Search

IntroductionUtility instruments are used to assess patients' health-related quality of life for cost-utility analysis (CUA). However, for cancer patients, the dimensions of generic utility instruments may not capture all the information relevant to the impact of cancer. Cancer-specific utilities provide a useful alternative. Under the auspices of the Multi-Attribute Utility in Cancer Consortium, a cancer-specific utility algorithm was derived from the FACT-G. The new FACT-8D contains eight dimensions: pain, fatigue, nausea, sleep, work, support from family/friends, sadness, and worry health will get worse. The aim of the study was to obtain a Canadian value set for the FACT-8D.MethodsA discrete choice experiment was administered to a Canadian general population online panel, quota sampled by age, sex, and province/territory of residence. Respondents provided responses to 16 choice sets. Each choice set consisted of two health states described by the FACT-8D dimensions plus an attribute representing survival duration. Sample weights were applied and the responses were analyzed using conditional logistic regression, parameterized to fit the quality-adjusted life year framework. The results were converted into utility weights by evaluating the marginal rate of substitution between each level of each FACT-8D dimension with respect to duration.Results2228 individuals were recruited. The analysis dataset included n = 1582 individuals, who completed at least one choice set; of which, n = 1501 completed all choice sets. After constraining to ensure monotonicity in the utility function, the largest decrements were for the highest levels of pain (- 0.38), nausea (- 0.30), and problems doing work (- 0.23). The decrements of the remaining dimensions ranged from - 0.08 to - 0.18 for their highest levels. The utility of the worst possible health state was defined as - 0.65, considerably worse than dead.ConclusionsThe largest impacts on utility included three generic dimensions (i

IntroductionUtility instruments are used to assess patients' health-related quality of life for cost-utility analysis (CUA). However, for cancer patients, the dimensions of generic utility instruments may not capture all the information relevant to the impact of cancer. Cancer-specific utilities provide a useful alternative. Under the auspices of the Multi-Attribute Utility in Cancer Consortium, a cancer-specific utility algorithm was derived from the FACT-G. The new FACT-8D contains eight dimensions: pain, fatigue, nausea, sleep, work, support from family/friends, sadness, and worry health will get worse. The aim of the study was to obtain a Canadian value set for the FACT-8D.MethodsA discrete choice experiment was administered to a Canadian general population online panel, quota sampled by age, sex, and province/territory of residence. Respondents provided responses to 16 choice sets. Each choice set consisted of two health states described by the FACT-8D dimensions plus an attribute representing survival duration. Sample weights were applied and the responses were analyzed using conditional logistic regression, parameterized to fit the quality-adjusted life year framework. The results were converted into utility weights by evaluating the marginal rate of substitution between each level of each FACT-8D dimension with respect to duration.Results2228 individuals were recruited. The analysis dataset included n = 1582 individuals, who completed at least one choice set; of which, n = 1501 completed all choice sets. After constraining to ensure monotonicity in the utility function, the largest decrements were for the highest levels of pain (- 0.38), nausea (- 0.30), and problems doing work (- 0.23). The decrements of the remaining dimensions ranged from - 0.08 to - 0.18 for their highest levels. The utility of the worst possible health state was defined as - 0.65, considerably worse than dead.ConclusionsThe largest impacts on utility included three generic dimensions (i

BackgroundAn important challenge with the application of next-generation sequencing technology is the possibility of uncovering incidental genomic findings. A paucity of evidence on personal utility for incidental findings has hindered clinical guidelines. Our objective was to estimate personal utility for complex information derived from incidental genomic findings.MethodsWe used a discrete-choice experiment to evaluate participants' personal utility for the following attributes: disease penetrance, disease treatability, disease severity, carrier status and cost. Study participants were drawn from the Canadian public. We analyzed the data with a mixed logit model.ResultsIn total, 1200 participants completed our questionnaire (available in English and French). Participants valued receiving information about high-penetrance disorders but expressed disutility for receiving information on low-penetrance disorders. The average willingness to pay was $445 (95% confidence interval [CI] $322-$567) to receive incidental findings in a scenario where clinicians returned information about high-penetrance, medically treatable disorders, but only 66% of participants (95% CI 63%-71%) indicated that they would choose to receive information in that scenario. On average, participants placed an important value ($725, 95% CI $600-$850) on having a choice about what type of findings they would receive, including receipt of information about high-penetrance, treatable disorders or receipt of information about high-penetrance disorders with or without available treatment. The predicted uptake of that scenario was 76% (95% CI 72%-79%).InterpretationMost participants valued receiving incidental findings, but personal utility depended on the type of finding, and not all participants wanted to receive incidental results, regardless of the potential health implications. These results indicate that to maximize benefit, participant-level preferences should inform the decision about whether to r

