73 research outputs found
Interpreting cost-effectiveness ratios in a cost-effectiveness analysis of risk-tailored prostate screening: A critique of Callender et al.
Callender et al. recently published a model-based cost-effectiveness analysis of a risk-tailored approach to prostate cancer screening. It considers the costs and effects of prostate cancer screening offered to all men aged 55-69 without any risk selection and, alternatively, over a range of risk-tailored strategies in which screen eligibility is determined by a varying threshold of disease risk. The analysis finds that the strategy of screening men once they reach a 10-year absolute risk of disease of 5% or more is cost-effective in a UK context. I believe there are several problems with the study, mostly stemming from an incorrect interpretation of the cost-effectiveness estimates. I show that one reinterpretation of their results indicates that screening is much less cost-effective than the original analysis suggests, indicating that screening should be restricted to a much smaller group of higher risk men. More broadly, I explain the challenges of attempting to meaningfully reinterpret the originally published results due to the simulation of non-mutually exclusive intervention strategies. Finally, I consider the relevance of considering sufficient alternative screening intensities. This critique highlights the need for appropriate interpretation of cost-effectiveness results for policymakers, especially as risk stratification within screening becomes increasingly feasible.Health Research Boar
Comment on Keeney et al.'s "Delphi Analysis of Relevant Comparators in a Cost-Effectiveness Model of Prostate Cancer Screening"
Keeney et al. recently published a Delphi analysis on identifying strategies for simulation in a cost-effectiveness analysis (CEA) of prostate cancer screening [1]. The research usefully elucidates the screening strategies experts consider the priority for assessment. This commentary addresses three points, the first two relate specifically to Keeney et al. regarding: (1) the questions asked of experts and (2) the sample of experts consulted. The third relates to the more general issue of how best to specify strategies in cancer screening CEAs.Access provided by IREL Consortium c/o Maynooth University The Library Maynooth Universit
Revision of Ireland's Cost-Effectiveness Threshold: New State-Industry Drug Pricing Deal Should Adequately Reflect Opportunity Costs
Ireland's cost-effectiveness threshold is currently €45,000 per quality-adjusted life-year (QALY). It has previously been determined by periodic agreements between the State and a pharma industry lobby body. A new deal is due in July 2021 and it is therefore timely to re-examine Ireland's threshold, how it is set and transparency around adherence to it. Previous research has noted a series of problems with the threshold, including that it is likely too high relative to the opportunity cost of unmet need within Ireland's health system. This means reimbursement at the threshold may do net harm to population health. The high threshold may also mean the Irish health system is failing to satisfy existing legislation on healthcare resource allocation. Recent COVID-19-related pressures on healthcare capacity and public spending appear to increase the urgency for an evidence-based revision of threshold to better reflect opportunity costs within the Irish healthcare system. Despite these problems, the prospects for reform of the threshold do not appear strong as the political and institutional incentives may favour the status quo. At the very least, the State should provide greater transparency regarding how the threshold is set and adhered to. A potential reform for consideration in the longer run could include a partial abandonment of thresholds in favour of an auction process to achieve the lowest cost per QALY from new drug interventions.Health Research Boar
Risk Stratification in Cost-Effectiveness Analyses of Cancer Screening: Intervention Eligibility, Strategy Choice, and Optimality
Introduction: There is increasing interest in risk-stratified approaches to cancer screening in cost-effectiveness analysis (CEA). Current CEA practice regarding risk stratification is heterogeneous and guidance on the best approach is lacking. This article suggests how stratification in CEA can be improved. Methods: I use a simple example of a hypothetical screening intervention with 3 potential recipient risk strata. The screening intervention has 6 alternative intensities, each with different costs and effects, all of which vary between strata. I consider a series of alternative stratification approaches, demonstrating the consequences for estimated costs, effects, and the choice of optimal strategy. I supplement this analysis with applied examples from the literature. Results: Adopting the same screening policy for all strata yields the least efficient strategies, where efficiency is understood as the volume of net health benefit generated across a range of cost-effectiveness threshold values. Basic stratification that withholds screening from lower-risk strata while adopting a common strategy for those screened increases efficiency. Greatest efficiency is achieved when different strata receive separate strategies. While complete optimization can be achieved within a single analysis by considering all possible policy combinations, the resulting number of strategy combinations may be inconveniently large. Optimization with separate strata-specific analyses is simpler and more transparent. Despite this, there can be good reasons to simulate all strata together in a single analysis. Conclusions: If the benefits of risk stratification are to be fully realized, policy makers need to consider the extent to which stratification is feasible, and modelers need to simulate those choices adequately. It is hoped this analysis will clarify those policy and modeling choices and therefore lead to improved population health outcomes.Health Research Boar
