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Body Mass Index and Asthma in the Military Population of the Northwestern United States
Sylvia Y. N. Young, MD, MPH;
Jeffrey D. Gunzenhauser, MD, MPH;
Kathleen E. Malone, PhD;
Anne McTiernan, MD, PhD
Arch Intern Med. 2001;161:1605-1611.
ABSTRACT
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Background Patients with asthma commonly have other medical problems such as obesity,
but it is unclear if obesity independently relates to asthma occurrence.
Objective To examine the association between asthma and obesity.
Methods We studied enrollees aged 17 to 96 years in region 11 of TRICARE, a
military managed health care program encompassing Washington, Oregon, and
northern Idaho, using an enrollment questionnaire from January 1997 to December
1998. We performed case-control analyses on 2788 asthma cases and 39 637
controls. From these cases and controls, we selected a random sample of 1000
asthma cases and 1000 controls, linking them to a computerized military health
record system to verify if medications indicated for asthma therapy were prescribed.
After excluding cases not prescribed bronchodilator medications and excluding
controls prescribed bronchodilator medications or steroids, we used logistic
regression to estimate associations among asthma, body mass index, and demographic,
lifestyle, and comorbid risk factors in 386 verified cases and 744 verified
controls.
Results Increasing body mass index, younger age, female sex, non–active
duty beneficiary status, and arthritis were significant independent predictors
of asthma prevalence in both our larger analysis and our verified substudy,
whereas stomach ulcer, depression, hypertension, and white race are also independent
predictors of asthma prevalence in our larger analysis.
Conclusions Increasing body mass index is a key factor predicting prevalence of
asthma and, if determined to be etiologically related to asthma incidence,
is a potentially modifiable risk factor for asthma.
INTRODUCTION
IN 1990, health care expenditures for illness related to asthma were
estimated at $6.2 billion or nearly 1% of all US health care costs.1-2 Asthma affects approximately 5.6% of
the general population.3 Based on National
Health Interview Survey results from 1980 through 1990, the age-adjusted prevalence
rate for self-reported asthma increased 38%, from 3100 to 4290 per 100 000
population (from 6.8 million to 10.3 million persons affected).1
During the 1980s, asthma mortality increased by 6.2% per annum (±1.2%).4
In the US population, 54.9% of adults are overweight (body mass index
[BMI]  25.0 kg/m2) and 22% are obese (BMI 30.0 kg/m2).5 The health care costs attributable
to obesity were an estimated $99.2 billion in 1995 (5.7% of health care expenditure).6
Hypertension, obesity, arthritis, and diabetes are common in asthmatic
individuals.7-11
Hypertension, arthritis, and diabetes are common in obese individuals.7, 12-14 Therefore,
obesity might be associated with the likelihood of having asthma.7-8,11, 15-19
The few studies7-8,11, 15-19
to date that have examined the relation between BMI and asthma have involved
limited population groups such as children or patients at tertiary care institutions.
Our objective was to examine the relation between BMI and asthma in a well-defined
study population ascertained via a military health care system.
SUBJECTS AND METHODS
STUDY POPULATION AND SAMPLE DESIGN
This study was a population-based prevalence case-control study of patients
enrolled in TRICARE region 11, which encompasses Washington, Oregon, and northern
Idaho. TRICARE is a military health care system comparable to a traditional
health maintenance organization. The population includes active and retired
service members of the US Air Force, US Army, US Coast Guard, US Navy, and
US Marine Corps and their family members residing in region 11. The Health
Enrollment Assessment Review (HEAR) is a self-administered questionnaire developed
by the Office for Prevention and Health Services Assessment, the National
Center for Environmental Health, and the Batelle Memorial Institute for TRICARE
regions 6 and 4 through a Memorandum of Agreement among Armstrong Laboratory
Human Services Command, US Air Force Materiel Command, and the Centers for
Disease Control and Prevention. The questionnaires were designed to assist
the Department of Defense and the managed care contractor in identifying patient
health care resource needs as TRICARE beneficiaries. Questionnaires are mailed
to patients enrolled in TRICARE after enrollment and then annually. Questionnaire
data include demographic information, height, weight, lifestyle information,
and history of medical problems. Based on institutional data, the questionnaire
response rate was estimated to be 50%. The study was approved by the institutional
review board of Madigan Army Medical Center.
