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Thresholds for the cost–effectiveness of interventions: alternative approaches Elliot Marseille a, Bruce Larson b, Dhruv S Kazi c, James G Kahn d & Sydney Rosen ba. Health Strategies International, 555 Fifty-ninth Street, Oakland, California, 94609, United States of America (USA).b. Center for Global Health and Development, Boston University, Boston, USA.c. Division of Cardiology, San Francisco General Hospital, San Francisco, USA.d.
Institute for Health Policy Studies, University of California – San Francisco, San Francisco, USA.Correspondence to Elliot Marseille (email: ).(Submitted: 05 March 2014 – Revised version received: 27 October 2014 – Accepted: 26 November 2014 – Published online: 15 December 2014.)Bulletin of the World Health Organization 2015;93:118-124. Doi: IntroductionIn public health, cost–effectiveness analyses compare the costs and effectiveness of two or more health interventions – with effectiveness measured in the same units.
When comparing interventions, the incremental cost–effectiveness ratio (ICER) – i.e. The second limitation is that thresholds are too easily attained. Beyond the virtue of availability, we are puzzled why per capita gross domestic products were chosen as the main units for cost–effectiveness thresholds. Too many health interventions are found to cost less, per DALY averted, than the relevant annual per capita gross domestic product. Box 2 illustrates this problem for diarrhoeal disease control. Making the threshold harder to meet – e.g. By only categorizing an intervention as highly cost–effective if, per DALY averted, it costs less than half of the annual per capita GDP – does not address the fundamental problem, which is that any threshold is arbitrary.
More stringent thresholds would rule interventions out with as little justification as more lenient thresholds would rule them in. Demonstrably effective interventions are almost certain to be cost–effective according to WHO-CHOICE: the example of diarrhoeal disease control.In sub-Saharan Africa, most diarrhoea-related deaths occur in children, the annual risk of death from diarrhoea in a household is often 1% or more, and 28 discounted life-years are lost per death. Thus, ignoring morbidity, the anticipated annual burden of diarrhoea can be estimated at 0.3 (0.01 × 28) disability-adjusted life-years (DALYs) per household with one child. In Kenya, a clean water intervention to reduce such deaths – e.g.
Chlorine or filters – could annually cost about 37 international dollars (I$) per household.,Well-funded trials are powered to detect risk reductions of 20% or more, and particularly large trials can detect a 10% reduction. – If we found that the clean water intervention had 20% effectiveness, implementing the intervention should avert 0.06 (0.2 × 0.3) of a DALY per household with one child. The incremental cost–effectiveness ratio, compared with doing nothing, is thus I$ 37 per 0.06 DALY averted – i.e. I$ 614 per DALY averted. At 10% effectiveness, this ratio rises to I$ 1228 per DALY averted. Both values given here for the ratio fall well below I$ 5211, which is the WHO-CHOICE threshold for a cost–effective intervention in Kenya – i.e. Three times the annual per capita gross domestic product. Even if its costs were twice as high or its effectiveness were only 5% – which is probably beyond trial precision – the intervention would still be deemed cost–effective according to WHO’s criterion.
Thus, if any benefit can be detected in a large trial, the intervention will be considered cost–effective. The third limitation is the untested assumptions on which this approach is based. Social willingness to pay for health benefits is, conceptually, an appropriate way to define social value that could be informed by the results of non-market valuations based on revealed- and stated-preference approaches., In using a cost–effectiveness threshold that is based on a country’s per capita GDP, analysts tacitly assume that the country is willing to pay up to that threshold for the health benefit – usually without any concrete evidence of that willingness to pay. While willingness to pay for health care is related to income, there is little evidence that the relationship is linear.
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Other factors are also important. If averted DALYs are more highly valued in high-income countries than in low-income ones, use of cost–effectiveness thresholds based on per capita GDP per DALY averted will give a biased measure of the willingness to pay. Such thresholds will tend to be too stringent in high-income countries – thus ruling some efficient options out – and too lax in low-income countries – thus ruling some inefficient options in.The fourth limitation is that affordability is not adequately appraised. Cost–effectiveness analyses are typically addressed to governments or international donors and aim to assist decision-making about how to spend finite budgets. Recent experience with international funding for HIV programmes may have fostered the notion that budget constraints are illusory.
