The impact of different perspectives on the cost-effectiveness of remote patient monitoring for patients with heart failure in different European countries.

BACKGROUND AND OBJECTIVE: Heart failure (HF) is a complex clinical syndrome with high mortality and hospitalization rates. Non-invasive remote patient monitoring (RPM) interventions have the potential to prevent disease worsening. However, the long-term cost-effectiveness of RPM remains unclear. This study aimed to assess the cost-effectiveness of RPM in the Netherlands (NL), the United Kingdom (UK), and Germany (DE) highlighting the differences between cost-effectiveness from a societal and healthcare perspective. METHODS: We developed a Markov model with a lifetime horizon to assess the cost-effectiveness of RPM compared with usual care. We included HF-related hospitalization and non-hospitalization costs, intervention costs, other medical costs, informal care costs, and costs of non-medical consumption. A probabilistic sensitivity analysis and scenario analyses were performed. RESULTS: RPM led to reductions in HF-related hospitalization costs, but total lifetime costs were higher in all three countries compared to usual care. The estimated incremental cost-effectiveness ratios (ICERs), from a societal perspective, were €27,921, €32,263, and €35,258 in NL, UK, and DE respectively. The lower ICER in the Netherlands was mainly explained by lower costs of non-medical consumption and HF-related costs outside of the hospital. ICERs, from a healthcare perspective, were €12,977, €11,432, and €11,546 in NL, the UK, and DE, respectively. The ICER was most sensitive to the effectiveness of RPM and utility values. CONCLUSIONS: This study demonstrates that RPM for HF can be cost-effective from both healthcare and societal perspective. Including costs of living longer, such as informal care and non-medical consumption during life years gained, increased the ICER.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.