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D A T A W A T C H
M E D I C A R E Q U A L I T Y
W E B E X C L U S I V E
07 April 2004
Medicare Spending,
The Physician Workforce,
And Beneficiaries’ Quality Of Care
Areas with a high concentration
of specialists also show higher spending
and less use of high-quality, effective care.
By Katherine Baicker and Amitabh
Chandra
ABSTRACT:
The quality of care received by Medicare beneficiaries varies across areas.
We find that states with higher Medicare spending have lower-quality care. This
negative relationship may be driven by the use of intensive, costly care that
crowds out the use of more effective care. One mechanism for this trade-off
may be the mix of the provider workforce: States with more general practitioners
use more effective care and have lower spending, while those with more specialists
have higher costs and lower quality. Improving the quality of beneficiaries’
care could be accomplished with more effective use of existing dollars.
Recent research has found large and persistent differences across states in
the quality of care that Medicare beneficiaries receive.1
One way to measure these differences is through differences in the use of effective,
high-quality care, such as the administration of beta-blockers after heart attacks,
mammograms for older women, influenza vaccines, or eye exams for diabetics.
These procedures are relatively inexpensive, are known to have desirable medical
benefits, and are rarely contraindicated. It is therefore puzzling that the
use of these procedures varies so widely between states; for example, in 2000
the use of beta-blockers within twenty-four hours of admission for patients
with heart attacks and without contraindications ranged from 50 percent in Alabama
to 86 percent in New Hampshire.2
In this paper, we first determine whether quality differences can be explained
by differences in Medicare spending. That is, are states where there is more
spending per Medicare beneficiary also more likely to provide effective care?
Clearly, spending more is unlikely to cause lower-quality care but rather serves
as a marker for a particular style of health care provision or use of resources.
Something in the underlying infrastructure or allocation of resources may drive
both higher spending and lower quality of care. We next examine whether high-spending
states provide more care along other dimensions, such as multiple specialist
consultations, hospitalizations, and use of intensive care units (ICUs) in the
last six months of life. Prior research has shown that end-of-life care is extraordinarily
costly but not correlated with the underlying sickness of the population, patient
outcomes, or patient satisfaction.3 Finally, we
explore potential mechanisms through which intensive care might crowd out high-quality
care. We analyze the effect of the underlying physician workforce (generalists
versus specialists) on both spending and quality differences across states.
Study Data And Methods
Empirical design.
We first examined the relationship between the provision of high-quality care
for Medicare beneficiaries and Medicare spending at the state level. We performed
this analysis at the state level mainly because of the availability of external
state quality measures, but also because the states serve as useful proxies
for geographic variation in care.4
We explored the determinants of state spending and quality using generalized
least squares regressions weighted by the size of the Medicare population in
each state (although unweighted regressions provide similar results). These
cross-section results are not sensitive to the price and demographic adjustments
described below, or to the exclusion of Medicare home health spending, which
was dramatically curtailed by the Balanced Budget Act (BBA) of 1997.5
Neither are the results affected by the choice of year of the quality measures
(1998–99 or 2000–01) or by the inclusion of state-level health maintenance
organization (HMO) enrollment and the rate of heart attack discharges (adjusted
for the age, sex, and race of the state population).
To assess whether these findings are driven by unmeasured differences in the
underlying sickness of state populations, we used a subset of four highly informative
quality measures that were available for both 1995 and 1999 from Medicare claims
data from the Dartmouth Atlas project: beta-blockers administered at
discharge, mammography every two years for women ages 65–69, and hemoglobin
(HbA1c) monitoring and annual eye exams for diabetics. We analyzed the relationship
between changes in the use of these quality measures and changes in spending
within each state. This technique eliminates unobserved confounders that are
fixed over time (for example, fixed differences in demographic structure, patients’
severity of illness, or reliance on outpatient clinics), although unobserved
state-level confounders that change over time may still affect these results.
We also included changes in the level of HMO enrollment among Medicare beneficiaries
and the rate of heart attack discharges.
