Obesity Among Older Americans

Obesity Among Older Americans
February 6, 2008
Andrew R. Sommers
Analyst in Public Health and Epidemiology
Domestic Social Policy Division

Obesity Among Older Americans
Obesity, defined as an unhealthy excess of body fat, is a serious and growing
public health problem. The number and proportion of people who are obese have
risen notably in recent decades; since the 1970s, the prevalence of obesity has more
than doubled in the adult population. In 2004, nearly one in three adults (32.2%)
were classified as obese. Overweight and obese people are more likely to have
chronic health problems such as diabetes, high blood pressure, and arthritis. They
also are at greater risk for developing heart disease, cancer, and Alzheimer’s disease.
Obesity-related health care costs in the United States were estimated to be 9.1% of
total annual medical expenditures in 1998 and may have been as high as $78.5 billion
($92.6 billion in 2002 dollars). Nearly half of these costs are borne by public
programs — Medicaid and Medicare.
Although Americans of all ages are increasingly overweight, policy makers and
health care providers have tended to overlook the problem of obesity in middle-aged
and older populations. Some physicians have neglected discussing the problem with
older patients, believing it too late to encourage substantive changes in their health
behavior, and the media have tended to focus on obesity among children, for whom
excessive weight has been a rarity until recently.
Currently, nearly one in eight Americans is age 65 or older. This ratio is
expected to jump to one in five by 2030, due in part to longer life expectancies and
the aging of the baby boom generation. Because the highest rates of obesity are
found among baby boomers, aged 44-62 in 2008, it is likely that the prevalence of
obesity among older adults will continue to climb in coming decades as this
population ages. By 2010, 37% of adults aged 65 and older are anticipated to be
obese. If this trajectory continues unabated, it is projected that nearly half of the
elderly population will be obese in 2030.
Increasing levels of obesity among the elderly will be a challenging policy issue
at the state and federal levels, because excessive weight gain is associated with an
array of chronic conditions, and because persons with multiple chronic illnesses
generate more than 65% of all Medicare costs.
To help inform Congress about patterns of weight distribution among older
Americans, as well as to describe potential future trends in elderly obesity, this report
presents estimates of the prevalence of obesity for adults aged 65 and older.
Differences in obesity between various race/ethnicity groups and across age and
gender lines are discussed. In addition, disparities by region are presented, including
state-level obesity estimates for 1996, 1999, 2002, and 2005. The report concludes
with a brief description of possible policy approaches to addressing the obesity
epidemic that the United States is facing. This report will be updated as new data
become available.

In troduction ......................................................1
Factors Contributing to Increases in Obesity.............................3
Defining Obesity in the Elderly Population..............................5
Risks of Obesity...................................................7
Diabetes .....................................................8
Cardiovascular and Cerebrovascular Disease........................9
Cancer .....................................................10
Physical Function.............................................11
Alzheimer’s Disease..........................................11
Effect of BMI on Mortality Risk at Various Ages....................12
Prevalence of Obesity.............................................13
Obesity Trends...............................................15
Weight Distribution Dynamics..................................17
Regional Variations in Obesity..................................17
Policy Implications...............................................23
Federal Efforts to Combat Obesity...................................26
Appendix A. Notes on Methodology..................................27
Appendix B. Selected Federal Obesity Programs........................28
Department of Health and Human Services (HHS)...................28
Dietary Guidelines for Americans............................28
“Calories Count” Initiative.................................28
“Steps to a Healthier U.S.” Program..........................28
“Control Your Diabetes. For Life” Campaign..................28
Congregate Meal Programs for the Elderly.....................29
Department of Agriculture (USDA)..............................29
Food Stamp Program......................................29
“Fit WIC” Program.......................................29®
“Eat Smart. Play Hard” Campaign..........................30
National School Lunch Program.............................30
MyPyramid ..............................................30
Senior Farmers’ Market Nutrition Program (SFMNP)............30
Department of the Interior......................................31
Department of Transportation...................................31
Department of Education.......................................31

Figure 1. Obesity Trends Among Elderly Americans, 1976-2004...........16
Figure 2. Changes in Weight Distribution Among Persons Aged 65 and Older,
1988-2002 ..................................................18
Figure 3. Prevalence of Obesity Among Adults Aged 65 and Older, 1996,
1999, 2002, 2005 .............................................22
List of Tables
Table 1. Body Mass Index Chart......................................6
Table 2. Prevalence of Overweight, Obesity, and Extreme Obesity in Adults,
by Age and Racial/Ethnic Group, 2003-2004.......................14
Table 3. Prevalence of Obesity Among the Elderly (Age 65+), by State,
1994-2005 ..................................................20

Obesity Among Older Americans
Obesity is an abnormal accumulation of body fat, usually 20% or more, over an
individual’s ideal body weight. In recent years, obesity has become an increasing
concern for public health officials and policy makers, as the number of obese
Americans has increased significantly. Obesity increases mortality risk and is
associated with serious chronic conditions, including cardiovascular disease, stroke,
diabetes, a growing list of cancers, and, according to recent reports, Alzheimer’s
disease.1 Moreover, excessive weight gain is a major cause of functional limitations2
among elderly individuals. Obesity-related health care costs for adults in the United
States were estimated to be 9.1% of total annual medical expenditures in 1998 and3
may have been as high as $78.5 billion ($92.6 billion in 2002 dollars). Nearly half
of these costs were borne by federal health care programs — Medicaid and Medicare.
While the attention of policy makers has focused largely on the implications of
obesity among children and adolescents, the effects of obesity on the health of older
adults have been somewhat overlooked. This report examines the causes and
consequences of obesity among older Americans, and includes a discussion of recent
trends in obesity within this age group and disparities in body mass that exist across
race, ethnicity, and gender lines. The report concludes by reviewing some of the
policy implications associated with increasing rates of obesity among elderly

1 Research studies supporting correlations between overweight, obesity, and a wide variety
of chronic conditions are discussed in detail later in this report.
2 Denise K. Houston, June Stevens, Jianwen Cai, and Miriam C. Morey, “Role of Weight
History on Functional Limitations and Disability in Late Adulthood: The ARIC study,”
Obesity Research, vol. 13 (2005), pp. 1793-1802; Janet M. Friedmann, Tom Elasy, and
Gordon L. Jensen, “The Relationship Between Body Mass Index and Self-Reported
Functional Limitation Among Older Adults,” Journal of the American Geriatrics Society,
vol. 49, no. 4 (2001), pp. 398-403.
3 Eric A. Finkelstein, Ian C. Fiebelkorn, and Guijing Wang, “National Medical Spending
Attributable to Overweight and Obesity: How Much and Who Is Paying?” Health Affairs,
Datawatch: The Costs of Obesity, Web Exclusive (May 14, 2003),
[http://content.healthaffairs.org/cgi/content/full/hlthaff.w3.219v1/DC1]; Martha L. Daviglus
et al., “Relation of Body Mass Index in Young Adulthood and Middle Age to Medicare
Expenditures in Older Age,” Journal of the American Medical Association (hereafter,
JAMA), vol. 292, no. 22 (December 8, 2004), pp. 2743-2749.

The number and proportion of older people who are obese have risen notably
in recent decades. Since the 1970s, the prevalence4 of obesity has more than doubled
to 32.2% of the general adult population5 and 31.0% of the elderly population.6 The
sharp increase in obesity is due to a confluence of factors, including technological
advances that have lowered the costs of food, more sedentary forms of employment,
and a decline in the amount of leisure time adults spend engaging in physical activity
(e.g., walking, biking, swimming).7 Eating patterns have also changed significantly,
with Americans consuming on average 300 calories more per day in 2002 than in
1985.8 In part, this may be explained by a 129% increase in per capita consumption
of highly caloric, energy-dense corn syrup between 1980 and 2000.9 The general
trend toward a less nutritious diet coincides with a general shift toward eating out.
In 1975, for instance, 25% of food spending went toward meals in restaurants; by

2004, individuals spent on average 44% of their food budget at restaurants or fast-

food establishments.10 As a result, fast-food revenue has skyrocketed since the

4 In epidemiology, the prevalence of a given disease is defined as the total number of cases
of that disease in the population at a specific point in time. The prevalence rate is the
prevalence divided by the number of individuals in the population, and is typically
expressed as a percentage. This report, however, follows linguistic convention by using the
term “prevalence” to refer to the prevalence rate of a condition.
5 In this report, the term “general adult population” refers to persons between age 18 and 59,
“middle age” individuals are 40-59, “near-elderly” are 60-64, and “elderly” are 65+. “Older
population” or “older age persons” generically refers to people aged 50 or older.
6 These estimates are derived using data from the National Health and Nutrition
Examination Survey (NHANES), Centers for Disease Control and Prevention (CDC),
National Center for Health Statistics (NCHS), Hyattsville, MD: U.S. Department of Health
and Human Services, Centers for Disease Control and Prevention, 1976-2004,
[http://www.cdc.gov/nchs/nhanes.htm]. For details, see Alison A. Hedley et al., “Prevalence
of Overweight and Obesity among U.S. Children, Adolescents, and Adults, 1999-2002,”
JAMA, vol. 291 (2004), pp. 2847-2850; Virginia W. Chang and Diane S. Lauderdale,
“Income Disparities in Body Mass Index and Obesity in the United States, 1971-2002,”
Archives of Internal Medicine, vol. 165 (2005), pp. 2122-2128.
7 David M. Cutler, Edward L. Glaeser, and Jesse M. Shapiro, “Why Have Americans
Become More Obese?” Journal of Economic Perspectives, vol. 17, no. 3 (Fall 2003), pp. 93-


8 Samara J. Nielsen and Barry M. Popkin, “Patterns and Trends in Food Portion Sizes,
1977-1998,” JAMA, vol. 289 (2003), pp. 450-453; Judith J. Putnam, Jane Allshouse, and
Linda S. Kantor, “U.S. per capita Food Supply Trends,” Food Review, United States
Department of Agriculture(Winter 2002), at [http://www.ers.usda.gov/publications/Food
Revi ew/DEC2002/frvol25i3a.pdf].
9 Judith J. Putnam et al., “U.S. Food Supply Trends.”
10 Households, by contrast, spent between 34% and 39% of their food budgets in 2003-2004
on eating out. See Noel Blisard and Hayden Stewart, “Food Spending in American
Households, 2003-04,” Economic Information Bulletin — 23, Economic Research Service,
U.S. Department of Agriculture, March 2007.

