20  Monthly Labor Review • February 2010

Medical Consumer Price Indexes

Producing disease-based 

price indexes 

Using a total-expenditure scope and adjusting

for utilizations under a treatment concept for measuring

health care costs slows down the rate of growth of medical

prices; the downside is that most of the saving is seen

in insurance benefit payments and not in out-of-pocket

payments or lower insurance premiums for consumers

Ralph Bradley,

Elaine Cardenas,

Daniel H. Ginsburg,

Lyubov Rozental,

and

Frankie Velez

Ralph Bradley is a research 

economist, Elaine M. Cárdenas 

is an economist, and Daniel H. 

Ginsburg and Frankie Vélez are

supervisory economists, all in the

Office of Prices and Living Conditions, Bureau of Labor Statistics;

Lyubov Rozental is a software

engineer with BAE Systems, a

contractor for the Bureau of

Labor Statistics. E-mail: bradley.

ralph@bls.gov

There are two basic ways of measuring health care costs. The first, labeled the “goods-and-services” 

concept, measures the cost of each medical good and service separately. The second, called the “treatment concept,” 

measures the cost of all the goods and 

services used to treat a particular disease. 

With an eye toward improving the accuracy of the Consumer Price Index (CPI), 

the National Academies’ Committee on 

National Statistics (CNSTAT) recommends the latter approach. In pursuit of 

satisfying the CNSTAT recommendation, 

this article compares these two concepts 

as they apply to constructing price indexes 

for medical care. The article does not select which concept is best: each approach 

provides different information. The first 

measures the contribution of each medical 

input to total health care inflation, whereas the second indicates how each disease 

influences health care inflation.

Ideally, what is sought to be measured 

is the cost of the healing that is derived 

from using medical goods and services. 

However, the amount of healing derived from a service cannot be directly 

measured; instead, only what is readily 

observable, such as the physician office 

visit, the hospital stay, or the prescription drug 

purchase, can be measured. Accordingly, in 

measuring medical care inflation as part of 

the CPI, the BLS collects prices for goods and 

services such as physician visits, emergency 

room visits, and prescription drug purchases. The resulting measures of medical price 

change are published, under the goods-andservices concept, as distinct indexes for physicians’ services, hospital and related services, 

prescription drugs, and nonprescription drugs 

and medical supplies.1

As long ago as 1967, it was recognized that 

“the average consumer of medical care is not 

as interested in the price of a visit or hospital 

day as he is in the total cost of an episode of 

illness.”2 Several well-known economists have 

been interested in the “total cost of an episode 

of illness” (the treatment concept) because 

there is evidence that, over time, the mix of 

goods and services used to treat a particular 

disease has changed and less expensive treatments have become substitutes for more expensive ones. In addition, interest has arisen 

in the economic effects of improved healing 

outcomes for certain diseases.

Over the years, economists have attempted 

to compute price indexes for the entire treatment of an episode of disease, rather than 

computing separate indexes for each of the 
Monthly Labor Review • February 2010  21

goods and services used to treat a particular disease. 

Matthew D. Shapiro and David Wilcox constructed 

a price index for treating a cataract and found that, 

during the last quarter of the 20th century, there was 

a shift in point of service for this procedure from an 

inpatient hospital setting to an outpatient surgical 

center.3 This move away from the inpatient hospital 

reduced the price of treating an episode of surgery for 

removal of a cataract. David M. Cutler, Mark McClellan, Joseph P. Newhouse, and Dahlia Remler examined 

how acute myocardial infarction (one kind of heart attack) was treated and found that prices for treating the 

condition had actually decreased when the increased longevity resulting from new surgical procedures was taken 

into account.4 Finally, Ernst R. Berndt and colleagues 

argued that prices for treating depression fell with the introduction of a new generation of antidepressants—the 