There were 50 075 questionnaires collected from January 1997 to
December 1998. If a person filled out the questionnaire more than once, data
from the most recent questionnaire were used, for a yield of 45 743 questionnaires.
Asthma cases were defined as patients responding positively to the question,
"Have you ever been told by a health care provider that you have asthma?"
All other individuals were defined as controls.
To eliminate the misclassification of asthma with emphysema, persons
responding positively to the question, "Have you ever been told by a health
care provider that you have emphysema/chronic bronchitis?" were excluded (n
= 1973 positive, n = 1345 data missing). Of the remaining 42 425 persons,
2788 were defined as cases and 39 637 were defined as controls. Of the
2788 cases, we excluded 193 (6.9%) who had no information for BMI and 18 (0.6%)
who had implausible data (such as BMI <7 kg/m2 or >60 kg/m2), leaving 2577 cases for our general analysis. Of the 39 637
controls, we excluded 3098 (7.8%) who had no information for BMI and 192 (0.4%)
who had implausible data, leaving 36 347 controls for our general case-control
analysis.
To analyze agreement of self-reported asthma diagnoses, a random sample
of 1000 cases was selected from the 2788 asthma cases and linked to the Composite
Health Care System (CHCS), a computerized military health record system that
includes information such as medication profile history, to determine if medications
indicated for asthma therapy were prescribed. Verified asthma cases were those
cases who were prescribed medications commonly indicated for treatment of
asthma, including inhaled bronchodilators (such as albuterol inhalers, metaproterenol
sulfate inhalers), oral bronchodilators (terbutaline sulfate tablets, albuterol
tablets, metaproterenol sulfate syrup), and orally inhaled steroids (triamcinolone
inhalers). Since nasally inhaled beclomethasone dipropionate and inhaled triamcinolone
aqueous nasal spray are indicated for allergic rhinitis, persons who were
prescribed beclomethasone nasal inhalers alone or triamcinolone aqueous nasal
spray alone were excluded. Since oral steroids are indicated for therapy of
other conditions, such as autoimmune disorders, persons who were prescribed
oral steroids alone were excluded. Persons prescribed a combination of beclomethasone
nasal inhaler, triamcinolone aqueous nasal spray, and/or oral steroids were
also excluded as cases. Since inhaled ipratropium bromide is indicated for
the treatment of bronchospasm associated with chronic obstructive pulmonary
disease, persons who were prescribed an ipratropium inhaler alone were also
excluded.
In verifying the random sample of 1000 asthma cases linked to the CHCS,
we excluded 248 (24.8%) not found in the CHCS, 46 (4.6%) listed as "no prescription
information," 2 (0.2%) younger than 17 years, and 1 (0.1%) listed as having
died, with no prescription information listed. Of the 703 remaining cases,
the following were excluded: 213 cases not prescribed any bronchodilators
or steroids; 73 prescribed intranasal beclomethasone, intranasal triamcinolone,
or oral steroids alone; and 1 prescribed an ipratropium inhaler alone.
Most (99%) of the remaining 416 had been prescribed an inhaler of some
sort, including bronchodilators such as albuterol, steroids such as triamcinolone,
and other inhaled medications such as cromolyn sodium. Four patients (1%)
had been prescribed oral bronchodilators alone, and 174 patients (41.8%) had
been prescribed an oral steroid in addition to other asthma therapy. Of the
416 cases remaining, we excluded 28 (7.3%) who had no information for BMI
and 2 (0.5%) who had implausible data. This left 386 verified cases.
From the initial 39 637 controls, we reviewed all medication prescription
information on a random sample of 1000 controls linked to the CHCS to determine
if medications indicated for asthma therapy were prescribed. Controls prescribed
asthma medication were excluded. We included as controls those listed in CHCS
as "no prescription information" and controls not found in the CHCS, since
most probably were not in the CHCS because they did not use any prescription
medications.
