Transcript Predictors of non-compliance in primary care of patients with chronic disease Roger Zoorob, MD, MPH, FAAFP; Mohamad Sidani, MD, MS; Medhat Kalliny, MD, PhD;

Predictors of non-compliance in
primary care of patients with
chronic disease
Roger Zoorob, MD, MPH, FAAFP; Mohamad Sidani, MD,
MS; Medhat Kalliny, MD, PhD; Kristy M. Durkin, MSW,
LCSW; and Robert Levine, MD
Department of Family and Community Medicine, Meharry
Medical College
Nashville, Tennessee
Presenter Disclosures
Mohamad Sidani, MD, MS
The following personal financial relationships with
commercial interests relevant to this presentation
existed during the past 12 months:
“No relationships to disclose”
Objectives/Issue
OJECTIVES:
1) What are the barriers of compliance among patients
with chronic Diseases?
Scope of the Issue
 Compliance
is a key concept in health care and affects
all areas of health care including chronic diseases.
 Non-compliance
has previously been a label attached to
many patients without much thought having been given
to the causes of poor compliance.
 A better
understanding of the factors affecting
compliance, is imperative in order to improved
outcomes.
Our Study
Study Purpose
 The
purpose of our study was to investigate
the predictors of non-compliance among
patients living with chronic diseases (n=267)
who were seen at our two Family Medicine
Residency-based clinics.
Our Study
Participant Criteria
 Diagnosed
with Type 2 diabetes, hypertension,
hyperlipidemia, or obesity
 Patients
seen at either of our two Family Health
Clinics
 Meharry Family Clinic (49.2%) in a metropolitan
area of Nashville
 Madison Family Clinic (50.8%) in a suburban
area half an hour outside Nashville city.
Our Study
Design Method
Data
was collected from the patient's electronic
health record from 2008 to 2010.
We
compared the effect of gender, age, ethnicity,
marital status, employment, insurance, tobacco or
alcohol use, clinic location, and co-morbidity on
compliance.
Used
Chi-Square, t-tests, and logistic regression to
analyze results.
Compliance
Patients were deemed compliant if they had a minimum
of two regular check-ups per year.
Percent Compliant
60.00%
50.00%
40.00%
30.00%
Percent
Compliant
20.00%
10.00%
0.00%
2008
2009
2010
Gender
Gender was not found to be a predictor of non-compliance
(p=.379).
Percent Compliant
36.00%
35.00%
34.00%
33.00%
32.00%
31.00%
30.00%
29.00%
28.00%
27.00%
Percent
Compliant
Male
Female
Ethnicity
Ethnicity was not found to be a predictor of noncompliance (p=.379)
Percent Compliant
40.00%
35.00%
30.00%
25.00%
20.00%
15.00%
10.00%
5.00%
0.00%
Percent
Compliant
AGE
Age was not found to be a predictor of non-compliance
(p=.930)
percent Compliant
70.00%
60.00%
50.00%
40.00%
30.00%
20.00%
10.00%
0.00%
percent
Compliant
29 - 30-39 40-49 50-59 60-69 70 under
over
Marital Status
Marital Status was not found to be a predictor of noncompliance (p=.721)
60.00%
50.00%
40.00%
Single
Married
Divorced
Widowed
30.00%
20.00%
10.00%
0.00%
percent Compliant
Substance Use
Tobacco (p=.375) was not found to be a predictor of noncompliance
percent Compliant
37.00%
36.00%
35.00%
34.00%
33.00%
32.00%
31.00%
30.00%
29.00%
28.00%
27.00%
percent
Compliant
Smoker
Non-Smoker
Substance Use
Alcohol use (p=.535) was not found to be a predictor of
non-compliance.
Percent Compliant
66.00%
64.00%
62.00%
60.00%
58.00%
56.00%
54.00%
52.00%
50.00%
48.00%
Percent
Compliant
Drinker
Non-Drinker
Location of residence (p=.117) was not found to be a
predictor of non-compliance.
Percent Compliant
45.00%
40.00%
35.00%
30.00%
25.00%
20.00%
15.00%
10.00%
5.00%
0.00%
Percent
Compliant
Urban
Suburban
clinic location was not found to be a predictor of noncompliance (p<.001)
Percent Compliant
50.00%
45.00%
40.00%
35.00%
30.00%
25.00%
20.00%
15.00%
10.00%
5.00%
0.00%
Percent
Compliant
Meharry
Clinic
Madison
Clinic
Economics
Being Unemployed/Disabled was not found to be a
predictor of non-compliance (p=.290)
50.00%
percent Compliant
40.00%
30.00%
percent
Compliant
20.00%
10.00%
0.00%
Employed
Unemployed
Economics
Patients having insurance were significantly more
compliant (53.5%) than those without (5.7%) (p<.000).
percent compliant
60.00%
50.00%
40.00%
30.00%
percent
compliant
20.00%
10.00%
0.00%
Insurance No Insurance
Comorbidity
 Having
Comorbidity was not found to be a predictor of
non-compliance (p=.168) 31.7% With and 43.2%
Without were compliant
 Having
Hypertension was not found to be a predictor of
non-compliance (p=.671) 32.7% With and 35.7%
Without were compliant
 Having
Hyperlipidemia was not found to be a predictor
of non-compliance (p=.209) 36.1% With and 28.6%
Without were compliant
 Those
having Obesity were significantly more
compliant than those who did not (40.1% versus 10%)
(p<.000).
Co-morbidity
percent Compliant
45.00%
40.00%
35.00%
30.00%
25.00%
20.00%
15.00%
10.00%
5.00%
0.00%
percent
Compliant
Counseling

Patients who received Diet Counseling (n=185) from the clinic’s
nutritionist were significantly more complaint than those who did not
(n=82) (43.8% verses 9.8%) (p< .000).

patients who received Exercise Counseling (n=156) from the clinic’s
nutritionist were significantly more complaint than those who did not
(n=111) (44.9% verses 17.1%) (p< .000).
Findings
Predictors of Non-Compliance
Clinic Location
 patients seen in the suburban clinic (Madison) were
more likely to be compliant than those from the urban
clinic (Meharry) (44.2% versus 24.8%)(p<.01).
 When
both insurance and clinic were entered into a
logistic regression model, insurance status was a
significant predictor of compliance while clinic location
was not.
 A follow-up
chi-square revealed that patients from the
suburban clinic (Madison) were significantly more likely
to have insurance than the urban clinic (72% versus
48%) (p<.01).
Findings
Predictors of Non-Compliance
Obesity
 Our
findings suggest that those with obesity make
more office visits.
 Obese
were significantly more compliant than those
who were not (40.1% vs.10%)(p<.000).
 Almost
all of the patients who were compliant were
also obese (93.3%) compared to patients who were
compliant and not obese (6.7%)(p<.01).
Findings
Predictors of Non-Compliance
Insurance
 Not
having insurance is a significant risk factor for
noncompliance among patients with chronic diseases.
 Patients
having insurance were significantly more
compliant (53.5%) than those without (5.7%) (p<.000).
 Almost
all patients with insurance (93.3%) were more
compliant than those without (36%)(p<.01)
 Those
with insurance were also less likely to drop out
after the first visit compared to those without (22%
versus 73%)(p<.01).
Any Questions or
Comments?