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.     

DM3 Outcomes Research in Medication use: Hcfa''s Health Care Quality Improvement Program

The objective of public policy decision making is to choose the set of interventions that maximize the net benefit to society. Given a set of mutually exclusive interventions, the one with the lowest cost-effectiveness ratio is not necessarily the one that maximizes net benefit. Thus, a treatment with a higher cost-effectiveness ratio compared to baseline may result in higher net benefits if its incremental cost-effectiveness is less than the dollar value of the outcome. In this presentation we describe how to use cost-effectiveness results to determine the intervention that maximizes net benefit. We also show how cost-effectiveness results can be used to determine threshold values for the benefit than another. We then examine the effect of budget constraints on this decision making problem. We also present a graphical means of representing cost-effectiveness results that allow for easy interpretation and use of the results. We describe a simple rule for identifying the net benefit maximizing intervention from this graph. We will illustrate the issue discussed using examples from the medical literature. This workshop should be beneficial to health care decision makers who have to interpret cost-effectiveness results and incorporate them in their decision making process.     

Uncertainty in cost-effectiveness analysis. Probabilistic uncertainty analysis and stochastic league tables

Interest is growing in the application of standard statistical inferential techniques to the calculation of cost-effectiveness ratios (CER), but individual level data will not be available in many cases because it is very difficult to undertake prospective controlled trials of many public health interventions. We propose the application of probabilistic uncertainty analysis using Monte Carlo simulations, in combination with nonparametric bootstrapping techniques where appropriate. This paper also discusses how decision makers should interpret the CER of interventions where uncertainty intervals overlap. We show how the incorporation of uncertainty around costs and effects of interventions into a stochastic league table provides additional information to decision makers for priority setting. Stochastic league tables inform decision makers about the probability that a specific intervention would be included in the optimal mix of interventions for different resource levels, given the uncertainty surrounding the interventions.     

Assessing health system performance in developing countries: a review of the literature

With the setting of ambitious international health goals and an influx of additional development assistance for health, there is growing interest in assessing the performance of health systems in developing countries. This paper proposes a framework for the assessment of health system performance and reviews the literature on indicators currently in use to measure performance using online medical and public health databases. This was complemented by a review of relevant books and reports in the grey literature. The indicators were organized into three categories: effectiveness, equity, and efficiency. Measures of health system effectiveness were improvement in health status, access to and quality of care and, increasingly, patient satisfaction. Measures of equity included access and quality of care for disadvantaged groups together with fair financing, risk protection and accountability. Measures of efficiency were appropriate levels of funding, the cost-effectiveness of interventions, and effective administration. This framework and review of indicators may be helpful to health policy makers interested in assessing the effects of different policies, expenditures, and organizational structures on health outputs and outcomes in developing countries.     

Cost-Effectiveness Analysis Alongside Clinical Trials II—An ISPOR Good Research Practices Task Force Report

Clinical trials evaluating medicines, medical devices, and procedures now commonly assess the economic value of these interventions. The growing number of prospective clinical/economic trials reflects both widespread interest in economic information for new technologies and the regulatory and reimbursement requirements of many countries that now consider evidence of economic value along with clinical efficacy. As decision makers increasingly demand evidence of economic value for health care interventions, conducting high-quality economic analyses alongside clinical studies is desirable because they broaden the scope of information available on a particular intervention, and can efficiently provide timely information with high internal and, when designed and analyzed properly, reasonable external validity. In 2005, ISPOR published the Good Research Practices for Cost-Effectiveness Analysis Alongside Clinical Trials: The ISPOR RCT-CEA Task Force report. ISPOR initiated an update of the report in 2014 to include the methodological developments over the last 9 years. This report provides updated recommendations reflecting advances in several areas related to trial design, selecting data elements, database design and management, analysis, and reporting of results. Task force members note that trials should be designed to evaluate effectiveness (rather than efficacy) when possible, should include clinical outcome measures, and should obtain health resource use and health state utilities directly from study subjects. Collection of economic data should be fully integrated into the study. An incremental analysis should be conducted with an intention-to-treat approach, complemented by relevant subgroup analyses. Uncertainty should be characterized. Articles should adhere to established standards for reporting results of cost-effectiveness analyses. Economic studies alongside trials are complementary to other evaluations (e.g., modeling studies) as information for decision makers who consider evidence of economic value along with clinical efficacy when making resource allocation decisions.     

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.     