selected serotonin reuptake inhibitors—as the improved 

pharmaceuticals became a cheaper alternative to expensive psychotherapy.5

In treating medical conditions, not only do the 

relative proportions of goods and services change over 

time, but the average intensity of use also changes. For 

instance, in the treatment of diabetes, the utilization 

of all medical goods and services has increased. The 

treatment concept allows changes to be incorporated 

into the composition and intensity of use of the goods 

and services utilized to treat particular diseases. But 

because the BLS computes medical indexes under the 

goods-and-services concept, it does not incorporate 

either the substitution of less expensive treatments for 

more expensive ones or the change in intensity of use 

of treatments into its medical price indexes.6

Although current national accounts measure medical consumption and output with a goods-and-services 

concept, the U.S. Bureau of Economic Analysis (BEA)

is seeking to create an alternative, or satellite, account 

that would redefine the final medical good as the entire treatment of a disease under the treatment concept.7

 Deriving a real-dollar amount for this nominal 

expenditure requires a price index that is categorized 

by disease, not medical services and products. As a 

result, there is a need for experimental disease-based 

price indexes that would properly deflate medical expenditures measured under a treatment concept.8

 This 

article is a summary of BLS research into the production of these indexes.

The BLS is not the only agency that is producing 

indexes under the two concepts described. Having 

found evidence of input substitution and changes in 

intensity of use of treatments, Ana Aizcorbe, of the BEA, 

and Nicole Nestoriak have generated disease-based indexes 

that account for both phenomena.9 The Steering Committee 

for the Workshop to Provide Guidance for Development of 

a Satellite Health Care Account at the BEA published the 

proceedings of a meeting between academic economists and 

government agencies that discussed implementing a satellite 

account for medical expenditures by disease. Disease-based 

price indexes also were discussed at the meeting.10

The BLS’s first experience with the production of diseasebased indexes derives from the following recommendation 

made by CNSTAT:

BLS should select between 15–40 diagnoses from the ICD

(International Classification of Diseases), chosen randomly 

in proportion to their direct medical treatment expenditures 

and use information from retrospective claims databases to 

identify and quantify the inputs used in their treatment and 

to estimate their cost. On a monthly basis, the BLS could reprice the current set of specific items (e.g., anesthesia, surgery, 

and medications), keeping quantity weights temporarily fixed. 

Then, at appropriate intervals, perhaps every year or two, the 

BLS should reconstruct the medical price index by pricing the 

treatment episodes of the 15 to 40 diagnoses—including the 

effects of changed inputs on the overall cost of those treatments. The frequency with which these diagnosis adjustments 

should be made will depend in part on the cost to BLS of doing so. The resulting MCPI [medical consumer] price indexes 

should initially be published on an experimental basis. The 

panel also recommends that the BLS appoint a study group 

to consider, among other things, the possibility that the index 

will “jump” at the linkage points and whether a prospective 

smoothing technique should be used.11

Rather than producing the indexes in-house, the BLS contracted with Thomson Healthcare Company to construct 

price indexes using insurance claims filed by self-insured 

companies. Medical indexes were constructed for three metropolitan areas by randomly selecting from 40 narrowly defined diseases, with a probability of selection proportional to 

the area’s expenditure share on each disease. Each year, the 

inputs used to treat the selected diseases were updated and 

reflected in the index. The results of this study were reported 

in a work by Xue Song, William Marder, William Houchens, 

John E. Conklin, and Ralph Bradley.12

In the process of completing the Thomson study, BLS researchers discovered important characteristics and limitations 

of the data used to calculate the disease-based indexes. First, 

the insurance claims data did not represent those who had 

only public insurance or who were uninsured; this was because the data contained records for privately insured patients 
Medical Consumer Price Indexes

22  Monthly Labor Review • February 2010

alone. Second, because the claims data covered just those 

companies which had contracted with Thomson, the data 

may not have been representative of the overall privately 

insured population. Third, the data included unobserved 

additions of patients, as well as attrition; therefore, it was 

not possible to determine whether the change in inputs 

was the result of using inputs more efficiently or the result of a change in the patient mix.13 Fourth, several claim 

records did not have a diagnosis (records of this kind are 

known as orphan records); hence, it could not be guaranteed that all the treatments being used to treat a particular 

disease were included. Fifth, under the CNSTAT recommendation, it was possible to track only the price indexes 

of randomly selected diseases; consequently, the aggregate 

treatment price for a disease that was not in the sample 

could not be tracked. 

