Methods for public health economic evaluation: A Delphi survey of decision makers in English and Welsh local government

Standard reference case methods recommended for health technology appraisals do not translate well to a public health setting. This paper reports on a Delphi survey designed to elicit views of public health decision makers in England and Wales, about different methodological elements of economic evaluation. This is important as methods should align with the objective function of decision makers. The Delphi survey comprised two rounds, with round 1 allowing open‐ended recommendations in addition to 5‐point Likert scale responses. The final survey comprised 36 questions, and levels and strength of agreement were assessed using median values and mean absolute deviation of the median. The Delphi panel (n = 66) achieved high levels of agreement for costs, health, well‐being, and productivity impact to be important elements within an economic evaluation. The panel agreed that evaluations should be relevant to the local context and include costs and consequences over a lifetime horizon. There was a call for the transparent reporting of costs and effects for different population subgroups, and for different sectors. Overall, the panel revealed a preference for a flexible approach, understanding that economic evidence fits within a dynamic process of decision making. These results provide empirical evidence to inform guidelines for public health economic evaluation.     

Decision makers' views on health care objectives and budget constraints: results from a pilot study

Economic evaluations aim to inform policy makers about the costs and effects of medical interventions to support their decisions on the allocation of health care resources. Decision makers combine information on cost-effectiveness with their preferences and with possible constraints for the allocation of health care resources. That is, decision makers need to specify an optimality criterion and all possible (budget) constraints. Usually this is a more or less implicit process. The aim of our pilot study was to find out whether decision makers consider the objectives and budget constraints we selected for a theoretical model of resource allocation relevant, and to set priorities for these objectives.     

Do Profit Margins of Pharmaceuticals Influence Reimbursement Decisions? A Discrete Choice Experiment Among Dutch Healthcare Decision Makers

ObjectivesThis study aimed to investigate whether the profit margins of pharmaceuticals would influence the outcome of reimbursement decisions within the Dutch policy context.MethodsWe conducted a discrete choice experiment among 58 Dutch decision makers. In 20 choice sets, we asked respondents to indicate which of 2 pharmaceutical treatment options they would select for reimbursement. Options were described using 5 attributes (disease severity, incremental costs per quality-adjusted life-year, health gain, budget impact, and profit margin) with 3 levels each. Additionally, cognitive debriefing questions were presented, and for validation debriefing, interviews were conducted. Choice data were analyzed using mixed logit models, also to calculate marginal effects and choice probabilities.ResultsResults indicated that the specified levels of profit margins significantly influenced choices made. Decision makers were less likely to reimburse a product with a higher profit margin. The relative importance of profit margins was lower than that of the included traditional health technology assessment criteria, but not negligible. When asked directly, 61% of respondents indicated that profit margin should play a role in reimbursement decision making, although concerns about feasibility and the connection to price negotiations were voiced.ConclusionsOur results suggest that if available to decision makers the profit margin of pharmaceutical products would influence reimbursement decisions within the Dutch policy context. Higher profit margins would reduce the likelihood of reimbursement. Whether adding profit margin as an additional, explicit criterion to the health technology assessment decision framework would be feasible and desirable is open to further exploration.     

Communicating the Parameter Uncertainty in the IQWiG Efficiency Frontier to Decision‐Makers

The Institute for Quality and Efficiency in Health Care (IQWiG) developed—in a consultation process with an international expert panel—the efficiency frontier (EF) approach to satisfy a range of legal requirements for economic evaluation in Germany's statutory health insurance system. The EF approach is distinctly different from other health economic approaches. Here, we evaluate established tools for assessing and communicating parameter uncertainty in terms of their applicability to the EF approach. Among these are tools that perform the following: (i) graphically display overall uncertainty within the IQWiG EF (scatter plots, confidence bands, and contour plots) and (ii) communicate the uncertainty around the reimbursable price. We found that, within the EF approach, most established plots were not always easy to interpret. Hence, we propose the use of price reimbursement acceptability curves—a modification of the well‐known cost‐effectiveness acceptability curves. Furthermore, it emerges that the net monetary benefit allows an intuitive interpretation of parameter uncertainty within the EF approach. This research closes a gap for handling uncertainty in the economic evaluation approach of the IQWiG methods when using the EF. However, the precise consequences of uncertainty when determining prices are yet to be defined. © 2014 The Authors. Health Economics published by John Wiley & Sons Ltd.     

