|
H E A L T H T R A C K I N G
T R E N D S
W E B E X C L U S I V E
20 October 2004
The Impact Of Obesity On
Rising Medical Spending
Higher spending for obese
patients is mainly attributable
to treatment for diabetes and hypertension.
By Kenneth E. Thorpe,
Curtis S. Florence, David H. Howard,
and Peter Joski
ABSTRACT:
Obese people incur higher health care costs at a given
point in time, but how rising obesity rates affect spending growth over time
is unknown. We estimate obesity-attributable health care spending
increases between 1987 and 2001. Increases in the proportion of and spending
on obese people relative to people of normal weight account for 27 percent
of the rise in inflation-adjusted per capita spending between 1987 and 2001;
spending for diabetes, 38 percent; spending for hyperlipidemia, 22 percent;
and spending for heart disease, 41 percent. Increases in obesity prevalence
alone account for 12 percent of the growth in health spending.
Evaluating the causes and health
benefits of rising health care spending is important for designing effective
cost containment interventions. The introduction of new medical technology
is thought to account for most of the growth in health care spending, while
aging and population growth account for smaller portions of the rise.1 Several
studies have estimated the impact of smoking, obesity, and other risk factors
on spending at a given point in time. However, studies have not addressed the
relationship between the increase in obesity prevalence and the growth in costs
over time. Through 1980 there were only moderate changes in the prevalence
of obesity, justifying its omission from the list of sources of health spending
growth.2 However, since 1980
the prevalence of obesity has doubled to 30 percent of the adult population;
it has increased by eight percentage points since 1990.3
The risk of developing diabetes, gallstones, hypertension,
heart disease, hyperlipidemia, stroke, and some forms of cancer is higher among
obese people.4 Moreover, the risk of
death is higher among moderately and severely overweight men and women, regardless
of age. Among the near-elderly (ages 50–69) medical care spending among
the severely obese (body mass index, or BMI, 35.0 or higher) is 60 percent
higher than for those of normal weight.5
Recent studies have estimated that health care spending is approximately 36
percent higher among obese adults under age sixty-five.6
These findings lead to the question: To what degree do increases in obesity
prevalence and relative costs contribute to the growth in health care spending?
In this paper we estimate the share of spending growth
attributable to changes in obesity and relative per capita spending among obese
people, using nationally representative data from 1987 and 2001. We also examine
the contribution of obesity-related factors to the growth in spending for three
conditions clinically linked to obesity: diabetes, hyperlipidemia, and heart
disease (including hypertension).
Study Data And Methods
Data sources. The
data for our analysis were drawn from the 1987 National Medical Expenditure
Survey (NMES) and the 2001 Medical Expenditure Panel Survey, Household Component
(MEPS-HC).7 These
surveys, conducted by the Agency for Healthcare Research and Quality (AHRQ),
provide nationally representative estimates of health care spending among the
noninstitutionalized civilian U.S. population. A more detailed description
of both surveys has been published elsewhere.8 The
1987 survey includes self-reported measures of each respondent’s
height and weight. We used these data to construct the BMI (calculated as weight
in kilograms divided by the square of height in meters) for each respondent
in the sample and classified respondents as underweight (BMI under 18.5), normal
weight (BMI between 18.5 and 24.9), overweight (BMI between 25.0 and 29.9),
and obese (BMI 30.0 or higher).9 The
2001 MEPS-HC calculates and reports BMI using self-reported weight
and height from the survey. Respondents to both surveys also self-report all
medical conditions. These data were professionally coded using the International
Classification of Diseases, Ninth Revision (ICD-9). The ICD-9
codes were collapsed to three-digit codes and subsequently coded into 259 clinically
relevant medical conditions by AHRQ researchers using the AHRQ Clinical Classification
System.10 Excluding respondents
under age nineteen and a small number of respondents (404 in 1987, 174 in 2001)
with missing values for education and marital status and implausible (BMI less
than 10; 12 in 1987, none in 2001) or missing values for BMI (3,150 in 1987,
864 in 2001) among adults, the sample sizes from the 1987 NMES and the 2001
MEPS are 20,989 and 21,460, respectively.11
Analysis.
Using the two-part regression model, we estimated per capita total health care
spending as well as spending to treat diabetes, heart disease/hypertension,
and hyperlipidemia
for underweight, normal, overweight, and obese adults age nineteen and older
in 1987 and 2001.12 We estimated separate
models for each year. Both parts of the two-part model include the following
as co-variates: weight (underweight, normal, overweight, obese),
age (19–29, 30–39, 40–49, 50– 64, 65 and older),
smoking, sex, education (less than high school, high school graduate, some
college, college degree), health insurance status (months of private insurance,
Medicaid, Medicare, other public insurance, CHAMPUS/TRICARE, uninsured), race/ethnicity
(Hispanic, non-Hispanic black, other), income as a percentage of the federal
poverty level (less than 100 percent, 100–199 percent, 200–399
percent, 400 percent or more), marital status (married, not married), and region
(Midwest, South, West, Northeast).
