Future Costs, Fixed Healthcare Budgets, and the Decision Rules of Cost-Effectiveness Analysis

Assessing the Health and Welfare Benefits of Interventions Using the Wider Societal Impacts Framework

OBJECTIVES

Health technology assessment bodies advocate capturing the value of interventions in terms of their benefits to health and broader welfare. The wider societal impacts (WSI) framework considers how changes in health alter a person's net contribution to society-that is, what they produce minus what they consume. In this research, we review this framework and explore the scope to differentiate WSI by equity-relevant sociodemographic characteristics.

METHODS

This research updates previous calculations using publicly available data from population-based surveys in the United Kingdom. We then estimate for 199 chronic conditions: (1) WSI for the average person with the condition and (2) gain in WSI for an improvement of 0.1 in health-related quality of life score.

RESULTS

The nature and availability of information varied across population-based surveys and precluded analyses to examine WSI by population subgroup. Our updated estimates mirrored earlier findings that consideration of the broader societal impacts of health would reprioritize interventions compared with assessment on health alone. For example, for the same improvement in health, a woman experiencing diseases of the circulatory system has the highest potential gain in WSI (£354/month) whereas a man experiencing HIV has the lowest potential gain (£233/month).

CONCLUSIONS

The WSI framework provides a simple, indirect method to inform resource allocation decisions. Understanding the equity implications of this approach was hindered by differences in the information collected across population-based surveys. Findings demonstrate the potential reprioritization that may occur if the broader welfare benefits of health interventions were used to inform coverage decisions.

The economic costs of alcohol-related harms at the local level in New South Wales

INTRODUCTION

Alcohol is a harmful, toxic and addictive substance that causes many diseases and injuries. Alcohol use also incurs a financial cost to the health care system and wider economy. This project aimed to undertake a cost impact analysis of alcohol-related harms at the local level in New South Wales (NSW). The alcohol-related harms costing model is an interactive tool designed for use by local health districts, stakeholders such as Liquor and Gaming NSW, NSW Independent Liquor and Gaming Authority and community stakeholders.

METHODS

Costs included in the analysis were alcohol-related hospitalisations, deaths, crimes, emergency department attendances, outpatient presentations and their impacts on productivity. Two local government areas (LGA) were used as case studies to demonstrate local impacts.

RESULTS

In 2019-2020, the total cost of alcohol-related harms for NSW was estimated at $9 billion, at a rate of $120.3 million per 100,000 population. The total costs were comprised of alcohol-attributable premature mortality ($8.3 billion), non-fatal health care costs ($275 million) and crime costs ($457 million). A comparative analysis of two case study LGAs estimated that alcohol-related harms cost $195 million for the Northern Beaches LGA and $351 million for the Central Coast LGA.

DISCUSSION AND CONCLUSIONS

This research has developed a 'proof-of-concept' model to estimate the cost of alcohol-related harms at the local level in Australia, empowering health agencies and local community stakeholders to use economic evidence in their submissions in response to new liquor licence applications and other policies that impact their local community. This economic evidence can be used to improve the quality of decisions on alcohol regulation and policies. There are a number of future research opportunities that would enhance the economic evidence available to liquor licensing decision-makers.

Context-Specific Estimation of Future Unrelated Medical Costs and Their Impact on Cost-Effectiveness Analyses

OBJECTIVES

This study constructed and applied procedures for the estimation of unrelated future medical costs (UFMC) of women with breast cancer in Israel (as a case study) and examined the influence of including UFMC in cost-effectiveness analyses (CEAs).

METHODS

Part I consisted of a retrospective cohort study based on patient-level claims data of both patients with breast cancer and matched controls during 14 years of follow-up. UFMC were estimated as (a) the annual average all-cause healthcare costs of the control subjects, and (b) as predicted values based on a generalized linear model (GLM) adjusted to patients' characteristics. Part II consisted of a CEA performed using a Markov simulation model comparing regimens of chemotherapy with/without trastuzumab, both excluding and including UFMC and for each of the UFMC estimates separately. All costs were adjusted to 2019 prices. Costs and QALYs were discounted at a yearly rate of 3%.

RESULTS

The average annual healthcare costs in the control group were $2328 (± $5662). The corresponding incremental cost-effectiveness ratio (ICER) was $53,411/QALY and $55,903/QALY, when UFMC were excluded or included, respectively. Hence, trastuzumab was not considered cost-effective compared with a threshold of willingness-to-pay of $37,000 per QALY, regardless of the inclusion of UFMC. When UFMC were estimated on the basis of the prediction model, the ICERs were $37,968/QALY and $39,033/QALY, when UFMC were excluded or included, respectively. Thus, in this simulation, trastuzumab was not considered cost-effective, independent of the inclusion of UFMC.

CONCLUSION

Our case study revealed that the inclusion of UFMC had modest effect on the ICERs, and thus did not alter the conclusion. Thus, we should estimate context-specific UFMC if they are expected to change the ICERs significantly, and transparently report the corresponding assumptions to uphold the integrity and reliability of the economic evaluation.

Associating Health-Related Quality-of-Life Score with Time Uses to Inform Productivity Measures in Cost-Effectiveness Analysis

BACKGROUND

The Second Panel on Cost Effectiveness in Health and Medicine recommended that cost-effectiveness analyses (CEA) explicitly incorporate the valuation of productive time from a societal perspective. We developed a new approach to capture productivity impacts in CEA without direct evidence on these impacts by associating varying levels of health-related quality-of-life (HrQoL) score with different time uses in the United States.

METHODS

We conceptualized a framework that estimates the association between HrQoL score with productivity through time uses. We used the American Time-Use Survey (ATUS) from year 2012-2013, when data on a Well-Being Module (WBM) was additionally collected alongside ATUS. The WBM measured the quality of life (QoL) score using a visual analog scale. To operationalize our conceptual framework, we employed an econometric approach that addressed three technical issues in the observed data: (i) distinction between overall QoL and HrQoL, (ii) correlation across different categories of time use and the share structure of time-use data, and (iii) reverse causality between time uses and HrQoL score in a cross-sectional setting. Furthermore, we developed a metamodel-based algorithm to summarize the numerous estimates from the primary econometric model efficiently. Finally, we illustrated the use of our algorithm to calculate productivity and time spent seeking care costs in an empirical CEA of a prostate cancer treatment.