The price indexes computed under the method recommended by CNSTAT did not differ statistically from the currently published medical CPI under the goods-and-services 

concept. The point estimates from the CNSTAT indexes, 

however, were lower than those of the BLS indexes.

Because of the limitations of the Thomson study, it was 

decided to recalculate disease-based price indexes with a 

data source that is more representative and has less attrition than an insurance claims database. Thus, instead of 

randomly selecting 40 narrowly defined disease categories, 

a price index was computed for every major disease so that 

it could readily be understood how each disease source 

contributed to the overall medical inflation rate. The final 

set of indexes computed complies with the CNSTAT recommendations and is representative of what the Nation is 

paying for treating each disease.

In what follows, the methods of computing medical 

price indexes under the goods-and-services concept and 

under the treatment concept are described and compared. 

Then, the data and the methods used to construct the 

disease-based indexes are presented. Finally, the resulting indexes are analyzed, and the article closes with an 

explication of how the disease-based indexes differ from 

indexes based on the goods-and-services concept.

No quality adjustment issues are addressed, because 

many improvements in medical care cannot be immediately observed—if they can be observed at all. The CPI

is a real-time index; consequently, the BLS must generate 

and publish indexes for price movements roughly 2 to 3 

weeks after the end of each month. This schedule does not 

allow enough time to observe the quality changes associated with, for example, the increased longevity resulting 

from heart bypass surgery, which could be measured only 

years after the surgery has been performed. When the BLS

collects a price quote for heart surgery, it cannot adjust 

for this increased longevity because it will not occur until long after the quote has been collected and the index 

published.

The CPI: the goods-and-services concept

The BLS currently publishes medical price indexes under 

the goods-and-services concept. The prices used in the 

generation of these indexes are collected from medical 

goods and services outlets (such as physicians’ offices) and 

hospitals. Indexes are calculated by the type of provider, 

expressed as a service (that is, physicians’ services, hospital 

and related services, and so forth) or good.

Sampling for prices is done at the outlet level. Outlets 

of a particular medical good or service are selected with 

a probability proportional to their share of total spending. The BLS identifies the responses of medical outlets 

through a household survey. For example, suppose that 

there are three physicians’ offices, labeled A, B, and C, in 

a certain geographical area. Suppose also that office A accounts for 50 percent of the area’s expenditures on physicians; then it will have a 50-percent chance of getting 

selected in a sample draw.

Once the outlet is selected, a particular good or service 

must be selected inside the outlet. Taking the example of 

physicians’ offices again, suppose that office B offers three 

services. Then, if each of the services accounts for a third 

of the office’s revenue, each service will have a one-third 

chance of being sampled.

The published CPI has four major medical indexes: 

prescription drugs, nonprescription drugs and medical 

supplies, professional services (physician, dental, and so 

forth), and hospital and related services (inpatient, outpatient, and emergency room). A fifth, minor, index, health 

insurance, essentially prices the part of the premium that 

does not finance the insurance benefit. 

Implicit quantity weights are derived when the sample 

is initiated, and they stay fixed throughout the entire 

sample period. Many claim that this method produces 

an upward bias because the savings from substituting less 

expensive or more efficient inputs are not incorporated 

into the index. But it also could be a source of downward 

bias because the method does not adjust for increases in 

utilization.

