Quality Adjustment for Health Care Spending on Chronic
Disease: Evidence from Diabetes Treatment, 1999–2009
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Eggleston, Karen N, Nilay D Shah, Steven A Smith, Ernst R
Berndt, and Joseph P Newhouse. “Quality Adjustment for Health
Care Spending on Chronic Disease: Evidence from Diabetes
Treatment, 1999–2009.” American Economic Review 101, no. 3
(May 2011): 206–211.
As Published
http://dx.doi.org/10.1257/aer.101.3.206
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American Economic Association
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Final published version
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Thu May 26 12:32:16 EDT 2016
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http://hdl.handle.net/1721.1/87608
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American Economic Review: Papers & Proceedings 2011, 101:3, 206–211
http://www.aeaweb.org/articles.php?doi=10.1257/aer.101.3.206
Quality Adjustment for Health Care Spending on Chronic
Disease: Evidence from Diabetes Treatment, 1999–2009
By Karen N. Eggleston, Nilay D. Shah, Steven A. Smith, Ernst R. Berndt,
and Joseph P. Newhouse*
$700 to more than $6,000 (David M. Cutler,
Allison B. Rosen, and Sandeep Vijan 2006).
Decomposing medical spending increases into
changes in quantity, quality, and medical care–
specific prices is difficult. Price indices based on
measuring bundles of inputs, such as a day in
the hospital or a physician visit, often yield misleading results, since they cannot fully account
for changes in the technology of treatment (such
as more intensive treatment for shorter lengths
of stay) or improved quality of care. Traditional
hedonic methods are ill-suited for measuring quality changes in health care: the insured
patient does not face the full marginal cost of
care, and the patient is not the sole (or even the
primary) decision maker for many facets of
treatment spending, such as hospital services.
One cannot assume that the marginal value of
care equals its social cost.
Researchers using varying methods to evaluate quality change in health care have differed in
their conclusions (Alan M. Garber and Jonathan
Skinner 2008). As Rosen and Cutler (2007) note,
“while some studies have suggested that [health
care] productivity growth is reasonable in aggregate (Cutler and [Mark] McClellan 2001; Cutler,
Rosen, and Vijan 2006), others argue that there
is substantial waste at the margin ([Elliott S.]
Fisher et al., 2003)”.
Research dating back to the pioneering work
of Anne A. Scitovsky (1967) suggests a promising approach: measuring the changes in quality
associated with treatment of a specific medical
condition, compared to changes in spending
on that condition. Although several researchers
have developed methods for allocating spending across diseases to supplement aggregate
health expenditure accounts (Rosen and Cutler
2007; Charles Roehrig et al. 2009), much work
remains to be done to link spending changes to
changes in health outcomes (Susan T. Stewart et
al. 2005; National Research Council 2010).
This study follows the line of research on
price adjustment in medical care started by
US health care expenditures reached $2.5 trillion in 2009, representing 17.6 percent of gross
domestic product (GDP) and $8,086 per person
(US Department of Health and Human Services
Centers for Medicare and Medicaid Services
2011). Since health care represents a large and
growing share of the economy, and factors such as
population aging imply that chronic disease treatment will continue to expand as a share of health
expenditures, developing methods for assessing
the value of quality improvement for chronic
disease spending is of increasing importance for
accurately measuring real economic activity.
This paper develops a method for assessing
the value of quality changes associated with
health care for patients living with one important chronic disease, diabetes mellitus, using
11 years of detailed data on spending and quality of care for over 800 patients. We first provide
an overview of measurement issues for health
care quality, and then present our data, methods,
results, and a brief discussion.
