Cost-effectiveness of remote haemodynamic monitoring by an implantable pulmonary artery pressure monitoring sensor (CardioMEMS-HF system) in chronic heart failure in the Netherlands

AIMS

Remote haemodynamic monitoring with an implantable pulmonary artery (PA) sensor has been shown to reduce heart failure (HF) hospitalizations and improve quality of life. Cost-effectiveness analyses studying the value of remote haemodynamic monitoring in a European healthcare system with a contemporary standard care group are lacking.

METHODS AND RESULTS

A Markov model was developed to estimate the cost-effectiveness of PA-guided therapy compared to the standard of care based upon patient-level data of the MONITOR-HF trial performed in the Netherlands in patients with chronic HF (New York Heart Association class III and at least one previous HF hospitalization). Cost-effectiveness was measured as the incremental cost per quality-adjusted life year (QALY) gained from the Dutch societal perspective with a lifetime horizon which encompasses a wide variety of costs including costs of hospitalizations, monitoring time, telephone contacts, laboratory assessments, and drug changes in both treatment groups. In the base-case analysis, PA-guided therapy increased costs compared to standard of care by €12 121. The QALYs per patient for PA-guided therapy and standard of care was 4.07 and 3.481, respectively, reflecting a gain of 0.58 QALYs. The resulting incremental cost-effectiveness ratio was €20 753 per QALY, which is below the Dutch willingness-to-pay threshold of €50 000 per QALY gained for HF.

CONCLUSIONS

The current cost-effectiveness study suggests that remote haemodynamic monitoring with PA-guided therapy on top of standard care is likely to be cost-effective for patients with symptomatic moderate-to-severe HF in the Netherlands.

Quantifying income inequality in years of life lost to COVID-19: a prediction model approach using Dutch administrative data

BACKGROUND

Low socioeconomic status and underlying health increase the risk of fatal outcomes from COVID-19, resulting in more years of life lost (YLL) among the poor. However, using standard life expectancy overestimates YLL to COVID-19. We aimed to quantify YLL associated with COVID-19 deaths by sex and income quartile, while accounting for the impact of individual-level pre-existing health on remaining life expectancy for all Dutch adults aged 50+.

METHODS

Extensive administrative data were used to model probability of dying within the year for the entire 50+ population in 2019, considering age, sex, disposable income and health care use (n = 6 885 958). The model is used to predict mortality probabilities for those who died of COVID-19 (had they not died) in 2020. Combining these probabilities in life tables, we estimated YLL by sex and income quartile. The estimates are compared with YLL based on standard life expectancy and income-stratified life expectancy.

RESULTS

Using standard life expectancy results in 167 315 YLL (8.4 YLL per death) which is comparable to estimates using income-stratified life tables (167 916 YLL with 8.2 YLL per death). Considering pre-existing health and income, YLL decreased to 100 743, with 40% of years lost in the poorest income quartile (5.0 YLL per death). Despite individuals in the poorest quartile dying at younger ages, there were minimal differences in average YLL per COVID-19 death compared with the richest quartile.

CONCLUSIONS

Accounting for prior health significantly affects estimates of YLL due to COVID-19. However, inequality in YLL at the population level is primarily driven by higher COVID-19 deaths among the poor. To reduce income inequality in the health burden of future pandemics, policies should focus on limiting structural differences in underlying health and exposure of lower income groups.

Optimizing cardiovascular disease risk screening in a low-resource setting: cost-effectiveness of program modifications in Sri Lanka modelled with nationally representative survey data

BACKGROUND

While screening for cardiovascular disease (CVD) risk can help low-resource health systems deliver low-cost, effective prevention, evidence is needed to adapt international screening guidelines for maximal impact in local settings. We aimed to establish how the cost-effectiveness of CVD risk screening in Sri Lanka varies with who is screened, how risk is assessed, and what thresholds are used for prescription of medicines.

METHODS

We used data for people aged 35 years and over from a 2018/19 nationally representative survey in Sri Lanka. We modelled the costs and quality adjusted life years (QALYs) for 128 screening program scenarios distinguished by a) age group screened, b) risk tool used, c) definition of high CVD risk, d) blood pressure threshold for treatment of high-risks, and e) prescription of statins to all diabetics. We used the current program as the base case. We used a Markov model of a one-year screening program with a lifetime horizon and a public health system perspective.

