Article

Health and economic burden of the projected trends in the USA and the UK

Department of Health Policy and Management, Mailman School of Public Health, Columbia University, New York, NY 10032, USA.
The Lancet (Impact Factor: 45.22). 08/2011; 378(9793):815-25. DOI: 10.1016/S0140-6736(11)60814-3
Source: PubMed

ABSTRACT

Rising prevalence of obesity is a worldwide health concern because excess weight gain within populations forecasts an increased burden from several diseases, most notably cardiovascular diseases, diabetes, and cancers. In this report, we used a simulation model to project the probable health and economic consequences in the next two decades from a continued rise in obesity in two ageing populations--the USA and the UK. These trends project 65 million more obese adults in the USA and 11 million more obese adults in the UK by 2030, consequently accruing an additional 6-8·5 million cases of diabetes, 5·7-7·3 million cases of heart disease and stroke, 492,000-669,000 additional cases of cancer, and 26-55 million quality-adjusted life years forgone for USA and UK combined. The combined medical costs associated with treatment of these preventable diseases are estimated to increase by $48-66 billion/year in the USA and by £1·9-2 billion/year in the UK by 2030. Hence, effective policies to promote healthier weight also have economic benefits.

Full-text

Available from: Tim Marsh
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www.thelancet.com Vol 378 August 27, 2011
815
Lancet 2011; 378: 815–25
See Editorial page 741
See Comment pages 743,
744, and 746
This is the second in a
Series of
four papers about obesity
Department of Health Policy
and Management, Mailman
School of Public Health,
Columbia University, New York,
NY, USA (Y C Wang MD);
New College, University
of Oxford, Oxford, UK
(Prof K McPherson PhD);
Department of Society, Human
Development and Health,
Harvard School of Public Health,
Harvard University, Boston,
MA, USA (S L Gortmaker PhD);
and National Heart Forum,
London, UK (T Marsh PG Dip,
M Brown PhD)
Correspondence to:
Y Claire
Wang, Department
of
Health Policy and Management,
Mailman School of Public Health,
Columbia University, New York,
NY 10032, USA
ycw2102@columbia.edu
Obesity 2
Health and economic burden of the projected obesity trends
in the USA and the UK
Y Claire Wang, Klim McPherson, Tim Marsh, Steven L Gortmaker, Martin Brown
Rising prevalence of obesity is a worldwide health concern because excess weight gain within populations forecasts
an increased burden from several diseases, most notably cardiovascular diseases, diabetes, and cancers. In this
report, we used a simulation model to project the probable health and economic consequences in the next two
decades from a continued rise in obesity in two ageing populations—the USA and the UK. These trends project
65 million more obese adults in the USA and 11 million more obese adults in the UK by 2030, consequently accruing
an additional 6–8·5 million cases of diabetes, 5·7–7·3 million cases of heart disease and stroke,
492 000–669 000 additional cases of cancer, and 26–55 million quality-adjusted life years forgone for USA and UK
combined. The combined medical costs associated with treatment of these preventable diseases are estimated to
increase by $48–66 billion/year in the USA and by £1·9–2 billion/year in the UK by 2030. Hence, eff ective policies
to promote healthier weight also have economic benefi ts.
Threat to population health
Increased prevalence of overweight and obesity is a
worldwide health concern.
1
In a systemic analysis of
epidemiological studies from 199 countries,
1
1·46 billion
adults worldwide were estimated to be overweight in 2008,
and of these 502 million were obese. Despite signs of
stabilisation in some populations,
2,3
the eff ects of
consistently high prevalence of obesity on population
health are far-reaching; societies are burdened by
premature mortality, morbidity associated with many
chronic disorders, and negative eff ects on health-related
quality of life. The challenge to quantify the eff ect of these
health burdens to inform public policies and health
services are pressing. Furthermore, projected increases in
these diseases in many ageing populations suggest a
substantial cost burden to the health-care system in an era
of ever-escalating medical expenditure. In a systematic
review of the economic burden of obesity worldwide,
Withrow and colleagues
4
concluded that obesity accounted
for 0·7–2·8% of a country’s total health-care costs, and that
obese individuals had medical costs 30% higher than those
with normal weight. The combination of rising obesity
prevalence and increased spending on obese people has
been estimated to account for 27% of the growth in US
health-care expenditure between 1987 and 2001.
5
Total
health-care costs attributable to obesity and overweight are
projected to double every decade to account for 16–18% of
total US health-care expenditure by 2030.
6
Figure 1 shows obesity prevalence in adults and children
in selected countries.
7
Since the 1970s, the USA and the
UK have had striking increases in the proportion of their
populations with a body-mass index (BMI) in overweight
(BMI 25–29·9 kg/m²) and obese (BMI ≥30 kg/m²) ranges.
If such trends were to continue unabated, the report’s
authors estimate that about three of four Americans and
seven of ten British people will be overweight or obese
by 2020.
7
Although population-wide secular trends seem
much the same, obesity and over weight cluster diff erently
according to socioeconomic status, educational attainment,
and race and ethnic group (fi gure 2 and fi gure 3).
Health burden from rising obesity
The health burden from obesity is largely driven by an
increased risk of type 2 diabetes, cardiovascular diseases,
and several forms of cancer. For instance, every additional
5 kg/m² in BMI increases a man’s risk of oesophageal
cancer by 52% and for colon cancer by 24%, and in
women, endometrial cancer by 59%, gall bladder cancer
by 59%, and postmenopausal breast cancer by 12% (the
association is strongest in women in the Asia–Pacifi c
Key messages
Excess bodyweight is associated with negative eff ects on longevity, disability
-free
life-years, quality-of-life, and productivity. The obesity epidemic aff ects both high and
middle-to-low income countries, posing a threat to population health and a
substantial burden to many health systems.
The burden of obesity includes an increased number of fatal and non-fatal
diseases—including diabetes, coronary heart disease, stroke, cancer, and
osteoarthritis—which impose substantial medical costs from treatment and productivity
losses (absenteeism, presenteeism, and loss of productivity from premature deaths).
