Variation in provider identification of
obesity by patientpatient-level and neighborhoodneighborhoodlevel characteristics among an insured
population
Sara N. Bleich1
Jeanne M. Clark2,3
Suzanne M. Goodwin1
Mary Margaret Huizinga2,3
Jonathan P. Weiner1
1Department
of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health
of Medicine, Division of General Internal Medicine, Johns Hopkins University School of Medicine
3Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Medical Institutions
2Department
Outline




Background and Significance
Research Objective, Data and Methods
Results
l
Conclusions, Limitations and Policy
Implications
Background




Obesity disproportionately affects minorities
and low SES groups
Significant racial and SES differences in
receipt of health care services
Cli i i
Clinicians
differentially
diff
i ll diagnose
di
andd treat
obesity
Prior studies on practice patterns of obesity
care focus on patientpatient-level characteristics
Rationale for focus neighborhood
characteristics

Place of residence matters (Living in low SES or minority
neighborhoods associated with an increased likelihood of
late--stage cancer diagnosis among Black and Hispanic
late
women, Barry
B
et al.,
l 2005)

Individual characteristics insufficient to explain disparities
in obesity diagnosis

Social Ecological Theory – Individuals are nested within
networks
Neighborhood context may capture
factors that impact obesity diagnosis

Availability of resources
Physician/nurse supply
 Health system infrastructure


Cultural norms

Higher coco-morbidity risk
Receipt of appropriate obesity care
matters for weight loss efforts

Patients told byy their pphysician
y
that theyy are overweight
g
are more likely to try to lose weight (Kant et al, 2007)

Patients counseled about their weight or weightweight-related
behaviors are more likely to report working on those
areas (Loureiro
(Loureiro et al,
al 2006)

Patients advised byy their pphysician
y
to modify
y their
behavior are generally more confident and motivated to
engage in lifestyle modifications ((Kreuter
Kreuter et al, 2000)
Outline




Background and Significance
Research Objective, Data and Methods
Results
l
Conclusions, Limitations and Policy
Implications
Goal of study
Objective
Objective:
j
: Identifyy variation in pprovider identification of
obesity by patientpatient-level and neighborhoodneighborhood-level
characteristics among an insured population
Research question:
question: Do obese plan members living in
minority or low SES communities receive an obesity
diagnosis?
Hypothesis
Hypothesis:
yp
: Members livingg in lower SES neighborhoods
g
or neighborhoods with a higher percent of minority
residents will have a lower likelihood of receiving an
g
from their pprovider.
obesityy diagnosis
Data

Blue Cross Blue Shield (BCBS) claims data,
data
2002--2005 (three plans) linked to:
2002

Health Risk Assessment (HRA) surveys



Member height and weight (self(self-reported)
ZIP code level neighborhood characteristics (2000
Census)
Included plans covered weight loss
medications, nutritional counseling and
bariatric surgery during the study period
Note: The zip code was based on the member ZIP code of enrollment.
Key advantages of data

Examination of a population not limited by
financial barriers to health care access

Largest and most current database that has
b
been
usedd to examine
i physician
h i i practice
i
pattern of adult obesity care

BCBS is the largest
g insurer in the countryy
Methods

Research Design

Cross--sectional analysis of BCBS enrollees
Cross

Exclusion criteria







Enrolled less than 6 months in the year in which their HRA was completed
Less than 18 years old or their data was missing
Had a pregnancy and/or delivery claim during the study period
Had a body mass index (BMI) less than 10 kg/m2 or greater than 100 kg/m2
Missing height or weight needed to calculate their BMI
St d sample:
Study
l 16,151
16 151 obese
b
plan
l members
b based
b d on self
lf
2
reported BMI ≥ 30 kg/m
Anal sis
Analysis

Logistic regression, adjusted for potential clustering of risk
factors by neighborhood
Outcome measure

Obesity diagnosis

The International Classification of Diseases, Ninth
Revision (ICD
(ICD--9) diagnostic codes:

259.9, 278.0, 280.1, 783.1, 783.6, V77.8, V85.0V85.0-V85.54
Covariates
Neighborhood-level (zipNeighborhood(zip-code) Patient
Patient--level
 Age
 Proportion of Black
 Gender
residents
 Obesity
Ob it class
l
 Median household income,
 class I: BMI 30.0
30.0--34.9 kg/m2
 class II: BMI 35.035.0-39.9 kg/m2
adjusted for inflation
 class III: BMI ≥ 40 kg/m2
 Percentage off residents
id
with
ih
 Co
Co--morbidities
a high school degree
 Type 2 diabetes
 Hypertension
yp
 Percentage living in an
 Dyslipidemia
urban area

Number of unique providers
seen
Note: All patient-level variables were obtained from the claims data with the exception
of body mass index. In the models, Census level variables were measured in tertiles.
Outline