RESULTS

We provide the estimates of the metamodel algorithm. Incorporating these estimates into the empirical CEA reduced the incremental cost-effectiveness ratio by 27%.

CONCLUSION

Our estimates can facilitate the inclusion of productivity and time spent seeking care in CEA as recommended by the Second Panel.

The Value of Health in a Cost-Effectiveness Analysis: Theory Versus Practice

A cost-effectiveness analysis has become an important method to inform allocation decisions and reimbursement of new technologies in healthcare. A cost-effectiveness analysis requires a threshold to which the cost effectiveness of a new intervention can be compared. In principle, the threshold ought to reflect opportunity costs of reimbursing a new technology. In this paper, we contrast the practical use of this threshold within a CEA with its theoretical underpinnings. We argue that several assumptions behind the theoretical models underlying this threshold are violated in practice. This implies that a simple application of the decision rules of CEA using a single estimate of the threshold does not necessarily improve population health or societal welfare. Conceptual differences regarding the interpretation of the threshold, widely varying estimates of its value, and an inconsistent use within and outside the healthcare sector are important challenges in informing policy makers on optimal reimbursement decision and setting appropriate healthcare budgets.

A framework for the estimation of treatment costs of cardiovascular conditions in the presence of disease transition

The current research aims to aid policymakers and healthcare service providers in estimating expected long-term costs of medical treatment, particularly for chronic conditions characterized by disease transition. The study comprised two phases (qualitative and quantitative), in which we developed linear optimization-based mathematical frameworks to ascertain the expected long-term treatment cost per patient considering the integration of various related dimensions such as the progression of the medical condition, the accuracy of medical treatment, treatment decisions at respective severity levels of the medical condition, and randomized/deterministic policies. At the qualitative research stage, we conducted the data collection and validation of various cogent hypotheses acting as inputs to the prescriptive modeling stage. We relied on data collected from 115 different cardio-vascular clinicians to understand the nuances of disease transition and related medical dimensions. The framework developed was implemented in the context of a multi-specialty hospital chain headquartered in the capital city of a state in Eastern India, the results of which have led to some interesting insights. For instance, at the prescriptive modeling stage, though one of our contributions related to the development of a novel medical decision-making framework, we illustrated that the randomized versus deterministic policy seemed more cost-competitive. We also identified that the expected treatment cost was most sensitive to variations in steady-state probability at the "major" as opposed to the "severe" stage of a medical condition, even though the steady-state probability of the "severe" state was less than that of the "major" state.

The Relevance of Including Future Healthcare Costs in Cost-Effectiveness Threshold Calculations for the UK NHS

BACKGROUND AND OBJECTIVE

The supply-side threshold for the UK National Health Service has been empirically estimated as the marginal returns to healthcare spending on health outcomes. These estimates implicitly exclude future healthcare costs, which is inconsistent with the objective of making the most efficient use of healthcare resources. This paper illustrates how empirical estimates of the threshold within healthcare can be adjusted to account for future healthcare costs.

METHODS

Using cause-deleted life tables and previous work on future costs in England and Wales, we illustrate how such estimates can be adjusted.

RESULTS

While the effect of including future healthcare costs can have substantial effects on incremental cost-effectiveness ratios of specific life-extending interventions, we find that including future costs has relatively little impact (an increase of £743 per quality-adjusted life-year) on the threshold estimate.

CONCLUSIONS

For some life-extending interventions the impact of including future costs on whether an intervention is deemed cost effective may be considerable.

Incorporating future unrelated medical costs in cost-effectiveness analysis in China

The occurrence of future unrelated medical costs is a direct consequence of life-prolonging interventions, but most pharmacoeconomic guidelines recommend the exclusion of these costs. The Chinese guidelines were updated in 2020, taking an exclusion approach for the future unrelated medical cost. We notice the research surrounding this issue continues in other countries and leads to an inclusion recommendation in some guidelines. Meanwhile, this issue has not been discussed in China, reflecting an urgent need for extensive research on its impact. We reviewed the theoretical and practical studies surrounding the inclusion of future unrelated medical costs, summarised the landscape of guidelines in other jurisdictions. We found that the inclusion would increase the internal and external consistency of economic evaluation and the comparability of results between different jurisdictions. However, more research is needed surrounding this issue. We proposed a future research agenda to inform the update of Chinese guidelines. We recommend research on individual-level healthcare reimbursement data and end-of-life costs from hospital administrative data to generate the age-specific, sex-specific and condition-specific costs. We also recommend establishing a formal process to evaluate the ethical and economic impact of including future unrelated medical costs and adjust the threshold accordingly in the guidelines.

Broadening the Concept of Value: A Scoping Review on the Option Value of Medical Technologies

OBJECTIVES

A recent debate in health economics and outcomes research community identified option value as one of the elements warranting consideration in the assessment of medical technologies. To conduct a scoping review of contributions on option value in the healthcare sector and identify relevant conceptual aspects and methods used to incorporate it in standard economic evaluations.

METHODS

A systematic search was conducted up to July 2020 to identify contributions from electronic bibliographic database and gray literature. Data on the proposed definitions of option value, theoretical implications of its use in economic evaluations, and methods used to estimate it were extracted and analyzed.

RESULTS

We found 57 eligible studies. Three different definitions emerged: insurance value, real option value, and option value of survival. Focusing on the latter (24 studies), we analyzed in depth 8 empirical applications across 7 therapeutic areas. The most relevant methodological challenges were on the perspective used in economic evaluations and how to robustly manage forecasting uncertainty, update cost-effectiveness thresholds, and avoid double-counting issues. For empirical studies assessing the total value of the technology, including option value, estimates ranged from +7% to +469% of its conventional value.

CONCLUSIONS

This review synthesizes theoretical and empirical aspects on option value of healthcare technologies and proposes a terminology to distinguish 3 different concepts identified. Future work should focus primarily on agreeing on whether option value should be included in economic evaluations and, if so, on developing and validating reliable methods for its ex-ante estimation.

