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.

Time trends and forecasts of body mass index from repeated cross-sectional data: a different approach.

In this paper, we report a case study on a technical generalization of the Lee-Carter model, originally developed to project mortality, to forecast body mass index (BMI, kg/m2). We present the method on an annually repeated cross-sectional data set, the Dutch Health Survey, covering years between 1981 and 2008. We applied generalized additive models for location, scale and shape semi-parametric regression models to estimate the probability distribution of BMI for each combination of age, gender and year assuming that BMI follows a Box-Cox power exponential distribution. We modelled and extrapolated the distribution parameters as a function of age and calendar time using the Lee-Carter model. The projected parameters defined future BMI distributions from which we derived the prevalence of normal weight, overweight and obesity. Our analysis showed that important changes occurred not only in the location and scale of the BMI distribution but also in the shape of it. The BMI distribution became flatter and more shifted to the right. Assuming that past trends in the distribution of BMI will continue in the future, we predicted a stable or slow increase in the prevalence of overweight until 2020 among men and women. We conclude that our adaptation of the Lee-Carter model provides an insightful and flexible way of forecasting BMI and that ignoring changes in the shape of the BMI distribution would likely result in biased forecasts.

Value of information analysis from a societal perspective: a case study in prevention of major depression.

OBJECTIVES: Productivity losses usually have a considerable impact on cost-effectiveness estimates while their estimated values are often relatively uncertain. Therefore, parameters related to these indirect costs play a role in setting priorities for future research from a societal perspective. Until now, however, value of information analyses have usually applied a health care perspective for economic evaluations. Hence, the effect of productivity losses has rarely been investigated in such analyses. The aim of the current study therefore was to investigate the effects of including or excluding productivity costs in value of information analyses. METHODS: Expected value of information analysis (EVPI) was performed in cost-effectiveness evaluation of prevention from both societal and health care perspectives, to give us the opportunity to compare different perspectives. Priorities for future research were determined by partial EVPI. The program to prevent major depression in patients with subthreshold depression was opportunistic screening followed by minimal contact psychotherapy. RESULTS: The EVPI indicated that regardless of perspective, further research is potentially worthwhile. Partial EVPI results underlined the importance of productivity losses when a societal perspective was considered. Furthermore, priority setting for future research differed according to perspective. CONCLUSIONS: The results illustrated that advise for future research will differ for a health care versus a societal perspective and hence the value of information analysis should be adjusted to the perspective that is relevant for the decision makers involved. The outcomes underlined the need for carefully choosing the suitable perspective for the decision problem at hand.

Forecasting lifetime and aggregate long-term care spending: accounting for changing disability patterns.

OBJECTIVE: The impact population aging exerts on future levels of long-term care (LTC) spending is an urgent topic in which few studies have accounted for disability trends. We forecast individual lifetime and population aggregate annual LTC spending for the Dutch 55+ population to 2030 accounting for changing disability patterns. METHODS: Three levels of (dis)ability were distinguished: none, mild, and severe. Two-part models were used to estimate LTC spending as a function of age, sex, and disability status. A multistate life table model was used to forecast age-specific prevalence of disability and life expectancy (LE) in each disability state. Finally, 2-part model estimates and multistate projections were combined to obtain forecasts of LTC expenditures. RESULTS: LE is expected to increase, whereas life years in severe disability remain constant, resulting in a relative compression of severe disability. Mild disability life years increase, especially for women. Lifetime homecare spending--mainly determined by mild disability--increases, whereas institutional spending remains fairly constant due to stable LE with severe disability. Lifetime LTC expenditures, largely determined by institutional spending, are thus hardly influenced by increasing LE. Aggregate spending for the 55+ population is expected to rise by 56.0% in the period of 2007-2030. CONCLUSIONS: Longevity gains accompanied by a compression of severe disability will not seriously increase lifetime spending. The growth of the elderly cohort, however, will considerably increase aggregate spending. Stimulating a compression of disability is among the main solutions to alleviate the consequences of longevity gains and population aging to growth of LTC spending.

Mortality risk associated with disability: a population-based record linkage study.

