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Background: Some patients with chronic hepatitis C virus (HCV) infection forego treatment due to concerns about treatment efficacy, treatment duration, and side effects., Objective: The purpose of this study was to quantify patient preferences among possible outcomes associated with new, direct-acting antiviral agents (DAAs) for the treatment of HCV infection and determine which treatment features are most important to patients in making treatment decisions., Methods: Adult participants with a self-reported physician diagnosis of HCV infection in five countries completed a web-enabled, choice-format conjoint analysis survey. The survey presented participants with 20 treatment-choice questions. Each treatment-choice question included a pair of hypothetical treatment profiles with varying levels of six attributes: treatment duration; chance of getting rid of the virus completely (i.e. likelihood of a sustained virologic response [SVR]); weeks on an additional, third medicine (i.e. a DAA); risk of a severe rash; risk of severe anemia; and number of times a day the third medicine is taken. Treatment-choice questions were based on a pre-determined experimental design with known statistical properties. Random-parameters logit was used to estimate preference weights for all attribute levels and the mean relative importance of each attribute., Results: 284 participants completed the survey. Likelihood of an SVR was the most important outcome to participants, followed by severe anemia risk, severe rash risk, therapy type (a combination of total weeks of treatment and weeks on the third medicine), and dosing of the third medicine. Controlling for other factors, preferences were similar across all therapy types examined., Conclusion: Patients with HCV infection indicate a willingness to accept an increased risk of side effects for sufficient improvement in the likelihood of treatment response.

The evolution of meningococcal vaccines illustrates nicely the incremental technological process that is common to much medical innovation and particularly appropriate to economic analysis. However, the economic evaluation of vaccines is complicated by several features unique to vaccines, including the possibility of indirect (herd immunity) benefits and the shear breadth of vaccination strategies available to decision makers. As with the vaccines themselves, the modelling approaches applied to the economic evaluation of meningococcal vaccines have evolved to become increasingly complex. Despite such innovation, concerns remain about the quality of economic studies of meningococcal vaccines. This article reviews evaluations of meningococcal vaccine strategies in developed countries and provides recommendations for future work in this area. Four potentially problematic areas in the existing literature are identified - indirect costs, herd immunity, quality of life and comparison programmes - and approaches to these issues are discussed. Recommendations for reporting a 'minimum analysis profile' case are also made. Although comparisons across studies are difficult at best, recent work shows that incorporating the indirect benefits and costs of vaccination substantially improves cost effectiveness.

Background: Abacavir sulfate (abacavir) is associated with a hypersensitivity reaction (HSR) that affects 5-8% of patients. While serious complications are rare, failure to identify it, or abacavir re-challenge following HSR, can be fatal. Genetic screening for HLA-B*5701 can identify patients who are likely to experience an HSR and reduces the incidence of the reaction., Objective: We assessed the intrinsic and practical value, from the US healthcare system perspective, of prospective HLA-B*5701 screening among a population of antiretroviral-naive patients without elevated risk factors for cardiovascular disease, plasma HIV RNA >100,000 copies/mL, or pre-existing renal insufficiency., Methods: Two approaches were used to evaluate the costs and benefits of prospective screening. First, the efficiency of HLA-B*5701 screening compared with no screening prior to abacavir initiation (intrinsic value of screening) was evaluated using a 60-day decision-tree model. Next, the practical value of screening was assessed using a lifetime discrete-event simulation model that compared HLA-B*5701 screening prior to abacavir use versus initiation with a tenofovir-containing regimen. Screening-effectiveness parameters were taken from an open-label trial that incorporated screening prior to abacavir initiation and other published studies. Treatment efficacy was derived from clinical trials. Modelling assumptions, costs ($US, year 2007 values) and other parameters were derived from published sources, primary data analysis and expert opinion. Multiple one-way sensitivity and scenario analyses were performed to assess parameter uncertainty. The primary outcome measure for the short-term screening versus no screening analysis was cost per patient. For the long-term analysis, outcomes were presented as QALYs. Costs and effects were discounted at 3% per year., Results: Over the first 60 days of treatment, prospective screening prior to abacavir initiation cost an additional $US17 per patient and avoided 537 HSRs per 10,000 patients. The per-patient cost of screening was sensitive to the cost of the genetic test, HSR costs and screening performance. In the lifetime model, screening-informed abacavir use was more effective and less costly than initiation with a tenofovir-containing regimen in the base case and in sensitivity analyses., Conclusions: Our results suggest that prospective HLA-B*5701 screening prior to abacavir initiation produces cost savings and should become a standard component of HIV care.

