Transcript A Price Deflator for Medical Care Ana Aizcorbe (BEA) Nicole Nestoriak (BLS) 2008 World Congress on National Accounts and Economic Performance Measures for Nations May.

A Price Deflator for Medical Care
Ana Aizcorbe (BEA)
Nicole Nestoriak (BLS)
2008 World Congress on National Accounts and
Economic Performance Measures for Nations
May 17, 2008
The views expressed in this paper are solely those of the authors
and not necessarily those of the
U.S. Bureau of Economic Analysis or the
U.S. Bureau of Labor Statistics.
www.bea.gov
2
Our research on price indexes for health spending is
part of a broader initiative.
BEA plans to construct a satellite account for
medical care that will include:
 Expenditures that are reconciled with CMS estimates.
 New measures of spending by disease that will
facilitate assessments of the returns on health
expenditures.
 Improved price deflators that will better-account for
changes in quantity vs changes in prices.
www.bea.gov
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Price deflators are the subject of today’s talk.
 Our research uses health claims data as a
laboratory to explore:
 Different ways to construct price deflators,
 How best to allocate spending by disease,
 The usefulness of claims data (relative to other data
sources),
 In particular, do we really need to use claims data?
 This project is very much at the research stage.
 Today’s results are for HMO patients only.
www.bea.gov
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Starting point for price deflator:
prescription from health economists.

“Output” of medical care as the “treatment of disease,” not the
individual treatments

Previous case studies suggest that this issue could be numerically
important:
 Psychiatric conditions (Berndt et. al.)
 Heart attacks (Cutler and McClellan)
 Cataract (Shapiro, Shapiro, and Wilcox)
 40 conditions in four cities (Bradley et. al.)

Two National Academies panels have recommended that statistical
agencies construct price indexes on this basis, even if one cannot
account for changes in outcomes of care (i.e., quality)
www.bea.gov
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Why is it important to define the “good” as the treatment of
a homogeneous medical condition?
Example: drug therapy introduced,
no price change for either treatment,
number of patients the same
Problem:
 As consumers switch to drug therapy,
nominal expenditures fall
 Usual price index shows no price change
 Real expenditures fall even if quantities
did not
Solution: redefine the “good” as the “treatment of depression” and the price as
Pdepression,t = expenditures on all types of treatments
-------------------------------------------------number of patients treated
This is really just a reincarnation of the “Reinsdorf” problem.
www.bea.gov
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Our data show large differences in cost per patient across
treatment types…
Table 1. Place of Service Variable, descriptive statistics
Place of Service
Ambulatory Surgical Center
Emergency Room-Hospital
Independent Lab
Inpatient Hospital Confinement
Office
Outpatient Hospital
Patient's Home
Pharmacy
Unknown
Rest
Total
per patient
$1,289
$581
$61
$14,619
$167
$573
$332
$155
$623
spending
$33,613,320
$90,341,578
$22,892,986
$488,913,387
$394,349,456
$327,756,307
$34,828,490
$431,708,333
$97,629,011
percent
1.7%
4.6%
1.2%
24.9%
20.1%
16.7%
1.8%
22.0%
5.0%
$39,607,301
$1,961,640,169
2.0%
100.0%
Shifts across treatments types will likely generate changes in costs.
www.bea.gov
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…and our indexes show substantial cost savings generated by
changes in treatment types.
Comparison of Treatment- and Patient-based Indexes
(CGAR)
1.3
Features of our index:
•Constructed using large
claims database for HMO
patients.
•Price = revenue from all
sources
1.2
•Price is defined as price per
patient treated for a
homogeneous condition
1.1
1
•Dollars are allocated to
conditions using “groupers”
0.9
2003
2004
Treatment (7.1%)
Source: Aizcorbe and Nestoriak (2008)
www.bea.gov
2005
Patient (5.0%)
•“Treatments” are identified
using “place of service”
variable.
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The differences are large and pervasive.
