.1)5.263.44-8.052.391.45-3.95
60+51 (3.2)13 (25.5)10.124.88-21.016.923.04-15.71
High body weightNo966 (59.6)67 (6.9)RefRefNS
Yes165 (10.2)20 (12.1)1.851.09-3.14
Unknown489 (30.2)58 (11.9)1.791.25-2.61
Calendar period of first attendance1996-2000484 (29.9)37 (7.6)RefRef  
2001-2005552 (34.1)43 (7.8)1.020.65-1.61  
2006-2010584 (36.1)65 (11.1)1.510.99-2.31  
Ever IVDUNo25833 (12.8)RefRefNS
Yes1,362112 (8.2)0.610.40-0.92
Duration of infection group, years0-960614 (2.3)RefRefRefRef
10-2060745 (7.4)3.391.84-6.242.681.43-5.03
>=2040786 (21.1)11.336.34-20.255.582.90-10.76
Viral genotype167852 (7.7)RefRef  
28210 (12.2)1.670.81-3.43  
367759 (8.7)1.150.78-1.70  
4-6/unknown18324 (12.9)1.821.09-3.04  
Current heavy alcohol use, >=50 units/weekNo/unknown1,505125 (8.3)RefRefNS
Yes11520 (17.4)2.321.39-3.89
Ever heavy alcohol use, >=50 units/week for sustained periodNo/unknown1,07660 (5.6)RefRefRefRef
Yes54485 (15.6)3.142.21-4.443.162.17-4.61
AF

Predicted probabilities of liver cirrhosis, according to age, infection duration, and alcohol subgroups, ranged from 1.1% (in patients who were <40 years of age, had acquired HCV <10 years previously, and had never engaged in heavy alcohol use) to 57.7% (in patients who were >60 years of age, had acquired HCV infection >=20 years previously, and had ever engaged in heavy alcohol use) (see Table 4).

Table 4. Fraction of Cases With Cirrhosis Attributable to Past Heavy Alcohol Use, According to Age Group, and Duration Category of HCV Infection
Past Heavy Alcohol UseDuration Group of HCV Infection (Years)Age Group (Years)Observed DataPredicted Data 
Number of PatientsNumber With CirrhosisObserved Probability of Cirrhosis (%)Predicted Probability of Cirrhosis (%)Expected Number With CirrhosisProbability of Cirrhosis, Not Attributable to Alcohol (%)Number With Cirrhosis, not Attributable to AlcoholProportion With Cirrhosis, not Attributable to Alcohol
No/not known0-9<4036130.831.103.971.103.970.027
40-595835.172.591.502.591.500.010
60+600.007.160.437.160.430.003
10-19<4023731.272.916.902.916.900.048
40-59156138.336.6710.416.6710.410.072
60+1600.0017.142.7417.142.740.019
20+<402229.095.861.295.861.290.009
40-591952512.8212.9425.2312.9425.230.174
60+251144.0030.097.5230.097.520.052
AllAll1,076605.585.5859.995.5859.990.414
Yes0-9<4013732.193.414.671.101.510.010
40-5944511.367.773.422.591.140.008
60+000.000.000.007.160.000.000
10-19<401181613.568.6510.212.913.430.024
40-59801316.2518.4514.766.675.340.037
60+000.000.000.0017.140.000.000
20+<4012216.6716.451.975.860.700.005
40-591494429.5331.9947.6712.9419.280.133
60+4250.0057.662.3130.091.200.008
AllAll5448515.6315.6385.006.0132.600.225
All0-9All606142.312.3113.991.418.550.059
10-19All607457.417.4245.014.7528.810.199
20+All4078621.1321.1385.9913.5755.230.381
All<40887293.273.2729.012.0117.800.123
All40-5968210315.1015.10102.989.2262.900.434
All60+511325.4925.4913.0023.3311.900.082
AllAll1,6201458.958.95144.992.0192.600.639

The total number of predicted cases of liver cirrhosis was 60 among patients without a history of heavy alcohol use and 85 among patients with a history of heavy alcohol use (in both cases, this was equal to the number of cases with cirrhosis observed). Of these 85 cases, 33 were not attributable to ever heavy alcohol use. On the basis of our method (summarized in Equation 2), the population-attributable fraction was 36.1% (95% CI: 24.4-47.4), and the exposed AF was 61.6% (95% CI: 47.0-72.2).