RESULTS

Scenarios that included the WHO-2019 office-based risk tool dominated most others. Switching to this tool and raising the age threshold for screening from 35 to 40 years gave an incremental cost-effectiveness ratio (ICER) of $113/QALY. Lowering the CVD high-risk threshold from 20 to 10% and prescribing antihypertensives at a lower threshold to diabetics and people at high risk of CVD gave an ICER of $1,159/QALY. The findings were sensitive to allowing for disutility of daily medication.

CONCLUSIONS

In Sri Lanka, CVD risk screening scenarios that used the WHO-2019 office-based risk tool, screened people above the age of 40, and lowered risk and blood pressure thresholds would likely be cost-effective, generating an additional QALY at less than half a GDP per capita.

Informal Care Costs According to Age and Proximity to Death to Support Cost-Effectiveness Analyses

BACKGROUND

Costs of informal care are ignored in many cost-effectiveness analyses (CEAs) conducted from a societal perspective; however, these costs are relevant for lifesaving interventions targeted at the older population. In this study, we estimated informal care costs by age and proximity to death across European regions and showed how these estimates can be included in CEAs.

METHODS

We estimated informal care costs by age and proximity to death using generalised linear mixed-effects models. For this, we selected deceased singles from the Survey of Health, Ageing and Retirement, which we grouped by four European regions. We combined the estimates of informal care costs with life tables to illustrate the impact of including informal care costs on the incremental cost-effectiveness ratio (ICER) of a hypothetical intervention that prevents a death at different ages.

RESULTS

Informal care use, and hence informal care costs, increase when approaching death and with increasing age. The impact of including informal care costs on the ICER varies between €200 and €17,700 per quality-adjusted life-year gained. The impact increases with age and is stronger for women and in southern European countries.

CONCLUSION

Our estimates of informal care costs facilitate including informal care costs in CEAs of life-extending healthcare interventions. Including these costs may influence decisions as it leads to reranking of life-extending interventions compared with interventions improving quality of life.

Future medical and non-medical costs and their impact on the cost-effectiveness of life-prolonging interventions: a comparison of five European countries

When healthcare interventions prolong life, people consume medical and non-medical goods during the years of life they gain. It has been argued that the costs for medical consumption should be included in cost-effectiveness analyses from both a healthcare and societal perspective, and the costs for non-medical consumption should additionally be included when a societal perspective is applied. Standardized estimates of these so-called future costs are available in only a few countries and the impact of inclusion of these costs is likely to differ between countries. In this paper we present and compare future costs for five European countries and estimate the impact of including these costs on the cost-effectiveness of life-prolonging interventions. As countries differ in the availability of data, we illustrate how both individual- and aggregate-level data sources can be used to construct standardized estimates of future costs. Results show a large variation in costs between countries. The medical costs for the Netherlands, Germany, and the United Kingdom are large compared to Spain and Greece. Non-medical costs are higher in Germany, Spain, and the United Kingdom than in Greece. The impact of including future costs on the ICER similarly varied between countries, ranging from €1000 to €35,000 per QALY gained. The variation between countries in impact on the ICER is largest when considering medical costs and indicate differences in both structure and level of healthcare financing in these countries. Case study analyses were performed in which we highlight the large impact of including future costs on ICER relative to willingness-to-pay thresholds.

The lifetime health and economic burden of obesity in five European countries: what is the potential impact of prevention?

AIM

Estimating the burden of obesity in five European countries (Germany, Greece, the Netherlands, Spain and the UK) and the potential health benefits and changes in health care costs associated with a reduction in body mass index (BMI).

MATERIALS AND METHODS

A Markov model was used to estimate the long-term burden of obesity. Health states were based on the occurrence of diabetes, ischaemic heart disease and stroke. Multiple registries and literature sources were used to derive the demographic, epidemiological and cost input parameters. For the base-case analyses, the model was run for a starting cohort of healthy obese people with a BMI of 30 and 35 kg/m² aged 40 years to estimate the lifetime impact of obesity and the impact of a one-unit decrease in BMI. Different scenario and sensitivity analyses were performed.

