T h e n e w e n g l a n d j o u r n a l o f m e d i c i n e
n engl j med 381;25 nejm.org December 19, 20192440
From the Center for Health Decision Sci-
ence (Z.J.W.) and the Departments of
Health Policy and Management (S.N.B.)
and Social and Behavioral Sciences (A.L.C.,
J.L.B., C.M.G., C.F., S.L.G.), Harvard T.H.
Chan School of Public Health, Boston;
and the Department of Prevention and
Community Health, Milken Institute School
of Public Health, George Washington
University, Washington, D.C. (M.W.L.).
Address reprint requests to Mr. Ward at
the Center for Health Decision Science,
Harvard T.H. Chan School of Public
Health, 718 Huntington Ave., Boston, MA,
02115, or at zward@ hsph . harvard . edu.
N Engl J Med 2019;381:2440-50.
DOI: 10.1056/NEJMsa1909301
Copyright © 2019 Massachusetts Medical Society.
BACKGROUND
Although the national obesity epidemic has been well documented, less is known
about obesity at the U.S. state level. Current estimates are based on body measures
reported by persons themselves that underestimate the prevalence of obesity, es-
pecially severe obesity.
METHODS
We developed methods to correct for self-reporting bias and to estimate state-
specific and demographic subgroup–specific trends and projections of the preva-
lence of categories of body-mass index (BMI). BMI data reported by 6,264,226
adults (18 years of age or older) who participated in the Behavioral Risk Factor
Surveillance System Survey (1993–1994 and 1999–2016) were obtained and cor-
rected for quantile-specific self-reporting bias with the use of measured data from
57,131 adults who participated in the National Health and Nutrition Examination
Survey. We fitted multinomial regressions for each state and subgroup to estimate
the prevalence of four BMI categories from 1990 through 2030: underweight or
normal weight (BMI [the weight in kilograms divided by the square of the height
in meters], <25), overweight (25 to <30), moderate obesity (30 to <35), and severe
obesity (≥35). We evaluated the accuracy of our approach using data from 1990
through 2010 to predict 2016 outcomes.
RESULTS
The findings from our approach suggest with high predictive accuracy that by
2030 nearly 1 in 2 adults will have obesity (48.9%; 95% confidence interval [CI],
47.7 to 50.1), and the prevalence will be higher than 50% in 29 states and not
below 35% in any state. Nearly 1 in 4 adults is projected to have severe obesity
by 2030 (24.2%; 95% CI, 22.9 to 25.5), and the prevalence will be higher than
25% in 25 states. We predict that, nationally, severe obesity is likely to become the
most common BMI category among women (27.6%; 95% CI, 26.1 to 29.2), non-
Hispanic black adults (31.7%; 95% CI, 29.9 to 33.4), and low-income adults (31.7%;
95% CI, 30.2 to 33.2).
CONCLUSIONS
Our analysis indicates that the prevalence of adult obesity and severe obesity will
continue to increase nationwide, with large disparities across states and demo-
graphic subgroups. (Funded by the JPB Foundation.)
A B S T R A C T
Projected U.S. State-Level Prevalence
of Adult Obesity and Severe Obesity
Zachary J. Ward, M.P.H., Sara N. Bleich, Ph.D., Angie L. Cradock, Sc.D.,
Jessica L. Barrett, M.P.H., Catherine M. Giles, M.P.H., Chasmine Flax, M.P.H.,
Michael W. Long, Sc.D., and Steven L. Gortmaker, Ph.D.
Special Article
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Projec ted Pr e va lence of Obesit y a nd Se v er e Obesit y
Although the growing obesity epi-demic in the United States has been well documented,1-4 less is known about long-
term trends and the future of obesity prevalence.
Although national projections of obesity have
been made previously,5-7 state-specific analyses
are limited. State-specific projections of the bur-
den of obesity are important for policymakers,
given the considerable variation in the prevalence
of obesity across states,8 the substantial state-
level financial implications,9 and the opportunity
for obesity-prevention interventions to be imple-
mented at a local level.10-13
However, a barrier to accurate state-level pro-
jections is the paucity of objectively measured
body-mass index (BMI) data according to state.
The Behavioral Risk Factor Surveillance System
(BRFSS), a nationally representative telephone
survey of more than 400,000 adults each year,14
provides participants’ estimates of height and
weight according to state. These data have been
used to track obesity prevalence and are the
basis of maps that have illustrated the growth of
the obesity epidemic.1 Although the BRFSS pro-
vides valuable state-level estimates over time, the
reliance on subjective body measures reported by
participants substantially underestimates the prev-
alence of obesity owing to the well-documented
self-reporting bias.8,15,16 We developed a method
of bias correction to adjust the entire distribu-
tion of BMI in the BRFSS surveys from 1993
through 2016 and estimated state-level historical
trends and projections of the prevalence of BMI
categories from 1990 through 2030 according to
demographic subgroup.