Sensitivity Analyses

When adopting the original Bruzi approach,20 the population and exposed AFs were, as expected, higher, with 40.1% (95% CI: 29.5-50.8) and 68.4% (95% CI: 61.8-75.2), respectively. When excluding patients with unknown ever heavy alcohol use, the population and exposed AFs were again higher with 43.5% (95% CI: 29.6-57.2) and 65.1% (95% CI: 50.8-76.0), respectively. Finally, when patients of all risk groups were considered (i.e., the analysis was not confined to patients infected by IVDU or blood transfusion), the population and exposed AFs were marginally lower with 33.2% (95% CI: 22.5-43.7) and 59.4% (95% CI: 46.2-69.5), respectively.

Discussion

  1. Top of page
  2. Abstract
  3. Patients and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References

To our knowledge, this is the first study to determine alcohol-related AFs for liver cirrhosis in patients with chronic HCV. Our findings from this approach are illuminating. Among liver clinic attendees, the fraction of cases with cirrhosis attributable to alcohol is substantial. For example, more than one third of cases with cirrhosis (36.1%; 95% CI: 24.4-47.4) would not have occurred, over the time frame of this study, had patients not previously engaged in heavy alcohol use. Moreover, among patients with a history of heavy alcohol use, this fraction exceeded 50% (61.6%; 95% CI: 47.0-72.2). These data are consistent with previous work, indicating that a substantial proportion of liver disease among patients with chronic HCV is attributable to lifestyle factors.4-6 Our AFs intuitively suggest that targeting alcohol abstinence interventions at patients with chronic HCV could alleviate the future burden of HCV-related liver disease considerably; an implication of consequence for healthcare policymakers.

Given the clear importance of alcohol, we opted to explore the extent to which this exposure is acknowledged in estimates of treatment cost-effectiveness.23-30 The rationale for this line of enquiry was 2-fold: (1) Failing to adequately acknowledge the contribution of ever heavy alcohol use on liver disease progression may lead to skewed estimates of cost-effectiveness and (2) cost-effectiveness hugely influences the decisions of healthcare policymakers. In the UK, for example, interventions generally need to be provide one additional quality-adjusted life-year for no more than £20,000-£30,000 to be approved.

To determine cost-effectiveness, cost utility analyses (CUAs) simulate progression of liver disease, according to various treatment scenarios. These simulations are particularly sensitive to the assumed risk of noncirrhotic treatment failures developing liver cirrhosis (to note, 85% of treatment failures in the UK are noncirrhotic31). This sensitivity reflects liver cirrhosis being a prerequisite to all of the most costly, and most quality-of-life–impairing, health states that comprise the natural history of chronic HCV infection. It is noteworthy that the risk of cirrhosis progression, assumed by CUAs, for noncirrhotic treatment failures, is not determined from this population directly. Instead, risks are based mainly on fibrosis progression incurred in patients up to their first attendance at a liver clinic (i.e., the same time frame as this study).23-30 From here, risks are adjusted to fit the characteristics of noncirrhotic treatment failures (e.g., in terms of age, sex, genotype, and so forth). However, we note that risks are not adjusted for ever heavy alcohol use. Hence, a similar prevalence of ever heavy alcohol use after treatment as before attending a liver clinic for the first time is tacitly assumed (see Table 5). The chief finding of this study was that heavy alcohol use, at some point before first attendance at a liver clinic, was widespread and fueled progression to liver cirrhosis (i.e., more than one third of all cases with cirrhosis were attributable to this exposure). Yet, heavy alcohol use after antiviral therapy may be far less frequent. For example, among a cohort of patients with chronic HCV treatment in the United States, more than 93% of patients were drinking regularly before their diagnosis. However, upon diagnosis, this fell dramatically to 31% of patients and fell further still after treatment to 11%.32 Therefore, taken together, it follows that post-treatment risks of liver cirrhosis progression, assumed by CUAs, may be boosted by levels of heavy alcohol use far beyond that appropriate to post-treatment patients. On this basis, CUAs may exaggerate the cost-effectiveness of antiviral therapy. Nonetheless, data describing the medium- to long-term prevalence of heavy alcohol use after treatment are sparse. Further research is warranted.

Table 5. Risk of Liver Cirrhosis Progression Assumed in CUAs
CUA (Year)Risk of Liver CirrhosisDerivation of Liver Cirrhosis Progression Risk
Assumed Risk of Progression to Liver Cirrhosis (Expressed as an Annual % Probability)Implied Incidence of Cirrhosis (Among Noncirrhotic Treatment Failures) 10 Years After Failing Treatment*Reference (Year)Derived From:Criteria Described to Account for a Lower Post-Treatment Prevalence of Heavy Alcohol Use
  • Data according to measures adopted to account for a lower post-treatment prevalence of heavy alcohol use.

