1. 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

2. Cardiovascular Disease (CVD) Risk Factors
Glucose Control
Hemoglobin A1c
Goal: <= 7.0%
Blood Pressure
Goal: <= 130/80
Lipids
LDL Cholesterol
Goal: <= 100 mg/dl (if no CAD)

3. Self-Care Activities
Diet, Exercise, Glucose Monitoring, Medication Adherence
5 Stages of Change:
Pre-contemplation
Contemplation
Preparation
Action
Maintenance: adherence for 6 months or more

4. The Chronic Care Model (CCM)

5. 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.

6. 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)

7. Subjects and Data Collection
Patients
30 consecutive presenting pts with an established dx of type 2 DM
Exit survey: demographics, stage of change for self-care 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)

8. ACIC Survey: Sub-Scales
Organizational Leadership
Community Linkages
Self-Management Support
Decision Support
Delivery System Design
Clinical Information Systems

9. 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

10. Results: Patient Characteristics
Age
58.6 (12.93)
Female
51%
Hispanic
57%
Maintenance Stage of change for all 4 self-care behaviors?
25%

11. Results: CVD Risk Factors
Percent of total (range by clinic)
A1c <= 7.0%
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)

12. 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

13. HLM Model: No Clinic-level Predictors
Patient Characteristic
Odds Ratio
95% C.I.
Age
1.01
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

14. 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

15. 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

16. 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

17. Community Linkages
Linking clinicians to diabetes specialists and educators
Patient diabetes education resources
Coordinates implementation of diabetes care guidelines with assessment/treatment by specialists

18. Delivery System Design
Practice Team Functioning
Practice Team Leadership
Appointment System
Follow-up
Planned Visits for diabetes care
Continuity and Coordination of Care

19. 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

20. 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

21. 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 DK

22. 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