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.

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.

You Only Die Once: Accounting for Multi-Attributable Mortality Risks in Multi-Disease Models for Health-Economic Analyses.

Mortality rates in Markov models, as used in health economic studies, are often estimated from summary statistics that allow limited adjustment for confounders. If interventions are targeted at multiple diseases and/or risk factors, these mortality rates need to be combined in a single model. This requires them to be mutually adjusted to avoid 'double counting' of mortality. We present a mathematical modeling approach to describe the joint effect of mutually dependent risk factors and chronic diseases on mortality in a consistent manner. Most importantly, this approach explicitly allows the use of readily available external data sources. An additional advantage is that existing models can be smoothly expanded to encompass more diseases/risk factors. To illustrate the usefulness of this method and how it should be implemented, we present a health economic model that links risk factors for diseases to mortality from these diseases, and describe the causal chain running from these risk factors (e.g., obesity) through to the occurrence of disease (e.g., diabetes, CVD) and death. Our results suggest that these adjustment procedures may have a large impact on estimated mortality rates. An improper adjustment of the mortality rates could result in an underestimation of disease prevalence and, therefore, disease costs.

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.

Quality of life and time to death: have the health gains of preventive interventions been underestimated?

This article explores the implications of the relation between quality of life (QoL) and time to death (TTD) for economic evaluations of preventive interventions. By using health survey data on QoL for the general Dutch population linked to the mortality registry, we quantify the magnitude of this relationship. For addressing specific features of the nonstandard QoL distribution such as boundness, skewness, and heteroscedasticity, we modeled QoL using a generalized additive model for location, scale, and shape (GAMLSS) with a ß inflated outcome distribution. Our empirical results indicate that QoL decreases when approaching death, suggesting that there is a strong relationship between TTD and QoL. Predictions of different regression models revealed that ignoring this relationship results in an underestimation of the quality-adjusted life year (QALY) gains for preventive interventions. The underestimation ranged between 3% and 7% and depended on age, the number of years gained from the intervention, and the discount rate used.

Did the health of the Dutch population improve between 2001 and 2008? Investigating age- and gender-specific trends in quality of life.

Although many countries' populations have experienced increasing life expectancy in recent decades, quality of life (QoL) trends in the general population have yet to be investigated. This paper investigates whether QoL changed for the general Dutch population over the period 2001-2008. A beta regression model was employed to address specific features of the QoL distribution (i.e., boundedness, skewness, and heteroskedasticity), as well non-linear age and time trends. Quality-adjusted life expectancy (QALE) was calculated by combining model estimates of mean QoL with mortality rates provided by Statistics Netherlands. Changes in QALE were decomposed into those changes caused by QoL changes and those caused by mortality-rate changes. The results revealed a significant increase in QoL over 2001-2008 for both genders and most ages. For example, QALE for a man/woman aged 20 was found to have increased by 2.3/1.9 healthy years, of which 0.6/0.8 was due to QoL improvements.

Disease prevention: saving lives or reducing health care costs?

BACKGROUND: Disease prevention has been claimed to reduce health care costs. However, preventing lethal diseases increases life expectancy and, thereby, indirectly increases the demand for health care. Previous studies have argued that on balance preventing diseases that reduce longevity increases health care costs while preventing non-fatal diseases could lead to health care savings. The objective of this research is to investigate if disease prevention could result in both increased longevity and lower lifetime health care costs. METHODS: Mortality rates for Netherlands in 2009 were used to construct cause-deleted life tables. Data originating from the Dutch Costs of Illness study was incorporated in order to estimate lifetime health care costs in the absence of selected disease categories. We took into account that for most diseases health care expenditures are concentrated in the last year of life. RESULTS: Elimination of diseases that reduce life expectancy considerably increase lifetime health care costs. Exemplary are neoplasms that, when eliminated would increase both life expectancy and lifetime health care spending with roughly 5% for men and women. Costs savings are incurred when prevention has only a small effect on longevity such as in the case of mental and behavioural disorders. Diseases of the circulatory system stand out as their elimination would increase life expectancy while reducing health care spending. CONCLUSION: The stronger the negative impact of a disease on longevity, the higher health care costs would be after elimination. Successful treatment of fatal diseases leaves less room for longevity gains due to effective prevention but more room for health care savings.