  • *

    Determined by Markov modeling, as per CUA studies (see Dore et al.42 for further explanation). Incidence assumes (1) each patient is 49 years of age when failing treatment (median treatment age in ADVANCE2) and (2) a fibrosis distribution according to treatment initiates of the ADVANCE TRIAL.2 nota bene (N.B.) mortality is not considered in these calculations.

      
Grieve et al. (2006),23 Wright et al. (2006),24 Shepherd et al. (2007),25 Martin et al. (2011),26 and Hartwell et al. (2011)27from Ishak 0-2 to Ishak 3-5 = 2.5%; from Ishak 3-5 to Ishak 6 = 3.7%20%Wright et al. (2006)24321 UK liver clinic patients33Patients with current alcohol consumption ≥40 units/wk at first attendance were excluded.
Grishchenko et al. (2009),28 NICE guidance on telaprevir (2012)29Probabilities are dependent on age and genotype: for GT1 patients 50 years of age at treatment: from Ishak 0-2 to Ishak 3-5 = 3.5%; from Ishak 3-5 to Ishak 6 = 4.8%24%Grishchenko et al. (2009)28UK liver clinic patientsNo indication progression risks were adjusted in any way, to reflect a lower post-treatment prevalence of heavy alcohol use.
NICE guidance on boceprevir (2012)30from Metavir 0 to Metavir 1 = 11.7%; from Metavir 1 to Metavir 2 = 8.5%; from Metavir 2 to Metavir 3 = 12.0%; from Metavir 3 to Metavir 4 = 11.6%27%Thien et al. (2008)41Meta analysis of 111 international studies19% of patients assumed to engage in sustained heavy alcohol use post-treatment; this is based on past consumption at first clinic attendance (if available) and current consumption otherwise.

Certain limitations of this study necessitate discussion. Foremost, we defined cirrhosis on the basis of a clinical approach (see Fig. 1) and not a blanket liver biopsy approach, as per previous studies.33 The majority of patients with cirrhosis in our cohort were diagnosed by liver biopsy/laparoscopy, FibroScan, or through clinical symptoms consistent with decompensated cirrhosis (97 patients, equating to 67% of all patients with cirrhosis). However the diagnosis may be considered less robust among the remaining 48 patients with cirrhosis (33% of all patients with cirrhosis) diagnosed only on the basis of MRI, CT, ultrasound exams, or results of hyaluronic acid tests. In spite of this, we note that the prevalence of cirrhosis in our cohort (9%) is similar to that of other UK cohorts that did pursue the blanket liver biopsy approach (8%-11%).33 Furthermore, a sensitivity analysis was conducted, whereby these 48 patients with cirrhosis, diagnosed through less-robust procedures, were reclassified as noncirrhotic. Under this analysis, the population and exposed attributable fractions were both even higher (40.2% and 65.4%, respectively). Thus, we do not believe this limitation undermines our findings.

The extent to which our findings are generalizable to other geographic settings is uncertain. The attributable fraction of an exposure depends on the prevalence of that exposure (i.e., the lower the prevalence, the lower the attributable fraction). Thus, in settings where the prevalence of heavy alcohol use is lower, the AF (all other factors being equal) will be lower too, and the relative contribution of other risk factors will increase. Further work exploring the contribution of heavy alcohol use (and other risk factors omitted here; see discussion on nonalcoholic fatty liver disease [NAFLD] below) in diverse chronic HCV populations is warranted.

NAFLD is an important cause of liver disease. In western countries, the prevalence of NAFLD is 20%-30%,34 and in the United States, NAFLD is the leading contributor to the burden of HCC.35 NAFLD is strongly associated with metabolic syndrome (MetS), a cluster of obesity-related risk factors (e.g., central obesity and insulin resistance) that predispose persons to adverse health outcomes. Among chronic HCV patients, NAFLD is more prevalent than in the general population and is associated with advanced liver fibrosis.36 Although, for HCV patients, NAFLD can have an HCV-related etiology (thus accounting for the higher prevalence), it is clear that MetS contributes, too.37 Therefore, that our cohort was not characterized in terms of obesity-related metabolic factors is a noteworthy limitation. Instead, only partial data on body weight was available (i.e., unknown in 30% of patients). This hinders us from determining the fraction of cases with cirrhosis attributable to MetS. Of further note, previous work suggests that a synergistic effect may exist between alcohol and MetS (MetS measured by BMI >30) regarding liver disease.38 If these findings apply equally to chronic HCV patients, then the fraction of cases attributable to alcohol will depend on the overlap of patients with MetS and heavy alcohol use (i.e., not just on the overall prevalence of heavy alcohol use); this overlap may vary across geographical settings, so, again, there is a question as to how representative our results will be to settings outside the UK.