Economic Evaluation of Vaccination Programs: A Guide for Selecting Modeling Approaches

ObjectivesIllustrate 3 economic evaluation methods whose value measures may be useful to decision makers considering vaccination programs.MethodsKeyword searches identified example publications of cost-effectiveness analysis (CEA), fiscal health modeling (FHM), and constrained optimization (CO) for economic evaluation of a vaccination program in countries where at least 2 of the methods had been used. We examined the extent to which different value measures may be useful for decision makers considering adoption of a new vaccination program. With these findings, we created a guide for selecting modeling approaches illustrating the decision-maker contexts and policy objectives for which each method may be useful.ResultsWe identified 8 countries with published evaluations for vaccination programs using >1 method for 4 infections: influenza, human papilloma virus, rotavirus, and malaria. CEA studies targeted health system decision makers using a threshold to determine the efficiency of a new vaccination program. FHM studies targeted public sector spending decision makers estimating lifetime changes in government tax revenue net of transfer payments. CO studies targeted decision makers selecting from a mix of options for preventing an infectious disease within budget and feasibility constraints. Cost and utility inputs, epidemiologic models, comparators, and constraints varied by modeling method.ConclusionsAlthough CEAs measures of incremental cost-effectiveness ratios are critical for understanding vaccination program efficiency for all decision makers determining access and reimbursement, FHMs provide measures of the program’s impact on public spending for government officials, and COs provide measures of the optimal mix of all prevention interventions for public health officials.     

Budget Impact Analysis—Principles of Good Practice: Report of the ISPOR 2012 Budget Impact Analysis Good Practice II Task Force

BackgroundBudget impact analyses (BIAs) are an essential part of a comprehensive economic assessment of a health care intervention and are increasingly required by reimbursement authorities as part of a listing or reimbursement submission.ObjectivesThe objective of this report was to present updated guidance on methods for those undertaking such analyses or for those reviewing the results of such analyses. This update was needed, in part, because of developments in BIA methods as well as a growing interest, particularly in emerging markets, in matters related to affordability and population health impacts of health care interventions.MethodsThe Task Force was approved by the International Society for Pharmacoeconomics and Outcomes Research Health Sciences Policy Council and appointed by its Board of Directors. Members were experienced developers or users of BIAs; worked in academia and industry and as advisors to governments; and came from several countries in North America and South America, Oceania, Asia, and Europe. The Task Force solicited comments on the drafts from a core group of external reviewers and, more broadly, from the membership of the International Society for Pharmacoeconomics and Outcomes Research.ResultsThe Task Force recommends that the design of a BIA for a new health care intervention should take into account relevant features of the health care system, possible access restrictions, the anticipated uptake of the new intervention, and the use and effects of the current and new interventions. The key elements of a BIA include estimating the size of the eligible population, the current mix of treatments and the expected mix after the introduction of the new intervention, the cost of the treatment mixes, and any changes expected in condition-related costs. Where possible, the BIA calculations should be performed by using a simple cost calculator approach because of its ease of use for budget holders. In instances, however, in which the changes in eligible population size, disease severity mix, or treatment patterns cannot be credibly captured by using the cost calculator approach, a cohort or patient-level condition-specific model may be used to estimate the budget impact of the new intervention, accounting appropriately for those entering and leaving the eligible population over time. In either case, the BIA should use data that reflect values specific to a particular decision maker’s population. Sensitivity analysis should be of alternative scenarios chosen from the perspective of the decision maker. The validation of the model should include at least face validity with decision makers and verification of the calculations. Data sources for the BIA should include published clinical trial estimates and comparator studies for the efficacy and safety of the current and new interventions as well as the decision maker’s own population for the other parameter estimates, where possible. Other data sources include the use of published data, well-recognized local or national statistical information, and, in special circumstances, expert opinion. Reporting of the BIA should provide detailed information about the input parameter values and calculations at a level of detail that would allow another modeler to replicate the analysis. The outcomes of the BIA should be presented in the format of interest to health care decision makers. In a computer program, options should be provided for different categories of costs to be included or excluded from the analysis.ConclusionsWe recommend a framework for the BIA, provide guidance on the acquisition and use of data, and offer a common reporting format that will promote standardization and transparency. Adherence to these good research practice principles would not necessarily supersede jurisdiction-specific BIA guidelines but may support and enhance local recommendations or serve as a starting point for payers wishing to promulgate methodology guidelines.     

