[PHNUTR-L] Lifetime Medical Costs of Obesity: Prevention No Cure for Increasing Health Expenditure

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Lifetime Medical Costs of Obesity: Prevention No Cure for Increasing 
Health Expenditure
http://medicine.plosjournals.org/perlserv/?request=get-document&doi=10.1371/journal.pmed.0050029

Pieter H. M. van Baal1*, Johan J. Polder2,3, G. Ardine de Wit1, Rudolf 
T. Hoogenveen1, Talitha L. Feenstra1, Hendriek C. Boshuizen1, Peter M. 
Engelfriet1, Werner B. F. Brouwer4

1 National Institute for Public Health and the Environment (RIVM), 
Centre for Prevention and Health Services Research, Bilthoven, The 
Netherlands, 2 National Institute for Public Health and the Environment, 
Centre for Public Health Forecasting, Bilthoven, The Netherlands, 3 
Tilburg University, Department Tranzo, Tilburg, The Netherlands, 4 
Erasmus University, Medical Center, Rotterdam, The Netherlands

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.

Funding: This work was funded by the Dutch Ministry of Health, Welfare 
and Sports. The funder did not have any role in study design, data 
collection and analysis, decision to publish, or preparation of the 
manuscript.

Competing Interests: The authors have declared that no competing 
interest exist.

Academic Editor: Andrew Prentice, London School of Hygiene & Tropical 
Medicine, United Kingdom

Citation: van Baal PHM, Polder JJ, de Wit GA, Hoogenveen RT, Feenstra 
TL, et al. (2008) Lifetime Medical Costs of Obesity: Prevention No Cure 
for Increasing Health Expenditure. PLoS Med 5(2): e29 
doi:10.1371/journal.pmed.0050029

Received: June 20, 2007; Accepted: November 30, 2007; Published: 
February 5, 2008

Copyright: © 2008 van Baal et al. This is an open-access article 
distributed under the terms of the Creative Commons Attribution License, 
which permits unrestricted use, distribution, and reproduction in any 
medium, provided the original author and source are credited.

Abbreviations: BMI, body mass index; COI, cost of illness; RIVM-CDM, 
National Institute for Public Health and the Environment chronic disease 
model; SHA, System of Health Accounts

* To whom correspondence should be addressed. E-mail: 
pieter.van.baal at rivm.nl
Editors' Summary
Background.

Since the mid 1970s, the proportion of people who are obese (people who 
have an unhealthy amount of body fat) has increased sharply in many 
countries. One-third of all US adults, for example, are now classified 
as obese, and recent forecasts suggest that by 2025 half of US adults 
will be obese. A person is overweight if their body mass index (BMI, 
calculated by dividing their weight in kilograms by their height in 
meters squared) is between 25 and 30, and obese if BMI is greater than 
30. Compared to people with a healthy weight (a BMI between 18.5 and 
25), overweight and obese individuals have an increased risk of 
developing many diseases, such as diabetes, coronary heart disease and 
stroke, and tend to die younger. People become unhealthily fat by 
consuming food and drink that contains more energy than they need for 
their daily activities. In these circumstances, the body converts the 
excess energy into fat for use at a later date. Obesity can be 
prevented, therefore, by having a healthy diet and exercising regularly.
Why Was This Study Done?

Because obesity causes so much illness and premature death, many 
governments have public-health policies that aim to prevent obesity. 
Clearly, the improvement in health associated with the prevention of 
obesity is a worthwhile goal in itself but the prevention of obesity 
might also reduce national spending on medical care. It would do this, 
the argument goes, by reducing the amount of money spent on treating the 
diseases for which obesity is a risk factor. However, some experts have 
suggested that these short-term savings might be offset by spending on 
treating the diseases that would occur during the extra lifespan 
experienced by non-obese individuals. In this study, therefore, the 
researchers have used a computer model to calculate yearly and lifetime 
medical costs associated with obesity in The Netherlands.
What Did the Researchers Do and Find?