We used similar methods to examine whether higher Medicare spending is a marker
for a different pattern of spending. We analyzed the relationship between spending
and end-of-life care, such as the fraction of patients admitted to the ICU and
the number of days spent in the hospital. Although this care may be costly and
have less observed impact on health, it may be highly valued by patients, so
we also analyzed the effect of spending on patient satisfaction.
Finally, we explored one of the mechanisms that may be responsible for the trade-off
between high-quality health care and costly end-of-life care: the composition
of the medical workforce. We regressed spending per Medicare beneficiary and
overall quality rank on the number of specialists, general practitioners, and
registered nurses per capita, controlling for the total number of physicians
per capita, to explore the effect of changing the composition of the medical
workforce.
Data.
Quality measures. This study uses the twenty-four quality measures developed
by the Medicare Quality Improvement Organization (QIO) and computed at the state
level by Steve Jencks for 2000–01.6 These measures
use samples of patient discharge records for the treatment of six common medical
conditions (acute myocardial infarction, breast cancer, diabetes mellitus, heart
failure, pneumonia, and stroke) and capture interventions and evaluations “for
which there is strong scientific evidence and professional consensus that the
process of care either directly improves outcomes or is a necessary step in
a chain of care that does so,” such as the prescription of warfarin for
atrial fibrillation or biennial eye examination for diabetics.7
Detailed risk adjustment is thus not critical for these measures, as few patients
are contraindicated for these procedures. Jencks and colleagues ranked states
for each measure and averaged the ranks (weighting each measure equally) to
compute each state’s overall quality rank.
Medicare spending and use measures. We calculated Medicare reimbursement
per beneficiary at the state level using Medicare claims data from the Dartmouth
Atlas projects; we included spending for fee-for-service (FFS) Medicare
beneficiaries.8 This spending is adjusted in three
ways. First, spending is adjusted for inflation using the Consumer Price Index
(CPI).9 Second, differences in state price levels
are taken into account using a state-specific cost-of-living adjustment.10
Third, spending is adjusted for the age, sex, and race of the states’ Medicare
populations.
We used several other measures computed from the Medicare claims data, including
the number of days Medicare beneficiaries in their last six months of life spent
in a hospital and what fraction of these beneficiaries are admitted to the ICU.
These measures abstract from unmeasured illness confounders by focusing on the
deceased. Other research has established that such care is pervasive in areas
that have a lot of beds, specialists, and health care facilities and has also
demonstrated that its provision does not improve patient outcomes or satisfaction.11
In some specifications we controlled for the number of acute myocardial infarction
(AMI) discharges and AMI mortality in each state, adjusted for the age, sex,
and race composition of the population, also computed from the claims data.
Satisfaction measures and HMO penetration rates. Patient satisfaction
measures come from the 1992–1995 Medicare Current Beneficiary Survey (MCBS).
We used measures of overall satisfaction, satisfaction with access to care,
and satisfaction with providers’ technical proficiency. We also used these
data to compute state-specific measures of Medicare HMO enrollment.
Workforce measures. Data on the number of specialists, general practitioners,
and registered nurses (RNs) were obtained from the 2003 Area Resource File (ARF).12
The ARF gathers information from the American Medical Association (AMA) Physician
Masterfile and the County Hospital File and is reported at the county level.
We summed county-level data into state measures. We computed per capita workforce
measures for each state by dividing state physician workforce counts by population
counts from the 2000 census.