M e t h o d s
Overview
We adjusted reported BMI data from the BRFSS
to align the data with objectively measured BMI
distributions from the National Health and Nu-
trition Examination Survey (NHANES), a nation-
ally representative survey in which measured
data on height and weight are collected with the
use of standardized examination procedures.17
We estimated trends in the prevalence of BMI
categories according to subgroup in each state
and made projections through 2030. The first
author designed the study, gathered and analyzed
the data, and vouches for the accuracy and com-
pleteness of the data. All the authors critically
revised the manuscript and made the decision to
submit the manuscript for publication.
Data
We obtained BRFSS data from 1993 through
1994 and 1999 through 2016, periods during
which annual data were collected for all 50 states
and Washington, D.C. (except for Wyoming in
1993, Rhode Island in 1994, and Hawaii in 2004).
We obtained nationally representative NHANES
data from 1991 through 1994 (phase 2 of
NHANES III) and from 1999 through 2016 (con-
tinuous NHANES). Data from pre-1999 BRFSS
surveys were restricted to 1993 and 1994 to co-
incide with phase 2 of NHANES III. (Before
1993, not all states were included in the BRFSS.)
We cleaned each data set to ensure that the vari-
ables of interest were not missing and ensured
that reported height and weight in the BRFSS
were biologically plausible. Our final BRFSS data
set included 6,264,226 adults (18 years of age
or older), and our NHANES data set included
57,131 adults. (Exclusion criteria and respondent
characteristics are provided in Section 1 in the
Supplementary Appendix, available with the full
text of this article at NEJM.org.)
Adjustment for Self-Reporting Bias
We adjusted reported BMI data from the BRFSS
so that the distribution was similar to measured
BMI from NHANES. Because both the BRFSS
and NHANES are designed to be nationally repre-
sentative surveys, data from NHANES can be
used to adjust participant-reported body measures
in the BRFSS. By adjusting the entire distribution
of reported BMI to be consistent with measured
BMI in NHANES, we adjusted for self-reporting
bias while preserving the relative position of each
person’s BMI.8 Specifically, we estimated the dif-
ference between participant-reported BMI and
measured BMI according to quantile and then fit
cubic splines to smoothly estimate self-reporting
bias across the entire BMI distribution. Each per-
son’s BMI was then adjusted for this bias given
his or her BMI quantile. We adjusted BMI dis-
tributions separately according to sex and time
period (1993–1994, 1999–2004, 2005–2010, and
2011–2016) to control for potential time trends
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T h e n e w e n g l a n d j o u r n a l o f m e d i c i n e
in self-reporting bias and composition of demo-
graphic subgroups. (Additional details are provid-
ed in Section 2 in the Supplementary Appendix.)
State-Specific
Trends and Projections
BMI categories were defined according to the
Centers for Disease Control and Prevention (CDC)
guidelines: underweight or normal weight (BMI
[the weight in kilograms divided by the square
of the height in meters], <25), overweight (25 to
<30), moderate obesity (30 to <35), and severe
obesity (≥35).18 We used multinomial (renormal-
ized logistic) regressions to predict the preva-
lence of each BMI category as a function of time.
This method ensures that the prevalence of all
categories sums to 100% in each year and allows
estimation of nonlinear trends in the prevalence
of BMI categories. Our reduced covariate model
(i.e., with year as the independent variable) im-
plicitly accounts for trends in the composition of
demographic subgroups (e.g., age distribution
and composition of race or ethnic group catego-
ries) within each state, since the relative contri-
butions of these various factors (and their po-
tential changing effect over time) are already
reflected in the prevalence estimates. Such an ap-
proach also implicitly controls for trends in other
variables that may affect BMI, such as smoking or
illness. Although it is important to explicitly con-
trol for these variables when estimating the ef-
fect of BMI on related health outcomes, because
our outcome of interest was the prevalence of
BMI categories over time, it was not necessary to
control for these variables because their effect
was already reflected in the observed prevalence
estimates used to fit the models. (Additional de-
tails and a discussion of previous approaches are
provided in Sections 3.1 and 3.2 in the Supple-
mentary Appendix.)
Regressions were performed nationally and
for each state independently, while taking the
complex survey structure of the BRFSS into ac-
count. We estimated historical trends and pro-
jections of the prevalence of each BMI category
from 1990 through 2030, as well as the preva-
lence of overall obesity (BMI, ≥30). We also
made projections for demographic subgroups to
examine trends and explore the effect of geogra-
phy (i.e., state of residence) on obesity trends
within subgroups. We estimated trends accord-
ing to sex (male or female), race or ethnic group
(non-Hispanic white, non-Hispanic black, His-
panic, or non-Hispanic other), annual house-
hold income (<$20,000, $20,000 to <$50,000, or
≥$50,000), education (less than high-school grad-
uate, high-school graduate to some college, or
college graduate), and age group (18 to 39, 40 to
64, or ≥65 years) (Section 3.3 in the Supplemen-
tary Appendix). Because of the small sample
sizes and changing BRFSS categories of race or
ethnic group over time, we combined five groups
(“American Indian or Alaskan Native,” “Asian,”
“Native Hawaiian or Pacific Islander,” “other,”
and “multiracial”) into one “non-Hispanic other”
category.
