1. THE PREVALENCE AND PREDICTORS OF LOW-COST GENERIC PROGRAM USE IN A NATIONALLY REPRESENTATIVE ADULT POPULATION: IMPLICATIONS FOR PATIENTS, RESEARCH, AND THE HEALTHCARE SYSTEM. Joshua Brown, PharmD

2. Background Low-cost generic programs originated in 2006 Kmart Walmart $4 program 8 of 10 top pharmacy retailers offer a generic program Vary based on medications offered, enrollment fees, copays

3. Previous studies 2008 survey - 25% of adults have used low-cost generic programs 83% had some type of insurance, 17% uninsured 1 70 million people use the programs Using claims data, 10% of people with INR tests had no warfarin prescription claims 2 A comparison of plans across the country One-third of the top 100 generics are included in programs 25,000 pharmacies nationwide Many medication classes are represented 3 1 CS Mott Children’s Hospital http://mottnpch.org/sites/default/files/documents/021108GenericRxPrograms.pdf 2 Lauffenburger (2013). doi:10.1002/pds.3458 3 Czechowski (2010)doi:10.1331/JAPhA.2010.09114

4. Process of claims adjudication 1. Prescription submitted to pharmacy 2. Filled by pharmacy, submitted to insurance company 3. Insurance company processes and determines copay/payment 4. Patient gets prescription What happens when a patient uses a low-cost generic program?? Claims data!!!

5. What are claims data used for? Quality assurance/measurement in the healthcare system Drug benefit design and policy Research Drug benefits, drug harms If claims are used to measure exposure, and medication use is missing from the claims, this may result in exposure misclassification 1 1 Jacobus (2004) doi:10.1002/pds.981

6. Research questions What is the trend of use of low-cost generic programs? What is the prevalence of use of these programs? What medication classes are being acquired? What are the demographic predictors of using these programs? What effect can misclassification exposure have on a research study?

7. Data source Medical Expenditure Panel Survey (MEPS) Publicly available, de-identified Enrolls a panel for two years and collects data over 5 rounds Includes information related to demographics and medication use Medication use is collected directly from pharmacy Survey weights allow for population estimates

8. Study sample Individuals surveyed during the years 2005 to 2011 Inclusion Adults Aged 18 to 64 years of age surveyed over all 5 rounds of their panel Reporting pharmacy use data Exclusion Persons having Medicare coverage Age >65

9. Methods – trends of use Prevalence of program use, generic utilization, and total prescription utilization will be observed by year starting in 2005 through 2011 2005 used as a baseline

10. Methods – comparison between users and non-users Cohort consisting of the years 2007-2011 will be categorized as users or non-users (binary) Having a prescription <$10 for 30 day-supply or ≤$15 for a 90 days supply Univariable comparisons will be made for each demographic between groups Logistic regression will determine the predictors of program use in unadjusted and adjusted analyses SAS survey procedures will be used to account for survey design and sampling weights to provide population estimate Demographics included: Age, gender, race, insurance, income, Charlson comorbidities, # of prescriptions

11. Methods example Observed exposure is what would be seen in the claims data True exposure includes claim exposure plus the exposure from low-cost generics Misclassified individuals are exposed by low-cost generics but not claims data Relative exposure = Observed/True exposure If a person fills lisinopril for $4 at a pharmacy and no other insurance pays, they are considered users of a low-cost generic program. If they only get lisinopril this way, they will be misclassified. If there is another record for lisinopril (or another ACE inhibitor) that indicates an third-party payer contribution, this person is still a user but is no longer misclassified for exposure to ACE inhibitors.

