Drost RMWA, van der Putten IM, Ruwaard D, Evers SMAA, Paulus ATG. Conceptualizations of the Societal Perspective within Economic Evaluations: a Systematic Review. Int.J.Technol.Assess.Health Care. Epub 2017 Jun 23. PMID: 28641592.

OBJECTIVES:
The aim of this study was to investigate how the societal perspective is conceptualized in economic evaluations and to assess how intersectoral costs and benefits (ICBs), that is, the costs and benefits pertaining to sectors outside the healthcare sector, impact their results.
METHODS:
Based on a search in July 2015 using PubMed, Embase, CINAHL, and PsychINFO, a systematic literature review was performed for economic evaluations which were conducted from a societal perspective. Conceptualizations were assessed in NVivo version 11 using conventional and directed content analysis. Trial-based evaluations in the fields of musculoskeletal and mental disorders were analyzed further, focusing on the way ICBs impact the results of economic evaluations.
RESULTS:
A total of 107 studies were assessed, of which 74 (69.1 percent) provided conceptualizations of the societal perspective. These varied in types of costs included and in descriptions of cost bearers. Labor productivity costs were included in seventy-two studies (67.3 percent), while only thirty-eight studies (35.5 percent) included other ICBs, most of which entailed informal care and/or social care costs. ICBs within the educational and criminal justice sectors were each included five times. Most of the trial-based evaluations analyzed further (n = 21 of 28) reported productivity costs. In nine, these took up more than 50 percent of total costs. In several studies, criminal justice and informal care costs were also important.
CONCLUSIONS:
There is great variety in the way the societal perspective is conceptualized and interpreted within economic evaluations. Use of the term "societal perspective" is often related to including merely productivity costs, while other ICBs could be relevant as well.

DOI: http://dx.doi.org/10.1017/S0266462317000526.
PubMed: https://www.ncbi.nlm.nih.gov/pubmed/?term=28641592.

Samson AL, Schokkaert E, Thébaut C, Dormont B, Fleurbaey M, Luchini S, Van de Voorde C. Fairness in cost-benefit analysis: A methodology for health technology assessment. Health Econ. Epub 2017 Jun 16. PMID: 28620934.

We evaluate the introduction of various forms of antihypertensive treatments in France with a distribution-sensitive cost-benefit analysis. Compared to traditional cost-benefit analysis, we implement distributional weighting based on equivalent incomes, a new concept of individual well-being that does respect individual preferences but is not subjectively welfarist. Individual preferences are estimated on the basis of a contingent valuation question, introduced into a representative survey of the French population. Compared to traditional cost-effectiveness analysis in health technology assessment, we show that it is feasible to go beyond a narrow evaluation of health outcomes while still fully exploiting the sophistication of medical information. Sensitivity analysis illustrates the relevancy of this richer welfare framework, the importance of the distinction between an ex ante and an ex post approach, and the need to consider distributional effects in a broader institutional setting.

DOI: http://dx.doi.org/10.1002/hec.3515.
PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28620934.

Del Paggio JC. Deconstructing Clinical Trials-Help From Oncology Value Frameworks. JAMA Oncol. Epub 2017 Jun 1. PMID: 28570734.

[First paragraph]

The practice of oncology relies heavily on clinical trials. Trainees are faced with an overwhelming amount of data that must be meticulously critiqued to provide guidance to patients when deciding on the “right” therapy. This can pose a considerable challenge, particularly in an era where molecular oncology forms the foundation of many modern treatment regimens, and the basic science of oncology demands a trainee’s attention. How do trainees navigate through the haze of clinical trial evaluation?

DOI: http://dx.doi.org/10.1001/jamaoncol.2017.1312.
PubMed: https://www.ncbi.nlm.nih.gov/pubmed/?term=28570734.

Schnipper LE, Schilsky RL. Converging on the Value of Value Frameworks. J.Clin.Oncol. Epub 2017 Jun 06. PMID: 28586244.

