Disparities in Physician-Patient
Communication by Obesity Status
Patrick Richard, PhD, MA
Assistant Research Professor
Christine Ferguson,
Ferguson JD
Research Professor
Jennifer Leonard, JD
Associate Research Professor
Anthony Lara, MHSA, PhD Candidate
Senior Research Associate
Anna Muldoon, MPH Candidate
Research Assistant
Funding for this research was provided by Strategies to Overcome and
Prevent (STOP) Obesity Alliance
Motivation/Why should we care about this?
•
There is evidence suggesting that the quality of physician-patient communication varies
by individuals’ BMI (Puhl and Heuer 2009, Huizinga et al 2009, Puhl and Brownell,
2003)
•
Clinical Significance
May lead to increased morbidity and mortality for individuals with obesity
– Provision of poor quality of care (Cohen et al 2008, Ferrante et al 2006, Chao et al
2004, Rosent et al 2004, Wee et al 2000)
– Delay in seeking preventive care and complying with treatment recommendations
byy obese ppatients (Puhl
(
& Heuer 2009)
9)
•
Public Policy
– Obesity is costly and a risk factor for several chronic health conditions including
t
type
II di
diabetes,
b t coronary hheartt di
disease, hi
highh bl
blood
d pressure, hi
highh bl
blood
d cholesterol,
h l t l
and asthma (Finklestein et al 2009; Must et al 1999)
– Significant public and private resources have been invested to expand access to high
quality preventive services to individuals with obesity to improve health outcomes
and contain costs.
– However, poor physician-patient communication may be detrimental to prevention
and intervention efforts
Previous Literature
• There is a small but growing literature examining the relationship
between physician
physician-patient
patient communication and obesity status (see Puhl
and Heuer 2009)
• Conceptual Limitations
– Inconsistency in physician-patient communication measures
– Combining communication and quality of care measures in
attempting to measure physician-patient
physician patient communication (Fong et al
2009)
• M
Methodological
th d l i l Li
Limitations
it ti
– Physicians’ perspectives in assessing physician-patient
communication (Huizinga et al 2009)
– Small
S ll non-representative
i samples
l (L
(Loomis
i 2001,
2001 S
Schwartz
h
et all
2003)
Study Objective and New Contribution
• Objective: Examine the relationship between physicianpatient communication and obesity status
• New Contribution
– Uses a large and nationally representative household survey (Medical
E
Expenditure
di
Panel
P l Survey,
S
or MEPS) that
h incorporates
i
patient
i
assessments of physician-patient communication
– Focuses only on interactions between primary care physicians and
patients
• Differs from previous studies that grouped physicians with other primary care
providers such as nurses, nurse practitioners and physician assistants
– F
Focuses on interactions
i t
ti
in
i ambulatory
b l t
care suchh as office-based
ffi b d visits,
i it
clinics and hospital outpatient settings
Data Source
• Medical Expenditure Panel Survey (MEPS)
– Nationally representative survey of health services use,
insurance coverage, medical expenditures and sources of
pa ment for the U
payment
U.S.
S ci
civilian,
ilian non-institutionalized
non instit tionali ed population
pop lation
– Pooled three waves of data from 2005-2007 from MEPS’
Household Component (HC) file
– Restricted sample to individuals who are between 18 and 64
years old with non-missing observations, receiving ambulatory
care from primary care physicians, with usual source of care, and
have had at least one visit
–
–
–
Sample
Sa
p e ssize:
e: Total
ota : N
N= 66
6628;
8; 32.3%
3 .3% (2041)
( 0 ) are
a e obese
Male: N=2437; 31.9% (777) are obese
Female: N: 4191; 32.8% (1375) are obese
Dependent and Independent Variables
• Dependent Variables
1)
2)
3)
4)
Physician-Patient Communication Composite Score
How often have providers showed respect for what you had to say?
How often have health care providers listened carefully to you?
H often
How
ft hhave hhealth
lth care providers
id explained
l i d things
thi
so you
understood?
5) How often have health providers spent enough time with you?
6) How
H often
ft hhave providers
id iinvolved
l d you iin ttreatment
t
t conditions?
diti ?