1970s, increasing 19-fold; the number of fast-food restaurants in the United States
has similarly increased, from 33,000 (in 1970) to 222,000 (in 2001).11
Policy makers are justifiably concerned about the spike of obesity among the
elderly, but there may be even more reason to worry about the baby boom generation
(born between 1946 and 1964, aged 44-62 in 2008), a large cohort that will soon join
the ranks of Medicare beneficiaries. The highest rates of obesity among adults are
found among baby boomers. For example, 37.5% of women in their 50s and early
60s are obese; among their male counterparts, the prevalence is 34.7%.12 Such high
rates of obesity have important implications because excessive weight gain is
associated with an array of chronic conditions; among all program beneficiaries,
persons with multiple chronic illnesses generate nearly two-thirds of all Medicare
If trends persist and the baby boomers are unable to alter their current weight
trajectories, projections suggest that more than 37% of the population aged 65 and
older will be obese by 2010, thus constituting the heaviest generation of elderly
persons in U.S. history.13 If this trajectory continues unabated, it is projected that
nearly half of the elderly population will be obese in 2030. Experts anticipate that
this high level of obesity among older Americans will strain the country’s economic
well-being in several ways. First, obesity-related health problems will contribute to
injuries, absenteeism, and decreases in overall productivity among working-age
caregivers. Second, unprecedented levels of obesity and overweight among the
elderly could hasten functional decline after age 65, possibly causing significant
social, psychological, or financial hardships for senior citizens or their families.
Finally, given that obesity is a major risk factor for costly and deadly conditions,
including heart disease, stroke, cancer, and diabetes, public insurance programs such
as Medicare are likely to face notable increases in costs as baby boomers become
eligible for health care entitlements.14
Factors Contributing to Increases in Obesity
Despite the scope and increasing severity of excessive body weight among
Americans, no consensus has been reached about how to address the “obesity
epidemic” in the United States. Public health officials have tended to emphasize

11 Jeffrey Levi, Laura M. Segal, and Chrissie Juliano, F as in Fat: How Obesity Policies Are
Failing in America 2006, Washington, D.C.: Trust for America’s Health, 2006.
12 By contrast, age-adjusted obesity prevalence statistics for the 65-and-older population are
as follows: women, 30.9%; men, 26.3%.
13 David E. Arterburn, Paul K. Crane, and Sean D. Sullivan, “The Coming Epidemic of
Obesity in Elderly Americans,” Journal of the American Geriatric Society, vol. 52 (2004),
pp. 1907-1912.
14 American Federation of Aging Research, “Boom, Boom, Boom: Obesity among Baby
Boomers and Older Adults,” Washington, D.C.: AFAR, March 2005, paper prepared
following The Politics of Older Adult Obesity, a conference held in Washington, D.C., on
December 2, 2004.

messages of personal responsibility, orchestrating campaigns in public service
announcements that focus on eating nutritious foods and engaging in physical
activities. Sociologists and social epidemiologists, by contrast, have suggested that
the recent rise in obesity is attributable to contextual factors such as increases in the
amount of time spent watching television or using computers, or the lack of
pedestrian-friendly infrastructure (e.g., sidewalks, bicycle paths, crosswalks). They
also point to aspects of the sociocultural environment that may contribute to society’s
expanding waistline. For instance, suburban sprawl has led to greater reliance on the
automobile. “Single-use zoning” in housing developments has compounded matters
by segregating residences from schools, retail stores, and recreational facilities.15 By
designing and building neighborhoods that are increasingly less “walkable,” urban
planners and architects have helped reduce the average level of physical activity that
Americans get each day.16
Obesity rates in the United States may also be related to the increasing number
of women who entered the workforce in the waning decades of the 20th century.17
This shift prompted many families both to rely on pre-packaged meals, or “frozen
dinners,” and to start eating in restaurants more often. Research demonstrates that
individuals consume larger portions and higher-calorie foods in restaurants than
when they eat meals at home.18 Over time, therefore, the increasing reliance on
restaurants for meals may have had an impact on obesity rates.19
Economists note that one reason obesity and overweight have seen
disproportionate increases among low-income minorities is that fruits and vegetables
are either prohibitively costly or simply inaccessible in their neighborhoods. One
study, for instance, has demonstrated that black communities have five times fewer

15 Single-use zoning started appearing in the 1920s in the United States. As increasing
numbers of families acquired automobiles, it became possible to travel longer distances for
shopping, entertainment, and work. Urban planners began to allow parcels of land to be set
aside for one sole purpose. The single-use zoning of residential and commercial areas
defines suburban life in the United States today: large tracts of housing, surrounded by large
arterial roads to handle a significant volume of automobile traffic. Industrial areas and retail
centers are typically tucked away from residences and schools; even single-family dwellings
are often not proximate to apartment complexes.
16 Andres Duany, Elizabeth Plater-Zyberk, and J. Speck, Suburban Nation, New York: North
Point Press, 2000; D. Berrigan, R. Troiano, “The Association Between Urban Form and
Physical Activity in U.S. Adults,” American Journal of Preventive Medicine (hereafter,
AJPM), vol. 23, suppl. 2, pp. 74-79; S. Jandy et al., “How the Built Environment Affects
Physical Activity,” AJPM, vol. 23, suppl. 2, pp. 64-73; C. Craig et al., “Exploring the Effect
of the Environment on Physical Activity,” AJPM, vol. 23, suppl. 2, pp. 36-43.
17 Shin-Yi Chou, Michael Grossman, and Henry Saffer, “An Economic Analysis of Adult
Obesity: Results from the Behavioral Risk Factor Surveillance System,” Journal of Health
Economics, vol. 23, no. 3 (Fall 2004), pp. 565-587.
18 Lisa R. Young and Marion Nestle, “Portion Sizes in Dietary Assessment: Issues and
Policy Implications,” Nutrition Review, vol. 53 (1995), pp. 149-158.
19 Lisa R. Young and Marion Nestle, “The Contribution of Expanding Portion Sizes to the
U.S. Obesity Epidemic,” American Journal of Public Health (hereafter, AJPH), vol. 92, no.

2 (2002), pp. 246-249.

grocery stores than white neighborhoods.20 This dearth of competition tends to drive
up the costs of nutritious goods such as fresh fruit, thus encouraging the consumption
of less-expensive, high-sugar snack foods.21
Finally, social scientists contend that an increase in the number of sedentary
jobs, and sharp increases in television viewing, computer use, and video gaming,
have led to a dramatic change in American lifestyles that has reduced individuals’
daily energy expenditures, without promoting a commensurate reduction in caloric
Defining Obesity in the Elderly Population
Obesity is defined as an unhealthy excess of body fat, which increases the risk
of medical illness and premature mortality. Because accurate measures of body fat23
mass require sophisticated technologies that are often available only in clinical
settings, an approximation called the Body Mass Index (BMI) is used to screen and
monitor overweight and obesity. BMI expresses the relationship of weight-to-height
and correlates with the percentage of body fat a person carries.24 It is calculated as25
body weight (in kilograms) divided by the square of height (in meters). BMI is
widely used and accepted as a simple method to categorize people as “healthy26
weight,” “overweight,” or “obese.”
An adult with a BMI greater than or equal to 25.0 and less than 30.0 is
considered “overweight”; persons with body mass indices of 30.0 or more are

20 Kimberly Morland, S. Wing, and A.D. Roux, “The Contextual Effect of the Local Food
Environment on Residents’ Diets: The Atherosclerosis Risk in Communities Study,” AJPH,
vol. 92, no. 11 (November 2002), pp. 1761-1767; Kimberly Morland, “Neighborhood
Characteristics Associated with the Location of Food Stores and Food Service Places,”
AJPM, vol. 22, no. 1 (2002), pp. 23-29.
21 Jamy D. Ard et al., “The Impact of Cost on the Availability of Fruits and Vegetables in
the Homes of Schoolchildren in Birmingham, Alabama,” AJPH, vol. 97, no. 2 (February

2007), pp. 367-372.

22 Darius N. Lakdawalla and Tomas J. Philipson, “Technological Change and the Growth
of Obesity,” NBER Working Paper no. 8946, Cambridge, MA: National Bureau of
Economic Research, 2002.
23 These technologies include dual energy X-ray absorptiometry (DEXA or DXA), Bod Pod®
(a tool that relies on air displacement to determine body fat), and magnetic resonance
imaging (MRI).
24 Walter C. Willett, William H. Dietz, and Graham A. Colditz, “Guidelines for Healthy
Weight,” New England Journal of Medicine (hereafter, NEJM), vol. 341 (1999), pp. 427-


25 BMI may also be estimated using English, non-metric measures: BMI = [(Weight in
pounds) / ( Height in inches ) x ( Height in inches )] x 703.
26 NHLBI Expert Panel, Clinical Guidelines on the Identification, Evaluation, and
Treatment of Overweight and Obesity in Adults: Evidence Report, NIH publication no.

02-4084, Bethesda, MD: NIH, 2002.

“obese.”27 Both the CDC and the World Health Organization concur that these BMI
thresholds are gender- and age-neutral despite distinct differences in body
composition for men and women, young and old. Table 1 illustrates BMIs for fairly
common height-weight combinations. For example, an adult who is 5’10” and
weighs between 174.0 and 208.9 pounds is overweight; at 209.0 pounds and above,
he or she is classified as obese.
Although BMI is proportional to the amount of body fat a person has, it is
important to emphasize that BMI is an inexact measure. Some people (e.g., muscular
athletes) may have a BMI that identifies them as overweight even though they do not
have excess body fat. Conversely, in elderly adults, BMI measures may
underestimate fatness because of age-related changes in body composition. For
instance, when a person gets older, muscle mass naturally decreases, whereas
abdominal and intramuscular fat increase.28 As a result, the BMI formula may
underestimate fatness in elderly individuals. On the other hand, loss of height
resulting from vertebral compression (a problem commonly associated with aging)29
may lead to overestimates of body fat.
Table 1. Body Mass Index Chart
19 21 23 25 26 27 28 29 30 32 34 36 38 40
Weight (lbs.)
Height Healthy O verw eight O bese
4’10” 91 100 110 119 124 129 134 138 143 153 162 172 181 191
5’0” 97 107 118 128 133 138 143 148 153 163 174 184 194 204
5’2” 104 115 126 136 142 147 153 158 164 175 186 196 207 218
5’4” 110 122 134 145 151 157 163 169 174 186 197 209 221 232
5’6” 118 130 142 155 161 167 173 179 186 198 210 223 235 247
5’8” 125 138 151 164 171 177 184 190 197 210 223 236 249 262
5’10” 132 146 160 174 181 188 195 202 209 222 236 250 264 278
6’0” 140 154 169 184 191 199 206 213 221 235 250 265 279 294
6’2” 148 163 179 194 202 210 218 225 233 249 264 280 295 311
Source: CRS adaption of chart from the National Heart, Lung, and Blood Institute, National Institutes
of Health, available at [http://www.nhlbi.nih.gov/guidelines/obesity/bmi_tbl.pdf].

