Pieter van Baal

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.

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

AbstractWhich 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.

Disability weights for comorbidity and their influence on Health-adjusted Life Expectancy

BackgroundComorbidity complicates estimations of health-adjusted life expectancy (HALE) using disease prevalences and disability weights from Burden of Disease studies. Usually, the exact amount of comorbidity is unknown and no disability weights are defined for comorbidity.MethodsUsing data of the Dutch national burden of disease study, the effects of different methods to adjust for comorbidity on HALE calculations are estimated. The default multiplicative adjustment method to define disability weights for comorbidity is compared to HALE estimates without adjustment for comorbidity and to HALE estimates in which the amount of disability in patients with multiple diseases is solely determined by the disease that leads to most disability (the maximum adjustment method). To estimate the amount of comorbidity, independence between diseases is assumed.ResultsCompared to the multiplicative adjustment method, the maximum adjustment method lowers HALE estimates by 1.2 years for males and 1.9 years for females. Compared to no adjustment, a multiplicative adjustment lowers HALE estimates by 1.0 years for males and 1.4 years for females.ConclusionThe differences in HALE caused by the different adjustment methods demonstrate that adjusting for comorbidity in HALE calculations is an important topic that needs more attention. More empirical research is needed to develop a more general theory as to how comorbidity influences disability.

Dynamic effects of smoking cessation on disease incidence, mortality and quality of life: The role of time since cessation

BackgroundTo support health policy makers in setting priorities, quantifying the potential effects of tobacco control on the burden of disease is useful. However, smoking is related to a variety of diseases and the dynamic effects of smoking cessation on the incidence of these diseases differ. Furthermore, many people who quit smoking relapse, most of them within a relatively short period.MethodsIn this paper, a method is presented for calculating the effects of smoking cessation interventions on disease incidence that allows to deal with relapse and the effect of time since quitting. A simulation model is described that links smoking to the incidence of 14 smoking related diseases. To demonstrate the model, health effects are estimated of two interventions in which part of current smokers in the Netherlands quits smoking.To illustrate the advantages of the model its results are compared with those of two simpler versions of the model. In one version we assumed no relapse after quitting and equal incidence rates for all former smokers. In the second version, incidence rates depend on time since cessation, but we assumed still no relapse after quitting.ResultsNot taking into account time since smoking cessation on disease incidence rates results in biased estimates of the effects of interventions. The immediate public health effects are overestimated, since the health risk of quitters immediately drops to the mean level of all former smokers. However, the long-term public health effects are underestimated since after longer periods of time the effects of past smoking disappear and so surviving quitters start to resemble never smokers. On balance, total health gains of smoking cessation are underestimated if one does not account for the effect of time since cessation on disease incidence rates. Not taking into account relapse of quitters overestimates health gains substantially.ConclusionThe results show that simulation models are sensitive to assumptions made in specifying the model. The model should be specified carefully in accordance with the questions it is supposed to answer. If the aim of the model is to estimate effects of smoking cessation interventions on mortality and morbidity, one should include relapse of quitters and dependency on time since cessation of incidence rates of smoking-related chronic diseases. A drawback of such models is that data requirements are extensive.

Cost-Effectiveness of Lifestyle Modification in Diabetic Patients

OBJECTIVE To explore the potential long-term health and economic consequences of lifestyle interventions for diabetic patients. RESEARCH DESIGN AND METHODS A literature search was performed to identify interventions for diabetic patients in which lifestyle issues were addressed. We selected recent (2003–2008), randomized controlled trials with a minimum follow-up of 12 months. The long-term outcomes for these interventions, if implemented in the Dutch diabetic population, were simulated with a computer-based model. Costs and effects were discounted at, respectively, 4 and 1.5% annually. A lifelong time horizon was applied. Probabilistic sensitivity analyses were performed, taking account of variability in intervention costs and (long-term) treatment effects. RESULTS Seven trials with 147–5,145 participants met our predefined criteria. All interventions improved cardiovascular risk factors at ≥1 year follow-up and were projected to reduce cardiovascular morbidity over lifetime. The interventions resulted in an average gain of 0.01–0.14 quality-adjusted life-years (QALYs) per participant. Health benefits were generally achieved at reasonable costs (≤€50,000/QALY). A self-management education program (X-PERT) and physical activity counseling achieved the best results with ≥0.10 QALYs gained and ≥99% probability to be very cost-effective (≤€20,000/QALY). CONCLUSIONS Implementation of lifestyle interventions would probably yield important health benefits at reasonable costs. However, essential evidence for long-term maintenance of health benefits was limited. Future research should be focused on long-term effectiveness and multiple treatment strategies should be compared to determine incremental costs and benefits of one over the other.