The researchers used their model to estimate the number of surviving 
individuals and the occurrence of various diseases for three 
hypothetical groups of men and women, examining data from the age of 20 
until the time when the model predicted that everyone had died. The 
“obese” group consisted of never-smoking people with a BMI of more than 
30; the “healthy-living” group consisted of never-smoking people with a 
healthy weight; the “smoking” group consisted of lifetime smokers with a 
healthy weight. Data from the Netherlands on the costs of illness were 
fed into the model to calculate the yearly and lifetime health-care 
costs of all three groups. The model predicted that until the age of 56, 
yearly health costs were highest for obese people and lowest for 
healthy-living people. At older ages, the highest yearly costs were 
incurred by the smoking group. However, because of differences in life 
expectancy (life expectancy at age 20 was 5 years less for the obese 
group, and 8 years less for the smoking group, compared to the 
healthy-living group), total lifetime health spending was greatest for 
the healthy-living people, lowest for the smokers, and intermediate for 
the obese people.
What Do These Findings Mean?

As with all mathematical models such as this, the accuracy of these 
findings depend on how well the model reflects real life and the data 
fed into it. In this case, the model does not take into account varying 
degrees of obesity, which are likely to affect lifetime health-care 
costs, nor indirect costs of obesity such as reduced productivity. 
Nevertheless, these findings suggest that although effective obesity 
prevention reduces the costs of obesity-related diseases, this reduction 
is offset by the increased costs of diseases unrelated to obesity that 
occur during the extra years of life gained by slimming down.
Additional Information.

Please access these Web sites via the online version of this summary at 
http://dx.doi.org/doi:10.1371/journal.pmed.0050029.

     * The MedlinePlus encyclopedia has a page on obesity (in English 
and Spanish)
     * The US Centers for Disease Control and Prevention provides 
information on all aspects of obesity (in English and Spanish)
     * The UK National Health Service's health Web site (NHS Direct) 
provides information about obesity
     * The International Obesity Taskforce provides information about 
preventing obesity
     * The UK Foods Standards Agency, the United States Department of 
Agriculture, and Shaping America's Health all provide useful advice 
about healthy eating
     * The Netherlands National Institute for Public Health and the 
Environment (RIVM) Web site provides more information on the cost of 
illness and illness prevention in the Netherlands (in English and Dutch)

Introduction

Because obesity is acknowledged as a major cause of morbidity and 
mortality [1,2], prevention of obesity is a target of health policy in 
many countries [3,4]. At the same time, many countries struggle to 
control ever-increasing health-care expenditures. The Organization for 
Economic Cooperation and Development suggested in 2005 that both goals 
could be achieved simultaneously, since “well-designed public health 
programmes may contribute to the prevention of illness and help relieve 
some of the cost pressures on health care systems” [5]. Such a promise 
of better health equaling lower costs is not new [6], yet is debatable. 
In fact, for smoking it has been argued that successful prevention will 
in the end increase expenditure exactly because it is successful [7,8]. 
The explanation for this hypothesis is that the life-years gained by 
prevention are not all lived in full health. While effective prevention 
will lead to a decrease in risk factor-related diseases, which may 
result in savings, these savings may be offset by cost increases related 
to an increase in diseases in life-years gained. Therefore, prevention 
may induce more health-care costs in the long run than it saves in the 
short run. Whether this possibility is true, however, will strongly 
depend on the risk factor concerned. An important determinant is whether 
this risk factor primarily causes relatively cheap lethal diseases or 
rather expensive chronic ones [9]. Since the diseases associated with 
obesity differ from those associated with smoking it is worthwhile to 
investigate whether or not prevention of obesity might indeed, as is 
sometimes suggested, relieve financial pressures on health-care systems. 
If it does not, of course, it does not imply that preventing obesity is 
not worthwhile, since the associated health gain is valuable in itself, 
for society and the individuals concerned.

In recent years several estimates of health-care costs attributable to 
obesity have been published [4,10–20]. Not only do such estimates vary 
enormously because of differences in methodology and definitions of 
health-care costs, these studies do not take into account the additional 
costs of “substitute” diseases that might occur during life-years 
gained. To our knowledge only two studies used the appropriate lifetime 
perspective [19,20], while only one [20] took into account medical costs 
of substitute diseases in life-years gained. It concluded that obesity 
causes higher lifetime medical costs, implying that prevention in this 
area can indeed result in cost savings.

