The value of implementation and the value of information: combined and uneven development

<i>Aim</i>: In a budget-constrained health care system, the decision to invest in strategies to improve the implementation of cost-effective technologies must be made alongside decisions regarding investment in the technologies themselves and investment in further research. This article presents a single, unified framework that simultaneously addresses the problem of allocating funds between these separate but linked activities. <i>Methods</i>: The framework presents a simple 4-state world where both information and implementation can be either at the current level or "perfect". Through this framework, it is possible to determine the maximum return to further research and an upper bound on the value of adopting implementation strategies. The framework is illustrated through case studies of health care technologies selected from those previously considered by the UK National Institute for Health and Clinical Excellence (NICE). <i>Results</i>: Through the case studies, several key factors that influence the expected values of perfect information and perfect implementation are identified. These factors include the maximum acceptable cost-effectiveness ratio, the level of uncertainty surrounding the adoption decision, the expected net benefits associated with the technologies, the current level of implementation, and the size of the eligible population. <i>Conclusions</i>: Previous methods for valuing implementation strategies have not distinguished the value of efficacy research and the value of strategies to change the level of implementation. This framework demonstrates that the value of information and the value of implementation can be examined separately but simultaneously in a single framework. This can usefully inform policy decisions about investment in health care services, further research, and adopting implementation strategies that are likely to differ between technologies

Mark versus Luke? Appropriate Methods for the Evaluation of Public Health Interventions

The purpose of this paper is to demonstrate that a social decision making approach to evaluation can be generalised to interventions such as public health and national policies which have multiple objectives and impact on multiple constraints within and beyond the health sector. We demonstrate that a mathematical programming solution to this problem is possible, but the information requirements make it impractical. Instead we propose a simple compensation test for interventions with multiple and cross-sectoral effects. However, rather than compensation based on individual preferences, it can be based on the net benefits falling on different sectors. The valuation of outcomes is based on the shadow prices of the existing budget constraints, which are implicit in existing public expenditure and its allocation across different sectors. A ‘welfarist’ societal perspective is not sufficient; rather, a multiple perspective evaluation which accounts for costs and effects falling on each sector is required.cost-effectiveness analysis, decision rules, public health

Pre-operative optimisation employing dopexamine or adrenaline for patients undergoing major elective surgery: a cost-effectiveness analysis

<b>Objective</b>: To compare the cost and cost-effectiveness of a policy of pre-operative optimisation of oxygen delivery (using either adrenaline or dopexamine) to reduce the risk associated with major elective surgery, in high-risk patients. <b>Methods</b>: A cost-effectiveness analysis using data from a randomised controlled trial (RCT). In the RCT 138 patients undergoing major elective surgery were allocated to receive pre-operative optimisation employing either adrenaline or dopexamine (assigned randomly), or to receive routine peri-operative care. Differential health service costs were based on trial data on the number and cause of hospital in-patient days and the utilisation of health care resources. These were costed using unit costs from a UK hospital. The cost-effectiveness analysis related differential costs to differential life-years during a 2 year trial follow-up. <b>Results</b>: The mean number of in-patient days was 16 in the pre-optimised groups (19 adrenaline; 13 dopexamine) and 22 in the standard care group. The number (%) of deaths, over a 2 year follow-up, was 24 (26%) in the pre-optimised groups and 15 (33%) in the standard care group. The mean total costs were EUR 11,310 in the pre-optimised groups and EUR 16,965 in the standard care group. Life-years were 1.68 in the pre-optimised groups and 1.46 in the standard care group. The probability that pre-operative optimisation is less costly than standard care is 98%. The probability that it dominates standard care is 93%. Conclusions: Based on resource use and effectiveness data collected in the trial, pre-operative optimisation of high-risk surgical patients undergoing major elective surgery is cost-effective compared with standard treatment

A critical structured review of economic evaluations of interventions for the prevention and treatment of osteoporosis