Based on data from a U.S. cohort of IVDUs,19 we assumed that injectors acquired infection 3.4 years after their first injection. The size of this lag was further corroborated in an analysis based on injectors recorded on the Scottish HCV clinical database who presented themselves to Scottish health services with acute HCV (acute HCV defined according to Centers for Disease Control definition39) and with a known month and year of injecting onset. Among 24 such patients, median time from onset of injecting to infection, at 2.8 years (interquartile range: 0.28-13.0), was comparable to that of Hagan's estimate. Adjusting duration of infection for the lag time between first injecting and acquiring HCV is important, because assuming IVDUs acquire HCV at first injection (as per previous studies) will skew the relationship between risk group and liver disease.

Finally, it is necessary to clearly stipulate what our results do, and do not, signify. To this end, our findings indicate that among patients with chronic HCV attending liver clinics, 36% of cases with liver cirrhosis are attributable to alcohol and would not have occurred had patients not engaged in heavy alcohol use. However, this is not tantamount to saying that among patients with chronic HCV attending liver clinics, 36% of cases with liver cirrhosis are attributable to alcohol and are not attributable to chronic HCV infection. This is particularly true because previous studies point to a synergistic effect between alcohol and chronic HCV infection; in other words, the pathogenic effect of alcohol may be heightened in the presence of chronic HCV.40 For this reason, one would expect the fraction of cases with liver cirrhosis attributable to alcohol, but not attributable to chronic HCV infection, to be less than 36%; it is a limitation of this study that we cannot quantify this more precisely.

In summary, our work illustrates that the development of liver cirrhosis, among patients with chronic HCV attending liver clinics, is frequently etiologically attributable to ever heavy alcohol use. Forthcoming treatment CUAs must go to greater lengths to ensure that the baseline risk of liver disease is adequately incorporated.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Patients and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References

The authors are grateful to Shirley McLeary and Cathy Scott for their help in retrieving and checking data items for patients in this study. The authors thank also Douglas Ferguson (for his help in retrieving the amalgam of references that went into the writing of this articles) and Elizabeth Spence (for communicating the liver-cirrhosis assessment process in Glasgow). The authors thank members of the Hepatitis C Clinical Database Monitoring Committee who are not authors on this paper, as follows: Bill Carmen, Ray Fox, Nick Kennedy, and David Wilkes. Finally, the authors are grateful to the Scottish Government for funding the Scottish Hepatitis C Clinical Database.