Disease-based indexes: the treatment concept

Under the treatment concept, disease-based indexes are 

computed for each disease, following the guidelines of the 
Monthly Labor Review • February 2010  23

CNSTAT recommendation. The disease categories used are 

set forth in the chapters of the ICD-9 manual and are as 

follows: 

•	 Infectious diseases

•	 Neoplasms

•	 Endocrine, nutritional, and related diseases

•	 Diseases of the blood

•	 Mental disorders

•	 Diseases of the nervous system

•	 Diseases of the circulatory system

•	 Diseases of the respiratory system

•	 Diseases of the digestive system

•	 Diseases of the genitourinary system

•	 Complications of pregnancy

•	 Diseases of the skin

•	 Diseases of the musculoskeletal system

•	 Congenital anomalies

•	 Certain conditions in the prenatal period

•	 Injury and poisoning

•	 Other conditions

To compute disease-based indexes, data are needed on 

the amounts of goods and services used to treat each disease for each year.14 For example, one needs to know how 

many emergency room visits took place in 2003 to treat 

diseases of the skin. The data source for this important 

information is the Medical Expenditures Panel Survey 

(MEPS), a survey administered by the U.S. Agency for 

Healthcare Research and Quality. This panel survey queries households about the diseases they contract and their 

expenditures and utilizations for the goods and services 

used to treat those diseases.15

Because monthly indexes had to be computed, but 

MEPS data had only yearly prices, a monthly update was 

imputed by increasing the yearly price by the growth 

in the monthly price index counterpart in the CPI. For 

physicians’ services, the yearly price was increased by the 

growth in the monthly CPI index for that expenditure category. For outpatient and inpatient services, the monthly 

price was increased by the CPI index for hospital services; 

for pharmaceuticals, the yearly price was increased by the 

CPI for pharmaceutical goods.

The year-opening quantities of each type of good and 

service used to treat any disease were updated to account 

for substitutions of products or services and changes in 

their intensity of utilization. Thus, if there was a substitution away from expensive inpatient hospitals to inexpensive prescription medicines, then the index would be 

lower than it would have been if that substitution had not 

been incorporated.

A simple example will serve to explain how diseasebased indexes are generated, both for this article and in 

general. Suppose that there are two diseases, A and B, and 

two services, 1 and 2, used to treat these diseases. Suppose also that in 2002 the price of service 1 is $1,000 per 

visit and the price of service 2 is $100 per visit. To treat 

disease A in 2002 requires 2 visits of service 1 and 2 visits 

of service 2. (These figures represent the utilization of the 

two services.) To treat disease B in 2002 requires 1 visit 

of service 1 and 1 visit of service 2. Now, suppose further 

that there is a substitution away from the higher priced 

service 1 to the lower priced service 2 in 2003, so that the 

treatment of disease A now requires 1 visit of service 1 

and 4 visits of service 2. Suppose also that it has become 

more difficult to treat disease B in 2003, so that utilizations have doubled for both services and it now requires 

2 visits each of service 1 and service 2 to treat disease B. 

Finally, suppose that the price for both services increases 

by 10 percent from 2002 to 2003. Then, under the services 

approach, the price index for medical care would increase 

by 10 percent. Under the disease approach, there would 

be a 30-percent drop in the price index for treating disease 

A, because the index would account for the substitution 

from the high-priced to the low-priced service. By contrast, the price index for treating disease B would increase 

by 120 percent, because the utilization of each service has 

doubled and the price for each service has increased by 

10 percent. Applying the broad outlines of this example 

to utilizations in the MEPS database reveals that there are 

some diseases like disease A, such as mental disorders, for 

which there has been a substitution from higher priced 

services, such as visits to a therapist, to lower priced pharmaceuticals, and some diseases like disease B, such as endocrine disease, for which the utilization of all goods and 

services has increased over time.

In constructing disease-based indexes, the problem of 

comorbidities—instances in which the patient has more 

than one condition or disease and the doctor is treating 

more than one disease in a single office visit—needs to be 

addressed. As table 1 shows, comorbidities for physician 

visits are increasing over time. What is the best approach 

to measure utilizations in situations with comorbidities? 