For many treatments, a patient's prognosis may be refined by conducting a mid-therapy assessment (MTA). When therapy lasts some time, an MTA predicts a patient's ultimate outcome based on early signs of response. The availability and timing of such assessments also serve as a mechanism by which policy makers can influence patients' treatment-initiation decisions. In this article, we examine how patients evaluate treatment options using MTAs of treatment effectiveness and discuss the policy implications of patient decision making in such contexts. To this end, we use indifference-curve analysis to characterize trade-offs between potential treatment success versus potential adverse effects in therapeutic situations where patients are encouraged to consider their own motivations for initiating treatment. Treatment initiation for chronic hepatitis C virus (HCV) infection is used for illustration, but the analysis can be easily adapted to a variety of clinical scenarios.Analysis shows that the existence of MTAs influences patients' initial treatment decisions by affecting the expectations of treatment success and adverse effects. Earlier MTAs have lower sensitivity and higher specificity, and the prior expectation of adverse effects is accordingly lower for two reasons. First, the lower chance for a false signal of treatment response and the higher chance for a false signal of no response make it more likely that the patient receives a signal leading to discontinuation. Second, if a signal to discontinue is received, fewer weeks of adverse effects have been endured before discontinuation occurs. Both factors make the expected burden of adverse effects lower when the MTA occurs earlier. Later MTAs have higher sensitivity and lower specificity, both of which serve to increase the probability of treatment completion, causing a higher prior likelihood of treatment success.These findings indicate that treatment demand may be increased by changing the timing of MTAs, depending on the information content of the alternative mid-therapy signals and the nature of patient preferences. In HCV infection and other diseases, clinical practice guidelines for conducting MTAs might be modified to better achieve public health or other policy objectives by studying the economics of patient choice.

Objective: The choice of evidence used in decision modelling of healthcare interventions divides analysts into 2 groups: (i) those who favour randomised clinical trial (RCT) data; and (ii) those who prefer 'real world' data. This preference may have serious consequences if the end result is to inform healthcare policy. This paper uses Medicare coverage of epoetin-alpha [erythropoietin (EPO)] as a case study to illustrate a technique which can be used to overcome some of the bias inherent in RCT data while avoiding some of the common pitfalls associated with the use of observational data., Design and Setting: Cost analysis of 2 treatments for anaemia of renal failure primarily in an outpatient setting is modelled in a decision tree. This method can be used to analyse healthcare interventions or policies in any setting., Patients and Participants: Patients with nontransplanted end-stage renal disease (ESRD) who received either EPO or blood transfusion for treatment of anaemia at any time during the 1-year study period (July 1989 to June 1990) were included in the sample., Methods: Outcome effects in the natural setting are decomposed into 2 parts: a treatment effect and a population effect. This is then extended to the special case of policy analysis. Logistic and multiple regression are used to estimate branch probabilities and payoffs, respectively, for 2 treatment options., Main Outcome Measures and Results: Under standard methods of decision analysis, an increase of $US7032 per patient following EPO coverage is observed. With the decomposition technique, the policy effect is estimated to be less, $US6172, the difference coming from the population effect., Conclusions: Failure to remove population effects from observed outcome effects may lead to biased decision-making. Although not directly observable, the population effect can be imputed from secondary data. The decomposition and imputting technique allows for a more meaningful interpretation of the results for the purpose of policy analysis.