The higher the proportion of the population that is overweight and obese, the greater
the use of health services, resulting in higher treatment costs for the many
obesity-related diseases than in a less obese population.
The health and cost burden of overweight and obesity has a protracted time course.
Epidemiological models such as the one we present enable us to link changes in
obesity at the population level to disease burdens decades later, a crucial exercise for
public policy.
A systematic understanding of the potential morbidity and cost implications of
specifi ed hypothetical changes in body-mass index trajectories, driven by policy
changes or otherwise, is crucial for formation of eff ective and cost-eff ective strategies,
establishment of research and funding priorities, and creation of the political will to
address the obesity epidemic.
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region).
8
Excess bodyweight also contributes to non-fatal
but costly or disabling disorders such as osteoarthritis.
9
Moreover, rapidly expanding evidence suggests that
excess bodyweight is linked to many additional disorders,
including benign prostate hypertrophy,
3
infertility,
4
asthma,
5,6
and sleep apnoea,
7
further contributing to the
cost burden. Maternal obesity has been linked to an
increased risk of congenital anomalies.
10
Because in
many populations the prevalence of obesity is greater at a
much younger age than in previous generations, present
trends in obesity project a growth in the proportion of the
population living with chronic disabilities. Some
researchers have postulated a potential threat to the
continued increase in life expectancy achieved by medical
and public health advances during the past century.
11
Economic cost of rising obesity
The many chronic and acute health disorders associated
with excess bodyweight burden a society not only by
negatively aff ecting the health-related quality of life
12,13
of
its people but also by incurring substantial costs to the
individuals aff ected and to society, notably from increased
health-care costs and lost productivity.
The medical costs of obesity represent the monetary
value of health-care resources devoted to managing
obesity-related disorders, including the costs incurred by
excess use of ambulatory care, hospitalisation, drugs,
radiological or laboratory tests, and long term care
(including nursing homes). In a systematic review of the
direct health-care costs of obesity, Withrow and colleages
4
estimated that obesity accounted for up to 2·8% of health-
care expenditure, noting that the studies were generally
very conservative, such that the actual amount was likely
to be higher. On the basis of the most recent US data,
Finkelstein and colleages
14
reported that, compared with
normal-weight individuals, obese patients incur 46%
increased inpatient costs, 27% more physician visits and
outpatient costs, and 80% increased spending on
prescription drugs. The annual extra medical costs of
obesity in the USA were estimated as $75 billion in 2003
15
and accounted for 4–7% of total health-care expenditure.
16
In the early 1990s, obesity was estimated to account for
2% of health-care costs in France,
17
4% in the Netherlands,
18
and 2% in Australia.
19
The application of similar
methodology to all member states of the European Union
has provided estimates for the combined direct and
indirect costs of obesity in 2002 of roughly €33 billion a
year.
20,21
In 2007, a report developed by the UK’s Offi ce for
Science Foresight Programme
22
projected that the
Figure 1: Past and projected prevalence of overweight (BMI ≥25 kg/m²)
Reproduced from the Organisation for Economic Co-operation and Development.
7
1970
20
10
0
30
40
50
60
70
80
1980 1990 2000
Year
Proportion overweight (%)
2010 2020
USA
England
Spain
Austria
Australia
France
Korea
Canada
Italy
Figure 2: Relative index of inequality in obesity by education level
The relative index of inequality provides a measure of how many times more likely to be obese are those at the lower end of the education spectrum relative to those
at the upper end. Reproduced from the Organisation for Economic Co-operation and Development.
7
3·2
0
France
Sweden
Austria
Italy
Spain
Australia
Canada
Hungary
England
United States
Korea
Korea
Spain
Italy
France
Sweden
Hungary
Austria
Canada
England
Australia
United States
1
5
Relative index for overweight by education level
10
15
20
Men
Women
2·8
2·3
2·2
2·2
1·8
1·6
1·6
0·8
18·4
16·9
6·8
4·8
3·3
2·9
2·2
2·0 1·9 1·9
1·6
1·4
1·0
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continuing rise in obesity will add £5·5 billion in medical
costs to the National Health Service by 2050.
In addition to medical costs, society incurs substantial
indirect costs from obesity as a result of decreased years
of disability-free life, increased mortality before retire-
ment, early retirement, disability pensions, and work
absenteeism or reduced productivity (also known as
presenteeism). Although individual estimates vary,
several studies suggest that the monetary value of lost
productivity is several times larger than medical costs.
23–25
For example, in Sweden individuals who are obese are
1·5–1·9 times more likely to take sick leave, and 12% of
obese women have disability pensions attributable to
obesity, togther costing about US$300 for every adult
woman in the population.
26
For US employees,
Finkelstein
25
reported that annual missed workdays
ranged from 0·5 more days in men who were overweight
to 5·9 more days in men who were classifi ed grade III
obese (BMI ≥40 kg/m²) than in men of healthy weight.
Moreover, they estimated that the annual cost from
presenteeism in men who were very obese (BMI
≥40 kg/m²) was the equivalent of 1 month of lost
productivity and cost employers $3792 per year.
Quantifi cation of the costs from the health consequences
of obesity is complex; costs are mediated by factors such
as a changing demography, food system, and the
economy. Estimation of the cost from lost productivity is
especially challenging because of the scarcity of data and
the assumptions needed for the labour market structure.
However, a valuable lesson learned from the UK Foresight
Project is that defi nition of the size of the problem can be
the beginning of a movement to raise awareness and
mobilise political will to address the problem. In the
following case studies, we applied the Foresight
modelling framework to the US and the UK to provide
updated projections for obesity trends and increases in
health-care expenditure consequent on increases in
obesity-related diseases. Of the 11 countries described in
the OECD report,
7
the USA and UK had the highest
prevalence of obesity (fi gure 1) and were two of only three
countries (the other being South Korea) with periodic
objectively measured BMI data.
