Cardiovascular Risk Factors, Type 2 Diabetes &

Primary Care Clinic StructureMichael 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 ControlHemoglobin A1c Goal: <= 7.0%

Blood PressureGoal: <= 130/80

LipidsLDL CholesterolGoal: <= 100 mg/dl (if no CAD)

Self-Care Activities

Diet, Exercise, Glucose Monitoring, Medication Adherence

5 Stages of Change:Pre-contemplationContemplationPreparationActionMaintenance: adherence for 6 months or

more

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 clinicsSolo 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 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)

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 CharacteristicsAge 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 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)

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 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

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 LinkagesDelivery System DesignClinical 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 registryReminders to providersFeedback on performance Identification of patients needing attentionPatient 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 risksGreater burden of illnessWorse overall health status

Current/Future Research*

Organizational Intervention in Primary Care Clinics to improve risk factor controlPrimary care clinics are complex adaptive

systems with non-linear dynamic behaviorNo “one-size-fits-all” approach to improving

risk factorsFacilitation of organizational change with a

focus on inter-dependence among agentsSee 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