Pre-assessment to assess the match between cost-effectiveness results and decision makers' information needs: an illustration using two cases in rehabilitation medicine in The Netherlands

OBJECTIVE: To determine if a pre-assessment can be used to establish whether cost-effectiveness results would meet the actual information needs of Dutch healthcare decision makers. METHODS: Two recent studies in rehabilitation medicine served as study material. Based on Wholey, a limited pre-assessment was performed in which the potential impact of cost-effectiveness analysis (CEA) results on intended users' decision making was assessed. Desk research and semi-structured interviews with several intended users of CEA results were performed. These included general practitioners, representatives of health insurance companies, the Health Care Insurance Board (CvZ), and medical guidelines committees. RESULTS: In day-to-day decision making of the interviewed decision makers, a cost-effectiveness criterion seemed to be of limited importance. Instead, results from clinical effectiveness studies and budget impact studies appeared to be sufficient. CvZ, however, preferred relative cost-effectiveness to be a criterion for inclusion in future reimbursement guidelines. In both cases the limited pre-assessments changed the expectations of the investigators regarding decision-making impact of an economic evaluation. CONCLUSION: This study revealed that the use of CEA results for Dutch micro- and meso-level healthcare decision making is not self-evident. The main purpose of CEA results is to support health policy making and planning at a macroeconomic level. Pre-assessment can be a valuable tool in designing a CEA to support the actual information needs of the decision makers.     

Application of cost-effectiveness analysis of health care interventions in developing countries. A case study in Mauritius

OBJECTIVE: Cost-effectiveness analysis is a tool to help inform the decision maker of efficient allocation of scarce health care resources and its application has increased in developing countries during the past decade. There are, however, a variety of different approaches used to calculate cost-effectiveness ratios, given the range and the controversies surrounding the use of some components of total cost, depending on the constraints faced by various decision-making bodies. This study is an investigation of cost-effectiveness of both currently delivered and prospective health interventions in Mauritius to set priorities and assess allocative efficiency by taking into account such constraints. METHODS: Resource use and unit cost data were collected from the representative health facilities and the Ministry of Health to estimate costs of each health intervention per person. Effectiveness of each intervention was estimated from the results of the national burden of disease study and the efficacy database compiled for this exercise. Several types of cost-effectiveness were calculated for each intervention according to its characteristics and the constraints imposed by the existing infrastructures and other health interventions. RESULTS: Cost-effectiveness ratios with and without the decision maker's constraints differed significantly. Infrastructure-constrained average cost-effectiveness of thirteen currently delivered and twenty one prospective interventions ranged from $127 to $92,949 and from $77 to $66,302 per DALY averted, respectively. Incremental cost-effectiveness of the prospective interventions was from $83 to $70,553. Among the currently delivered interventions, those for perinatal disorders, mental illness, and ischemic heart disease were particularly less cost-effective than the prospective interventions. Sensitivity analysis of both effectiveness and discount rates did not change the cost-effectiveness ranking significantly. CONCLUSION: The present study showed that cost-effectiveness ratios differ significantly depending on the decision maker's constraints and that an interpretation of each cost-effectiveness study should be made with great caution when implementing its results in practice. Both average cost-effectiveness of the currently delivered interventions and incremental cost-effectiveness of the prospective interventions suggest that there is an allocative inefficiency among the currently delivered health interventions in Mauritius and a possibility of enhancing allocative efficiency through introducing alternative interventions.     

Cost-effectiveness ratios: in a league of their own

There is an increasing tendency for papers appearing in the medical literature to propose or use league tables of cost-effectiveness ratios as a means of comparing health-care interventions. In this paper we identify what the information in cost-effectiveness league tables tells us and how this is inadequate and inappropriate for addressing questions about improving efficiency in the use of resources at either the broad system level or at the individual care-group level. We present an alternative approach which provides decision makers with a practical way of deciding whether the adoption of a particular programme represents an unambiguous improvement in economic efficiency.     

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.     

Generalised cost-effectiveness analysis: an aid to decision making in health

Health economics literature provides ample evidence for existing inefficiencies in health. Economic appraisal seeks to improve efficiency by guiding policy makers in how scarce resources can be used to derive the greatest possible social benefit. In the past many cost-effectiveness (CE) studies have addressed sector-wide cost-effectiveness in health. However, as described in this paper, current studies suffer from a number of shortcomings including the inability to assess the current mix of interventions, low generalisability and inconsistent methodological approaches. Most importantly, it is argued that the current incremental approach to cost-effectiveness analysis (CEA) does not provide decision makers with sufficient guidance for sector-wide priority setting in health. Instead, a broader complementary sectorial approach is proposed via the application of a generalised CEA framework, which allows the examination of existing inefficiencies in health systems. The wide variations in cost-effectiveness ratios observed among interventions that are currently in use, suggest there is considerable room to improve efficiency by moving from inefficient interventions to efficient interventions that are underutilized. This information will contribute to a more informed debate on resource allocation in the long-term.     