The health-care burden of obesity
BMI trends
We analysed two nationally representative surveys to
obtain trends in BMI: the National Health and Nutrition
Examination Survey (NHANES)
27
from the USA and the
Healthy Survey for England (HSE)
28
from the UK. Both
surveys contain objectively measured weight and height
data (table 1 and panel). Separately for the two countries,
a set of two projections were made to provide a probable
range of the outlook of growth in obesity prevalence
within populations in the next 20 years. The historic
trend projection was constructed from two decades of
measured BMI data (since 1988 in the USA and 1993 in
the UK)—depicting the fast-growing obesity trend that is
repeatedly reported. The recent trend, a more optimistic
projection, is based on data from 2000 onwards, guided
by several publications suggesting a levelling-off of
obesity trends.
2,49,50
Past trends in BMI growth projected an increase of
obesity prevalence in US adults from about 32% in
2007–08, (the latest available data) to 50–51% (corres-
ponding to recent–historic projections) in 2030 for men,
and from 35% to 45–52% for women (fi gure 4). These
projections are similar to previously published estimates
with diff erent projection models.
6,51
By contrast, past
trends in the UK would forecast a rise in obesity
prevalence in men from 26% to 41–48% and in women
from 26% to 35–43%. With the exception of US men, the
recent trend projections have slopes that are substantially
atter than the slopes under the historic trend. Because
of the fewer datapoints, these projections also have more
uncertainty than historic trends, as shown by wider
confi dence intervals.
Combined with the shift in age structure—the ageing of
the so-called baby boom generation in both countries—
these projections suggest that, for the USA, there would be
as many as 65 million more obese adults in 2030 than
in 2010, 24 million of whom would be older than 60 years
(fi gure 5); and for the UK, up to 11 million more obese
adults, 3·3 million of whom would be older than 60 years.
Health burden of obesity epidemic
For the USA, in the next two decades, the historic trends
since the early 1990s would project an excess of 8 million
cases of diabetes, 6·8 million cases of coronary heart
disease and stroke, and over 0·5 million cases of cancer
(table 2, scenario 1). By comparison, from a more optimistic
recent trend we would predict an excess of 6 million cases
of diabetes, 5 million of coronary heart disease and stroke,
and more than 400 000 of cancer. Although the prevalence
of obesity in the UK is less than in the USA, and data
since 2000 suggest some stabilisation of projected growth
(fi gure 4), a substantial disease burden is associated with
obesity and overweight in the UK population. During the
next 20 years, we projected that obesity-attributable disease
risks will add an excess of 544 000–668 000 cases of
Figure 3: Adult prevalence of obesity by ethnicity in USA and UK
Reproduced from the Organisation for Economic Co-operation and Development.
7
Non-Hispanic white
Non-Hispanic black
Men
29%
28%
26%
24%
29%
46%
35%
24%
19% 19% 17%
32%
17%
11%
Women Men Women
40
50
60
USA UK
0
10
20
30
Obesity prevalence (%)
White
Mexican American
Other ethnicity
Black Asian
A
B
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diabetes, 331 000–461 000 of coronary heart disease and
strokes, and 87 000–130 000 of cancer.
In addition to these diseases are several non-fatal, but
nevertheless debilitating disorders such as osteoarthritis
and hypertension; together they pose a substantial threat
to the population’s healthy life span. We estimated that a
continuing trend in obesity would present a loss of
2·2–6·3 million quality-adjusted life-years (QALYs)
in the UK and 24·5–48·2 million QALYs in the USA
during 2010–30.
Projected health-care costs attributable to
obesity-related diseases
If trends continue, further increases in obesity in the
two populations project an expansion of obesity-related
and mostly chronic diseases with substantial implications
for health-care expenditure. Compounded by an ageing
population, in the next two decades, extrapolation of the
historic trend in the USA would project an increase in
annual medical cost from treating obesity-related
disorders of US$28 (95% CI 8–49) billion per year by
2020 and $66 (19–112) billion per year by 2030 (fi gure 6).
The recent trend would project a lower, but still
substantial increase in costs: $22 (–28 to 72) billion per
year by 2020 and $48 (–47 to 143) billion per year by 2030.
To put these numbers into context, a $22–66 billion
increase in health-care spending represents a
0·8%–2·6% increase from the $2·5 trillion US health-
care spending in 2009. The top contributors to this cost
burden are arthritis, coronary heart disease, and diabetes,
and about half these costs would be incurred by
individuals 65 years and older (covered by the publicly
funded Medicare programme).
A substantial health-care cost burden is expected in
the UK (fi gure 6). Historic BMI trends would project
£648 (95% CI 352–944) million higher costs annually
USA UK
Population characteristics
BMI distribution NHANES 1988–2008
27
HSE 1993–2008
28
Population size US census and projections
29
UK census and projections
30
Incidence of disease
Hypertension NHLBI 2006 chart book
31
British Heart Foundation statistics
Coronary heart disease Framingham heart study 1980–2003, National Institute of
Health–NHLBI
32
European cardiovascular disease statistics 2008
33
Diabetes National health interview survey, Centers for Disease Control
and Prevention, National Centre for Health Statistics.
34
British Heart Foundation statistics
Stroke Framingham heart study 1980–2003
32
Stroke statistics 2009,British Heart Foundation
35
Cancer US cancer statistics: 1999–2005 UK Cancer Research statistics (CancerStat)
36
Arthritis Cohort study based on population-based administrative
health-care database
37
Offi ce for National Statistics
Relative risks of obesity on
disease risks
International Association for the Study of Obesity, 2010
38
··
Cost of treatment
Hypertension Heart disease and stroke statistics, 2009 update
39
British Heart Foundation statistics (adjusted for infl ation)
Coronary heart disease Heart disease and stroke statistics, 2009 update
39
UK coronary heart disease statistics 2009–10
40
Diabetes American Diabetes Association
41
Diabetes in the NHS report
42
Stroke Heart disease and stroke statistics, 2009 update
39
Stroke statistics 2009 from British Heart Foundation,
35
calculated for per-case cost based on estimated prevalence.
