FULL COVERAGE FOR PREVENTIVE MEDICATIONS AFTER MYOCARDIAL INFARCTION IMPACT ON RACIAL AND ETHNIC DISPARITIES

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1 FULL COVERAGE FOR PREVENTIVE MEDICATIONS AFTER MYOCARDIAL INFARCTION IMPACT ON RACIAL AND ETHNIC DISPARITIES Niteesh K. Choudhry, MD, PhD Harvard Medical School Division of Pharmacoepidemiology and Pharmacoeconomics Department of Medicine, Brigham and Women s Hospital FUNDED BY A GRANT FROM THE AETNA FOUNDATION

2 Lowering patient out-of-pocket costs improves adherence to evidence-based medications Adherence to evidence-based medications prescribed after myocardial infarction (MI) remains poor Within 2 years of initiating therapy, only half of patients remain adherent to their prescribed statins, beta-blockers, or ACEI/ARBs Drug costs appear to be a central reason for medication underuse Even among patients with insurance, utilization varies according to the comprehensiveness of Eliminating out-of-pocket costs for evidence-based therapies may promote adherence and improve outcomes Referred to as value-based insurance design or evidence-based plan design SOURCE: Choudhry et al. Circulation 2008;117;

3 MI Free Rx Event and Economic Evaluation (MI FREEE) Trial AETNA BENEFICIARIES DISCHARGED AFTER ACUTE MI (n=5855) FULL COVERAGE all beta-blockers, ACEI/ARBs and statins cluster randomized by plan sponsor CONTROL Usual prescription insurance CHANGE: FULL v. USUAL COVERAGE 4-6% NS 14% MAJOR VASCULAR EVENT (secondary outcome) $499 ADHERENCE MAJOR VASCULAR EVENT OR RESVASCULARIZATION (primary outcome) PATIENT OUT-OF-POCKET COSTS Both groups contacted by mail and phone Median follow-up: 394 days (interquartile range: 201 to 663 days) NS OVERALL SPENDING SOURCE: Choudhry et al. Am Heart J 2008; 156: 31; Choudhry et al. NEJM 2011; 365:

4 Could the impact of copayment reductions differ by race/ethnicity? MI FREEE Racial and ethnic differences in CV outcomes well documented Attributed to differences in the use of evidence-based therapies including medications Racial and ethnic minorities report more cost-related nonadherence May be more likely to respond to interventions that address high out-ofpocket drug costs Minority patients appear to have difficulty navigating the complexities of health insurance programs Including expansion like Medicare Part D SOURCE: Lewey, Choudhry. Am Heart J 2013 % of patients fully adherent Non-white (v. white) race -53% Female (v. male) -10%

5 Research Goals IMPACT OF MI FREEE ON RACIAL AND ETHNIC DISPARITIES Evaluate whether the impact of eliminating copayments for secondary prevention cardiovascular medications differed by self-reported race/ethnicity Aetna routinely collects racial/ethnic data via self-report (available for about 1/3 of beneficiaries) Determine whether indirect racial/ethnic identification methods achieve the same results as self-reported race/ethnicity assignment Indirect methods assign the predicted probability that patients belong to different race/ethnicity groups based upon geocoding and surname widely available RAND methodology highly correlated with self-reported race/ethnicity specifically among Aetna beneficiaries (r 2 = 0.76) Comparing the impact of copayment elimination using self-reported and indirect identification could help establish the ability to use indirect methods for evaluating disparities in other conditions or interventions

6 METHODS MI FREEE DISPARITIES Restricted MI FREEE trial participants to those with both self-reported and indirect race/ethnicity information available (n=2,387) Categorized race/ethnicity using self-reported information A priori classified patients as white v. non-white Considered white v. black in sensitivity analysis (very similar results but less power) Evaluated whether impact of full differed by race/ethnicity Primary outcome (first major vascular event or revascularization) - Cox proportional hazards Health care expenditures and adherence - generalized estimating Multivariable adjustment: cluster and block design, age, gender, comorbidity Ran models with and without zip code level income adjustment (no change) Repeated analyses using indirect race/ethnicity assignment (predicted probability of belonged to each race/ethnic group) Classified as white if indirect predicted probability of white race 55% Creating 100 imputed race variables based on predicted probabilities