RESULTS

The base-case analyses showed that total lifetime health care costs (for obese people aged 40 and BMI 35 kg/m² ) ranged from €75 376 in Greece to €343 354 in the Netherlands, with life expectancies ranging from 37.9 years in Germany to 39.7 years in Spain. A one-unit decrease in BMI showed gains in life expectancy ranging from 0.65 to 0.68 year and changes in total health care costs varying from -€1563 to +€4832.

CONCLUSIONS

The economic burden of obesity is substantial in the five countries. Decreasing BMI results in health gains, reductions in obesity-related health care costs, but an increase in non-obesity related health care costs, which emphasizes the relevance of including all costs in decision making on implementation of preventive interventions.

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.

Estimating Quality of Life Decrements in Oncology Using Time to Death

OBJECTIVES

The estimation of lifetime quality-adjusted life-years (QALYs) requires the extrapolation of both length and quality of life (QoL). The extrapolation of QoL has received little attention in the literature. Here we explore the predictive value of "time to death" (TTD) for extrapolating QoL in oncology.

METHODS

We used QoL and survival data from the Patient Reported Outcomes Following Initial Treatment and Long-Term Evaluation of Survivorship registry, which is linked to The Netherlands Cancer Registry. QoL was assessed with EQ-5D and SF-6D. We tested the relationship between TTD and QoL using linear, 2-part, and beta regression models. Incremental QALYs were compared using the TTD approach and an annual age-related disutility approach using artificial survival data with varying mortality rates.

RESULTS

A total of 6 samples with >100 patients each were used for the analysis. A declining pattern in QoL was observed when patients were closer to death, confirming the predictive value of TTD for QoL. The declining pattern in QoL was most pronounced when QoL was measured with SF-6D. Proximity to death had a larger impact on QoL than age. Incremental QALYs were higher using the TTD approach than annual age-related disutility, ranging from +0.139 to +0.00003 depending on mortality rates.

CONCLUSIONS

TTD is a predictor variable for QoL. Using TTD allows cost-effectiveness models that lack QoL data to extrapolate morbidity using overall survival estimates. The TTD approach generates more incremental QALYs than an annual age-related disutility, most notably for longer survival periods.

Quality-Adjusted Life-Years Lost Due to COVID-19 Mortality: Methods and Application for The Netherlands

OBJECTIVES

The COVID-19 pandemic has increased mortality worldwide considerably in 2020. Nevertheless, it is unknown how the increase in mortality translates into a loss in quality-adjusted life-years (QALYs), which is a function of age and the health condition of the deceased patient at time of death. We estimate the QALYs lost in The Netherlands as a result of deaths because of COVID-19 in 2020.

METHODS

As a starting point, we use estimates of underlying diseases and the number of COVID-19 deaths in nursing homes as a proxy for underlying health status. In a next step, these are combined with estimates of excess mortality rates and quality of life for different groups to calculate QALYs lost. We compare the results with an alternative scenario, in which COVID-19 deaths occurred randomly across the population regardless of underlying conditions. For this alternative scenario, we use population mortality and average quality of life by age and sex.

RESULTS

Accounting for underlying health status, we estimate that QALYs lost because of COVID-19 mortality are on average 3.9 per death for men and 3.5 for women. This is approximately 3.5 QALYs less than when not taking selective mortality into account. Given 16 308 excess deaths, this translates into 61 032 QALYs lost because of COVID-19.

CONCLUSIONS

We conclude that QALYs lost because of COVID-19 mortality are still substantial, even if mortality is strongly concentrated in people with poor health.

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.

Improving maternal and child health in Pakistan: a programme evaluation using a difference in difference analysis

Introduction

Pakistan is a country with high maternal and infant mortality. Several large foreign funded projects were targeted at improving maternal, neonatal and child health. The Norway-Pakistan Partnership Initiative (NPPI) was one of these projects. This study aims to evaluate whether NPPI was successful in improving access and use of skilled maternal healthcare.