However, even HIV funding is less secure now than it was a few years ago. – There is no evidence that, in the short term at least, the world will contribute the sums needed to implement all interventions that meet WHO’s criteria for cost–effectiveness. Thus, in any timeframe relevant to policy-makers, trade-offs have to be considered.Ignoring the overall budget assigned to a health programme may be just as problematic in a high-income country as in a lower-income one – particularly for conditions that are highly prevalent. Consider a drug that adds a year to everyone’s life and costs the annual per capita GDP per person treated.
Although such a drug would be categorized as highly cost–effective by WHO’s thresholds, we would have to spend the entire GDP of the country each year to give the drug to every eligible individual – i.e. To the country’s entire population. Benchmark interventionsOriginally proposed by Weinstein and Zeckhauser, a second solution to the cost–effectiveness standard problem is to cite the cost–effectiveness of a benchmark intervention that has already been adopted in the relevant country and to use that as a threshold for acceptable cost–effectiveness. In this approach we are again using a threshold but – unlike the thresholds based on per capita GDP – this threshold is established by a retrospective analysis of existing practice.
In the USA, for example, a threshold still used in cost–effectiveness analyses – US$ 50 000 per QALY gained – was based on an estimate of the cost–effectiveness of dialysis for chronic renal disease. This threshold has recently been updated to US$ 100 000 or US$ 150 000 per QALY gained. Since there is already evidence of a willingness to pay US$ 150 000 per QALY gained, it should be possible to increase overall health benefits by transferring funds from activities that cost more than this sum to activities that cost less. Thus, this approach appears to justify the adoption of any option that has a lower ICER than the benchmark.Although such an approach may have better local relevance than thresholds based on per capita GDP, it also has substantial shortcomings. The ICER of the benchmark intervention may be a high or low outlier.
For example, it may have resulted from a political decision that does not reflect the current, true measure of societal willingness to pay for health benefits. In addition, benchmarks do not take affordability into account and are not regularly updated to reflect changes in opportunity costs resulting from new technologies or delivery models, or changes in the burden of disease.Most importantly, using a single benchmark does not address the critical question of whether there might be available options that have a better cost–effectiveness ratio than either the benchmark intervention or the intervention under evaluation. In the USA, for example, an analysis might reveal that an intervention can add a QALY for US$ 80 000 – i.e. Well under the US$ 150 000 benchmark cited above. Although this would indicate that the intervention is much more cost–effective than the current benchmark, it would not tell us anything about the set of possible interventions that might add a QALY for less than US$ 80 000. Other techniques for establishing thresholds, such as human capital, contingent valuation and revealed preference approaches share the same basic strengths and weaknesses as the benchmark approach.
An option to justify the one under study can almost always be found., One way to mitigate this problem is to consider a range of interventions adopted by public health programmes in the setting of interest and the range of ICERs from these adopted interventions. This could be achieved via a research agenda that aims to aggregate more data on willingness to pay for a unit of health benefit in a wide range of countries.
In high-income countries, progress has been made on such an agenda by the translation of the available data on lives saved to data on QALYs gained. League tablesA third approach side-steps the threshold question and focuses instead on getting the largest health impact for the budget.
Conceptually, a complete set of relevant interventions would be chosen to maximize health effects. For example, if all of the interventions considered are at least somewhat scalable, they can be ranked into a so-called league table according to their ICERs. The league-table approach is based on the principle that, for any budget, health outcomes are maximized if selection of the options for implementation begins at the top of the league table – i.e. With the option with the lowest ICER – and then moves down the list, to interventions with successively higher ratios, until the budget is exhausted.Several generic league tables have been developed. WHO-CHOICE has reported simple information on the ICERs for many interventions.
Separate regional league tables are available for several diseases or risk factors. For example, for the Africa D region there are tables for 60 different interventions (Table 1). Other league tables have been created for specific diseases or conditions. A 2005 article assessed the ICERs of several major HIV interventions and arranged these in a league table for sub-Saharan Africa and South-East Asia (Table 2).
Other league tables are large repositories of cost–effectiveness information that can be used to assess the ranking of many interventions for wide ranges of diseases and conditions. One of the largest of these is the cost–effectiveness analysis registry maintained by Tufts Medical Center, which provides over 3600 ICERs for over 2000 health interventions.A limitation of league tables is that ICERs may not be available for many relevant options or settings. Diy retractable large opening screen. Many low resource countries lack data on the costs and effectiveness of specific interventions. In these countries, the only recourse for local policy-makers is to use findings from similar countries.
