BACKGROUND/RATIONALE:
The immediate goal of this project is to develop a psychometrically robust tool using Adaptive Conjoint Analysis (ACA) that can be applied to improve patient education about antiviral treatment for HCV, elicit patient treatment preferences (i.e., whether or not to accept antiviral therapy), and facilitate decision-making at the individual patient level. If positive, the results from this project will support an intervention trial to determine whether explicit elicitation of individual patient preferences using ACA facilitates decision-making and improves clinical outcomes in veterans with HCV. Our long-term goal is to disseminate a reliable and valid tool for use throughout the Veterans Affairs (VA) healthcare systems in order to improve delivery of health services to veterans with HCV.
OBJECTIVE(S):
SPECIFIC AIM 1: To develop an Adaptive Conjoint Analysis questionnaire for patients with HCV.
1a. To determine which attributes physicians take into consideration when deciding whether or not patients should receive antiviral therapy for HCV.
1b. To determine which attributes patients take into consideration when deciding whether or not to accept antiviral therapy for HCV.
1c. To design and pilot an ACA questionnaire, and to subsequently revise the questionnaire, based on patient and physician feedback.
SPECIFIC AIM 2: To describe patient preferences for treatment of HCV using ACA.
2a. To quantify the influence of specific medication characteristics on treatment preference.
2b. To describe the proportion of patients willing to accept antiviral therapy.
2c. To describe how patients' sociodemographic characteristics, comorbidity, and health beliefs relate to treatment preferences.
2d.To compare treatment preferences in veteran and nonveteran populations.
SPECIFIC AIM 3: To test the value and acceptability of ACA as a decision aid for patients with HCV in clinical practice.
3a. To test the psychometric properties of the ACA questionnaire.
3b. To determine whether patients and their treating physicians consider ACA to be an acceptable tool to facilitate decision-making in clinical practice.
METHODS:
We recruited consecutive patients eligible for treatment of HCV. Baseline data were collected in face-to-face interviews with a research assistant. Participants then completed an ACA task designed to help patients evaluate the pros and cons related to treatment of HCV with pegylated interferon and ribavirin before seeing their physician. Attributes for the ACA questionnaire were chosen based on patient testimonials obtained from focus groups. Preferences were measured for two choices: 1. Treatment associated with mild side effects versus no treatment, and 2. Treatment associated with severe side effects versus no treatment. Criterion validity was assessed by measuring the association between preferences predicted by ACA and the treatment plan, as well as by ascertaining the correlations between preferences predicted by ACA and patient values.
FINDINGS/RESULTS:
PATIENTS' PREFERENCES FOR TREATMENT OF HEPATITIS C
Liana Fraenkel, MD, MPH 1,2
Diane Chodkowski, RN 1,2
Joseph Lim, MD 1,2
Guadalupe Garcia-Tsao, MD, Director, Hepatitis C Resource Center 1,2
1: VA Connecticut Healthcare System
2: Yale University School of Medicine
3: New England Research Institutes
This study was funded by the VA Health Services Research Department Grant IIR 03-621-1 and the Yale Liver Center Pilot Project Grant DK P30 34989. Dr. Fraenkel is also supported by the K23 Award AR048826-01 A1.
Please correspond with:
Liana Fraenkel, MD, MPH
Yale University School of Medicine
Section of Rheumatology
300 Cedar St, TAC#525
P.O. Box 208031
New Haven, CT
06520-8031
E-mail: liana.fraenkel@yale.edu
Key words: Hepatitis C decision-making pegylated-interferon ribavirin
Short title: Preferences in HCV
Word count: 2737
Background: The objective of this study was to ascertain patient preferences for treatment of HCV and to determine which treatment characteristics most strongly impact on patients' choices.
Methods: We recruited consecutive patients eligible for treatment of HCV and used Adaptive Conjoint Analysis (ACA) to elicit preferences for pegylated-interferon and ribavirin.
Results: 140 subjects completed the ACA task. The mean ( SD) age of the sample was 51 8, 85% male, and 59% White. When described as being associated with mild side effects, 67% (N=94) of subjects' preferred treatment for HCV. The percentage of subjects preferring therapy decreased to 51% (N=72) when it was described as being associated with severe side effects. Preferences for treatment of HCV were stronger among subjects with a higher perceived risk of developing cirrhosis, more severe underlying liver disease, and worse HCV-related quality of life. The risk of toxicity had the greatest influence on preference in patients with milder liver disease, among which 53% would choose treatment if associated with mild side effects and 29% would choose treatment if associated with severe side effects. The severity of toxicity had no effect on preferences in subjects with cirrhosis, with the majority (93%) choosing treatment regardless of risk.