References

  1. Top of page
  2. Abstract
  3. Patients and Methods
  4. Results
  5. Discussion
  6. Acknowledgements
  7. References
  • 1
    Lettmeier B, Muhlberger N, Schwarzer R, Sroczynski G, Wright D, Zeuzem S, Siebert U. Market uptake of new antiviral drugs for the treatment of hepatitis C. J Hepatol 2008; 49: 528-536.
  • 2
    Jacobson IM, McHutchison JG, Dusheiko G, Di Bisceglie AM, Reddy KR, Bzowej NH, et al. Telaprevir for previously untreated chronic hepatitis C virus infection. N Engl J Med 2011; 364: 2405-2416.
  • 3
    Poordad F, McCone J, Jr., Bacon BR, Bruno S, Manns MP, Sulkowski MS, et al. Boceprevir for untreated chronic HCV genotype 1 infection. N Engl J Med 2011; 364: 1195-1206.
  • 4
    McMahon BJ, Bruden D, Bruce MG, Livingston S, Christensen C, Homan C, et al. Adverse outcomes in Alaska natives who recovered from or have chronic hepatitis C infection. Gastroenterology 2010; 138: 922-931.
  • 5
    Prasad L, Spicher VM, Negro F, Rickenbach M, Zwahlen M. Little evidence that hepatitis C virus leads to a higher risk of mortality in the absence of cirrhosis and excess alcohol intake: the Swiss Hepatitis C Cohort Study. J Viral Hepat 2009; 16: 644-649.
  • 6
    Innes HA, Hutchinson SJ, Allen S, Bhattacharyya D, Bramley P, Delahooke TE, et al. Excess liver-related morbidity of chronic hepatitis C patients, who achieve a sustained viral response, and are discharged from care. HEPATOLOGY 2011; 54: 1547-1558.
  • 7
    Amin J, Law MG, Micallef J, Jauncey M, Van Beek I, Kaldor JM, Dore G.J. Potential biases in estimates of hepatitis C RNA clearance in newly acquired hepatitis C infection among a cohort of injecting drug users. Epidemiol Infect 2007; 135: 144-150.
  • 8
    O'shea RS, Dasarathy S, McCullough AJ. Alcoholic liver disease. HEPATOLOGY 2010; 51: 307-328.
  • 9
    Armstrong GL, Wasley A, Simard EP, McQuillan GM, Kuhnert WL, Alter MJ. The prevalence of hepatitis C virus infection in the United States, 1999 through 2002. Ann Intern Med 2006; 144: 705-714.
  • 10
    Dalgard O, Jeansson S, Skaug K, Raknerud N, Bell H. Hepatitis C in the general adult population of Oslo: prevalence and clinical spectrum. Scand J Gastroenterol 2003; 38: 864-870.
  • 11
    Prasad L, Spicher VM, Zwahlen M, Rickenbach M, Helbling B, Negro F. Cohort Profile: the Swiss Hepatitis C Cohort Study (SCCS). Int J Epidemiol 2007; 36: 731-737.
  • 12
    Pessione F, Degos F, Marcellin P, Duchatelle V, Njapoum C, Martinot-Peignoux M, et al. Effect of alcohol consumption on serum hepatitis C virus RNA and histological lesions in chronic hepatitis C. HEPATOLOGY 1998; 27: 1717-1722.
  • 13
    Roudot-Thoraval F, Bastie A, Pawlotsky JM, Dhumeaux D. Epidemiological factors affecting the severity of hepatitis C virus-related liver disease: a French survey of 6,664 patients. The Study Group for the Prevalence and the Epidemiology of Hepatitis C Virus. HEPATOLOGY 1997; 26: 485-490.
  • 14
    Verbaan H, Widell A, Bondeson L, Andersson K, Eriksson S. Factors associated with cirrhosis development in chronic hepatitis C patients from an area of low prevalence. J Viral Hepat 1998; 5: 43-51.
  • 15
    Wiley TE, McCarthy M, Breidi L, Layden TJ. Impact of alcohol on the histological and clinical progression of hepatitis C infection. HEPATOLOGY 1998; 28: 805-809.
  • 16
    Pol S, Lamorthe B, Thi NT, Thiers V, Carnot F, Zylberberg H, et al. Retrospective analysis of the impact of HIV infection and alcohol use on chronic hepatitis C in a large cohort of drug users. J Hepatol 1998; 28: 945-950.
  • 17
    Hutchinson SJ, Bird SM, Goldberg DJ. Influence of alcohol on the progression of hepatitis C virus infection: a meta-analysis. Clin Gastroenterol Hepatol 2005; 3: 1150-1159.
  • 18
    The Scottish Health Survey 2008. Available at: http://www.scotland.gov.uk/Publications/2009/09/28102003/79. Accessed 1st May 2012.
  • 19
    Hagan H, Thiede H, Des Jarlais DC. Hepatitis C virus infection among injection drug users: survival analysis of time to seroconversion. Epidemiology 2004; 15: 543-549.
  • 20
    Bruzzi P, Green SB, Byar DP, Brinton LA, Schairer C. Estimating the population attributable risk for multiple risk factors using case-control data. Am J Epidemiol 1985; 122: 904-914.
  • 21
    Davies HT, Crombie IK, Tavakoli M. When can odds ratios mislead? BMJ 1998; 316: 989-991.
  • 22
    Efron B, Tibshirani R. An Introduction to the Bootstrap (Chapman & Hall/CRC Monographs on Statistics & Applied Probability). Boca Raton, FL: Chapman & Hall/CRC; 1993.
