|
D A T A W A T C H
H E A L T H S P E N D I N G
W E B E X C L U S I V E
25 August 2004
Which Medical Conditions Account
For The Rise In Health Care Spending?
The fifteen most costly medical
conditions accounted for
half of the overall growth in health care spending between 1987 and 2000.
By Kenneth E. Thorpe, Curtis
S. Florence, and Peter Joski
ABSTRACT:
We calculate the level
and growth in health care spending attributable to the fifteen most expensive
medical conditions in 1987 and 2000. Growth in spending by medical condition
is decomposed into changes attributable to rising cost per treated case, treated
prevalence, and population growth. We find that a small number of conditions
account for most of the growth in health care spending—the top five medical
conditions accounted for 31 percent. For four of the conditions, a rise in treated
prevalence, rather than rising treatment costs per case or population growth,
accounted for most of the spending growth.
The rising cost of health care, and what to do about it, is perhaps the most
challenging health policy issue facing the United States. Health care is projected
to account for 15.2 percent of U.S. gross domestic product (GDP) in 2004, compared
with 11.1 percent fifteen years ago.1 During this
period health care spending increased at an average annual rate of 7.5 percent
per year (in nominal dollars) and 5.1 percent per year when adjusting for inflation
(using the GDP deflator).2 During the past three
years, the cost of health insurance has increased by an average of 12.5 percent
per year.3
The most common factor cited as driving rising health costs has been the explosion
of new medical technologies, which can improve care but tend to cost more than
older modalities of treatment.4 However, total cost
is also a function of how many people are receiving treatment for a given condition.
The rise in treated-case prevalence may reflect improvements in medical technology
that allow expanded treatment of a particular condition. It could also reflect
changes in the diagnosis or reporting of disease. Finally, the rise could reflect
factors such as the aging of the population. Distinguishing among these scenarios—increasing
cost per case and increasing population-based use of treatments—could provide
an important context for understanding U.S. health care spending. In particular,
it could allow us to more effectively target interventions designed to rein
in the growth in health care spending.
Although several studies have examined the factors associated with the rise
in health care spending, they have largely tracked overall changes in payments
for hospitals, physicians, and pharmaceuticals.5
Linking health care spending to the treatment of specific medical conditions
can establish a framework for understanding (1) changes in real health care
spending by disaggregating the effects of new technologies developed for treating
those conditions and (2) increases in the number of people who are treated.
Such an analysis also allows a better match between underlying cost drivers
and potential interventions/solutions. Moreover, a disease-based analysis affords
a more natural comparison to changes in medical benefits purchased.
To address the issue of what is driving health care spending growth, we undertook
a study designed to track changes in spending over time by medical condition.
We examined the change in this spending as a percentage of the change in total
health care spending. We then decomposed this change, by medical condition,
into changes in treated prevalence, treated cost per case, and population growth.
Study Data And Methods
Data.
Our analytic approach was to estimate the level and change in nominal health
care spending over time by patients with the fifteen most expensive medical
conditions. Data for our study are from the 1987 National Medical Expenditure
Survey (NMES) and the 2000 Medical Expenditure Panel Survey, Household Component
(MEPS-HC).6 The 1987 NMES surveyed 34,459 people,
and the 2000 MEPS, 25,096 people. Both surveys are nationally representative
samples of the U.S civilian noninstitutionalized population. They contain detailed
information on health spending, use of services, patient demographics, insurance
coverage, markers of health status, and self-reported medical conditions. We
adjusted the 1987 and 2000 spending data to make them comparable using the methods
developed by the Agency for Healthcare Research and Quality (AHRQ).7
The process adjusted the 1987 data from charges to payments, the same measure
used in the 2000 data.