27 Individuals with BMIs greater than or equal to 40.0 are said to be “extremely obese,” or
“morbidly obese.”
28 Bernard Beaufrere and Beatrice Morio, “Fat and Protein Redistribution with Aging:
Metabolic Considerations,” European Journal of Clinical Nutrition, vol. 54 supplement
(2000), pp. S48-S53.
29 As old people age, spinal vertebrae often “collapse” much like a sponge collapses under
the pressure of one’s hand. Over many years, spinal compression reduces the length of the
spine and diminishes a person’s height.

Given these age-related changes, recent studies suggest that the ideal BMI for
elderly people should be slightly higher than that for the young and middle-aged.30
Statistics presented in this report, however, use the adult thresholds for overweight
and obesity, because researchers have yet to establish and validate a different set of
BMI standards for elderly persons.
Risks of Obesity
Obesity can cause or exacerbate serious medical conditions as people age.
Obese people are prone to arthritis, liver disease, gout, gallstones, and pulmonary31
difficulties, such as sleep apnea (the temporary cessation of breathing while asleep).
This section examines health conditions that obesity has been shown to cause or
exacerbate. In particular, it discusses the links between obesity and type 2 diabetes,
hypertension (high blood pressure), heart disease, and cancer. Because of these
associations, obesity ranks as the nation’s second-leading risk factor for mortality;
in 2000, for instance, obesity was associated with 112,000 deaths, well behind
smoking (435,000 deaths) but somewhat greater than alcohol consumption (85,000
deat hs). 32
While there is little disagreement about the fact that obesity increases an
individual’s risk of mortality, a December 2007 study concludes that being physically
fit may mitigate some of the dangers associated with being fat.33 The authors
observed that fit individuals who were obese (defined as having a BMI of 30.0-34.9)
had a lower risk of all-cause mortality than did unfit, healthy weight, or lean
individuals. “Our data provide further evidence regarding the complex long-term

30 Asefeh Heiat, “Impact of Age on Definition of Standards for Ideal Weight,” Preventive
Cardiology, vol. 6 (2003), pp. 104-107; Asefeh Heiat, Viola Vaccarino, and Harlan M.
Krumholz, “An Evidence-Based Assessment of Federal Guidelines for Overweight and
Obesity as They Apply to Elderly Persons,” Archives of Internal Medicine, vol. 161 (2001),
pp. 1194-1203.
31 Ross Lazarus, David Sparrow, and Scott T. Weiss, “Effects of Obesity and Fat
Distribution on Ventilatory Function: The Normative Aging Study,” Chest, vol. 111, pp.


32 Previous estimates by the Centers for Disease Control and Prevention (CDC) had
indicated that as many as 400,000 Americans die annually from causes related to excess
body weight. For details, see Ali H. Mokdad et al., “Actual Causes of Death in the United
States, 2000,” JAMA, vol. 291 (2004), pp. 1238-1245. After this study’s results were
challenged on methodological grounds, the CDC acknowledged its error and revised its
obesity-related mortality estimate to 365,000. Later, however, these findings were retracted
entirely. The most recent estimates published regarding obesity-attributable deaths suggest
that obesity kills about 112,000 Americans annually. See Katherine M. Flegal et al.,
“Excess Deaths Associated with Underweight, Overweight, and Obesity,” JAMA, vol. 293
(2005), pp. 1861-1867.
33 Xuemei Sui, Michael J. LaMonte, James N. Laditka, James W. Hardin, Nancy Chase,
Steven P. Hooker, and Steven N. Blair, “Cardiorespiratory Fitness and Adiposity as
Mortality Predictors in Older Adults,” JAMA, vol. 298 (2007), no. 21, pp. 2507-2516.

relationship among fitness, body size, and survival. It may be possible to reduce
all-cause death rates among older adults, including those who are obese, by
promoting regular physical activity, such as brisk walking for 30 minutes or more on
most days of the week (about 8 kcal/kg per week), which will keep most individuals
out of the low-fitness category,” they concluded. “Enhancing functional capacity
also should allow older adults to achieve a healthy lifestyle and to enjoy longer life
in better health.”
Diabetes, the sixth-leading cause of death among elderly Americans,34 is a
disease in which blood glucose levels are excessive. It is a serious chronic illness
that may lead to numerous health complications, including heart disease, blindness,
and kidney failure.35
Normally, the pancreas secretes a hormone called insulin, which facilitates the
absorption of glucose by cells in our bodies. In type 1 diabetes, the immune system
attacks and destroys the cells in the pancreas that produce insulin. In order to survive,
type 1 individuals must intravenously inject insulin into their bloodstream every
day.36 By contrast, people with type 2 diabetes produce enough insulin, but their
bodies do not use the insulin effectively, a condition called “insulin resistance.”
Type 2 diabetes is most often associated with older age and physical inactivity.37
Type 2 individuals must monitor their blood sugar levels carefully and watch their
diets accordingly, though they do not necessarily need to self-administer insulin.
Obesity is known to induce insulin resistance. To compensate for insulin
resistance, the pancreas produces even more insulin, which leads to a higher
concentration of insulin in the blood stream. This situation may continue for years,
but the pancreas is ultimately unable to maintain this high insulin output. At that
point, blood sugar levels increase and type 2 diabetes is diagnosed. Extremely obese
women (BMI$40.0) are 12 times more likely to have diabetes, and extremely obese

34 Hsiang-Chink Kung, Donna L. Hoyert, Jiaquan Xu, and Sherry L. Murphy, “Deaths: Final
Data for 2005,” National Vital Statistics Reports, vol. 56, no 10 (2008). Hyattsville, MD:
National Center for Health Statistics. See also CRS Report RL34125, Mortality of
Americans Age 65 and Older: 1980 to 2004, by Andrew R. Sommers.
35 One in two diabetic individuals develops heart disease, and 67% of all amputees are
diabetic. See Oscar H. Franco et al., “Associations of Diabetes Mellitus with Total Life
Expectancy and Life Expectancy with and without Cardiovascular Disease,” Archives of
Internal Medicine, vol. 167, no. 11 (2007), pp. 1145-1151.
36 Type 1 diabetes, previously known as “juvenile-onset diabetes,” accounts for about 5%
to 10% of all diagnosed cases of diabetes.
37 Type 2 diabetes, previously known as “adult-onset diabetes,” accounts for about 90% to
95% of all diagnosed cases of diabetes. Persons with a family history of diabetes, a history
of gestational diabetes, and with certain ethnic backgrounds may be at greater risk for
developing this form of diabetes.

men 8 times more likely, than their peers who maintain BMIs between 18.5 and 25.0
(“healthy weight”).38
About 90% of all people who develop non-insulin dependent diabetes (type 2)
are overweight. It is not surprising, therefore, that the prevalence of type 2 diabetes
has risen with recent increases in obesity.39 In 2005, the percentage of individuals
aged 65 and older who had been diagnosed with type 2 diabetes was 34.1%, an
increase of 57.1% since 1995.40
Although researchers in the past have argued that insulin resistance could be a
function of the aging process,41 recent studies suggest that the decline in insulin
sensitivity among people over age 50 is actually a result of abdominal obesity and
physical inactivity.42 Elderly persons, particularly women, who get moderate
amounts of exercise and maintain a healthy weight are three times less likely to
develop diabetes than peers whose poor nutrition or sedentary lifestyles promote
weight gain.43
Cardiovascular and Cerebrovascular Disease
The likelihood of developing cardiovascular conditions (e.g., heart disease) or
experiencing cerebrovascular problems (e.g., stroke) rises steeply with increasing
body fatness. Obesity is additionally a critical risk factor for developing hypertension
— the medical term for high blood pressure (HBP). HBP makes the heart exert
additional force to pump blood through the circulatory system.44 This extra work

38 Ruth E. Patterson et al., “A Comprehensive Examination of Health Conditions Associated
with Obesity in Older Adults,” AJPM, vol. 27, no. 5 (December 2004), pp. 385-390.
39 Some researchers question whether diabetes rates are actually increasing sharply. A CDC
epidemiologist, Katherine Flegal, for instance, argues that the overall age-adjusted
proportion of the population that has diabetes has been largely stable since 1988. What has
changed, she contends, is that the frequency with which people receive diagnoses of diabetes
has increased significantly, thus creating a perception that diabetes is becoming epidemic.
See also Edward W. Gregg et al., “Trends in the Prevalence and Ratio of Diagnosed to
Undiagnosed Diabetes According to Obesity Levels in the U.S.,” Diabetes Care, vol. 27
(2004), pp. 2806-2812.
40 National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), Diabetes
Overview, NIH Publication No. 06-3873, Bethesda: U.S. Department of Health and Human
Services, NIH, National Diabetes Information Clearinghouse, August 2006.
41 Annette M. Chang and Jeffrey B. Halter, “Aging and Insulin Secretion,” American
Journal of Physiology-Endocrinology and Metabolism, vol. 284 (2003), pp. E7-E12.
42 Steven M. Haffner, “Abdominal Obesity, Insulin Resistance, and Cardiovascular Risk in
Pre-diabetes and Type 2 Diabetes,” European Journal of Cardiology, vol. 8, suppl. B
(2006), pp. B20-B25.
43 Frank B. Hu et al., “Television Watching and Other Sedentary Behaviors in Relation to
Risk of Obesity and Type 2 Diabetes Mellitus in Women,” JAMA, vol. 289, no. 14 (2003),
pp. 1785-1791.
44 Hypertension is considered to be present when a person’s systolic blood pressure is

increases the risk of heart attacks and strokes, as well as kidney failure. Severely
hypertensive people may increase their chances of having a stroke 10-fold. HBP may
also weaken the heart or harden and scar the arteries. While this type of damage
happens naturally as people age, HBP accelerates the process among elderly
individuals because it promotes atherosclerosis, the accumulation of cholesterol or
plaque in the arteries.45
Data from longitudinal population studies suggest that obesity significantly
increases the risk of cardiovascular disease in elderly men, but not necessarily in
women. Among men over age 65, increased BMI is associated with an increase in
new cases of coronary heart disease, fatal and nonfatal heart attacks, and
cardiovascular disease mortality. For women, research findings on obesity and
cardiovascular disease risk are mixed; some studies have demonstrated a correlation,
whereas others have not.46
Obesity is also related to certain types of life-threatening cancers. In older
adults, the correlation is particularly strong between obesity and cancer of the breast,
colon, endometrium (lining of the uterus), pancreas, esophagus, and kidney.47 Men
who are obese are more likely than non-obese men to develop cancer of the colon,
rectum, or prostate. Women who are obese are more likely to develop cancer of the
gallbladder, uterus, cervix, or ovaries.
Researchers suggest that the link between obesity and cancer may involve a
similar etiologic mechanism to the one that exists between obesity and diabetes.48
As discussed above, obesity is known to induce insulin resistance. The high
concentrations of insulin that are often present for years could potentially affect a

44 (...continued)
consistently 140mmHg or greater, and/or their diastolic blood pressure is consistently

90mmHg or greater.