I. Measuring the Value of Quality Improvements
in Health Care
Since 1960, real annual medical spending per
person in the United States has risen from about
* Eggleston: Stanford University Shorenstein AsiaPacific Research Center, 616 Serra Street, Encina Hall
E301, Stanford, CA 94305-6055 (e-mail: karene@stanford.
edu); Shah: Division of Health Care Policy and Research,
College of Medicine, Mayo Clinic, 200 First Street SW,
Rochester, MN 55905 (e-mail: Shah.Nilay@mayo.edu);
Smith: Division of Endocrinology, Mayo Clinic College
of Medicine, 200 First Street SW, Rochester, MN 55905
(e-mail: smith.steven@mayo.edu); Berndt: Massachusetts
Institute of Technology Sloan School of Management and
NBER, 50 Memorial Drive, Cambridge, MA 02142 (e-mail:
eberndt@mit.edu); Newhouse: Harvard Kennedy School;
Harvard Medical School; Harvard School of Public Health;
and NBER, 180 Longwood Avenue, Boston, MA 02115-5899
(e-mail: joseph_newhouse@harvard.edu). We are grateful for
comments from Marshall B. Reinsdorf as well as other discussants and seminar participants at the American Society of
Health Economists 2010, AEA 2011, and Stanford University.
206
Quality Adjustment for Health care Spending
VOL. 101 NO. 3
Cutler et al. (1998), who analyzed spending
and outcomes for patients with heart attacks.
Contrary to the perception that health care
spending growth far exceeds associated benefits,
Cutler and coauthors estimated that a qualityadjusted price index for heart attack treatments
declined about 1 percent annually between
1983 and 1994. Jonathan S. Skinner, Douglas
O. Staiger, and Elliott S. Fisher (2006) find
that overall gains in heart attack survival more
than justified the increases in costs during the
1986–2002 period, but since 1996 survival gains
stagnated while spending continued to increase.
Ernst R. Berndt et al. (2002) found that the incremental cost of treating an episode of acute phase
major depression fell between 1991 and 1996.
Focusing on cataract surgery between 1969 and
1993, Irving Shapiro, Matthew D. Shapiro, and
David W. Wilcox (2001) found that spending has
not increased faster than the general price level.
Only three studies of which we are aware
apply this approach to quality adjustment for
expenditures on chronic disease. Alexandra
Constant et al. (2006) use data from the
Canadian Cancer Registry and the Ontario
Case Costing Initiative to calculate that a quality-adjusted price index for cancer treatment
declined 5.4 percent annually between 1995
and 2003. Examining colorectal cancer drugs,
Claudio Lucarelli and Sean Nicholson (2009)
estimate that two quality-adjusted price indices
were roughly constant between 1993 and 2005.
Eggleston et al. (2009) study diabetes, a chronic
condition of growing importance and with an
expanding literature on the economics of its
management (CDC Diabetes Cost-effectiveness
Group 2002; Nancy Beaulieu et al. 2006; Elbert
S. Huang et al. 2007; Thomas J. Hoerger 2009).
We extend the Eggleston et al. (2009) research,
which found that between 1997 and 2005 the
benefits of the additional or newer care for diabetes outweighed the greater costs for the average patient.
II. Data and Methods
A. Data
Adjusting spending for quality improvement
requires detailed data on changes in spending
and quality over time. We use a sample of 821
employees and dependents diagnosed with diabetes enrolled in the Mayo Clinic’s self-funded
207
health plan between 1999 and 2009.1 The data
include total direct medical spending for those
patients, including all payments made for service use. Prices are actual transaction prices, not
list charges. Our estimates are therefore based
on “supply” prices, and if these results were
used to compare to extant prices indices, they
would be akin to the producer price index (PPI)
published by the Bureau of Labor Statistics
(BLS).2 Spending data are converted to constant
2009 dollars using the GDP deflator.3
B. Methods
The net value of total spending is defined
as the difference between the monetary value
of improved quality (better health status and
avoided treatment spending, ΔV ) and the
increase in annual inflation-adjusted treatment
spending over the 11-year period (ΔC) (i.e.,
ΔV − ΔC).
To measure quality (ΔV ), we use a clinically
relevant metric: “modifiable cardiovascular risk”
based on the equation for predicting cardiovascular complications from the United Kingdom
Prospective Diabetes Study (UKPDS; Richard J.