In this study we present new estimates of annual and lifetime 
health-care costs of obesity in The Netherlands, and make comparisons 
between cohorts of people with different patterns of morbidity and 
mortality—namely, on the one hand smokers and on the other 
“healthy-living” people. This comparison provides two clear reference 
points for the case of obesity. A cohort approach was chosen to avoid 
blurring the comparison by demographic heterogeneity and to allow for a 
lifetime perspective. We included both the costs of diseases directly 
associated with obesity and smoking and those of other diseases that 
tend to occur as life-years are gained.
Methods

To estimate annual and lifetime health-care costs conditional on the 
presence of risk factors, the National Institute for Public Health and 
the Environment chronic disease model (RIVM-CDM) was used. The RIVM-CDM 
is a dynamic population model that describes the life course of cohorts 
in terms of transitions between risk factor classes and changes between 
disease states over time. Smoking classes distinguished in the model are 
never-smokers, current smokers, and former smokers. Body weight is 
modeled in three classes using body mass index (BMI) as an indicator: 
18.5 ≤ BMI < 25 (normal weight), 25 ≤ BMI < 30 (overweight), BMI ≥ 30 
(obese). The RIVM-CDM has been used in disease projections and cost 
effectiveness analyses [21–25]. With the model we estimated survivor 
numbers and disease prevalence numbers for three different hypothetical 
cohorts consisting of 500 men and 500 women aged 20 y at baseline: (1) 
an “obese” cohort, never-smoking men and women aged 20 with a BMI above 
30; (2) a “healthy-living” cohort, never-smoking men and women aged 20 
with normal weight (18.5 ≤ BMI < 25); and (3) a “smoking” cohort, men 
and women aged 20 with normal weight who had smoked throughout their life.

Cohorts were simulated until everybody in the cohort had died. The 
methods and input data we used to estimate survivor and disease 
prevalence numbers for the different cohorts with the model were 
discussed in depth elsewhere [26] (see also Table S1 and Texts S1 and S2 
for more information on the RIVM-CDM). In short, risk factors were 
linked to 22 obesity- and/or smoking-related chronic diseases through 
relative risks of disease incidence for each risk factor level, to model 
the chain leading from risk factor to disease to death. In addition, 
risk factor levels influence mortality directly through mortality from 
diseases that are not explicitly modeled. The diseases modeled account 
for roughly 60% of total morbidity [27] and mortality, and 15% of total 
health-care costs in The Netherlands [28]. The RIVM-CDM is programmed as 
a deterministic Markov model, i.e., the simulation model calculates the 
expected outcomes in one run. Therefore, more replications would not 
improve the results, which differs from a so-called microsimulation or 
Monte Carlo simulation model. We chose 500 men and 500 women purely for 
convenience.

No ethics committee approval was required for this study.

Cost of illness (COI) data from The Netherlands for 2003 were used to 
estimate health-care expenditure for the different cohorts [28]. The 
2003 COI study was a sequel to earlier COI 1999 studies in the 
Netherlands [29–31] and COI estimates were made using the health-care 
cost definitions of the System of Health Accounts (SHA) for reasons of 
international comparability of costs [32]. Average annual costs per 
patient having a certain disease were calculated by dividing total 
annual costs by Dutch prevalence numbers for each disease in 2003. 
Health-care costs for the different cohorts were then calculated as 
follows. First, the annual disease costs per patient were multiplied by 
RIVM-CDM projections of future prevalence numbers for each chronic 
disease in the model. Then, to calculate health-care costs for all 
“other” diseases, the numbers of survivors were multiplied by age- and 
sex-specific cost profiles of “remaining” costs. These latter are the 
difference between total health-care costs and the costs of the diseases 
incorporated explicitly in the model. These costs include, for instance, 
the costs of mental and behavioral disorders. Finally, these two 
categories of costs, one related and the other unrelated to the risk 
factor under study, were added to estimate annual costs. To calculate 
lifetime health-care costs of the three different cohorts [33], annual 
costs were added over time. To reflect the concept of time preference, 
meaning that an amount of money spent or saved in the future is worth 
less than the same amount today, net present values were calculated 
using discount rates of 3% and 4%. Using the differences in lifetime 
health-care costs compared to the healthy-living cohort we calculated 
whether or not avoidance of obesity and smoking resulted in lower 
health-care costs.

To investigate the robustness of our results with respect to future 
changes in disease epidemiology and health-care costs, and different 
definitions of health-care costs, a series of sensitivity analyses were 
performed by estimating the lifetime health-care costs in different 
scenarios:

Scenario 1. Assumes a yearly decrease of 1% in the incidence and 
mortality rates for all diseases included in the model. This is roughly 
the same yearly decrease as was used in the Global Burden of Disease 
projections of global mortality and burden of disease [34].

