Bruce Stuart, Thomas Shaffer, Linda Simoni-Wastila, Ilene Zuckerman

1. Drug Coverage, Disease Burden, and the Intensity of Medication Use among Medicare Beneficiaries Seattle, WashingtonAcademyHealth June 27, 2006 Bruce Stuart, Thomas Shaffer, Linda Simoni-Wastila, Ilene Zuckerman The Peter Lamy Center on Drug Therapy and Aging University of Maryland Baltimore

2. Outline • Sponsor acknowledgment: funding provided by The Commonwealth Fund under grant Benchmarking the Quality of Medication Use by Medicare Beneficiaries • Motivation: need for new empirical models of medication demand • Study objectives • Data and study sample • Measures • Statistical strategy • Results • Discussion and study implications for policy

3. Motivation: Need for New Empirical Models of Demand for Prescription Drugs by Medicare Beneficiaries Traditional studies of demand for drugs by Medicare beneficiaries • Most studies assume a linear demand response to price signals. • Complements and substitutes for drug therapy are generally acknowledged but not formally modeled • Disease burden is considered an important demand shifter, but is not assumed to directly impact price elasticity because.. • No explicit account is taken of changes in the marginal contribution of drug therapy to health across the spectrum of disease burden

4. Motivation: Need for New Empirical Models of Demand for Prescription Drugs by Medicare Beneficiaries Prescription coverage can induce 3 types of demand 1. Increased intensity (better adherence /persistence) of drug use for existing medication sensitive conditions (MSCs) 2. Increased “demand” for new MSCs 3. Demand for medications to treat the new MSCs Why it matters • Traditional empirical models underestimate moral hazard because new MSC effects (2 and 3 above) are co-varied out with risk adjustment • Policy impact of giving beneficiaries drug coverage ignores potential increase in cost for physician services (2 above) • Cost impacts may vary depending on the relative distribution of disease burden among those gaining coverage

5. Study Objectives • Estimate impact of prescription coverage on • prescription fills, • MSCs • Medication intensity (prescription fills per MSC) • Model without risk adjustment for comorbidities using stratification by decile of total annual medical spending as a strategy to minimize selection bias • Compare results with models using risk adjustment for comorbidity • Learn more about the differential effects of prescription coverage along the continuum of disease burden

6. Data and Study Sample Data • 2002 MCBS Cost and Use files (N=12,697) Study Sample • Inclusion criteria • Community-dwelling (excludes institutional residents) • Enrolled in Part A and B in January 2002 (excludes new enrollees) • Fee-for service (excludes Medicare HMO enrollees due to lack of claims) • Complete surveys (excludes respondents with missed survey rounds) • Minimum of 1 medication sensitive condition (MSC) • Final study sample: N=7,751

7. Measures Overall Burden of Illness • Stratify study sample into 10 equal sized groups (deciles) by cumulative spending for all medical services including drugs Dependent Variables • Counts of medication sensitive conditions (RxHCCs) • Counts of prescription drug fills (PME events) • Prescription fills per RxHCC (medication intensity measure) Explanatory Variables • 4 domains: (1) decile assignment, (2) demographics (age, sex, race, census region), (3) economic variables (income, prescription coverage), (4) health (self-reported, ADLs, BMI, any inpatient hospital, SNF, or hospice stay, and home health visit, and denominator days)

8. Statistical Strategy Descriptive charts • Plot prevalence rates for common comorbidities by decile of medical spending • Plot unadjusted rates for RxHCCs, prescription fills, and Rx fills per RxHCC by prescription coverage status and disease burden Regression analysis/plots of predicted values • OLS regression models for RxHCCs, Rx counts, Rx fills per RxHCC • Output predicted values for RxHCCs, Rx counts, and Rx fills per RxHCC for beneficiaries with and without drug coverage by decile of disease burden • Plot and compare the adjusted and unadjusted rates across the spectrum of disease burden