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.

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.

A new prevention paradox: the trade-off between reducing incentives for risk selection and increasing the incentives for prevention for health insurers.

The Dutch risk equalization scheme has been improved over the years by including health related risk adjusters. The purpose of the Dutch risk equalization scheme is to prevent risk selection and to correct for predictable losses and gains for insurers. The objective of this paper is to explore the financial incentives for risk selection under the Dutch risk equalization scheme. We used a simulation model to estimate lifetime health care costs and risk equalization contributions for three cohorts (a smoking; an obese; and a healthy living cohort). Financial differences for the three cohorts were assessed by subtracting health care costs from risk equalization contributions. Even under an elaborate risk equalization system, the healthy living cohort was still most financially attractive for insurers. Smokers were somewhat less attractive, while the obese cohort was least attractive. Lifetime differences with healthy living individuals (revenues minus costs) were modest: €4840 for obese individuals and €1101 for smokers. Under a simple form of risk equalization these differences were higher, €8542 and €4620 respectively. Improvement of the risk equalization scheme reduced the gap between costs and revenues. Incentives for undesirable risk selection were reduced, but simultaneously incentives for health promotion were weakened. This highlights a new prevention paradox: improving the level playing field for health insurers will inevitably limit their incentives for promoting the health of their clients.

Modeling and forecasting health expectancy: theoretical framework and application.

Life expectancy continues to grow in most Western countries; however, a major remaining question is whether longer life expectancy will be associated with more or fewer life years spent with poor health. Therefore, complementing forecasts of life expectancy with forecasts of health expectancies is useful. To forecast health expectancy, an extension of the stochastic extrapolative models developed for forecasting total life expectancy could be applied, but instead of projecting total mortality and using regular life tables, one could project transition probabilities between health states simultaneously and use multistate life table methods. In this article, we present a theoretical framework for a multistate life table model in which the transition probabilities depend on age and calendar time. The goal of our study is to describe a model that projects transition probabilities by the Lee-Carter method, and to illustrate how it can be used to forecast future health expectancy with prediction intervals around the estimates. We applied the method to data on the Dutch population aged 55 and older, and projected transition probabilities until 2030 to obtain forecasts of life expectancy, disability-free life expectancy, and probability of compression of disability.

The DYNAMO-HIA model: an efficient implementation of a risk factor/chronic disease Markov model for use in Health Impact Assessment (HIA).

In Health Impact Assessment (HIA), or priority-setting for health policy, effects of risk factors (exposures) on health need to be modeled, such as with a Markov model, in which exposure influences mortality and disease incidence rates. Because many risk factors are related to a variety of chronic diseases, these Markov models potentially contain a large number of states (risk factor and disease combinations), providing a challenge both technically (keeping down execution time and memory use) and practically (estimating the model parameters and retaining transparency). To meet this challenge, we propose an approach that combines micro-simulation of the exposure information with macro-simulation of the diseases and survival. This approach allows users to simulate exposure in detail while avoiding the need for large simulated populations because of the relative rareness of chronic disease events. Further efficiency is gained by splitting the disease state space into smaller spaces, each of which contains a cluster of diseases that is independent of the other clusters. The challenge of feasible input data requirements is met by including parameter calculation routines, which use marginal population data to estimate the transitions between states. As an illustration, we present the recently developed model DYNAMO-HIA (DYNAMIC MODEL for Health Impact Assessment) that implements this approach.

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.

Cost-effectiveness of opportunistic screening and minimal contact psychotherapy to prevent depression in primary care patients.

