Economische evaluaties en de zorgkosten van levensverlenging

Inleiding: Als iemand dankzij een preventieve of curatieve interventie langer leeft is het zeer waarschijnlijk dat deze persoon in zijn of haar extra levensjaren medische zorg consumeert. Neem als voorbeeld Jan die op 60-jarige leeftijd een succesvolle harttransplantatie heeft ondergaan. Dankzij de harttransplantatie sterft Jan niet in zijn 60ste levensjaar maar in zijn 75ste levensjaar en in deze 15 extra levensjaren zal Jan medische zorg consumeren. Deze medische zorg in gewonnen levensjaren wordt in de vakliteratuur vaak aangeduid met de term ‘indirecte medische kosten’. In een binnenkort te verschijnen artikel in het blad Health Economics hebben we gepoogd de theoretische discussie rondom indirecte medische kosten in het licht te zetten van de empirische literatuur rondom de kosten van vergrijzing en de huidige praktijk van economische evaluaties. In dit stuk wordt alvast een voorproefje op dat artikel gegeven

Costs and benefits of early response in the Ebola virus disease outbreak in Sierra Leone

Background: The 2014-2016 Ebola virus disease (EVD) outbreak in West Africa was the largest EVD outbreak recorded, which has triggered calls for investments that would facilitate an even earlier response. This study aims to estimate the costs and health effects of earlier interventions in Sierra Leone. Methods: A deterministic and a stochastic compartment model describing the EVD outbreak was estimated using a variety of data sources. Costs and Disability-Adjusted Life Years were used to estimate and compare scenarios of earlier interventions. Results: Four weeks earlier interventions would have averted 10,257 (IQR 4353-18,813) cases and 8835 (IQR 3766-16,316) deaths. This implies 456 (IQR 194-841) thousand DALYs and 203 (IQR 87-374) million $US saved. The greatest losses occurred outside the healthcare sector. Conclusions: Earlier response in an Ebola outbreak saves lives and costs. Investments in healthcare system facilitating such responses are needed and can offer good value for money

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

Forecasting differences in life expectancy by education

Forecasts of life expectancy (LE) have fuelled debates about the sustainability and dependability of pension and healthcare systems. O

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 fut

The influence of health care spending on life expectancy

Health care expenditures and life expectancy have both been rising in many countries, including in the Netherlands. However, it is unclear to what extent increased health care spending caused the increase in life expectancy. Establishing a causal link between health care expenditures and mortality is difficult for several reasons. In medicine, randomized clinical trials are the gold standard to demonstrate causality and thereby the effectiveness of clinical interventions. However, data from randomized trials are not available to estimate the influence of health care spending on life expectanc

Practical Guidance for Including Future Costs in Economic Evaluations in the Netherlands: Introducing and Applying PAID 3.0

Objectives: A consensus has been reached in The Netherlands that all future medical costs should be included in economic evaluations. Furthermore, internationally, there is the recognition that in countries that adopt a societal perspective estimates of future nonmedical consumption are relevant for decision makers as much as production gains are. The aims of this paper are twofold: (1) to update the tool Practical Application to Include Future Disease Costs (PAID 1.1), based on 2013 data, for the estimation of future unrelated medical costs and introduce future nonmedical consumption costs, further standardizing and facilitating the inclusion of future costs; and (2) to demonstrate how to use the tool in practice, showing the impact of including future unrelated medical costs and future nonmedical consumption in a case-study where a life is hypothetically saved at different ages and 2 additional cases where published studies are updated by including future costs. Methods: Using the latest published cost of illness data from the year 2017, we model future unrelated medical costs as a function of age, sex, and time to death, which varies per disease. The Household Survey from Centraal Bureau Statistiek is used to estimate future nonmedical consumption by age. Results: The updated incremental cost-effectiveness ratios (ICERs) from the case studies show that including future costs can have a substantial effect on the ICER, possibly affecting choices made by decision makers. Conclusion: This article improves upon previous work and provides the first tool for the inclusion of future nonmedical consumption in The Netherlands

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

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