Osteoporosis is a major cause of morbidity, mortality and resource cost amongst the elderly population. Hip fracture is the most serious of the osteoporotic fractures, with approximately 10-20% of patients dying within six months of sustaining a fracture. Furthermore, hip fractures are the most expensive manifestation of osteoporosis, incurring about 87% of the total costs of osteoporotic fractures. This public health and economic burden is likely to increase in developed nations due, in part, to ageing populations. In addition, there is strong evidence that the age-specific incidence of fracture is rising. There are a number of treatments which can be used to prevent fracture including hormone replacement therapy (HRT), bisphosphonates, vitamin D and calcium. These interventions have been used for primary prevention, secondary prevention and the treatment of established osteoporosis. This Discussion Paper details the results of a structured review, the purpose of which was to identify and critically appraise economic evaluations relating to interventions for osteoporosis. The focus of the work is a critical assessment of the methodology of those studies. A total of 16 economic evaluations was identified on the basis of a computerised search of three bibliographic databases. All studies were based on decision analytical models and all took the form of cost-effectiveness analysis. Seven studies were from the US and four from the UK. The majority of studies focused on either primary prevention alone (seven) or both primary and secondary prevention where high-risk women were identified on the basis of bone mineral density screening (seven). Most studies considered the cost-effectiveness of HRT. Most of the published studies conclude that treatment using HRT is relatively cost-effective among symptomatic women or women who have had a prior hysterectomy. In contrast, for asymptomatic women, the results are more equivocal. The most recent cost-effectiveness analysis was undertaken by the National Osteoporosis Foundation (NOF) which makes the explicit assumption that HRT is the treatment of choice. For women unwilling or unable to take HRT, the next recommended treatment was alendronate; should alendronate not be tolerated, calcitonin was recommended. Many of the models included in the review exhibit methodological weaknesses which suggest heir results should be treated with some caution. One of these concerns the dearth of formally elicited health state preference data from patients or members of the public: only two studies in the review derive preferences empirically rather than use the authors’ judgement. A second limitation of many studies is the inappropriate application of costeffectiveness decision rules with the frequent use of average cost-effectiveness ratios. Areas of methodological controversy, such as whether or not to include costs unrelated to osteoporosis in life-years added as a result of treatment, increase uncertainty regarding how to interpret the results of the studies.osteoporosis, HRT

Appropriate Perspectives for Health Care Decisions

NICE uses cost-effectiveness analysis to compare the health benefits expected to be gained by using a technology with the health that is likely to be forgone due to additional costs falling on the health care budget and displacing other activities that improve health. This approach to informing decisions will be appropriate if the social objective is to improve health, the measure of health is adequate and the budget for health care can reasonably be regarded as fixed. If NICE were to recommend a broader =societal perspective‘, wider effects impacting on other areas of the public sector and the wider economy would be formally incorporated into analyses and decisions. The problem for policy is that, in the face of budgets legitimately set by government, it is not clear how or whether a societal perspective can be implemented, particularly if transfers between sectors are not possible. It poses the question of how the trade-offs between health, consumption and other social arguments, as well as the valuation of market and non market activities, ought to be undertaken.Perspective. Cost-effectiveness analysis. Economic evaluation.

Priority setting for research in health care: An application of value of information analysis to glycoprotein IIb/IIIa antagonists in non-ST elevation acute coronary syndrome

The purpose of this study is to explain the rationale for the value of information approach to priority setting for research and to describe the methods intuitively for those familiar with basic decision analytical modeling. A policy-relevant case study is used to show the feasibility of the method and to illustrate the type of output that is generated and how these might be used to frame research recommendations. The case study relates to the use of glycoprotein IIb/IIIa antagonists for the treatment of patients with non-ST elevation acute coronary syndrome. This is an area that recently has been appraised by the National Institute for Health and Clinical Excellence

The value of implementation and the value of information: combined and uneven development