OBJECTIVES: We assessed the association between mortality and disability and quantified the effect of disability-associated risk factors. METHODS: We linked data from cross-sectional health surveys in the Netherlands to the population registry to create a large data set comprising baseline covariates and an indicator of death. We used Cox regression models to estimate the hazard ratio of disability on mortality. RESULTS: Among men, the unadjusted hazard ratio for activities of daily living, mobility, or mild disability defined by the Organization for Economic Co-operation and Development at age 55 years was 7.85 (95% confidence interval [CI] = 4.36, 14.13), 5.21 (95% CI = 3.19, 8.51), and 1.87 (95% CI = 1.58, 2.22), respectively. People with disability in activities of daily living and mobility had a 10-year shorter life expectancy than nondisabled people had, of which 6 years could be explained by differences in lifestyle, sociodemographics, and major chronic diseases. CONCLUSIONS: Disabled people face a higher mortality risk than nondisabled people do. Although the difference can be explained by diseases and other risk factors for those with mild disability, we cannot rule out that more severe disabilities have an independent effect on mortality.

Co-occurrence of diabetes, myocardial infarction, stroke, and cancer: quantifying age patterns in the Dutch population using health survey data.

BACKGROUND: The high prevalence of chronic diseases in Western countries implies that the presence of multiple chronic diseases within one person is common. Especially at older ages, when the likelihood of having a chronic disease increases, the co-occurrence of distinct diseases will be encountered more frequently. The aim of this study was to estimate the age-specific prevalence of multimorbidity in the general population. In particular, we investigate to what extent specific pairs of diseases cluster within people and how this deviates from what is to be expected under the assumption of the independent occurrence of diseases (i.e., sheer coincidence). METHODS: We used data from a Dutch health survey to estimate the prevalence of pairs of chronic diseases specified by age. Diseases we focused on were diabetes, myocardial infarction, stroke, and cancer. Multinomial P-splines were fitted to the data to model the relation between age and disease status (single versus two diseases). To assess to what extent co-occurrence cannot be explained by independent occurrence, we estimated observed/expected co-occurrence ratios using predictions of the fitted regression models. RESULTS: Prevalence increased with age for all disease pairs. For all disease pairs, prevalence at most ages was much higher than is to be expected on the basis of coincidence. Observed/expected ratios of disease combinations decreased with age. CONCLUSION: Common chronic diseases co-occur in one individual more frequently than is due to chance. In monitoring the occurrence of diseases among the population at large, such multimorbidity is insufficiently taken into account.

Exploring the influence of proximity to death on disease-specific hospital expenditures: a carpaccio of red herrings.

It has been demonstrated repeatedly that time to death is a much better predictor of health care expenditures than age. This is known as the 'red herring' hypothesis. In this article, we investigate whether this is also the case regarding disease-specific hospital expenditures. Longitudinal data samples from the Dutch hospital register (n=11 253 455) were used to estimate 94 disease-specific two-part models. Based on these models, Monte Carlo simulations were used to assess the predictive value of proximity to death and age on disease-specific expenditures. Results revealed that there was a clear effect of proximity of death on health care expenditures. This effect was present for most diseases and was strongest for most cancers. However, even for some less fatal diseases, proximity to death was found to be an important predictor of expenditures. Controlling for proximity to death, age was found to be a significant predictor of expenditures for most diseases. However, its impact is modest when compared to proximity to death. Considering the large variation in the degree to which proximity to death and age matter for each specific disease, we may speak not only of age as a 'red herring' but also of a 'carpaccio of red herrings'.

Economic evaluation and the postponement of health care costs.

The inclusion of medical costs in life years gained in economic evaluations of health care technologies has long been controversial. Arguments in favour of the inclusion of such costs are gaining support, which shifts the question from whether to how to include these costs. This paper elaborates on the issue how to include cost in life years gained in cost effectiveness analysis given the current practice of economic evaluations in which costs of related diseases are included. We combine insights from the theoretical literature on the inclusion of unrelated medical costs in life years gained with insights from the so-called 'red herring' literature. It is argued that for most interventions it would be incorrect to simply add all medical costs in life years gained to an ICER, even when these are corrected for postponement of the expensive last year of life. This is the case since some of the postponement mechanism is already captured in the unadjusted ICER by modelling the costs of related diseases. Using the example of smoking cessation, we illustrate the differences and similarities between different approaches. The paper concludes with a discussion about the proper way to account for medical costs in life years gained in economic evaluations.

