Lifetime Medical Costs of Obesity: Prevention No Cure for Increasing Health Expenditure
et al. (2008) Lifetime medical costs of
obesity: Prevention no cure for
increasing health expenditure. PLoS
Med 5(2): e29. doi:10.1371/journal.
Lifetime Medical Costs of Obesity: Prevention No Cure for Increasing Health Expenditure
Pieter H. M. van Baal 0 1 2
Johan J. Polder 0 1 2
G. Ardine de Wit 0 1 2
Rudolf T. Hoogenveen 0 1 2
Talitha L. Feenstra 0 1 2
Hendriek C. Boshuizen 0 1 2
Peter M. Engelfriet 0 1 2
Werner B. F. Brouwer 0 1 2
0 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
1 Academic Editor: Andrew Prentice, London School of Hygiene & Tropical Medicine , United Kingdom
2 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
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.
The Editors Summary of this article follows the references.
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
healthcare 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 . Such a
promise of better health equaling lower costs is not new ,
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 . 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 [4,1020]. 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 
took into account medical costs of substitute diseases in
lifeyears 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 mortalitynamely, 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
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 . 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
 (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  and mortality, and 15% of total
health-care costs in The Netherlands . 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 . The 2003 COI study was a sequel to
earlier COI 1999 studies in the Netherlands  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 . 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
ageand 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 ,
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
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 .
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) fRR(t 1) 1g 3 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 ), 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 . 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. 
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.  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.  as
input for the simulation model for the obese cohort.
Table 1 shows remaining life expectancy and the lifetime
health-care costs for the three cohorts, specified by disease
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
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
healthyliving persons. Until age 56, average annual health-care costs
are highest for an obese person. In higher age groups smokers
are more expensive.
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-BMIrelated 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
lowerback 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 E7.1 and E3.4 million
(assuming costless intervention). For obesity prevention these
figures would amount to E1.8 and E1.0 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%
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
Table 1. Life Expectancy (Years) and Expected Lifetime Health-Care Costs per Capita (Price Level 2003 3 E1,000) at 20 Years of Age for
narrow the differences between the obese and healthy-living
cohort (scenarios 6 and 7). The rank order of lifetime
healthcare costs for the cohorts, however, is the same in all
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 . As smoking is in particular related to
lethal (and relatively inexpensive) diseases, the ratio of cost
savings from a reduced incidence of risk factorrelated
diseases to the medical costs in life-years gained is more
favorable for obesity prevention than for smoking
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  that serve to determine which proportion of
health care may be attributed to particular risk factors .
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 [4,8,1018]. With respect
to lifetime medical costs for smokers, our results are in line
with other studies that used a lifetime perspective.
Barendregt et al. , using Dutch data, and Sloane et al. , 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 . This discrepancy may be
explained in several ways. First, Allison and colleagues
truncated their analysis at age 85, whicheven 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 ; 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
healthcare 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.,
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 . However, by modeling most
primary causes of death (coronary heart disease, stroke, and
Figure 1. Average Additional Annual per Capita Costs for Smoking and Obese Individuals Compared to Healthy-Living Individuals
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
factorrelated 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
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., ). 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 , 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 .
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.  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
Listed are expected lifetime health-care costs per capita for the different cohorts in different scenarios. In parentheses: relative difference in expected lifetime health-care costs with the
NHANES, National Health and Nutrition Examination Survey.
cake. If prevention can bring additional health to a
population at relatively low costs, it is a good candidate for
funding . 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
costeffectiveness 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.
Found at doi:10.1371/journal.pmed.0050029.st001 (335 KB XLS).
Text S1. Details on the Structure of the RIVM Chronic Disease Model
Found at doi:10.1371/journal.pmed.0050029.sd001 (106 KB PDF).
Text S2. Additional Results Specified by Disease
Found at doi:10.1371/journal.pmed.0050029.sd002 (141 KB PDF).
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.
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
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
healthyliving 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.
The MedlinePlus encyclopedia has a page on obesity (in English and
The US Centers for Disease Control and Prevention provides
information on all aspects of obesity (in English and Spanish)
The UK National Health Services health Web site (NHS Direct) provides
information about obesity
The International Obesity Taskforce provides information about
The UK Foods Standards Agency, the United States Department of
Agriculture, and Shaping Americas 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
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