Study Results
Higher spending is associated with lower quality of care as seen in Exhibits
1–3. These relationships are statistically significant: Spending is
not merely uncorrelated with the quality of care provided.13
Exhibit
4 quantifies the relationship between an increase in spending of $1,000
per beneficiary (roughly the rise in average spending from 1995 to 1999) and
the twenty-four individual quality measures, as well as end-of-life care and
patient satisfaction. For convenience, we also report (from the work of Jencks
and colleagues) the percentage of Medicare beneficiaries nationwide who received
the indicated evaluation/intervention in 2000. The effect of increased spending
on fifteen of the measures is estimated to be negative and statistically significant,
and there is no statistical effect on the remaining nine. The first row demonstrates
that a state spending $1,000 more per beneficiary dropped almost ten positions
in overall quality ranking (p < .001). Similarly, states spending
$1,000 more per Medicare beneficiary had beta-blocker usage rates at discharge
that were 3.5 percentage points lower (p < .02), and mammography rates
that were 2.1 percentage points lower (p < .01) than the average usage
in 2000.14
We also explored the role of two other covariates. We included the fraction
of Medicare beneficiaries enrolled in HMOs and discharges for heart attacks
(or AMIs) adjusted for age, sex, and race. HMO enrollment may affect the cost
and quality of care provided, and AMI discharges capture an important component
of the overall health of the Medicare population. The results were not affected
by the inclusion of these variables.
To ensure that these results are not the artifact of omitted variables, such
as the possibility that some states have (unobserved) systematically different
patients than others, the same relationship is illustrated in changes: Do states
that increase their spending also improve their quality of care? The results
in Exhibit
5 show that there is no correlation between changes in the use of high-quality
care and changes in Medicare spending. The negative association between spending
and quality persists; states that increased spending reduced their usage of
beta-blockers, mammograms, and annual eye exams for diabetics.
Although this method eliminates all confounders that are fixed over time by
differencing them away, we may not have accounted for some state-specific factors
that change over time. Here, too, we included the change in the fraction of
beneficiaries enrolled in an HMO (which increased substantially over this period)
and the change in adjusted discharges for AMI (which would proxy for an overall
change in the health of the state population). The bottom panel of Exhibit
5 shows that the inclusion of these measures does not affect the reported
coefficients: There is still no correlation between quality of care and Medicare
spending. Together, these results also validate the claim that the quality measures
are not sensitive to risk adjustment.
Where does the money in high-spending states go, if not to highly effective
care? It seems to be spent on expensive health care that has not been shown
to have a positive effect on patient satisfaction or health outcomes. Exhibit
4 shows a positive relationship between Medicare spending and the percentage
of Medicare beneficiaries who were admitted to the ICU (or the number of days
beneficiaries spent in the hospital) during their last six months of life. Medicare
patients in states that spent $1,000 more per beneficiary spent an average of
1.3 more days in the hospital (p < .01) and were 3.9 percent more
likely to be admitted to an ICU (p < .005).15
These increases do not seem to be associated with higher levels of patient satisfaction.
As shown at the bottom of Exhibit 4, spending is uncorrelated with several different
measures of patient satisfaction.
What causes some states to be high spenders and provide lower-quality care,
while others are low spenders and provide higher-quality care? One possibility
is the composition of the medical workforce. Exhibits 6–11 examine this
hypothesis, illustrating the relationship between the medical workforce, spending,
and quality. The exhibits adjust for the total number of physicians in a state
and study the effect of specialists (Exhibits
6 and 7),
general practitioners (Exhibits
8 and 9),
and nurses (Exhibits
10 and 11)
per capita on overall quality rank and Medicare spending. We are thus examining
the effect of changing the composition of the medical workforce, holding the
overall size of the physician workforce constant. Together, these workforce
measures can explain 42 percent of state-level variation in Medicare spending
per beneficiary. These exhibits show that states where more physicians are general
practitioners show greater use of high-quality care and lower cost per beneficiary.
Increasing the number of general practitioners in a state by 1 per 10,000 population
(while decreasing the number of specialists to hold constant the total number
of physicians) is associated with a rise in that state’s quality rank of
more than 10 places (p < .0005) as well as a reduction in overall
spending of $684 per beneficiary (p < .0005). Conversely, states where
more physicians are specialists have lower-quality care and higher cost per
beneficiary. The estimated effect of increasing the fraction of specialists
by 1 per 10,000 is a drop in overall quality rank of almost 9 places (p
< .005) and an increase in spending of $526 per beneficiary (p <
.004). The supply of nurses does not seem to affect either the use of high-quality
care or total spending.
