Ranking Age-at-Death Distributions Using Dominance: Robust Evaluation of United States Mortality Trends, 2006-2021.

Diverging mortality trends at different ages motivate the monitoring of lifespan inequality alongside life expectancy. Conclusions are ambiguous when life expectancy and lifespan inequality move in the same direction or when inequality measures display inconsistent trends. We propose using nonparametric dominance analysis to obtain a robust ranking of age-at-death distributions. Application to U.S. period life tables for 2006-2021 reveals that, until 2014, more recent years generally dominate earlier years, implying improvement if longer lifespans that are less unequally distributed are considered better. Improvements were more pronounced for non-Hispanic Black and Hispanic individuals than for non-Hispanic White individuals. Since 2014, for all subpopulations-particularly Hispanics-earlier years often dominate more recent years, indicating worsening age-at-death distributions if shorter and more unequal lifespans are considered worse. Dramatic deterioration of the distributions in 2020-2021 during the COVID-19 pandemic is most evident for Hispanic individuals.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Health inequalities in the Netherlands: trends in quality-adjusted life expectancy (QALE) by educational level.

BACKGROUND: Quality-adjusted life expectancy (QALE) has been proposed as a summary measure of population health because it encompasses multiple health domains as well as length of life. However, trends in QALE by education or other socio-economic measure have not yet been reported. This study investigates changes in QALE stratified by educational level for the Dutch population in the period 2001-2011. METHODS: Using data from multiple sources, we estimated mortality rates and health-related quality of life (HRQoL) as functions of age, gender, calendar year and educational level. Subsequently, predictions from these regressions were combined for calculating QALE at ages 25 and 65. QALE changes were decomposed into effects of mortality and HRQoL. RESULTS: In 2001-2011, QALE increased for men and women at all educational levels, the largest increases being for highly educated resulting in a widening gap by education. In 2001, at age 25, the absolute QALE difference between the low and the highly educated was 7.4 healthy years (36.7 vs. 44.1) for men and 6.3 healthy years (39.5 vs. 45.8) for women. By 2011, the QALE difference increased to 8.1 healthy years (38.8 vs. 46.9) for men and to 7.1 healthy years (41.3 vs. 48.4) for women. Similar results were observed at age 65. Although the gap was largely attributable to widening inequalities in mortality, widening inequalities in HRQoL were also substantial. CONCLUSIONS: In the Netherlands, population health as measured by QALE has improved, but QALE inequalities have widened more than inequalities in life expectancy alone.

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.

Does living longer in good health facilitate longer working lives? The relationship between disability and working lives.

BACKGROUND: Improvements in life expectancy have fuelled debates about the statutory retirement age in many European countries. This article contributes to this debate by investigating how changes in disability may influence both employment outcomes and disability-free life expectancy. METHODS: We used data from the European Community Household Panel to estimate the impact of disability incidence on labour supply by country using propensity score techniques. In a second step, we translated the estimated effects of disability incidence into effects on working life expectancy as well as disability-free life expectancy using multi-state life tables. RESULTS: Results from the matching analysis show that individuals who become disabled are more likely to leave the labour market. However, the size of the effect is much weaker than a simple descriptive analysis suggests and varies by country. A 10% decrease in disability incidence results in increases in disability-free life expectancy and working life expectancy of respectively 0.6 and 0.07 years on average. CONCLUSION: A large part of the differences in employment between disabled and non-disabled individuals is not due to a causal effect of disability on employment. Policies that reduce disability incidence increase disability-free life expectancy but have only a limited impact on working life expectancy.

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.

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.