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Cardiovascular Risk Factors, Type 2 Diabetes & Primary Care Clinic Structure Michael L. Parchman, MD 1 Amer Kassai, PhD 2 Jacqueline A. Pugh, MD 1 Raquel.

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Presentation on theme: "Cardiovascular Risk Factors, Type 2 Diabetes & Primary Care Clinic Structure Michael L. Parchman, MD 1 Amer Kassai, PhD 2 Jacqueline A. Pugh, MD 1 Raquel."— Presentation transcript:

1 Cardiovascular Risk Factors, Type 2 Diabetes & Primary Care Clinic Structure Michael L. Parchman, MD 1 Amer Kassai, PhD 2 Jacqueline A. Pugh, MD 1 Raquel L. Romero, MD 1 1 University of Texas Health Science Center, San Antonio, Texas 2 Trinity 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 Age58.6 (12.93) Female51% Hispanic57% Maintenance Stage of change for all 4 self-care behaviors? 25%

11 Results: CVD Risk Factors Risk FactorPercent of total (range by clinic) A1c <= 7.0%43% (20 to 69.7) BP <= 130/8049% (0 to 72.7) LDL <= 10050% (0 to 73.3) All 3 well controlled13% (0 to 31.3)

12 ACIC Sub-scale Scores Mean (S.D.)Range* Orgnzn Leadership6.5 (2.3)2.5 – 10.0 Comm Linkage7.1 (1.7)4.3 – 10.7 Self-Care Support6.9 (1.9)2.8 – 10.3 Decision Support6.0 (1.8)2.7 – 9.0 Delivery System6.7 (2.2)3.4 – 11.0 Clinical Info System5.2 (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 Ratio95% C.I. Age , 1.02 Female0.66*0.48, 0.92 Hispanic , 1.19 All Maintenance1.55*1.09, 2.21

14 HLM: No Patient-level predictors CCM componentO.R.95% C.I. Org Leader , 1.11 Comm Linkage1.65*1.31, 2.09 Self-Care Support , 1.21 Decision Support , 1.63 Delivery System1.38*1.40, 1.67 Clin Info System0.58*0.42, 0.81

15 HLM Final Model PredictorO.R.95%C.I. Female , 0.98 All Maintenance , 4.07 Comm Linkages , 1.98 Delivery System , 1.86 Clin Info System , 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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