A bare league table omits much of the information that decision-makers might want to consider when choosing among options – e.g. The size of the affected population, whether the intervention is scalable, the health benefit per recipient and the degree of uncertainty around the ICERs., Perhaps, given these, we need an extended league table approach in which a list of ICERs is complemented by information on context-sensitive costs and benefits of competing options.Against these disadvantages must be weighed several virtues. A league table indicates graduated distinctions between ICERs.
Since the length of the list of interventions deemed cost–effective varies according to the budget, league tables combine considerations of cost–effectiveness with affordability. The last (least cost–effective) intervention in the table to be adopted is more likely to approximate society’s willingness to pay for health benefits than the open-ended set of commitments implied by global thresholds. Finally, league tables need not be comprehensive to support improved resource allocation. They can still indicate the potential health benefits of cancelling an existing programme and using the resources freed to fund another programme., DiscussionIf one intervention is deemed more cost–effective than another in the context of a fixed budget, we can say that it will yield more health benefit per unit of expenditure than that other option. However, the results of a cost–effectiveness analysis cannot indicate if an intervention is a good use of the health budget because the comparator may itself be inefficient relative to other feasible options. In addition, the notion of a fixed budget depends on the level or authority of the decision-maker.
In the context of HIV treatment, for example, ICERs might indicate that viral load testing is less cost–effective than adding patients to the caseload. Although the decision-makers responsible for an HIV programme’s budgets might therefore recommend the latter approach, they might ignore – or be unaware of – the possibility that the same money spent on vaccines for childhood diseases might give greater health benefits. Funders can get a better idea of the policy relevance of the results of new cost–effectiveness analyses if they are given the ICERs for interventions that they already support. However, there is no substitute for careful reflection by policy-makers on the most efficient ways to maximize national welfare. WHO’s current cost–effectiveness thresholds can short-circuit this task, by using annual per capita GDP as a proxy for social willingness to pay.Part of the appeal of thresholds may be the perception that cost–effectiveness analysis does not allow for fine distinctions.
Rather than pretending that unrealistic precision has been achieved, thresholds have the apparent virtue of simply distinguishing interventions that meet, from those that fail to meet, a fixed criterion. It is widely acknowledged that certain aspects of cost–effectiveness theory are contentious., Practice is also imperfect and inconsistent, often making it difficult to compare results from different studies. For example, between-study variation in the selection of analytic perspective, time horizons and criteria for including or excluding particular cost components can hamper comparisons of different investigations, even when sensitivity analyses document the impact of these choices.
Transparency in the assumptions made and methods used is therefore essential, as suggested by the Consolidated Health Economic Evaluation Reporting Standards. When cost–effectiveness analyses of an important policy question produce substantially different results, funders should sponsor efforts to document the source of the difference and to make appropriate adjustments, where possible.Whether because of these uncertainties or merely for expediency, many individuals appear to believe that a statement about the ICER for an intervention – relative to a threshold based on the annual per capita GDP – is sufficient to determine cost–effectiveness. For researchers, a simple threshold removes the need to compare results to other locally relevant findings and to place their studies in context. For the editors and reviewers of journals, use of a globally accepted threshold provides reassurance that methods and results meet international norms. Use of such a threshold allows authors and reviewers to choose convenience over a more nuanced and context-specific interpretation of results. The widespread acceptance of global thresholds may thus undermine both the supply and demand for more policy-relevant analyses. On the demand side, decision-makers are offered the results of cost–effectiveness analyses that neither distinguish between programme options with widely divergent ICERs nor account for budget constraints.
Decision-makers may therefore tend to dismiss cost–effectiveness analyses and revert to political or organizational interests as decision criteria. On the supply side, the availability of global cost–effectiveness thresholds undercuts the incentive of investigators to generate the nuanced, context-specific information that decision-makers need. ConclusionFor cost–effectiveness analyses to contribute to sound resource allocation, we argue that the estimates of both costs and effectiveness must be situated firmly within the relevant context, which includes the disease burden and budget of the setting in question. Simple cost–effectiveness thresholds – whether based on per-capita incomes or benchmark interventions – fail to evaluate and rank interventions within countries and disregard budgetary constraints.
By short-circuiting a more thorough assessment of policy-relevant alternatives, they contribute little to good decision-making and can actually mislead. While the currently available data will not support comprehensive off-the-shelf league tables for most settings, the results of cost–effectiveness analyses should be compared with as many relevant interventions as reasonable in a given situation.
