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Michael L. Parchman, MD1 Amer Kassai, PhD2 Jacqueline A. Pugh, MD1

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Presentation on theme: "Michael L. Parchman, MD1 Amer Kassai, PhD2 Jacqueline A. Pugh, MD1"— Presentation transcript:

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

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