Health Care Costs in Women with and without Sleep Apnea
Expenditure on Health Care in Obese Women with and without Sleep Apnea
Katsuhisa Banno, MD1; Clare Ramsey, MD2; Randy Walld, BSc2; Meir H. Kryger, MD, FRCPC3
Sleep Disorders Centre, Kitatsushima Hospital, Inazawa-city, Aichi, Japan; 2Manitoba Centre for Health Policy and Evaluation, University of
Manitoba, Winnipeg, Manitoba, Canada; 3Sleep Research and Education, Gaylord Hospital, Wallingford, CT
1
Study Objectives: To determine the effect of obesity and sleep apnea
on health care expenditure in women over 10 years.
Design: Retrospective observational study
Setting: Tertiary university-based medical center
Patients and controls: Three groups of age-matched women: 223
obese women with OSAS (body mass index: 39.3 ± 0.6 kg/m2), and
from the general population, 223 obese controls (BMI 36.3 ± 0.4) and
223 normal weight controls (BMI 23.9 ± 0.4).
Interventions: None
Measurements and Results: We examined health care utilization in
the 3 matched groups for the 10 years leading up to the documentation of OSAS. The mean physician fees and the number of physician
visits were significantly higher in obese controls than in normal weight
controls during the observed period. Physician fees and physician visits
progressively increased in the 10 years before diagnosis in the OSAS
cases and were significantly higher than in the matched obese controls.
Physician fees, in Canadian dollars, one year before diagnosis in the
OSAS cases were higher than in obese controls: $547.49 ± 34.79 vs
$246.85 ± 20.88 (P < 0.0001). More was spent for OSAS cases on
physician fees for circulatory, endocrine and metabolic diseases, and
mental disorders than the obese controls. Physician visits one year
before diagnosis in the OSAS cases were more frequent than in the
obese controls: 13.2 ± 0.73 visits vs 7.26 ± 0.49 visits (P < 0.0001).
Conclusions: Obese women are heavier users of health services than
normal weight controls. Obese women with OSAS use significantly
more health services than obese controls. Since OSAS imposes a
greater financial burden, treatment of OSAS may reduce other comorbidities and lower overall medical costs.
Keywords: Obesity, obstructive sleep apnea, female, medical economics, health services
Citation: Banno K; Ramsey C; Walld R; Kryger MH. Expenditure on
health care in obese women with and without sleep apnea. SLEEP
2009;32(2):274-252.
METHODS
THE PREVALENCE OF OBESITY HAS INCREASED SIGNIFICANTLY IN RECENT DECADES; THIS IS CONSIDERED A MAJOR PUBLIC HEALTH ISSUE.1-7 EPIDEMIOLOGIC reports suggest that obesity may be responsible for huge
health care costs.8-11 Since obesity is a known risk factor for the
development of diabetes mellitus, hypertension, ischemic heart
disease, and obstructive sleep apnea syndrome (OSAS),1,2,4,12-14
these comorbidities may lead to increased health care utilization
in obese people. OSAS is a common sleep disorder in which
there is repetitive cessation of breathing during sleep and resultant daytime sleepiness. The disorder, affecting about 5% of
adults in the general population, is associated with obesity, cardiovascular diseases, neurocognitive impairment, and metabolic dysfunction.12-14 The majority of OSAS patients are obese.14,15
Thus, both obesity and OSAS may compound the risk for some
diseases, resulting in additional healthcare costs.16-20 A previous
study in Israel showed that age and female gender significantly
affect increased health care costs in patients with OSAS.21 We
hypothesize that obese people use health care resources at a
greater rate than normal weight people, and further that the increase in health care utilization in obese people is affected by
overweight and coexisting OSAS. We thus investigated health
care utilization in 3 matched groups: normal weight women,
obese women, and obese women with documented OSAS.
This study was conducted in the Canadian Province of Manitoba. Surrogates of health care utilization, physician fees, and
physician visits were investigated in three groups: obese women, normal weight women, and obese women with OSAS by
using the Manitoba Health database. Obese women with OSAS
were selected first, and then 2 control groups were randomly
selected by matching those with OSAS based on the severity of
obesity (see below).
The Manitoba Health database, described in detail elsewhere,22
allows longitudinal tracking of the health care utilization of individuals and their diagnoses based on the International Classification of Disease (ICD-9) codes.16-19 All residents of Manitoba have
unrestricted access to government-funded health care services,
including physician services and hospitalizations. When physicians see a patient, a standardized claim is submitted to the government agency responsible (Manitoba Health), which then renders payment (one payer system). To ensure the confidentiality of
all subjects, an encrypted health insurance number was used as
the only identifier of each person in the study, to construct a final
working database that included the complete health care utilization data for the 3 study populations. The Human Ethics Committee of the University of Manitoba and the Health Information
Privacy Committee of Manitoba Health approved this project.