• Independent Variables
– Obesity, binary indicator measuring whether patients reported a BMI
greater than 30 kg/m2
– Demographic, socioeconomic, behavioral, and regional variables
– Cardiovascular co-morbid conditions (high blood pressure, angina,
coronary heart attack, other heart disease, and stroke)
Estimation Strategy
• T and chi-square tests to analyze differences in
physician-patient communication by obesity status
• OLS regression
g
for Physician-Patient
y
Communication
Composite Score
• Logistic Regression for individual components of the
composite score
• Survey weights used to account for MEPS’
MEPS complex
survey design
Table 1: Weighted Demographic and Health Characteristics by Gender, Pooled
MEPS 2005-2007
Characteristics (%)
Total
(n=6628)
Male
(n=2437)
Female
(n=4191)
60.4
--
--
Age 18-24
7.8
9.2
6.9
Age 25-34
15.9
12.9
17.9
Age 35-44
21.2
21.0
21.3
Age 45-54
28.2
28.7
27.8
Age 55-64
26.9
28.2
26.1
Non-Hispanic
Non
Hispanic White
75 3
75.3
76 7
76.7
74 9
74.9
Non-Hispanic Black
10.6
9.3
11.4
Hispanic
8.5
8.0
8.9
Asian
3.8
4.2
3.6
Other
1.8
1.8
1.8
Obese
32.3
31.9
32.8
Co-morbid
Co
morbid cardiovascular conditions
35 0
35.0
41 1
41.1
31 5
31.5
Smokes
17.8
19.8
16.5
Physically Active
58.1
59.5
57.2
Female
Age Group
Race
Health Conditions and Behaviors
Table 1 (continued): Weighted Demographic and Socioeconomic
Characteristics by Gender, Pooled MEPS 2005-2007
Ch
Characteristics
i i (%)
Totall
T
(n=6628)
Male
M
l
(n=2437)
Female
F
l
(n=4191)
< High School
17.6
17.7
17.6
High School Graduate
47.4
45.9
48.4
College Graduate
22.2
22.1
22.2
Graduate School
12.8
14.3
11.8
Under 100% of FPL
7.6
5.7
8.1
100-199% of FPL
9.8
8.9
11.3
200-400% of FPL
28.6
26.9
29.6
Over 400% of FPL
54.0
58.5
51.0
Private Insurance
86.1
87.3
85.2
P bli IInsurance
Public
77
7.7
58
5.8
90
9.0
Uninsured
6.2
6.9
5.8
East
25.1
26.3
24.2
Midwest
22.5
22.5
22.5
South
36.1
34.9
36.9
West
16.3
16.3
16.4
Education
Income
Insurance Status
Region
Table 2: Weighted Sample Characteristics by Gender,
Dependent Variables, Pooled MEPS 2005
2005-2007
2007
Dependent Variables (%)
Total
(n=6628)
Male
(n=2437)
Female
(n=4191)
Physician-patient communication
composite score *
(standard deviation)
12.5 (2.5)
12.5 (2.5)
12.5 (2.5)
Physician shows respect to patients
93 5
93.5
93 5
93.5
93 5
93.5
Physician listens to patients
91.8
92.1
91.6
Physician explains things to patients
93.2
93.2
93.1
Physician spends enough time with
patients
87.4
87.6
87.3
Physician involves patients in
treatment decisions
85.7
85.5
85.8
* Expressed as score, not percent
Table 3: Bivariate Results of Dependent Variables and Obesity
Status byy Gender,, Pooled MEPS 2005-2007
Total
(n=6628)
Male
(n=2437)
Female
(n=4191)
p
Variables ((%))
Dependent
Obese
Nonobese
P
value
Obese
Nonobese
P
value
Obese
Nonobese
P value
Physician-Patient
Communication Composite
Score +
12.4
(2.5)
12.5
(2.5)
0.04**
12.4
(2.6)
12.6
(2.4)
0.24
12.4
(2.5)
12.5
(2.5)
0.09*
Physician shows respect to
patients
92.3
94.0
0.02**
91.9
94.3
0.04**
92.6
93.9
0.19
Physician
i i listens
i
to patients
i
91 0
91.0
92 2
92.2
01
0.17
90 5
90.5
92 9
92.9
0 05**
0.05**
91 4
91.4
91
91.7
0
0.77
Physician explains well
92.8
93.4
0.46
92.7
93.5
0.51
92.9
93.3
0.70
Physician spends enough
time with patients
86.5
87.9
0.19
86.0
88.3
0.16
86.8
87.5
0.56
Physician involves patients
in treatment
84.8
86.2
0.23
84.9
85.9
0.57
84.8
86.3
0.27
* p < 0.10; ** p < 0.05; +Expressed as score, not percent; () = Standard Deviation
Table 4: OLS Regression and Odds Ratio Results for Total
Sample by Obesity Status, Pooled MEPS 2005-2007
Dependent Variables
Obese
Physician-Patient Communication Composite Score
Physician shows respect to patients
Physician listens to patients
Physician explains well
Physician spends enough time with patients
Physician involves patients in treatment
* p < 0.10;
0 10; ** p < 0.05
0 05
OLS Coefficient
- 0.19
95% Confidence Interval
-0.34 to -0.03
P value