Both surveys collect detailed information on respondents’ reports of their
medical conditions and other measures of health status. When a survey respondent
reports a medical event, such as a physician office visit, he or she is asked
to describe the reason for the visit. In both years the data were professionally
coded from respondents’ verbatim text using the International Classification
of Diseases, Ninth Revision (ICD-9). Up to four ICD-9 codes are listed per
medical event. The ICD-9 codes are collapsed to three-digit codes and subsequently
coded into 259 clinically relevant medical conditions using the Clinical Classification
System (CCS) developed by the U.S. Department of Health and Human Services (HHS).8
Although the medical conditions were self-reported, previous research has found
a high level of agreement between descriptions of conditions (at the CCS level)
reported by patients and those provided by physicians.9
Study methods.
Following the methods of Benjamin Druss and colleagues (2002) and of Joel Cohen
and Nancy Krauss (2003), we linked diagnosis codes for each self-reported medical
encounter (provider visits of any type and prescribed drugs) that prompted a
patient to seek medical care.10 For each patient,
we calculated total annual spending and total spending for each of the 259 CCS
medical conditions reported. We compiled the fifteen conditions with the largest
nominal growth in spending between 1987 and 2000. We then tabulated total annual
spending by medical condition in 1987 and 2000, and the change in spending by
medical condition. For each condition, we tabulated the change in spending as
a percentage of the change in national health spending among the noninstitutionalized
population—both are reported in nominal dollars. Since the NMES and MEPS
samples include a complex stratification design, we used STATA version 8 and
used the “svymean” for the means and standard errors of all spending
data. This accounts for both the complex sample design and the weighting of
observations.
Some medical events were associated with multiple conditions. For example, a
patient may seek care to treat an existing heart condition as well as hypertension.
As a result, this approach will double-count the spending associated with some
medical conditions. On the other hand, simply using the principal diagnosis,
perhaps through the use of a disease hierarchy, may understate spending associated
with a medical condition. Recognizing this potential, we present a range of
estimates. Our upper-bound estimate added up total spending for each health
care event for which a given condition is reported. Since up to four medical
conditions can be reported for each event, this will obviously include some
double-counting. As a lower bound, we summed spending from each medical event
for which only a single condition is reported. Although the total spending calculated
from this approach obviously does not account for all spending associated with
a given condition, it does not include any double-counting. Finally, we developed
a “best guess” estimate of condition attributable spending using the
following approach. We tabulated spending per event for those reporting a single
medical condition (for example, heart disease and no other condition). We then
tabulated spending per event for those reporting two or more medical conditions
associated with the event (for example, heart disease and hypertension). We
calculated the ratio of these two spending totals and used it to determine how
much of the spending associated with heart disease plus other conditions should
be attributed to heart disease.11
Study Results
Nominal health care spending among the noninstitutionalized population increased
by $314 billion—5.5 percent per year—between 1987 and 2000 (Exhibit
1). After inflation was adjusted for using the GDP deflator, total spending
increased by $199 billion—about 3 percent per year.
Exhibit
2 shows the growth in nominal spending over time by medical condition. Between
43 and 61 percent of the total nominal change in spending between 1987 and 2000
is attributable to the fifteen most costly conditions. Our “best guess”
estimate, adjusted for double-counting, approximates the share to be 56 percent.
Most of this change is concentrated in the five most expensive conditions: heart
disease, mental disorders, pulmonary disorders, cancer, and trauma, which account
for approximately 31 percent of the overall change in spending between 1987
and 2000.
The data presented in Exhibit
2 reveal a substantial rise in treated prevalence in eight of the fifteen
conditions experiencing the largest rise in spending. For instance, treatment
of mental disorders nearly doubled, and cases involving a pulmonary disorder,
such as asthma and upper and lower respiratory diseases, increased 50 percent.
There also was a substantial rise in the treated prevalence of hypertension
and diabetes (Exhibit
2).
We now turn to decomposing the change in spending, by medical condition, into
changes traced to population growth, changes in treated disease prevalence,
and a change in annual spending on the condition per person reporting the condition.
Since we are primarily interested in explaining the factors associated with
increased spending within each medical condition, we are not concerned with
double-counting across conditions. Exhibit
3 presents the results of our decomposition.12
For several medical conditions, the rise in treated disease prevalence was a
key factor accounting for the rise in spending. It accounted for 59 percent
of the increased spending on mental disorders and figured prominently in the
rise in spending on cerebrovascular disease (stroke and cerebral ischemia, 60
percent), pulmonary conditions (42 percent), and diabetes (50 percent).