Chronic disease projections in heterogeneous ageing populations: approximating multi-state models of joint distributions by modelling marginal distributions.

To quantify the effects of changes in risk factors for chronic diseases on morbidity and mortality, Markov-type multi-state models are used. However, with multiple risk factors and many diseases relating to these risk factors, these models contain a large number of states. In this paper, we present an alternative modelling methodology implemented in the National Institute for Public Health and the Environment chronic disease model. This model includes multiple states based on risk factor levels and disease stages but only keeps track of the marginal probability values. Starting from the multi-state model, differential equations are derived that describe the change of the marginal distribution for each risk factor class and disease stage, taking into account population heterogeneity and competing mortality risks. The model is illustrated by presenting results of a scenario affecting disease incidence by altering the risk factor distribution of the population. To show the strength of the approximating model, we compare its results to those of the multi-state Markov model.

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.

DYNAMO-HIA-A Dynamic Modeling Tool for Generic Health Impact Assessments

Background Currently, no standard tool is publicly available that allows researchers or policy-makers to quantify the impact of policies using epidemiological evidence within the causal framework of Health Impact Assessment (HIA). A standard tool should comply with three technical criteria (real-life population, dynamic projection, explicit risk-factor states) and three usability criteria (modest data requirements, rich model output, generally accessible) to be useful in the applied setting of HIA. With DYNAMO-HIA (Dynamic Modeling for Health Impact Assessment), we introduce such a generic software tool specifically designed to facilitate quantification in the assessment of the health impacts of policies. Methods and Results DYNAMO-HIA quantifies the impact of user-specified risk-factor changes on multiple diseases and in turn on overall population health, comparing one reference scenario with one or more intervention scenarios. The Markov-based modeling approach allows for explicit risk-factor states and simulation of a real-life population. A built-in parameter estimation module ensures that only standard population-level epidemiological evidence is required, i.e. data on incidence, prevalence, relative risks, and mortality. DYNAMO-HIA provides a rich output of summary measures – e.g. life expectancy and disease-free life expectancy – and detailed data – e.g. prevalences and mortality/survival rates – by age, sex, and risk-factor status over time. DYNAMO-HIA is controlled via a graphical user interface and is publicly available from the internet, ensuring general accessibility. We illustrate the use of DYNAMO-HIA with two example applications: a policy causing an overall increase in alcohol consumption and quantifying the disease-burden of smoking. Conclusion By combining modest data needs with general accessibility and user friendliness within the causal framework of HIA, DYNAMO-HIA is a potential standard tool for health impact assessment based on epidemiologic evidence.

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 lifeyears 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.

Cost-Effectiveness of an Opportunistic Screening Programme and Brief Intervention for Excessive Alcohol Use in Primary Care

Background Effective prevention of excessive alcohol use has the potential to reduce the public burden of disease considerably. We investigated the cost-effectiveness of Screening and Brief Intervention (SBI) for excessive alcohol use in primary care in the Netherlands, which is targeted at early detection and treatment of ‘at-risk’ drinkers. Methodology and Results We compared a SBI scenario (opportunistic screening and brief intervention for ‘at-risk’ drinkers) in general practices with the current practice scenario (no SBI) in the Netherlands. We used the RIVM Chronic Disease Model (CDM) to extrapolate from decreased alcohol consumption to effects on health care costs and Quality Adjusted Life Years (QALYs) gained. Probabilistic sensitivity analysis was employed to study the effect of uncertainty in the model parameters. In total, 56,000 QALYs were gained at an additional cost of €298,000,000 due to providing alcohol SBI in the target population, resulting in a cost-effectiveness ratio of €5,400 per QALY gained. Conclusion Prevention of excessive alcohol use by implementing SBI for excessive alcohol use in primary care settings appears to be cost-effective.