401 research outputs found

    Approaches to Aggregation and Decision Making - A Health Economics Approach : An ISPOR Special Task Force Report [5]

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    The fifth section of our Special Task Force report identifies and discusses two aggregation issues: 1) aggregation of cost and benefit information across individuals to a population level for benefit plan decision making and 2) combining multiple elements of value into a single value metric for individuals. First, we argue that additional elements could be included in measures of value, but such elements have not generally been included in measures of quality-adjusted lifeyears. For example, we describe a recently developed extended costeffectiveness analysis (ECEA) that provides a good example of how to use a broader concept of utility. ECEA adds two features—measures of financial risk protection and income distributional consequences. We then discuss a further option for expanding this approach—augmented CEA, which can introduce many value measures. Neither of these approaches, however, provide a comprehensive measure of value. To resolve this issue, we review a technique called multicriteria decision analysis that can provide a comprehensive measure of value. We then discuss budget-setting and prioritization using multicriteria decision analysis, issues not yet fully resolved. Next, we discuss deliberative processes, which represent another important approach for population- or plan-level decisions used by many health technology assessment bodies. These use quantitative information on CEA and other elements, but the group decisions are reached by a deliberative voting process. Finally, we briefly discuss the use of stated preference methods for developing “hedonic” value frameworks, and conclude with some recommendations in this area

    Objectives, Budgets, Thresholds, and Opportunity Costs—A Health Economics Approach: An ISPOR Special Task Force Report [4]

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    The fourth section of our Special Task Force report focuses on a health plan or payer’s technology adoption or reimbursement decision, given the array of technologies, on the basis of their different values and costs. We discuss the role of budgets, thresholds, opportunity costs, and affordability in making decisions. First, we discuss the use of budgets and thresholds in private and public health plans, their interdependence, and connection to opportunity cost. Essentially, each payer should adopt a decision rule about what is good value for money given their budget; consistent use of a cost-per-qualityadjusted life-year threshold will ensure the maximum health gain for the budget. In the United States, different public and private insurance programs could use different thresholds, reflecting the differing generosity of their budgets and implying different levels of access to technologies. In addition, different insurance plans could consider different additional elements to the quality-adjusted life-year metric discussed elsewhere in our Special Task Force report. We then define affordability and discuss approaches to deal with it, including consideration of disinvestment and related adjustment costs, the impact of delaying new technologies, and comparative cost effectiveness of technologies. Over time, the availability of new technologies may increase the amount that populations want to spend on health care. We then discuss potential modifiers to thresholds, including uncertainty about the evidence used in the decision-making process. This article concludes by discussing the application of these concepts in the context of the pluralistic US health care system, as well as the “excess burden” of tax-financed public programs versus private programs

    Socioeconomic and geographic determinants of survival of patients with digestive cancer in France

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    Using a multilevel Cox model, the association between socioeconomic and geographical aggregate variables and survival was investigated in 81 268 patients with digestive tract cancer diagnosed in the years 1980–1997 and registered in 12 registries in the French Network of Cancer Registries. This association differed according to cancer site: it was clear for colon (relative risk (RR)=1.10 (1.04–1.16), 1.10 (1.04–1.16) and 1.14 (1.05–1.23), respectively, for distances to nearest reference cancer care centre between 10 and 30, 30 and 50 and more than 90 km, in comparison with distance of less than 10 km; P-trend=0.003) and rectal cancer (RR=1.09 (1.03–1.15), RR=1.08 (1.02–1.14) and RR=1.12 (1.05–1.19), respectively, for distances between 10 and 30 km, 30 and 50 km and 50 and 70 km, P-trend=0.024) (n=28 010 and n=18 080, respectively) but was not significant for gall bladder and biliary tract cancer (n=2893) or small intestine cancer (n=1038). Even though the influence of socioeconomic status on prognosis is modest compared to clinical prognostic factors such as histology or stage at diagnosis, socioeconomic deprivation and distance to nearest cancer centre need to be considered as potential survival predictors in digestive tract cancer

    Buying big into biotech: scale, financing, and the industrial dynamics of UK biotech, 1980--2009

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    This article explores how the UK's biotech firms have evolved in response to their financial environment. As investors' expectations about the potential of biotech have changed, funding options have opened up and closed down, leading firms to develop new business models and routes of technology development. After a favorable period, new constraints on stock market funding have forced UK biotech firms to compress their life cycles, constraining their ability to generate the late-stage drug candidates sought by large pharmaceutical firms. These changes are analyzed within a neo-Chandlerian framework in the context of a selection environment where rather than firms of varying inefficiencies being selected by an efficient market, we find entrepreneurs submitting themselves to an inefficient investment-selection process at the intersection of industries attempting to achieve their own scale economies. The article highlights the importance of the scale of investment at the firm and industry level, and suggests that decline in the size of the industry can have adverse consequences for investment and firm performance in this setting. Copyright 2013 The Author 2013. Published by Oxford University Press on behalf of Associazione ICC. All rights reserved., Oxford University Press

    Price regulation, new entry, and information shock on pharmaceutical market in Taiwan: a nationwide data-based study from 2001 to 2004

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    <p>Abstract</p> <p>Background</p> <p>Using non-steroidal anti-inflammatory drugs (NSAIDs) as a case, we used Taiwan's National Health Insurance (NHI) database, to empirically explore the association between policy interventions (price regulation, new drug entry, and an information shock) and drug expenditures, utilization, and market structure between 2001 and 2004.</p> <p>Methods</p> <p>All NSAIDs prescribed in ambulatory visits in the NHI system during our study period were included and aggregated quarterly. Segmented regression analysis for interrupted time series was used to examine the associations between two price regulations, two new drug entries (cyclooxygennase-2 inhibitors) and the rofecoxib safety signal and expenditures and utilization of all NSAIDs. Herfindahl index (HHI) was applied to further examine the association between these interventions and market structure of NSAIDs.</p> <p>Results</p> <p>New entry was the only variable that was significantly correlated with changes of expenditures (positive change, p = 0.02) and market structure of the NSAIDs market in the NHI system. The correlation between price regulation (first price regulation, p = 0.62; second price regulation, p = 0.26) and information shock (p = 0.31) and drug expenditure were not statistically significant. There was no significant change in the prescribing volume of NSAIDs per rheumatoid arthritis (RA) or osteoarthritis (OA) ambulatory visit during the observational period. The market share of NSAIDs had also been largely substituted by these new drugs up to 50%, in a three-year period and resulted in a more concentrated market structure (HHI 0.17).</p> <p>Conclusions</p> <p>Our empirical study found that new drug entry was the main driving force behind escalating drug spending, especially by altering the market share.</p
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