Scenario 2. Assumes a yearly decrease in all relative risks of the obese 
and smoking cohort to reflect selective disease prevention efforts in 
smokers and obese as has been observed in the past [35,36]. This was 
done using the following formula: RR(t) = {RR(t − 1) − 1} × 0.99 + 1 
where RR(t) is the relative risk in year t.

Scenario 3. Assumes a yearly increase of 1% in health-care costs for all 
diseases per person.

Scenario 4. Adopts a broader definition of health-care costs (like the 
one commonly used in The Netherlands [37]), which includes a broader 
range of long-term and residential care than in the SHA as used in 
baseline estimates, which is especially relevant in case of increased 
longevity.

Scenario 5. Adopts a narrower definition of health-care costs by 
excluding all expenditure on nursing and residential care mentioned in 
the SHA definition. These costs can cause substantial variation in 
cost-of-illness estimations between countries [37]. The narrower set of 
costs improves the international comparability of the figures presented.

Scenario 6. Uses relative mortality risk estimates for persons with 30 ≤ 
BMI < 35, published by Flegal et al. [38] as input for the simulation 
model for the obese cohort. Mortality estimates vary substantially as a 
function of BMI in the higher ranges beyond the cutoff BMI value of 30. 
Lumping together all values above 30 into one category masks this 
significant variation in mortality and thus also in lifetime health-care 
costs, possibly leading to biased estimates. In fact, there still is 
scientific debate about the exact values of the mortality risks 
associated with different levels of BMI. The article by Flegal et al. 
[38] attracted much attention because their estimates of the excess 
mortality associated with obesity were much lower than previously thought.

Scenario 7. Uses relative mortality risk estimates for persons with a 
BMI ≥ 35, published by Flegal et al. [38] as input for the simulation 
model for the obese cohort.
Results

Table 1 shows remaining life expectancy and the lifetime health-care 
costs for the three cohorts, specified by disease category.
thumbnail
Table 1.

Life Expectancy (Years) and Expected Lifetime Health-Care Costs per 
Capita (Price Level 2003 × 1,000) at 20 Years of Age for the Three Cohorts

The obese cohort has the highest health-care costs for diabetes and 
musculoskeletal diseases compared to the other cohorts. Lifetime costs 
for cancers other than lung cancer are equal for all cohorts. Despite 
differences in life expectancy, the costs for stroke are similar for all 
cohorts. The most pronounced difference in costs occurs in the category 
“costs of other diseases,” which is purely the result of different life 
expectancies.

Figure 1 displays average annual health-care costs per healthy-living 
person, smoker, and obese person. At all ages, smokers and obese people 
incur more costs than do healthy-living persons. Until age 56, average 
annual health-care costs are highest for an obese person. In higher age 
groups smokers are more expensive.
thumbnail
Figure 1. Average Additional Annual per Capita Costs for Smoking and 
Obese Individuals Compared to “Healthy-Living” Individuals

Despite the higher annual costs of the obese and smoking cohorts, the 
healthy-living cohort incurs highest lifetime costs, due to its higher 
life expectancy, as shown in Table 1. Furthermore, the greatest 
differences in health-care costs are not caused by smoking- and 
obesity-related diseases, but by the other, unrelated, diseases that 
occur as life-years are gained (Table 1). Therefore, successful 
prevention of obesity and smoking would result in lower health-care 
costs in the short run (assuming no costs of prevention), but in the 
long run they would result in higher costs.

To zoom in on what might happen to health-care costs if successful 
prevention converts the obese and smoking cohort to a healthy-living 
cohort, Figure 2 displays the differences in total health-care costs 
over time between the obese and smoking cohorts compared to the 
healthy-living cohort. Figure 2 shows that in approximately the first 50 
years after the hypothetical lifestyle change of the cohort, cost 
savings are realized through the reduced incidence of smoking- and 
high-BMI–related diseases. After this period, additional health-care 
costs occur during life-years gained. The initial savings are higher for 
the converted obese cohort, primarily the result of savings due to a 
reduced incidence of diabetes and of nonlethal diseases such as 
osteoarthritis and lower-back pain. Furthermore, Figure 2 demonstrates 
that the initial savings weigh more heavily than do additional costs in 
the long run if costs are discounted. Cumulative differences in 
health-care costs are lower for obesity prevention than for smoking 
prevention: at discount rates of, respectively, 3% and 4% successful 
smoking prevention would result in additional health-care costs of 7.1 
and 3.4 million (assuming costless intervention). For obesity prevention 
these figures would amount to 1.8 and 1.0 million. Only for discount 
rates above 4.7% would costless obesity prevention be cost saving. For 
smoking prevention to be cost saving, the discount rate for costs should 
be at least 5.7%
thumbnail
Figure 2. Differences in Aggregated Health-Care Costs over Time if 
Successful Prevention Converts the Obese and Smoking Cohort into a 
Healthy-Living Cohort (Undiscounted and with a Discount Rate of 3%)