In accordance with the CDC guidelines that
consider BRFSS estimates unreliable if they are
based on a sample of fewer than 50 people,19 we
suppressed state-level estimates from subgroups
with fewer than 1000 respondents; given our
data set of 20 rounds of BRFSS surveys, we sup-
pressed estimates from subgroups with fewer
than 50 respondents on average per year in a
state. Thus, estimates for the following sub-
groups were suppressed: non-Hispanic black
adults in 12 states (Alaska, Hawaii, Idaho, Maine,
Montana, New Hampshire, North Dakota, Ore-
gon, South Dakota, Utah, Vermont, and Wyo-
ming) and Hispanic adults in 2 states (North
Dakota and West Virginia).
To account for uncertainty, we bootstrapped
both data sets (NHANES and BRFSS) 1000
times, considering the complex structure of each
survey (Section 3.4 in the Supplementary Ap-
pendix) and repeated all analyses (i.e., adjustment
for self-reporting bias and state-specific projec-
tions). We report the mean and 95% confidence
interval (calculated as the 2.5 and 97.5 percen-
tiles of the bootstrapped values) for all esti-
mates.
Assessment of Predictive Accuracy
and Sensitivity Analyses
To evaluate the accuracy of our approach, we
restricted our data sets (NHANES and BRFSS) to
include only data from 1999 through 2010. We
then repeated our analyses with this subset of
data and predicted the prevalence of each BMI
category in 2016 (i.e., 6 years after the last ob-
served year in our truncated data). We compared
our predictions with the observed prevalence
(corrected for self-reporting bias) in 2016. This
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Projec ted Pr e va lence of Obesit y a nd Se v er e Obesit y
exercise allowed us to evaluate the accuracy of
our approach in predicting future values and
allowed us to assess the potential effect of the
change in the BRFSS sample design in 2011 to
include cell-phone interviews on our estimation
of trends. For our predictions, we calculated the
coverage probability (i.e., the percentage of ob-
served estimates that fell within our 95% confi-
dence intervals), the percentage of our mean
predictions that fell within a certain distance
(e.g., 10% relative error) of the observed esti-
mate, and the mean absolute error.
In a sensitivity analysis, we also made projec-
tions based on self-reported body measures (i.e.,
no adjustment for self-reporting bias). Statistical
analyses were performed with the use of R soft-
ware, version 3.2.5 (R Foundation for Statistical
Computing), with BRFSS bootstrapping per-
formed in Java for computational efficiency.
R e s u l t s
Bias-Corrected BMI Data
After we corrected for self-reporting bias, our
adjusted BMI distributions in the BRFSS data set
did not differ significantly (P>0.05) from those
in the NHANES data set for each sex and time
period. Adjustment of the entire BMI distribu-
tion also ensured that the prevalence of each
BMI category in the BRFSS data set was similar
to that in the NHANES data set. BMI values for
men and women were adjusted on average by
0.71 and 1.76 units, respectively, with differential
(increasing) adjustment according to reported
BMI. (Additional details are provided in Sec-
tion 2 in the Supplementary Appendix.)
Predictive Accuracy
Our coverage probability (i.e., the percentage of
time that our 95% confidence intervals con-
tained the observed estimate) for state-level prev-
alence in 2016 was 94.6% across the four BMI
categories. Subgroup-specific coverage probabil-
ities were 92.5% on average (Section 4 in the
Supplementary Appendix). Our mean predictions
for states were within 10% (relative error) of the
reported estimate 95.6% of the time, with a mean
absolute error of 0.85 percentage points. Although
our coverage probabilities are high, our mean
predictions are less accurate for subgroups with
smaller sample sizes.
Trends and Projections
Our projections show that the national preva-
lence of adult obesity and severe obesity will rise
to 48.9% (95% confidence interval [CI], 47.7 to
50.1) and 24.2% (95% CI, 22.9 to 25.5), respec-
tively, by 2030, with large variation across states.
Maps of state-level prevalence of obesity and
severe obesity over time are provided in Figure 1.
Based on current trends, our projections show
that the prevalence of overall obesity (BMI, ≥30)
will rise above 50% in 29 states by 2030 and will
not be below 35% in any state. We also project
that the prevalence of severe obesity (BMI, ≥35)
will rise above 25% in 25 states (Table 1). State-
level trends in the prevalence of each BMI cate-
gory are presented according to subgroup in
Section 5 in the Supplementary Appendix. These
trends show that the prevalence of overweight is
declining as obesity develops in more people.
