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.

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.

Health outcomes in Bulgaria: Simulated effects of obesogenic environmental changes in adulthood versus childhood.

OBJECTIVE: Bulgarian government efforts to tackle obesity are focused mainly on guidelines affecting children. However, it is unclear whether targeting children for obesity-related health policies yields better long-term health outcomes as opposed to changing the risk of obesity in adulthood. This study aims to evaluate where policy efforts should be directed to alleviate the health burden associated with obesity. METHODS: We compare the impact on population health of two simulated scenarios when (a) the prevalence of obesity upon entering adulthood is lowered; (b) the risk of getting an unhealthy weight as an adult is reduced. Additionally, we run (c) combinations of the two and (d) childhood obesity prevention on the one hand, and worsening (increasing) obesity incidence later in adulthood on the other. RESULTS: Our findings show that obesogenic environmental changes throughout adulthood have a stronger effect on life expectancy (LE), diabetes-free life expectancy (DFLE) and type 2 diabetes prevalence outcomes compared to lowering the proportion of individuals with obesity during adolescence. Nevertheless, a sizable reduction in the number of young adults with unhealthy weight has the potential to recover years of LE/DFLE that would be lost if the risk of obesity in adulthood would continue to grow in time. CONCLUSIONS: The two types of policies' (a-b) effects are not equivalent in strength and the best way forward is dependent on future obesity incidence trends.

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.

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.

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.