Methods

We used data from three rounds (2009–2010, 2011–2012 and 2013–2014) of the Pakistan Social and Living Standards Measurement Survey (PSLM). A difference-in-difference regression framework was used to estimate the effectiveness of NPPI and its different programme components with respect to maternal healthcare seeking behaviour of pregnant women. Various parts of the PSLM were combined to examine the healthcare seeking behaviour response of pregnant women to exposure to NPPI.

Results

Trends in maternal care seeking behaviour of pregnant women were similar in districts exposed to NPPI and control districts. Consequently, only a weak and insignificant impact of NPPI on maternal care seeking behaviour was found. However, women in districts which used vouchers or which implemented contracting were more likely to seek skilled assistance with their delivery.

Conclusion

We conclude that the objective to improve access to and use of skilled care was not achieved by NPPI. The small effects identified for vouchers and contracts on skilled birth attendance hold some promise for further experimentation.

Estimating the costs of non-medical consumption in life-years gained for economic evaluations

Including the costs of non-medical consumption in life years gained in economic evaluations of medical interventions has been controversial. This paper focuses on the estimation of these costs using Dutch data coming from cross-sectional household surveys consisting of 56,569 observations covering the years 1978-2004. We decomposed the costs of consumption into age, period and cohort effects and modelled the non-linear age and cohort patterns of consumption using P-splines. As consumption patterns depend on household composition, we also estimated household size using the same regression modeling strategy. Estimates of non-medical consumption and household size were combined with life tables to estimate the impact of including non-medical survivor costs on an incremental cost-effectiveness ratio (ICER). Results revealed that including non-medical survivor costs substantially increases the ICER, but the effect varies strongly with age. The impact of cohort effects is limited but ignoring household economies of scale results in a significant overestimation of non-medical costs. We conclude that a) ignoring the costs of non-medical consumption results in an underestimation of the costs of life prolonging interventions b) economies of scale within households with respect to consumption should be accounted for when estimating future costs.

Distributional consequences of including survivor costs in economic evaluations

Medical interventions that increase life expectancy of patients result in additional consumption of non-medical goods and services in 'added life years'. This paper focuses on the distributional consequences across socio-economic groups of including these costs in cost effectiveness analysis. In that context, it also highlights the role of remaining quality of life and household economies of scale. Data from a Dutch household spending survey was used to estimate non-medical consumption and household size by age and educational attainment. Estimates of non-medical consumption and household size were combined with life tables to estimate what the impact of including non-medical survivor costs would be on the incremental cost effectiveness ratio (ICER) of preventing a death at a certain age. Results show that including non-medical survivor costs increases estimated ICERs most strongly when interventions are targeted at the higher educated. Adjusting for household size (lower educated people less often live additional life years in multi-person households) and quality of life (lower educated people on average spend added life years in poorer health) mitigates this difference. Ignoring costs of non-medical consumption in economic evaluations implicitly favors interventions targeted at the higher educated and thus potentially amplifies socio-economic inequalities in health.

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.

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.

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.

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.

Prospective impact of tobacco eradication and overweight and obesity eradication on future morbidity and health-adjusted life expectancy: simulation study

BACKGROUND

Interventions that reduce morbidity, in addition to mortality, warrant prioritisation. It is important to understand the magnitude of potential morbidity and health gains from changing risk factor distributions. We quantified the impact of tobacco compared with overweight/obesity eradication on future morbidity and health-adjusted life expectancy (HALE) for the New Zealand population alive in 2011.

METHODS

Business-as-usual (BAU) future smoking rates were set based on past falling rates, but we assumed no future change in Body Mass Index (BMI) distribution, given historic trends. Population impact fractions and the percentage reduction in incidence rates for 16 tobacco-related and 14 overweight/obesity-related diseases (allowing for time lags) were calculated using the difference between BAU and eradication risk factor scenarios combined with tobacco and BMI incidence rate ratios. We used two multistate lifetable models to estimate HALE changes over the remaining lifespan and morbidity rate changes 30 years hence.