Background and Significance
Research Objective, Data and Methods
Results
l
Conclusions, Limitations and Policy
Implications
Sample characteristics
Individual-level
SD
Age
g
48.4 yyears
12.7
Female
48.0%
--
Hypertensive
29.2%
--
T
Type
2 Diabetes
Di b t
10 3%
10.3%
--
Dyslipidemia
26.8%
--
Number of unique provider visits
8.2
9.9
Proportion black residents
5.3%
14.4
Median household income
$46,759
$14, 202
High school graduates
70.5%
7.3
Proportion urban
81.7%
28.9
7.7%
--
Zip code-level
Ob it Diagnosis
Obesity
Di
i
Physician identification of obesity
Morbidly obese BCBS enrollees more likely to
receive
i an obesity
b i diagnosis
di
i
BMI in obese study sample for persons with and without obesity claim diagnosis;
mean BMIdiagnosed 38.4 kg/m2 vs. mean BMIun-diagnosed 34.5 kg/m2, p < 0.001)
Patient-level characteristics influence
Patientphysician
h i i identification
id ifi i off obesity
b i
OR (95% CI)
Female
1.48† (1.29, 1.70)
Age
Age 44 and below
1 64† (1
1.64†
(1.42,
42 1.90)
1 90)
Age 45 and above
1.00 (reference)
Hypertension
1.55† (1.32, 1.81)
Type 2 Diabetes
0.98 (0.81, 1.19)
Dyslipidemia
1.54† (1.32, 1.80)
Obesity class
Class III
4.08† (3.46, 4.82)
Class II
2.05† (1.76, 2.39)
Class I
1 00 (reference)
1.00
Number of unique providers visits in HRA year
† Statistically significant at p<0.05
1.20† (1.16, 1.24)
Neighborhood-level characteristics influence
Neighborhoodphysician
h i i identification
id ifi i off obesity
b i
OR (95% CI)
Proportion of Black residents
Tertile 3 (highest)
0.75† (0.57, 0.98)
Tertile 2
0.98 ((0.78,, 1.24))
Tertile 1 (lowest)
1.00 (reference)
Median household income
Tertile 3
0.94 (0.70, 1.26)
Tertile 2
1.07 (0.87, 1.31)
Tertile 1
1.00 (reference)
(
)
Percent high school graduate
Tertile 3
0.95 (0.74, 1.22)
Tertile
il 2
1.07 (0.87,
(
1.31))
Tertile 1
1.00 (reference)
† Statistically significant at p<0.05; The model also controlled for percent urban; result non-significant.
Outline




Background and Significance
Research Objective, Data and Methods
Results
l
Conclusions, Limitations and Policy
Implications
Conclusions

Consistent with national data,, pprovider identification of
obesity was low and varied by patient characteristics

Neighborhood characteristics (e.g., zip code with a
higher concentration of blacks) were moderately
important to provider diagnosis of obesity

A keyy contribution of this paper
p p is the examination of
the relationship between neighborhood characteristics
and obesity diagnosis, which has received little
attention to date
Limitations

Cross--sectional data
Cross

Reliance on selfself-reported height and weight

Differential member completion of HRA data

Physician
y
obesity
y coding
g

Mismatch between zip code of residence and location of care

2000 Census estimates

Lack
c of
o membermember
e be -level
eve race
ce data
d
Policy implications and next steps

Considerable missed opportunities
pp
in the diagnosis
g
of
obesity, particularly for members at higher risk and
members living in neighborhoods with a higher
concentration of blacks

More precise measures needed to understand how
neighborhood effects disparities in obesity diagnosis

A better understanding of these influences may help
better target federal efforts aimed at reducing obesity
rates and eliminating health disparities
Acknowledgements

The data and inin-kind database development
p
support
pp and
guidance were provided by the BCBS Association,
BCBS of Tennessee, BCBS of Hawaii, BCBS of
Michigan,
g , BCBS of North Carolina,, Highmark,
g
, Inc. (of
(
Pennsylvania), Independence Blue Cross (of
Pennsylvania), Wellmark BCBS of Iowa, and
Wellmark BCBS of South Dakota

Technical assistance with the claims data was provided
by Thomas Richards,
Richards Andrew Shore
Shore, and Hsien
Hsien--Yen
Chang of Johns Hopkins Bloomberg School of Public
Health
Contact information
Sara Bleich
Johns Hopkins Bloomberg School of Public Health
Health
l h Policy
li andd Management
sbleich@jhsph.edu
410.502.6604
Appendix
Diagnosis
g
Obesity
Hypertension
Type 2
Diabetes
Dyslipidemia
ICD-9 Code
278.0, 280.1, 783.1, 783.6, V77.8, or V85.0V85.54
401-405
250, 250.0, 250.00, 250.02, 250.1, 250.10,
250 12 250
250.12,
250.2,
2 250
250.20,
20 250
250.22,
22 250.3,
250 3 250.30,
250 30
250.32, 250.4, 250.40, 250.42, 250.5, 250.50,
250.52, 250.6, 250.60, 250.62, 250.7, 250.70,
250.72, 250.8, 250.80, 250.82, 250.9, 250.90,
250.92
272 2 – 272.9
272.2
272 9