45 Atherosclerosis is a chronic disease in which arterial walls thicken and harden, thus
restricting blood circulation to one’s organs and tissues. It is in large part a response to the
accumulation of deposits of plasma proteins (which carry cholesterol and triglycerides)
within blood vessels. Although atherosclerosis develops with aging, it is exacerbated by
hypertension and diabetes.
46 Debashish K. Dey and Lauren Lissner, “Obesity in 70-Year-Old Subjects as a Risk Factor
for 15-year Coronary Heart Disease Incidence,” Obesity Research, vol. 11 (2003), pp. 817-
827; Aaron R. Folsom et al., “Associations of General and Abdominal Obesity with
Multiple Health Outcomes in Older Women,” Archives of Internal Medicine, vol. 160
(2000), pp. 2117-2128.
47 Alicia Wolk et al., “A Prospective Study of Obesity and Cancer Risk,” Cancer Causes and
Control, vol. 12 (2001), pp. 13-21; Joanne M. Jordan et al., “The Role of Sociodemographic
Factors, Obesity, and Knee Pain,” Arthritis Care Research, vol. 9 (1996), pp. 273-278.
48 Rachael Z. Stolzenberg-Solomon et al., “Insulin, Glucose, Insulin Resistance, and
Pancreatic Cancer in Male Smokers,” JAMA, vol. 294 (2005), pp. 2872-2878; Maurizio
Trevisan et al., “Markers of Insulin Resistance and Colorectal Cancer Mortality,” Cancer
Epidemiology Biomarkers & Prevention, vol. 10 (2001), pp. 937-94.

person’s likelihood of developing cancer. This is because a consistent characteristic
of cancer cells is their ability to grow uncontrollably, and insulin is an important
growth factor for body tissues that signals cells to proliferate. It is reasonable to
hypothesize that in an insulin-resistant state, which may be induced by obesity,
higher circulating levels of growth factors such as insulin could be critical to the
initial development of cancer.49
Physical Function
Obesity has important functional implications in the older population because
it can exacerbate the age-related decline in physical function.50 Various studies have
shown that BMI and fat mass are positively related to disability.51 Specifically, high
BMI is associated with an increased risk of osteoarthritis (OA). A quantitative
synthesis of research studies over the past 35 years showed a positive association
between obesity and OA. Obese adults are 25% more likely than their non-obese
peers to have OA of the hip joint, and 300 to 400% more likely to have OA in their
knees.52 This relationship is even stronger for adults age 65 and older. Elderly
individuals who are obese are approximately 37% more likely to develop hip OA and
as much as 10 times as likely to develop knee OA.53
Alzheimer’s Disease
Obesity has also been shown to correlate strongly with Alzheimer’s disease.54
A 2005 study estimated that relative to “healthy weight” persons, obese individuals
had a 74% greater risk of developing dementia, and overweight persons had a 35%

49 American Institute for Cancer Research, Cancers, Food, Nutrition and the Prevention of
Cancer: A Global Perspective (Washington, D.C.: AICR, July 1997).
50 Dennis T. Villareal et al., “Physical Frailty and Body Composition in Obese Elderly Men
and Women,” Obesity Research, vol. 12, (2004), pp. 913-920.
51 Kenneth F. Ferraro et al., “Body Mass Index and Disability in Adulthood: A 20-year Panel
Study,” AJPH, vol. 92, no. 5 (2002), pp. 834-840.
52 Richard N. Baumgartner et al., “Sarcopenic Obesity Predicts Instrumental Activities of
Daily Living Disability in the Elderly,” Obesity Research, vol. 12, no. 12 (December 2004),
pp. 1995-2004.
53 David T. Felson, “Weight and osteoarthritis,” Journal of Rheumatology, vol. 43 (1995),
pp. 7-9.
54 Susan Craft, “Insulin Resistance Syndrome and Alzheimer’s Disease: Age- and
Obesity-related Effects on Memory, Amyloid, and Inflammation,” Neurobiology of Aging,
vol. 26, supplement 1 (2005), pp. 65-69; S. Gandy, “The Role of Cerebral Amyloid ß
Accumulation in Common Forms of Alzheimer’s Disease,” Journal of Clinical Evaluation,
vol. 115 (2005), pp. 1121-1129; Emmanuel C. Gorospe and Jatin K. Dave, “The Risk of
Dementia with Increased Body Mass Index,” Age and Ageing, vol. 36, no. 1 (2007), pp.
23-29; Miia Kivipelto et al., “Obesity and Vascular Risk Factors at Midlife and the Risk of
Dementia and Alzheimer’s Disease,” Archives of Neurology, vol. 62, no. 10 (2005), pp.
1556-1560; George Razay, Anthea Vreugdenhil, and Gordon Wilcock, “Obesity, Abdominal
Obesity and Alzheimer Disease,” Dementia and Geriatric Cognitive Disease, vol. 22
(2006), pp. 173-176.

higher risk.55 Moreover, Swedish researchers have concluded that every unit increase
in body mass index after age 70 increases a woman’s chance of developing
Alzheimer’s by 36%.56
While researchers have long suspected a link between Alzheimer’s disease and
diabetes, only recently have scientists demonstrated a correlation between these
conditions. Two competing explanations have been offered for this association.
Both hinge on the fact that overweight and insulin-resistant individuals produce
excess insulin as their pancreas tries to reduce the body’s blood sugar level. This
extra insulin can provoke inflammation of blood vessels. One hypothesis is that
swollen vessels in the brain may increase production of the protein beta-amyloid,
which is an important component of the sticky plaques that accumulate in the brain
and lead to the development of Alzheimer’s disease. The notion that Alzheimer’s
may result when the brain is overwhelmed by insulin is supported by the results of
recent research.57
Effect of BMI on Mortality Risk at Various Ages
Researchers have found that among adults between the ages of 30 and 74,
increased BMI is associated with an increase in the risk of death from all causes.58
Indeed, a life-table analysis showed that 40-year-old nonsmokers, who have a BMI
of at least 30.0, generally live six to seven fewer years than their “healthy weight”
However, the relative risk associated with increased BMI appears to decline
with age. For example, among 30-to-44-year-olds, the increase in mortality risk
among overweight and obese individuals relative to those of healthy weight was
greater than the corresponding relative increase in risk among 65-to-74-year-olds
with an elevated BMI. This observation is often misinterpreted as evidence that

55 Rachel Whitmer, Charles P. Quesenberry, Jr., and Kristine Yaffe, “Obesity in Middle Age
and Future Risk of Dementia: A 27-year Longitudinal Population-based Study,” British
Medical Journal, vol. 62 (2005), pp. 1556-1560.
56 Deborah Gustafson et al., “An 18-Year Follow-Up of Overweight and Risk of Alzheimer’s
Disease,” Archives of Internal Medicine, vol. 163 (2003), pp. 1524-1528. This study’s
design did not include male subjects; therefore, no conclusions were reached about the
impact of BMI on dementia among men.
57 Akiko Taguchi, Lynn M. Wartschow, and Morris F. White, “Brain IRS2 Signaling
Coordinates Life Span and Nutrient Homeostasis,” Science, vol. 317 (July 20, 2007), pp.

369-372; Colin Barras, “Excess Insulin May Be Bad for the Brain,” New Scientist (July 28,

2007), p. 26.

58 Eugenia E. Calle et al., “Body-Mass Index and Mortality in a Prospective Cohort of U.S.
Adults,” NEJM, vol. 341, no. 15 (October 1999), pp. 1097-1105; David B. Allison et al.,
“Body Mass Index and All-Cause Mortality among People Age 70 and Over: The
Longitudinal Study of Aging,” International Journal of Obesity-Related Metabolic
Disorders, vol. 21 (1997), pp. 424-431.
59 Anna Peeters et al., “Obesity in Adulthood and its Consequences for Life Expectancy: A
Life-Table Analysis,” Annals of Internal Medicine, vol. 138 (2003), pp. 24-32.

obesity is less harmful in older adults.60 On the contrary, as individuals age, the
effect of obesity on mortality is not less pronounced; it is simply less evident.
Reasons for this obscuring are twofold. First, the mortality risk attributable to body
mass loses prominence because so many other mortality risks become manifest as
persons grow old. Second, it is more difficult to find an association between obesity
and mortality after age 75 because many obese individuals have already died by that
age, thus leaving behind persons who are healthier, leaner, or more resistant to
potential health problems associated with high BMI values.61
Prevalence of Obesity
In 2003-2004, of Americans aged 60 and older, 40% were overweight, while62
31% were obese. This suggests that more than 7 of every 10 senior citizens in the
United States have a higher-than-recommended body mass index. Table 2 details
prevalence estimates for 2003-2004 of overweight, obesity, and extreme obesity for
the adult population by racial/ethnic group. Data for three age subgroups are
followed by statistics for the entire population aged 20 and above (see Appendix A
for notes on the data sources and the methodology used to calculate the estimates
presented in this report).
There is broad consensus in the scientific community that the level of obesity63
in the United States is unprecedented and epidemic. However, expanding girth and
bulging waistlines do not affect different racial and ethnic groups uniformly. Certain64
subsets of the population have been affected more seriously than others. First, the
prevalence of overweight, obesity, and extreme obesity among non-Hispanic blacks

60 Jerome P. Kassirer and Marcia Angell, “Losing Weight — An Ill-Fated New Year’s
Resolution,” NEJM, vol. 338 (1998), pp. 52-54.
61 William B. Kannel, Tavia Gordon, and William P. Castelli, “Obesity, Lipids, and Glucose
Intolerance: The Framingham Study,” American Journal of Clinical Nutrition, vol. 32
(1979), pp. 1238-45; June Stevens et al., “The Effect of Age on the Association Between
Body-Mass Index and Mortality,” NEJM, vol. 338 (1998), pp. 1-7; JoAnn E. Manson et al.,
“Body Weight and Mortality among Women,” NEJM, vol. 333 (1995), pp. 677-85.
62 28.0% of elderly individuals were “obese,” with BMIs from 25.0 to 39.9; 3.0%were
“extremely obese,” with BMIs over 40.0. For details, see Katherine M. Flegal et al.,
“Overweight and Obesity in the United States: Prevalence and Trends, 1960-1994,”
International Journal of Obesity-Related Metabolic Disorders, vol. 22 (1998), pp. 39-47;
Cynthia L. Ogden et al., “Prevalence of Overweight and Obesity in the United States, 1999-
2004, JAMA, vol. 295, no. 13 (April 5, 2006), pp. 1549-1555; Ali H. Mokdad et al., “The
Continuing Epidemics of Obesity and Diabetes in the United States,” JAMA, vol. 286, no.

10 (September 21, 2001), pp. 1195-1200.