Our sensitivity analyses, which did not cor-
rect for self-reporting bias, revealed similar trends
over time but with an overall projected obesity
prevalence that was on average 5.3 percentage
points lower than the bias-corrected obesity
prevalence (relative error of approximately 10%)
and similar underestimates according to sub-
group (Section 6 in the Supplementary Appendix).
Our projections also revealed large disparities
in obesity prevalence across subgroups. We project
that by 2030 severe obesity will be the most com-
mon BMI category nationwide among women,
black non-Hispanic adults, and low-income adults
(i.e., household income <$50,000) (Fig. 2).
In addition, we found large geographic dis-
parities within subgroups (Fig. 3). (State-level
maps and tables are provided in Sections 7 and 8
in the Supplementary Appendix.) In general, we
found a higher prevalence of obesity among non-
Hispanic black and Hispanic adults than among
non-Hispanic white adults, and the heterogene-
ity in the composition of the non-Hispanic other
category of race or ethnic group across states
was ref lected by the variation in obesity preva-
lence across states for this group.
We also found a large gradient in the preva-
lence of obesity according to income. For exam-
ple, our projections show that severe obesity will
be the most common BMI category in 44 states
among adults with an annual household income
of less than $20,000, as compared with only 1 state
among adults with an annual household income
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T h e n e w e n g l a n d j o u r n a l o f m e d i c i n e
B Prevalence of Severe Obesity (BMI, ≥35)A Prevalence of Overall Obesity (BMI, ≥30)
1990 1990
2000 2000
2010 2010
2020 2020
2030 2030
0 10 20 30 40 50 60
Prevalence (%)
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Projec ted Pr e va lence of Obesit y a nd Se v er e Obesit y
of greater than $50,000 (Fig. 3). State-specific
analyses according to subgroup are provided in
Sections 7 through 9 in the Supplementary Ap-
pendix, including the results for education and
age subgroups, as well as suppressed estimates
for race or ethnic groups.
D i s c u s s i o n
In this study, we used more than 20 years of data
from more than 6 million adults and applied an
analytical approach that provided more accurate
state-level estimates of BMI trends, corrected for
self-reporting bias. Our method differentially ad-
justed the entire BMI distribution, an approach
that preserves heterogeneity, in contrast to regres-
sion-based approaches that adjust mean values.6,15
Adjustment of the entire BMI distribution has
been shown to better capture the tails of the
BMI distribution, resulting in more accurate es-
timates of obesity prevalence, especially for severe
obesity.8
Although analyses of trends in adult obesity
in the United States have been performed previ-
ously,1-6,15,20-23 a strength of our analysis is that
we provided both national and state-level, sub-
group-specific estimates (i.e., 832 demographic
subgroups) based on bias-corrected data from
more than 6 million adults over many years.
Although previous criticisms of obesity projec-
tions — often based on small samples over short
periods — argue that changes in obesity preva-
lence have not followed a predictable pattern,24
we observed remarkably stable and predictable
trends across a wide range of states and demo-
graphic subgroups. Moreover, we provided em-
pirical evidence of the predictive validity of our
approach, showing that our model has a high
degree of accuracy. Our coverage probabilities of
approximately 95% indicate that our 95% confi-
dence intervals appropriately reflect the uncer-
tainty around our estimates.
Our sensitivity analyses, which did not adjust
for self-reporting bias, revealed similar trends to
those in our main analysis but with a lower
prevalence, as expected. For example, our unad-
justed projections of the prevalence of obesity
among women in 2030 were on average 13%
(6.4 percentage points) lower than our bias-
corrected projections, a finding that highlights
the importance of correcting for self-reporting
bias to obtain accurate prevalence estimates.
We found that nearly 1 in 2 adults nationwide
will probably have obesity by 2030, with large
disparities across states and demographic sub-
groups. Using our model, we projected that by
2030 the majority of adults in 29 states will have
obesity and that the prevalence of obesity will
approach 60% in some states and not be below
35% in any state. These results are similar to
previous estimates showing that 57% of children
2 to 19 years of age in 2016 are projected to have
obesity by the age of 35 years.7
We noted that as more adults cross the
threshold to obesity, the prevalence of overweight
is declining, a finding that highlights the impor-
tance of assessing changes in weight across the
entire BMI distribution rather than focusing on
only one category. Especially worrisome is the
projected rise in the prevalence of severe obesity,
which is associated with even higher mortality
and morbidity25 and health care costs.9 Using
our model, we projected that by 2030 nearly 1 in 4
U.S. adults will have severe obesity, and the
prevalence will be higher than 25% in 25 states.
Severe obesity is thus poised to become as preva-
lent as overall obesity was in the 1990s. Indeed,
our projections suggest that severe obesity may
become the most common BMI category among
adults in 10 states by 2030 and even more common
in some subgroups, especially among women,
non-Hispanic black adults, and low-income adults;
these findings highlight persistent disparities
according to sex, race or ethnic group, and in-
come. The high projected prevalence of severe
obesity among low-income adults and the high
medical costs of severe obesity have substantial
implications for future health care costs,9 espe-
cially as states expand access to obesity-related
services for adult Medicaid beneficiaries.26
Although severe obesity was once a rare con-
Figure 1 (facing page). Estimated Prevalence of Overall
Obesity and Severe Obesity in Each State, from 1990
through 2030.