For each person in the sample, we calculated predicted
(retransformed from log dollars to dollars) per capita spending levels by multiplying
predicted values from the first and second stage. To summarize the impact of
weight on per capita spending, we computed four predicted spending levels.
The first is what per capita spending would be if every person were underweight.
Next, we computed what per capita spending would be if every person were normal,
and then if every person were overweight and obese. Computing predicted values
in this manner nets out the impact of observable individual characteristics
(such as age, insurance status, income) on per capita spending predictions.
Since the NMES and MEPS samples include a complex stratification
design, we used the svymean command in Stata, version 8, for the means and
standard errors of per capita spending by obesity category. This accounts for
both the complex sample design and the weighting of observations. We calculated
standard errors and 95 percent confidence intervals for the regression-adjusted
per capita spending estimates using the bootstrap technique with 1,000 replications.13 The
alpha value was set at .05, and all tests were two-sided. We used the gross
domestic product (GDP) personal consumption deflator to adjust per capita spending
levels for changes in economy-wide price levels.14 Per
capita cost estimates are expressed in 2001 dollars.
Decomposition of spending
growth over time.
To evaluate the contribution of rising obesity rates and changes in the relative
spending of underweight, normal-weight, obese, and seriously obese people,
we decomposed the actual per capita spending increase between 1987 and 2001
into a portion attributable to these factors and a portion attributable to
other causes. The decomposition was performed by computing a “counterfactual” per
capita spending level equal to what per capita spending would have been in
2001 if obesity rates and relative per capita spending levels by weight category
had remained unchanged from 1987 levels.15 Using
this counterfactual level, we then computed how much per capita spending levels
would have increased if none of these factors had changed and compared it with
the actual spending increase, thus deriving an “obesity-attributable” share
of spending growth.
We repeated the analysis for disease-specific
spending on three conditions linked to obesity: diabetes, hyperlipidemia, and
heart disease (which includes hypertension, congestive heart failure, pulmonary
heart disease, and acute myocardial infarction). Following previously published
methods, we linked diagnosis codes from NMES and MEPS-HC for each self-reported
medical encounter (provider visits of any type and prescribed drugs) that prompted
a person to seek medical care.16 We
calculated total spending for these three medical conditions for each person
and then reran the regression models and decomposition analysis. Sample size
and lack of statistical power precluded us from including other conditions
linked to obesity such as gallstones and stroke.
Results
Over the fourteen-year study period, the proportion of
the population with normal weight decreased by thirteen percentage points,
and the proportion categorized as obese increased by 10.3 percentage points
(both p < .05)
(Exhibit
1). This increase in the proportion of the population with BMI greater
than 30.00 is similar to the change in obesity prevalence observed from clinically
derived estimates from the National Health and Nutrition Examination Survey
(NHANES), although the self-reported rates of obesity are lower.17
Using the results from our multivariate analysis, we tabulated
adjusted per capita spending among underweight, normal-weight, overweight,
and obese people in 1987 and 2001 (Exhibit
2). By presenting results in terms
of per capita spending levels, we net out the contribution of population increase
to cost growth. We find statistically significant differences in mean per capita
health care spending between the obese and normal-weight categories in 1987
and 2001. Estimated per capita spending in 1987 (in 2001 dollars) was $2,188
overall; there was a 15.2 percent difference between spending for normal-weight
and obese people. By 2001 we find larger differences in spending by weight
category (p < .05):
Health care spending among the obese was 37 percent higher than it was among
the normal-weight group. Moreover, the increase in per capita spending within
the normal-weight and obese groups was 37 percent and 63 percent, respectively.
The rate of growth among the obese was much higher than the overall growth
rate in per capita spending (51 percent).
Using these multivariate results, we calculated the share
of growth in real per capita spending attributable to the rise in obesity
prevalence and the rise in relative spending among the obese. Between 1987
and 2001, inflation-adjusted spending per capita increased by $1,110 (Exhibit
3). Per capita spending would have increased by an estimated $809 had the
prevalence of obesity and relative spending among people in each weight
category remained at 1987 levels. We attribute the residual, $301 or 27
percent of the growth, to changes in prevalence and relative spending among
the obese relative to the nonobese. When we isolate the impact of changes
in obesity prevalence alone, we find that the increase in the proportion
of the population that is obese accounts for 12 percent of real per capita
spending growth.