63 U.S. Department of Health and Human Services, The Surgeon General’s Call to Action
to Prevent and Decrease Overweight and Obesity, Rockville, MD: Office of the Surgeon
General, 2001; World Health Organization, Obesity: Preventing and Managing the Global
Epidemic, WHO Obesity Technical Report, Series 894, Geneva, Switzerland: WHO, 2000.
64 Nicole Cossrow and Bonita Falkner, “Race/ethnic Issues in Obesity and Obesity-Related
Comorbidities,” The Journal of Clinical Endocrinology & Metabolism, vol. 89, no. 6
(2004), pp. 2590-2594.

and Mexican Americans exceeds that of non-Hispanic whites in most age categories
(see Table 2).
Table 2. Prevalence of Overweight, Obesity, and Extreme
Obesity in Adults, by Age and Racial/Ethnic Group, 2003-2004
No n-Hispa nic No n-Hispa nic Mexican-
white bla c k American All
20-39 yrs.
O ve r we i ght a 27.0% 27.8% 39.0% 28.6%
Ob ese b 20.7 30.2 32.3 23.1
Extremely obesec4.811.74.55.4
Subt o t a l 5 2 . 5 % 6 9 . 7 % 7 5 . 8 % 5 7 . 1 %
40-59 yrs.
Overweight 35.8% 32.8% 39.6% 36.3%
Ob ese 32.1 36.6 35.1 31.4
Extremely obese4.611.84.75.4
Subt o t a l 7 2 . 5 % 8 1 . 2 % 7 9 . 4 % 7 3 . 1 %
60 yrs. or older
Overweight 41.4% 33.9% 41.2% 40.0%
Ob ese 26.9 38.3 33.0 28.0
Extremely obese2.
Subt o t a l 7 1 . 1 % 7 8 . 8 % 7 8 . 1 % 7 1 . 0 %
All adults, 20 years or older
Overweight 33.6% 31.1% 39.0% 34.1%
Ob ese 26.3 34.5 32.3 27.4
Extremely obese4.310.54.54.8
Subt o t a l 6 4 . 2 % 7 6 . 1 % 7 5 . 8 % 6 6 . 3 %
Source: CRS compilation of NHANES 2003-2004 data.
a. Defined as BMI of 25.0-29.9.
b. Defined as BMI of 30.0-39.9.
c. Defined as BMI of 40.0 or greater.
Second, important gender disparities exist. For instance, the percentage of
Americans aged 60 or older who are overweight is greater for men than women
(43.3% versus 37.4%, respectively); however, the prevalence of obesity and extreme
obesity are each greater among women.65 Within racial groups, a greater fraction of
non-Hispanic black and Mexican American women are overweight or obese than
men.66 After age 60, though, this pattern changes; for blacks and Mexican Americans
in this age group, obesity is more common among women than men (54.0% versus

65 In 2003-2004, the prevalence of obesity for men and women age 60 and older was 27.9%
and 28.2%, respectively, while, the prevalence of extreme obesity was 2.5% for men and

3.3% for women. Source: CRS compilation of NHANES 2003-2004 data.

66 Hedley et al., “Prevalence of Overweight and Obesity,” pp. 2847-2850.

31.1% and 43.8% versus 29.5%, respectively)67. Some researchers argue that higher
proportions of obesity among minority women may be explained by higher rates of
binge eating among black women,68 less social or cultural pressure for black or
Hispanic women to control their weight, limited access to healthy foods, and other
“obesity-tolerant” attitudes that limit the motivation to maintain a healthy weight.69
Third, overweight and obesity in the United States disproportionately affect
individuals of lower socioeconomic status (SES). Previous research, for instance,
indicates that the prevalence of obesity declines with both education and income.
Conservative estimates70 are that 26% of high school dropouts were obese in 2000,
versus 22% of individuals with a high school diploma and 15% of college
graduates.71 It has also been shown that the prevalence of obesity is inversely related
to income. As an example, 23% of white women with family incomes greater than
400% of the federal poverty level (FPL) were obese in 1999-2002, compared with

40% of their counterparts at or below the FPL.72

Obesity Trends
The prevalence of obesity in the United States has increased significantly for
all age categories during the past 25 years.73 Figure 1 illustrates changes in the
prevalence of obesity among elderly men and women since the mid-1970s. Between74
1976-1980 and 2003-2004, Americans aged 65-74 experienced significant increases
in obesity; the prevalence among men increased from 13.2% to 33.0%, whereas
women experienced an increase of nearly 15 percentage points during the same
period, from 21.5% to 36.1%.75 The steepest increases occurred in the 1990s, when
obesity rose among all elderly individuals by a factor of nearly 50%. Between

2001/2002 and 2003/2004, a notable drop in obesity is evident for women aged 65-

74. Researchers are still speculating about the cause(s) of this shift. One possibility,

67 These figures represent the prevalence of obesity and extreme obesity combined.
68 Ruth H. Striegel-Moore et al., “Recurrent Binge Eating in Black American Women,”
Archives of Family Medicine, vol. 9 (2000), pp. 83-87.
69 Shiriki K. Kumanyika, J.F. Wilson, and M. Guilford-Davenport, “Weight-Related
Attitudes and Behaviors of Black Women,” Journal of the American Dietetic Association,
vol. 93 (1993), pp. 416-422; H.S. Kahn et al., “Race and Weight Change in U.S. Women:
The Roles of Socioeconomic and Marital Status,” AJPH, vol. 81 (1991), pp. 319-323.
70 These estimates are conservative because they rely on self-reported data.
71 Mokdad et al., “Continuing Epidemic of Obesity,” pp. 1195-1200.
72 Analogous obesity figures for white males in 1999-2002 were 14% and 34%.
73 Hedley et al., “Prevalence of Overweight and Obesity, 1999-2002,” pp. 2847-2850; Flegal
et al., “Overweight and Obesity in the United States,” pp. 39-47; Robert J. Kuczmarski et
al., “Increasing Prevalence of Overweight among U.S. Adults: The National Health and
Nutrition Examination Surveys, 1960 to 1991,” JAMA, vol. 272 (1994), pp. 205-211.
74 Note that prior to 1999, the CDC released NHANES data only in multiyear blocks. The

2003-2004 data are the most recent NHANES data currently available.

75 Note that these percentages are rounded to the nearest integer in Figure 1.

raised by William Dietz, Director of the CDC’s Division of Nutrition and Physical
Activity, is that women are taking the health threats associated with obesity
somewhat more seriously than men and, upon retirement, they are modifying their
eating habits or levels of physical activity accordingly.76
Figure 1. Obesity Trends Among Elderly Americans, 1976-2004

Source: Federal Interagency Forum on Aging-Related Statistics.
Notes: Data points rounded to nearest integer. Data were not collected for the 75+ age category when
the NHANES was first fielded (1976-1980).
Another explanation, which deserves further investigation, concerns the
increasing use of bariatric surgery in the United States. This surgery, which includes
procedures such as gastric bypass and gastric banding, alters the gastrointestinal tract
in a manner that limits the quantity food a person is capable of ingesting.77 Between
1998 and 2002, the use of bariatric surgery increased 450% in the United States, and
more than four out of five persons who underwent the procedure were female.78
Much of the overall increase in bariatric surgery was attributable to a 900% rise in
76 Mike Stobbe, “More Kids, Men Getting Fat,” Associated Press, April 4, 2006.
77 Gastric bypass surgery reduces the stomach’s size and bypasses part of the small intestine
where food is absorbed. In gastric banding surgery, an inflatable band is placed around the
upper part of the stomach, creating a small pouch, which helps restrict the amount of food
78 William E. Encinosa, Didem M. Bernard, Claudia A. Steiner, and Chi-Chang Chen, “Use
and Costs of Bariatric Surgery and Prescription Weight-Loss Medications,” Health Affairs,
vol. 24, no. 4 (July/August 2005), pp. 1039-1046.

number of operations performed on people aged 55 to 64. This group accounted for

11% of all bariatric surgeries in 2002.79

Weight Distribution Dynamics
Figure 1 illustrates recent significant increases in obesity among the elderly;
however, it masks important shifts in the overall distribution of body weight. Figure
2 shows the distribution of BMI scores among older men and women (aged 65 and
older) in two time periods: 1988-1994 and 1999-2002.
Specifically, Figure 2 demonstrates that the percentage of overweight elderly
people has not dropped, as the prevalence of obesity and extreme obesity has
increased in recent years. Instead, something somewhat more complicated has
occurred. Between the two time periods highlighted, the share of the elderly that was
overweight remained virtually unchanged (going from 37.7% to 39.3%), while the
ranks of the obese and the severely obese both significantly increased (from 21.5%
to 29.0% and from 1.8% to 3.2%, respectively). During the same time period, the
fraction of individuals aged 65 and older who fell into the healthy weight category
dropped significantly — from 37.8% to 29.7%.
Because the highest rates of obesity can be found among “early” baby boomers
(aged 52-61 in 2008), followed closely by “late” boomers (aged 42-51 in 2008),
obesity rates among the elderly are expected to rise within the next two decades as
these individuals enter their retirement years. This is especially true for women, as
the highest rates of obesity among women occur in their 50s. The current degree of
obesity among boomers has significant implications for the care that these individuals
are likely to require in coming years. It may also have policy ramifications, given
that persons who are obese in middle-age are projected to incur twice as many
medical expenses as Medicare beneficiaries as will their healthy weight peers.80
Regional Variations in Obesity
Table 3 provides annual estimates of the prevalence of obesity among elderly
adults for each state from 1994 to 2005. States are listed alphabetically. In 2005, the
states with the greatest prevalence of obesity among the elderly were Louisiana
(25.3%), Alaska (24.8%), and Iowa (24.5%), followed closely by Wisconsin (24.4%)
and Michigan (24.3%). The states with the lowest level of obesity were Hawaii
(13.4%), Colorado (15.5%), and New Mexico (15.6%), then Arizona (17.5%) and
Massachusetts (17.9%).
Since 1994, the prevalence of obesity among adults has increased in every state,
for both sexes, and across all age, race, and socioeconomic groups. No region in the

79 Encinosa et al., “Use and Costs of Bariatric Surgery,” pp. 1039-1046.
80 Daviglus et al., “Relation of Body Mass Index to Medicare Expenditures,” pp. 2743-2749.
This study estimated that individuals who were extremely obese as young adults have
Medicare expenditures after age 65 of $12,342 on average; by contrast, persons who were
neither overweight nor obese early in life had Medicare expenditures that averaged $6,224.