Conclusions: Whether or not to choose treatment for HCV is a difficult decision for many patients. Treatment is usually recommended for those with moderate to severe liver disease and our results demonstrate that most patients' preferences are concordant with this view.
Chronic hepatitis C (HCV) infection is a major healthcare burden among Americans, particularly in American veterans. At present, it is the most common indication for liver transplantation in the country, accounting for 30% of transplant cases (1). It is estimated that the seroprevalence of HCV is 1.8% in the general US population (2) while it is 5.4% in a population of Veterans Affairs medical center users (3).
Although definitive data on long-term outcomes are lacking, studies have shown that sustained virological response (SVR) to treatment appears to be associated with a decreased rate of disease progression and improved survival (4-8). Over the past decade, treatment of HCV has evolved favorably from a 5% to 10% SVR with 24 weeks of interferon monotherapy, to the current 54-56% SVR with 48 weeks of pegylated-interferon and ribavirin (9, 10).
The efficacy of antiviral therapy, however, has to be considered in the context of the natural history of HCV. Cirrhosis develops in 8% to 20% of patients while liver-related mortality occurs in up to 3.7% of patients over an average follow-up of 16 years (11-13). Moreover, chronic HCV does not progress at a uniform rate in all patients. Therefore, although antiviral therapy is effective at reducing progression to cirrhosis and the development of complications related to cirrhosis, at the individual patient level many patients may not derive any benefit from treatment. Furthermore, the medications which are currently available can cause potentially serious adverse effects.
Given the trade-offs involving uncertain benefits and risk of toxicity, treatment for HCV should incorporate patients' treatment preferences. The objective of this study was to ascertain patients' treatment preferences for HCV and to determine which treatment characteristics most strongly impact on patients' choices.
Methods
Subjects
The study was conducted at two sites: the VA Connecticut Healthcare System Liver Clinic and the Yale University Liver Clinic. The Hepatitis C Resource Center at VA Connecticut is an innovative multidisciplinary model of care that specifically targets veterans with chronic HCV who have psychiatric co-morbidities including mental illness and alcohol or drug abuse. Patients referred to this clinic undergo a pre-treatment intervention that includes, in addition to medical evaluation, a standardized group education class and psychiatric evaluation. Both the VA and University clinics are attended by the same hepatologists.
We recruited consecutive patients eligible for treatment of HCV. Eligibility criteria included chronic HCV, no prior treatment for HCV, known genotype, and liver biopsy within the preceding two years or clinical evidence of cirrhosis. Subjects were recruited by their treating physician, advanced practice registered nurse (APRN), or by the research nurse at their liver biopsy appointments. Eligible patients either signed informed consent at their biopsy visit, or when they arrived for their study visit, which was held on the same day as their follow-up appointment with their hepatologist. Subjects who did not have a scheduled liver biopsy were invited to participate by clinic staff, and consented by the research nurse on the day of their study visit.
Data Collection
Data were collected on the day subjects were scheduled to talk to their physician about treatment for HCV. Subjects were asked to come in 90 minutes before their appointment in order to participate in the study. All patients were informed of their biopsy results by their physician or APRN before completing the surveys.
All data were collected in a private room with the help of the research nurse. We elicited treatment preferences using Adaptive Conjoint Analysis (ACA, Sawtooth Software ). Conjoint analysis is a well-validated tool originally developed to understand consumer preferences and predict market shares of innovative products (15-17) and is now an accepted method of eliciting preferences for health care (18, 19). When faced with complex decisions, people typically evaluate a number of characteristics and then make trade-offs to arrive at a final choice. Conjoint analysis evaluates these trade-offs to determine which combination of characteristics are most preferred. For example, consider having to choose from four insurance plans which differ on amount of co-pays, access to subspecialists, drug coverage, and deductibles. By asking subjects to trade-off between these characteristics using rating and paired comparison tasks (described in detail below), conjoint analysis can determine which plan is best suited to each individual subject and which characteristics most strongly influence their choice.