Shown is the estimated prevalence of overall obesity
(Panel A) and severe obesity (Panel B) among adults in
each U.S. state from 1990 through 2030. Overall obesity
includes the BMI (body-mass index) categories of
moderate obesity (BMI, 30 to <35) and severe obesity
(BMI, ≥35).
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T h e n e w e n g l a n d j o u r n a l o f m e d i c i n e
State Overall Obesity (BMI, ≥30)* Severe Obesity (BMI, ≥35)
Overall Men Women Overall Men Women
percentage (95% confidence interval)
U.S. overall 48.9 (47.7–50.1) 48.2 (46.8–49.6) 49.9 (48.5–51.4) 24.2 (22.9–25.5) 21.1 (19.6–22.6) 27.6 (26.1–29.2)
Alabama 58.2 (56.2–60.2) 56.7 (53.8–59.4) 59.7 (57.3–62.3) 30.6 (28.5–32.8) 25.6 (22.6–28.5) 35.7 (33.2–38.3)
Alaska 49.3 (46.3–52.2) 48.9 (45.0–53.1) 50.0 (46.1–54.1) 24.2 (21.4–26.8) 21.7 (17.5–25.7) 27.6 (24.1–31.4)
Arizona 51.4 (48.9–53.9) 49.3 (45.7–53.0) 53.6 (50.5–56.6) 24.4 (22.1–26.7) 20.8 (17.5–24.2) 28.3 (25.3–31.2)
Arkansas 58.2 (55.7–60.4) 56.7 (53.2–59.9) 59.9 (57.0–62.8) 32.6 (30.1–35.1) 29.6 (26.2–33.1) 36.1 (33.0–39.1)
California 41.5 (39.9–43.3) 41.1 (39.0–43.4) 42.1 (40.0–44.3) 18.3 (16.8–19.8) 16.1 (14.1–18.1) 20.9 (19.0–22.8)
Colorado 38.2 (36.3–40.3) 37.5 (34.8–40.0) 39.2 (36.7–42.0) 16.8 (15.2–18.6) 14.3 (12.1–16.6) 19.8 (17.6–22.2)
Connecticut 46.6 (44.4–48.9) 46.5 (43.5–49.4) 46.9 (44.3–49.6) 22.5 (20.6–24.6) 19.8 (17.2–22.7) 25.3 (22.9–27.9)
Delaware 53.2 (51.0–55.7) 51.4 (48.2–55.0) 55.0 (51.9–58.1) 27.1 (24.8–29.6) 22.2 (19.0–25.6) 31.7 (28.7–34.8)
District of Columbia 35.3 (33.0–37.8) 32.3 (29.1–36.3) 39.0 (35.9–42.2) 17.3 (15.2–19.3) 11.3 (8.9–13.9) 23.1 (20.3–26.1)
Florida 47.0 (45.0–48.9) 47.9 (45.5–50.2) 46.3 (43.9–48.8) 21.3 (19.7–23.1) 19.0 (16.7–21.1) 24.0 (22.0–26.3)
Georgia 51.9 (49.9–54.2) 49.6 (46.6–52.7) 54.5 (51.8–57.2) 26.6 (24.3–28.8) 21.2 (18.3–24.2) 32.1 (29.6–34.7)
Hawaii 41.3 (39.2–43.4) 43.3 (40.3–46.1) 39.1 (36.4–41.9) 18.2 (16.4–20.2) 17.5 (14.9–20.1) 19.1 (17.0–21.7)
Idaho 47.7 (45.4–50.0) 48.0 (44.5–51.3) 47.7 (44.6–50.6) 23.0 (20.8–25.2) 20.8 (17.9–23.8) 26.0 (23.3–28.7)
Illinois 50.0 (47.8–52.1) 48.6 (45.3–51.3) 51.6 (48.9–54.5) 25.5 (23.5–27.7) 20.7 (17.8–23.5) 30.4 (27.5–33.0)
Indiana 51.6 (49.7–53.6) 50.7 (48.1–53.5) 52.9 (50.3–55.4) 26.9 (24.8–29.0) 24.1 (21.2–26.9) 30.3 (27.8–32.8)
Iowa 52.0 (50.0–54.0) 52.6 (49.8–55.2) 51.9 (49.2–54.4) 26.4 (24.4–28.5) 24.8 (22.0–27.7) 28.8 (26.1–31.5)
Kansas 55.6 (53.8–57.5) 54.3 (51.8–56.9) 57.0 (54.7–59.5) 30.6 (28.7–32.5) 26.7 (24.3–29.3) 34.8 (32.6–37.2)