Price Indexes:
Major Disease Category
INFECTIOUS DISEASES
ENDOCRINOLOGY
HEMATOLOGY
PSYCHIATRY
CHEMICAL DEPENDENCY
NEUROLOGY
OPHTHALMOLOGY
CARDIOLOGY
OTOLARYNGOLOGY
PULMONOLOGY
GASTROENTEROLOGY
HEPATOLOGY
NEPHROLOGY
UROLOGY
OBSTETRICS
GYNECOLOGY
DERMATOLOGY
ORTHOPEDICS & RHEUMATOLOGY
NEONATOLOGY
Source: Aizcorbe and Nestoriak (2008)
www.bea.gov
P(2005:4)/P(2003:1)
Patient
1.36
1.15
1.14
1.06
1.16
1.19
1.08
1.06
1.10
1.17
1.15
1.12
0.96
1.10
1.10
1.15
1.16
1.14
1.22
Visit
diff
1.37
1.21
1.22
1.08
1.19
1.26
1.10
1.24
1.15
1.22
1.23
1.23
1.09
1.20
1.18
1.26
1.18
1.24
1.23
(0.01)
(0.05)
(0.08)
(0.02)
(0.03)
(0.07)
(0.02)
(0.18)
(0.05)
(0.05)
(0.07)
(0.11)
(0.14)
(0.10)
(0.08)
(0.11)
(0.02)
(0.10)
(0.00)
For example, the cost of treating
infectious diseases rose, on average,
36 percent from 2003:1 to 2005:4,
while the costs of the underlying
treatments rose 37 percent.
Conclusion: viewing the bundle of
treatments as the “good” implies
slower increases in price (and faster
increases in quantity).
Health economists view these
differences as productivity.
Caveat: these indexes do not
account for changes in “quality” of
treatment.
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Can we say more about the differences in the
indexes? Is it substitution?
We developed an algebraic mapping to further explore sources of these
differences.
The unit value index for treating disease d can be interpreted as a
Laspeyres index of treatments that is adjusted for shifts in
utilization of treatments.
Id(P) = cd1 / cd0 = S i
cd,i0 xd,i0
---------------S i cd,i0 xd,i0
xd,i1/ P d 1
--------------xd,i0/ P d 0
( cd,i1 / cd,i0 )
The difference in the two indexes is then:
Id(P)- Id(T)
www.bea.gov
= Si
cd,i0 xd,i0
xd,i1/ Pd1
---------------- [ ----------- - 1 ] ( cd,i1 / cd,i0 )
S i cd,i0 xd,i0
xd,i0/ Pd0
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We found that reduced use of hospitals and office visits generated
cost savings not offset by increased use of drugs and other, lowercost treatments.
Indexes
Contributions to differences (percentage points)
Hospital
Major Disease Category
INFECTIOUS DISEASES
ENDOCRINOLOGY
HEMATOLOGY
PSYCHIATRY
CHEMICAL DEPENDENCY
NEUROLOGY
OPHTHALMOLOGY
CARDIOLOGY
OTOLARYNGOLOGY
PULMONOLOGY
GASTROENTEROLOGY
HEPATOLOGY
NEPHROLOGY
UROLOGY
OBSTETRICS
GYNECOLOGY
DERMATOLOGY
ORTHOPEDICS & RHEUMATOLOGY
NEONATOLOGY
Patient
1.36
1.15
1.14
1.06
1.16
1.19
1.08
1.06
1.10
1.17
1.15
1.12
0.96
1.10
1.10
1.15
1.16
1.14
1.22
Visit
diff
1.37
1.21
1.22
1.08
1.19
1.26
1.10
1.24
1.15
1.22
1.23
1.23
1.09
1.20
1.18
1.26
1.18
1.24
1.23
(0.01)
(0.05)
(0.08)
(0.02)
(0.03)
(0.07)
(0.02)
(0.18)
(0.05)
(0.05)
(0.07)
(0.11)
(0.14)
(0.10)
(0.08)
(0.11)
(0.02)
(0.10)
(0.00)
Inpatient
(0.02)
(0.06)
(0.08)
(0.02)
(0.07)
(0.05)
(0.00)
(0.17)
(0.01)
(0.05)
(0.05)
(0.10)