In eight of the top fifteen conditions, a rise in the cost per treated case,
not rising numbers of cases treated, accounted for most of the growth in spending.
For instance, the treated prevalence of heart disease remained constant between
1987 and 2000. Thus, a rise in the cost per treated heart disease case accounted
for nearly 70 percent of the rise in medical care spending between 1987 and
2000. The rise in cost per treated hypertension case accounted for 60 percent
of the overall growth in spending. The rise in spending is traced to several
new prescription drugs available to treat hypertensive patients. The treated
prevalence of trauma declined during the period, with a rise in cost per treated
case accounting for the rise in medical care spending.
Finally, population growth has also contributed to the rise in spending by medical
condition. In our tabulations, it accounted for about 19–35 percent of
the increase in condition-specific spending across the top fifteen medical conditions.
This shows that demographic factors, in addition to factors such as changes
in medical technology, have a large impact on nominal spending changes over
time.
Discussion
A small number of medical conditions were associated with much of the increase
in health care spending between 1987 and 2000. The top fifteen conditions accounted
for approximately half of the overall growth in spending. For some of these
conditions, such as mental disorders, most of the increase was associated with
increased treated prevalence. A rise in treated prevalence, in turn, might represent
either an increase in epidemiological prevalence or more widespread access to
care among people with a disease. This mix varies across conditions. For instance,
the prevalence of mental disorders has remained relatively stable over time;
however, rates of treatment have been rising.13
The sharp rise in treated prevalence reflects two trends: increasing recognition
and diagnosis of mental disorders, particularly depression and a rapid expansion
of new psychotropic medications. Given the historical underdiagnosis and treatment
of disorders such as depression, this wider use of treatments, and the associated
increase in health care spending, is likely to represent benefits that outweigh
the cost.14
Potential interventions.
For several conditions, the rise in the epidemiological prevalence appears to
be responsible for the growth in treated cases. This result highlights the importance
of developing interventions designed to reverse the rise in disease prevalence.
This appears to be the case for pulmonary disease, which accounted for nearly
8 percent of the rise in spending over the decade. Prevalence and death rates
for asthma have been rising since 1975.15 Factors
accounting for the rise in asthma and other pulmonary disorders are not well
understood. They have been linked to environmental exposures (both indoor, such
as dust mites and smoking, and outdoor air quality).16
In addition, diabetes accounted for up to 3 percent of the rise in health care
spending, with about 50 percent of the rise traced to a rise in treated prevalence.
The U.S. Centers for Disease Control and Prevention (CDC) reports a continued
rise in diabetes prevalence that now exceeds eighteen million among adults alone.17
The rise in the treated prevalence of diabetes closely tracks the substantial
rise in obesity in the population.18 Since effective
treatments exist for both of these conditions, however, it would be a mistake
to see increased spending to treat them in a completely negative light.
Value of increased spending.
Increased spending per person for these top fifteen medical conditions may appear
at first glance to reflect a truly “wasteful” increase in health care
spending. However, the technologies used to treat patients with heart disease—such
as new drugs, the use of diagnostic cardiac catheterization, and angioplasty—increased
sharply during this period.19 These new approaches
replaced less costly (and less effective) means for treating heart disease,
and heart attacks in particular. While spending per person with heart disease
is going up, death rates associated with this condition continue to go down.20
Health policy analysts, policymakers, employers, families, and the media pay
a great deal of attention to annual increases in nominal U.S. spending for health
care. In recent years the rate of increase in health spending has been greater
than the growth of the overall economy and has therefore led to an increase
in the share of economic output devoted to health care.21
This is usually viewed negatively, because an increasing share of the economy
devoted to health care means a lower share devoted to other goods and services.