[First paragraph, reference html links removed]

The relentless increase in health care costs has been difficult to control and is widely believed to be approaching a tipping point that threatens the economic well-being of many nations and their citizens. Although cancer represents only a fraction of total health care expenditures, economists are predicting an increase in the costs of cancer care in the United States from $120 billion in 2010 to $158 billion in 2020.1 Many factors contribute to this increase including larger numbers of patients with cancer, which is a reflection of the aging population; higher costs of hospitalizations and procedures; and longer survival while on treatment.1 The steepest increments in cost are projected to be related to the cost of cancer drugs.2

DOI: http://dx.doi.org/10.1200/JCO.2017.73.5704.
PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28586244.

Oortwijn W, Sampietro-Colom L, Habens F. Developments in Value Frameworks to Inform the Allocation of Healthcare Resources. Int.J.Technol.Assess.Health Care. Epub 2017 Jun 05. PMID: 28578735.

BACKGROUND:
In recent years, there has been a surge in the development of frameworks to assess the value of different types of health technologies to inform healthcare resource allocation. The reasons for, and the potential of, these value frameworks were discussed during the 2017 Health Technology Assessment International (HTAi) Policy Forum Meeting.
METHODS:
This study reflects the discussion, drawing on presentations from invited experts and Policy Forum members, as well as a background paper.
RESULTS:
The reasons given for a proliferation of value frameworks included: rising healthcare costs; more complex health technology; perceived disconnect between price and value in some cases; changes in societal values; the need for inclusion of additional considerations, such as ethical issues; and greater empowerment of clinicians and patients in defining and using value frameworks. Many Policy Forum participants recommended learning from existing frameworks. Furthermore, there was a desire to agree on the core components of value frameworks, defining the additional value elements as necessary and considering how they might be measured and used in practice. Furthermore, adherence to the principles of transparency, predictability, broad stakeholder involvement, and accountability were widely supported, along with being forward looking, explicit, and consistent across decisions.
CONCLUSIONS:
Value frameworks continue to evolve with significant implications for global incentives for innovation and access to health technologies. There is a role for the HTA community to address some of the key areas discussed during the meeting, such as defining the core components for assessing the value of a health technology.

DOI: http://dx.doi.org/10.1017/S0266462317000502.
PubMed: https://www.ncbi.nlm.nih.gov/pubmed/?term=28578735.

Cookson R, Mirelman AJ, Griffin S, Asaria M, Dawkins B, Norheim OF, Verguet S, J Culyer A. Using Cost-Effectiveness Analysis to Address Health Equity Concerns. Value Health. 2017 Feb;20(2):206-12. PMID: 28237196.

This articles serves as a guide to using cost-effectiveness analysis (CEA) to address health equity concerns. We first introduce the "equity impact plane," a tool for considering trade-offs between improving total health-the objective underpinning conventional CEA-and equity objectives, such as reducing social inequality in health or prioritizing the severely ill. Improving total health may clash with reducing social inequality in health, for example, when effective delivery of services to disadvantaged communities requires additional costs. Who gains and who loses from a cost-increasing health program depends on differences among people in terms of health risks, uptake, quality, adherence, capacity to benefit, and-crucially-who bears the opportunity costs of diverting scarce resources from other uses. We describe two main ways of using CEA to address health equity concerns: 1) equity impact analysis, which quantifies the distribution of costs and effects by equity-relevant variables, such as socioeconomic status, location, ethnicity, sex, and severity of illness; and 2) equity trade-off analysis, which quantifies trade-offs between improving total health and other equity objectives. One way to analyze equity trade-offs is to count the cost of fairer but less cost-effective options in terms of health forgone. Another method is to explore how much concern for equity is required to choose fairer but less cost-effective options using equity weights or parameters. We hope this article will help the health technology assessment community navigate the practical options now available for conducting equity-informative CEA that gives policymakers a better understanding of equity impacts and trade-offs.

FREE FULL TEXT: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5340318/
DOI: http://dx.doi.org/10.1016/j.jval.2016.11.027.
PubMed: https://www.ncbi.nlm.nih.gov/pubmed/?term=28237196.
PubMed Central: https://www.ncbi.nlm.nih.gov/pubmed/?term=PMC5340318.

Hawkins N, Grieve R. Extrapolation of Survival Data in Cost-effectiveness Analyses: The Need for Causal Clarity. Med.Decis.Making. 2017 May;37(4):337-9. PMID: 28355938.