Cancer National Cancer Institute
43
UK Foresight programme
22
Arthritis Medical expenditure panel survey
44
UK Foresight programme
22
Disease-specifi c mortality
Coronary heart disease Heart disease and stroke statistics, 2009 update
39
British Heart Foundation statistics
Diabetes American Diabetes Association
41
British Heart Foundation statistics
Stroke Heart disease and stroke statistics, 2009 update
39
British Heart Foundation statistics
Cancer National Cancer Institute (SEER database 1999–2006) UK Cancer Research statistics
36
Quality-of-life weights Published HRQL estimates
12
using the EQ-5D measures in the
2000 US MEPS data (n=13 646)
N/A (assumed same as US HQRL weights)
Forgone productivity
Absenteeism 2008 National health and wellness survey
45
N/A
Presenteeism 2008 National health and wellness survey
45
N/A
NHANES=National Health and Nutrition Examination Survey. HSE=Health Survey for England. NHLBI=National Heart, Lung and Blood Institute. HRQL=health-related
quality-of-life weights. SEER=Surveillance Epidemiology and End Results. MEPS=Medical Expenditure Panel Survey. NA=not available.
Table 1: Sources of data inputs for USA and UK
For more on the British Heart
Foundation statistics see
http://www.heartstats.org
For more on US cancer statistics
see http://wonder.cdc.gov
For more on the UK Offi ce for
National Statistics see http://
www.statistics.gov.uk
For more on the US National
Cancer Institute SEER database
see http://seer.cancer.gov
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in 2020 and £2 (95% CI 1·2–3·0) billion higher costs
annually in 2030 to be spent on treating obesity-related
diseases. The equivalent estimates with the recent trend
projections would amount to £613 (–426 to 1653) million
excess spending in 2020 and £1·9 (–0·8 to 4·5) billion
in 2030. A £613 million–£2 billion increase would
Population-representative measured body-mass index data
UK–Healthy Survey for England (HSE): HSE is a nationally
representative, cross-sectional survey of health and nutrition in
adults and children in England. Since 1993, HSE datasets have
been produced annually. In this report, we used 16 waves of
HSE from 1993 to 2008 (n=241 580) to produce the historic
trend, and the 2001–08 surveys to produce the recent trend.
US–National Health and Nutrition Examination Survey
(NHANES): NHANES is a nationally representative,
cross-sectional survey of health and nutrition in adults and
children in the USA. Since 1999, NHANES datasets have been
produced every two years. We used the most recent fi ve waves
of surveys (1999–2000, 2001–2, 2003–4, 2005–6, and 2007–8)
in addition to NHANES III (1988–1994) to produce the historic
trend, and the post 1999 data to produce the recent trend. The
total sample size from NHANES was 85 602.
Categorisation of body-mass index
We defi ned three mutually exclusive categories of body-mass
index (BMI): not overweight (<25 kg/m²), overweight
(25–29·9 kg/m²), and obese (≥30 kg/m²). For children and
adolescents between 2 and 19 years of age, we applied the
age-specifi c and sex-specifi c BMI percentiles from the US
Centers for Disease Control and Prevention growth standards:
not overweight (BMI <85th percentile), overweight (85–94th
percentile), and obese (≥95th percentile).
Statistical methods
We undertook the two-part modelling process developed by the
UK Foresight working group.
22,46,47
The rst module implements
a regression analysis based on series of cross-sectional data; the
second module implements a microsimulation programme to
produce longitudinal projections.
In the fi rst module, we fi t multivariate, categorical regression
models to the cross-sectional BMI data series from each country
and by sex. We included age and calendar year as covariates,
and constrained the predicted proportions of population in
each BMI category to always sum up to 100%. The 95% CI for
the projected prevalence were calculated from the Bayesian
posterior distribution of the regression parameters.
Microsimulation of obesity-related disease consequences
Within the Foresight microsimulation framework,
22
we created
virtual US and UK individuals on the basis of projected BMI
distributions in 2010–30. We probabilistically assigned BMI
values as a function of age, sex, and calendar year. Assuming an
individual’s BMI ranking (ie, percentile) in the same-age cohort
is constant over time, we longitudinally simulated the BMI
trajectories of a large number of individuals as they age.
Population size and age distributions were based on the
published projections from the US and UK censuses.
Every year, each simulated individual in the model had a
probability of getting a specifi c disease if he or she was free of
the disease at the beginning of the year. This risk is a
predetermined function of age, sex, and BMI. For individuals
with a disease, possible outcomes are recovery, continuation of
the disease, or death from the disease. The progress of any
disease was determined by the appropriate survival and
case-fatality statistics.
A review of epidemiological publications was undertaken to
determine country-specifi c incidence, case-fatality rates, and
rough annual treatment costs for the obesity-related diseases of
type 2 diabetes, coronary heart disease, stroke, arthritis, and
obesity-related cancer, by age and BMI. Relative risks of BMI for
these diseases individually are taken from a systematic review of
epidemiological studies.
32
Health-related quality of life (HRQL)
weights as a function of BMI were based on published US
estimates done with the EQ-5D instrument.
12
We assumed that
the relative risks of high BMI on the incidence of diseases and the
average quality-of-life weights were the same for the US and the
UK. We calculated quality-adjusted life-years by taking the
product of length of life and HRQL, aggregated for 20 years.
Excess annual costs of each disease due to rising obesity were
obtained from estimates from governmental data or the best
available published work (table 1). For instance, cost of coronary
heart disease in the USA was obtained from Heart Disease and
Stroke Statistics—2009 update, which reports the aggregate
direct medical expenditure due to coronary heart disease,
including costs from hospitals, nursing homes, physicians and
other professionals, and drugs. These aggregate values were then
divided by total number of patients at baseline to estimate
annual medical cost per case. We probabilistically assigned
diseases and associated costs, and quality-of-life weights, in all
subsequent years as a function of individual BMI trajectories
using a Monte Carlo simulation method.