BACKGROUND: Depression causes a large burden of disease worldwide. Effective prevention has the potential to reduce that burden considerably. This study aimed to investigate the cost-effectiveness of minimal contact psychotherapy, based on Lewinsohn's 'Coping with depression' course, targeted at opportunistically screened individuals with sub-threshold depression. METHODS AND RESULTS: Using a Markov model, future health effects and costs of an intervention scenario and a current practice scenario were estimated. The time horizon was five years. Incremental cost-effectiveness ratios were expressed in euro per Disability Adjusted Life Year (DALY) averted. Probabilistic sensitivity analysis was employed to study the effect of uncertainty in the model parameters. From the health care perspective the incremental cost-effectiveness ratio was € 1,400 per DALY, and from the societal perspective the intervention was cost-saving. Although the estimated incremental costs and effects were surrounded with large uncertainty, given a willingness to pay of € 20,000 per DALY, the probability that the intervention is cost-effective was around 80%. CONCLUSION: This modelling study showed that opportunistic screening in primary care for sub-threshold depression in combination with minimal contact psychotherapy may be cost-effective in the prevention of major depression.

To die with or from heart failure: a difference that counts: is heart failure underrepresented in national mortality statistics?

AIMS: Mortality attributed to a disease is an important public health measure of the 'burden' of that disease. A discrepancy has been noted between the high mortality rates associated with heart failure (HF) and the share of deaths ascribed to HF in official mortality statistics. It was our main aim to estimate excess mortality associated with HF and use the estimates to better understand the burden of HF. METHODS AND RESULTS: Excess mortality was defined as the difference in mortality rates between individuals with and those without HF. An epidemiological model was formulated that allowed deriving age-specific excess mortality rates in HF patients from HF incidence and prevalence. Incidence and prevalence were estimated from yearly collected cross-sectional data from four nationally representative General Practice registries in the Netherlands. The year 2007 was chosen as a reference. Next, excess mortality rates were used to calculate numbers of deaths among HF patients and compare the figures with national cause-of-death statistics. The latter were found to be more than three times smaller than the former (roughly 6000 vs. 21 000). Further, by applying HF prevalence and mortality rates to a life table of the Dutch population, average numbers of life years lost due to HF were calculated to be 6.9 years. CONCLUSION: National mortality statistics strongly underestimate the number of deaths associated with HF. Moreover, the high mortality rate in HF patients amounts to a remarkably large number of life years lost given the advanced age of disease onset.

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.

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'.

Standardizing the inclusion of indirect medical costs in economic evaluations.

A shortcoming of many economic evaluations is that they do not include all medical costs in life-years gained (also termed indirect medical costs). One of the reasons for this is the practical difficulties in the estimation of these costs. While some methods have been proposed to estimate indirect medical costs in a standardized manner, these methods fail to take into account that not all costs in life-years gained can be estimated in such a way. Costs in life-years gained caused by diseases related to the intervention are difficult to estimate in a standardized manner and should always be explicitly modelled. However, costs of all other (unrelated) diseases in life-years gained can be estimated in such a way. We propose a conceptual model of how to estimate costs of unrelated diseases in life-years gained in a standardized manner. Furthermore, we describe how we estimated the parameters of this conceptual model using various data sources and studies conducted in the Netherlands. Results of the estimates are embedded in a software package called 'Practical Application to Include future Disease costs' (PAID 1.0). PAID 1.0 is available as a Microsoft® Excel tool (available as Supplemental Digital Content via a link in this article) and enables researchers to 'switch off' those disease categories that were already included in their own analysis and to estimate future healthcare costs of all other diseases for incorporation in their economic evaluations. We assumed that total healthcare expenditure can be explained by age, sex and time to death, while the relationship between costs and these three variables differs per disease. To estimate values for age- and sex-specific per capita health expenditure per disease and healthcare provider stratified by time to death we used Dutch cost-of-illness (COI) data for the year 2005 as a backbone. The COI data consisted of age- and sex-specific per capita health expenditure uniquely attributed to 107 disease categories and eight healthcare provider categories. Since the Dutch COI figures do not distinguish between costs of those who die at a certain age (decedents) and those who survive that age (survivors), we decomposed average per capita expenditure into parts that are attributable to decedents and survivors, respectively, using other data sources.

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.