Moreover, rising health care costs have also been shown to reduce the number
of people with health insurance.22
In light of our results, however, we believe that some of the concern about
the growth in spending may be misplaced. Discussion of the magnitude of health
care spending growth usually does not take into account changes in disease prevalence
and demographic factors behind spending growth. Moreover, at issue is whether
the higher growth in spending is purchasing larger increments in medical care
benefits or whether the same improvements in health can be purchased at lower
cost. However, in light of how we track trends in health care spending—by
provider (such as hospital, prescribed drugs)—analysts have been largely
unable to address this key issue. Our focus on tracking the level and growth
in spending by medical condition allows a more natural evaluation of this important
issue, because it can provide a direct comparison to changes in health benefits.
Recent research has found that higher spending on treating heart attacks, low-birthweight
babies, cataracts, and depression has benefits that outweigh the increased costs.23
Inasmuch as treatments for these conditions are cost-effective, their more widespread
use is likely to represent an appropriate if costly expenditure by society.
Study limitations.
These findings should be considered in light of several limitations. First,
use of treatments and diagnoses are based on self-reports, which may have led
to underreporting of medical conditions and spending. Second, the analysis excludes
health care spending among the institutionalized population. Spending on some
of the medical conditions reported here may have been incurred in nursing homes,
which we do not observe.
Our current approaches for tracking spending are useful, although they provide
little information for policymakers or purchasers for assessing what we are
buying and whether the additional spending is worth it. Addressing this key
issue requires a focus on changes in spending and benefits along the lines presented
here: by medical condition.
The authors thank their colleague Benjamin Druss for comments on an earlier
draft.
NOTES
1. Centers for Medicare and Medicaid Services, “Health
Accounts,” 24 March 2004, www.cms.hhs.gov/statistics/nhe/default.asp
(26 July 2004). These are the National Health Accounts (NHA) estimates of total
health care spending.
2. Ibid.
3. Henry J. Kaiser Family Foundation and Health Research and
Educational Trust, “Summary of Findings,” Employer Health Benefits:
2003 Annual Survey, September 2003,
www.kff.org/insurance/ehbs2003-1-set.cfm
(28 July 2004).
4. J.P. Newhouse, “An Iconoclastic View of Care Cost Containment,”
Health Affairs 12 Supplement (1993): 152–171.
5. B.C. Strunk and P.B. Ginsburg, “Tracking Health Care
Costs: Trends Stabilize but Remain High in 2003,” Health Affairs,
9 June 2004, content.healthaffairs.org/cgi/content/abstract/hlthaff.w4.354
(26 July 2004).
6. Agency for Healthcare Research and Quality, “Overview
of the MEPS Web Site,” www.ahrq.gov/data/mepsweb.htm#full-year
(26 July 2004). Compared with the spending estimates developed by the Department
of Health and Human Services (the NHA estimates), the MEPS spending estimates
focus on the noninstitutionalized population and do not include the same breadth
of services (for example, spending for nursing home care). As a result, MEPS
produces estimates of national health care spending that are lower than those
produced through the NHA approach. However, both the populations and the services
included in MEPS are those typically financed through private insurance. A detailed
crosswalk between the two estimates has been developed by T. Selden et al.,
“Reconciling Medical Expenditure Estimates from the MEPS and NHA, 1996,”
Health Care Financing Review 23, no. 1 (2001): 161–178. This review
found substantial agreement in the estimates for the noninstitutionalized population
for services generally included in private health insurance plans. When the
NHA figures are compared with MEPS (on a comparable basis, focusing on spending
included in both surveys among the civilian, noninstitutionalized population),
spending totals were within 6.7 percent of one another.
7. S. Zuvekas and J.W. Cohen, “A Guide to Comparing Health
Care Expenditures in the 1996 MEPS to the 1987 NMES,” Inquiry 39,
no. 1 (2002): 76–86. The unadjusted spending data from the 1987 were based
on charges, while the MEPS spending data used payments to providers. We used
the approach outlined by AHRQ to make the two surveys comparable by transforming
the 1987 NMES data to payments. The unadjusted charge-based total spending in
the 1987 NMES was $363.6 billion. The adjusted NMES total based on payments
used in our analysis was $314.1 billion.