ACA is a specific type of conjoint analysis that elicits preferences using an interactive computer program. The method uses individual respondent's answers to update and refine upcoming questions through a series of graded-paired comparisons. Because of its interactive property, ACA is much more efficient than traditional surveys, and can generate precise preference data without information overload or respondent fatigue.
We composed an ACA questionnaire to elicit preferences for treatment of HCV with pegylated-interferon and ribavirin. Preferences were measured for two choices: 1. Treatment associated with mild side effects versus no treatment and 2. Treatment associated with severe side effects versus no treatment. The five treatment characteristics included in the ACA questionnaire were: benefit, need to monitor blood tests, and risks of flu-like illness, fatigue and depression. In order to ensure that subjects were presented with appropriate information related to potential benefits, we created eight versions of the ACA questionnaire corresponding to varying degrees of fibrosis on biopsy (none, mild, moderate or cirrhosis) and genotype (1 or 2). The characteristics were chosen based on themes which emerged during preparatory focus groups (20, 21). Probabilistic data were obtained from the literature (9, 10, 22) and all characteristics were described using patient testimonials obtained from the focus groups (see Appendix). In addition, we used pictographs to facilitate risk communication and to decrease framing bias.
The ACA task included two groups of questions. Participants were first asked to rate the importance of the difference between best and worst estimates of each characteristic on a nine-point scale (Figure 1). Second, respondents completed a series of paired comparisons (Figure 2). ACA constructs the paired comparison tasks by examining all possible ways for levels of each characteristic to be combined and uses the information obtained from each new paired comparison to select the next pair of options (23). Answers to these patient-specific questions are used to calculate values for each estimate of each medication characteristic. These values are then used to predict which option most closely suits each patient's individual priorities.
Before performing the ACA task, subjects completed a questionnaire to ascertain sociodemographic characteristics, alcohol and drug use (24-26), social support (27), overall health status (28), HCV-related quality of life (29), mental illness, trust in physician (30) and decisional conflict (31). For patients without cirrhosis, we also ascertained subjects' perceived risk of developing cirrhosis without treatment on a 0 to 100 numeric rating scale (32).
Statistical Analyses
Patient characteristics were entered into SAS computer files (SAS Software, version 9.1, SAS Institute, Inc., Cary, North Carolina). Preference data derived from ACA were imported into and merged with the patient characteristics data set. ACA predicts preferences based on the utilities derived from the conjoint questionnaire using least squares regression analysis. In this context "utility" is a number that represents the value a respondent associates with a particular characteristic, with higher utilities indicating increased value. Market simulators are used to convert the raw utilities into preferences for specific options based on the assumption that subjects' prefer the option with the highest utility. Details on how ACA calculates utilities and predicts treatment preferences using this model have been previously published (23, 33).
We examined the association between patient characteristics and treatment preference using the Mann-Whitney U test and chi-square statistic for continuous and categorical variables respectively. We subsequently calculated adjusted odds ratios and 95% confidence intervals using logistic regression. All variables found to be significant at p 0.05 in bivariate analyses were entered into the model. We used the post-estimation Wald test to assess the individual contribution of each variable. Given possible differences between the VA and University settings, we also performed exploratory subgroup analyses by site. Lastly, in order to gain insight into the factors influencing subjects' decisions, we calculated the impact of each characteristic on subjects' preferences by dividing the range of values for each characteristic by the sum of ranges, and multiplying by 100. The relative importances are proportions and sum to 100 (34).
Results
Patient Characteristics
Of 212 eligible subjects, 178 agreed to participate and 140 completed the ACA task. The computer task was not performed in 38 eligible patients for the following reasons: 21 patients cancelled or did not come to their appointment, six patients did not have the time to complete the task, eight could not participate because of a scheduling error, and the computer malfunctioned on three occasions. The mean ( SD) age of the sample was 51 8, 85% were male, 59% were White and 30% Black. Further details regarding subjects' characteristics are provided in Table 1.
Treatment Preferences
Sixty-seven percent (N=94) of subjects' preferred treatment for HCV if associated with mild side effects. The percentage of subjects preferring therapy decreased to 51% (N=72) when it was described as being associated with severe side effects.