PMD12 The Paradox of League Tables

Allocation of limited health care resources is a problem faced by decision makers. In an effort to ease this burden, the idea has developed that economic efficiency can be compared among interventions by the use of league tables—lists of interventions and their corresponding cost-effectiveness ratios derived from various studies. League tables simplify a huge amount of information into a few numbers postulated to be critical to the decision. Desirable as it may be, this simplicity is not warranted. The very concept is suspect as the ratios listed do not compare the cited intervention to the one of interest. Even when all compare to a common reference intervention, they represent inappropriate average ratios. Methodology and assumptions made in creating the individual economic models vary greatly but are typically ignored in the league table. Even without the questionable methods used in some analyses, real and important differences will always exist among models (e.g., disease variation, purpose of studies, and data availability). Even among studies conducted by the same researchers, relevant differences arise. For example, in one study on prevention of cardiovascular disease the cost estimates included only initial acute care. In another on stroke prevention, also by us, costs included subsequent care for up to 15 years after the initial event. This difference can substantially influence the resulting cost-effectiveness ratios. Both approaches are correct for their purpose, but the difference would be unknown to the decision maker viewing the league table. League tables mislead decision makers into feeling informed without realizing how little is known. These problems cannot be cured by methodological guidelines. Pharmacoeconomic researchers should resist creating league tables and thereby providing both too little and too much information to decision makers.     

Cost-effectiveness of influenza vaccination of healthy children

Influenza vaccination of children 6-23 months of age is recommended in the United States and Canada because of high rates of influenza-associated hospitalisations, but few other countries have adopted similar policies. Most children with influenza are treated in the primary care setting, and the cost-effectiveness of influenza vaccination of children has not been fully established. We used a decision analysis model to assess the cost-effectiveness of influenza vaccination of children 6 months to 13 years of age in Finland. The analyses were based on comprehensive clinical data on virologically confirmed influenza infections, hospital medical records, and national registers. We estimated the impact of influenza on outpatient and hospitalised children and their families, and performed the analyses from the health care provider and societal perspective. Influenza vaccination resulted in savings in all programs including children 相似文献   

Both clinical epidemiology and population health perspectives can define the role of health care in reducing health disparities

OBJECTIVE: To compare and contrast clinical epidemiology and population health perspectives on the role of health care in reducing socioeconomic disparities in health. STUDY DESIGN AND SETTING: A review of concepts outlined in selected articles on population health and clinical epidemiology and a systematic literature search for randomized controlled trials (RCTs) of therapeutic interventions for cardiovascular disease that contained analysis of outcomes by socioeconomic status. RESULTS: Population health has a focus on health disparities, particularly disparities related to socioeconomic status, and many of its proponents have a pessimistic view of the degree to which health care can reduce these disparities. Clinical epidemiology has a focus on the production of valid evidence on the impact of health care interventions; however, RCTs rarely report the impact of interventions across socioeconomic strata. Both population health and clinical epidemiology share the view that efficacy, effectiveness, and cost-effectiveness are all important in defining the impact of health care on health disparities. CONCLUSION: Principles drawn from both population health and clinical epidemiology could be used to provide a clearer picture of the role that health care interventions can have on socioeconomic disparities in health and to identify implications for policy, research, and clinical practice.     

The cost-effectiveness of introducing hepatitis B vaccine into infant immunization services in Mozambique

OBJECTIVE: To estimate the cost-effectiveness of introducing hepatitis B vaccine into routine infant immunization services in Mozambique, which took place in the year 2001. METHODS: A decision analytic model was used to estimate the impact of hepatitis B vaccination. This model was developed for the WHO to estimate the global burden of disease from hepatitis B. Cost data of vaccine delivery and medical treatment related to hepatitis B infection were collected for the analysis. FINDINGS: The introduction of hepatitis B vaccine has increased the annual budget for immunization services by approximately 56%. It is predicted that more than 4000 future deaths are averted annually by the intervention. In the base case scenario, the incremental costs per undiscounted deaths averted amount to US$436, and the costs per undiscounted DALY averted amount to US$36. Since the major impact of hepatitis B vaccination will not start to be evident for at least another 40 years (deaths from hepatitis B mainly occur between 40-60 years of age), the cost per DALY averted rises to US$47, when using a discount rate of 3% on health effects. We found that the monovalent hepatitis B vaccine was considerably more cost-effective than the hepatitis B vaccine in combination with DTP. INTERPRETATION: If policy makers value future health benefits equal to current benefits, the cost-effectiveness of infant hepatitis B vaccination is in the range of other primary health care interventions for which similar analysis has been undertaken.     