Study Populations
Submitted for publication May, 2008
Submitted in final revised form October, 2008
Accepted for publication October, 2008
Address correspondence to: Dr. M. Kryger, Sleep Research and Education, Gaylord Hospital, 400 Gaylord Farm Road, Wallingford, CT 06492;
Tel: (203)741-3410; Fax: (203) 741-3409; E-mail: [email protected]
SLEEP, Vol. 32, No. 2, 2009
Obese women with OSAS: We selected 223 consecutive
women diagnosed as having OSAS based on overnight polysomnography (see below) at the Sleep Disorders Centre, St.
Boniface General Hospital�����������������������������������
from 1990 to 2003�����������������
. They were resi247
Health Care Use in Obese Women—Banno et al
dents of Manitoba who had complete health care utilization data
stored in the Manitoba Health database during the period under
study, 10 years before the initial diagnosis of OSAS. The diagnosis of OSAS was based on the clinical features and overnight
sleep study data, using conventional criteria23 and the cases
were independently reviewed by one of the authors (MK). The
Epworth Sleepiness Scale (ESS) was used to document subjective daytime sleepiness.24
Obese controls: 223 women who met the WHO classification25,26
of obesity with body mass index (BMI) > 30 were selected from
the general population using the 1996 National Population Health
Survey.27,28 They were randomly matched to one obese woman
with OSAS each, based on age and BMI.
Normal weight controls: In addition to obese women mentioned above, non-obese women were selected in a similar fashion. They were matched for age from the general population
using the 1996 National Population Health Survey.
Both groups selected from the general population were residents of Manitoba continuously during the 10-year period under
study. Health care utilization information for the 2 groups was
investigated by linking the 1996 National Population Health
Survey with the Manitoba Health database.
People whose entire health care service was not covered by
the province of Manitoba for the 10 years of observation were
excluded; also excluded were those who were not permanent
residents in Manitoba, and Indians and military personal, whose
health care is covered by the Canadian Federal Government (n =
12). Data for people on dialysis were excluded because the very
high costs of this treatment could skew the results (n = 1). In addition, morbidly obese women with OSAS with BMI > 60 were
excluded because it was difficult to match them with obese women from the general population with respect to BMI (n = 8).
Table 1—Demographics and Characteristics of Study Populations
at Baseline
Obese women Obese women Normal weight
with OSAS (n=223) (n=223) women (n=223)
Age (year)
50.3 ± 0.7
49.1 ± 0.7
49.9 ± 0.6
BMI (kg/m2) 39.3 ± 0.6
36.3 ± 0.4*
23.9 ± 0.4*
ESS
12.4 ± 0.4
N/A
N/A
AHI (/hr)
28.1 ± 1.9
N/A
N/A
OSAS: obstructive sleep apnea syndrome, BMI: body mass index,
ESS: Epworth sleepiness scale, AHI: apnea-hypopnea index, Significant pairwise differences (adjusted for multiple comparisons
by Tukey-Kramer method): *P < 0.0001 vs obese women with
OSAS. Values are expressed in mean ± standard error
All statistical analyses were performed using SAS software
(SAS Institute, Cary, NC, USA). Physician visits were modeled
using a Poisson regression model; physician fees were log transformed and then modeled using a normal regression model to
compare the 3 study groups over the observed period. The correction for repeated measures overtime was controlled for in each
model using generalized estimating equations (GEE). The GEE
estimates combine cross-sectional and longitudinal associations
that takes into account the within-group correlation and produce
robust standard errors for testing hypotheses and computing confidence intervals.30 A univariate ANOVA was used to compare
characteristics such as age and BMI between the 3 groups. Significance of individual differences was evaluated by the TukeyKramer test. Significance levels were set at P < 0.05 or P < 0.01.
RESULTS
Polysomnography
There were 223 obese controls and 223 normal weight controls from the general population and 223 obese women with
OSAS in the study. The characteristics of the 3 groups at baseline are presented in Table 1.
