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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 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 Previous studies • 2008 survey - 25% of adults have used low-cost generic programs 83% had some type of insurance, 17% uninsured1 • 70 million people use the programs • • Using claims data, 10% of people with INR tests had no warfarin prescription claims2 • 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 represented3 • 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 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 Claims data!!! What happens when a patient uses a low-cost generic program?? 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 misclassification1 1Jacobus (2004) doi:10.1002/pds.981 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? 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 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 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 30 90% 80% 25 70% 20 60% 50% 15 40% 10 30% 20% 5 10% 0 0% 2005 2006 2007 Total Prescription 2008 2009 Total generics 2010 2011 % low-cost generics 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 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. Results Figure 1 – Application of the inclusion and exclusion criteria on the cohort Table 1 – Cohort characteristics by use of low-cost generic programs (2007-2011) Characteristic Users Non-users p-value % (n) % (n) Age, % (n) 18-34 years 35-54 years 55-64 years Female, % (n) Race, % (n) # of original sample # excluded because of age # excluded because of Medicare # not eligible in all rounds # excluded without prescription medication reported Final cohort 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] Table 2 – Medication use in the cohort (2007 – 2011) Medication Category* Cardiovascular Diureticsa 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 Users Non-users % (n) % (n) Observed exposure % (n) True exposure Misclassified % (n) % (n) Relative Exposure Table 3 – Logistic regression results for each age group predicting use of low-cost generic programs Results • Will come soon enough…. Odds Ratios (95% Confidence Intervals) Unadjusted Adjusted Variable Age 18-34 years Ref. 35-54 years 55-64 years Ref. White Ref. Hispanic African-American Asian Other Ref. Uninsured Ref. Private Medicare Medicaid Other Federal Other Coverage Any insurance Dual coverage Ref. Female Race Insurance status Income level <$25,000 $25,001-$50,000 >$50,000 Ref. Number of Prescriptions Ref. Table 4 – Demonstration of the effect of exposure misclassification % Exposure (excludes exposure to low-cost generics) 5% 10% 20% Relative exposure 1.01 1.05 1.1 1.2 1.25 1.3 1.5 1.75 1.9 2 Sensitivity 99.0% 95.2% 90.9% 83.3% 80.0% 76.9% 66.7% 57.1% 52.6% 50.0% Corrected exposure 5.05% 5.25% 5.50% 6.00% 6.25% 6.50% 7.50% 8.75% 9.50% 10.00% Corrected RR 2.00 2.01 2.01 2.02 2.03 2.03 2.06 2.09 2.10 2.12 Bias Corrected exposure 0.1% 0.3% 0.5% 1.1% 1.3% 1.6% 2.7% 4.1% 5.0% 5.6% 10.10% 10.50% 11.00% 12.00% 12.50% 13.00% 15.00% 17.50% 19.00% 20.00% 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 Corrected RR 2.00 2.01 2.02 2.05 2.06 2.07 2.13 2.20 2.25 2.29 Bias 0.1% 0.6% 1.1% 2.3% 2.9% 3.4% 5.9% 9.1% 11.1% 12.5% Corrected exposure 20.20% 21.00% 22.00% 24.00% 25.00% 26.00% 30.00% 35.00% 38.00% 40.00% Corrected RR 2.01 2.03 2.05 2.11 2.14 2.18 2.33 2.60 2.82 3.00 50% Bias Corrected exposure 0.3% 1.3% 2.6% 5.3% 6.7% 8.1% 14.3% 23.1% 29.0% 33.3% 50.50% 52.50% 55.00% 60.00% 62.50% 65.00% 75.00% 87.50% 95.00% 100.00% Corrected RR Bias 2.02 2.11 2.25 2.67 3.00 3.50 1.0% 5.3% 11.1% 25.0% 33.3% 42.9% ----- ----- 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