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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 - PowerPoint PPT PresentationTRANSCRIPT
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
Need for a common metric for evaluation
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
AA deaths prevented/delayed =
absolute disparity x absolute risk reduction
Components of absolute disparity (AD)
Disparity in provision/prescription of intervention
Disparities in use of or adherence to intervention
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 blacksAdB= Adherence to the intervention for blacks
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
Baseline mortality in the absence of intervention
Relative risk reduction associated with the intervention
ARR= RRR x base mortality rate
CVD Interventions
AMI following hosp discharge – drug treatment
AMI – reperfusion and revascularization
Chronic angina - drug treatment
Chronic heart failure - drug treatment
Heart failure following hosp discharge – drug treatment
Hyperlipidemia – drug treatment
Hypertension - drug treatment
Long-term post MI – drug treatment
Unstable angina –drug treatment
Unstable angina - drug treatment
Sudden death prevention – ICD insertion
Population size and mortality rates
Condition Size of population (crude)
Base annual
Mortality (crude)
AMI admits 83,490 22%
HF admits 110,000 33%
UA admits 54,000 16%
Chronic AMI 950,000 5%
Chronic angina 575,00 2.5%
Chronic HF 444,000 10%
Hypertension 9.4 million 1.6%
Hyperipidemia 10.4 million 0.5%
Sudden death 13,600 15%
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
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
Findings
Condition Disparity Deaths AMI first year following admission 1,200
Chronic angina 450
Heart failure (> 1 year following admission) 1,750
Heart failure first year following admission 1,930
Hyperlipidemia 430
Hypertension 1420
AMI (>1 year following admission) 930
Sudden death prevention- ICD 200
Unstable angina first year following admission 800
TOTAL 8,800
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 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
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
Funding: RWJF and NHLBI/NIH
Collaborators: Richard Dressler
Advice: Simon Capewell
Sensitivity
95% CI - 5,700-11,110
Adherence disparity: 0.70-.90 - 6,310-
11,290