Kentucky 54.8 (52.9–56.8) 54.5 (51.8–57.2) 55.4 (53.0–57.9) 29.4 (27.4–31.4) 26.0 (23.3–28.8) 33.1 (30.5–35.7)
Louisiana 57.2 (55.1–59.2) 56.3 (53.2–59.3) 58.3 (55.6–61.0) 31.2 (28.9–33.5) 26.8 (23.5–29.9) 36.0 (33.2–38.9)
Maine 50.3 (48.1–52.6) 49.4 (46.3–52.5) 51.3 (48.5–54.0) 24.2 (22.1–26.4) 20.9 (18.2–23.7) 27.7 (25.0–30.3)
Maryland 50.0 (48.1–52.0) 48.0 (45.4–50.8) 52.1 (49.7–54.5) 24.6 (22.8–26.6) 19.7 (17.5–22.1) 29.4 (27.0–31.9)
Massachusetts 42.3 (40.2–44.3) 43.1 (40.4–45.7) 41.7 (39.1–44.2) 20.0 (18.2–22.1) 18.7 (16.3–21.4) 21.5 (19.3–24.0)
Michigan 51.9 (50.2–53.7) 51.2 (48.8–53.6) 53.0 (50.8–55.2) 27.2 (25.5–29.1) 24.4 (21.9–26.9) 30.7 (28.3–33.1)
Minnesota 46.1 (44.3–48.0) 48.2 (46.0–50.4) 44.3 (41.9–46.6) 20.4 (18.7–22.2) 20.0 (17.7–22.3) 21.6 (19.5–23.6)
Mississippi 58.2 (56.0–60.2) 54.3 (51.1–57.2) 62.0 (59.3–64.6) 31.7 (29.5–33.9) 24.6 (21.4–28.0) 38.6 (35.9–41.2)
Missouri 52.4 (50.2–54.6) 51.0 (47.8–54.1) 53.9 (51.0–56.5) 28.3 (26.1–30.5) 24.4 (21.5–27.5) 32.4 (29.6–35.1)
Montana 44.2 (41.8–46.6) 44.5 (41.4–47.6) 44.3 (41.3–47.5) 21.4 (19.3–23.5) 19.6 (16.7–22.6) 23.9 (21.2–26.8)
Nebraska 51.3 (49.3–53.3) 51.0 (48.3–53.7) 51.7 (49.2–54.1) 25.4 (23.4–27.4) 21.5 (18.9–24.1) 29.6 (27.0–32.2)
Nevada 45.5 (42.7–48.3) 45.3 (41.5–49.0) 45.8 (42.1–49.6) 20.6 (18.1–23.4) 18.1 (14.7–22.1) 23.4 (20.0–26.8)
New Hampshire 48.8 (46.6–51.1) 50.5 (47.3–53.5) 47.1 (44.1–50.0) 24.1 (21.9–26.5) 21.9 (18.8–25.2) 26.6 (23.7–29.6)
New Jersey 46.6 (44.4–48.6) 48.6 (45.6–51.6) 44.8 (42.0–47.4) 21.7 (19.8–23.5) 19.9 (17.2–22.7) 23.8 (21.4–26.2)
New Mexico 51.8 (49.5–54.1) 49.5 (46.0–52.6) 54.6 (51.8–57.3) 24.8 (22.6–27.0) 22.7 (19.6–26.0) 27.5 (24.9–30.3)
New York 42.8 (41.0–44.8) 42.0 (39.5–44.7) 43.9 (41.4–46.3) 19.8 (18.2–21.6) 17.5 (15.2–19.9) 22.5 (20.4–24.8)
North Carolina 50.3 (48.3–52.2) 47.3 (44.8–49.9) 53.4 (50.8–55.7) 25.7 (23.6–27.5) 21.0 (18.3–23.6) 30.6 (28.0–33.0)
North Dakota 53.9 (51.6–56.1) 56.5 (53.4–59.4) 51.3 (48.5–54.0) 26.9 (24.7–29.0) 26.6 (23.4–29.6) 27.9 (24.9–30.7)
Ohio 53.2 (51.0–55.3) 52.4 (49.5–55.3) 54.1 (51.3–56.9) 26.8 (24.8–28.8) 23.8 (21.1–26.6) 30.0 (27.2–32.7)
Oklahoma 58.4 (56.4–60.2) 59.5 (56.9–61.9) 57.5 (54.9–59.8) 31.7 (29.7–33.9) 29.0 (26.1–32.0) 34.9 (32.6–37.6)
Oregon 47.5 (45.5–49.5) 47.9 (45.1–50.8) 47.3 (44.7–49.8) 24.1 (22.0–26.1) 21.6 (18.7–24.5) 27.1 (24.5–29.7)
Pennsylvania 50.2 (48.2–52.1) 50.8 (48.1–53.2) 50.0 (47.7–52.5) 24.8 (22.7–26.8) 23.3 (20.7–25.8) 27.0 (24.5–29.6)
Table 1. Projected State-Specific Prevalence of Adult Obesity and Severe Obesity in 2030.