A Simple Cost-Effectiveness Model of Screening:An Open-Source Teaching and Research Tool Coded in R
Applied cost-effectiveness analysis models are an important tool for assessing health and economic effects of healthcare interventions but are not best suited for illustrating methods. Our objective is to provide a simple, open-source model for the simulation of disease-screening cost-effectiveness for teaching and research purposes. We introduce our model and provide an initial application to examine changes to the efficiency frontier as input parameters vary and to demonstrate face validity. We described a vectorised, discrete-event simulation of screening in R with an Excel interface to define parameters and inspect principal results. An R Shiny app permits dynamic interpretation of simulation outputs. An example with 8161 screening strategies illustrates the cost and effectiveness of varying the disease sojourn time, treatment effectiveness, and test performance characteristics and costs on screening policies. Many of our findings are intuitive and straightforward, such as a reduction in screening costs leading to decreased overall costs and improved cost-effectiveness. Others are less obvious and depend on whether we consider gross outcomes or those net to no screening. For instance, enhanced treatment of symptomatic disease increases gross effectiveness, but reduces the net effectiveness and cost-effectiveness of screening. A lengthening of the preclinical sojourn time has ambiguous effects relative to no screening, as cost-effectiveness improves for some strategies but deteriorates for others. Our simple model offers an accessible platform for methods research and teaching. We hope it will serve as a public good and promote an intuitive understanding of the cost-effectiveness of screening.Health Research Boar
The Irish Cost-Effectiveness Threshold: Does it Support Rational Rationing or Might it Lead to Unintended Harm to Ireland's Health System?
Ireland is one of the few countries worldwide to have an explicit cost-effectiveness threshold. In 2012, an agreement between government and the pharmaceutical industry that provided substantial savings on existing medications set the threshold at €45,000/quality-adjusted life-year (QALY). This replaced a previously unofficial threshold of €20,000/QALY. According to the agreement, drugs within the threshold will be granted reimbursement, whereas those exceeding it may still be approved following further negotiation. A number of drugs far exceeding the threshold have been approved recently. The agreement only applies to pharmaceuticals. There are four reasons for concern regarding Ireland's threshold. The absence of an explicit threshold for non-drug interventions leaves it unclear if there is parity in willingness to pay across all interventions. As the threshold resembles a price floor rather than a ceiling, in principle it only offers a weak barrier to cost-ineffective interventions. It has no empirical basis. Finally, it is probably too high given recent estimates of a threshold for the UK based on the cost effectiveness of services forgone of approximately £13,000/QALY. An excessive threshold risks causing the Irish health system unintended harm. The lack of an empirically informed threshold means the policy recommendations of cost-effectiveness analysis cannot be considered as fully evidence- based rational rationing. Policy makers should consider these issues and recent Irish legislation that defined cost effectiveness in terms of the opportunity cost of services forgone when choosing what threshold to apply once the current industry agreement expires at the end of 2015.Health Research BoardAccess provided by IREL Consortium c/o Maynooth University The Library Maynooth Universit
NICE's Discounting Review: Clear Thinking on Rational Revision Meets Obstacle of Industrial Interests
The National Institute of Health and Care Excellence (NICE) recently published a review of discounting practice and theory as part of a consultation on its current methods guidelines. The review examines the case for revision or retention of current methods. The changes considered include eliminating favourable rates in certain special cases and the reduction of the base-case rate for costs and health effects from 3.5 to 1.5%. The review also notes the potential need to reduce the cost-effectiveness threshold to accommodate a discount rate reduction, explaining that an agreement between the UK government and the pharmaceutical industry proscribes changing NICE's threshold range until the end of 2023. We believe NICE should be commended for a useful overview of the existing literature and relevant issues. We firmly endorse NICE's view that favourable discount rates are not a good way to apply a preference for certain interventions. Similarly, we support the option of reducing the discount rate to 1.5%, which better accords with real government borrowing costs. We suggest further work to clarify the appropriate theoretical basis for the NICE's social discount rate and the sensitivity of the threshold to changes in discounting. The prospects of a necessary discount rate reduction appear to depend on whether a threshold reduction can be achieved within NICE's current range or if the range itself must be revised downwards. NICE has usefully informed the debate around discount rates. Ultimately, the path to a methodologically consistent and evidence-based revision of discounting depends on whether NICE needs to adjust the threshold too and if it is free to do so.Access provided by IREL Consortium c/o Maynooth University The Library Maynooth Universit