9. Figure 1. Prevalence of Selected Diseases among Medicare Beneficiaries Stratified by Decile of Annual Medical Spending, 2002

10. Figure 2a. Unadjusted Medication Sensitive Condition Counts (RxHCCs) for Medicare Beneficiaries by Full or No Rx Coverage Stratified by Spending Decile, 2002

11. Figure 2b. Adjusted Comorbidity Counts (RxHCCs) for Medicare Beneficiaries by Full or No Rx Coverage Stratified by Spending Decile, 2002

12. Figure 3a. Unadjusted Prescription Drug Fills for Medicare Beneficiaries with Full Year or No Rx Coverage Stratified by Spending Decile, 2002

13. Figure 3b. Adjusted Prescription Drug Fills for Medicare Beneficiaries with Full Year or No Rx Coverage Stratified by Spending Decile, 2002

14. Figure 4a. Unadjusted Prescription Drug Fills Per RxHCC for Medicare Beneficiaries with Full Year and No Rx Coverage Stratified by Spending Decile, 2002

15. Figure 4b. Adjusted Prescription Drug Fills Per RxHCC for Medicare Beneficiaries with Full Year and No Rx Coverage Stratified by Spending Decile, 2002

16. Main Points Medication Sensitive Conditions • Beneficiaries with prescription coverage have small but significantly higher MSC counts up through the 8th decile of total medical spending Prescription coverage effects • Increasing disease burden is associated with a steady rise in drug use for both those with and without coverage, and the differential increases with disease burden Medication intensity curve • Distinct inverted “U” pattern in medication intensity for both those with and without coverage • Higher overall intensity of drug treatment for those with coverage implies that health spending for those individuals is more heavily weighted toward drug therapy • Medication intensity rises faster with disease burden among those with Rx coverage, and falls less sharply after inflection point is reached

17. How Much Difference Does it Make When Moral Hazard Effects are Estimated Using the New Methodology? Standard method using risk adjustment with RxHCCs (assumes difference in MSCs between those with and without prescription coverage is due to selection) • Estimated price elasticity of= -0.45 New method assuming difference in MSCs are due to prescription coverage • Estimated price elasticity = -0.50 or about 11% higher • Plus cost for physician services to treat new MSCs (about 4% more) So which method is correct? • Two methods may bound the true value

18. How to Interpret the Medication Intensity Curve?Some Plausible Explanations Rising segment (deciles 1-5) • Reflects beneficiary learning curve for effective drug use • Addition of therapy or co-therapy for existing chronic conditions • More physician contacts increase likelihood of optimal prescribing (surveillance hypothesis) Middle segment (deciles 4-6) • Beneficiaries perceptions of positive returns from drug therapy balanced against rising rates of adverse drug effects and difficulty in managing drug regimen • Physicians balance benefits and harms from prescribed drug therapy Falling segment (deciles 5-10) • Beneficiary/physician perceptions that negative returns to drug therapy outweigh positive returns • Beneficiary lapses in medication management skills • Beneficiaries place lower value on treatment effects when seriously ill • Complex morbidity leads physicians to cut back treatment for specific conditions (competing demands hypothesis)

19. Other Analytic Considerations/ Study limitations • Cross-sectional study design precludes causal inferences • Cannot distinguish between patient and prescriber behavior • Data may under-report true drug utilization • RxHCC measures medication sensitive conditions but not severity • Beneficiaries in the top deciles are more likely to be hospitalized and therefore more likely to “collect” ICD-9 codes • Some drugs are used to treat multiple conditions • Stratification and limitation of sample to beneficiaries with at least 1 MSC may not fully control for selection bias

20. Conclusions: Implications for Part D • Part D is likely to lead to a small short-run bump in Part B spending as those with newly minted drug coverage begin to seek treatment for formerly untreated MSCs • Major increase in drug use among Part D enrollees with no former drug coverage, with largest increases among those at the upper end of the disease spectrum • Overall rise in medication intensity with bigger increases at the upper end of the disease spectrum

21. Thank You!