偏远及农村地区卫生人力留用计划成本分析技术框架

背景：通过加强卫生人力留用以提高偏远和农村地区卫生人力的可及性,对于人口健康至关重要。然而,此类干预措施有关成本方面的信息往往零散、不全面、甚至缺失。政策措施的成本分析对于政策的选择、规划、实施和评估至关重要,但是由于缺少成本分析的系统方法,为卫生政策制定和决策带来了严峻的挑战。方法：本文提出了提高偏远及农村地区卫生人力可及性政策干预措施的成本分析实施框架,旨在为卫生政策决策者提供帮助。同时,该框架也强调了确定财政的主要来源和财政资金可持续性的重要性。文章对加强边远农村地区卫生人力留用相关政策措施的成本分析进行了文献综述,为此类干预措施的成本分析提供了指导框架,并进一步调查分析了成本分析在整个卫生人力规划与管理中的角色与重要意义。结果：尽管对于人力留用相关政策措施效益的争论得到越来越多的关注,但如何对此类政策措施进行成本分析,目前仍缺乏相关的知识与证据。为了解决这个问题,文章提出了实施成本分析框架的关键要素,并对此进行讨论分析。结论：这些关键要素能够帮助决策者对政策措施有更加深入的了解,明晰与理解财政来源和运行机制,同时确保财政资金的可持续性。     

Economic evaluation of programmes aiming at eradicating infectious diseases

The subject of this article is to discuss the global and regional economic aspects of programmes for eradicating infectious diseases. The perspective on the issue is that scarce health care resources should be employed in the best possible way. The task is then to examine what the best possible use might be. A starting point is by asking the relevant policy questions attached to eradications: Is there a suitable technique, what are the costs, and what are the benefits? Of particular importance is the opportunity cost of using resources on eradication when other health care problems may require more urgent donation of resources. The paper attempts to set up a generic model for economic evaluation of eradication programmes. But, as there are many different treatment strategies varying from one country to another, such a generic model may need modifications to suit a particular strategy. However, the paper is contextual and should only be viewed as guidance for those wishing to conduct an economic analysis of eradication programmes, or as a initiative to create discussion among health care decision makers.     

Applying Dynamic Simulation Modeling Methods in Health Care Delivery Research—The SIMULATE Checklist: Report of the ISPOR Simulation Modeling Emerging Good Practices Task Force

Health care delivery systems are inherently complex, consisting of multiple tiers of interdependent subsystems and processes that are adaptive to changes in the environment and behave in a nonlinear fashion. Traditional health technology assessment and modeling methods often neglect the wider health system impacts that can be critical for achieving desired health system goals and are often of limited usefulness when applied to complex health systems. Researchers and health care decision makers can either underestimate or fail to consider the interactions among the people, processes, technology, and facility designs. Health care delivery system interventions need to incorporate the dynamics and complexities of the health care system context in which the intervention is delivered. This report provides an overview of common dynamic simulation modeling methods and examples of health care system interventions in which such methods could be useful. Three dynamic simulation modeling methods are presented to evaluate system interventions for health care delivery: system dynamics, discrete event simulation, and agent-based modeling. In contrast to conventional evaluations, a dynamic systems approach incorporates the complexity of the system and anticipates the upstream and downstream consequences of changes in complex health care delivery systems. This report assists researchers and decision makers in deciding whether these simulation methods are appropriate to address specific health system problems through an eight-point checklist referred to as the SIMULATE (System, Interactions, Multilevel, Understanding, Loops, Agents, Time, Emergence) tool. It is a primer for researchers and decision makers working in health care delivery and implementation sciences who face complex challenges in delivering effective and efficient care that can be addressed with system interventions. On reviewing this report, the readers should be able to identify whether these simulation modeling methods are appropriate to answer the problem they are addressing and to recognize the differences of these methods from other modeling approaches used typically in health technology assessment applications.     