Comprehensive polysomnography included the monitoring of neurophysiological variables (EEG, chin EMG, EOG,
anterior tibialis EMG) and cardiorespiratory variables (chest
wall motion, abdominal motion, nasal pressure, ETCO2, SpO2,
and EKG). The sleep data was analyzed using 30-sec epochs.29
Apnea-hypopnea index (AHI) was used as the measure of sleep
apnea severity.23
Physician Visits
The trends in physician visits 10 years before diagnosis are
presented in Figure 1. Obese controls visited clinics more frequently than normal weight controls during the observed period
(P < 0.0001). Physician visits by women with OSAS were more
frequent than by obese controls in 10 years before diagnosis (P
< 0.0001). Physician visits gradually increased in the 10 years
before diagnosis in the women with OSAS. Physician visits in
the year before diagnosis in the OSAS cases were more frequent
than in the obese controls: 13.2 ± 0.73 visits vs 7.26 ± 0.49 visits (P < 0.0001). The trend prior to diagnosis was an increase of
1.16 visits per person per year in with the OSAS cases; among
obese controls there was a decrease of 0.09 visits, and among
normal weight controls there was a decrease of 0.16 visits.
Data Analysis
Physician fees and the number of physician visits were used
as the measure of health care utilization. Costs are presented in
Canadian dollars. Continuous variables were expressed as mean
± standard error (SE). The study period analyzed in this report
was 10 years, spanning the period from 9 years prior to the diagnosis and the year of OSAS diagnosis. Annual health care
utilization in OSAS patients was compared to health care costs
of each control group in the same year. To assess the trend of
physician fees and the number of physician visits before OSAS
diagnosis, we calculated the slope of these 2 variables (per person per year) for the 2 years before OSAS diagnosis. Because
use of health care may peak proximate to the time an OSAS
diagnosis is made, costs or clinic visits related to the diagnosis
were excluded in the year of the diagnosis to avoid this bias.
SLEEP, Vol. 32, No. 2, 2009
Physician Fees
Physician fees for the 3 groups are presented in Figure 2. Physician fees were higher for obese controls compared to normal
weight controls over the observed period (P < 0.0001); physician
248
Health Care Use in Obese Women—Banno et al
Table 2—Mean Physician Fees Related to Specific Categories for the Obese Women with OSAS, Obese Women, and Normal Weight Women
During the 10-Year Period
Obese women
Obese women
P value
Normal weight
with OSAS
obese women
women
with OSAS vs.
obese women
Major diagnostic categories
Endocrine and metabolic diseases
38.30 ± 5.47
15.73 ± 2.87
< 0.001
4.67 ± 1.41
Mental disorders
54.77 ± 12.08
22.88 ± 10.20
< 0.0001
6.29 ± 1.49
Diseases of the nervous system
41.69 ± 5.58
25.50 ± 5.76
< 0.0001
15.85 ± 4.28
Diseases of the circulatory system
33.30 ± 4.12
21.34 ± 3.44
< 0.01
6.72 ± 1.70
Diseases of the respiratory system
43.85 ± 5.29
19.41 ± 2.66
< 0.001
13.82 ± 2.18
Diseases of the digestive system
14.11 ± 2.73
14.13 ± 4.04
NS
7.35 ± 1.91
Diseases of the genitourinary system
32.75 ± 4.93
16.43 ± 2.81
< 0.05
15.12 ± 2.35
Diseases of the musculoskeletal system 58.07 ± 9.37
28.13 ± 5.14
< 0.001
8.97 ± 1.72
Cancer
13.99 ± 3.83
2.62 ± 1.08
< 0.01
6.09 ± 1.88
Infectious diseases
4.62 ± 1.40
2.15 ± 0.82
NS
2.56 ± 0.91
Other categories
Medical tests
143.81 ± 9.74
42.99 ± 4.88
< 0.001
33.12 ± 3.61
Psychotherapy
53.00 ± 16.8
8.63 ± 6.1
< 0.05
1.41 ± 1.41
P value
obese women
vs. normal
weight women
< 0.0001
NS
NS
< 0.0001
< 0.05
< 0.01
NS
< 0.0001
NS
NS
NS
NS
OSAS: obstructive sleep apnea syndrome. Values are expressed in mean ± standard error. Costs are presented in the Canadian dollars.
Figure 2—Mean physician fees, in Canadian dollars, for obese
women, normal weight women, and OSAS cases 9 years before
diagnosis. The bars represent standard error. OSAS: obstructive
sleep apnea syndrome.
Figure 1—Mean number of physician visits for obese women,
normal weight women, and obese women with OSAS 9 years before OSAS diagnosis. The bars represent standard error. OSAS:
obstructive sleep apnea syndrome.
Additional analysis of the relative risk of pregnancy or birth
for obese women with OSAS showed that OSAS cases are less
likely to have a baby than obese women; the cases also were
less likely to become pregnant than obese controls.