Catalog of Age- and Medical Condition-Specific Healthcare Costs in the United States to Inform Future Costs Calculations in Cost-Effectiveness Analysis

OBJECTIVES

This study aims to develop a catalog of annual age- and medical condition-specific healthcare costs per capita among those who are living at a certain age (survivors) and the costs attributable to death itself for those who die at that age (decedents) in the United States. These estimates can be used to inform future cost calculations in cost-effectiveness analysis (CEA).

METHODS

We discussed a theoretical framework to incorporate futures costs in CEA. We used the nationally representative Medical Expenditure Panel Survey data to estimate costs among survivors and death costs. For survivors, we obtained cost estimates nonparametrically using kernel-based regression and locally weighted scatterplot smoothing. We estimated costs attributable to death using inverse probability weights comparing decedents with appropriately weighted survivors at a given age after controlling for more than 270 clinical condition classifications, demographics, and interactions. Cost estimates were expressed in 2019 US dollar and also separately by sex and specific clinical conditions.

RESULTS

Average healthcare costs per capita among survivors, expectedly, rose over age from $2062 (95% confidence interval [CI] $1553-$2478) during the first year of life to $14 307 (95% CI $13 706-$14 956) at 85 years or older. Average costs of death were $44 569 (95% CI $14 304-$67 369) during the first year of life and declined by -$321 (95% CI -$620 to -$22) per 1 year older.

CONCLUSIONS

The US catalog of healthcare costs among survivors and decedents can facilitate calculations of future costs in CEA as recommended by the Second Panel on Cost-Effectiveness in Health and Medicine.

Don't forget about the future: The impact of including future costs on the cost-effectiveness of adult pneumococcal conjugate vaccination with PCV13 in the Netherlands

BACKGROUND

When vaccines increase longevity, vaccinated people may experience costs and benefits during added life-years. These future benefits and costs may include increased productivity as well as medical and non-medical costs. Such impacts should be considered in cost-effectiveness analyses (CEA) of vaccines but are often omitted. Here, we illustrate the impact of including future costs on the cost-effectiveness of vaccination against pneumococcus disease. We emphasize the relevance of differentiating cost estimates between risk groups.

METHODS

We updated an existing Dutch CEA of vaccination against pneumococcus disease with the 13-valent pneumococcal conjugate vaccine (PCV13) to include all future medical and non-medical costs. We linked costs by age and risk with survival information and estimates of cases prevented per vaccination strategy based on the original study to calculate the impact of inclusion. Future medical costs were adjusted for relevant risk groups.

RESULTS

For the base-case strategy, the original incremental cost-effectiveness ratio (ICER) of PVC13 was €9,157 per quality adjusted life-year (QALY). Including all future medical costs increased the ICER to €28,540 per QALY. Also including future non-medical costs resulted in an ICER of €45,691 per QALY. The impact of future medical costs varied considerably per risk group and generally increased with age.

DISCUSSION AND CONCLUSION

This study showed a substantial effect of the inclusion of future costs on the ICER of vaccinating with PCV13. Especially when lives of people with underlying health conditions are extended, the impact of future medical costs is large. This inclusion may make vaccination a less attractive option, especially in relation to low thresholds as often applied for prevention. Although this raises important questions, ignoring these real future costs may lead to an inefficient use of healthcare resources. Our results may imply that prices for some vaccines need to be lowered to be cost-effective.

Trading off health and financial protection benefits with multiobjective optimization

Countries which are introducing a system of Universal health coverage have to make a number of key tradeoffs, of which one is the tradeoff between the level of coverage and the degree to which patients are exposed to potentially catastrophic financial risk. In this study, we first present a way in which decision makers might be supported to focus on in a particular part of the tradeoff curve and ultimately choose an efficient solution. We then introduce some multiobjective optimization models for generating the tradeoff curves given data about potential treatment numbers, costs, and benefits. Using a dataset from Malawi, we demonstrate the approach and suggest a core index metric to make specific observations on the individual treatments. Moreover, as there has been some debate about the best way to measure financial exposure, we also investigate the extent to sensitivity of our results to the precise technical choice of financial exposure metric. Specifically, we consider two metrics, which are the total number of cases protected from catastrophic expenditure and a convex penalty function that penalizes out-of-pocket expenditures in an increasingly growing way, respectively.

Costs and benefits of interventions aimed at major infectious disease threats: lessons from the literature

Pandemics and major outbreaks have the potential to cause large health losses and major economic costs. To prioritize between preventive and responsive interventions, it is important to understand the costs and health losses interventions may prevent. We review the literature, investigating the type of studies performed, the costs and benefits included, and the methods employed against perceived major outbreak threats. We searched PubMed and SCOPUS for studies concerning the outbreaks of SARS in 2003, H5N1 in 2003, H1N1 in 2009, Cholera in Haiti in 2010, MERS-CoV in 2013, H7N9 in 2013, and Ebola in West-Africa in 2014. We screened titles and abstracts of papers, and subsequently examined remaining full-text papers. Data were extracted according to a pre-constructed protocol. We included 34 studies of which the majority evaluated interventions related to the H1N1 outbreak in a high-income setting. Most interventions concerned pharmaceuticals. Included costs and benefits, as well as the methods applied, varied substantially between studies. Most studies used a short time horizon and did not include future costs and benefits. We found substantial variation in the included elements and methods used. Policymakers need to be aware of this and the bias toward high-income countries and pharmaceutical interventions, which hampers generalizability. More standardization of included elements, methodology, and reporting would improve economic evaluations and their usefulness for policy.

Cured Today, Ill Tomorrow: A Method for Including Future Unrelated Medical Costs in Economic Evaluation in England and Wales

OBJECTIVES

In many countries, future unrelated medical costs occurring during life-years gained are excluded from economic evaluation, and benefits of unrelated medical care are implicitly included, leading to life-extending interventions being disproportionately favored over quality of life-improving interventions. This article provides a standardized framework for the inclusion of future unrelated medical costs and demonstrates how this framework can be applied in England and Wales.

