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

  • Burgeoning health care disparities literature
  • Challenge of prioritizing health care disparities
  • Need for a common metric for evaluation
  • 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
the model
The Model

AA deaths prevented/delayed =

absolute disparity x absolute risk reduction

components of absolute disparity ad
Components of absolute disparity (AD)
  • Disparity in provision/prescription of intervention
  • Disparities in use of or adherence to intervention
estimating ad
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

common thread clinician patient communication
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
absolute risk reduction
Absolute risk reduction
  • Baseline mortality in the absence of intervention
  • Relative risk reduction associated with the intervention
  • ARR= RRR x base mortality rate
key disparity black white ratio estimates
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)
adjusting summed deaths
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
key findings
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.
  • 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
  • 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

Funding: RWJF and NHLBI/NIH

Collaborators: Richard Dressler

Advice: Simon Capewell

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