Stevens et al. 2001). We define “modifiable” risk
by holding constant the patient’s age and duration of diabetes at their values at cohort entry.
Changes in modifiable risk reflect changes in
risk factors amenable to control through medical care. A reduction in modifiable risk indicates
a quality improvement.
We estimate the value of improved probability of survival from reduction in ten-year risk
of fatal coronary heart disease (CHD) or fatal
stroke using $200,000 as the value of a life
year. Since nominal GDP has roughly doubled
since the early 1990s when analysts often used
1
We chose to begin in 1999 because earlier administrative records do not provide as comprehensive and consistent
data on quality, spending, and absenteeism. 2
See the discussion in Berndt et al. (2000, p. 465).
Focusing only on patient out-of-pocket expenditures would
be similar to the CPI concept, but changes in out-of-pocket
spending reflect not only supply price changes but also
insurance benefit design. 3
To reflect opportunity cost in the overall economy, we
also tried using the gross domestic purchases price index
(which measures the prices of goods and services purchased
by US residents, regardless of where the goods and services
are produced); the results are very similar to those reported
here. 208
$100,000 for the value of a life year, and since
W. Kip Viscusi and Joseph E. Aldy (2003) estimate $7 million for the value of a statistical life
for a prime age worker in 2000 with an income
elasticity of 0.5–0.6, we posit that $200,000 is
an appropriate value to use currently. We add the
present discounted value of avoided CHD treatment spending, using the Rosen et al. (2007)
estimate and assuming a 3 percent real discount
rate.
Calculating ΔV requires taking the difference
between the present discounted value of remaining life at two points in time that correspond to
the two technologies being compared: treatment
prevailing in the baseline (1999–2001) and final
(2007–2009) observation periods, respectively.
To do so, we approximate the UKPDS predicted
probability of a cardiovascular event in the next
ten years, conditional on risk factors in that
observation period, by giving a tenth of the predicted probability to each of the first ten years,
and assuming that all patients surviving beyond
year 10 die at the end of year 20.4
To smooth the variation in annual spending, the increase in spending or costs (ΔC) for
each diagnosis cohort is defined by the difference between three-year spending averages for
the baseline and final observation periods. For
individuals diagnosed within a given three-year
interval, we include only spending since diagnosis. For the 33 deaths (Table 1, second row), we
include total spending in the year of death.
To isolate the impact of technological change
while allowing for differences across cohorts
diagnosed in earlier or later years, we estimate
net value three ways: (i) separately based on
the average values for five different diagnosis
cohorts, (ii) using the weighted average across
diagnosis cohorts; and (iii) at the individual
patient level by simulating quality-adjusted life
years (QALYs) in the UKPDS Outcomes Model
based on patient-specific trajectories of diabetes
risk factors and complications.
Although we attribute all the change in modifiable risk to medical care, we believe our estimates of net value are conservative for several
reasons. First, the spending figures include all
medical spending for these individuals, without
attempting to isolate what fraction of spending is
directly attributable to diabetes. We also assume
4
MAY 2011
AEA PAPERS AND PROCEEDINGS
Our results are not very sensitive to this assumption. it costs the same amount to treat CHD in 1999
and 2009 and up to ten years into the future,
even though spending for CHD treatment has
been rising. We assume a patient with fatal CHD
dies before receiving any medical care, even
though many patients receive treatment before
death. Finally, our estimates do not include the
value of reduced treatment spending for strokes
or other complications that are avoided by better
metabolic control. As greater attention has
been given to treatment guidelines for people
with diabetes, there is some evidence that the
predictions of risk based on the UKPDS trial
may underestimate the strength of the association between the level of metabolic control and
cardiovascular risk (ADVANCE Collaborative
Group 2008; Action to Control Cardiovascular
Risk in Diabetes Study Group 2008).
III. Results
Total real annual health care spending of
patients in our sample increased 48.5 percent
between 1999 and 2009, for an average annual
increase of 4.4 percent based on the weighted
average across diagnosis cohorts.