It is possible that although areas with more specialists do not provide higher-quality
care along these dimensions, they may be better at the treatment of more acute
conditions.16 It is also possible that areas “specialize”
in different types of care: Some areas specialize in primary care, while others
may specialize in the delivery of technologically aggressive care for heart
attacks.17 We do not find evidence of this here:
States with more specialists have neither lower mortality rates from all causes
nor reduced post-AMI mortality.18
Discussion And Policy Implications
States that spend more per Medicare beneficiary are not states that provide
higher quality care. In fact, additional spending is positively correlated with
end-of-life care but negatively correlated with the use of effective care. While
higher spending per se is unlikely to cause a drop in the use of high-quality
care, it seems to be a marker for a particular pattern of care. Our analysis
suggests that the mix of the physician workforce plays a critical role in the
use of highly effective care. States with relatively more general practitioners
have both higher rates of use of effective care and lower spending. Surprisingly,
we find no relationship between nurses and the provision of high-quality care.
Given the reliance on cross-area variation in spending and quality, inferences
about causal mechanisms should be made with great caution. First, ecological
inferences always raise concerns about omitted variables, such as risk adjustment
or legal environment. This is unlikely to be a problem in this analysis, for
three reasons. First, the QIO quality measures were specifically selected to
be robust to the absence of risk adjustment. Second, for incomplete risk adjustment
to drive the cross-section results in Exhibit
4, it must also be the case that sicker patients medically require less
of the “high-quality” care—a highly unlikely scenario. Third,
the results are equally strong in the within-state panel-data analysis summarized
in Exhibit
5, which controls for any persistent differences in illness in state populations,
malpractice laws, or regulations.
A second concern might be that specialists locate in areas where patients are
sicker and that sicker patients are more likely to be hospitalized for longer
stays or admitted to the ICU. If this were true, then the positive relationship
between specialists and end-of-life spending could be spurious. Here, too, several
factors limit this potential bias. First, examining care that is based only
on the sample of deceased people implicitly controls for the underlying sickness
of the patient population. Furthermore, other researchers have found that underlying
population risk does not seem to drive the presence of specialists and that
outcomes are not improved by increased access to these specialists. In particular,
in the area of neonatology, specialists are associated with neither higher risk
nor lower mortality.19 The results on the ineffectiveness
of specialists for the provision of high-quality care are thus consistent with
the findings of a broader literature.
What, then, are the policy implications of the negative relationship between
spending and quality? It clearly does not suggest that we mandate lower spending,
because it is probably not spending per se that reduces quality. Spending captures
many aspects of local health care delivery systems, such as physician practice
styles, composition of the medical workforce, and capacity constraints. Therefore,
naïve policies that simply target spending could have the undesirable effect
of reducing the quality of care in high-spending states even more. Also, the
quality measures we use do not capture the totality of health care provision.
Although specialists may not drive the provision of effective care, they often
provide better care in their area of specialty.20
This suggests that specialists are clustered in areas where costly intensive
care crowds out high-quality care and that one mechanism for this is a lesser
presence of general practitioners. Encouraging greater access to general practitioners,
or involving specialists in the provision of effective care, could improve the
overall quality of care received by elderly Americans.
With Medicare’s mounting fiscal crisis, understanding the relationship
between the variation in Medicare spending and beneficiaries’ quality of
care is critical. The negative relationship we found between spending and quality
and the factors that drive it are of immediate concern. Policies that improve
quality of care (such as establishing national practice benchmarks for basic
quality measures) need not be costly and could even improve Medicare’s
financial solvency.21
This research was funded by National Institute on Aging (NIA) Grant no. P01
AG19783-02. Amitabh Chandra acknowledges the support of the Rockefeller Center
at Dartmouth. The authors are grateful to Jonathan Skinner, Douglas Staiger,
Jack Wennberg, and two astute anonymous referees for helpful comments and to
Dan Gottlieb for help with the Medicare survey data. The opinions reflected
in this paper are those of the authors and should not be attributed to the National
Bureau of Economic Research or the NIA.
