Reimbursement Policy and
Cancer Chemotherapy Treatment
Mireille Jacobson, RAND and NBER
Craig C. Earle, MD, Cancer Care Ontario
Mary Price, Kaiser Permanente
Joseph P. Newhouse, Harvard and NBER
Support from the Robert Wood Johnson
Foundation HCFO Program
g
How do changes in reimbursement
rates affect treatment?
• Many studies find that non-clinical factors play an
important role in physician decisions:
– Training and local practice patterns
– Reimbursements unrelated to quality-adj. costs
• Medicare Modernization Act dramatically changed
reimbursement methods for Part B drugs
– Did this change affect cancer treatment?
Important Institutional Detail
• Chemotherapy typically provided in physician’s
offices/clinics
– Physicians
Ph i i
purchase
h
chemotherapy
h
th
d
drugs
– Payers reimburse physicians for the drugs
• Chemotherapy accounts for a large share of oncologist’s
income
• Ch
Changing
i reimbursement
i b
t rates
t plausibly
l
ibl affect
ff t ttreatment
t
t
decisions
How did the Medicare Modernization Act
change reimbursement?
• Prior to January 2005, rates based on list price (AWP)
– AWP unrelated to transaction prices
– Some chemotherapy agents were reimbursed at over
60% of wholesale transaction prices
• Since January 2005, rates are based on wholesale
prices (ASP) in the previous 2 quarters + 6%
– Reimbursement rates for many drugs have declined
dramatically since 1/2005
Change in Nominal Quarterly Reimbursement
Rates Relative to the Payment Change
Nomiinal Reimb
bursement Per Mon
nthly
Dose
3500
3000
2500
2000
1500
1000
500
0
-10
-5
0
5
10
Quarter Relative to January 2005
Carboplatin
p
Paclitaxel
Docetaxel
Etoposide
p
Gemcitabine HCL
15
Potential Changes
• Oncologists might turn away patients
– refer them to hospitals for treatment
• MMA could increase physician prescribing
– Make up
p for lost margins in volume
ol me
• Equalized 6% margin incentivizes use of higher priced
agents
– Potentially unintended effect of MMA
Our Analytic Approach
• Study lung cancer
• Analyze regression-adjusted changes in:
– Chemotherapy treatment rates overall
– Chemotherapy treatment rates by setting
– Use of different agents conditional on chemotherapy
treatment
• Look at treatment within 1 month of diagnosis
– Analyze data 24 months prior to and 10 months after
the implementation of the new payment regime
Some comments about the approach
• Currently just using a pre-post design:
– Monumental shift in how chemotherapy is reimbursed
– No other major changes to lung cancer treatment
– Look for sharp changes in tx at time of change
• Test stability of patient characteristics across
regime change
• In ongoing work, consider a control group:
– Kaiser Permanente data for Northern California
– Medicare pays a flat fee for enrollees
Data sources
• Rely exclusively on claims data
– Received initial cohort of Medicare beneficiaries with
g cancer-related claim,, 2003-2005
at least 1 lung
(N=878,923)
• ICD-9 codes: 162.0-162.9
• Have all 2002-2006 claims for these beneficiaries from
the following files:
– Carrier (physician data)
– Outpatient
– MedPAR
M dPAR (inpatient
(i
ti t data)
d t )
– Hospice
Analytic Sample
• Restrict
R t i t to
t Fee-for-service
F f
i patients
ti t (95%)
• Apply algorithm to “confirm”
confirm a lung cancer dx.
dx Require:
– 2+ non-institutional claims separated by at least 28
days and no more than 365 with a lung cancer dx
– 1 institutional claim with a lung cancer dx
• Exclude those with more than one primary cancer
• 222,478 beneficiaries with a “confirmed” lung cancer
diagnosis between 2003 and 2005
Some Sample Characteristics
Overall
Pre
Post
Age at Dx
74.0
74.1
74.0
Share White
0.878
0.878
0.878
Share AfricanAmerican
0.089
0.090
0.088
Share male
0.516
0.519
0.511
Share with
Metastasis at Dx
0.286
0.287
0.285
Analysis: What did we do?
• Visual analysis of trends by month of dx in:
– the probability of chemotherapy tx
– the probability of receiving different agents among
chemotherapy-treated
• From visual analysis, specify a regression model to
estimate change in probability of tx pre versus post MMA
• Narrow the window around the pre versus post MMA
periods to ensure estimates are driven by policy
What did we find (I)?
• Probability of chemotherapy increased about 2pp, off a
base of 17% after the new payment regime took effect
• Increase came entirely in physician office-setting not in
hospital settings
• Increase not driven by patent expiration of Carboplatin
• Vi
Visuall analysis
l i off ttrends
d suggests
t th
the paymentt regime
i
was likely the cause
What did we find (II)?
• A
Among those
th
receiving
i i chemotherapy
h
th
ttreatment:
t
t
– Patients less likely to get Paclitaxel & Carboplatin:
3 4 pp decline each or 14% and 5% respectively
3-4
• Previously reimbursed well above cost
– Patients more likely to receive Docetaxel:1pp
increase but 10% in relative terms!
• Profit margin
g changed
g little with regime
g
change
g
• Higher priced agents favored by flat mark-up rate
• Visual analysis of trends suggests some anticipation of
the payment regime change
Interpretation
• Patient choice unlikely to play a role/works against
effects
ff
we are finding
fi di
– With lower payment rates, physicians increasingly
collect 20% coinsurance
• Physicians increase volume to make up for lost income
due to reduction in margins (PID)
– Income effect > substitution effect
• Some potential savings from the payment changes were
offset
ff t by
b increases
i
in
i use
Summary
• MMA’s reduction in payment rates for outpatient
chemotherapy drugs:
– Did not adversely affect access
– Increased
I
d chemotherapy
h
th
ttreatment
t
t for
f lung
l
cancer
– Changed the relative mix of agents
– Implications
p
for health are unclear in this context
Ongoing and Future Work
Ongoing and Future Work
• Quantify impact on spending: how much did increase in
volume and change in drugs offset savings?
• Analyze SEER data (confirmatory analysis)
• E
Expanding
di analysis
l i tto C
Colorectal
l
t lC
Cancer patients
ti t
– Preliminary results suggest a different response
• Will analyze trends in treatment patterns for Kaiser
Permanente, Northern California
– Control group, Medicare pays a flat fee for enrollees
• Will assess heterogeneity by race
race, SES and geography