Obesity has been linked to several medical conditions,
including diabetes, hyperlipidemia, and heart disease. Our tabulations
from NMES and MEPS-HC reveal a sharp rise in the number of treated cases of
diabetes (79 percent) and hypertension (29 percent) during this period. Thus,
the rise in health spending traced to obesity is most likely concentrated
in higher spending for treating these medical conditions. From the regression
models, we predicted per capita spending levels for each person for each of
the three conditions by weight category. Spending predictions differed significantly
by weight group in both 1987 and 2001 (p < .05)
across these conditions. The rise in obesity prevalence and relative spending
accounted for a significant portion of the rise in spending on each of the
three medical conditions examined (Exhibit
4). The trends in obesity accounted
for more than 38 percent of diabetes spending growth, 22 percent of hyperlipidemia
spending growth, and 41 percent of heart disease spending growth. Collectively,
these three medical conditions accounted for 22 percent of the overall rise
in spending attributable to obese people ($65 of the $301 increase per capita
from Exhibit 4). These medical conditions are among the fifteen priority
medical conditions identified by the Institute of Medicine (IOM) for needed
improvements in the efficiency of treatment, prevention, and quality.18
Concluding Comments
Both the rising prevalence of obesity and higher relative
per capita spending among obese Americans accounted for 27 percent of the
growth in real per capita spending between 1987 and 2001. During this period,
the prevalence of obesity increased by 10.3 percentage points—to nearly
24 percent of the adult population. The rise in obesity contributed
to large spending increases for the three medical conditions examined (diabetes,
hyperlipidemia, and heart disease). Our estimates are valid only for the civilian,
noninstitutionalized population. To the extent that changes in obesity prevalence
and the impact of obesity on spending differ in the institutionalized population,
our estimates may over- or understate the impact of obesity on cost growth
nationally.
The obesity-attributable cost estimate of 27 percent incorporates
two trends: the increase in obesity prevalence and the increase in spending
on the obese relative to those in the normal-weight category. This latter
component captures changes in medical technology that provide physicians
better options for treating obese patients and the diseases common among
them.19 Thus,
our obesity-attributable spending growth estimate is inclusive, rather
than exclusive, of changes in medical technology and simply represents
a different approach to characterizing spending growth.
Obesity has a sizable impact on the U.S. health care system.
It is associated with higher rates of mortality, even among those without
other risk factors such as smoking or a previous medical condition. Similar
to previous estimates, our results indicate that costs incurred by the
obese were 37 percent higher than costs for those with normal weight in
2001.20 Moreover,
growth in obesity and spending on obese people accounted for 27 percent
of the growth in inflation-adjusted per capita health care spending between
1987 and 2001. To date, there is no evidence that the rise in the share
of the U.S. population with BMI greater than 30.00 is abating. These
results suggest that future cost containment efforts need to attack the rising
prevalence and costs of obesity head on. This will require a focus on
developing effective interventions to promote weight loss among obese people.
NOTES
1. J.P. Newhouse, “Medical Care Costs: How Much Welfare Loss?” Journal
of Economic Perspectives 6, no. 3 (1992): 3–21; and S.
Glied, “Health Care Costs on the Rise Again,” Journal
of Economic Perspectives 17,
no. 2 (2003): 125–148.
2. K.M. Flegal et al., “Overweight and Obesity in the United
States: Prevalence and Trends, 1960– 1994,” International
Journal of Obesity and Related Metabolic Disorders 22, no.
1 (1998): 39–47.
3. K.M. Flegal et al., “Prevalence and Trends in Obesity among
U.S. Adults, 1999–2000,” Journal of the American
Medical Association 288, no. 14 (2002): 1723–1727.
4. A.D. Field et al., “Impact of Overweight on the Risk of Developing
Common Chronic Diseases during a Ten-Year Period,” Archives
of Internal Medicine 161, no. 13 (2001): 1581–1586; and
A. Must et al., “The Disease Burden Associated with Overweight
and Obesity,” Journal
of the American Medical Association 282, no. 16 (1999): 1523–1529.
5. R. Sturm, “The Effects of Obesity, Smoking, and Drinking
on Medical Problems and Costs,” Health Affairs 21,
no. 2 (2002): 245–253; and E.A. Finkelstein, I.C. Fiebelkorn, and G.
Wang, “National Medical Spending Attributable to Overweight and Obesity:
How Much, and Who’s Paying?” Health Affairs, 14
May 2004,
content.healthaffairs.org/cgi/content/abstract/hlthaff.w3.219 (20
September 2004).