NOTES
1. S.F. Jencks et al., “Quality of Medical Care Delivered
to Medicare Beneficiaries,” Journal of the American Medical Association
284, no. 13 (2000): 1670–1676; S.F. Jencks, E.D. Huff, and T. Cuerdon,
“Change in the Quality of Care Delivered to Medicare Beneficiaries, 1998–1999
to 2000–2001,” Journal of the American Medical Association
289, no. 3 (2003): 305–312; E.A. McGlynn et al., “The Quality of Health
Care Delivered to Adults in the United States,” New England Journal
of Medicine 348, no. 26 (2003): 2635–2645; E.S. Fisher and J.S. Skinner,
“Comparing the Health Care of States,” Providence Journal-Bulletin,
17 March 2001; E.S. Fisher et al., “The Implications of Regional Variations
in Medicare Spending, Part 1: The Content, Quality, and Accessibility of Care,”
Annals of Internal Medicine 138, no. 4 (2003): 273–287; and E.S.
Fisher et al., “The Implications of Regional Variations in Medicare Spending,
Part 2: Health Outcomes and Satisfaction with Care,” Annals of Internal
Medicine 138, no. 4 (2003): 288–298.
2. Jencks et al., “Quality of Medical Care”; and Jencks
et al., “Change in the Quality of Care.”
3. Fisher et al., “The Implications of Regional Variations
in Medicare Spending, Part 1”; Fisher et al., “The Implications of
Regional Variations in Medicare Spending, Part 2”; and J.E. Wennberg, E.S.
Fisher, and J.S. Skinner, “Geography and the Debate over Medicare Reform,”
Health Affairs, 13 February 2002, content.healthaffairs.org/cgi/content/abstract/hlthaff.w2.96
(3 March 2004).
4. An alternative geographic unit of analysis is the Hospital
Referral Region (HRR) constructed by the Dartmouth Atlas of Health Care.
These (smaller) regions capture the geographic level at which health care for
the Medicare population is delivered. In parallel work we have performed similar
analyses for the subset of quality indicators that we could construct at the
HRR level. K. Baicker and A. Chandra, “The Productivity of Physician Specialization:
Evidence from the Medicare Program,” American Economic Review (forthcoming).
This analysis yielded similar results.
5. H.L. Komisar, “Rolling Back Medicare Home Health,”
Health Care Financing Review 24, no. 2 (2002): 33–55.
6. Jencks et al., “Quality of Medical Care”; and Jencks
et al., “Change in the Quality of Care.”
7. Jencks et al., “Change in the Quality of Care,”
1670.
8. J.E. Wennberg and M.M. Cooper, eds., The Dartmouth Atlas
of Health Care 1999 (Chicago: American Hospital Association, 1999); J.E.
Wennberg and M.M. Cooper, eds., The Dartmouth Atlas of Health Care 1996
(Chicago: American Hospital Association, 1998); and D.E. Wennberg and J.D. Birkmeyer,
eds., The Dartmouth Atlas of Cardiovascular Health Care (Chicago: American
Hospital Publishing, 2000).
9. Bureau of Labor Statistics, Inflation Calculator, data.bls.gov/cgi-bin/cpicalc.pl
(3 March 2004); and U.S. Census Bureau, “Resident Population of the Fifty
States, the District of Columbia, and Puerto Rico,” 1 April 2000, www.census.gov/population/www/cen2000/respop.html
(3 March 2004).
10. H.B. Leonard and J.H. Walder, The Federal Budget and
the States: Fiscal Year 1999, 15 December 2000, www.library.unt.edu/gpo/ACIR/ir/FY1999.pdf
(3 March 2004).