8. J.W. Cohen and N.A. Krauss, “Spending and Service Use
among People with the Fifteen Most Costly Medical Conditions, 1997,” Health
Affairs 22, no. 2 (2003): 129–138.
9. N. Krauss and B. Kass, “Comparison of Household and
Medical Provider Reports of Medical Conditions” (Paper presented at Joint
Statistical Meetings, Indianapolis, Indiana, August 2000).
10. B. Druss et al., “The Most Expensive Medical Conditions
in America,” Health Affairs 21, no. 4 (2002): 105–111; and
Cohen and Krauss, “Spending and Service Use.” We replicated the totals
reported by Cohen and Krauss for 1997 in their Exhibit 1.
11.For four of the top fifteen medical conditions, this ratio
was close to 1. These were cases where a substantial share of total spending
was traced to events with a single medical condition. As a result, in these
four cases the best guess and the upper-bound estimate are the same. Moreover,
the upper- and lower-bound estimates for these four conditions were virtually
identical.
12. We divide the change in spending, by condition, into the
overall change in national health spending among the noninstitutionalized population.
This is done by evaluating the change in spending that would be generated by
the observed changes in one of these components, leaving the others constant.
Algebraically, the decomposition is derived in the following way: Cost in any
year is the product of cost per case in that year, treated prevalence in that
year, and population in that year. Change in expenditures is the difference
in cost in 2000 and 1987. Change in expenditures is equal to the sum of change
in cost per case, change in treated prevalence, and change in population. This,
in turn, is equal to a more complex expression that sums three products, each
involving a difference and two other multiplicands. The multiplicands in each
product are as follows (each group of three multiplicands is separated by a
semicolon): difference in cost per case in 2000 and 1987, treated prevalence
in 1987, and population 1987; difference in treated prevalence in 2000 and 1987,
cost per case 2000, and population 1987; and difference in population in 2000
and 1987, cost per case in 2000, and treated prevalence in 2000.
13. M. Olfson et al., “National Trends in Outpatient Treatment
of Depression” (2002).
14. Ibid.
15. D.M. Mannino et al., “Surveillance for Asthma—United
States, 1960–1995,” Morbidity and Mortality Weekly Report,
Vol. 47, No. RR-05 (24 April 1998): 1–28.
16. See National Center for Environmental Health, “Asthma:
General Information,” 6 May 2004, www.cdc.gov/nceh/airpollution/asthma/basics.htm
(26 July 2004).
17. U.S. Centers for Disease Control and Prevention, National
Diabetes Fact Sheet: General Information and National Estimates in the United
States, 2003 (Atlanta: CDC, 2003).
18. See National Center for Chronic Disease Prevention and
Health Promotion, “Data and Trends: Diabetes Surveillance System,”
14 May 2004, www.cdc.gov/diabetes/statistics/comp/table4dtl.htm
(26 July 2004).
19. American Heart Association, Heart Disease and Stroke
Statistics—2004 Update (Dallas: American Heart Association, 2003).
20. National Center for Health Statistics, Health, United
States, 2003 (Hyattsville, Md.: CDC, 2003), Table 29.
21. K. Levit et al., “Health Spending Rebound Continues
in 2002,” Health Affairs 23, no. 1 (2004): 147–159.
22. D.M. Cutler, “Employee Costs and the Decline in Health
Insurance Coverage,” NBER Working Paper no. 9036 (Cambridge Mass.: National
Bureau of Economic Research, July 2002).
23. D.M. Cutler and M. McClellan, “Is Technological Change
in Medicine Worth It?” Health Affairs 20, no. 5 (2001): 11–29.
Ken Thorpe (kthorpe{at}sph.emory.edu)
is the Robert W. Woodruff Professor and Chair, Department of Health Policy and
Management, Rollins School of Public Health, at Emory University in Atlanta,
Georgia. Curtis Florence is an assistant professor and Peter Joski, a research
associate, in the same department.
DOI: 10.1377/hlthaff.W4.437
©2004 Project HOPE–The People-to-People Health Foundation, Inc.
|