(0.04)
(0.06)
(0.09)
(0.07)
(0.00)
(0.07)
(0.02)
Outpatient
(0.00)
(0.00)
(0.00)
0.01
(0.02)
(0.01)
(0.02)
(0.01)
(0.03)
(0.01)
(0.02)
(0.01)
(0.02)
(0.02)
0.00
(0.01)
(0.00)
(0.03)
0.00
Office
Visits
(0.01)
(0.02)
(0.01)
(0.03)
(0.03)
(0.02)
(0.01)
(0.01)
(0.02)
(0.01)
(0.02)
(0.00)
(0.00)
(0.02)
0.00
(0.02)
(0.02)
(0.00)
0.00
Home
Drugs
0.02
0.03
0.01
0.02
0.01
0.01
0.00
(0.00)
(0.01)
0.00
(0.00)
(0.01)
0.01
0.00
(0.00)
(0.00)
(0.00)
0.00
(0.00)
ER
(0.00)
(0.00)
(0.00)
(0.00)
0.04
(0.00)
(0.01)
(0.00)
(0.01)
(0.01)
(0.01)
0.00
0.00
(0.01)
0.00
0.00
(0.01)
(0.01)
(0.00)
Lab
Care
ASC
Other
(0.00)
(0.00)
(0.00)
(0.00)
0.00
0.00
(0.00)
0.00
0.00
(0.00)
0.00
(0.00)
0.00
0.00
(0.00)
(0.00)
(0.00)
0.00
0.00
0.00
0.01
(0.01)
(0.00)
(0.00)
0.00
0.00
0.00
0.00
0.01
0.00
(0.00)
(0.00)
0.00
0.00
0.00
0.00
0.01
0.00
(0.00)
(0.00)
(0.00)
0.00
0.00
(0.00)
0.01
(0.00)
(0.00)
(0.00)
0.01
0.00
0.00
(0.00)
(0.00)
(0.00)
(0.00)
0.00
(0.00)
0.00
0.00
0.01
0.00
0.04
(0.00)
0.01
0.00
0.01
0.02
0.01
0.01
(0.07)
0.01
0.01
0.01
0.02
0.00
0.01
This is consistent with long-run trends in the CMS data.
www.bea.gov
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Some Examples
Differences in price indexes: Hospital
Major Disease Category
Patient
Visit
diff
Inpatient Outpatient
Office
Visits
Emergenccy
IndependentHome Ambulatory
Drugs
Room
Labs
Care
Surg. Ctrs.
Other
U se of Ambula tory Surgica l Ce nte rs:
GAST R OEN T ER OLOGY
OPH T H ALMOLOGY
15.5%
8.2%
22.6%
10.2%
-7.1%
-2.0%
-5.3%
-0.4%
-1.9%
-1.6%
-1.5%
-1.1%
-0.1%
0.1%
-0.5%
-0.9%
0.0%
0.0%
0.0%
0.0%
0.6%
1.0%
1.5%
0.9%
U se of D rugs, H ome Ca re :
OR T H OPED ICS & R H EU MAT OLOGY
PU LMON OLOGY
14.2%
17.3%
24.0%
22.4%
-9.8%
-5.1%
-7.0%
-4.5%
-3.0%
-0.6%
-0.2%
-1.1%
0.2%
0.2%
-0.6%
-1.4%
0.0%
0.0%
0.5%
0.8%
0.1%
0.0%
0.1%
1.6%
6.0%
8.1%
-2.1%
-1.9%
0.5%
-3.2%
2.3%
0.0%
0.0%
0.0%
0.0%
0.2%
PSYCH IAT R Y
Ambulatory Surgical Centers: Small, growing fast, particularly in the treatment
of gastrointestinal and eye conditions.
Home Care: There is anecdotal evidence of shifting medical equipment from
hospitals to the home in the treatment of lung conditions.
www.bea.gov
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Other lessons from this research:
 In theory, reweighting treatment indexes will
not fix the problem.
 Empirically, reweighting does narrow some of the
gap.
 In theory, differences in the indexes can go
either way.
 Empirically, the net effect is dominated by
substitution towards lower cost treatments.
www.bea.gov
13
We will continue to explore:
 An economic interpretation for this deflator.
 Volume measures (as an alternative to deflation).
 Alternative types of data sources for prices.
 Alternative ways to allocate spending by disease.
www.bea.gov
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