Quality generally improved. All diagnosis
cohorts except the most recent (2002–2004)
experienced a statistically and economically
significant reduction in modifiable risk of
developing CHD, ranging from −41 percent
(1 − (16.7/28.3)) for the pre-1985 diagnosis
cohort to −3 percent for the 2002–2004 diagnosis cohort (Table 1). Trends for the other clinical
measures were more mixed, generally exhibiting declines in modifiable risk for earlier diagnosis cohorts and little change or an increase for
the patients diagnosed most recently.
We find that the value of reduced mortality and avoided treatment spending, net of the
increase in annual spending, averaged over
all cohorts, was $9,094 (although three of the
five cohorts experienced a decline in net value;
Table 1).
Estimates based on the UKPDS Outcomes
Model are similar, suggesting that the average
patient gained 0.123 QALYs and a net value
of US$23,627 ($27,095 when adding avoided
CHD treatment spending).5
5
Assuming drugs and other treatment for diabetes
must be maintained into the future to achieve the gains the
Quality Adjustment for Health care Spending
VOL. 101 NO. 3
209
Table 1—Net Value of Increased Medical Spending between 1999 and 2009, by Diabetes Diagnosis Cohort
Before
1985
1985–1996
1997–1998
1999–2001
2002–2004
Weighted
average
40
5
24.3
178
12
11.7
116
2
6.5
279
9
7.5
208
5
4.9
821
33
8.4
15.1
9.3
7.3
7.9
6.1
6.5
28.3
18.4
14
15.8
12.4
15.9
16.7
11.2
11.1
11.6
12
11.8
$9,206
$186,332
$195,538
$5,803
$48,608
$54,412
$42,106
$30,808
$30,958
$30,821
$195,675
$22,525
$28,003
$21,589
$26,533
$49,468
Year of diagnosis
Number of patients
Number of deaths
1999 modifiable 10-year risk of
fatal CDH or fatal stroke
2009 modifiable 10-year risk of
fatal CDH or fatal stroke
1999 modifiable 10-year risk of
CHD
2009 modifiable 10-year risk of
CHD
Saved CHD treatment spending
Value of improved survival
Total value (improved
survival + saved treatment costs)
1999 Mean spending*
2009 Mean spending*
Mean spending 1999–2001*
Mean spending 2007–2009*
Net value of increased spending
−$16,202
−$13,157
$3,045
−$8,101
−$4,172
−$24,304
−$22,760
$1,544
$3,863
$8,417
$12,281
$18,235
$18,013
$17,574
$18,898
$13,213
$20,302
$14,816
$19,379
n/a
$14,840
n/a
n/a
n/a
n/a
$9,094
−$14,481
$3,929
−$8,735
n/a
$45,564
−$24,277
Notes: CHD = coronary heart disease. For the 2002–2004 diagnosis cohort, the baseline modifiable risk is for 2002, not 1999.
n/a = Not applicable.
*In 2009 dollars.
Source: Authors’ calculations.
IV. Conclusion
These results suggest that despite significant expenditure increases, the value of quality improvements for patients with diabetes has
generally kept pace with expenditures between
1999 and 2009. In other words, the unit cost of
treatment for diabetes, adjusting for the value
of health outcomes, has been roughly constant.
Since we believe that input prices for health
care have not been declining, our results are
consistent with productivity improvement in
health care.
UKPDS model predicts, our values should be (but are not)
net of the cost of that continuing treatment for the additional years of life and QALYs. In our study, however, the
net increase in life expectancy is a fraction of a year (0.12
years for the average patient based on the UKPDS Outcomes
Model), and results about net value are far more sensitive to
assumptions about the monetary value of a life-year than to
adding “maintenance costs” during additional years of life. Medical care represents a large and growing
component of the services sector in the United
States and globally (Jack E. Triplett and Barry
Bosworth 2004; National Research Council
2010); therefore, further efforts to measure
the value of quality changes in health care are
important for accurately assessing economic
productivity.
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