METHODS

Data sources are combined to construct estimates of per-capita National Health Service spending by age, sex, and time to death, and a framework is developed for adjusting these estimates for costs of related diseases. Using survival curves from 3 empirical examples illustrates how our estimates for unrelated National Health Service spending can be used to include unrelated medical costs in cost-effectiveness analysis and the impact depending on age, life-years gained, and baseline costs of the target group.

RESULTS

Our results show that including future unrelated medical costs is feasible and standardizable. Empirical examples show that this inclusion leads to an increase in the ICER of between 7% and 13%.

CONCLUSIONS

This article contributes to the methodology debate over unrelated costs and how to systematically include them in economic evaluation. Results show that it is both important and possible to include future unrelated medical costs.

After 20 Years of Using Economic Evaluation, Should NICE be Considered a Methods Innovator?

The National Institute for Health and Care Excellence (NICE) is only one of several organisations internationally that uses economic evaluation as part of decision making regarding funding and pricing of new medical technologies. However, it can be argued that NICE has developed a more prominent international profile than most in their use of economics. After 20 years of operation, it is timely to assess the extent of NICE's achievements, including the economic evaluation methods it has used and its willingness to adapt these as new evaluative approaches emerge and when NICE faces particular policy challenges. This paper considers some of the important policy and contextual developments in the UK over the last 20 years and how these may have shaped NICE's approach to economic evaluation. It then assesses key areas of NICE methods, including perspective, defining benefits, modelling and uncertainty. The paper concludes that NICE has provided important support for the development of new methods, in particular through its role in identifying priorities for methods research funding and its sponsorship of the NICE Decision Support Unit. However, potentially important developments in methods in a number of important areas have yet to be formally included in NICE's methods guidance and this should be addressed in the Institute's 2019/2020 methods review.

Comprehensive value assessment of drugs using a multi-criteria decision analysis: An example of targeted therapies for metastatic colorectal cancer treatment

OBJECTIVE

This study is aimed toward establishing a decision-making model with multiple criteria for appraisal and reimbursement to compare the attitudes of different stakeholders toward various dimensions and criteria and to evaluate the five targeted therapies (bevacizumab, cetuximab, panitumumab, aflibercept, and regorafenib) for metastatic colorectal cancer.

METHOD

This study is a multi-criteria decision analysis (MCDA) using a model that includes three dimensions and nine criteria. Both the overall and individual scores of the respective targeted therapies in different dimensions and criteria were calculated. A sensitivity analysis was carried out in order to evaluate the robustness of the research results. An interview-based questionnaire survey was applied to obtain the performance information for the targeted therapies and the weights of the dimensions and criteria.

RESULTS

Overall, the clinical dimension had the highest weight, followed by the economic dimension, and finally, the social dimension. In the clinical dimension, the "comparative efficacy" criterion had the highest weight; in the economic dimension, the "cost-effectiveness" criterion" was given the greatest importance; in the social dimension, the "social concern and patient needs" criterion was given more emphasis. The overall values ranked from high to low as follows: cetuximab (overall score 3.3666), bevacizumab (3.3043), panitumumab (3.2030), aflibercept (2.8923) and regorafenib (2.8366).

CONCLUSIONS

A comprehensive value assessment system combining "multi-dimensional criteria," "multi-perspectives," and an "integrative assessment" is necessary to evaluate the value of medicines. The results showed not only the order of weights of different dimensions or criteria, but also the rankings of the value of the targeted therapies.

Cost-Effectiveness of Cancer Screening: Health and Costs in Life Years Gained

INTRODUCTION

Studies reporting on the cost-effectiveness of cancer screening usually account for quality of life losses and healthcare costs owing to cancer but do not account for future costs and quality of life losses related to competing risks. This study aims to demonstrate the impact of medical costs and quality of life losses of other diseases in the life years gained on the cost-effectiveness of U.S. cancer screening.

METHODS

Cost-effectiveness studies of breast, cervical, and colorectal cancer screening in the U.S. were identified using a systematic literature review. Incremental cost-effectiveness ratios of the eligible articles were updated by adding lifetime expenditures and health losses per quality-adjusted life year gained because of competing risks. This was accomplished using data on medical spending and quality of life by age and disease from the Medical Expenditure Panel Survey (2011-2015) combined with cause-deleted life tables. The study was conducted in 2018.

RESULTS

The impact of quality of life losses and healthcare expenditures of competing risks in life years gained incurred owing to screening were the highest for breast cancer and the lowest for cervical cancer. The updates suggest that incremental cost-effectiveness ratios are underestimated by $10,300-$13,700 per quality-adjusted life year gained if quality of life losses and healthcare expenditures of competing risks are omitted in economic evaluations. Furthermore, cancer screening programs that were considered cost saving, were found not to be so following the inclusion of medical expenditures of competing risks.

CONCLUSIONS

Practical difficulties in quantifying quality of life losses and healthcare expenditures owing to competing risks in life years gained can be overcome. Their inclusion can have a substantial impact on the cost-effectiveness of cancer screening programs.

Incorporating Future Medical Costs: Impact on Cost-Effectiveness Analysis in Cancer Patients

BACKGROUND

The inclusion of future medical costs in cost-effectiveness analyses remains a controversial issue. The impact of capturing future medical costs is likely to be particularly important in patients with cancer where costly lifelong medical care is necessary. The lack of clear, definitive pharmacoeconomic guidelines can limit comparability and has implications for decision making.

OBJECTIVE

The aim of this study was to demonstrate the impact of incorporating future medical costs through an applied example using original data from a clinical study evaluating the cost effectiveness of a sepsis intervention in cancer patients.

METHODS

A decision analytic model was used to capture quality-adjusted life-years (QALYs) and lifetime costs of cancer patients from an Australian healthcare system perspective over a lifetime horizon. The evaluation considered three scenarios: (1) intervention-related costs (no future medical cost), (2) lifetime cancer costs and (3) all future healthcare costs. Inputs to the model included patient-level data from the clinical study, relative risk of death due to sepsis, cancer mortality and future medical costs sourced from published literature. All costs are expressed in 2017 Australian dollars and discounted at 5%. To further assess the impact of future costs on cancer heterogeneity, variation in survival and lifetime costs between cancer types and the implications for cost-effectiveness analysis were explored.