Gesundheitsökonomie und HTA

For a comprehensive health technology assessment (HTA), health economic studies are analysed as well as medical studies. Significance and effects of HTA are rather different in European countries. In Germany HTA is conducted by two official agencies: DIMDI and IQWiG. While DIMDI requires health economic assessments, IQWiG is prohibited from considering economic aspects by law (pure assessment of benefits). For the health economic assessment in HTA reports mainly secondary scientific evidence is exploited, i.e. primarily cost effectiveness and cost utility analyses. The study perspective, the study alternatives and the modelling methods are of great importance for the evaluation and have to be justified in depth. The cost effectiveness ratio and the budget impact are the most important results of the health economic assessment, which is the basis for the appraisal by the health care authorities. For this decision medical, utilitarian or other aspects like the avoidance of unwanted distribution effects are considered. On the other hand often only a few studies are available for the decision making when the appraisal has to be done. This problem might be less relevant in the near future as the number of HTAs and the number of health economic evaluations increases.     

Comparative effectiveness research: Policy context,methods development and research infrastructure

Comparative effectiveness research (CER) has received substantial attention as a potential approach for improving health outcomes while lowering costs of care, and for improving the relevance and quality of clinical and health services research. The Institute of Medicine defines CER as ‘the conduct and synthesis of systematic research comparing different interventions and strategies to prevent, diagnose, treat, and monitor health conditions. The purpose of this research is to inform patients, providers, and decision‐makers, responding to their expressed needs, about which interventions are most effective for which patients under specific circumstances.’ Improving the methods and infrastructure for CER will require sustained attention to the following issues: (1) Meaningful involvement of patients, consumers, clinicians, payers, and policymakers in key phases of CER study design and implementation; (2) Development of methodological ‘best practices’ for the design of CER studies that reflect decision‐maker needs and balance internal validity with relevance, feasibility and timeliness; and (3) Improvements in research infrastructure to enhance the validity and efficiency with which CER studies are implemented. The approach to addressing each of these issues should be informed by the understanding that the primary purpose of CER is to help health care decision makers make informed clinical and health policy decisions. Copyright © 2010 John Wiley & Sons, Ltd.     

Budgeting in health care systems

During the last decade there has been a recognition that all health care systems, public and private, are characterised by perverse incentives (especially moral hazard and third party pays) which generate inefficiency in the use of scarce economic resources. Inefficiency is unethical: doctors who use resources inefficiently deprive potential patients of care from which they could benefit. To eradicate unethical and inefficient practices two economic rules have to be followed: (i) no service should be provided if its total costs exceed its total benefits; (ii) if total benefits exceed total costs, the level of provision should be at that level at which the additional input cost (marginal cost) is equal to the additional benefits (marginal benefit). This efficiency test can be applied to health care systems, their component parts and the individuals (especially doctors) who control resource allocation within them. Unfortunately, all health care systems neither generate this relevant decision making data nor are they flexible enough to use it to affect health care decisions. There are two basic varieties of budgeting system: resource based and production targeted. The former generates obsession with cash limits and too little regard of the benefits, particularly at the margins, of alternative patterns of resource allocation. The latter generates undue attention to the production of processes of care and scant regard for costs, especially at the margins. Consequently, one set of budget rules may lead to cost containment regardless of benefits and the other set of budget rules may lead to output maximization regardless of costs. To close this circle of inefficiency it is necessary to evolve market-like structures. To do this a system of client group (defined broadly across all existing activities public and private) budgets is advocated with an identification of the budget holder who has the capacity to shift resources and seek out cost effective policies. Negotiated output targets with defined budgets and incentives for decision makers to economise in their use of resources are being incorporated into experiments in the health care systems of Western Europe and the United States. Undue optimism about the success of these experiments must be avoided because these problems have existed in the West and in the Soviet bloc for decades and efficient solutions are noticeable by their absence.     

