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Background

IMPACT OF DISPARITIES IN CARDIOVASCULAR CARE ON AFRICAN AMERICAN DEATHS Kevin Fiscella, MD, MPH University of Rochester School of Medicine & Dentistry. Background. Burgeoning health care disparities literature Challenge of prioritizing health care disparities

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Background

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  1. IMPACT OF DISPARITIES IN CARDIOVASCULAR CAREON AFRICAN AMERICAN DEATHSKevin Fiscella, MD, MPH University of Rochester School of Medicine & Dentistry

  2. Background • Burgeoning health care disparities literature • Challenge of prioritizing health care disparities • Need for a common metric for evaluation

  3. Purpose • Population impact - annual deaths • Present a simple model using black-white disparities in CVD • Estimate the number of African American CVD deaths that would be avoided/delayed if disparities in CVD care were eliminated

  4. The Model AA deaths prevented/delayed = absolute disparity x absolute risk reduction

  5. Components of absolute disparity (AD) • Disparity in provision/prescription of intervention • Disparities in use of or adherence to intervention

  6. Estimating AD AD= (EPB x Rxw x Adw) - (EPB x RxB x AdB) EPB = Eligible black population i.e. the number who are candidates for the intervention annually Rxw = Provision/prescription of the intervention for whites Adw= Adherence to the intervention for whites RxB = Provision/prescription of the intervention for blacks AdB= Adherence to the intervention for blacks

  7. Common thread: clinician-patient communication • Communication affects patients’ willingness to accept a treatment and clinician’s willingness to provide or prescribe it • Communication affects patients’ adherence

  8. Absolute risk reduction • Baseline mortality in the absence of intervention • Relative risk reduction associated with the intervention • ARR= RRR x base mortality rate

  9. CVD Interventions

  10. Population size and mortality rates

  11. Key disparity (black/white ratio) estimates • Drug treatment in the year following hospital discharge - 0.95 (0.92- 0.98) • CABG - 0.80 (0.6-0.8) • PTCA - 0.90 (0.7-0.9) • Fibrinolysis - 0.90 (0.85-0.95) • Adherence to treatment for chronic condition – 0.80 (0.7-0.9)

  12. Adjusting summed deaths • Avoiding double counting from hospital readmissions from same year and transfers • Avoiding double counting from comoribidity e.g. AMI and HF, CAD and hypertension • Adjusting for less than additive relative risk

  13. Findings

  14. Key findings • Common conditions with high mortality requiring daily adherence have the greatest impact on disparities e.g. heart failure and AMI. • Interventions with high reach e.g. cardiac rehabilitation (990) have greater impact than those with smaller reach e.g. reperfusion therapy (740) or ICDs (200). • Disparities in drug adherence is a major driver accounting for 4,980 deaths.

  15. Limitations • Lack of reliable data for many estimates • Assumptions e.g. differential impact, sustained benefit, synergistic effects • No stratification by age or gender • Annual deaths not QALYS

  16. Conclusions • Population impact represents a key (though not the only) metric for prioritizing health care disparities • The population impact model could be adapted by health care organizations that care for defined populations using their own internal data to assess the impact of health care disparities

  17. Acknowledgements Funding: RWJF and NHLBI/NIH Collaborators: Richard Dressler Advice: Simon Capewell

  18. Sensitivity • 95% CI - 5,700-11,110 • Adherence disparity: 0.70-.90 - 6,310-11,290

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