NHLBI Workshop

Data Needs for Cardiovascular Events, Management, and Outcomes

HMO Populations and Clinical Databases as a Source for Monitoring Trends in CVD Morbidity & Mortality -
Dr. Joseph Selby


Key Points

  • Integrated HMOs are an increasingly rich source of longitudinal data on CVD events, risk factors and quality of care
  • Membership is quite stable
  • Large registries of persons with CVD diagnoses are being created
  • Many of these HMOs share the same electronic medical record, and data definitions are being harmonized across HMOs

HMO Research Network (HMORN) -- 13 million members

  • Fallon Health Care, Worcester, MA
  • Group Health Cooperative, Seattle, WA
  • Harvard Pilgrim Health Care, Boston, MA
  • HealthPartners Research Foundation, Minneapolis, MN
  • Henry Ford Health System, Detroit, MI
  • Kaiser Permanente Colorado, Denver, CO
  • Kaiser Permanente Georgia, Atlanta, GA
  • Kaiser Permanente Hawaii, Honolulu, HI
  • Kaiser Permanente Northwest, Portland, OR
  • Kaiser Permanente Northern CA, Oakland, CA
  • Kaiser Permanente Southern CA, Pasadena, CA
  • Lovelace Clinic Foundation, Albuquerque, NM
  • Scott and White Health System, Temple, Texas
  • United Healthcare, Minnetonka, MN

Major Collaborative Projects of the HMO Research Network (HMORN)

Project
Funder
Coordinating Center
Vaccine Safety Datalink - CDC CDC CDC
Cancer Research Network - National Cancer Institute NCI Group Health Cooperative
Center for Education and Research on Therapeutics (CERT) AHRQ Harvard Pilgrim
Coordinated Clinical Studies Network Roadmap/
NHLBI
Group Health Cooperative

Data are Improving

  • Membership (denominators & demographics)
  • Complete hospital discharge data (endpoints, procedures, comorbidities)
  • Outpatient diagnoses & procedures (comorbidities and endpoints)
  • Pharmacy data (comorbidities, quality of care)
  • Laboratory results (risk factors)
  • Outpatient measurements (BP, smoking status, BMI)

Availability of CVD Risk Factor Data among 1.2 million persons, age 45 and above, 1/1/04; Kaiser Permanente Northern California

  Men (n=580,817) Women (n=663,661)
Blood Pressure 86% 92%
LDL-C 68% 69%
HDL-C 73% 75%
Fasting Glucose 63% 64%
Current Smoking Status 85% 92%
Creatinine 81% 84%
BMI 55% 60%
Race/ethnicity 70% 75%

Examples of Studies Conducted in Kaiser Permanente, Northern California

  • Joint Control of Three CVD Risk Factors in the KP Population, 2001 to 2003:
    The 3D Study -- Support: Pfizer, Inc.
  • Troponin Measurement and Trends in Acute Coronary Syndrome Hospital Discharge Diagnosis
    • To determine the role of troponin measurement in the changing distribution of ACS hospitalization discharge diagnoses in Kaiser Permanente Northern California (KPNC)
    • All data, including cardiac biomarkers, were obtained from KPNC electronic databases.
    • All hospital discharges for ACS from KPNC hospitals between 1994-2003 were determined (primary discharge codes 410.x, 411.1, 414.x primary with 411.1 secondary)
  • Kaiser Permanente Acute Coronary Syndrome Registry (KP-ACS)
    • Modeled after National Registry of Myocardial Infarction (NRMI)
    • Systematic chart review of all hospitalized acute myocardial infarction and troponin (+) unstable angina patients at 17 Kaiser facilities since 2002 (AMI) & 2004 (UA)
    • ~9,000-10,000 reviewed cases annually
  • Kaiser Permanente Chronic Heart Failure Registry (KP-CHF)
    • Chronic heart failure (CHF) identified from hospitalizations and ambulatory visit (outpatient, ED) databases
    • 96% of primary hospital discharge diagnoses and 85-90% of cases identified from outpatient diagnoses are verified using Framingham criteria at chart review
    • 1996-2002: 59,772 adults > or = 20 years met registry criteria for CHF in Kaiser No. Cal.
    • Registry is updated annually and linked to treatment & clinical outcomes

Conclusions

  • Data from integrated HMOs can provide a timely look at incidence, complications, mortality for a variety of CVD conditions
  • Rich clinical data allows adjustment for population differences in comorbidities and disease severity
  • Data definitions can be standardized across settings
  • Data may allow exploration of possible artifactual differences in observed patterns

Limitations

  • HMO patients do not represent the extremes of U.S. SES spectrum
  • Time trends, geographic variation, subject to differences and changes in clinical and coding practices
  • Data completeness is sometimes a question
  • Potentially, time trends may be affected by differing enrollment/departure over time

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