Supporting and resourcing treatment decision‐making: some policy considerations

This paper considers some of the policy implications of issues raised during a conference about treatment decision‐making in the clinical encounter held in Hamilton, Ontario in May 1999. Policies promoting patient participation in treatment decision‐making need to be flexible enough to ensure that they are appropriate across the range of contexts in which health care decisions are made and acceptable to people with diverse preferences and abilities. They should also be formulated in consideration of other health policies and of available resources. Policies of informing people and involving them in decisions about their care are unlikely to be simple to implement. Various strategies might be needed to support them. These include the development of appropriate skills among health professionals and in the general population, the use of interventions to encourage people to play more active roles in decisions about their health care, the provision of decision aids for people facing specific decisions and the provision and accreditation of more general information resources and services. If information and other facilitators of patient participation in decision‐making are seen as integral to good quality health care, then funding should be made available for them. This will, however, have opportunity costs. Policy makers’ decisions about how much health care funding should be invested in which strategies should be underpinned by good research evidence about the effects that different types of intervention have on a range of outcomes for individuals, health care systems and populations. The knowledge on which current policies are based is limited. The development of future policies will be enhanced if policy makers invest in critical conceptual thinking, reflective practice, imaginative development work and good quality evaluative research.     

Optimal cost reimbursement of health insurers to reduce risk selection

In the absence of a perfect risk adjustment scheme, reimbursing health insurers' costs can reduce risk selection in community‐rated health insurance markets. In this paper, we develop a model in which insurers determine the cost efficiency of health care and have incentives for risk selection. We derive the optimal cost reimbursement function, which balances the incentives for cost efficiency and risk selection. For health cost data from a Swiss health insurer, we find that an optimal cost reimbursement scheme should reimburse costs only up to a threshold. Copyright © 2010 John Wiley & Sons, Ltd.     

Discounting future health benefits: the poverty of consistency arguments

In economic evaluation of health care, main stream practice is to discount benefits at the same rate as costs. But main papers in which this practice is advocated have missed a distinction between two quite different evaluation problems: (1) How much does the time of program occurrence matter for value and (2) how much do delays in health benefits from programs implemented at a given time matter? The papers have furthermore focused on logical and arithmetic arguments rather than on real value considerations. These ‘consistency arguments’ are at best trivial, at worst logically flawed. At the end of the day, there is a sensible argument for equal discounting of costs and benefits rooted in microeconomic theory of rational, utility maximising consumers' saving behaviour. But even this argument is problematic, first because the model is not clearly supported by empirical observations of individuals' time preferences for health, second because it relates only to evaluation in terms of overall individual utility. It does not provide grounds for claiming that decision makers with a wider societal perspective, which may include concerns for fair distribution, need to discount health benefits and costs equally. This applies even if health benefits are measured in monetary terms. Copyright © 2010 John Wiley & Sons, Ltd.     

Recommendations for treating depression in community-based older adults

OBJECTIVE: To present recommendations for community-based treatment of late-life depression to public health and aging networks. METHODS: An expert panel of mental health and public health researchers and community-based practitioners in aging was convened in April 2006 to form consensus-based recommendations. When making recommendations, panelists considered feasibility and appropriateness for community-based delivery, as well as strength of evidence on program effectiveness from a systematic literature review of articles published through 2005. RESULTS: The expert panel strongly recommended depression care management-modeled interventions delivered at home or at primary care clinics. The panel recommended individual cognitive behavioral therapy. Interventions not recommended as primary treatments for late-life depression included education and skills training, comprehensive geriatric health evaluation programs, exercise, and physical rehabilitation/occupational therapy. There was insufficient evidence for making recommendations for several intervention categories, including group psychotherapy and psychotherapies other than cognitive behavioral therapy. CONCLUSIONS: This interdisciplinary expert panel determined that recommended interventions should be disseminated throughout the public health and aging networks, while acknowledging the challenges and obstacles involved. Interventions that were not recommended or had insufficient evidence often did not treat depression primarily and/or did not include a clinically depressed sample while attempting to establish efficacy. These interventions may provide other benefits, but should not be presumed to effectively treat depression by themselves. Panelists also identified primary prevention of depression as a much under-studied area. These findings should aid individual clinicians as well as public health decision makers in the delivery of population-based mental health services in diverse community settings.     