RESULTS

The inclusion of future medical costs increased incremental cost-effectiveness ratios (ICERs) resulting in a shift from the intervention being a dominant strategy (cheaper and more effective) to an ICER of $7526/QALY. Across different cancer types, longer life expectancies did not necessarily result in greater lifetime healthcare costs. Incremental costs differed across cancers depending on the respective costs of managing cancer and survivorship, thus resulting in variations in ICERs.

CONCLUSIONS

There is scope for including costs beyond intervention costs in economic evaluations. The inclusion of future medical costs can result in markedly different cost-effectiveness results, leading to higher ICERs in a cancer population, with possible implications for funding decisions.

Future Costs in Cost-Effectiveness Analyses: Past, Present, Future

There has been considerable debate on the extent to which future costs should be included in cost-effectiveness analyses of health technologies. In this article, we summarize the theoretical debates and empirical research in this area and highlight the conclusions that can be drawn for current practice. For future related and future unrelated medical costs, the literature suggests that inclusion is required to obtain optimal outcomes from available resources. This conclusion does not depend on the perspective adopted by the decision maker. Future non-medical costs are only relevant when adopting a societal perspective; these should be included if the benefits of non-medical consumption and production are also included in the evaluation. Whether this is the case currently remains unclear, given that benefits are typically quantified in quality-adjusted life-years and only limited research has been performed on the extent to which these (implicitly) capture benefits beyond health. Empirical research has shown that the impact of including future costs can be large, and that estimation of such costs is feasible. In practice, however, future unrelated medical costs and future unrelated non-medical consumption costs are typically excluded from economic evaluations. This is explicitly prescribed in some pharmacoeconomic guidelines. Further research is warranted on the development and improvement of methods for the estimation of future costs. Standardization of methods is needed to enhance the practical applicability of inclusion for the analyst and the comparability of the outcomes of different studies. For future non-medical costs, further research is also needed on the extent to which benefits related to this spending are captured in the measurement and valuation of health benefits, and how to broaden the scope of the evaluation if they are not sufficiently captured.

Future unrelated medical costs need to be considered in cost effectiveness analysis

New medical technologies that prolong life result in additional health care use in life years gained. Some of these costs in life years gained are considered to be related to the intervention while other costs are considered unrelated. Here, we argue that ignoring these so-called future medical costs in cost effectiveness analysis is contrary to common sense, results in lost health and fails to inform decision makers for whom cost effectiveness is supposed to serve.

A cost-effectiveness threshold based on the marginal returns of cardiovascular hospital spending

Traditionally, threshold levels of cost-effectiveness have been derived from willingness-to-pay studies, indicating the consumption value of health (v-thresholds). However, it has been argued that v-thresholds need to be supplemented by so-called k-thresholds, which are based on the marginal returns to health care. The objective of this research is to estimate a k-threshold based on the marginal returns to cardiovascular disease (CVD) hospital care in the Netherlands. To estimate a k-threshold for hospital care on CVD, we proceed in two steps: First, we estimate the impact of hospital spending on mortality using a Bayesian regression modelling framework, using data on CVD mortality and CVD hospital spending by age and gender for the period 1994-2010. Second, we use life tables in combination with quality of life data to convert these estimates into a k-threshold expressed in euros per quality-adjusted life year gained. Our base case estimate resulted in an estimate of 41,000 per quality-adjusted life year gained. In our sensitivity analyses, we illustrated how the incorporation of prior evidence into the estimation pushes estimates downwards. We conclude that our base case estimate of the k-threshold may serve as a benchmark value for decision making in the Netherlands as well as for future research regarding k-thresholds.

Which Costs Matter? Costs Included in Economic Evaluation and their Impact on Decision Uncertainty for Stable Coronary Artery Disease

BACKGROUND

Variation exists in the resource categories included in economic evaluations, and National Institute for Health and Care Excellence (NICE) guidance suggests the inclusion only of costs related to the index condition or intervention. However, there is a growing consensus that all healthcare costs should be included in economic evaluations for Health Technology Assessments (HTAs), particularly those related to extended years of life.

OBJECTIVE AND METHODS

We aimed to quantify the impact of a range of cost categories on the adoption decision about a hypothetical intervention, and uncertainty around that decision, for stable coronary artery disease (SCAD) based on a dataset comprising 94,966 patients. Three costing scenarios were considered: coronary heart disease (CHD) costs only, cardiovascular disease (CVD) costs and all costs. The first two illustrate different interpretations of what might be regarded as related costs.

RESULTS

Employing a 20-year time horizon, the highest mean expected incremental cost was when all costs were included (£2468) and the lowest when CVD costs only were included (£2377). The probability of the treatment being cost effective, estimating health opportunity costs using a ratio of £30,000 per quality-adjusted life-year (QALY), was different for each of the CHD (70%) costs, CVD costs (73%) and all costs (56%) scenarios. The results concern a hypothetical intervention and are illustrative only, as such they cannot necessarily be generalised to all interventions and diseases.

CONCLUSIONS

Cost categories included in an economic evaluation of SCAD impact on estimates of both cost effectiveness and decision uncertainty. With an aging and co-morbid population, the inclusion of all healthcare costs may have important ramifications for the selection of healthcare provision on economic grounds.

Consensus-based cross-European recommendations for the identification, measurement and valuation of costs in health economic evaluations: a European Delphi study

OBJECTIVES

Differences between country-specific guidelines for economic evaluations complicate the execution of international economic evaluations. The aim of this study was to develop cross-European recommendations for the identification, measurement and valuation of resource use and lost productivity in economic evaluations using a Delphi procedure.

METHODS

A comprehensive literature search was conducted to identify European guidelines on the execution of economic evaluations or costing studies as part of economic evaluations. Guideline recommendations were extracted by two independent reviewers and formed the basis for the first round of the Delphi study, which was conducted among European health economic experts. During three written rounds, consensus (agreement of 67% or higher) was sought on items concerning the identification, measurement and valuation of costs.