Table 2 displays the results of the sensitivity analyses. Expected 
health-care costs for all cohorts, and relative differences between 
cohorts increase in scenario 1 (decreasing incidence and mortality 
rates) due to increases in life expectancy. In scenario 2 (decreasing 
relative risks), differences between the cohorts become less pronounced. 
In scenario 3 (increasing health-care costs) absolute estimates of 
lifetime health-care costs and differences between the cohorts increase. 
This is due to the fact that the yearly increase in health-care costs 
will be mostly felt at older ages. Under the broader Dutch definition of 
health-care costs (scenario 4), differences between cohorts increase. 
Excluding costs of nursing homes (scenario 5) attenuates the differences 
between cohorts. Estimates of lifetime health-care costs using lower 
relative mortality risks for the obese cohort as input narrow the 
differences between the obese and healthy-living cohort (scenarios 6 and 
7). The rank order of lifetime health-care costs for the cohorts, 
however, is the same in all scenarios.
thumbnail
Table 2.

Results of Sensitivity Analyses
Discussion

In this study we have shown that, although obese people induce high 
medical costs during their lives, their lifetime health-care costs are 
lower than those of healthy-living people but higher than those of 
smokers. Obesity increases the risk of diseases such as diabetes and 
coronary heart disease, thereby increasing health-care utilization but 
decreasing life expectancy. Successful prevention of obesity, in turn, 
increases life expectancy. Unfortunately, these life-years gained are 
not lived in full health and come at a price: people suffer from other 
diseases, which increases health-care costs. Obesity prevention, just 
like smoking prevention, will not stem the tide of increasing 
health-care expenditures. The underlying mechanism is that there is a 
substitution of inexpensive, lethal diseases toward less lethal, and 
therefore more costly, diseases [9]. As smoking is in particular related 
to lethal (and relatively inexpensive) diseases, the ratio of cost 
savings from a reduced incidence of risk factor–related diseases to the 
medical costs in life-years gained is more favorable for obesity 
prevention than for smoking prevention.

The simulation model we used to study the lifetime medical costs 
associated with obesity employs data and assumptions similar to those 
used to calculate so-called “attributable fractions” [39] that serve to 
determine which proportion of health care may be attributed to 
particular risk factors [10]. The main difference between the two 
approaches is that our model can take into account differences in life 
expectancy. Using the same simulation model and methodology employed in 
this paper we calculated that 2.0% of total health-care costs in The 
Netherlands in 2003 could be attributed to overweight (BMI > 25). For 
smoking, this percentage equaled 3.7%. Given differences in overweight 
and smoking prevalence between the US and The Netherlands, these figures 
compare well with previous research [4,8,10–18]. With respect to 
lifetime medical costs for smokers, our results are in line with other 
studies that used a lifetime perspective. Barendregt et al. [7], using 
Dutch data, and Sloane et al. [8], in the US health-care setting, both 
found that the high medical costs of smoking-related diseases are more 
than offset by lower survival of smokers. For costs attributable to 
obesity, only one previous study used a similar methodology, employing a 
lifetime perspective and including all medical costs. In contrast to our 
analysis, it concluded that obesity increases lifetime medical costs 
[20]. This discrepancy may be explained in several ways. First, Allison 
and colleagues truncated their analysis at age 85, which—even assuming 
no differences in mortality between cohorts after this age—biases the 
results toward relatively higher lifetime medical costs attributable to 
obesity. Second, they based their estimates on another study in which 
the costs attributable to obesity were not stratified by age group [10]; 
to compensate for this lack of information they hypothesized an age 
gradient. Third, their age-related costs increased more gradually, which 
may be due to a narrower definition of health-care costs. Fourth, it 
could well be the case that our cost definition was broader and included 
more so-called costs of care instead of cure, which are related to age 
rather than to disease per se.

