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PREDICTORS OF OBESITY MEDICATION USE IN AMBULATORY SETTING:
NAMCS 2006-07 ANALYSIS
Hemalkumar B. Mehta*, Jeetvan G. Patel, Rohan C. Parikh, and Susan S. Abughosh, PhD.
Department of Clinical Sciences & Administration, College of Pharmacy, University of Houston, Houston, Texas, USA.
BACKGROUND & SIGNIFICANCE
United States has the highest prevalence of overweight (66.3%) and obese
(33.2%) individuals among all developed countries.1
Approximately 9.1% of the annual US medical expenditure ($117 billion) is
attributed to obesity.2
Cawley et. al. demonstrated disparities in anti-obesity medication use across
factors such as race, ethnicity, gender etc.; however important factors like
physician specialty, counseling and co-morbidity status are yet to be
explored.3
To our knowledge there is no study which describes anti-obesity drug use in
ambulatory setting.
RESEARCH OBJECTIVE
To determine predictors affecting obesity medication use amongst adults
who are diagnosed with obesity in ambulatory setting.
METHODS
♦Data Source: NAMCS ♦Identification: Obesity Diagnosis ♦Years: 2006-07
 Study Design: National probability sample survey of visits to office-based
physician.
 Inclusion Criteria: All patients ≥ 18 years of age and diagnosed with
obesity (ICD-9-CM: 278.00).
 Confounders: Race, sex, region, MSA, counseling, physician type, type of
insurance, and co-morbidity status.
 Medications: Only US-FDA approved medications for obesity treatment
were included in the analysis. Multum Lexicon codes were used to identify
the drugs.
 Unit of Analysis: Patient visit level and prescription level.
 Statistical Analyses: Descriptive analysis and chi-square tests were
performed on the demographic variables. Domain analysis was performed
to select adult visits with a diagnosis of obesity . A multivariate logistic
regression model was constructed to test the association of drug use with
the independent variables. SAS v9.2 was used with the survey procedures
to perform statistical analysis.
Table1: US-FDA approved anti-obesity medications
Generic name
Trade name
Drug type
Orlistat
Xenical
Lipase Inhibitor
Sibutramine
Meridia
Appetite Suppressant
Diethylpropion
Tenuate
Appetite Suppressant
Phendimetrazine
Bontril
Appetite Suppressant
Phentermine
Adipex-P
Appetite Suppressant
For further information contact at : [email protected]
RESULTS
Table 2: Patient and Provider characteristics for obese adults with
outcome variable of receiving a prescription or not.
Visits
No
Characteristics
Prescription
p-value
(thousands)
Prescription
All Adult Visits
112,964
6,550
106,414
NA
(Percent)
(5.79%)
(94.21%)
Sex
Female
73,817
89.61
63.85
<0.0001*
Male
39,147
10.39
36.15
Race
Whites
92,775
86.37
81.87
0.2743
Non - Whites
20,189
13.63
18.13
Region
South
47,343
67.00
40.37
0.0308*
Others
65.621
33.00
59.63
Paytype
Private Insurance 59,276
32.33
57.15
Public Insurance 38,960
12.22
38.13
<0.0001*
No Insurance
8,181
55.45
4.72
Provider
PCP
62,468
32.98
56.57
0.0201*
Other
50,496
67.02
43.33
Counseling
Yes
52,619
78.19
44.63
<0.0001*
No
60,345
21.81
55.37
Comorbidity
Very-high or High 99,008
67.32
88.89
<0.001*
Low or No
13,956
32.68
11.11
MSA region
MSA
95,413
81.09
84.67
0.6663
Non-MSA
17,551
18.91
15.32
Table 3: Patient and Provider characteristics and associations with
prescription of at least one anti-obesity. (Multivariate Logistic Model).
Odds ratio
Confidence
Characteristics
p-value
β
(e )
Interval (95%)
Sex
Female
Reference
Reference
<0.0001*
Male
0.244
0.128-0.463
Race
Whites
Reference
Reference
0.2201
Non - Whites
0.686
0.375-1.325
Age
(1 yr. increments)
0.976
0.962 - 0.990
<0.001*
Region
South
Reference
Reference
0.3195
Other
0.628
0.251-1.570
Paytype
Private Insurance
0.056
0.021-0.146
<0.0001*
Public Insurance
0.080
0.034-0.189
<0.0001*
No Insurance
Reference
Reference
Provider
PCP
0.597
0.253-1.408
0.2384
Other
Reference
Reference
Counseling
Yes
3.730
1.878-7.407
<0.001*
No
Reference
Reference
Comorbidity
Low or No
Reference
Reference
0.3717
Very High or High
0.718
0.347-1.486
MSA region
MSA
Reference
Reference
0.2162
Non-MSA
1.638
0.749-3.579
DISCUSSION
Obese women are more likely to be associated with social stigma and lower
self esteem as compared to obese men and thus, utilization of drugs in women
is expected to be higher among women compared to men.3
Obesity is known to be a harbinger for other chronic diseases like CHF,
diabetes, hypertension, arthritis and some cancers.
Reducing access to anti-obesity medications might eventually lead to an
overall increase in healthcare expenditure in United States through long term
complications.
Obesity related counseling increases awareness about obesity epidemic and
thus could lead to better management of obesity.4
LIMITATIONS
Differences across different socio-economic status in adults could not be
assessed.
Quality of obesity counseling could not be assessed.
About
6% visits with an obesity
diagnosis received an anti-obesity
medication prescription.
 Almost 90% visits where
an antiobesity medication was prescribed
were by females and 86% visits were
by whites.
Unadjusted
analysis showed that
females, uninsured and people from
south region had significantly higher
chance of getting a drug prescription.
 In
adjusted analysis, males were
about 75% less likely to receive a
prescription. There was no difference
amongst different races and visits at
different region.
Chances
of
an
anti-obesity
medication prescription progressively
with age.
Patients visits with private or public
insurance were less likely to receive an
anti-obesity medication prescription
as compared to uninsured.
Visits where obesity counseling was
given were almost 4 times more likely
to get an anti-obesity medication
prescription.
CONCLUSIONS
Study findings suggest that adequate coverage for anti-obesity medications
might not be available.
Patients who received counseling were more likely to receive an anti-obesity
medication prescription.
Further research to evaluate prescribing practices across people from
different socio-economic status is warranted.
REFERENCES
1. Low S et al. Review on Epidemic of obesity. Annals Academy of Medicine.
2009;38(1):57-65.
2. Powers KA et al. Financial impact of obesity and bariatric surgery. The Medical Clinics of
North America. 2007;91(1):321-38.
3. Cawley et. al. One pill makes you smaller: The demand for anti-obesity drugs. Advances
in Health Economics and Health Services Research. 2006;17:149-183.
4. Dastani et al. Combating the obesity epidemic: Community Pharmacists’ counseling on
obesity management. The Annals of Pharmacotherapy. 2004;38:1800-1804.