12. Table 1 – Cohort characteristics by use of low-cost generic programs (2007-2011) Characteristic Users % (n) Non-users % (n) p-value Age, % (n) 18-34 years 35-54 years 55-64 years Female, % (n) Race, % (n) Whites Hispanics African-Americans Asians Other Insurance coverage, % (n) Uninsured Private Medicaid Other Federal Other coverage Multiple coverage Income level, % (n) <$25,000 $25,001 - $50,000 >$50,000 Prescription utilization, % (n) Total Generics % Low-cost generics Out-of-pocket medication costs Per prescription, mean [SD] Per person, mean [SD] Results # of original sample # excluded because of age # excluded because of Medicare Final cohort # not eligible in all rounds # excluded without prescription medication reported Figure 1 – Application of the inclusion and exclusion criteria on the cohort

13. Table 2 – Medication use in the cohort (2007 – 2011) Medication Category* Users % (n) Non-users % (n) Observed exposure % (n) True exposure % (n) Misclassified % (n) Relative Exposure Cardiovascular Diuretics a Beta-blockers ACE Inhibitors Ca-channel blockers Alpha-blockers Statins Warfarin Antibiotics Penicillins Cephalosporins SMZ-TMP Fluoroquinolones Anti-fungals Arthritis and Pain NSAIDs Steroids Muscle Relaxants Allergy and Cold Antihistamines Anti-diabetes Metformin Sulfonylureas Gastrointestinal H2 Blockers

14. Results Will come soon enough…. Table 3 – Logistic regression results for each age group predicting use of low-cost generic programs Variable Odds Ratios (95% Confidence Intervals) UnadjustedAdjusted Age 18-34 years 35-54 years 55-64 years Ref. Ref. Female Race White Hispanic African-American Asian Other Ref. Ref. Insurance status Uninsured Private Medicare Medicaid Other Federal Other Coverage Any insurance Dual coverage Ref. Ref. Income level <$25,000 $25,001-$50,000 >$50,000 Ref. Ref. Number of Prescriptions

15. Table 4 – Demonstration of the effect of exposure misclassification % Exposure (excludes exposure to low-cost generics) 5%10%20%50% Relative exposure Sensitivity Corrected exposure Corrected RR Bias Corrected exposure Corrected RR Bias Corrected exposure Corrected RR Bias Corrected exposure Corrected RR Bias 1.01 99.0%5.05%2.000.1%10.10%2.000.1%20.20%2.010.3%50.50%2.021.0% 1.05 95.2%5.25%2.010.3%10.50%2.010.6%21.00%2.031.3%52.50%2.115.3% 1.1 90.9%5.50%2.010.5%11.00%2.021.1%22.00%2.052.6%55.00%2.2511.1% 1.2 83.3%6.00%2.021.1%12.00%2.052.3%24.00%2.115.3%60.00%2.6725.0% 1.25 80.0%6.25%2.031.3%12.50%2.062.9%25.00%2.146.7%62.50%3.0033.3% 1.3 76.9%6.50%2.031.6%13.00%2.073.4%26.00%2.188.1%65.00%3.5042.9% 1.5 66.7%7.50%2.062.7%15.00%2.135.9%30.00%2.3314.3%75.00% -- 1.75 57.1%8.75%2.094.1%17.50%2.209.1%35.00%2.6023.1%87.50% -- 1.9 52.6%9.50%2.105.0%19.00%2.2511.1%38.00%2.8229.0%95.00% -- 2 50.0%10.00%2.125.6%20.00%2.2912.5%40.00%3.0033.3%100.00% -- Assumptions: Uncorrected rate ratio (RR) = 2 Event rate in exposed = 50 events per 1000 person-years Event rate in uneposed = 25 events per 1000 person-years % Corrected exposure = Relative exposure * % Exposure % Bias = (Corrected RR – Uncorrected RR)/Corrected RR * 100

16. Implications Administrative claims data are used for a variety of purposes including research and quality assurance Low-cost generics provide a source of misclassification of exposure Misclassification of exposure can bias the results of studies E.g. – underestimate the benefits or harms of medication use This is the first study to describe the use of low-cost generics in detail and the first to include analyses investigating medications used and the differences between demographic groups