In verifying the random sample of 1000 controls, we excluded 3 (0.3%)
who had no prescription information because they were listed as having died
and 189 (18.9%) prescribed an inhaled bronchodilator, oral bronchodilator,
inhaled steroid, or oral steroid. Of 808 controls remaining, we excluded 59
(7.3%) with no information for BMI and 5 (0.6%) with implausible data for
BMI. This left 744 verified controls. Using our 386 verified cases and 744
verified controls, we performed a substudy case-control analysis.
EXPOSURES OF INTEREST
Selected self-reported demographic (age, sex, race, marital status,
and beneficiary status), lifestyle (smoking, drinking, and physical activity),
and comorbid medical condition variables were studied. The BMI was calculated
as weight in kilograms divided by the square of height in meters from self-reported
weight and height in individuals 17 years and older and was classified as
follows: normal, less than 25; overweight, 25.0 or higher; preobese, 25.0
to 29.9; class I obesity, 30.0 to 34.9; class II obesity, 35.0 to 39.9; and
class III obesity, 40 or higher.5
DATA ANALYSIS
We determined the prevalence of asthma for different age groups, sexes,
racial/ethnic groups, marital status, and military status. We also determined
the prevalence of possible confounding factors such as smoking, drinking,
and physical activity.
For univariate analysis, we assessed the unadjusted association of asthma
with other factors and compared proportions with the  2 test
with 2-sided significance at the .05 level. We calculated crude odds ratios
(ORs) and 95% confidence intervals (CIs) as an approximation of relative risk
estimates and to measure the association between overweight or obesity and
prevalence of asthma.20-21
To evaluate trend patterns in the BMI association, we compared the proportions
of enrollees with asthma within incremental categories of BMI and tested the
differences using the 2 test for trend.
We performed multiple logistic regression analysis using SPSS Base 8.0
for Windows (SPSS Inc, Chicago, Ill) to determine the odds of having asthma,
given the BMI plus possible confounders such as physical activity. We adjusted
for the demographic and lifestyle variables listed previously, related to
the prevalence of asthma, obesity, and comorbid conditions in the analyses,
by fitting a series of hierarchical models.22
We also adjusted for the following comorbid conditions: stomach ulcer, depression,
arthritis, hypertension, cancer, diabetes, elevated cholesterol level, myocardial
infarction, heart disease or angina, kidney disease, liver disease, neurologic
disease, and stroke.
RESULTS
Compared with those without asthma, our initial analysis showed that
TRICARE enrollees with asthma were more likely to be female and younger and
less likely to be active duty service members and to engage in exercise at
least 3 times per week (Table 1).
Enrollees with and without asthma were similar in terms of race, marital status,
smoking history, and drinking history.
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Table 1. Selected Characteristics of Patients Enrolled in TRICARE Region
11*
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Verified asthma cases were more likely to be female and married and
less likely to be active duty service members compared with verified noncases
(Table 1). The verified cases
and controls were similar in terms of age, race, smoking history, drinking
history, and exercise history.
In the overall analysis, TRICARE enrollees with asthma were more likely
to be preobese (defined as BMI of 25.0-29.9) than enrollees without asthma,
even when the analysis was simultaneously adjusted for age and sex (OR, 1.2;
95% CI, 1.1-1.4; Table 2). Similarly,
in the substudy, TRICARE enrollees verified with asthma were more likely to
be preobese than enrollees verified without asthma, even when the analysis
was simultaneously adjusted for age and sex (OR, 1.4; 95% CI, 1.1-2.0; Table 3). Both in the entire group of subjects
and the verified subgroup, asthma cases had an increased odds for being obese
(defined as BMI 30) compared with controls, even when the analysis was
simultaneously adjusted for age and sex. In the entire group, the risk of
having asthma increased with increasing BMI, with the greatest increase among
individuals with a BMI between 40 and 60 (OR, 2.8; 95% CI, 2.3-3.5; P for trend <.001). We found similar results in the
verified subgroup of subjects in that verified asthma cases had increased
odds of being obese, with the greatest increase in risk for asthma prevalence
seen among individuals with a BMI between 35 and 40 (OR, 4.8; 95% CI, 2.6-9.1; P for trend <.001). In both analyses, adjustment for
race had no material effect.