Some aspects of our study methodology need to be emphasized. First, in 
the simulation model employed, disease incidence rates are coupled to 
risk factor levels. Linking costs per disease to the estimated disease 
prevalence over time then allows for an explicit causal link between BMI 
status and health-care costs. This is an important point, since in 
studies using individual-level data comprising both BMI and health-care 
use, the causality of the relationship between BMI and health-care use 
is usually left unspecified [11,15,40]. As a result, the observed 
differences between the groups might have been associated with 
confounding variables, e.g., socioeconomic status.

Second, the health-care costs employed in the model were a function of 
age and disease status but not of proximity to death, which has been 
proposed as an important determinant of health-care costs [41–43]. 
However, by modeling most primary causes of death (coronary heart 
disease, stroke, and different types of cancers) in our example, we 
implicitly have taken into account “time to death” as an important 
explanatory variable of health-care costs, since postponement of these 
lethal diseases through prevention also postpones the costs of these 
diseases.

Third, we assumed that costs per patient for each risk factor–related 
disease are equal, irrespective of risk factor status. This similarity 
might not always be the case. For instance, treatment costs of 
lower-back pain could depend on BMI status.

Fourth, it is important to stress that we have focused solely on 
health-care costs related to smoking and obesity, ignoring broader cost 
categories and consequences of these risk factors to society. It is 
likely, however, that these impacts will be substantial. For instance, 
reduced morbidity in people of working age may improve productivity and 
thus result in sizeable productivity gains in society (e.g., [44]). In 
the case of smoking and obesity, these indirect costs could well be 
higher than the direct medical costs [8,18]. Moreover, from a societal 
perspective, other potentially substantial costs and consequences need 
to be considered, such as those related to informal care, the damage due 
to fires caused by smoking, or the reduced well-being of family members 
due to morbidity and premature death. These different cost categories 
emphasize the influence the perspective taken in economic analyses has 
on the conclusions. From a welfare economic perspective the societal 
perspective is, in fact, the most relevant [45], although in practice 
many evaluations take a narrower perspective, which more closely 
conforms to the perspective most relevant to the decision-maker they are 
trying to inform [46].

Fifth, in our simulation model there are no gradations of obesity, which 
are critical to relative risks, mortality impact, and thus also to 
lifetime health-care costs. However, our sensitivity analyses revealed 
that even if mortality risks for the obese group were based solely on 
the group 30 ≤ BMI < 35, lifetime health-care costs of the obese would 
still be lower than those of healthy-living persons.

Finally, we assumed that no transitions occur between risk factor 
classes over time. In reality, of course, transitions between classes do 
occur: some smokers quit (and some of them might start again later) and 
obese people of course might lose and regain weight over time.

A remaining and most important question is whether prevention should be 
cost-saving in order to be attractive. Obviously, the answer is that it 
need not be cost-saving: like other forms of care it “merely” needs to 
be cost-effective. Bonneux et al. [9] made this very clear: “The aim of 
health care is not to save money but to save people from preventable 
suffering and death. Any potential savings on health care costs would be 
icing on the cake.” If prevention can bring additional health to a 
population at relatively low costs, it is a good candidate for funding 
[47]. However, the present study demonstrates that sound estimates of 
medical costs in life-years gained should be taken into account in 
cost-effectiveness analysis of prevention. In this respect it is 
interesting to note that in the area of smoking cessation and weight 
loss, favorable cost-effectiveness results have been shown even if 
medical costs in life-years gained are taken into account [22,26,33]. 
Prevention may therefore not be a cure for increasing 
expenditures—instead it may well be a cost-effective cure for much 
morbidity and mortality and, importantly, contribute to the health of 
nations.
Supporting Information
Table S1. Input Data of the RIVM Chronic Disease Model Used in the 
Calculations

(335 KB XLS)
Text S1. Details on the Structure of the RIVM Chronic Disease Model

(106 KB PDF)
Text S2. Additional Results Specified by Disease

(141 KB PDF)
Acknowledgments

Author contributions. PHMvB had the original idea for the paper, carried 
out the analyses, and drafted the initial manuscript. RTH developed the 
simulation model. All authors contributed substantially in developing 
and writing the paper. HCB is the guarantor.
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