[First paragraph]

Decision makers require predictions of cost-effectiveness over a time horizon that is sufficient to capture material differences in costs and health outcomes across relevant comparators. The ideal Randomized Controlled Trial (RCT) for cost-effectiveness analysis (CEA) would: include all relevant comparators, produce unbiased precise estimates when analyzed as randomized, and measure all endpoints relevant to the decision over a sufficient time horizon. Such trials are rare. To fill this gap, decision analytical models are developed to synthesize the available evidence in an attempt to recreate an “ideal trial”. Decision models that include “time to event” endpoints typically need to extrapolate beyond the observed data to account for censoring.

FREE FULL TEXT: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5459311/pdf/nihms862159.pdf
DOI: http://dx.doi.org/10.1177/0272989X17697019.
PubMed: https://www.ncbi.nlm.nih.gov/pubmed/?term=28355938.

Jim HS, McLeod HL. American Society of Clinical Oncology Value Framework: Importance of Accurate Toxicity Data. J.Clin.Oncol. 2017 Apr;35(10):1133-4. PMID: 28165908.

[First paragraph, reference html links removed]

The ASCO value framework1,2 provides an important first step in the evaluation of the clinical benefit of cancer treatments against their toxicity and costs. Separate algorithms were created for advanced and potentially curable disease with the expectation that survival and toxicity data from a head-to-head clinical trial would be input into the relevant algorithm to arrive at a net health benefit score that compared two regimens. The net health benefit score, together with the cost of the regimen, is intended to form the basis of shared decision making between oncologist and patient to arrive at the best treatment option for that patient.

DOI: http://dx.doi.org/10.1200/JCO.2016.69.2079.
PubMed: https://www.ncbi.nlm.nih.gov/pubmed/?term=28165908.

Phelps CE, Madhavan G. Using Multicriteria Approaches to Assess the Value of Health Care. Value Health. 2017 Feb;20(2):251-5. PMID: 28237204.

Practitioners of cost-utility analysis know that their models omit several important factors that often affect real-world decisions about health care options. Furthermore, cost-utility analyses typically reflect only single perspectives (e.g., individual, business, and societal), further limiting the value for those with different perspectives (patients, providers, payers, producers, and planners-the 5Ps). We discuss how models based on multicriteria analyses, which look at problems from many perspectives, can fill this void. Each of the 5Ps can use multicriteria analyses in different ways to aid their decisions. Each perspective may lead to different value measures and outcomes, whereas no single-metric approach (such as cost-utility analysis) can satisfy all these stakeholders. All stakeholders have unique ways to measure value, even if assessing the same health intervention. We illustrate the benefits of this approach by comparing the value of five different hypothetical treatment choices for five hypothetical patients with cancer, each with different preference structures. Nine attributes describe each treatment option. We add a brief discussion regarding the use of these approaches in group-based decisions. We urge that methods to value health interventions embrace the multicriteria approaches that we discuss, because these approaches 1) increase transparency about the decision process, 2) allow flight simulator-type evaluation of alternative interventions before actual investment or deployment, 3) help focus efforts to improve data in an efficient manner, 4) at least in some cases help facilitate decision convergence among stakeholders with differing perspectives, and 5) help avoid potential cognitive errors known to impair intuitive judgments.

DOI: http://dx.doi.org/10.1016/j.jval.2016.11.011.
PubMed: https://www.ncbi.nlm.nih.gov/pubmed/?term=28237204.

Sculpher M, Claxton K, Pearson SD. Developing a Value Framework: The Need to Reflect the Opportunity Costs of Funding Decisions. Value Health. 2017 Feb;20(2):234-9. PMID: 28237201.