The associations of subjects' characteristics and strength of treatment preference for treatment described as being associated with severe side effects are reported in Tables 2a and 2b. In unadjusted analyses, preferences for treatment of HCV were stronger among women, as well as subjects with a higher perceived risk of developing cirrhosis, more severe liver disease, and worse HCV-related quality of life. Those with greater decisional conflict had weaker preferences for treatment. The individual contribution of each variable is reported in Table 3. In this model, female gender and extent of underlying liver disease were the strongest predictors of preference for HCV treatment. We found no other relationships between the remaining demographic characteristics, the use of drugs or alcohol, current health status, social support or trust in physician and treatment preference. No differences were found when the analyses were repeated for treatment associated with mild side effects.
Given that we recruited patients from two distinct sites, we also performed exploratory subgroup analyses by site (Tables 4a and 4b). In these analyses, patients reporting a history of mental illness were more likely to prefer treatment at the VA (60% versus 37%, p=0.02), but less likely to prefer treatment at the University Clinic (44% versus 71%, p=0.07). In addition, the extent of underlying liver disease was a strong predictor of preference at the VA, but not at the University Clinic (Table 5). At both sites, the majority of patients with either moderate or severe fibrosis preferred treatment. However, subjects at the University Clinic with mild or no fibrosis were more likely to prefer treatment compared to those recruited from the VA (50% versus 24%, p=0.08).
Relative Impact of Treatment Characteristics
The relative impact of each treatment characteristic on subjects' preferences is illustrated in Figure 3. Overall, the likelihood of benefit was most important to subjects and the need for blood test monitoring least important. The risk of fatigue, depression and flu-like illness all had similar influences on subjects' preferences. Table 6 displays the impact of each characteristic by severity of liver disease. These results demonstrate significant associations between the severity of liver disease on biopsy and the relative impact of risk and benefits, with subjects having more severe disease placing greater weight on the importance of expected benefits and less on the risk of toxicity compared to those with mild or no fibrosis. This pattern was also observed in subgroup analyses by site.
Discussion
In summary, we found that subjects' treatment preferences for HCV are driven primarily by the severity of underlying liver disease, perceived treatment effectiveness, and the severity of side effects. Our results suggest that, when effectively informed of the risks and benefits associated with treatment, patients' preferences vary substantially unless they have cirrhosis, in which case almost all patients have a strong preference to be treated. The risk of toxicity had the greatest influence on preference in patients with milder underlying liver disease, among which 53% prefer treatment that is associated with mild side effects where only 29% prefer treatment that is associated with severe side effects. In contrast, for those with more severe fibrosis (i.e. cirrhosis), the severity of toxicity had no effect on preferences, with the vast majority of these subjects (93%) choosing treatment under both conditions. Preferences mirror current recommendations that advocate antiviral therapy for patients who have more than portal fibrosis, including those with compensated cirrhosis (35).
We found that women had a stronger preference for treatment compared to men, although this association did not remain significant in multivariable analyses, possibly because of the small number of women recruited. We did not find any effect of age or race on patient preferences. These results suggest that reasons outside of patient preferences account for the lower frequency of treatment observed among women, older adults, and non-white patients found by Morrill et al (36) and Butt et al (37) respectively.
In this study, subjects with more uncertainty about treatment (as measured by decisional conflict) were less likely to prefer treatment compared to their counterparts with similar disease severity, indicating that subjects with greater uncertainty may be more likely to opt out of treatment than to consent. Although there are no data linking decision conflict to compliance in HCV, the latter also emphasizes the importance of decreasing decisional conflict (through education and support) before beginning treatment to maximize the likelihood of adherence.
Exploratory subgroup analyses by site revealed several interesting findings. First, treatment preferences amongst subjects recruited from the VA were strongly related to severity of underlying liver disease and perceived risk of cirrhosis where as this association was much weaker amongst subjects cared for at the University Clinic. This difference may be due to the formal education classes that most veterans with HCV attend. An alternative explanation may be that subjects outside of the VA may have stronger opinions regarding therapy before consulting with a specialist compared to the veteran population. In addition, we found that subjects who reported having a history of mental illness were more likely to prefer therapy at the VA, while they were less likely to prefer therapy in the University Clinic. This difference is almost certainly due to the specialized care afforded to patients with co-morbidities at the Hepatitis C Resource Centers developed within the VA healthcare system.