In a budget constrained healthcare system the decision to invest in strategies to improve the implementation of cost-effective technologies must be made alongside decisions regarding investment in the technologies themselves and investment in further research. This paper presents a single, unified framework that simultaneously addresses the problem of allocating funds between these separate but linked activities. The framework presents a simple 4 state world where both information and implementation can be either at the current level or ‘perfect’. Through this framework it is possible to determine the maximum return to further research and an upper bound on the value of adopting implementation strategies. The framework is illustrated through case studies of health care technologies selected from those previously considered by the UK National Institute for Health and Clinical Excellence (NICE). Through the case studies, several key factors that influence the expected values of perfect information and perfect implementation are identified. These factors include the maximum acceptable cost-effectiveness ratio, the level of uncertainty surrounding the adoption decision, the expected net benefits associated with the technologies, the current level of implementation and the size of the eligible population. Previous methods for valuing implementation strategies have confused the value of research and the value of implementation. This framework demonstrates that the value of information and the value of implementation can be examined separately but simultaneously in a single framework. This can usefully inform policy decisions about investment in healthcare services, further research and adopting implementation strategies which are likely to differ between technologies.Value of information analysis; value of implementation; healthcare decisionmaking, Bayesian analysis

Model parameter estimation and uncertainty analysis: a report of the ISPOR-SMDM modeling good research practices task force working group - 6

A model’s purpose is to inform medical decisions and health care resource allocation. Modelers employ quantitative methods to structure the clinical, epidemiological, and economic evidence base and gain qualitative insight to assist decision makers in making better decisions. From a policy perspective, the value of a model-based analysis lies not simply in its ability to generate a precise point estimate for a specific outcome but also in the systematic examination and responsible reporting of uncertainty surrounding this outcome and the ultimate decision being addressed. Different concepts relating to uncertainty in decision modeling are explored. Stochastic (first-order) uncertainty is distinguished from both parameter (second-order) uncertainty and from heterogeneity, with structural uncertainty relating to the model itself forming another level of uncertainty to consider. The article argues that the estimation of point estimates and uncertainty in parameters is part of a single process and explores the link between parameter uncertainty through to decision uncertainty and the relationship to value-of-information analysis. The article also makes extensive recommendations around the reporting of uncertainty, both in terms of deterministic sensitivity analysis techniques and probabilistic methods. Expected value of perfect information is argued to be the most appropriate presentational technique, alongside cost-effectiveness acceptability curves, for representing decision uncertainty from probabilistic analysis

Laparoscopic fundoplication compared with medical management for gastro-oesophageal reflux disease : cost effectiveness study

Peer reviewedPublisher PD

Improving the efficiency and relevance of health technology assessent: the role of iterative decision analytic modelling

Decision making in health care involves two sets of related decisions: those concerning appropriate service provision on the basis of existing information; and those concerned with whether to fund additional research to reduce the uncertainty relating to the decision. Information acquisition is not costless, and the allocation of funds to the enhancement of the decision makers’ information set, in a budgetconstrained health service, reduces the ‘pot’ of resources available for health service provision. Hence, a framework is necessary to unify these decisions and ensure that HTA is subject to the same evaluation of efficiency as service provision. A framework is presented which addresses these two sets of decisions through the employment of decision analytic models and Bayesian value of information analysis, early and regularly within the health technology assessment process. The model becomes the vehicle of health technology assessment, managing and directing future research effort on an iterative basis over the lifetime of the technology. This ensures consistency in decision making between service provision, research and development priorities and research methods. Fulfilling the aim of the National Health Service HTA programme, that research is “produced in the most economical way” using “cost effective research protocols”. The proposed framework is applied to the decision concerning the appropriate management of female patients with symptoms of urinary tract infection, which was the subject of a recent NHS HTA call for proposals. A probabilistic model is employed to fully characterise and assess the uncertainty surrounding the decision. The expected value of perfect information (EVPI) is then calculated for the full model, for each individual management strategy and for particular model parameters. Research effort can then be focused on those areas where the cost of uncertainty is high and where additional research is potentially cost-effective. The analysis can be used to identify the most appropriate research protocol and to concentrate research upon particular parameters where more precise estimates would be of most value.assessment