RESULTS

Recommendations from 18 guidelines were extracted. Consensus among 26 panellists from 17 European countries was reached on 61 of 68 items. The recommendations from the Delphi study are to adopt a societal perspective, to use patient report for measuring resource use and lost productivity, to value both constructs with use of country-specific standardized/unit costs and to use country-specific discounting rates.

CONCLUSION

This study provides consensus-based cross-European recommendations on how to measure and value resource use and lost productivity in economic evaluations. These recommendations are expected to support researchers, healthcare professionals, and policymakers in executing and appraising economic evaluations performed in international contexts.

Estimating comparable English healthcare costs for multiple diseases and unrelated future costs for use in health and public health economic modelling

OBJECTIVES

Healthcare interventions, and particularly those in public health may affect multiple diseases and significantly prolong life. No consensus currently exists for how to estimate comparable healthcare costs across multiple diseases for use in health and public health cost-effectiveness models. We aim to describe a method for estimating comparable disease specific English healthcare costs as well as future healthcare costs from diseases unrelated to those modelled.

METHODS

We use routine national datasets including programme budgeting data and cost curves from NHS England to estimate annual per person costs for diseases included in the PRIMEtime model as well as age and sex specific costs due to unrelated diseases.

RESULTS

The 2013/14 annual cost to NHS England per prevalent case varied between £3,074 for pancreatic cancer and £314 for liver disease. Costs due to unrelated diseases increase with age except for a secondary peak at 30-34 years for women reflecting maternity resource use.

CONCLUSIONS

The methodology described allows health and public health economic modellers to estimate comparable English healthcare costs for multiple diseases. This facilitates the direct comparison of different health and public health interventions enabling better decision making.

Implications of Coronary Artery Calcium Testing for Treatment Decisions Among Statin Candidates According to the ACC/AHA Cholesterol Management Guidelines: A Cost-Effectiveness Analysis

This review evaluates the cost-effectiveness of using coronary artery calcium (CAC) to guide long-term statin therapy compared with treating all patients eligible for statins according to 2013 American College of Cardiology/American Heart Association cholesterol management guidelines for atherosclerotic cardiovascular disease. The authors used a microsimulation model to compare costs and effectiveness from a societal perspective over a lifetime horizon. Both strategies resulted in similar costs and quality-adjusted life years (QALYs). CAC resulted in increased costs (+$81) and near-equal QALY (+0.01) for an incremental cost-effectiveness ratio of $8,100/QALY compared with the treat-all strategy. For 10,000 patients, the treat-all strategy would theoretically avert 21 atherosclerotic cardiovascular disease events, but would add 47,294 person-years of statins. With CAC costs <$100, and higher cost and/or disutility associated with statin therapy, CAC strategy was favored. These findings suggest the economic value of both approaches were similar. Clinicians should account for individual preferences in context of shared decision making when choosing the most appropriate strategy to guide statin decisions.

Decision Making for Healthcare Resource Allocation: Joint v. Separate Decisions on Interacting Interventions

Standard guidance for allocating healthcare resources based on cost-effectiveness recommends using different decision rules for independent and mutually exclusive alternatives, although there is some confusion around the definition of “mutually exclusive.” This paper reviews the definitions used in the literature and shows that interactions (i.e., non-additive effects, whereby the effect of giving 2 interventions simultaneously does not equal the sum of their individual effects) are the defining feature of mutually exclusive alternatives: treatments cannot be considered independent if the costs and/or benefits of one treatment are affected by the other treatment. The paper then identifies and categorizes the situations in which interventions are likely to have non-additive effects, including interventions targeting the same goal or clinical event, or life-saving interventions given to overlapping populations. We demonstrate that making separate decisions on interventions that have non-additive effects can prevent us from maximizing health gained from the healthcare budget. In contrast, treating combinations of independent options as though they were “mutually exclusive” makes the analysis more complicated but does not affect the conclusions. Although interactions are considered by the World Health Organization, other decision makers, such as the National Institute for Health and Care Excellence (NICE), currently make independent decisions on treatments likely to have non-additive effects. We propose a framework by which interactions could be considered when selecting, prioritizing, and appraising healthcare technologies to ensure efficient, evidence-based decision making.

Allocation rules for global donors

In recent years, donors such as the Bill and Melinda Gates Foundation have made an enormous contribution to the reduction of the global burden of disease. It has been argued that such donors should prioritise interventions based on their cost-effectiveness, that is to say, the ratio of costs to benefits. Against this, we argue that the donor should fund not the most cost-effective interventions, but rather interventions which are just cost-ineffective for the country, thus encouraging the country to contribute its own domestic resources to the fight against disease. We demonstrate that our proposed algorithm can be justified within the context of a model of the problem as a leader-follower game, in which a donor chooses to subsidise interventions which are implemented by a country. We argue that the decision rule we propose provides a basis for the allocation of aid money which is efficient, fair and sustainable.

Systematic review of model-based economic evaluations of heart valve implantations

OBJECTIVE

To review the evidence on the cost-effectiveness of heart valve implantations generated by decision analytic models and to assess their methodological quality.

METHODS

A systematic review was performed including model-based cost-effectiveness analyses of heart valve implantations. Study and model characteristics and cost-effectiveness results were extracted and the methodological quality was assessed using the Philips checklist.

RESULTS

Fourteen decision-analytic models regarding the cost-effectiveness of heart valve implantations were identified. In most studies transcatheter aortic valve implantation (TAVI) was cost-effective compared to standard treatment (ST) in inoperable or high-risk operable patients (ICER range 18,421-120,779 €) and in all studies surgical aortic valve replacement (SAVR) was cost-effective compared to ST in operable patients (ICER range 14,108-40,944 €), but the results were not consistent on the cost-effectiveness of TAVI versus SAVR in high-risk operable patients (ICER range: dominant to dominated by SAVR). Mechanical mitral valve replacement (MVR) had the lowest costs per success compared to mitral valve repair and biological MVR. The methodological quality of the studies was moderate to good.