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Projec ted Pr e va lence of Obesit y a nd Se v er e Obesit y
State Overall Obesity (BMI, ≥30)* Severe Obesity (BMI, ≥35)
Overall Men Women Overall Men Women
percentage (95% confidence interval)
Rhode Island 47.3 (45.0–49.9) 48.8 (45.3–52.3) 46.3 (42.8–49.7) 22.9 (20.6–25.4) 21.9 (18.7–25.3) 24.5 (21.6–27.6)
South Carolina 52.8 (51.0–54.6) 49.6 (47.0–52.3) 56.0 (53.6–58.3) 27.2 (25.3–29.1) 21.2 (18.8–23.8) 33.0 (30.7–35.4)
South Dakota 50.6 (48.1–52.9) 53.0 (49.6–56.1) 48.2 (45.1–51.4) 25.2 (22.9–27.7) 24.1 (20.8–27.3) 26.9 (24.1–29.9)
Tennessee 55.8 (53.9–57.8) 55.0 (52.1–57.8) 56.9 (54.4–59.5) 29.9 (27.8–32.1) 26.5 (23.5–29.7) 33.7 (31.2–36.5)
Texas 52.9 (50.9–54.7) 50.1 (47.3–52.5) 55.9 (53.5–58.5) 26.6 (24.6–28.5) 22.5 (20.0–25.2) 31.1 (28.5–33.8)
Utah 43.2 (41.3–45.1) 43.9 (41.5–46.3) 42.7 (40.2–45.2) 20.6 (18.9–22.6) 18.8 (16.7–21.3) 23.0 (20.6–25.5)
Vermont 43.6 (41.5–45.8) 43.1 (40.2–46.1) 44.2 (41.7–47.0) 20.7 (18.9–22.7) 17.8 (15.4–20.2) 23.9 (21.5–26.4)
Virginia 48.9 (46.7–50.9) 46.0 (43.0–48.9) 51.8 (48.9–54.7) 25.3 (23.3–27.5) 20.7 (18.0–23.4) 30.0 (27.4–32.4)
Washington 47.4 (45.6–49.2) 48.0 (45.7–50.3) 47.2 (44.9–49.5) 22.6 (20.9–24.4) 20.9 (18.6–23.2) 25.0 (23.0–27.2)
West Virginia 57.5 (55.6–59.4) 57.0 (54.2–59.6) 58.3 (55.8–61.0) 30.8 (28.7–32.8) 27.0 (24.1–29.9) 35.2 (32.5–37.9)
Wisconsin 50.3 (48.0–52.7) 50.3 (47.0–53.2) 50.7 (47.6–53.7) 25.5 (23.4–27.8) 23.1 (20.2–26.1) 28.6 (25.7–31.7)
Wyoming 48.2 (45.6–50.9) 45.5 (41.6–49.3) 51.3 (47.7–54.8) 22.4 (19.8–25.0) 19.2 (16.0–22.4) 26.1 (22.7–29.8)
* “Overall obesity” includes the body-mass index (BMI) categories of moderate obesity (BMI, 30 to <35) and severe obesity (BMI, ≥35).
Table 1. (Continued.)
Figure 2. Projected National Prevalence of BMI Categories in 2030, According to Demographic Subgroup.
Shown is the projected national prevalence of BMI categories in 2030, according to sex, race or ethnic group, and
annual household income.
0 10 20 30 40 50 60 70 80 90 100
Prevalence (%)
Underweight or normal
weight (BMI, <25)
Overweight
(BMI, 25 to <30)
Moderate obesity
(BMI, 30 to <35)
Severe obesity
(BMI, ≥35)
Overall
Male
Female
Non-Hispanic white
Non-Hispanic black
Hispanic
Non-Hispanic other
<$20,000
$20,000 to <$50,000
≥$50,000
Annual Household Income
Race or Ethnic Group
Sex
21.5 (20.5−22.6)
17.9 (17.1−18.8)
19.8 (18.9−20.7)
37.9 (35.9−39.8)
17.1 (16.0−18.2)
17.5 (16.6−18.6)
21.7 (20.8−22.6)
23.5 (22.4−24.6)
19.4 (18.5−20.3)
21.4 (20.6−22.3)
31.4 (30.2−32.6)
27.7 (26.7−28.8)
24.6 (23.6−25.7)
31.7 (30.0−33.6)
30.5 (29.0−32.0)
25.6 (24.3−26.9)
30.2 (29.1−31.2)
26.6 (25.7−27.5)
32.5 (31.2−33.8)
29.7 (28.6−30.7)
25.6 (24.6−26.6)
25.8 (24.8−26.7)
23.9 (22.8−24.9)
16.8 (15.5−18.1)
27.9 (26.4−29.4)
25.2 (24.0−26.5)
24.7 (23.8−25.5)
22.3 (21.6−23.0)
27.1 (25.7−28.5)
24.8 (23.9−25.6)
21.5 (20.2−22.9)
28.6 (27.1−30.0)
31.7 (30.2−33.2)
13.7
(12.4−15.0)
24.5 (22.8−26.2)
31.7 (29.9−33.4)
23.4 (22.1−24.8)
27.6 (26.1−29.2)
21.1 (19.6−22.6)
24.2 (22.9−25.5)
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T h e n e w e n g l a n d j o u r n a l o f m e d i c i n e
A Sex
B Race or Ethnic Group
C Annual Household Income
Male Female
Non-Hispanic White Non-Hispanic Black
Hispanic Non-Hispanic Other
<$20,000 $20,000 to <$50,000
≥$50,000 Overall
Underweight or normal
weight (BMI, <25)
Overweight
(BMI, 25 to <30)
Moderate obesity
(BMI, 30 to <35)
Severe obesity
(BMI, ≥35)
Suppressed
estimate
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Projec ted Pr e va lence of Obesit y a nd Se v er e Obesit y
dition, our findings suggest that it will soon be
the most common BMI category in the patient
populations of many health care providers. Given
that health professionals are often poorly pre-
pared to treat obesity,27 this impending burden
of severe obesity and associated medical compli-
cations has implications for medical practice
and education. In addition to the profound health
effects, such as increased rates of chronic dis-
ease and negative consequences on life expec-
tancy,25,28 the effect of weight stigma29 may have
far-reaching implications for socioeconomic dis-
parities as severe obesity becomes the most
