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Cardiovascular Risk Factors, Type 2 Diabetes & Primary Care Clinic Structure

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.

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Cardiovascular Risk Factors, Type 2 Diabetes & Primary Care Clinic Structure

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  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

  11. Results: CVD Risk Factors

  12. ACIC Sub-scale Scores *Potential Range of each sub-scale: 0 to 11

  13. HLM Model: No Clinic-level Predictors

  14. HLM: No Patient-level predictors

  15. HLM Final Model

  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 DK067300-01

  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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