48
We simulated
20 million individuals, by sex, for all scenarios and scaled them
up to represent the total census population. Excess numbers of
diseases and associated health-care costs were calculated by
taking the diff erence between the estimates for a specifi c
scenario (eg, recent trend) and a reference scenario, which
assumed that the BMI distributions were xed at the
2008 level—the most recent data available. The 95% CI for the
projections were derived from simulation of the BMI
distributions corresponding to the upper and lower bounds of all
obesity growth scenarios.
The simulation model was programmed in C++ (version 12·0,
Embarcadero Technologies). Further details of the two-part
modelling process can be found in the Foresight report
31
and
webappendix pp 7–9.
Panel: Data sources and statistical methods
See Online for webappendix
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corres pond about 0·5%–2% of the total health-care
spending in 2009 in the UK (£109·7 billion).
Eff ects of ameliorating or reversing the obesity epidemic
In view of the substantial health and cost burdens of
obesity, an obvious policy question is; what would be the
health and economic benefi t were the rising obesity trend
to be ameliorated? Table 2 summarises the projected
downstream changes in disease burdens, QALYs, and
obesity-related health-care costs according to two
hypothetical scenarios.
Consider a hypothetical programme that enables a 1%
reduction in BMI across the entire population (scenario 2,
table 2). A 1% reduction is equivalent to a weight loss of
roughly 1 kg for an adult of average weight. This change
might sound small, but such a scenario would have a
substantial eff ect on consequent health burdens.
Compared with a scenario in which past trends continue
(recent vs historic), a 1% BMI reduction across the US
population would avoid up to 2·1–2·4 million incident
cases of diabetes, 1·4–1·7 million cardiovascular diseases,
and 73 000–127 000 cases of cancer, with a gain of about
16 million QALYs. The equivalent scenario in the UK
would avoid 179 000–202 000 incident cases of diabetes,
122 000 cardio vascular diseases, and 32 000–33 000 inci-
dent cases of cancer with a gain of about 3 million QALYs
over 20 years. Because a 1% reduction in BMI is roughly
1 kg weight reduction per person, according to the
principle developed by Hall and colleagues,
52
it would
need a net caloric reduction of 20 kcal per day that was
sustained for 3 years.
A more aggressive scenario (scenario 3, table 2)
envisions a drastically lower prevalence of obesity by a
return to 1990 prevalence. Between 1990 and 2007–08,
the period for which we have similar data for both the
USA and the UK, the average bodyweight had risen by
9–18 kg (dependent on country and sex). This diff erence
in weight corresponds to a 200–400 kcal per day
diff erence in energy intake or expenditure sustained for
3 years.
Figure 4: Historic and recent trends in adult obesity prevalence in men and women in USA and UK
A=US men; B=US women; C=UK men; D=UK women. Black dots (bars=95% CI) show recorded prevalence from national surveys; each dot=one data point. Historic
trend used all data points; recent trend used data points after 2000.
C D
A B
20
30
40
50
60
70 Historic trend
95% CI
Recent trend
95% CI
Obesity prevalence (%)Obesity prevalence (%)
10
0
1985 1990 1995 2000 2005
Year Year
2010 2015 2020 2025 2030 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030
20
30
40
50
60
70
10
0
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Loss of productivity
The economic costs from the excess morbidity and
mortality attributable to obesity-related diseases go
beyond health-care costs alone, perhaps most notable are
the consequent losses in productivity. The shortage of
consistent and high-quality data precludes cross-country
comparisons. We explored the size of this indirect cost
burden for the USA alone in the context of the health-
care costs projected by our model. After incorporation of
estimates by Finkelstein and colleagues
43
of the incre-
mental lost workdays and costs of absenteeism and
presenteeism from high BMI—based on the 2008
National Health and Wellness Survey—we would expect
a loss of 1·7–3 million productive person-years in working
US adults, representing an economic cost as high as
$390–580 billion.
43
Discussion
In this report, by drawing similar statistics from the USA
and the UK into the same modelling structure, we have
had the opportunity to describe how the seemingly
similar obesity epidemic unfolds in two populations. In
the years 2010–30, the continuing rise in obesity was
projected to add a combined 6–8·5 million incident cases
of diabetes, 5·6–7·3 million incident cardiovascular
diseases, and more than half a million new cancers in the
USA and the UK. In addition to compromising the
populations’ healthy, productive life span, by 2030, these
increases in obesity-related diseases were projected to
add to health-care costs by $48–66 billion a year in the
USA and by £1·9–2 billion a year in the UK. The
prevalence of obesity is lower in the UK than in the USA;
however, we projected a more rapid increase in health-
care costs in the UK during the next 20 years than in the
USA. This rapid increase is partly attributable to the UK’s
older population (fi gure 5), for example, US men in 2007
were on average 2 years younger than men in the UK
(average ages 36·1 and 38·3 years, respectively). If past
trends continue, during the next 20 years, we projected a
13–16% increase in annual costs of obesity-related
diseases in the USA, 4% of which is from population
ageing alone. In the UK, the equivalent annual increase
would be 24–25%, 10% from ageing alone.
These projections are mere extrapolations from
available data, and inherent uncertainties exist when
making predictions. Although the increase in obesity
prevalence in the past several decades has been steady in
the USA and the UK (fi gure 4) and the rest of the world,
1,3
past trends do not always predict the future. Consideration
of several projection scenarios is therefore vital. For
instance, we produced a more pessimistic trend from
data since 1990, and a more optimistic trend using the
atter, more recent data, both with confi dence bounds.
How the continuing trend will respond to the changing
world (eg, food prices, agriculture policy, or technological
innovation) in the next 5 or 10 years can only be examined
with hindsight. However, human physiology for energy
regulation suggests that weight change in response to a
shift in energy intake or expenditure is a gradual process,
with a half-life spanning several years.