United States has been immune from this trend. By 2005, 37 states had elderly
obesity rates of 20% or more, compared with just one in 1994. During this 1994-
2005 period, three of every four states saw obesity among elderly residents increase
by more than 50% (see Table 3).81 Eleven additional states experienced increases
in excess of 70%. Utah led the way, with obesity prevalence more than doubling
among those aged 65 and older. Figure 3 maps the prevalence of obesity at the state-
level between 1996 and 2005.
Figure 2. Changes in Weight Distribution Among Persons Aged 65
and Older, 1988-2002

Source: CRS compilation of NHANES data, 1988-2002.
While the highest prevalence of obesity in 1994 was evident in Alaska, followed
by Southern rural states, including Louisiana and Alabama, the first states in the
continental United States to have more than 20% of their adult populations qualify
as obese were Iowa, Illinois, Wisconsin, and Michigan — all located in the Midwest
(see Table 3). Although some researchers have suggested that these rates of obesity
reflect differences in race/ethnicity, income inequality, or educational achievement,
there is somewhat greater support for the theory that these regional trends in obesity
are related to suburbanization.82 High levels of suburban sprawl characterize the
81 It should be noted that this figure and the statistics depicted in Figure 3 and presented in
Table 3 are conservative estimates of the prevalence of obesity, because they are based on
BRFSS data, which is self-reported. Because survey respondents often underestimate their
weight and overestimate their height, it is likely that the “true” level of obesity in each state
is even higher than BRFSS data suggest.
82 Roland Sturm and Deborah A. Cohen, “Suburban Sprawl and Physical and Mental

areas where obesity rates have climbed most steeply since 1994. Some studies
suggest that low-density residential development fosters automobile dependency and
discourages walking and bicycling.83 That may explain in part why states like Iowa,
Utah, and Michigan have seen large increases in obesity since the mid-1990s (see
Table 3).

82 (...continued)
Health,” Public Health, vol. 118, no. 7 (October 2004), pp. 488-496; Nicholas Freudenberg,
Sandro Galea, and David Vlahov, “Beyond Urban Penalty and Urban Sprawl: Back to
Living Conditions as the Focus of Urban Health,” Journal of Community Health, vol. 30,
no. 1 (2005), pp. 1-11; Howard Frumkin, “Urban Sprawl and Public Health,” Public Health
Reports, vol. 117 (2002), pp. 201-217; Susan L. Handy et al., “How the Built Environment
Affects Physical Activity,” AJPM, vol. 23, no. 2 (2002), pp. 64-73.
83 Russ Lopez, “Urban Sprawl and Risk for Being Overweight or Obese,” AJPH, vol. 94, no.
9 (September 2004), pp. 1574-1579; Reid Ewing et al., “Relationship between Urban Sprawl
and Physical Activity, Obesity, and Morbidity,” American Journal of Health Promotion, vol.
18, no. 1 (2003), pp. 47-57; Barbara A. McCann and Reid Ewing, Measuring the Health
Effects of Sprawl, Smart Growth America (SGA), Surface Transportation Project,
Washington, D.C.: SGA, September 2003.

Table 3. Prevalence of Obesity Among the Elderly (Age 65+), by State, 1994-2005
(percentage of elderly population characterized as “obese”)
Absolute %Relative
Point Increase
2005b Change (%)
St a t e a 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Ra nk 1994-2005 1994-2005
a 16.3 18.3 17.5 16.8 16.6 17.9 18.8 19.7 19.5 21.0 20.8 22.1 1 8 5 .8 35.6
ka 21.7 24.0 24.3 23.2 22.7 23.5 25. 0 24.2 24.0 22.6 25.2 24.8 2 3.1 14.3
zona 10.4 11.7 9 .4 8.5 7 .3 10.9 13.0 15.2 16.0 16.9 18.7 17.5 4 8 7 .1 68.3
ansas 14.1 14.5 15.6 15.9 16.8 17.5 17.8 17.9 18.0 18.0 18.9 19.4 4 0 5 .3 37.6
ifornia 11.9 11.9 11.7 12.0 14.1 16.7 18.5 18.5 19.8 19.7 20.2 19.6 3 8 7 .7 64.7
o rado 8.5 10.0 11.3 12.6 13.3 14.1 14.5 14.3 13.6 13.4 14.0 15.5 5 0 7 .0 82.4
necticut 10.0 9 .9 11.4 14.1 14.8 15.4 15.2 16.5 18.1 18.3 18.4 18.4 4 5 8 .4 84.0
aware 15.4 15.0 14.8 14.2 14.9 15.1 16.6 19.0 21.8 21.5 20.6 20.1 3 7 4 .7 30.5
iki/CRS-RL34358trict of Columbia15.519.018.918.716.
g/wd a 12.0 12.3 13.6 14.1 14.6 14.9 15.6 16.3 18.1 18.5 19.1 18.8 4 3 6 .8 56.7
s.oria 12.3 12.1 11.5 12.5 14.2 16.6 17.0 19.2 19.5 20.9 22.2 22.0 1 9 9 .7 78.9
leakaii 7.5 6 .7 7.4 9 .3 10.4 10.3 9 .6 10.5 10.7 n/a 12.0 13.4 5 1 5 .9 78.7
://wikio 12.7 14.6 15.4 15.7 16.3 16.1 17.0 16.7 18.5 19.6 20.6 20.5 3 1 7 .8 61.4o is 1 3 . 1 1 4 . 3 1 5 . 0 1 7 . 3 1 9 . 5 2 0 . 8 2 1 . 4 2 1 . 4 2 1 . 8 2 1 . 0 2 1 . 6 2 2 . 7 1 5 9 .6 7 3 . 3
ana 16.0 16.2 17.6 16.8 17.4 19.1 20.7 21.6 21.4 20.8 22.1 23.4 8 7.4 46.3
a 16.0 16.3 17.6 18.1 20.6 20.6 21.3 21.0 22.9 24.2 24.5 24.5 3 8.5 53.1
sas 12.5 12.5 14.0 14.3 16.3 16.8 18.8 19.1 19.0 18.9 19.4 20.5 3 1 8 .0 64.0
tucky 12.6 13.1 14.4 14.9 16.8 16.7 17.9 18.9 19.8 20.0 20.5 21.0 2 6 8 .4 66.7
isiana 17.9 19.3 19.0 18.3 17.6 19.2 21.1 23.1 22.5 22.8 24.2 25.3 1 7.4 41.3
ne 13.2 13.0 12.2 13.2 14.0 15.0 15.7 16.1 18.4 19.7 19.7 19.6 3 8 6 .4 48.5
land 14.5 15.3 16.9 17.2 18.1 18.0 18.1 16.6 17.3 19.1 21.4 23.2 1 1 8 .7 60.0
sachusetts 11.8 12.0 12.7 12.8 13.9 15.0 15.8 16.9 17.5 18.3 17.6 17.9 4 7 6 .1 51.7
igan 13.0 13.9 16.3 18.1 19.5 20.2 22.6 24.7 25.5 24.2 23.8 24.3 5 11.3 86.9
neso ta 15.3 15.5 16.6 18.0 18.5 18.7 18.3 18.5 18.7 20.4 22.4 23.3 1 0 8 .0 52.3
sissippi 15.5 16.2 17.3 16.8 18.1 18.6 20.5 20.4 22.2 21.8 22.2 22.6 1 6 7 .1 45.8
so uri 12.2 13.9 14.7 15.8 17.7 17.5 18. 6 18.8 19.7 20.0 20.5 22.4 1 7 10.2 83.6
tana 12.0 12.7 13.2 14.3 15.0 16.4 17.0 16.1 16.1 16.2 18.4 19.3 4 1 7 .3 60.8
ka 14.9 14.6 14.9 16.1 18.7 19.1 19.4 18.9 20.3 21.3 22.3 23.9 7 9.0 60.4

Absolute %Relative
Point Increase
2005b Change (%)
St a t e a 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Ra nk 1994-2005 1994-2005
ada 11.3 12.9 11.7 10.7 11.9 13.6 17.3 18.5 19.7 18.6 17.9 18.2 4 6 6 .9 61.1
h Carolina14.615.216.115.315.015.417.819.720.120.920.421.7217.148.6
h Dakota15.415.115.917.219.521.221.521.822.924.023.923.488.051.9
o 15.4 17.1 16.8 17.4 18.0 19.5 20.3 21.6 22.8 22.7 22.0 22.8 1 4 7 .4 48.1
aho ma 10.6 10.4 9 .9 11.9 14.5 16.1 17.0 16.7 18.1 18.0 18.9 20.3 3 5 9 .7 91.5
on 12.0 12.6 13.6 13.1 15.1 16.3 17. 6 16.7 17.2 17.7 18.8 19.3 4 1 7 .3 60.8
nsylvania 14.9 15.6 15.9 16.3 18.6 19.2 20.5 20.9 22.3 23.0 23.2 23.0 1 2 8 .1 54.4
iki/CRS-RL34358land 11.1 11.9 12.3 14.4 14.8 16.7 16.9 18.1 17.5 18.3 18.6 21.1 2 5 10.0 90.1
g/wth Carolina14.714.113.614.915.717.818.219.019.520.621.422.9138.255.8
s.orth Dakota14.415.215.815.016.416.819.218.719.720.121.521.9207.552.1
leaknessee 14.5 14.4 15.5 15.3 15.2 16.0 17.4 18.6 18.6 18.5 20.0 20.7 2 8 6 .2 42.8
://wikias 12.2 12.3 14.2 15.9 17.5 17.4 18.7 19.1 19.6 20.1 19.8 20.7 2 8 8 .5 69.710.1 11.1 12.4 13.6 15.0 15.5 15.6 16.6 18.5 20.2 20.9 21.3 2 4 11.2 110.9
httpont 11.7 12.3 14.7 15.2 17.1 18.1 19.0 19.0 18.2 17.9 18.0 18.7 4 4 7 .0 59.8
inia 14.6 15.1 15.8 15.3 15.9 17.0 17.8 18.7 18.2 18.6 19.7 20.4 3 3 5 .8 39.7
hington 12.4 12.0 13.2 14.0 16.3 16.9 17.5 17.3 18.0 18.7 19.9 20.4 3 3 8 .0 64.5
t Virginia13.714.414.416.017.618.217.617.519.321.022.324.0610.375.2
consin 15.2 15.4 15.5 18.4 19.7 21.5 21.2 21.2 21.5 21.9 24.2 24.4 4 9.2 60.5
oming 12.6 13.3 13.4 15.5 16.5 17.1 17.1 16.2 17.8 18.7 20.9 20.2 3 6 7 .6 60.3
Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System Survey Data. Atlanta, Georgia: U.S. Department of Health and Human
ices, Centers for Disease Control and Prevention, 1993-2004.
BRFSS data are released in overlapping two-year blocks (e.g., 1993-1995, 1994-1996, 1995-1997). In the table above, 1993-1995 data are labeled “1994” data, 1994-1996
are labeled “1995,” and so on.
are listed alphabetically.
2005 Rank reflects the overall prevalence of obesity for the 65-and-over population in 2005. The state with the greatest percentage of obese elderly persons has a rank of1;th
a “17” indicates the state with the 17-highest prevalence of obesity among the elderly. Note that states with identical prevalence levels in 2005 receive identical rankings. For
instance, Oregon and Montana both rank41.”