BackgroundScientific advances have led to the discovery of novel treatments with high prices. The cost to publicly fund high-cost drugs may threaten the sustainability of drug budgets in different health care systems. In oncology, there are concerns that health-benefit gains are diminishing over time and that the economic evidence to support funding decisions is too limited.MethodsTo assess the additional costs and benefits gained from oncology drugs over time, we used treatment protocols and efficacy results from U.S. Food and Drug Administration records to calculate cost-effectiveness ratios for drugs approved to treat first- and second-line metastatic or advanced breast, colorectal, and non-small cell lung cancer during the years 1994-2013. We assessed reimbursement recommendations reached by health technology assessment agencies in the U.K., Australia, and Canada.ResultsCost-effectiveness ratios were calculated for 50 drugs approved by the U.S. regulator. The more recent approvals were often based on surrogate efficacy outcomes and had extremely high costs, often triple the costs of drugs approved in previous years. Over time, the effectiveness gains have increased for some cancer indications; however, for other indications (non-small cell lung and second-line colorectal cancer), the magnitude of gains in effectiveness decreased. Reimbursement recommendations for drugs with the highest cost-effectiveness ratios were the most inconsistent.ConclusionEvaluation of the clinical benefits that oncology drugs offer as a function of their cost has become highly complex, and for some clinical indications, health benefits are diminishing over time. There is an urgent need for better economic evidence from oncology drug trials and systematic processes to inform funding decisions.Implications for practiceHigh-cost oncology drugs may threaten the ability of health care systems to provide access to promising new drugs for patients. In order to make better drug-funding decisio

Background Multi attribute utility (MAU) instruments are used to include the health related quality of life (HRQoL) in economic evaluations of health programs. Comparative studies suggest different MAU instruments measure related but different constructs. The objective of this paper is to describe the methods employed to achieve content validity in the descriptive system of the Assessment of Quality of Life (AQoL)-6D, MAU instrument. Methods The AQoL program introduced the use of psychometric methods in the construction of health related MAU instruments. To develop the AQoL-6D we selected 112 items from previous research, focus groups and expert judgment and administered them to 316 members of the public and 302 hospital patients. The search for content validity across a broad spectrum of health states required both formative and reflective modelling. We employed Exploratory Factor Analysis and Structural Equation Modelling (SEM) to meet these dual requirements. Results and Discussion The resulting instrument employs 20 items in a multi-tier descriptive system. Latent dimension variables achieve sensitive descriptions of 6 dimensions which, in turn, combine to form a single latent QoL variable. Diagnostic statistics from the SEM analysis are exceptionally good and confirm the hypothesised structure of the model. Conclusions The AQoL-6D descriptive system has good psychometric properties. They imply that the instrument has achieved construct validity and provides a sensitive description of HRQoL. This means that it may be used with confidence for measuring health related quality of life and that it is a suitable basis for modelling utilities for inclusion in the economic evaluation of health programs.

This book provides a concise synthesis of leading edge research in the theory and practise of efficiency measurement in health and health care. Whilst much of the literature in this area is confusing and impregnable, Hollingsworth and Peacock show the logical links between the economic theory underlying efficiency, the methods used in analysis and practical application of measurement techniques including Data Envelopment Analysis and Stochastic Frontiers Analysis. The book outlines which methods are most suitable in which setting, how to specify valid models, and how to undertake a study and effectively disseminate results. The current state of the art is assessed in terms of methods and published applications, and undertakes practical applications of advanced methods, including analysis of economies of scale and scope, variable weightings, specification testing, and estimation of the efficient production of health. Finally, the way forward in efficiency measurement in health is outlined, mapping out an agenda for future research and policy development.

This book provides a concise synthesis of leading edge research in the theory and practise of efficiency measurement in health and health care. Whilst much of the literature in this area is confusing and impregnable, Hollingsworth and Peacock show the logical links between the economic theory underlying efficiency, the methods used in analysis and practical application of measurement techniques including Data Envelopment Analysis and Stochastic Frontiers Analysis. The book outlines which methods are most suitable in which setting, how to specify valid models, and how to undertake a study and effectively disseminate results. The current state of the art is assessed in terms of methods and published applications, and undertakes practical applications of advanced methods, including analysis of economies of scale and scope, variable weightings, specification testing, and estimation of the efficient production of health. Finally, the way forward in efficiency measurement in health is outlined, mapping out an agenda for future research and policy development.