The strengths of this study lie in the methods used to evaluate preferences as well as the successful recruitment of a substantial number of patients at the time of decision making. ACA measures preferences based on how subjects evaluate specific risks and benefits. Values are computed based on how each respondent makes trade-offs between competing risks and benefits related to the treatment options under consideration. These values are then used to predict which option most closely suits each patient's individual priorities. Therefore, subjects do not evaluate treatment alternatives directly and preferences are not biased by physicians' preferences. Use of conjoint analysis also enabled us to quantify the importance that patients attach to specific medication characteristics, thus making it possible to gain insight into the reasons underlying differences in patients' preferences. In addition, we conducted preparatory focus groups to ensure that questionnaires included information salient to patients and provided subjects with patient testimonials describing varying levels of severity for each side effect so that they would understand the impact of toxicity from the patients' perspective. We also used human figure graphs to facilitate understanding of probabilistic information and to decrease framing bias (by emphasizing the denominator).
There are also several limitations of this study, most notably the small number of subjects recruited from the Hospital Clinic and consequently the small number of women included. The subgroup results should therefore be viewed as hypotheses generating and should be confirmed in larger clinics. Although we did recruit subjects from two distinct settings, participants may not be representative of other community-based samples. We could not include all medication characteristics, since this would have overly complicated the questionnaire. We therefore chose medication characteristics based on the results of focus groups.
Whether or not to choose treatment for HCV is a difficult decision for many patients. Treatment is usually recommended for those with moderate to severe liver disease and our results demonstrate that many patients' preferences are concordant with this view. Predictors of treatment preferences differ by site of care, suggesting that further research is needed to evaluate the individual impact of educational programs, patient factors and physicians' opinions on patients' preferences and ultimately on treatment decision making and outcomes in HCV.
Acknowledgements
We very much appreciate the support of Carol Eggers, APRN and Martha Shea, RN who greatly facilitated recruitment for this study.
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Table 1. Subjects' Characteristics.
Characteristic Number (%) Total = 140
Age, years (mean SD)51 8
Male 119 (85)
Hispanic 19 (14)
Race: White82 (59)
Black43 (31)
Married33 (24)
At least some college education63 (45)
Employed54 (39)
Veteran96 (69)
Excellent or very good overall health status30 (21)
HCV-related quality of life* (median, range)18 (0-84)
Liver Biopsy: No fibrosis - Genotype 16 (4)
No fibrosis - Genotype 20
Mild fibrosis - Genotype 143 (31)
Mild fibrosis - Genotype 29 (6)
Moderate fibrosis - Genotype 159 (42)
Moderate fibrosis - Genotype 28 (6)
Cirrhosis - Genotype 19 (6)
Cirrhosis - Genotype 26 (4)
History of mental illness54 (39)
Alcohol abuse ( 5 drinks per day): Never50 (36)
Ever90 (64)
Table 1. Subjects' Characteristics (Cont'd)
Drug abuse: Never19 (14)
Ever121 (86)
*Possible range 0-100 with larger scores representing worse quality of life.
Table 2a. Associations between subject characteristics (dichotomous) and preferences.
CharacteristicPercent Preferring Treatment (N)P value
RaceWhite
Other50 (41)
53 (31)0.7
GenderMale
Female48 (57)
71 (15)0.05
Marital statusMarried
Not married39 (13)
55 (59)0.1
EducationSome college
No college49 (31)
53 (41)0.6
Employment statusEmployed
Unemployed43 (23)
57 (49)0.1
SiteVeteran
Nonveteran47 (45)
61 (27)0.1
Health statusExcellent/very good
Good/fair/poor40 (12)
55 (60)0.2
Mental illnessNo
Yes48 (41)
55 (30)0.4
Degree of fibrosis Mild/none
Moderate/severe 29 (17)
67 (55)0.0001
Genotype1
249 (54)
74 (17)0.03
Alcohol useNever
Ever58 (29)
48 (43)0.2
Drug abuseNever
Ever53 (10)
51 (62)0.9
Table 2b. Associations between subject characteristics (continuous) and preferences.
Median (range)
CharacteristicSubjects preferring treatment (N=72)Subjects not preferring treatment (N=68)P value
Age 52 (23-64)53 (26-70)0.1
HCV-related quality of life28 (0-77)14 (0-84)0.03
Expectation of developing cirrhosis 50 (0-100)33 (0-100) <0.0004
Social support 62 (5-100)63 (12-100)0.7
Trust in physician66 (36-77)64 (45-80)0.1
Decisional conflict 25 (0-58)34 (0-73)0.02
Table 3. Associations between subject characteristics and preferences.