RESULTS

HALE gains always exceeded life expectancy (LE) gains for overweight/obesity eradication (ie, absolute compression of morbidity), but for eradication of tobacco, the pattern was mixed. For example, among 32-year-olds in 2011, overweight/obesity eradication increased HALE by 2.06 years and LE by 1.21 years, compared with 0.54 and 0.50 years for tobacco eradication.Morbidity rate reductions 30 years into the future were considerably greater for overweight/obesity eradication (eg, a 15.8% reduction for 72-year-olds in 2041, or the cohort that was aged 42 years in 2011) than for tobacco eradication (2.7%). The same rate of morbidity experienced at age 65 years under BAU was deferred by 5 years with overweight/obesity eradication.

CONCLUSIONS

Preventive programmes that reduce overweight and obesity have strong potential to reduce or compress morbidity, improving the average health status of ageing populations. This paper simulated eradication of tobacco and overweight/obesity; actual interventions will have lesser health impacts, but the relativities of morbidity to mortality gains should be similar.

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.

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.

The Impact of Hospital Payment Schemes on Healthcare and Mortality: Evidence from Hospital Payment Reforms in OECD Countries

In this study, aggregate-level panel data from 20 Organization for Economic Cooperation and Development countries over three decades (1980-2009) were used to investigate the impact of hospital payment reforms on healthcare output and mortality. Hospital payment schemes were classified as fixed-budget (i.e. not directly based on activities), fee-for-service (FFS) or patient-based payment (PBP) schemes. The data were analysed using a difference-in-difference model that allows for a structural change in outcomes due to payment reform. The results suggest that FFS schemes increase the growth rate of healthcare output, whereas PBP schemes positively affect life expectancy at age 65 years. However, these results should be interpreted with caution, as results are sensitive to model specification. Copyright © 2015 John Wiley & Sons, Ltd.

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.

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. Of relevance to these debates are inequalities in LE by education. In this paper, we present a method of forecasting LE for different educational groups within a population. As a basic framework we use the Li-Lee model that was developed to forecast mortality coherently for different groups. We adapted this model to distinguish between overall, sex-specific, and education-specific trends in mortality, and extrapolated these time trends in a flexible manner. We illustrate our method for the population aged 65 and over in the Netherlands, using several data sources and spanning different periods. The results suggest that LE is likely to increase for all educational groups, but that differences in LE between educational groups will widen. Sensitivity analyses illustrate the advantages of our proposed method.

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

Life-saving medical technologies result in additional demand for health care due to increased life expectancy. However, most economic evaluations do not include all medical costs that may result from this additional demand in health care and include only future costs of related illnesses. Although there has been much debate regarding the question to which extent future costs should be included from a societal perspective, the appropriate role of future medical costs in the widely adopted but more narrow healthcare perspective has been neglected. Using a theoretical model, we demonstrate that optimal decision rules for cost-effectiveness analyses assuming fixed healthcare budgets dictate that future costs of both related and unrelated medical care should be included. Practical relevance of including the costs of future unrelated medical care is illustrated using the example of transcatheter aortic valve implantation. Our findings suggest that guidelines should prescribe inclusion of these costs.

[Less need for prevention through better care? Towards an effective deployment of preventive and curative care]

Scientists and politicians repeatedly call for more investment in prevention. Besides improving public health, this would reduce health care spending. This article discusses two mechanisms that are relevant to the debate regarding the efficiency of prevention. The first mechanism concerns the additional demand for health care as a result of increased life expectancy. The second mechanism concerns the impact that improvements in curative care have on the consequences of prevention. Both mechanisms show that prevention and curative care cannot be considered separately. Consequently, decisions on investments in preventive and curative care should ideally be based on the same decision-making framework. An effective deployment of prevention and care will benefit from economic evaluations that give as full a picture as possible of both the costs and benefits of new interventions.

DYNAMO-HIA--a Dynamic Modeling tool for generic Health Impact Assessments

BACKGROUND

Currently, no standard tool is publicly available that allows researchers or policy-makers to quantify the impact of policies using epidemiological evidence within the causal framework of Health Impact Assessment (HIA). A standard tool should comply with three technical criteria (real-life population, dynamic projection, explicit risk-factor states) and three usability criteria (modest data requirements, rich model output, generally accessible) to be useful in the applied setting of HIA. With DYNAMO-HIA (Dynamic Modeling for Health Impact Assessment), we introduce such a generic software tool specifically designed to facilitate quantification in the assessment of the health impacts of policies.

