Download The prevalence and predictors of low-cost generic

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%
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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