Cardiovascular Risk
Factors, Type 2 Diabetes &
Primary Care Clinic Structure
Michael L. Parchman, MD1
Amer Kassai, PhD2
Jacqueline A. Pugh, MD1
Raquel L. Romero, MD1
1University
of Texas Health Science Center, San Antonio, Texas
2Trinity
University, San Antonio, Texas
Cardiovascular Disease (CVD)
Risk Factors

Glucose Control
 Hemoglobin A1c
 Goal:

Blood Pressure
 Goal:

<= 7.0%
<= 130/80
Lipids
 LDL
Cholesterol
 Goal: <= 100 mg/dl (if no CAD)
Self-Care Activities
Diet, Exercise, Glucose Monitoring,
Medication Adherence
 5 Stages of Change:

 Pre-contemplation
 Contemplation
 Preparation
 Action
 Maintenance:
more
adherence for 6 months or
The Chronic Care Model (CCM)
Purpose
 Examine
the relationship between
control of CVD risk factors, patient
self-care behaviors, and the presence
of the CCM model elements across a
diverse group of primary care clinic
settings.
Methods

20 small autonomous primary care clinics
 Solo
practice physicians (n=11)
 Small group practices (n=3)
 Community Health Clinic (n=1)
 VHA Primary Care OPC (n=2)
 City/County Indigent Health Clinics (n=3)

Recruited from a Primary Care Practice
Based Research Network (PBRN)
Subjects and Data Collection

Patients
 30
consecutive presenting pts with an established dx
of type 2 DM
 Exit survey: demographics, stage of change for selfcare behaviors, health status (excellent, v. good,
good, fair, poor)
 Chart Abstraction: most recent values of A1c, BP and
LDL-cholesterol

Clinicians
 Assessment
of Chronic Illness Care (ACIC) Survey.
(Bonomi, Wagner et al 2002) (25 items)
ACIC Survey: Sub-Scales
Organizational Leadership
 Community Linkages
 Self-Management Support
 Decision Support
 Delivery System Design
 Clinical Information Systems

Analysis


Outcome: All 3 risk factors well controlled (Y/N)
Hierarchical Logistic Model (Random Effects Model)
 Patients

clustered within clinic
Predictors:
 Patient:




Age (years)
Hispanic ethnicity (Y/N)
Female gender
Maintenance Stage of Change for all 4 behaviors (Y/N)
 Clinic

Sub-scale scores from ACIC survey
Results: Patient Characteristics
Age
58.6 (12.93)
Female
51%
Hispanic
57%
Maintenance Stage of change
for all 4 self-care behaviors?
25%
Results: CVD Risk Factors
Risk Factor
A1c <= 7.0%
Percent of total (range
by clinic)
43% (20 to 69.7)
BP <= 130/80
49% (0 to 72.7)
LDL <= 100
50% (0 to 73.3)
All 3 well controlled
13% (0 to 31.3)
ACIC Sub-scale Scores
Mean (S.D.)
Range*
Orgnzn Leadership
6.5 (2.3)
2.5 – 10.0
Comm Linkage
7.1 (1.7)
4.3 – 10.7
Self-Care Support
6.9 (1.9)
2.8 – 10.3
Decision Support
6.0 (1.8)
2.7 – 9.0
Delivery System
6.7 (2.2)
3.4 – 11.0
Clinical Info System
5.2 (2.4)
0.6 – 10.2
*Potential Range of each sub-scale: 0 to 11
HLM Model: No Clinic-level Predictors
Patient
Characteristic
Odds Ratio
1.01
95% C.I.
1.00, 1.02
Female
0.66*
0.48, 0.92
Hispanic
0.86
0.62, 1.19
All Maintenance
1.55*
1.09, 2.21
Age
HLM: No Patient-level predictors
CCM component
O.R.
95% C.I.
Org Leader
0.89
0.72, 1.11
Comm Linkage
1.65*
1.31, 2.09
Self-Care Support
0.97
0.78, 1.21
Decision Support
1.10
0.75, 1.63
Delivery System
1.38*
1.40, 1.67
Clin Info System
0.58*
0.42, 0.81
HLM Final Model
Predictor
O.R.
95%C.I.
Female
0.59
0.36, 0.98
All Maintenance
1.82
1.08, 4.07
Comm Linkages
1.56
1.23, 1.98
Delivery System
1.47
1.17, 1.86
Clin Info System
0.58
0.44, 0.73
Conclusions

Control of CVD risk factors among patients
with T2DM is associated with structural
characteristics of primary care clinic:
 Community
Linkages
 Delivery System Design
 Clinical Information Systems
Community Linkages
Linking clinicians to diabetes specialists
and educators
 Patient diabetes education resources
 Coordinates implementation of diabetes
care guidelines with assessment/treatment
by specialists

Delivery System Design
Practice Team Functioning
 Practice Team Leadership
 Appointment System
 Follow-up
 Planned Visits for diabetes care
 Continuity and Coordination of Care

Clinical Information Systems

Inversely associated with CVD risk factor:
 Diabetes
registry
 Reminders to providers
 Feedback on performance
 Identification of patients needing attention
 Patient treatment plans
CIS may improve measurement of risk
factors but not efforts to control
 Implementation of CIS may distract from
risk factor control

Limitations
Small number of primary care clinics
 Cross-sectional data
 Selection bias of consecutive patients

 Bias
toward worse control of CVD risks
 Greater burden of illness
 Worse overall health status
Current/Future Research*

Organizational Intervention in Primary
Care Clinics to improve risk factor control
 Primary
care clinics are complex adaptive
systems with non-linear dynamic behavior
 No “one-size-fits-all” approach to improving
risk factors
 Facilitation of organizational change with a
focus on inter-dependence among agents
 See Poster by Leykum et al this afternoon
*Funded by NIH/NIDDK 1 R34 DK067300-01
Acknowledgements

Supported by:
 Agency
for Healthcare Research and Quality
(Grant #K08 HS013008)
 South Texas Health Research Center
 Office of Research and Development, Health
Services Research and Development Service,
Department of Veterans Affairs.
 The views expressed are those of the authors
and do not necessarily represent the views of
the Department of Veterans Affairs