Stochastic league tables: an application to diabetes interventions in the Netherlands

The aim of this paper is to discuss the use of stochastic league tables approach in cost-effectiveness analysis of diabetes interventions. It addresses the common grounds and differences with other methods of presenting uncertainty to decision-makers. This comparison uses the cost-effectiveness results of medical guidelines for Dutch diabetes type 2 patients in primary and secondary care. Stochastic league tables define the optimum expansion pathway as compared to baseline, starting with the least costly and most cost-effective intervention mix. Multi-intervention cost-effectiveness acceptability curves are used as a way to represent uncertainty information on the cost-effectiveness of single interventions as compared to a single alternative. The stochastic league table for diabetes interventions shows that in case of low budgets treatment of secondary care patients is the most likely optimum choice. Current care options of diabetes complications are shown to be inefficient compared to guidelines treatment. With more resources available one may implement all guidelines and improve efficiency. The stochastic league table approach and multi-intervention cost-effectiveness acceptability curves in uncertainty analysis lead to similar results. In addition, the stochastic league table approach provides policy makers with information on affordability by budget level. It fulfils more adequately the information requirements to choose between interventions, using the efficiency criterion.     

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.     

The use of economic evaluation in health care: Australian decision makers' perceptions

Decision making about the alternative uses of health care resources is an issue of critical concern for governments and administrators in all health care systems. While many factors need to be taken into consideration when making these decisions, economic evaluation can help to determine the relative efficiency of different choices. Research in various countries suggests that economic evaluation is not being used by health care decision makers to the extent that health economists think that it should be. Interest in the use of economic evaluation is increasing in Australia but, to date, there has been no Australian research which looks at its use from the point of view of its potential users--the decision makers. This study fills that gap. It was found that there was a high level of awareness of economic evaluation among the group of decision makers interviewed and that some had used it in their decision making. However decisions often have to be made quickly and take into account factors other than efficiency, hence limiting the use of economics. Other problems limiting its use were availability of data and lack of expertise. Those interviewed suggested a number of ways in which the problems they identified could be overcome. In particular, they recommended that researchers doing economic evaluations should be more responsive to the needs of the decision makers using them.     

Multicriteria Decision Analysis for Including Health Interventions in the Universal Health Coverage Benefit Package in Thailand

ObjectivesConsidering rising health expenditure on the one hand and increasing public expectations on the other hand, there is a need for explicit health care rationing to secure public acceptance of coverage decisions of health interventions. The National Health Security Office, the institute managing the Universal Coverage Scheme in Thailand, recently called for more rational, transparent, and fair decisions on the public reimbursement of health interventions. This article describes the application of multicriteria decision analysis (MCDA) to guide the coverage decisions on including health interventions in the Universal Coverage Scheme health benefit package in the period 2009–2010.MethodsWe described the MCDA priority-setting process through participatory observation and evaluated the rational, transparency, and fairness of the priority-setting process against the accountability for reasonableness framework.ResultsThe MCDA was applied in four steps: 1) 17 interventions were nominated for assessment; 2) nine interventions were selected for further quantitative assessment on the basis of the following criteria: size of population affected by disease, severity of disease, effectiveness of health intervention, variation in practice, economic impact on household expenditure, and equity and social implications; 3) these interventions were then assessed in terms of cost-effectiveness and budget impact; and 4) decision makers qualitatively appraised, deliberated, and reached consensus on which interventions should be adopted in the package.ConclusionThis project was carried out in a real-world context and has considerably contributed to the rational, transparent, and fair priority-setting process through the application of MCDA. Although the present project has applied MCDA in the Thai context, MCDA is adaptable to other settings.     

Cost-effectiveness analysis and innovation

While cost-effectiveness (CE) analysis has provided a guide to allocating often scarce resources spent on medical technologies, less emphasis has been placed on the effect of such criteria on the behavior of innovators who make health care technologies available in the first place. A better understanding of the link between innovation and cost-effectiveness analysis is particularly important given the large role of technological change in the growth in health care spending and the growing interest of explicit use of CE thresholds in leading technology adoption in several Westernized countries. We analyze CE analysis in a standard market context, and stress that a technology's cost-effectiveness is closely related to the consumer surplus it generates. Improved CE therefore often clashes with interventions to stimulate producer surplus, such as patents. We derive the inconsistency between technology adoption based on CE analysis and economic efficiency. Indeed, static efficiency, dynamic efficiency, and improved patient health may all be induced by the cost-effectiveness of the technology being at its worst level. As producer appropriation of the social surplus of an innovation is central to the dynamic efficiency that should guide CE adoption criteria, we exemplify how appropriation can be inferred from existing CE estimates. For an illustrative sample of technologies considered, we find that the median technology has an appropriation of about 15%. To the extent that such incentives are deemed either too low or too high compared to dynamically efficient levels, CE thresholds may be appropriately raised or lowered to improve dynamic efficiency.