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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


Background
Background

  • Burgeoning health care disparities literature

  • Challenge of prioritizing health care disparities

  • Need for a common metric for evaluation


Purpose
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


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.


Limitations
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


Conclusions
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


Acknowledgements
Acknowledgements

Funding: RWJF and NHLBI/NIH

Collaborators: Richard Dressler

Advice: Simon Capewell


Sensitivity
Sensitivity

  • 95% CI - 5,700-11,110

  • Adherence disparity: 0.70-.90 - 6,310-11,290


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