52
Whether the USA
UK USA
Recent trend Historic trend Recent trend Historic trend
Scenario 1. Past trends continue unabated
Diabetes (×1000)
+545 (432) +668 (159) +5503 (3524) +7855 (1618)
Coronary heart disease and
stroke (×1000)
+331 (407) +461 (128) +5365 (3359) +6836 (1537)
Cancer (×1000)
+87 (108) +130 (34) +405 (265) +539 (123)
Gain or loss in QALYs (×1000)
–2219 –6300 –24 488 –48 259
Scenario 2. 1% reduction in BMI for every adult at baseline
Diabetes (×1000)
–179 (385) –202 (139) –2051 (2922) –2420 (1461)
Coronary heart disease and
stroke (×1000)
–122 (374) –122 (116) –1431 (2799) –1704 (1400)
Cancer (×1000)
–32 (100) –33 (33) –73 (219) –127 (109)
Gain or loss in QALYs (×1000)
+3011 (930) +3195 (395) +15 988 (1911) +16 135 (781)
Scenario 3. If obesity rates had remained at 1990 levels
Diabetes (×1000)
–897 (216) –1021 (159) –8664 (3524) –11 016 (1618)
Coronary heart disease and
stroke (×1000)
–634 (204) –763 (128) –7670 (3359) –9141 (1537)
Cancer (×1000)
–177 (54) –220 (34) –534 (265) –668 (123)
Gain or loss in QALYs (×1000)
+7073 +11 155 +58 177 +81 948
Scenario 1=past trends continue unabated; scenario 2=1% reduction in BMI for every adult at baseline; scenario 3=obesity
rates remained at 1990 levels. Recent trend estimates were based on projections with data from 1990, which implied a
slower increase in obesity, while historic trend estimates were projected from all available data from 1988, showing a
steeper rate of increase in obesity. Data are cases (SE) unless otherwise stated. QALY=quality-adjusted life-years.
Table 2: Projected health and quality-adjusted life-year outcomes, 2010–30, under three hypothetical
scenarios of population-wide change in body-mass index distribution
Figure 5: US and UK population projections 2010 vs 2030, by overweight or obesity status, assuming historic
trend in BMI
A=USA, 2010; B=USA, 2030; C=UK, 2010; D= UK, 2030. Population pyramid. Size of bars shows the size of
projected census population (×100 000) by BMI status, sex, and age category in USA and UK.
0–9
10–19
20–29
30–39
40–49
50–59
Age (years)
60–69
70–79
≥80+
Obese (BMI ≥30 kg/m
2
) Overweight (BMI 25–29·9 kg/m
2
) Not overweight (BMI <25 kg/m
2
)
0–9
10–19
20–29
30–39
40–49
50–59
Age (years)
60–69
70–79
≥80+
Men Women Men Women
Men Women Men Women
30 3020 2010 100
BMI
30 3020 2010 100
BMI
B
A
D
C
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and the UK have plateaued or even turned the corner will
be a focal point for the next several datapoints from
periodic surveys. As we look beyond these two countries
and examine the similarities and diff erences across other
populations,
3
the availability of high-quality national
surveillance data becomes increasingly crucial. Never-
theless, we hope that our dire predictions will serve to
mobilise eff orts to reduce obesity so that our predictions
do not become reality.
Undoubtedly the costs associated with treatment of
obesity-related morbidity are high, but would a reduction
in obesity result in net cost savings?
53–56
Some researchers
argue that prevention of obesity could result in lengthened
lifespan, which in turn could lead to increased costs in a
person’s lifetime for treatment of diseases associated
with ageing but not directly related to obesity, such as
senile dementia. van Baal and colleagues
55
predicted that
a 20-year-old obese individual might incur lower total
cost for health-care during his or her lifetime than a
normal weight adult of the same age because of their
roughly 5 years shorter life expectancy. Rappange and
colleagues
54
advocated the inclusion of unrelated medical
costs in life-years gained in all economic evaluations of
preventive interventions, although they acknowledge the
practical challenge and scarcity of comprehensive data
for doing so. By contrast, another analysis
57
showed that
lifetime medical costs are substantially higher in adults
who are obese in the USA. Other researchers
53,58
argue
that inclusion of unrelated future costs distorts decision
making about resource allocation.
One key distinction is between the projected lower
lifetime health-care costs for an obese individual (versus
those of health weight) and the higher cost for an obese
population at a specifi c time or during a particular period
(eg, 2010–30). An obese population will incur greater
health-care costs at a particular time than will a lean
population of the same age distributions, and this
expenditure is preventable.
56
In our case studies, we
estimated the cross-sectional health-care costs of US and
UK adults older than 20 years according to counterfactual
scenarios of lower BMI distribution (eg, 1% lower or
resumption of 1990 prevalence). The eff ect of these
scenarios on life expectancy was relatively small.
However, we have accounted for obesity-related medical
costs for these added months, capturing the most costly
disorders such as cardiovascular diseases, cancer, and
osteoarthritis. Without a doubt, health-care expenditure
is high for elderly people, but these costs should not be
used to justify the cost-savings of dying younger, or to
suggest that obesity-prevention has no benefi t. In fact,
Figure 6: Projected health-care costs from obesity-related diseases in USA and UK, 2010–30
A=USA, historic trend; B=USA, recent trend; C=UK, historic trend; D=UK, recent trend. Dashed lines=95% CI. Costs are $ for USA and £ for UK.
120
5
4
3
2
Costs (in $billions)Costs (in £billions)
1
0
–1
2010 2012 2014 2016 2018 2020
Year
2022 2024 2026 2028 2030 2010 2012 2014 2016 2018 2020
Year
2022 2024 2026 2028 2030
100
80
60
40
20
0
–20
–40
–60
C D
A B
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823
van Baal and colleagues
55
emphasised that, although
prevention might not always be a cure for increasing
expenditures, it can be a “cost-eff ective cure for much
morbidity and mortality, and importantly, contribute to
the health of nations”.