Estimating and comparing incidence and prevalence of chronic diseases by combining GP registry data: the role of uncertainty.

BACKGROUND: Estimates of disease incidence and prevalence are core indicators of public health. The manner in which these indicators stand out against each other provide guidance as to which diseases are most common and what health problems deserve priority. Our aim was to investigate how routinely collected data from different general practitioner registration networks (GPRNs) can be combined to estimate incidence and prevalence of chronic diseases and to explore the role of uncertainty when comparing diseases. METHODS: Incidence and prevalence counts, specified by gender and age, of 18 chronic diseases from 5 GPRNs in the Netherlands from the year 2007 were used as input. Generalized linear mixed models were fitted with the GPRN identifier acting as random intercept, and age and gender as explanatory variables. Using predictions of the regression models we estimated the incidence and prevalence for 18 chronic diseases and calculated a stochastic ranking of diseases in terms of incidence and prevalence per 1,000. RESULTS: Incidence was highest for coronary heart disease and prevalence was highest for diabetes if we looked at the point estimates. The between GPRN variance in general was higher for incidence than for prevalence. Since uncertainty intervals were wide for some diseases and overlapped, the ranking of diseases was subject to uncertainty. For incidence shifts in rank of up to twelve positions were observed. For prevalence, most diseases shifted maximally three or four places in rank. CONCLUSION: Estimates of incidence and prevalence can be obtained by combining data from GPRNs. Uncertainty in the estimates of absolute figures may lead to different rankings of diseases and, hence, should be taken into consideration when comparing disease incidences and prevalences.

Weight change and incident diabetes: addressing an unresolved issue.

The impact of weight change on diabetes incidence remains unclear. To clarify the role of weight change as a risk factor for diabetes, the authors assessed the association between weight change and diabetes incidence conditional upon either initial or attained body mass index (BMI). They used 7,837 observations available from repeated measurements of 4,259 participants (men and women aged 20-59 years) in the Dutch population-based Doetinchem Cohort Study (1987-2007) to analyze the association between 5-year weight change and diabetes incidence (n = 124) in the subsequent 5 years. When adjusted for initial BMI, 5-year weight change was a significant risk factor for diabetes (odds ratio = 1.08, 95% confidence interval: 1.04, 1.13 per kilogram of weight change). However, no significant association was found between weight change and diabetes if the association was adjusted for attained BMI (odds ratio = 0.99, 95% confidence interval: 0.94, 1.04 per kilogram of weight change). Results suggest that weight change is associated with diabetes incidence because, conditional upon initial BMI, weight change determines attained BMI. This finding implies that lifestyle interventions can contribute to diabetes prevention because they affect attained BMI. Weight change appears to have no effect on diabetes incidence beyond its effect on attained BMI.

Lifestyle intervention: from cost savings to value for money.

Prevention of unhealthy lifestyles has sometimes been promoted as simultaneously reducing costs and improving public health but this will unlikely prove to be true. Additional medical costs in life years gained due to treatment of unrelated diseases may offset possible savings in related diseases, but are often ignored both in health promotion policies and in economic evaluations of life-prolonging interventions. Many national guidelines explicitly recommend excluding these costs from economic evaluations or leave inclusion up to the discretion of the analyst. This may result in too favorable estimations of cost-effectiveness, feeding the unjustified optimism among policymakers regarding lifestyle interventions as a cost-saving option. However, prevention may still be a cost-effective way to improve public health, even when it does not result in cost savings, but this should be judged taking all future costs into account and be based on the true value for money provided by lifestyle interventions.

Probabilistic sensitivity analysis: be a Bayesian.

OBJECTIVE: To give guidance in defining probability distributions for model inputs in probabilistic sensitivity analysis (PSA) from a full Bayesian perspective. METHODS: A common approach to defining probability distributions for model inputs in PSA on the basis of input-related data is to use the likelihood of the data on an appropriate scale as the foundation for the distribution around the inputs. We will look at this approach from a Bayesian perspective, derive the implicit prior distributions in two examples (proportions and relative risks), and compare these to alternative prior distributions. RESULTS: In cases where data are sparse (in which case sensitivity analysis is crucial), commonly used approaches can lead to unexpected results. Weshow that this is because of the prior distributions that are implicitly assumed, namely that these are not as "uninformative" or "vague" as believed. We propose priors that we believe are more sensible for two examples and which are just as easy to apply. CONCLUSIONS: Input probability distributions should not be based on the likelihood of the data, but on the Bayesian posterior distribution calculated from this likelihood and an explicitly stated prior distribution.