0 02**
0.02
Odds Ratio
0.77
95% Confidence Interval
0.61 to 0.98
P value
0.04**
Odds Ratio
0.82
95% Confidence Interval
0.65 to 1.02
P value
0.07*
Coefficient
0.93
95% Confidence Interval
0.74 to 1.17
P value
0.53
Coefficient
0.80
95% Confidence Interval
0.62 to 0.99
P value
0.04**
Coefficient
0.98
95% Confidence Interval
0.83 to 1.18
P value
0.82
Table 5. OLS Regression and Odds Ratio Results for Male
Sample by Obesity Status, Pooled MEPS 2005-2007
Dependent Variables
Obese
Physician-Patient Communication Composite Score
Physician shows respect to patients
Physician listens to patients
Physician explains well
Physician spends enough time with patients
Physician involves patients in treatment
* p < 0.10;
0 10; ** p < 0.05
0 05
OLS Coefficient
-0.28
95% Confidence Interval
-0.52 to – 0.04
P value
0 03**
0.03
Odds Ratio
0.67
95% Confidence Interval
0.45 to 0.96
P value
0.03**
Odds Ratio
0.68
95% Confidence Interval
0.48 to 0.95
P value
0.02**
Coefficient
0.87
95% Confidence Interval
0.59 to 1.29
P value
0.49
Coefficient
0.71
95% Confidence Interval
0.53 to 0.96
P value
0.03**
Coefficient
0.98
95% Confidence Interval
0.72 to 1.34
P value
0.92
Table 7: OLS Regression and Odds Ratio Results for Female
Sample by Obesity Status, Pooled MEPS 2005-2007
Dependent Variables
Obese
Physician-Patient Communication Composite Score
Physician shows respect to patients
Physician listens to patients
Physician explains well
Physician spends enough time with patients
Physician involves patients in treatment
* p < 0.10;
0 10; ** p < 0.05
0 05
OLS Coefficient
-0.12
95% Confidence Interval
-0.32 to 0.07
P value
0 20
0.20
Odds Ratio
0.85
95% Confidence Interval
0.61 to 1.18
P value
0.32
Odds Ratio
0.91
95% Confidence Interval
0.67 to 1.25
P value
0.57
Coefficient
0.98
95% Confidence Interval
0.72 to 1.32
P value
0.88
Coefficient
0.89
95% Confidence Interval
0.70 to 1.12
P value
0.31
Coefficient
0.96
95% Confidence Interval
0.77 – 1.20
P value
0.72
Discussion
• Findings are consistent with the literature
– For instance
instance, we found a negative association between BMI and
physician respect (OR = 0.77**)
– Similar to Huizinga et al, 2009 (OR = 0.83***)
– Similar to Puhl & Brownell, 2006 & Fung et al (2008)– patient’s
perspective
– However, different from Fong et al (2005) that used the MEPS and
found that obese patients reported significantly greater satisfaction with
their healthcare providers compared to non-obese patients
• May be due to internal inconsistency in measuring patients’ satisfaction by
combining
bi i diff
different measures off quality
li off care and
d communication
i i
• Physician’s negative attitudes and perceptions towards obese individuals
may be increasing over time
• Alternatively, patients may be more likely to report physicians’ negative
attitudes and interactions as the obesity epidemic increases
Discussion
• Significant
g
negative
g
association between BMI and
physician respect for males (OR=0.67**)
g
but not significant
g
association for females
• Negative
(OR=0.85)
• Females may be more involved in their interactions with
their physicians (Bertakis and Azari 2007)
Overweight female patients may be engaging in denial
• “Overweight
strategies or compensatory behaviors that assure them of
quality
q
y care” ((Hebl and Mason 2003))
Conclusions/Policy Implications
• Negative association between physician-patient communication and
patient’ss obesity status
patient
status, particularly for obese male patients
• Clinical: Train physicians on effective communication
– Lack of respect = weight-based
weight based stigma
– Awareness of patients’ diverse needs and circumstances
• P
Public
bli P
Policy
li
– Incentives to treat obese individuals and coordinate care (e.g. Medicaid
payment system reform)
• More frequent interactions
• Better patient satisfaction
– Strengthen patient-centered
patient centered health care model
• Usual source of care
• Continuity of care