The trend of physician fees before diagnosis was an increase
of $57.72 per person per year in the OSAS cases, an increase
of $6.97 in the obese controls, and a decrease of $3.24 in the
normal weight women.
fees were in turn higher for women with OSAS than for the obese
controls 10 years before diagnosis (P < 0.0001). Physician fees in
the OSAS group progressively increased in the 10 years leading
up to diagnosis. Physician fees one year before diagnosis in the
OSAS were significantly higher than in obese controls: $547.49
± 34.79 vs $246.85 ± 20.88 (P < 0.0001).
Physician fees related to specific organ systems one year
before diagnosis in normal weight women, obese women, and
obese women with OSAS are presented in Table 2. More health
care use was spent in the OSAS cases on physician fees for
diseases of the circulatory system than obese controls one year
prior to diagnosis. Physician fees for endocrine and metabolic
diseases in OSAS cases ($38.30 ± 5.47) were more than twice
those in obese women ($15.73 ± 2.87) in the year before diagnosis. In addition, higher health care use was present in the
obese women with OSAS for diseases of genitourinary system
(P < 0.05) and cancer (P < 0.01), compared to the obese women.
SLEEP, Vol. 32, No. 2, 2009
DISCUSSION
To our knowledge, this is the first report on long-term
healthcare utilization in obese women with OSAS, compared
to those without OSAS and normal weight women. Expenditure on health care was higher in obese women than normal
weight controls. Obesity is a risk factor for the development of
hypertension, diabetes, cardiovascular diseases, and stroke,1,2,4
and therefore higher health care expenditure in obese women
249
Health Care Use in Obese Women—Banno et al
than normal weight women would be expected. We found that
obese women with OSAS had health care utilization 2.2 times
as high as obese women before their diagnosis, and there was a
progressive increase in health care expenditure in obese women
with OSAS in the 9 years before diagnosis. This suggests that
OSAS may cause additional burden on the health care system,
potentially due to the development of more comorbidities in
these obese women. Ronald et al reported that physician fees in
patients with OSAS were approximately twice as high as those
of age-matched controls in Manitoba.16 Tarasiuk and colleagues
looked at health care utilization in patients with OSAS, using
one billing system data in Israel, and concluded that health care
expenditure in middle-aged and older patients with OSAS was
1.8 times as high as that of healthy controls.20 Those results
are similar to our findings, although subjects in both reports
included men and women. In contrast, a report on health care
utilization data in Manitoba, analyzed using only women with
OSAS showed women with OSAS spent 2.6 times as much in
physician fees in the year before diagnosis as controls matched
for gender and age.18 However severity of obesity was not taken
into account in control subjects in the previous study, which
may lead to the magnitude of difference between subjects and
controls on health care utilization. Health care expenditure on
specific disorders before a diagnosis will be discussed later.
The mean BMI in the obese controls was 3 units less than
that of obese women with OSAS. However, based on the recent
WHO classification of obesity,1,25,26 the 2 groups fall into the
same category: Obese class 2 (BMI: 35.0 to 39.9 kg/m2), as such
should have an equally severe risk of developing comorbidities.
Thus the actual difference of BMI between the 2 groups is considered to be acceptable in this study from the viewpoint of risk
of comorbidities, which may also reflect health care utilization
for the obesity-related comorbidities. Therefore, it is unlikely
that the difference in number of physician visits and health
care expenditures could be attributed to this small difference in
BMI. It is likely that some of the obese controls may actually
have OSAS, since obesity is a risk factor for this condition. This
would tend to strengthen our findings.
Based on the subsequent analysis of physician fees related
to specific disease categories, we found that obese women with
OSAS were heavier users of health care resources for metabolic diseases than obese controls. Obesity is a risk factor for the
development of diabetes mellitus and insulin resistance.2,4 Although the evidence about causal relationship between OSAS
and diabetes mellitus or insulin resistance14,31-33 is a topic of current research, our data suggest that there is negative effect of
OSAS on metabolic function. The exact mechanisms for metabolic abnormalities have not been clarified. Hypoxia caused
by repetitive apnea and fragmented sleep negatively affects
insulin resistance and glucose metabolism.14 Some intermediary mechanisms may be involved, including sustained sympathetic activation, increased oxidative stress, and activation of
inflammatory pathways.31,32 These may alter metabolic function
in patients with OSAS, which may lead to the development of
diabetes or insulin resistance.