7 Baseline characteristics (selected) by race MI FREEE White (N=1856) Non-White (N=531) CHARACTERISTIC Full Coverage (N=946) Usual Coverage (N=910) Full Coverage (N=260) Usual Coverage (N=271) Age, mean Male sex, % * Comorbidities, % Congestive heart failure Diabetes Hypertension Prior MI Stroke Procedures on index hospitalization, % Angiography PCI CABG Monthly baseline copayment, mean ACEI/ARB $13.57 $14.14 $12.61 $13.69 Beta-blocker $13.18 $14.46 $11.48 $13.50 Statin $27.20 $26.26 $25.55 $24.15 *p-value <0.05

8 Medication adherence MI FREEE Full Usual ACEI/ARBs Beta-blockers Statins All 3 classes WHITE % fully adherent NON-WHITE % fully adherent Interaction % 41% 44% 36% P=0.44 P=0.66 P=0.63 P= % 53% 43% 132%

9 Lowering copayments decreased major vascular events among non-white but not white patients MI FREEE White Non-White Full Usual Full Usual Rate/100 py Rate/100 py % 60% Cumulative Incidence 50% 40% 30% 20% 10% Full Usual Cumulative Incidence 50% 40% 30% 20% 10% Usual Full 0% % Months Months HR (95% CI): 0.96 ( ) P-value: 0.74 HR (95% CI): 0.62 ( ) P-value: 0.03

10 WHITE Total spending Health spending MI FREEE $125,000 $100,000 $75,000 $50,000 $25,000 $0 Full Usual Pharmacy Medical Total $6,168 9% 28% 26% $5,545 $67,587 $50,617 $73,775 $56,163 Interaction NON-WHITE Total spending $125,000 $100,000 $75,000 $50,000 $25,000 $0 P=0.83 P= P= % 76% 72% $115,114 $119,887 $5,577 $31,622 $37,198 $4,774

11 Indirect methods result in very different conclusions as direct measures MAJOR VASCULAR EVENT OR REVASCULARIZATION WHITE NON-WHITE Method FULL (Rate per 100 PY) USUAL (Rate per 100 PY) Hazard Ratio * (95% CI) FULL (Rate per 100 PY) USUAL (Rate per 100 PY) Hazard Ratio * (95% CI) Interaction p-value Self-report ( ) ( ) 0.03 Indirect (binary) ( ) ( ) 0.93 Indirect (imputation) ( ) ( ) 0.85 * Hazard Ratios adjusted for cluster and block randomized design

12 Implications Baseline rates of adherence were lower and cardiovascular events and health spending were much higher for non-white patients Eliminating cost-sharing for evidence-based post-mi medications may help address cardiovascular disparities Full similarly effective at improving adherence for patients self-identifying as white and non-white BUT, full significantly more effective at reducing rates of major vascular events or revascularization and total health care spending among non-white patients Rates of major vascular events/revascularization much more similar with full than with usual Quantitatively and qualitatively different results were obtained using indirect race/ethnicity methods Indirect measures may not accurately capture the impact of race and ethnicity on health behaviors

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14 MI Free Rx Event and Economic Evaluation (MI FREEE) Trial AETNA BENEFICIARIES DISCHARGED AFTER ACUTE MI Based on discharge claims submitted by hospitals (specificity 99%) EXCLUSIONS: Age > 65, didn t have both drug and medical, enrolled in ineligible plan cluster randomized by plan sponsor FULL COVERAGE all beta-blockers, ACEI/ARBs and statins CONTROL usual levels of prescription insurance Both groups contacted to tell them that taking their prescribed medications is important +/- inform them of their benefit change SOURCE: Choudhry et al. Am Heart J 2008; 156: 31

15 Lowering copayments improved medication adherence MI FREEE (N=5855) ACEI/ARBs Beta-blockers Statins All 3 classes % 4.4% 6.2% 5.4% % of days covered P<0.001 for all comparisons Full Usual SOURCE: Choudhry et al. NEJM 2011; 365:

16 Lowering copayments decreased major vascular events MI FREEE Major vascular event or revascularization Major vascular events Full Usual Full Usual Rate/100 py Rate/100 py Usual Usual Full Full HR (95% CI): 0.93 ( ) P-value: 0.21 HR (95% CI): 0.86 ( ) P-value: 0.03

17 Health spending MI FREEE Full Usual Pharmacy Medical Total $2,000 30% P< % P= % P<0.001 Patient Spending $1,500 $1,000 $500 $802 $1,164 $480 $618 $1,282 $1,781 $0 17% 10% 11% $75,000 P=0.02 P=0.72 P=0.68 Total spending $50,000 $25,000 $5,649 $5,085 $60,358 $66,693 $66,008 $71,778 $0