Irrespective of the aim of the models being descriptive,
explanatory, or assessable,
59
for a model to be useful, a
crucial capacity is to link changes in population weight
distribution to immediate and future health and cost
outcomes. By contrast with traditional epidemiological
investigations—such as randomised controlled trials,
observational studies, and meta-analyses—simulation
models such as the one we used fi ll a methodological
gap by overcoming several challenges with respect to
quanti fi cation of obesity-attributable health con-
sequences: the detrimental health eff ects of excess
weight take many years to manifest, and demographic
shifts (eg, ageing population) or health-system factors
can result in sub stantial diff erences in the magnitude
and bearers of such burden. In this analysis, we project
the future health and associated medical costs on the
basis of a list of obesity-related diseases. This so-called
top-down approach is similar to the methods used
previously
60–62
but tends to be conservative. Rapidly
expanding evidence suggests that many additional
disorders beyond those we included could be linked to
excess weight.
9
For example, increased abdominal
adiposity causes benign prostatic hypertrophy in men,
63
and infertility is clearly related to higher BMI categories
in young women in prospective studies.
64
Asthma risk is
directly related to adiposity in children
and possibly
adults.
65,66
Sleep apnoea is directly related to adiposity, yet
has been omitted from cost estimates to date.
67
Moreover,
this approach inevitably makes simplifi cations on
variations between individual patients such as treatment
intensity, stage of disease, and comorbidities. The
correlation between conditions is often not considered—
eg, an obese individual can have both high blood
pressure and diabetes, and the medical visits might treat
more than one condition. An alternative approach is to
bypass the process of making a list of obesity-related
disorders altogether and instead using existing health
services data systems to obtain direct estimates of use
for insured patients, classifi ed according to BMI.
In an increasing number of studies,the fi nancial eff ect
of overweight and obesity is examined by directly
contrasting medical expenditure or health-services use in
individuals at diff erent BMIs.
14,68
However, this approach
for projection, especially for multicountry comparison, is
problematic. Not only does a nationally representative
expenditure data system have to be available, but also
extensive adjustments need to be made to ensure the
reported expenditure diff erences can truly be attributed
to BMI. For instance, availability and type of health
insurance coverage might be correlated to BMI and
highly predictive of health-care use (especially in a
decentralised market-driven system like that in the USA).
The association between health-care costs and specifi c
disease categories (eg, cancer) is unclear.
The USA and the UK are unusual in having decades-
long, periodic population surveys that use objective
measures of BMI. Despite the excellent BMI
measurements, census and vital statistics, and disease
registry infrastructures in these two countries, several
methodological challenges exist. The surveys we used for
these two countries were not perfectly representative: US
NHANES samples only the non-institutionalised
population, and HSE only represents England, but not
Scotland, Northern Ireland, or Wales. We noted a
substantial variation in the quality, study population,
collection frequency, and disease defi nition in the
statistics available. For example, the disparate incidence
rates between the two countries could be a result of
diff erences in diagnosis and coding practices. This
variation is particularly challenging for non-fatal diseases
such as osteoarthritis. In addition to measurement
issues, because of the vastly diff erent health-care systems,
the cost of treatment of the same disease (a function of
treatment intensity and unit cost of a specifi c service) can
diff er drastically. Finally, despite many previous studies
69–71
suggesting that most of the cost burden of obesity could
come from productivity loss, consistent measures to
track and compare forgone productivity across diff erent
populations are scarce.
In addition to data inputs, our study had several other
limitations. Our model only partly addressed the
diff erences in medical costs by category of obesity
9,10
(ie, severely obese individuals use many more health
services than do moderately obese individuals
72
) and by
demo graphic factors such as ethnicity and socioeconomic
status.
11
We also had to make necessary mathematical
assumptions—for example, to ensure the simulated
population would produce BMI distributions that
matched cross-sectional data, we assumed BMI rankings
between same-aged individuals were the same over time.
This assumption, however, is likely to have a small eff ect,
because an individual’s bodyweight tracks strongly over
time, and instances of substantial weight gain or weight
loss are likely to negate each other when summed across
the whole population.
Because of the 20-year timeframe, we probably
underestimated the future eff ect of childhood obesity.
High bodyweight early in life increases future
cardiovascular disease risk, independent of adult BMI.
73
Bibbins-Domingo and colleagues
74
estimated that by 2035,
the present prevalence of overweight and obesity in
adolescents could lead to a 5–16% increase in coronary
heart disease. Finally, our projections incorporated
population ageing, but we have not accounted for other
less predictable, but important, population changes such
as immigration, health-care system reform, or
technological advances for disease treatment.
The morbidity and economic burden of obesity is a
practical metric for comparative assessment of health
Page 9
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risks, as exemplifi ed by its use by international
organisations such as the World Bank, WHO, and the
OECD.
7
Quantifi cation of the size of the problem creates
awareness of the need for action and garners political
will to mobilise resources, but it is only the fi rst step
towards a solution.
75
In their systematic review, Withrow
and colleagues
4
concluded that further investigation is
needed to answer when, where, why, and, how costs
accrue in obese populations.
For future studies, how the overall health burden of
obesity might diff erently aff ect the budgets of various
segments of health systems, and how these burdens
might create disparate incentives for obesity prevention
programmes, are important issues. Furthermore,
quantifi cation of health consequences and the potential
cost off sets forms the foundation of comparative
eff ectiveness inquiries into strategies to mitigate obesity.
One example is Australia’s accessing cost-eff ectiveness
programme,
76
which uses a simulation framework
(including a disease modelling component similar to
Foresight
22
) to weigh potential future health-care costs
avoided against the implementation costs of obesity
prevention programmes.
77
Cecchini and colleagues
78
used
a microsimulation framework to assess the cost-
eff ectiveness of a range of programmes tackling
unhealthy diets, physical inactivity, and obesity in seven
countries. They reported that many population-based
prevention policies are cost eff ective, largely paying for
themselves through future health gains and resulting
reductions in health expenditures.
Contributors
YCW and MB did the analyses and drafted the report. MB constructed the
Foresight model and did all simulations. YCW designed scenario analyses
and identifi ed US data inputs. KM, TM, and SLG provided critical
guidance and edits. All authors reviewed, approved and edited the report.
Confl icts of interest
We declare that we have no confl icts of interest.