  • 23
    Grieve R, Roberts J, Wright M, Sweeting M, DeAngelis D, Rosenberg W, et al. Cost effectiveness of interferon alpha or peginterferon alpha with ribavirin for histologically mild chronic hepatitis C. Gut 2006; 55: 1332-1338.
  • 24
    Wright M, Grieve R, Roberts J, Main J, Thomas HC. Health benefits of antiviral therapy for mild chronic hepatitis C: randomised controlled trial and economic evaluation. Health Technol Assess 2006; 10: 1-113, iii.
  • 25
    Shepherd J, Jones J, Hartwell D, Davidson P, Price A, Waugh N. Interferon alpha (pegylated and non-pegylated) and ribavirin for the treatment of mild chronic hepatitis C: a systematic review and economic evaluation. Health Technol Assess 2007; 11: 1-205, iii.
  • 26
    Martin NK, Vickerman P, Miners A, Foster GR, Hutchinson SJ, Goldberg DJ, Hickman M. Cost-effectiveness of hepatitis C virus antiviral treatment for injection drug user populations. HEPATOLOGY 2012; 55: 49-57.
  • 27
    Hartwell D, Jones J, Baxter L, Shepherd J. Peginterferon alfa and ribavirin for chronic hepatitis C in patients eligible for shortened treatment, re-treatment or in HCV/HIV co-infection: a systematic review and economic evaluation. Health Technol Assess 2011; 15: i-xii, 1-210.
  • 28
    Grishchenko M, Grieve RD, Sweeting MJ, De Angelis D, Thomson BJ, Ryder SD, Irving WL. Cost-effectiveness of pegylated interferon and ribavirin for patients with chronic hepatitis C treated in routine clinical practice. Int J Technol Assess Health Care 2009; 25: 171-180.
  • 29
    National Institute of Clinical Excellence. Available at: http://www.nice.org.uk/nicemedia/live/13486/58478/58478.pdf. Accessed 1st March 2012.
  • 30
    National Institute of Clinical Excellence. Available at: http://www.nice.org.uk/nicemedia/live/13482/58368/58368.pdf. Accessed 1st March 2012.
  • 31
    Innes HA, Hutchinson SJ, Allen S, Bhattacharyya D, Bramley P, Carman B, et al. Ranking predictors of a sustained viral response for patients with chronic hepatitis C treated with pegylated interferon and ribavirin in Scotland. Eur J Gastroenterol Hepatol 2012; 24: 646-655.
  • 32
    Russell M, Patricia P, Moore CD, Chia C, Dorrell J, Cunanan R, et al. The impact of lifetime alcohol use on hepatitis C treatment outcomes in privately insured members of an integrated health care plan. HEPATOLOGY 2012 Apr 5. doi: 10.1002/hep.25755.
  • 33
    Sweeting MJ, De Angelis D, Neal KR, Ramsay ME, Irving WL, Wright M, et al. Estimated progression rates in three United Kingdom hepatitis C cohorts differed according to method of recruitment. J Clin Epidemiol 2006; 59: 144-152
  • 34
    Targher G, Day CP, Bonora E. Risk of cardiovascular disease in patients with nonalcoholic fatty liver disease. N Engl J Med 2010; 363: 1341-1350.
  • 35
    Sanyal A, Poklepovic A, Moyneur E, Barghout V. Population-based risk factors and resource utilization for HCC: US perspective. Curr Med Res Opin 2010; 26: 2183-2191.
  • 36
    Leandro G, Mangia A, Hui J, Fabris P, Rubbia-Brandt L, Colloredo G, et al. Relationship between steatosis, inflammation, and fibrosis in chronic hepatitis C: a meta-analysis of individual patient data. Gastroenterology 2006; 130: 1636-1642.
  • 37
    Negro F. Hepatitis C virus and liver steatosis: is it the virus? Yes it is, but not always. HEPATOLOGY 2002; 36: 1050-1052.
  • 38
    Hart CL, Morrison DS, Batty GD, Mitchell RJ, Davey Smith G. Effect of body mass index and alcohol consumption on liver disease: analysis of data from two prospective cohort studies. BMJ 2010; 340: c912.
  • 39
    Centers for Disease Control. Hepatitis C Acute 2011 case definition. Available at: http://www.cdc.gov/osels/ph_surveillance/nndss/casedef/hepatitiscacutecurrent.htm. Accessed 1st March 2012.
  • 40
    Donato F, Tagger A, Gelatti U, Parrinello G, Boffetta P, Albertini A, et al. Alcohol and hepatocellular carcinoma: the effect of lifetime intake and hepatitis virus infections in men and women. Am J Epidemiol 2002; 155: 323-331.
  • 41
    Thein HH, Yi Q, Dore GJ, Krahn MD. Estimation of stage-specific fibrosis progression rates in chronic hepatitis C virus infection: a meta-analysis and meta-regression. HEPATOLOGY 2008; 48: 418-431.
  • 42
    Dore GJ, Freeman AJ, Law M, Kaldor JM. Natural history models for hepatitis C-related liver disease: different disease progression parameters for different settings. Antivir Ther 2003; 8: 365-372.