Figure 3. Prevalence of Obesity Among Adults Aged 65 and Older, 1996, 1999, 2002, 2005

: Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System Survey Data. Atlanta, Georgia: U.S. Department of Health and Human
ices, Centers for Disease Control and Prevention, 1993-2006.
Obesity is defined as having a BMI$30, or about 30 lbs. overweight for a 5’4” person. See Table 3 (above) for state-level obesity prevalence data, 1994-2005.

Policy Implications
Following the Surgeon General’s Call to Action to Prevent and Decrease84
Overweight and Obesity in 2001, an array of government programs was established
to promote physical activity and improve general nutrition. Although public health
officials have welcomed these programs, they urge nonetheless that more be done by
health care providers, schools, and municipalities now that overweight and obesity85
are so commonplace in the United States.
Their concerns reflect in part a growing consensus that obesity and overweight
are social epidemics driven by contextual factors. Health economists have
demonstrated that BMI is directly proportional to food prices and access to
restaurants.86 They also have shown that limited access to supermarkets contributes
to the risk of obesity because larger supermarkets are more likely to carry healthy
foods at affordable prices.87 Finally, the influence of technology on how individuals
spend their free time has contributed in important ways to reducing physical activity
levels. Personal computers, cell phones, and the growing popularity of text
messaging have all fostered sedentary behavior among Americans. While some
people find these devices to be enjoyable or efficient, others note that individuals
“pay” for this very utility by reducing the calories they would have expended by
walking next door to talk with neighbors or by engaging in leisure-based exercise.
In an agricultural or industrial society, work is typically strenuous; in effect, the
worker is “paid to exercise.” Technological change, however, has raised the costs
associated with expending calories, while at the same time lowering the cost of
calorie intake by making food cheaper. Increasingly sedentary lifestyles, driven in
large measure by the advent of the personal computer, have transformed physical
exercise from a vocational activity to an activity that must occur during leisure time.
In order to burn calories, therefore, people are increasingly forced to sacrifice “family
time” or recreational pursuits to make the time to burn calories by exercising.
Lakdawalla and Philipson summarize this dilemma, concluding that “the obesity

84 As examples, the CDC’s Nutrition and Physical Activity Program to Prevent Obesity and
Other Chronic Diseases works collaboratively with state health departments to implement
and evaluate interventions that promote physical activity and foster improved diets. The
CDC also sponsors the Coordinated School Health Program, which works with state
education and health agencies to improve nutrition and increase the amount of exercise
students get in school.
85 U.S. Department of Health and Human Services (HHS), The Surgeon General’s Call to
Action to Prevent and Decrease Overweight and Obesity, Rockville, MD: HHS, Public
Health Service, Office of the Surgeon General, 2001.
86 Shin-Yi Chou et al., “An Economic Analysis of Adult Obesity,” pp. 565-587; Roland
Sturm and Ashlesha Datar, “Body Mass Index in Elementary School Children, Metropolitan
Area Food Prices and Food Outlet Density,” Public Health, vol. 119, no. 12 (December

2005), pp. 1059-1068.

87 Richard E. Mantovani et al., Authorized Food Retailer Characteristics Study, Technical
Report IV, U.S. Department of Agriculture, Food and Consumer Service, Office of Analysis
and Evaluation, 1997; Chanjin Chung and Samuel L. Myers, Jr., “Do the Poor Pay More for
Food?” Journal of Consumer Affairs, vol. 33, no. 2 (1999), pp. 276-296.

epidemic in the U.S. is a result of two simple changes in incentives: the relative price
of consuming a calorie has fallen over time, while the opportunity cost of burning a
calorie has risen over time.”88
The emerging literature that identifies significant associations between
contextual factors and BMI/obesity is helping identify nonmedical policies that may
help combat obesity among Americans. Some of these policy options include the
!Providing funding both to evaluate food availability problems, for
instance limited access to fresh produce (e.g., fresh fruits and
vegetables) and to make recommendations to address these
!Testing financial incentives for purchasing nutritious foods under the
Food Stamp program, for instance by discounting the prices
beneficiaries are charged for fruits and vegetables.
!Improving or encouraging the distribution and use of fresh produce
in existing nutrition programs, such as congregate meal programs in
senior centers, community centers, and adult day care centers.
!Requiring nutrition labels on fast-food packaging, or requiring
restaurants to post calorie information on their menu boards.89
!Increasing financial assistance to farmers’ markets (particularly
those particpating in the Seniors’ Farmers’ Market Nutrition
Program) for the purposes of facilitating their ability to accept
Electronic Benefits Transfer (EBT) cards from the Food Stamp
!Taxing “junk food” to raise the costs of everything from hamburgers
to tacos to sodas. Alternatively, lawmakers could consider
regulating the use of certain food ingredients, such as corn syrup,90

88 Darius N. Lakdawalla and Tomas J. Philipson, “Technological Change and Obesity,”


89 In September 2007, California became the first state to require fast-food establishments
to post this information on menus.
90 Research indicates that high-fructose corn syrup may interfere with the heart’s use of
minerals like magnesium, copper, and chromium; it also has been linked to elevated blood
cholesterol levels; finally, it may inhibit the ability of white blood cells to effectively fight
infections or viruses. See Sharon S. Elliot et al., “Fructose, Weight Gain, and the Insulin
Resistance Syndrome,” American Journal of Clinical Nutrition, vol. 79 (April 2004), pp.


MSG (monosodium glutamate),91 and trans fats (partially
hydrogenated oil).92
A research study released in July 2007 showed just how “social” the obesity
epidemic in the United States truly is. Nicholas Christakis and James Fowler
analyzed 32 years of data for 12,067 adults who underwent repeated medical
assessments as part of the Framingham Heart Study. The study results demonstrate
that if one person becomes obese, those closely connected with him or her have a
greater chance of becoming obese themselves. The authors suggest, therefore, that
obesity is “socially contagious,” spreading from person to person in a social
network.93 The greatest effect, the researchers point out, is seen not among family
members or housemates, but among friends. Moreover, geography does not play a
role. The impact of friends on obesity seems to be independent of whether the
friends live in the same region or not.
Christakis and Fowler’s social network theory of obesity has additional policy
implications. They suggest that although people may directly influence each other’s
behaviors, the more significant mechanism is normative: “What appears to be
happening is that a person becoming obese most likely causes a change of norms
about what counts as an appropriate body size. People come to think that it is okay
to be bigger since those around them are bigger, and this sensibility spreads.”
Altering the physical activity norms of an entire neighborhood through community-
level interventions could have a snowball effect, both by altering the lifestyles of the
proximate social network and by influencing the health behaviors of friends at risk
of overweight who live far outside of the community.
From this perspective, policy makers may want to consider using the tools of
urban planning to address the growing obesity crisis, including smart growth
development of suburban areas and increased funding to improve local infrastructure
(e.g., street lighting, better sidewalks, common green space, and better public
transportation to facilitate the mobility of the citizenry). For the most part, these
changes in land use policy would fall under the purview of local jurisdictions.

91 Monosodium glutamate (MSG) has been shown indirectly to cause obesity in lab animals
by increasing appetite. For details: John W. Olney, “Brain Lesions, Obesity, and Other
Disturbances in Mice Treated with Monosodium Glutamate,” Science, vol. 165 (1969), pp.
719-271; John W. Olney, “Excitotoxins in Foods,” Neurobehavioral Toxicology and
Teratology, vol. 15, no. 3 (1994), pp. 535-544. A similar effect has not yet been observed
in humans, but some researchers speculate that the increasing prevalence of obesity in the
United States may relate to early exposure to food additives, such as MSG. See Michael
Hermanussen et al., “Obesity, Voracity, and Short Stature: the Impact of Glutamate on the
Regulation of Appetite,” European Journal of Clinical Nutrition, vol. 60, no. 1 (2006), pp.


92 Trans fats are effective as preservatives, but they can cause significant lowering of HDL
(good) cholesterol and a serious increase in LDL (bad) cholesterol. This may contribute to
atherosclerosis (clogging of arteries), insulin resistance, or even type 2 diabetes.
93 Nicholas A. Christakis and James H. Fowler, “The Spread of Obesity in a Large Social
Network over 32 Years,” NEJM, vol. 357, no. 4 (July 26, 2007), pp. 370-379.

Federal Efforts to Combat Obesity
Many federal departments and agencies administer obesity-related programs.
These include nutrition counseling, health promotion campaigns, program
evaluations, and quantitative studies of the causes and consequences of excessive
weight gain.
Although obesity prevention efforts are housed primarily in the Department of
Health and Human Services, a broad array of government policies affects the
behavior patterns that determine levels of exercise and dietary patterns among the
general populace. Agriculture regulations, for instance, heavily influence nutrition;
housing and commerce policies influence where supermarkets are built and how
accessible produce is, especially in low-income neighborhoods; and decisions about
transportation infrastructure heavily influence levels of physical activity.
Currently, no effective framework across the government exists to organize and
coordinate federal efforts to mitigate the growing problem of obesity in the United
States. Although the Department of Health and Human Services takes credit for
more than 300 obesity-related programs, and nearly every federal agency similarly
contends that it is actively engaged in countering the rising tide of obesity, most
federal programs deal with obesity somewhat indirectly. (See Appendix B below for
examples of programs.) For instance, the Indian Health Service has a variety of
diabetes programs, which indirectly address obesity by stressing the importance of
counting calories and getting regular exercise.
In addition, the Food and Drug Administration (FDA) monitors and regulates
food labeling requirements, and the Department of Agriculture administers an array
of food and nutrition programs, such as the Food Stamp program. The degree to
which food labeling or food stamps contribute to better nutrition, however, has been
questioned by many experts, who assert that food labeling is helpful only to people
who have the knowledge and ability to determine what they can purchase that will
be healthy, as well as an understanding of what could happen if they do not eat a
nutritious diet. Some contend that food stamps are of limited use to people who do
not have access to a wide range of healthful foods.94 The rural poor, for example,
may not have any large grocery stores near where they live or may not have
transportation available to get to a grocery store with fresh produce. Finally, in this
view, the food security programs may also be of limited benefit if beneficiaries lack
adequate food preparation skills or have difficulty shopping for healthy food on a
tight budget.

94 Food Research and Action Center (FRAC), Food Stamp Access in Urban America: A
City-by-City Snapshot, September 2005 (Revised April 2006), Washington, D.C.: FRAC,
at [http://www.frac.org]; Kimberly Morland, “Neighborhood Characteristics Associated
with the Location of Food Stores and Food Service Places,” AJPM, vol. 22, no. 1 (2002),
pp. 23-29.