METHODS AND RESULTS

DYNAMO-HIA quantifies the impact of user-specified risk-factor changes on multiple diseases and in turn on overall population health, comparing one reference scenario with one or more intervention scenarios. The Markov-based modeling approach allows for explicit risk-factor states and simulation of a real-life population. A built-in parameter estimation module ensures that only standard population-level epidemiological evidence is required, i.e. data on incidence, prevalence, relative risks, and mortality. DYNAMO-HIA provides a rich output of summary measures--e.g. life expectancy and disease-free life expectancy--and detailed data--e.g. prevalences and mortality/survival rates--by age, sex, and risk-factor status over time. DYNAMO-HIA is controlled via a graphical user interface and is publicly available from the internet, ensuring general accessibility. We illustrate the use of DYNAMO-HIA with two example applications: a policy causing an overall increase in alcohol consumption and quantifying the disease-burden of smoking.

CONCLUSION

By combining modest data needs with general accessibility and user friendliness within the causal framework of HIA, DYNAMO-HIA is a potential standard tool for health impact assessment based on epidemiologic evidence.

Decomposing cross-country differences in quality adjusted life expectancy: the impact of value sets

Background

The validity, reliability and cross-country comparability of summary measures of population health (SMPH) have been persistently debated. In this debate, the measurement and valuation of nonfatal health outcomes have been defined as key issues. Our goal was to quantify and decompose international differences in health expectancy based on health-related quality of life (HRQoL). We focused on the impact of value set choice on cross-country variation.

Methods

We calculated Quality Adjusted Life Expectancy (QALE) at age 20 for 15 countries in which EQ-5D population surveys had been conducted. We applied the Sullivan approach to combine the EQ-5D based HRQoL data with life tables from the Human Mortality Database. Mean HRQoL by country-gender-age was estimated using a parametric model. We used nonparametric bootstrap techniques to compute confidence intervals. QALE was then compared across the six country-specific time trade-off value sets that were available. Finally, three counterfactual estimates were generated in order to assess the contribution of mortality, health states and health-state values to cross-country differences in QALE.

Results

QALE at age 20 ranged from 33 years in Armenia to almost 61 years in Japan, using the UK value set. The value sets of the other five countries generated different estimates, up to seven years higher. The relative impact of choosing a different value set differed across country-gender strata between 2% and 20%. In 50% of the country-gender strata the ranking changed by two or more positions across value sets. The decomposition demonstrated a varying impact of health states, health-state values, and mortality on QALE differences across countries.

Conclusions

The choice of the value set in SMPH may seriously affect cross-country comparisons of health expectancy, even across populations of similar levels of wealth and education. In our opinion, it is essential to get more insight into the drivers of differences in health-state values across populations. This will enhance the usefulness of health-expectancy measures.

The costs, effects and cost-effectiveness of counteracting overweight on a population level. A scientific base for policy targets for the Dutch national plan for action

OBJECTIVES

To gain insight in realistic policy targets for overweight at a population level and the accompanying costs. Therefore, the effect on overweight prevalence was estimated of large scale implementation of a community intervention (applied to 90% of general population) and an intensive lifestyle program (applied to 10% of overweight adults), and costs and cost-effectiveness were assessed.

METHODS

Costs and effects were based on two Dutch projects and verified by similar international projects. A markov-type simulation model estimated long-term health benefits, health care costs and cost-effectiveness.

RESULTS

Combined implementation of the interventions--at the above mentioned scale--reduces prevalence rates of overweight by approximately 3 percentage points and of physical inactivity by 2 percentage points after 5 years, at a cost of 7 euros per adult capita per year. The cost-effectiveness ratio of combined implementation amounts to euro 6000 per life-year gained and euro 5700 per QALY gained (including costs of unrelated diseases in life years gained). Sensitivity analyses showed that these ratios are quite robust.

CONCLUSIONS

A realistic policy target is a decrease in overweight prevalence of three percentage points, compared to a situation with no interventions. In reality, large scale implementation of the interventions may not counteract the expected upward trends in The Netherlands completely. Nonetheless, implementation of the interventions is cost-effective.