CharacteristicAdjusted Odds Ratio (95% CI)
Gender4.56 (1.35 - 15.44)
HCV-related quality of life1.02 (1.00 - 1.03)
Expectation of developing cirrhosis1.02 (1.00 - 1.03)
Degree of fibrosis4.67 (2.03 - 10.76)
Decisional conflict0.97 (0.94 - 0.99)
Table 4a. Associations between subject characteristics (dichotomous) and preferences by site.
VA Clinic SubjectsUniversity Clinic Subjects
CharacteristicPercent Preferring Treatment (N= 45)P valuePercent Preferring Treatment (N = 27)P value
RaceWhite
Other46 (24)
48 (21)0.957 (17)
71 (10)0.3
Gender*Male
Female--50 (12)
75 (15)0.09
Marital statusMarried
Not married32 (7)
51 (38)0.155 (6)
64 (21)0.6
EducationSome college
No college46 (24)
48 (21)0.964 (7)
61 (20)0.9
Employment Employed
Unemployed36 (13)
53 (32)0.156 (10)
65 (17)0.5
Alcohol abuse
Never
Ever52 (15)
45 (30)0.567 (14)
56 (13)0.5
Drug abuse
Never
Ever33 (4)
49 (41)0.386 (6)
57 (21)0.1
Health statusExcellent/very good
Good/fair/poor36 (8)
50 (37)0.350 (4)
64 (23)0.5
Table 4a. Cont'd.
Mental illnessNo
Yes37 (21)
60 (23)0.0271 (20)
44 (7)0.07
Degree of fibrosis Moderate/severe
Mild/None68 (34)
24 (11)<0.000166 (21)
50 (6)0.3
Genotype1
245 (35)
64 (9)0.256 (19)
89 (8)0.08
* Unable to test the effect of gender at the VA.
Table 4b. Associations between subject characteristics (continuous) and preferences by site.
Median (range)
VA Clinic SubjectsMedian (range)
University Clinic Subjects
CharacteristicPreferring treatment
(N=45)Not preferring treatment
(N=51)P valuePreferring treatment (N=27)Not preferring treatment (N=17)P value
Age 53 (39-64)54 (34-70)0.946 (23-64)51 (26-69)0.1
HCV-related quality of life27 (90-70)11 (0-80)0.0830 (2-77)18 (0-84)0.5
Expectation of developing cirrhosis 50 (0-100)20 (0-85)0.000250 (0-100)50 (0-100)0.8
Social support 53 (5-98)62 (12-99)0.184 (32-100)67 (29-100)0.2
Trust in physician66 (36-75)64 (45-79)0.266 (48-77)64 (50-77)0.7
Decisional conflict 25 (0-50)33 (90-73)0.129 (0-58)42 (12-71)0.03
Table 5. Percent of subjects choosing treatment by severity of liver disease by site
SiteNo/Mild Fibrosis (N)Moderate Fibrosis (N)Severe Fibrosis (N)P Value
VA24% (11)61% (25)100% (9)<0.0001
University Clinic50% (6)61% (16)83% (5)<0.4
Table 6. Relative impact of treatment characteristics on subjects' preferences by severity of liver disease
Relative Impact**
CharacteristicMild/No FibrosisModerate FibrosisSevere Fibrosis
Benefit33 2451 25*64 16*
Monitor 7 5 8 6 8 7
Flu20 1113 11* 10 7*
Fatigue21 1213 10* 9 7*
Depression19 1115 15 9 8*
*p<0.05 compared to Mild/None (least square means adjusted for multiple comparisons using Tukey's procedure).
** The relative impact reflect the impact that each characteristic has on subjects' preferences. The values are proportions that
IMPACT:
We have now described patients percepectives related to the decision making process in HCV - and patietns expereinces related to adverse effects. THe updated abstract represents the results of the latest paper and describes the variability of patient preferences in this disorder using ACA. We are now currently working on the next manuscript which will describe the psychometrix proerties of the ACA survey.
PUBLICATIONS:
Journal Articles
- Fraenkel L, McGraw S, Wongcharatrawee S, Garcia-Tsao G. Patients' experiences related to anti-viral treatment for hepatitis C. Patient Education and Counseling. 2006; 62(1): 148-55.