Appendix A. Notes on Methodology
Data used in this report are drawn from the National Health and Nutrition
Examination Study (NHANES) and the Behavioral Risk Factor Surveillance System
(BRFSS) survey.
NHANES is fielded by the Centers for Disease Control and Prevention (CDC)
and is widely thought to have the most reliable obesity data available, as the survey
includes an actual physical examination and does not rely on self-reporting.
NHANES began in the early 1970s and has been conducted several times in the last
three decades: 1971-1975, 1976-1980, and 1988-1994. Since 1999, NHANES data
collection has become a continuous annual survey, with data being released for
public use in two-year groupings. Each year, approximately 7,000 randomly selected
residents across the United States from 15 different counties are visited and given the
opportunity to participate in the latest NHANES.
BRFSS is the world’s largest ongoing telephone health survey system, tracking
health conditions and risk behaviors in the United States yearly since 1984.
Conducted by the 50 state health departments, as well as those in the District of
Columbia, Puerto Rico, Guam, and the U.S. Virgin Islands, with support from the
CDC, BRFSS provides state-specific information about issues such as asthma,
diabetes, health care access, alcohol use, hypertension, obesity, cancer screening,
nutrition and physical activity, tobacco use, and more.
Because this report relies on previously published cross-tabulations from
NHANES and BRFSS, age groups have been predefined and may not be consistent.
For instance, it is necessary to report some age-adjusted prevalence statistics for
individuals “aged 60 and older” (e.g., Table 2), whereas other tables examine obesity
and overweight for “persons 65 and older.” Whenever possible, finer age categories
have been used (i.e., 55-64, 65-74, 75-84, and 85+).

Appendix B. Selected Federal Obesity Programs
Department of Health and Human Services (HHS)
Dietary Guidelines for Americans. The Department of Agriculture and the
Department of HHS jointly issue The Dietary Guidelines for Americans95 every five
years. The Guidelines provide authoritative advice for people two years old and
older about how good dietary habits can promote health and reduce risk for major
chronic diseases. They serve as the basis for federal food and nutrition education
“Calories Count” Initiative. The Food and Drug Administration (FDA),
which oversees food labeling requirements, is using “calories count” as the message96
of its obesity campaign. In essence, the FDA is focusing on caloric balance,
stressing that “calories in must equal calories out.” To this end, the agency seeks to
ensure that food labels display calories more prominently and use meaningful serving
sizes. In addition, it is encouraging restaurants to provide nutritional information to
consumers and increasing enforcement of the accuracy of food labeling. FDA is also
working with other government agencies, industry organizations, and academic
institutions on obesity research.
“Steps to a Healthier U.S.” Program. The primary goal of the Steps
Program97 is to foster physical activity and exercise among Americans. It also aims
to mitigate problems associated with chronic illness. The CDC provides grants to
communities to design and implement chronic disease prevention and health
promotion activities that address obesity, diabetes, and asthma, as well as high-risk
health behaviors, including sedentary lifestyle, poor nutrition, and tobacco use.
Target populations include minorities, immigrants, low-income populations, people
with disabilities, school-aged youth, and senior citizens.
“Control Your Diabetes. For Life” Campaign. The National Diabetes
Education Program (NDEP), a collaborative program between the National Institute
of Diabetes and Digestive and Kidney Diseases (NIDDK) and the CDC, launched
this national campaign98 in September 2007 to inform patients and their health care
providers about the strong relationship between diabetes and cardiovascular disease
(CVD). While this effort on its face is not an obesity prevention program, its core
messages include an emphasis on regular physical exercise, and its materials
underscore the importance of a balanced, nutritious diet. NDEP’s goal is to get
people with diabetes to understand the importance of controlling their “ABCs” —
that is, their hemoglobin A1C, their Blood pressure, and their Cholesterol levels.
Although this initiative is technically a diabetes program, it nevertheless is likely to
mitigate problems with overweight and obesity.

95 See [http://www.health.gov/dietaryguidelines/].
96 See [http://www.cfsan.fda.gov/~dms/nutrcal.html#calcount].
97 See [http://www.cdc.gov/steps/].
98 See [http://ndep.nih.gov/campaigns/ControlForLife/ControlForLife_index.htm].

Congregate Meal Programs for the Elderly. The Administration on
Aging in HHS awards funds for congregate nutrition services, home-delivered99
nutrition services, and nutrition services incentive grants to state agencies on aging.
Congregate meals programs operate in a variety of sites, such as senior centers,
community centers, schools, and adult day care centers. The purpose of these
programs is to reduce hunger and food insecurity and to promote the health and
well-being of older individuals by helping them access health promotion services and
delay the onset of adverse health conditions resulting from poor nutrition or sedentary100
Other offices within HHS, including the Health Resources and Services
Administration (HRSA), the Indian Health Service, and the Surgeon General’s
Office, independently manage obesity-related health promotion and education
Department of Agriculture (USDA)
The USDA’s Food and Nutrition Service administers a variety of programs that
directly affect the nutrition of vulnerable segments of the population. These include
the Food Stamp Program; the Special Supplemental Nutrition Program for Women,
Infants and Children (WIC);101 and the National School Lunch Program. Non-elderly
women and children are the primary beneficiaries of the nutrition and obesity
prevention initiatives that fall under the auspices of these programs.
Food Stamp Program. The Food Stamp Program is the largest federal
nutrition program for low-income households. It is available to nearly anyone with
low income and few resources. Eighty-four percent of all Food Stamp households
in FY2006 contained an elderly or disabled person or a child, and these households
received 89% of all benefits. The average monthly food stamp benefit for all
participants in FY2006 was $214 per household.
Eligibility is based on income and assets available to the household, as well as
household characteristics, namely, immigrant status and one’s ability to work. Only
legal immigrants are eligible for program benefits, most of whom must wait five
years in legal status before qualifying for benefits. The Program requires able-bodied
adults between 16 and 60 (with some exceptions) to register for work, to take part in
employment/training programs referred by the food stamp office, and to accept or
continue suitable employment. Benefits come to the household via electronic debit
cards, known as Electronic Benefit Transfer (EBT) cards, which can be used in

162,000 approved retail stores nationwide to purchase food.

“Fit WIC” Program. In 1998, the USDA funded a five-year childhood obesity
prevention initiative called “Fit WIC.” The purpose of this effort was to examine

99 See [http://www.aoa.gov/press/fact/alpha/fact_elderly_nutrition.asp].
100 See CRS Report RS21202, Older Americans Act: Nutrition Services Program, by Carol
101 See [http://www.fns.usda.gov/wic/].

how WIC could better respond to the issue of childhood obesity. The USDA
recognized that WIC has widespread access to the population of young low-income
children that is at greatest risk for obesity, and that reaching very young children is
critical to the success of any obesity prevention strategy.102
“Eat Smart. Play Hard”® Campaign. Among current USDA initiatives are®103
the “Eat Smart. Play Hard” campaign for schools, which features posters,
stickers, and brochures that encourage healthy eating and physical exercise, and “5
A Day for Better Health Program,” which encourages fruit and vegetable
consumption among Americans.104
National School Lunch Program. Administered by the Food and Nutrition
Service, this federally assisted meal program operates in over 97,700 public and
non-profit private schools and residential childcare institutions.105 Over 28 million
children each school day are served nutritionally balanced, low-cost or free lunches.
Since its inception, more than 180 billion lunches have been served. Congress
expanded the program in 1998 to include reimbursement for snacks served to
children through age 18 in after-school educational and enrichment programs.
MyPyramid. The original Food Guide Pyramid, released in 1992, was
updated, revised, and renamed “MyPyramid” in 2005. MyPyramid’s daily food
intake patterns identify amounts to consume from each food group and subgroup at
a variety of energy levels.106 The overall purpose of the revision was to improve its
effectiveness in motivating consumers to make healthier food choices and to ensure
that the USDA’s food guidance system reflected the latest nutritional science. To
ensure that these patterns reflect the latest science, they were updated to meet all
current nutrition standards through a technical research process.
Senior Farmers’ Market Nutrition Program (SFMNP). SFMNP awards
grants to states, U.S. territories, and federally recognized Indian tribal governments
to provide low-income seniors107 with coupons that can be exchanged for fresh,
unprepared, locally grown fruits, vegetables, and herbs from farmers’ markets,

102 The final report from the “Fit WIC” project describes the experiences of the five Fit WIC
teams, their goals, outcomes, the lessons learned, and policy recommendations which stem
from the project. It is available online at [http://www.fns.usda.gov/oane/MENU/Published/
WIC/FIL E S / f i t w ic.pdf#xml= xis/search/pdfhi.txt?query=Fit+
WIC&pr=FNS&order=r&c q=&i d=4592d0fc17].
103 See [http://www.fns.usda.gov/eatsmartplayhard/].
104 “5 A Day for Better Health” is a joint program with the National Cancer Institute and the
CDC. See [http://www.fruitsandveggiesmatter.gov/].
105 See [http://www.fns.usda.gov/cnd/lunch/].
106 See [http://www.mypyramid.gov/].
107 Low-income seniors, generally defined as individuals who are at least 60 years old and
who have household incomes of not more than 185% of the federal poverty income
guidelines (published each year by the Department of Health and Human Services), are the
targeted recipients of SFMNP benefits.

roadside stands, and community-supported agriculture programs.108 The majority of
the grant funds must be used to support the costs of the foods provided under this
program; state agencies may use up to 10% of their grants to cover SFMNP
administrative costs.
In 2006, these SFMNP provided products were available to 825,691 low-income
seniors from 14,575 farmers at 2,911 farmers’ markets, as well as 2,323 roadside
stands and 260 community supported agriculture programs.
Department of the Interior
In conjunction with HHS, USDA, and the Department of Defense, the
Department of the Interior has established a Memorandum of Understanding to
Promote Public Health and Recreation.109 The goal of this program is to
simultaneously promote physical activity and the use of public lands, such as national
parks. As part of this effort, the National Park Service administers a matching
federal grant program that helps states and municipalities acquire land to develop into
public outdoor recreation areas.110
Department of Transportation
The Federal Safe Routes to School Program offers a dedicated source of grant
funding for infrastructure improvements (e.g., sidewalks, crosswalks, bicycle paths,111
street lighting) that encourage children to walk and bicycle to and from school.
Department of Education
Competitive grants for the design, modification, and expansion of physical
education programs are available to elementary and secondary schools as part of the
Department of Education’s Carol M. White Physical Education Program.112

108 Certain foods are not eligible for purchase with SFMNP benefits; these include dried
fruits or vegetables such as prunes (dried plums), raisins (dried grapes), sun-dried tomatoes,
and dried chili peppers. Potted fruit or vegetable plants, potted or dried herbs, wild rice,
nuts, honey, maple syrup, cider, and molasses are also not allowed.
109 See [http://www.fhwa.dot.gov/environment/rectrails/mou_pubhealth.htm].
110 See [http://www.nps.gov/nts/memorandum2006.html].
111 See [http://safety.fhwa.dot.gov/saferoutes/].
112 See [http://www.ed.gov/programs/whitephysed/index.html].