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Table 2. Odds Ratios of Asthma Dependent on Body Mass Index in TRICARE
Region 11 Enrollees With Asthma Adjusted for Age and Sex*
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Table 3. Odds Ratios of Asthma Dependent on Body Mass Index in TRICARE
Region 11 Enrollees With Asthma After Medication Verification Adjusted for
Age and Sex*
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In our final multivariate regression model for the overall group, increasing
BMI, younger age, non–active duty beneficiary status, female sex, and
a history of being diagnosed as having stomach ulcer, depression, arthritis,
or hypertension were significant independent predictors of asthma, with white
race being a marginally significant independent predictor (Table 4). In our final multivariate regression model for the substudy,
increasing BMI, younger age, non–active duty beneficiary status, female
sex, and a history of being diagnosed as having arthritis were significant
independent positive predictors of asthma prevalence (Table 5).
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Table 4. Final Multivariate Regression Model for 2577 Cases and 36 347
Controls*
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Table 5. Final Multivariate Regression Model After Medication Verification
for Substudy of 387 Cases and 744 Controls*
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COMMENT
Asthma has various causal factors and multiple etiologic pathways, the
interrelations of which are not clearly understood. In this prevalence case-control
study, increasing BMI was linearly related to the risk of having asthma, independent
of potential confounders. Other independent associated factors were younger
age, non–active duty beneficiary status, female sex, and arthritis in
both our main analysis and our verified substudy. Surprisingly, smoking history
was not associated with asthma prevalence.
Stomach ulcer, depression, and hypertension are also independent predictors
of asthma prevalence in our main analysis, whereas white race is a marginally
significant predictor. It is unclear by what mechanism arthritis, stomach
ulcer, depression, or hypertension may be independent predictors, and verification
of these medical conditions would be necessary in future studies.
An association between gastroesophageal reflux disease and asthma has
long been recognized.23 The possible mechanisms
that may explain this association include the following: gastric contents
enter the bronchial tree, triggering spasms; gastric contents rise into the
esophagus, triggering bronchospasm by stimulating the vagus nerve; and both
mechanisms may be at work in some patients.24
The HEAR questionnaire did not ask patients if they had ever been diagnosed
as having gastroesophageal reflux disease, so it is unclear if patients who
responded that they had been diagnosed as having stomach ulcer included patients
who had been diagnosed as having gastroesophageal reflux disease.
There have also been many studies25-27
showing an association between asthma and psychopathologic conditions, particularly
depression and anxiety, with estimates of psychopathologic conditions in severe
asthmatic patients ranging from 30% to 63%. It is unclear whether patients
with asthma have higher rates of depression and anxiety as a consequence of
asthma or whether they have a comorbid genetic risk for affective or anxiety
disorders.28
Asthma is a reason for exclusion from active military service, which
likely explains beneficiary status as a family or retired member as significant
independent positive predictors of asthma. This may also be related to our
finding that female sex was an independent positive predictor of asthma, since
active duty service members are still predominantly male. It follows that
the female TRICARE region 11 enrollees are more likely to belong in the family
member category as spouses of active duty service members, thus women having
asthma are less likely to have been selected out in our population.
Few studies have been published regarding common correlates in patients
with asthma. Bailey et al8 noted obesity was
a common comorbidity in 479 adults with asthma seen by the University of Alabama
at Birmingham Asthma Program. A limitation noted by the authors was that University
of Alabama at Birmingham is a tertiary care institution; thus, the population
of patients seen may not be representative of the general population.
Other published studies have focused on children. Luder et al17 examined 209 black and Hispanic children seen in
a New York City medical center and compared them with 1017 black and Hispanic
children enrolled in city schools. They found that the prevalence of overweight
was significantly higher in children with moderate-to-severe asthma than in
their peers and that being overweight was associated with significantly more
severe asthma symptoms and outcomes (increased number of school days missed
per year, peak expiratory flow rate [PEFR] less than or equal to 60% of the
predicted PEFR, and increased asthma medications prescribed).