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.

A systematic literature review and network meta-analysis of effectiveness and safety outcomes in advanced melanoma.

BACKGROUND: Although a myriad of novel treatments entered the treatment paradigm for advanced melanoma, there is lack of head-to-head evidence. We conducted a network meta-analysis (NMA) to estimate each treatment's relative effectiveness and safety. METHODS: A systematic literature review (SLR) was conducted in Embase, MEDLINE and Cochrane to identify all phase III randomised controlled trials (RCTs) with a time frame from January 1, 2010 to March 11, 2019. We retrieved evidence on treatment-related grade III/IV adverse events, progression-free survival (PFS) and overall survival (OS). Evidence was synthesised using a Bayesian fixed-effect NMA. Reference treatment was dacarbazine. In accordance with RCTs, dacarbazine was pooled with temozolomide, paclitaxel and paclitaxel plus carboplatin. To increase homogeneity of the study populations, RCTs were only included if patients were not previously treated with novel treatments. RESULTS: The SLR identified 28 phase III RCTs involving 14,376 patients. Nineteen and seventeen treatments were included in the effectiveness and safety NMA, respectively. For PFS, dabrafenib plus trametinib (hazard ratio [HR] PFS: 0.21) and vemurafenib plus cobimetinib (HR PFS: 0.22) were identified as most favourable treatments. Both had, however, less favourable safety profiles. Five other treatments closely followed (dabrafenib [HR PFS: 0.30], nivolumab plus ipilimumab [HR PFS: 0.34], vemurafenib [HR PFS: 0.38], nivolumab [HR PFS: 0.42] and pembrolizumab [HR PFS: 0.46]). In contrast, for OS, nivolumab plus ipilimumab (HR OS: 0.39), nivolumab (HR OS: 0.46) and pembrolizumab (HR OS: 0.50) were more favourable than dabrafenib plus trametinib (HR OS: 0.55) and vemurafenib plus cobimetinib (HR OS: 0.57). CONCLUSIONS: Our NMA identified the most effective treatment options for advanced melanoma and provided valuable insights into each novel treatment's relative effectiveness and safety. This information may facilitate evidence-based decision-making and may support the optimisation of treatment and outcomes in everyday clinical practice.

Lifetime medical costs of obesity: prevention no cure for increasing health expenditure.

BACKGROUND: Obesity is a major cause of morbidity and mortality and is associated with high medical expenditures. It has been suggested that obesity prevention could result in cost savings. The objective of this study was to estimate the annual and lifetime medical costs attributable to obesity, to compare those to similar costs attributable to smoking, and to discuss the implications for prevention. METHODS AND FINDINGS: With a simulation model, lifetime health-care costs were estimated for a cohort of obese people aged 20 y at baseline. To assess the impact of obesity, comparisons were made with similar cohorts of smokers and "healthy-living" persons (defined as nonsmokers with a body mass index between 18.5 and 25). Except for relative risk values, all input parameters of the simulation model were based on data from The Netherlands. In sensitivity analyses the effects of epidemiologic parameters and cost definitions were assessed. Until age 56 y, annual health expenditure was highest for obese people. At older ages, smokers incurred higher costs. Because of differences in life expectancy, however, lifetime health expenditure was highest among healthy-living people and lowest for smokers. Obese individuals held an intermediate position. Alternative values of epidemiologic parameters and cost definitions did not alter these conclusions. CONCLUSIONS: Although effective obesity prevention leads to a decrease in costs of obesity-related diseases, this decrease is offset by cost increases due to diseases unrelated to obesity in life-years gained. Obesity prevention may be an important and cost-effective way of improving public health, but it is not a cure for increasing health expenditures.

Cost-effectiveness of a low-calorie diet and orlistat for obese persons: modeling long-term health gains through prevention of obesity-related chronic diseases.