Discounting the Recommendations of the Second Panel on Cost-Effectiveness in Health and Medicine
Twenty years ago, the "Panel on Cost-effectiveness in Health and Medicine" published a landmark text setting out appropriate methods for conducting cost-effectiveness analyses of health technologies. In the two decades since, the methods used for economic evaluations have advanced substantially. Recently, a "second panel" (hereafter "the panel") was convened to update the text and its recommendations were published in November 2016. The purpose of this paper is to critique the panel's updated guidance regarding the discounting of costs and health effects. The advances in discounting methodology since the first panel include greater theoretical clarity regarding the specification of discount rates, how these rates vary with the analytical perspective chosen, and whether the healthcare budget is constrained. More specifically, there has been an important resolution of the debate regarding the conditions under which differential discounting of costs and health effects is appropriate. We show that the panel's recommendations are inconsistent with this recent literature. Importantly, the panel's departures from previously published findings do not arise from an alternative interpretation of theory; rather, we demonstrate that this is due to fundamental errors in methodology and logic. The panel also failed to conduct a formal review of relevant empirical evidence. We provide a number of suggestions for how the panel's recommendations could be improved in future.Access provided by IREL Consortium c/o Maynooth University The Library Maynooth Universit
Colorectal Cancer Screening within Colonoscopy Capacity Constraints: Can FIT-Based Programs Save More Lives by Trading off More Sensitive Test Cutoffs against Longer Screening Intervals?
Introduction. Colorectal cancer (CRC) prevention programs using fecal immunochemical testing (FIT) in screening rely on colonoscopy for secondary and surveillance testing. Colonoscopy capacity is an important constraint. Some European programs lack sufficient capacity to provide optimal screening intensity regarding age ranges, intervals, and FIT cutoffs. It is currently unclear how to optimize programs within colonoscopy capacity constraints. Design. Microsimulation modeling, using the MISCAN-Colon model, was used to determine if more effective CRC screening programs can be identified within constrained colonoscopy capacity. A total of 525 strategies were modeled and compared, varying 3 key screening parameters: screening intervals, age ranges, and FIT cutoffs, including previously unevaluated 4- and 5-year screening intervals (using a lifetime horizon and 100% adherence). Results were compared with the policy decisions taken in Ireland to provide CRC screening within available colonoscopy capacity. Outcomes estimated net costs, quality-adjusted life-years (QALYs), and required colonoscopies. The optimal strategies within finite colonoscopy capacity constraints were identified. Results. Combining a reduced FIT cutoff of 10 µg Hb/g, an extended screening interval of 4 y and an age range of 60–72 y requires 6% fewer colonoscopies, reduces net costs by 23% while preventing 15% more CRC deaths and saving 16% more QALYs relative to a strategy (FIT 40 µg Hb/g, 2-yearly, 60–70 year) approximating current policy. Conclusion. Previously overlooked longer screening intervals may optimize cancer prevention with finite colonoscopy capacity constraints. Changes could save lives, reduce costs, and relieve colonoscopy capacity pressures. These findings are relevant to CRC screening programs across Europe that employ FIT-based testing, which face colonoscopy capacity constraints.Health Research BoardHealth and Social Care Northern IrelandNational Cancer Institute Health Economics FellowshipNational Institutes of Health/National Cancer Institute Cancer CenterCancer Intervention and Surveillance Modeling Network (CISNET
Dealing with Time in Health Economic Evaluation: Methodological Issues and Recommendations for Practice
Time is an important aspect of health economic evaluation, as the timing and duration of clinical events, healthcare interventions and their consequences all affect estimated costs and effects. These issues should be reflected in the design of health economic models. This article considers three important aspects of time in modelling: (1) which cohorts to simulate and how far into the future to extend the analysis; (2) the simulation of time, including the difference between discrete-time and continuous-time models, cycle lengths, and converting rates and probabilities; and (3) discounting future costs and effects to their present values. We provide a methodological overview of these issues and make recommendations to help inform both the conduct of cost-effectiveness analyses and the interpretation of their results. For choosing which cohorts to simulate and how many, we suggest analysts carefully assess potential reasons for variation in cost effectiveness between cohorts and the feasibility of subgroup-specific recommendations. For the simulation of time, we recommend using short cycles or continuous-time models to avoid biases and the need for half-cycle corrections, and provide advice on the correct conversion of transition probabilities in state transition models. Finally, for discounting, analysts should not only follow current guidance and report how discounting was conducted, especially in the case of differential discounting, but also seek to develop an understanding of its rationale. Our overall recommendations are that analysts explicitly state and justify their modelling choices regarding time and consider how alternative choices may impact on results
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