11. Fisher et al., “The Implications of Regional Variations
in Medicare Spending, Part 1”; Fisher et al., “The Implications of
Regional Variations in Medicare Spending, Part 2”; and Wennberg et al.,
“Geography and the Debate over Medicare Reform.”
12. National Center for Health Workforce Analysis, Area Resource
File, U.S. Department of Health and Human Services, 2003.
13. We focused on quality rank because of the attention given
to this statistic in the original Jencks papers. However, since small differences
in the overall index could result in large differences in the final ranking,
we studied the relationship between spending and an index of quality usage.
We constructed this index by summing the usage rate of each quality measure
(weighting each measure equally). We ignored the time to PTCA and thrombolytic
therapy since these measures are inversely correlated with quality. Our index
is virtually identical to the Jencks rankings. A regression of our index on
average spending predicts that a spending increase of $1,000 is correlated with
a decline of 0.7 standard deviation in the value of the index. Exhibits
1–3 illustrate the relationship between spending and two subcomponents
of the quality index, and Exhibit
4 shows the regression estimates for all subcomponents.
14. In Exhibits 1–3 it can be seen that
the inclusion of Hawaii reduces the magnitude of the estimated relationship;
eliminating Hawaii results in an even stronger negative relationship between
quality and spending.
15. Fisher et al., “The Implications of Regional Variations
in Medicare Spending, Part 1”; and Fisher et al., “The Implications
of Regional Variations in Medicare Spending, Part 2” demonstrate that the
correlation between end-of-life spending and average per capita Medicare spending
is 0.83. A large portion of the estimated relationship is therefore driven by
end-of-life spending. Since additional dollars are always spent on the margin,
it makes sense to include end-of-life spending in total spending.
16. J.Z. Ayanian et al., “Specialty of Ambulatory Care
Physicians and Mortality among Elderly Patients after Myocardial Infarction,”
New England Journal of Medicine 347, no. 21 (2002): 1678–1686; and
J.G. Jollis et al., “Outcome of Acute Myocardial Infarction According to
the Specialty of the Admitting Physician,” New England Journal of Medicine
335, no. 25 (1996): 1880–1887.
17. A. Chandra and D. Staiger, “Network Externalities
in Health Care: Evidence from Variation in the Treatment of Acute Myocardial
Infarction” (Working Paper, Department of Economics, Dartmouth College,
Hanover, New Hampshire, 2003).
18. We calculated state-specific mortality rates among patients
admitted post-AMI, adjusting for age, sex, race, and ten comorbidities using
the Medicare claims data.
19. D.C. Goodman et al., “Are Neonatal Intensive Care
Resources Located According to Need? Regional Variation in Neonatologists, Beds,
and Low Birth Weight Newborns,” Pediatrics 108, no. 2 (2001): 426–431;
and D.C. Goodman et al., “The Relation between the Availability of Neonatal
Intensive Care and Neonatal Mortality,” New England Journal of Medicine
346, no. 20 (2002): 1538–1544.
20. Ayanian et al., “Specialty of Ambulatory Care Physicians”;
and K.B. Wells and R. Sturm, “Care for Depression in a Changing Environment,”
Health Affairs 14, no. 3 (1995): 78–90.
21. G.R. Wilensky, “The Implications of Regional Variations
in Medicare—What Does It Mean for Medicare?” Annals of Internal
Medicine 138, no. 4 (2003): 350–351.
Katherine Baicker (kbaicker{at}dartmouth.edu)
is an assistant professor of economics at Dartmouth (Hanover, New Hampshire),
a senior research associate at the Center for the Evaluative Clinical Sciences
at Dartmouth Medical School, and a faculty research fellow at the National Bureau
of Economic Research (NBER). Amitabh Chandra (achandra{at}dartmouth.edu)
is an assistant professor of economics at Dartmouth; a senior research associate
at the center, a faculty research fellow at NBER; a research fellow of the IZA
in Bonn, Germany; and coeditor of Economics Letters.
DOI: 10.1377/hlthaff.W4.184
©2004 Project HOPE–The People-to-People Health Foundation, Inc.
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