common BMI category among low-income adults
in nearly every state.
Given the difficulty in achieving and main-
taining meaningful weight loss,30,31 these find-
ings highlight the importance of prevention ef-
forts. Although some cost-effective prevention
interventions have been identified,10 a range of
sustained approaches to maintain a healthy weight
over the life course, including policy and envi-
ronmental interventions at the community level
that address upstream social and cultural deter-
minants of obesity,32 will probably be needed to
prevent further weight gain across the BMI dis-
tribution.
Our analysis has certain limitations. Although
we found that our model predictions are accu-
rate for states overall, our point estimates (i.e.,
mean predictions) may be less accurate for sub-
groups with smaller sample sizes. However, our
high coverage probabilities for all subgroups
indicate that we appropriately accounted for the
uncertainty around our estimates, which high-
lights the importance of considering the 95%
confidence intervals of our projections as well.
In addition, our assessment of predictive accu-
racy reveals that our projections are robust to
the change in the BRFSS sample design in 2011
to include cell-phone interviews. Although our
predictive validity checks from 2010 through 2016
help build confidence in our approach, projec-
tions through 2030 involve a much longer period,
so the uncertainty around our projections may be
larger than estimated because we assumed that
current trends will continue.
Because of data limitations, we could not ex-
plore trends in obesity according to all race or
ethnic groups included in our “non-Hispanic
other” category. We found large differences in
the prevalence of obesity across states for this
category, a finding that is consistent with the
well-known differences in obesity prevalence
among Native American, Native Hawaiian, and
Asian populations that are included in this hetero-
geneous category, which differs in composition
from state to state. Also, because the BRFSS re-
ports categories of annual household income (as
opposed to actual dollar values), we were unable
to adjust the household income of respondents
for inflation over time.
Finally, because of the small sample size, we
combined underweight (BMI, <18.5) and normal
weight into one category. (Underweight com-
prises only 2% of respondents in our NHANES
data set.) Although this grouping may be prob-
lematic when used as the reference category for
estimating BMI-related health risks, it should
not present any problems for estimating the
prevalence of BMI categories.
We project that given current trends, nearly
1 in 2 U.S. adults will have obesity by 2030, and
the prevalence will be higher than 50% in 29
states and not below 35% in any state — a level
currently considered high. Furthermore, our pro-
jections show that severe obesity will affect
nearly 1 in 4 adults by 2030 and become the most
common BMI category among women, black non-
Hispanic adults, and low-income adults.
Supported by the JPB Foundation.
Disclosure forms provided by the authors are available with
the full text of this article at NEJM.org.
Figure 3 (facing page). Projected Most Common BMI
Category in 2030 in Each State, According to Demo-
graphic Subgroup.
Shown is the projected most common BMI category
(underweight or normal weight, overweight, moderate
obesity, or severe obesity) in 2030 in each U.S. state,
according to sex (Panel A), race or ethnic group (Panel B),
and annual household income (Panel C). In accordance
with the Centers for Disease Control and Prevention
guidelines that consider Behavioral Risk Factor Surveil-
lance System (BRFSS) survey estimates unreliable if
they are based on a sample of fewer than 50 respon-
dents,19 we suppressed state-level estimates from sub-
groups with fewer than 1000 respondents; given our
data set of 20 rounds of BRFSS surveys, we suppressed
estimates from subgroups with fewer than 50 respon-
dents on average per year in a state.
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Projec ted Pr e va lence of Obesit y a nd Se v er e Obesit y
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