CONCLUSION

This review showed that improvements can be made in the description and justification of methods and data sources, sensitivity analysis on extrapolation of results, subgroup analyses, consideration of methodological and structural uncertainty, and consistency (i.e. validity) of the models. There are several opportunities for future decision-analytic models of the cost-effectiveness of heart valve implantations: considering heart valve implantations in other valve positions besides the aortic valve, using a societal perspective, and developing patient-simulation models to investigate the impact of patient characteristics on outcomes.

Health care input constraints and cost effectiveness analysis decision rules

Results of cost effectiveness analyses (CEA) studies are most useful for decision makers if they face only one constraint: the health care budget. However, in practice, decision makers wishing to use the results of CEA studies may face multiple resource constraints relating to, for instance, constraints in health care inputs such as a shortage of skilled labour. The presence of multiple resource constraints influences the decision rules of CEA and limits the usefulness of traditional CEA studies for decision makers. The goal of this paper is to illustrate how results of CEA can be interpreted and used in case a decision maker faces a health care input constraint. We set up a theoretical model describing the optimal allocation of the health care budget in the presence of a health care input constraint. Insights derived from that model were used to analyse a stylized example based on a decision about a surgical robot as well as a published cost effectiveness study on eye care services in Zambia. Our theoretical model shows that applying default decision rules in the presence of a health care input constraint leads to suboptimal decisions but that there are ways of preserving the traditional decision rules of CEA by reweighing different cost categories. The examples illustrate how such adjustments can be made, and makes clear that optimal decisions depend crucially on such adjustments. We conclude that it is possible to use the results of cost effectiveness studies in the presence of health care input constraints if results are properly adjusted.

How to Appropriately Extrapolate Costs and Utilities in Cost-Effectiveness Analysis

Costs and utilities are key inputs into any cost-effectiveness analysis. Their estimates are typically derived from individual patient-level data collected as part of clinical studies the follow-up duration of which is often too short to allow a robust quantification of the likely costs and benefits a technology will yield over the patient's entire lifetime. In the absence of long-term data, some form of temporal extrapolation-to project short-term evidence over a longer time horizon-is required. Temporal extrapolation inevitably involves assumptions regarding the behaviour of the quantities of interest beyond the time horizon supported by the clinical evidence. Unfortunately, the implications for decisions made on the basis of evidence derived following this practice and the degree of uncertainty surrounding the validity of any assumptions made are often not fully appreciated. The issue is compounded by the absence of methodological guidance concerning the extrapolation of non-time-to-event outcomes such as costs and utilities. This paper considers current approaches to predict long-term costs and utilities, highlights some of the challenges with the existing methods, and provides recommendations for future applications. It finds that, typically, economic evaluation models employ a simplistic approach to temporal extrapolation of costs and utilities. For instance, their parameters (e.g. mean) are typically assumed to be homogeneous with respect to both time and patients' characteristics. Furthermore, costs and utilities have often been modelled to follow the dynamics of the associated time-to-event outcomes. However, cost and utility estimates may be more nuanced, and it is important to ensure extrapolation is carried out appropriately for these parameters.

Cost-effectiveness of population-based, community, workplace and individual policies for diabetes prevention in the UK

AIM

To analyse the cost-effectiveness of different interventions for Type 2 diabetes prevention within a common framework.

METHODS

A micro-simulation model was developed to evaluate the cost-effectiveness of a range of diabetes prevention interventions including: (1) soft drinks taxation; (2) retail policy in socially deprived areas; (3) workplace intervention; (4) community-based intervention; and (5) screening and intensive lifestyle intervention in individuals with high diabetes risk. Within the model, individuals follow metabolic trajectories (for BMI, cholesterol, systolic blood pressure and glycaemia); individuals may develop diabetes, and some may exhibit complications of diabetes and related disorders, including cardiovascular disease, and eventually die. Lifetime healthcare costs, employment costs and quality-adjusted life-years are collected for each person.

RESULTS

All interventions generate more life-years and lifetime quality-adjusted life-years and reduce healthcare spending compared with doing nothing. Screening and intensive lifestyle intervention generates greatest lifetime net benefit (£37) but is costly to implement. In comparison, soft drinks taxation or retail policy generate lower net benefit (£11 and £11) but are cost-saving in a shorter time period, preferentially benefit individuals from deprived backgrounds and reduce employer costs.

CONCLUSION

The model enables a wide range of diabetes prevention interventions to be evaluated according to cost-effectiveness, employment and equity impacts over the short and long term, allowing decision-makers to prioritize policies that maximize the expected benefits, as well as fulfilling other policy targets, such as addressing social inequalities.

The impact of Type 2 diabetes prevention programmes based on risk-identification and lifestyle intervention intensity strategies: a cost-effectiveness analysis

AIMS

To develop a cost-effectiveness model to compare Type 2 diabetes prevention programmes targeting different at-risk population subgroups with a lifestyle intervention of varying intensity.

METHODS

An individual patient simulation model was constructed to simulate the development of diabetes in a representative sample of adults without diabetes from the UK population. The model incorporates trajectories for HbA1c , 2-h glucose, fasting plasma glucose, BMI, systolic blood pressure, total cholesterol and HDL cholesterol. Patients can be diagnosed with diabetes, cardiovascular disease, microvascular complications of diabetes, cancer, osteoarthritis and depression, or can die. The model collects costs and utilities over a lifetime horizon. The perspective is the UK National Health Service and personal social services. We used the model to evaluate the population-wide impact of targeting a lifestyle intervention of varying intensity to six population subgroups defined as high risk for diabetes.

RESULTS

The intervention produces 0.0003 to 0.0009 incremental quality-adjusted life years and saves up to £1.04 per person in the general population, depending upon the subgroup targeted. Cost-effectiveness increases with intervention intensity. The most cost-effective options are to target individuals with HbA1c > 42 mmol/mol (6%) or with a high Finnish Diabetes Risk (FINDRISC) probability score (> 0.1).

CONCLUSION

The model indicates that diabetes prevention interventions are likely to be cost-effective and may be cost-saving over a lifetime. In the model, the criteria for selecting at-risk individuals differentially impact upon diabetes and cardiovascular disease outcomes, and on the timing of benefits. These findings have implications for deciding who should be targeted for diabetes prevention interventions.