Health care expenditure on circulatory diseases in OSAS cases was significantly higher than in obese controls. Patients with
OSAS are more frequently diagnosed with cardiovascular diseases and are heavy users of cardiovascular medications before
SLEEP, Vol. 32, No. 2, 2009
their OSAS is diagnosed.12,19 Recent reports suggest that the inflammatory state caused by sleep apnea may play an important
role in the development of atherosclerosis, which is associated
with cardiovascular diseases.34,35 There is strong evidence that
OSAS is an independent risk factor for hypertension.13,14 Other
heart diseases such as heart failure, angina pectoris, myocardial
infarction, and arrhythmia were also common in the women
with OSAS,12-14 which may explain the higher physician fees
for circulatory diseases in obese women with OSAS than obese
women in this study.
Higher health care costs related to mental conditions were also
found in OSAS cases than obese controls; cost for psychotherapy
was also higher in women with OSAS than those without OSAS.
Women with OSAS are more likely to receive a diagnosis of depression before OSAS diagnosis;12,36 female gender is predictive
of depression (odds ratio 2.24; 95% confidence interval: 1.45 to
3.44) in patients with OSAS.12 Several previous studies support
a positive association between depression or anxiety and OSAS;
however, the results are still controversial because of methodological issues in the studies.14,37,38 However, our results show
that mental conditions are more common in OSAS cases than in
obese controls, as documented by increased health care resources
used for mental conditions or psychotherapy.
Obese women with OSAS used more health care resources
for cancer than those without OSAS. By contrast, health care
expenditure for cancer in normal weight people was higher than
in obese people, although the statistical difference of physician
fees between the 2 groups was not significant. No previous
reports have shown that sleep apnea may be associated with
cancer in patients with OSAS. However, hypoxia, a common
finding in OSAS, has been associated with enhanced tumor aggressiveness and progression.39,40 Chronic physiological stress
and nocturnal hypoxia caused by abnormal breathing may thus
play a role in the increased incidence of cancer in patients with
OSAS. Further research needs to be done to demonstrate the
relationship between sleep apnea and the occurrence of cancer.
Another possibility that affects health care cost for cancer may
be due to patients who continue to be treated with chemotherapy. Cost of chemotherapy depends on type of regimen, how
long, and how often the treatment is performed. Thus, data of
patients on chemotherapy also may have an influence on health
care expenditure.
Healthcare costs for diseases of the genitourinary system in
obese women with OSAS were approximately double that of
the 2 control groups. Another analysis showed that women with
OSAS are less likely to become pregnant than obese women
controls. Sleep apnea frequently occurs in pregnant women;41
and this disorder is more common in obese pregnant women
than normal weight pregnant women.42 It is possible that severe
hypoxia, as well as obesity, may have adverse health effects in
pregnant obese women with OSAS, possibly increasing the risk
of pregnancy induced hypertension and/or having a negative
effect on the developing fetus. However many of the cases and
control subjects were older than 40 years of age. Thus further
research evaluating younger women is required to demonstrate
the influence of obesity and sleep apnea on the development of
genitourinary diseases.
Study limitations include lack of data analysis adjusted for
other potential confounders that may affect healthcare utiliza250
Health Care Use in Obese Women—Banno et al
tion, such as socioeconomic status, race, education, smoking
habit, and consumption of alcohol. Thus, the results should be
interpreted with caution. In addition, some obese women controls may have undiagnosed OSAS. It has been reported that
that more than 90% of women with moderate to severe OSAS
may be undiagnosed.43 Furthermore, we did not calculate the
costs related to reduced productivity due to cognitive impairment and excessive daytime sleepiness, a major symptom of
OSAS, which may increase the risk of traffic accidents.44 Deaths
and injuries associated with motor vehicle accidents may affect
indirect financial burden attributable to OSAS.45
In conclusion, obese women are heavier users of health care
resources than normal weight women, and those with OSAS are
in turn heavier users of health care resources than obese women. In contrast to the trend in the controls, physician fees and
physician visits progressively increased in the 10 years leading
up to diagnosis in obese women with OSAS. OSAS imposes a
greater financial burden in obese women. Treatment of OSAS
may reduce other comorbidities and contribute to the cost reduction of health care systems.
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Acknowledgments
The study was supported in part by NIH RO1 HL082672-01.
The results and conclusions of are those of the authors, and no
official endorsement by Manitoba Health is intended or should
be inferred.
Disclosure Statement
This was not an industry supported study. Dr. Kryger has
received research support from Respironics, Pfizer, Boehringer
Ingelheim, and Transoral and has consulted for Sanofi-Aventis
and Takeda. The other authors have indicated no financial conflicts of interest.
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