Unger et al7 noted that 18 (30%) of 61
obese children from Chicago, Ill, had asthma. The authors suggest that children
may limit exertion to control exertional asthma symptoms, thereby reducing
energy (caloric) expenditure and promoting weight gain.
Kaplan and Montana11 tested children
from Miami, Fla, with no history of asthma for spirometry performance before
and after an exercise challenge on a treadmill and found a greater frequency
and degree of exercise-induced bronchospasm in 13 obese children compared
with 14 control children. Eleven (85%) of the 13 obese children compared with
6 (43%) of the 14 control children showed a minimum decrease of 15% after
exercise challenge for any of the following pulmonary function test parameters
assessed: forced expiratory volume in the first second, forced expiratory
flow between 25% and 75%, and PEFR. They questioned whether exercise-induced
bronchospasm leads to exercise avoidance and obesity or whether obesity causes
or enhances bronchial hyperreactivity to exercise.
A population-based cohort study by Siersted et al18
in Odense, Denmark, studied 495 children, half selected at random and half
with a history of asthma. Subjects completed a questionnaire and underwent
testing that included peak expiratory flows, anthropometric measurements,
spirometry, treadmill exercise testing, and methacholine challenge. The study
found undiagnosed asthma was associated with low physical activity and high
BMI.
In a prospective cohort of 85 911 female nurses aged 26 to 46 years,
Camargo et al15 found that after controlling
for 9 potential confounders (including age, race, smoking, physical activity,
and energy intake), BMI was a strong risk factor for asthma (relative risk,
2.7 for BMI 30.0 vs BMI 20.0-22.4; 95% CI, 2.3-3.1). In a prospective
cohort of 16 862 children of female nurses, the same authors16 found that after controlling for Tanner stage, the
relative risk of asthma for the highest compared with the lowest BMI quintile
was 2.3 (95% CI, 1.3-4.1) among boys and 1.5 (95% CI, 0.9-2.6) among girls.
There are several strengths to our study. Our study was population-based,
since all enrollees in TRICARE region 11 were asked to complete the questionnaire.
Thus, our findings may be generalized to other similar populations. The large
sample size allowed sufficient power so that we were able to simultaneously
control for many potential confounders. Verification of self-reported asthma
diagnoses was accomplished through linkage to a medication database.
There are also several limitations in this study. Similar to other studies,
we were not able to answer the key question of causality; it is not clear
if obesity or asthma occurs first. There are several potential explanations
for the observed association between asthma prevalence and obesity: (1) asthma
may lead to exercise avoidance and obesity; (2) obesity might cause or enhance
bronchospasm; (3) severe asthma cases who die or experience extreme morbidity
may be underweight—leaving asthmatic patients who survive and are healthy
enough to complete a questionnaire with a higher average BMI; and (4) obese
individuals may have altered immune function, which could lead to respiratory
hyperreactivity. One mechanism proposed by Fredberg et al29
for the second potential explanation is that obesity may lead to an altered
pattern of tidal lung inflation and/or less effectiveness of those inflations
in stretching airway smooth muscle, which may result in hyperreactivity.
Missing or implausible BMI data led to the exclusion of 7.6% of the
2788 cases and 8.3% of the 39 637 controls in the general study and similar
percentages in the substudy. Since the response rate on the HEAR questionnaire
was only about 50%, there could have been selection bias, in which subjects
with multiple medical problems such as stomach ulcer and hypertension were
more likely to respond, thereby creating artifactual relations. Since asthma
and obesity are both reasons for rejection from military service, the findings
in this study may not reflect the general population. However, the prevalence
of overweight and obesity in our study population closely reflects the prevalence
of overweight and obesity in the US population.5
In view of the fact that asthma and obesity are both reasons for rejection
from military service, it is interesting to observe that retired service members
have twice the risk of asthma as active service members. Our data also indicate
that obesity is more prevalent in retired service members who are asthmatic
(15.9%) than in active service members who are asthmatic (7.3%). Although
one cannot rule out the possibility that some of these retired service members
may have developed asthma, then decreased their physical activity, and subsequently
became obese, it is possible that retired service members decrease the physical
activity they maintained during their active service years, increase their
BMI, and then develop asthma. This question of causality may be answered with
a prospective study in which a cohort with initial baseline pulmonary function
test and BMI measurements is followed up with repeated measurements throughout
subsequent years. Unfortunately, it is difficult to conduct a prospective
study in a military population due to the frequent moves of active service
members.