Unrelated Future Costs and Unrelated Future Benefits: Reflections on NICE Guide to the Methods of Technology Appraisal

In this editorial, we consider the vexing issue of 'unrelated future costs' (for example, the costs of caring for people with dementia or kidney failure after preventing their deaths from a heart attack). The National Institute of Health and Care Excellence (NICE) guidance is not to take such costs into account in technology appraisals. However, standard appraisal practice involves modelling the benefits of those unrelated technologies. We argue that there is a sound principled reason for including both the costs and benefits of unrelated care. Changing this practice would have material consequences for decisions about reimbursing particular technologies, and we urge future research to understand this better. Copyright © 2016 John Wiley & Sons, Ltd.

Preventing dementia by promoting physical activity and the long-term impact on health and social care expenditures

BACKGROUND

Preventing dementia has been proposed to increase population health as well as reduce the demand for health and social care. Our aim was to evaluate whether preventing dementia by promoting physical activity (PA) a) improves population health or b) reduces expenditure for both health and social care if one takes into account the additional demand in health and social care caused by increased life expectancy.

METHODS

A simulation model was developed that models the relation between PA, dementia, mortality, and the use of health care and social care in England. With this model, scenarios were evaluated in which different assumptions were made about the increase in PA level in (part of) the population.

RESULTS

Lifetime spending on health and social care related to dementia was highest for the physically inactive (£28,100/£28,900 for 40-year-old males/females), but spending on other diseases was highest for those that meet PA recommendations (£55,200/£43,300 for 40-year-old males/females) due to their longer life expectancies. If the English population aged 40-65 were to increase their PA by one level, life expectancy would increase by 0.23years and health and social care expenditures would decrease by £400 per person.

CONCLUSIONS

Preventing dementia by increasing PA increases life expectancy and can result in decreased spending overall on health and social care, even after additional spending during life years gained has been taken into account. If prevention is targeted at the physically inactive, savings in dementia-related costs outweigh the additional spending in life years gained.

How much might a society spend on life-saving interventions at different ages while remaining cost-effective? A case study in a country with detailed data

Objective

We aimed to estimate the maximum intervention cost (EMIC) a society could invest in a life-saving intervention at different ages while remaining cost-effective according to a user-specified cost-effectiveness threshold.

Methods

New Zealand (NZ) was used as a case study, and a health system perspective was taken. Data from NZ life tables and morbidity data from a burden of disease study were used to estimate health-adjusted life-years (HALYs) gained by a life-saving intervention. Health system costs were estimated from a national database of all publicly funded health events (hospitalizations, outpatient events, pharmaceuticals, etc.). For illustrative purposes we followed the WHO-CHOICE approach and used a cost-effectiveness threshold of the gross domestic product (GDP) per capita (NZ$45,000 or US$30,000 per HALY). We then calculated EMICs for an “ideal” life-saving intervention that fully returned survivors to the same average morbidity, mortality, and cost trajectories as the rest of their cohort.

Findings

The EMIC of the “ideal” life-saving intervention varied markedly by age: NZ$1.3 million (US$880,000) for an intervention to save the life of a child, NZ$0.8 million (US$540,000) for a 50-year-old, and NZ$0.235 million (US$158,000) for an 80-year-old. These results were predictably very sensitive to the choice of discount rate and to the selected cost-effectiveness threshold. Using WHO data, we produced an online calculator to allow the performance of similar calculations for all other countries.

Conclusions

We present an approach to estimating maximal cost-effective investment in life-saving health interventions, under various assumptions. Our online calculator allows this approach to be applied in other countries. Policymakers could use these estimates as a rapid screening tool to determine if more detailed cost-effectiveness analyses of potential life-saving interventions might be worthwhile or which proposed life-saving interventions are very unlikely to benefit from such additional research.

Electronic supplementary material

The online version of this article (doi:10.1186/s12963-015-0052-2) contains supplementary material, which is available to authorized users.

Health, Health Inequality, and Cost Impacts of Annual Increases in Tobacco Tax: Multistate Life Table Modeling in New Zealand

BACKGROUND

Countries are increasingly considering how to reduce or even end tobacco consumption, and raising tobacco taxes is a potential strategy to achieve these goals. We estimated the impacts on health, health inequalities, and health system costs of ongoing tobacco tax increases (10% annually from 2011 to 2031, compared to no tax increases from 2011 ["business as usual," BAU]), in a country (New Zealand) with large ethnic inequalities in smoking-related and noncommunicable disease (NCD) burden.

METHODS AND FINDINGS

We modeled 16 tobacco-related diseases in parallel, using rich national data by sex, age, and ethnicity, to estimate undiscounted quality-adjusted life-years (QALYs) gained and net health system costs over the remaining life of the 2011 population (n = 4.4 million). A total of 260,000 (95% uncertainty interval [UI]: 155,000-419,000) QALYs were gained among the 2011 cohort exposed to annual tobacco tax increases, compared to BAU, and cost savings were US$2,550 million (95% UI: US$1,480 to US$4,000). QALY gains and cost savings took 50 y to peak, owing to such factors as the price sensitivity of youth and young adult smokers. The QALY gains per capita were 3.7 times greater for Māori (indigenous population) compared to non-Māori because of higher background smoking prevalence and price sensitivity in Māori. Health inequalities measured by differences in 45+ y-old standardized mortality rates between Māori and non-Māori were projected to be 2.31% (95% UI: 1.49% to 3.41%) less in 2041 with ongoing tax rises, compared to BAU. Percentage reductions in inequalities in 2041 were maximal for 45-64-y-old women (3.01%). As with all such modeling, there were limitations pertaining to the model structure and input parameters.

CONCLUSIONS

Ongoing tobacco tax increases deliver sizeable health gains and health sector cost savings and are likely to reduce health inequalities. However, if policy makers are to achieve more rapid reductions in the NCD burden and health inequalities, they will also need to complement tobacco tax increases with additional tobacco control interventions focused on cessation.