A growing number of health care systems internationally use formal economic evaluation methods to support health care funding decisions. Recently, a range of organizations have been advocating forms of analysis that have been termed "value frameworks." There has also been a push for analytical methods to reflect a fuller range of benefits of interventions through multicriteria decision analysis. A key principle that is invariably neglected in current and proposed frameworks is the need to reflect evidence on the opportunity costs that health systems face when making funding decisions. The mechanisms by which opportunity costs are realized vary depending on the system's financial arrangements, but they always mean that a decision to fund a specific intervention for a particular patient group has the potential to impose costs on others in terms of forgone benefits. These opportunity costs are rarely explicitly reflected in analysis to support decisions, but recent developments to quantify benefits forgone make more appropriate analyses feasible. Opportunity costs also need to be reflected in decisions if a broader range of attributes of benefit is considered, and opportunity costs are a key consideration in determining the appropriate level of total expenditure in a system. The principles by which opportunity costs can be reflected in analysis are illustrated in this article by using the example of the proposed methods for value-based pricing in the United Kingdom.

DOI: http://dx.doi.org/10.1016/j.jval.2016.11.021.
PubMed: https://www.ncbi.nlm.nih.gov/pubmed/?term=28237201.

Shi CR, Nambudiri VE. Research Techniques Made Simple: Cost-Effectiveness Analysis. J.Invest.Dermatol. 2017 Jul;137(7):e143-7. PMID: 28647028.

Cost-effectiveness analysis (CEA) is a research method used to determine the clinical benefit-to-cost ratio of a given intervention. CEA offers a standardized means of comparing cost-effectiveness among interventions. Changes in quality-adjusted life-years, disability-adjusted life-years, or survival and mortality are some of the common clinical benefit measures incorporated into CEA. Because accounting for all associated costs and benefits of an intervention is complex and potentially introduces uncertainty into the analysis, sensitivity analyses can be performed to test the analytic model under varying conditions. CEA informs the identification of high-value clinical practices and can be used to evaluate preventative, diagnostic, and therapeutic interventions in dermatology.

DOI: http://dx.doi.org/10.1016/j.jid.2017.03.004.
PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28647028.

Sorenson C, Lavezzari G, Daniel G, Burkholder R, Boutin M, Pezalla E, Sanders G, McClellan M. Advancing Value Assessment in the United States: A Multistakeholder Perspective. Value Health. 2017 Feb;20(2):299-307. PMID: 28237214.

Rising costs without perceived proportional improvements in quality and outcomes have motivated fundamental shifts in health care delivery and payment to achieve better value. Aligned with these efforts, several value assessment frameworks have been introduced recently to help providers, patients, and payers better understand the potential value of drugs and other interventions and make informed decisions about their use. Given their early stage of development, it is imperative to evaluate these efforts on an ongoing basis to identify how best to support and improve them moving forward. This article provides a multistakeholder perspective on the key limitations and opportunities posed by the current value assessment frameworks and areas of and actions for improvement. In particular, we outline 10 fundamental guiding principles and associated strategies that should be considered in subsequent iterations of the existing frameworks or by emerging initiatives in the future. Although value assessment frameworks may not be able to meet all the needs and preferences of stakeholders, we contend that there are common elements and potential next steps that can be supported to advance value assessment in the United States.

DOI: http://dx.doi.org/10.1016/j.jval.2016.11.030.
PubMed: https://www.ncbi.nlm.nih.gov/pubmed/?term=28237214.

Grosse SD, Khoury MJ. Epidemiology matters: peering inside the "black box" in economic evaluations of genetic testing. Genet.Med. 2016 Oct;18(10):963-5. PMID: 27608172.

[First paragraph, reference html links removed]

Cost-effectiveness analyses (CEAs) of genetic testing are increasing in frequency. Such analyses can inform providers, policy makers, and payers’ coverage decisions.1 CEAs usually measure improved health outcomes in terms of either life-years gained (LYG) or quality-adjusted life-years saved (QALYs). If the cost of purchasing a LYG or QALY through the use of a new intervention (“incremental cost-effectiveness ratio”; ICER) is favorable relative to established interventions, the new intervention is considered costeffective (i.e., good value for money). Because cost-effectiveness estimates are dependent on assumptions, it is recommended that analysts conduct sensitivity analyses to determine how sensitive cost-effectiveness conclusions are to uncertainty in the model parameters

FREE FULL TEXT: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5459311/pdf/nihms862159.pdf
DOI: http://dx.doi.org/10.1038/gim.2016.121.
PubMed: https://www.ncbi.nlm.nih.gov/pubmed/?term=27608172.
PubMed Central: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5459311.