Decision-makers would then be in a far better position to interpret the results of cost–effectiveness analyses.A consensus process should be convened, perhaps by WHO, to develop a new framework for articulating cost–effectiveness in global health policy – specifically focusing on low- and middle-income countries. Rather than referencing a uniform standard, this new consensus should place ICERs in the context of other public health options available or already adopted in the relevant country setting – and in the context of the relevant budgets. While not resolving all of the issues affecting cost–effectiveness analysis as a guide for resource allocation, a new framework could offer an improvement on the use of simple thresholds based on per-capita incomes.Competing interests:None declared.
I'm an independent healthcare analyst with over 20 years of experience analyzing healthcare and pharmaceuticals. Specifically, I analyze the value (costs and benefits) of biologics and pharmaceuticals, patient access to prescription drugs, the regulatory framework for drug development and reimbursement, and ethics with respect to the distribution of healthcare resources. I have over 90 publications in peer-reviewed and trade journals, in addition to newspapers and periodicals.
I have also presented my work at numerous trade, industry, and academic conferences. From 1999 to 2017 I was a research associate professor at the Tufts Center for the Study of Drug Development. Prior to my Tufts appointment, I was a post-doctoral fellow at the University of Pennsylvania, and I completed my PhD in economics at the University of Amsterdam. Before pursuing my PhD I was a management consultant at Accenture in The Hague, Netherlands. Currently, I work on freelance basis on a variety of research, teaching, and writing projects.The author is a Forbes contributor. The opinions expressed are those of the writer.
7, 2018, photo shows a CVS Pharmacy entrance in Ridgeland, Miss. (AP Photo/Rogelio V. Solis)Rationing of some sort is part and parcel of every healthcare system. Simply put, it refers to any method to allocate scare resources. This may occur by implicit means. To illustrate, in the U.S. Allocation of healthcare resources happens largely implicitly on the basis of willingness or ability to pay, whether with or without health insurance.
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In most other industrialized nations allocation of healthcare resources occurs explicitly independently of an individual’s willingness or ability to pay, for example, by way of government-imposed formulary restrictions or price controls. It appears that a private entity in the US. – the pharmacy benefit manager CVS Caremark – is embarking on an explicit path towards rationing.Last month CVS Caremark published a in which it outlined several methods to “lower pharmaceutical costs,” including attempts to lower the prices at which drugs are launched.
CVS Caremark lamented the fact that launch prices of drugs have been steadily rising for decades. The pharmacy benefit manager advocated the use of methods, such as the cost per quality-adjusted life years (QALY), to influence initial pricing decisions made by drug manufacturers and inform formulary decision-making.For newly approved drugs, CVS Caremark indicated it would use cost per QALY ratios based on publicly available analyses conducted by the Institute for Clinical and Economic Review (ICER). ICER employs a methodology similar to the one used by the British National Institute for Health and Clinical Excellence (NICE).CVS Caremark intends to institute a above which it would deny patient access. Specifically, CVS Caremark will “allow clients to exclude any drug launched at a price greater than $100,000 per QALY from their plan.” Additionally, the white paper notes that drugs deemed “breakthrough” therapies by the Food and Drug Administration will be excluded from a cost per QALY assessment.
Rather, CVS Caremark says it will focus on “expensive, ‘me-too’ medications that are not cost effective.”The latter statement is puzzling because ICER evaluates newly approved drugs, many of which are not “me-too.” It’s also presumptuous because there are many expensive drugs that are cost-effective.Most importantly, there is considerable arbitrariness involved in the $100,000 per QALY figure. The empirical approach to determine a cost-effectiveness threshold would involve an algorithm whereby, given a fixed budget, healthcare services and technologies, such as prescription drugs, would be funded, starting with the most cost-effective, until the budget is exhausted. The last funded healthcare service or technology’s cost per QALY would delineate the threshold. But, such an approach has never been undertaken, even in the U.K., due to insufficient data on the cost per QALY for many healthcare services and technologies.
Therefore, policymakers are left with arbitrarily defining cost per QALY thresholds at $50,000, $100,000, or some other figure. Furthermore, the $100,000 per QALY number may encourage pricing up to the threshold. That is, it’s clear to a drug manufacturer that a threshold level exists above which drugs will likely not be reimbursed. But, of course, that doesn’t prevent manufacturers from pricing drugs up to the threshold. One has to then ask how such pricing can be considered value-based.There is definitely an important role to play for cost-effectiveness analysis. It’s the closest proxy policymakers have to rank the value of drugs.
Also, the more payers, patients, and healthcare providers know the better informed their decisions. But, without an empirically determined threshold, pricing and formulary decisions contingent on an arbitrarily defined threshold cannot be considered value-based.Joshua P.