Acknowledgments
This work was done under auspices of the Collaborative Obesity
Modeling Network as part of the Envision Project, supported by the
National Collaborative on Childhood Obesity Research, which
coordinates childhood obesity research across the National Institutes of
Health, Centers for Disease Control and Prevention (CDC), Department
of Agriculture, and the Robert Wood Johnson Foundation (RWJF). This
work was supported in part by grants from RWJF (numbers 260639 and
61468 and 66284), CDC (U48/DP00064-00S1 and 1U48DP001946). This
work is solely the responsibility of the authors and does not represent
offi cial views of the CDC or any of the other funders.
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    • "As a consequence, in the US, obesity has been associated with an annual medical care cost of US$209.7 billion, corresponding with 20.6 % of total annual spend- ing [3]. In the UK, obesity-related diseases are estimated to add £1.9–2 billion per year to healthcare costs by the year 2030 [4]. So far, behavioural obesity treatment has been largely ineffective at achieving sustained maintenance of weight loss that is required to curb the obesity pandemic [5]. "
    [Show abstract] [Hide abstract] ABSTRACT: Long-term weight loss maintenance is difficult to achieve. Effectiveness of obesity interventions could be increased by providing extended treatment, and by focusing on person-environment interactions. Ecological Momentary Intervention (EMI) can account for these two factors by allowing an indefinite extension of a treatment protocol in everyday life. EMI relies on observations in daily life to intervene by providing appropriate in-the-moment treatment. The Think Slim intervention is an EMI based on the principles of cognitive behavioural therapy (CBT), and its effectiveness will be investigated in the current study. A randomised controlled trial (RCT) will be conducted. At least 134 overweight adults (body mass index (BMI) above 25 kg/m 2 ) will be randomly assigned to an 8-week immediate intervention group (Diet + Think Slim intervention, n = 67) or to an 8-week diet-only control group (followed by the Think Slim intervention, n = 67). The Think Slim intervention consists of (1) an app-based EMI that estimates and intervenes when people are likely to overeat, based on Ecological Momentary Assessment data, and (2) ten online computerised CBT sessions which work in conjunction with an EMI module in the app. The primary outcome is BMI. Secondary outcomes include (1) scores on self-report questionnaires for dysfunctional thinking, eating styles, eating disorder pathology, general psychological symptomatology, and self-esteem, and (2) eating patterns, investigated via network analysis. Primary and secondary outcomes will be obtained at pre- and post-intervention measurements, and at 3- and 12-month follow-up measurements. This is the first EMI aimed at treating obesity via a cognitive approach, provided via a smartphone app and the Internet, in the context of an RCT. Trial registration This trial has been registered at the Netherlands Trial Register, part of the Dutch Cochrane Centre (NTR5473; registration date: 26 October 2015).
    Preview · Article · Dec 2016 · Trials
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    • "With obesity comes an increased risk of type-II diabetes, adult heart disease, as well as several forms of cancer and premature morbidity (Reilly, et al., 2003; Wang, McPherson, Marsh, Gortmaker, & Brown, 2011). In addition to its economic burden, obesity is also associated with a host of psychosocial problems, including anxiety, depression, and social discrimination (see Puhl & Heuer, 2009; Sutin, Stephan, & Terracciano, 2015; Wang et al., 2011). If we are to design effective interventions to ameliorate or even reverse this epidemic then research will need to elucidate those factors that contribute to the development and maintenance of this phenomenon. "
    [Show abstract] [Hide abstract] ABSTRACT: It has been argued that obese individuals evaluate high caloric, palatable foods more positively than their normal weight peers, and that this positivity bias causes them to consume such foods, even when healthy alternatives are available. Yet when self-reported and automatic food preferences are assessed no such evaluative biases tend to emerge. We argue that situational (food deprivation) and methodological factors may explain why implicit measures often fail to discriminate between the food-evaluations of these two groups. Across three studies we manipulated the food deprivation state of clinically obese and normal-weight participants and then exposed them to an indirect procedure (IRAP) and self-report questionnaires. We found that automatic food-related cognition was moderated by a person’s weight status and food deprivation state. Our findings suggest that the diagnostic and predictive value of implicit measures may be increased when (a) situational moderators are taken into consideration and (b) we pay greater attention to the different ways in which people automatically relate rather than simply categorize food stimuli.
    Full-text · Article · Oct 2016 · Appetite
    • "The number of obese people has increased dramatically at an alarming rate all over the world [26,27]. It is estimated that there will be 65 million more obese (only adults) in the U.S.A. and 11 million more obese (only adults) in the U.K. by 2030 [28]. An obesity prevalence map by state and territory in U.S. [29] indicates that prevalence of obesity is more than 20 percent in each state. "
    [Show abstract] [Hide abstract] ABSTRACT: Fat accumulation in the liver causes metabolic diseases such as obesity, hypertension, diabetes or dyslipidemia by affecting insulin resistance, and increasing the risk of cardiac complications and cardiovascular disease mortality. Fatty liver diseases are often reversible in their early stage; therefore, there is a recognized need to detect their presence and to assess its severity to recognize fat-related functional abnormalities in the liver. This is crucial in evaluating living liver donors prior to transplantation because fat content in the liver can change liver regeneration in the recipient and donor. There are several methods to diagnose fatty liver, measure the amount of fat, and to classify and stage liver diseases (e.g. hepatic steatosis, steatohepatitis, fibrosis and cirrhosis): biopsy (the gold-standard procedure), clinical (medical physics based) and image analysis (semi or fully automated approaches). Liver biopsy has many drawbacks: it is invasive, inappropriate for monitoring (i.e., repeated evaluation), and assessment of steatosis is somewhat subjective. Qualitative biomarkers are mostly insufficient for accurate detection since fat has to be quantified by a varying threshold to measure disease severity. Therefore, a quantitative biomarker is required for detection of steatosis, accurate measurement of severity of diseases, clinical decision-making, prognosis and longitudinal monitoring of therapy. This study presents a comprehensive review of both clinical and automated image analysis based approaches to quantify liver fat and evaluate fatty liver diseases from different medical imaging modalities.
    No preview · Article · Apr 2016 · Computers in Biology and Medicine
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