Cost-Effectiveness Thresholds in Global Health: Taking a Multisectoral Perspective

Good health is a function of a range of biological, environmental, behavioral, and social factors. The consumption of quality health care services is therefore only a part of how good health is produced. Although few would argue with this, the economic framework used to allocate resources to optimize population health is applied in a way that constrains the analyst and the decision maker to health care services. This approach risks missing two critical issues: 1) multiple sectors contribute to health gain and 2) the goods and services produced by the health sector can have multiple benefits besides health. We illustrate how present cost-effectiveness thresholds could result in health losses, particularly when considering health-producing interventions in other sectors or public health interventions with multisectoral outcomes. We then propose a potentially more optimal second best approach, the so-called cofinancing approach, in which the health payer could redistribute part of its budget to other sectors, where specific nonhealth interventions achieved a health gain more efficiently than the health sector’s marginal productivity (opportunity cost). Likewise, other sectors would determine how much to contribute toward such an intervention, given the current marginal productivity of their budgets. Further research is certainly required to test and validate different measurement approaches and to assess the efficiency gains from cofinancing after deducting the transaction costs that would come with such cross-sectoral coordination.     

Revisiting Rose: Comparing the Benefits and Costs of Population‐Wide and Targeted Interventions

Context: Geoffrey Rose's two principal approaches to public health intervention are (1) targeted strategies focusing on individuals at a personal increased risk of disease and (2) population‐wide approaches focusing on the whole population. Beyond his discussion of the strengths and weaknesses of these approaches, there is no empiric work examining the conditions under which one of these approaches may be better than the other. Methods: This article uses mathematical simulations to model the benefits and costs of the two approaches, varying the cut points for treatment, effect magnitudes, and costs of the interventions. These techniques then were applied to the specific example of an intervention on blood pressure to reduce cardiovascular disease. Findings: In the general simulation (using an inverse logit risk curve), lower costs of intervention, treating people with risk factor values at or above where the slope on the risk curve is at its steepest (for targeted interventions), and interventions with larger effects on reducing the risk factor (for population‐wide interventions) provided benefit/cost advantages. In the specific blood pressure intervention example, lower‐cost population‐wide interventions had better benefit/cost ratios, but some targeted treatments with lower cutoffs prevented more absolute cases of disease. Conclusions: These simulations empirically evaluate some of Rose's original arguments. They can be replicated for particular interventions being considered and may be useful in helping public health decision makers assess potential intervention strategies.     

Preferences of diabetes patients and physicians: A feasibility study to identify the key indicators for appraisal of health care values

Background  

Evidence-based medicine, the Institute of Medicine (IOM) and the German Institute for Quality and Efficiency in Health Care (IQWiG), support the inclusion of patients' preferences in health care decisions. In fact there are not many trials which include an assessment of patient's preferences. The aim of this study is to demonstrate that preferences of physicians and of patients can be assessed and that this information may be helpful for medical decision making.     

An illustration of the modelling of cost and efficacy data from a clinical trial

Health care providers, purchasers and policy makers need to make informed decisions regarding the provision of cost-effective care. When a new health care intervention is to be compared with the current standard, an economic evaluation alongside an evaluation of health benefits provides useful information for the decision making process. We consider the information on cost-effectiveness which arises from an individual clinical trial comparing the two interventions. Recent methods for conducting a cost-effectiveness analysis for a clinical trial have focused on the net benefit parameter. The net benefit parameter, a function of costs and health benefits, is positive if the new intervention is cost-effective compared with the standard. In this paper we describe frequentist and Bayesian approaches to cost-effectiveness analysis which have been suggested in the literature and apply them to data from a clinical trial comparing laparoscopic surgery with open mesh surgery for the repair of inguinal hernias. We extend the Bayesian model to allow the total cost to be divided into a number of different components. The advantages and disadvantages of the different approaches are discussed. In January 2001, NICE issued guidance on the type of surgery to be used for inguinal hernia repair. We discuss our example in the light of this information.