Since this study is questionnaire-based, it is possible that unverified
asthma could lead to misclassification of patients. Verification analysis
indicated that two thirds of the randomly sampled cases had been prescribed
an inhaled bronchodilator, oral bronchodilator, inhaled steroid, or oral steroid,
suggesting that most of the self-reported cases truly have asthma. It is possible
that unverified BMI may lead to misclassification of overweight patients as
not being overweight. Furthermore, women of reproductive age may be pregnant,
with a resulting increased BMI. Indeed, 54.4% of overweight and obese women
were in the 20- to 39-year age group compared with 45.1% of overweight and
obese men.
This study was limited in that the racial/ethnic distribution was predominantly
white. Furthermore, it was not possible to determine from the HEAR questionnaire
if the study groups differed in socioeconomic status, because there were no
questions available on income or educational level. The TRICARE system tends
to ensure that everyone in the military population has equal access to care,
although in some instances, beneficiaries who are not on active service may
be seen on a "space-available" basis.
In summary, increasing BMI is a key factor predicting prevalence of
asthma and, if determined to be etiologically related to asthma incidence,
is a potentially modifiable risk factor for this disease. Prospective cohort
data are needed to sort out issues of causality, ie, does obesity lead to
asthma or vice versa. Future studies may include interventional trials to
determine if a decrease in weight will cause a decrease in asthma symptoms
for overweight asthma patients. One small study30
performed in Finland involved 2 groups of 19 obese patients with asthma, with
the intervention of a supervised weight reduction program in the treatment
group, showing an improvement in lung function, symptoms, morbidity, and health
status. More studies are needed to evaluate the asthma-BMI association in
black and other minority populations and the general, nonmilitary population.
These further studies are key to better education and management of patients
with asthma. Instead of parents or clinicians discouraging strenuous exercise
in obese children or patients with "exercise-induced bronchospasm" to manage
asthma symptoms, better management may prove to be a program stressing reduced
energy intake and increased physical activity aimed at achieving an ideal
body weight, thereby reducing asthma symptoms.
AUTHOR INFORMATION
Accepted for publication October 31, 2000.
Presented as a poster at the 16th Annual National Preventive Medicine
Meeting, Arlington, Va, March 18-21, 1999, and the Third Annual Force Health
Protection Conference, Baltimore, Md, August 7-11, 2000.
Most of this work was completed while Dr Young was a public health resident
at Madigan Army Medical Center, as part of her requirement to earn the Master
of Public Health degree, Department of Epidemiology, University of Washington
School of Public Health and Community Medicine, Seattle.
The opinions or assertions herein are the private views of the authors
and should not be construed as official or as reflecting the views of the
US Departments of the Army, Navy, or Defense.
Corresponding author: Jeffrey D. Gunzenhauser, MD, MPH, Preventive
Medicine Service, Madigan Army Medical Center, Tacoma, WA 98431-5000.
Reprints:
Sylvia Y. N. Young, MD, MPH, PSC 824, Box 2760, FPO AE 09623.
From the Department of Epidemiology, Navy Environmental and Preventive
Medicine Unit No. 7, Sigonella, Italy (Dr Young); Preventive Medicine Service,
Madigan Army Medical Center, Tacoma, Wash (Drs Young and Gunzenhauser); and
Fred Hutchinson Cancer Research Center (Drs Malone and McTiernan), Department
of Epidemiology, University of Washington School of Public Health and Community
Medicine (Drs Malone and McTiernan), and Department of Geriatrics, University
of Washington School of Medicine (Dr McTiernan), Seattle.
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