OBJECTIVE: Our study estimated the cost-effectiveness of pharmacologic treatment of obesity in combination with a low-calorie diet in The Netherlands. METHODS: Costs and effects of a low-calorie diet-only intervention and of a low-calorie diet in combination with 1 year of orlistat were compared to no treatment. The RIVM Chronic Disease Model was used to project the differences in quality adjusted life years (QALYs) and lifetime health-care costs because of the effects of the interventions on body mass index (BMI) status. This was done by linking BMI status to the occurrence of obesity-related diseases and by relating quality of life to disease status. Probabilistic sensitivity analysis was employed to study the effect of uncertainty in the model parameters. In univariate sensitivity analysis, we assessed how sensitive the results were to several key assumptions. RESULTS: Incremental costs per QALY gained were Euro 17,900 for the low-calorie diet-only intervention compared to no intervention and Euro 58,800 for the low-calorie diet + orlistat compared to the low-calorie diet only. Assuming a direct relation between BMI and quality of life, these ratios decreased to Euro 6000 per QALY gained and Euro 24,100 per QALY gained. Costs per QALY gained were also sensitive to assumptions about long-term weight loss maintenance. CONCLUSIONS: Cost-effectiveness ratios of interventions aiming at weight reduction depend strongly on assumptions regarding the relation between BMI and quality of life. We recommend that a low-calorie diet should be the first option for policymakers in combating obesity.

Future costs in economic evaluation. A comment on Lee.

In a recent article in this journal Lee argued that indirect medical costs should be ignored in economic evaluations. To reach this conclusion, Lee uses an unrealistic and uncommon budget constraint. This comment highlights a number of methodological problems in Lee's analysis. Moreover, it highlights that looking at current practice of economic evaluation, Lee's model implies the inclusion rather than the exclusion of indirect medical costs.

Unrelated medical costs in life-years gained: should they be included in economic evaluations of healthcare interventions?

Which costs and benefits to consider in economic evaluations of healthcare interventions remains an area of much controversy. Unrelated medical costs in life-years gained is an important cost category that is normally ignored in economic evaluations, irrespective of the perspective chosen for the analysis. National guidelines for pharmacoeconomic research largely endorse this practice, either by explicitly requiring researchers to exclude these costs from the analysis or by leaving inclusion or exclusion up to the discretion of the analyst. However, the inclusion of unrelated medical costs in life-years gained appears to be gaining support in the literature.This article provides an overview of the discussions to date. The inclusion of unrelated medical costs in life-years gained seems warranted, in terms of both optimality and internal and external consistency. We use an example of a smoking-cessation intervention to highlight the consequences of different practices of accounting for costs and effects in economic evaluations. Only inclusion of all costs and effects of unrelated medical care in life-years gained can be considered both internally and externally consistent. Including or excluding unrelated future medical costs may have important distributional consequences, especially for interventions that substantially increase length of life. Regarding practical objections against inclusion of future costs, it is important to note that it is becoming increasingly possible to accurately estimate unrelated medical costs in life-years gained. We therefore conclude that the inclusion of unrelated medical costs should become the new standard.

Increasing tobacco taxes: a cheap tool to increase public health.

INTRODUCTION: Several studies have estimated health effects resulting from tobacco tax increases. However, studies on the cost effectiveness of tobacco taxes are scarce. The aim of this study was to estimate the cost effectiveness of tobacco tax increases from a health care perspective, explicitly considering medical costs in life years gained. METHODS: The effects of a tax increase were translated into effects on smoking quit rates. A dynamic population model then projected incidence, prevalence and health care costs of the major chronic diseases conditional on smoking status over time. Comparing to a current practice scenario, the differences in healthcare costs, tax revenues, life years and QALYs from a tobacco tax increase resulting in a price increase of 10% increase were estimated. RESULTS: Including effects on health care costs in life years gained, the tax increase costs about 2500 euro per QALY gained. Only 3% of additional tax revenues are enough to compensate additional health care costs in life years gained. CONCLUSIONS: Even if the health care costs in life years gained are taken into account and even if additional tax revenues do not flow to the health care sector a tax increase is a cost-effective intervention to increase public health from a health care perspective.