6. Sturm, “The Effects of Obesity, Smoking, and Drinking”;
Finkelstein et al., “National Medical Spending Attributable to Overweight
and Obesity”; N.P. Pronk, W. Tan, and P. O’Connor, “Obesity,
Fitness, and Health Care Costs,” Medicine and Science
in Sports and Exercise 31, no. 5 (1999): s66; A. Wolf and G.
Colditz, “Current Estimates of the Economic Cost of Obesity in
the United States,” Obesity Research 6, no. 2 (1998):
97–106; and C. Quesenberry, B. Caan, and A. Jacobson, “Obesity,
Health Services Use, and Health Care Costs among Members of a Health
Maintenance Organization,” Archives of Internal Medicine 158,
no. 5 (1998): 466–472.
7. W.S. Edwards and M. Berlin, National Medical Expenditure
Survey: Questionnaires and Data Collection Methods for the Household Survey
and Survey of American Indian and Alaska Natives, Pub. no.
89-3450 (Washington: U.S. Department of Health and Human Services,
1989); and J.W. Cohen et al., “The Medical Expenditure Panel
Survey: A National Health Information Resource,” Inquiry 33,
no. 4 (1996): 373–389.
8. Ibid.
9. U.S. Centers for Disease Control and Prevention, “What Is
BMI?,” 17 April 2003, www.cdc.gov/nccdphp/dnpa/bmi/bmi-adult.htm (30
August 2004).
10. J.W. Cohen and N.A. Krauss, “Spending and Service Use among
People with the Fifteen Most Costly Medical Conditions, 1997,” Health
Affairs 22, no. 2 (2003): 129–138.
11. We weighted observations using weights provided in the NMES data
that account for the missing values for weight and height, as in J.A. Rhoades,
B.M. Altman, and L.J. Cornelius, “Trends in Adult Obesity in the United
States, 1987 and 2001: Estimates for the Noninstitutionalized Population,
Age 20 to 64,” Statistical Brief no. 37, August 2004, www.meps.ahrq.gov/papers/st37/stat37.htm (20
September 2004).
12. We also estimated modified two-part models as suggested in W.G.
Manning and J. Mullahy, “Estimating Log Models: To Transform or Not
to Transform?” Journal of Health Economics 20,
no. 4 (2001): 461–494. However, we present results from the standard
two-part model here because predictions were closer to the actual sample means
and the Cook-Weisberg test could not reject the null of homoskedasticity in
both years. We transformed the estimates to their original dollar scale using
the smearing estimator. See N. Duan, “Smearing Estimate: A Nonparametric
Retransformation Method,” Journal of the American
Statistical Association 78, no. 383 (1983): 605–610.
13. B. Efron, “Bootstrap Methods: Another Look at the Jackknife,” Annals
of Statistics 7, no. 1 (1979): 1–26.
14. National Aeronautics and Space Administration, “GDP Deflator
Inflation Calculator,” 26 March 2004, www.jsc.nasa.gov/bu2/inflateGDP.html (20
September 2004).
15. The counterfactual levels equal per capita spending for normal-weight
people in 2001 multiplied by the sum of the products of per capita spending
ratios and prevalence levels for each weight group in 1987. This level displayed
in Exhibit 3 is $2,997 = $2,907 x (1.15 x 0.036 + 1.00 x 0.516 + 1.02
x 0.313 + 1.15 x 0.135).
16. Some medical events were associated with multiple medical conditions.
However, nearly 90 percent of total spending linked to an event reported
a single medical condition. Since we are interested in explaining the role
of obesity in influencing spending growth within a condition, we are not
concerned about double counting across conditions. We reach similar conclusions
when the sample is limited to spending associated with medical events that
report only the single condition (for example, diabetes only).
17. Flegal et al., “Prevalence and Trends in Obesity.”
18. Institute of Medicine, Crossing the Quality Chasm:
A New Health System for the Twenty-first Century (Washington:
National Academies Press, 2001).
19. D.M. Cutler and M. McClellan, “Is Technological Change in
Medicine Worth It?” Health Affairs 20,
no. 5 (2001): 11–29.
20. Finkelstein et al., “National Medical Spending Attributable
to Overweight and Obesity.”
Ken Thorpe (kthorpe{at}sph.emory.edu)
is the Robert W. Woodruff Professor and Chair, Department of Health Policy
and Management, Rollins School of Public Health, Emory University, in Atlanta,
Georgia. Curtis Florence and David Howard are assistant professors in that
department; Peter Joski is a research associate.
DOI: 10.1377/hlthaff.w4.480
©2004 Project HOPE–The People-to-People Health Foundation, Inc.
|