In what follows, two sets of indexes are generated that 

treat comorbidities differently. Under the first method, if 

a patient uses a service to treat more than one disease, 

then the use of that service is recorded for each disease 

treated. In the second method, the use of the service is 

prorated to each disease, so that if a patient had three 

diseases treated in one physician visit, only one-third of 
Medical Consumer Price Indexes

24  Monthly Labor Review • February 2010

Indicators of comorbidity, 1996–2004

 Physician office visits

Year Mean 

number of 

diseases per 

visit

Number of visits 

for one disease

Number of visits 

for two diseases

Number of 

visits for three 

diseases

1996................ 1.532 914,097,000 88,510,626 23,576,756

1997................ 1.802 857,015,927 105,222,051 27,585,681

1998................ 1.780 877,451,281 110,900,249 30,690,505

1999................ 1.800 845,212,132 116,441,032 27,143,362

2000................ 1.939 847,517,668 103,487,437 31,378,739

2001................ 1.900 936,244,257 110,942,893 36,068,550

2002................ 2.085 1,006,756,597 131,275,941 39,673,678

2003................ 2.216 1,012,850,592 143,401,176 40,693,481

2004................ 2.033 1,026,306,773 156,835,092 40,904,072

Table 1.

a visit is recorded for each of the diseases treated. Both 

methods have their shortcomings. The first method will 

overcount utilizations if the patient would have used less 

of the service if he or she were treated for just one of 

the diseases alone. In the second method, the increase in 

comorbidities by itself will increase the productivity of 

medical services solely because the patient is sicker and 

the service is treating more diseases per visit. This result 

might not be desirable. 

Another price index problem is that a substantial fraction of providers are not paid for their services and the 

cost of these uncompensated services must be defrayed 

from other sources. Current CPI methods do not account 

for this situation, because the price that the BLS collects is 

for services that get full reimbursement. However, when 

a patient pays nothing, the BLS does not collect any price 

data. The MEPS database, by contrast, does account for 

nonpayment. Average prices computed by sampling only 

those who do ultimately pay puts an upward bias on the 

average price that all patients pay. Tables 2 and 3 and the 

following tabulation of the relationship between growth 

in the incidence of unpaid emergency room visits and the 

difference of price growth for all emergency room visits 

and for reimbursed visits illustrate the problem: 

Yearly growth in incidence Difference of price growth

 of unpaid emergency for all visits and price growth

Year room visits (percent) for reimbursed visits

1999....... 5.61 0.46

2000....... 15.64 1.32

2001....... 5.62 .61

2002....... –29.64 –3.10

2003....... 17.28 1.28

2004....... 10.39 1.08

Six percent to nine percent of emergency room visits go unreimbursed. In years 

when there was an increase in the incidence of unreimbursed visits, the average 

price for reimbursed visits rose more rapidly than that for all visits. It is plausible to 

assume that part of this price increase for 

reimbursed visits financed the increases 

in delinquencies (unpaid visits). Likewise, 

in 2002 there was a dramatic drop in the 

unreimbursed share, and only in that year 

did the average price for all emergency 

room visits grow more rapidly than that 

for just the reimbursed visits. Over the 

1998–2004 period, the reimbursed price 

grew more rapidly than the all-visits price while, at the 

same time, the incidence of unpaid visits also increased. 

However, the all-visits price is reflective of all consumers, 

not just those who pay. The BLS prices reimbursed visits 

only and does not account for those patients who, for example, have been able to receive emergency room care for 

which no reimbursement was made on their behalf.

Finally, the notion of expenditure scope is important 

in the construction of price indexes. In the medical sector, there are several alternative scopes. At the Bureau of 

Economic Analysis, the scope for personal consumption 

expenditures is all expenditures, regardless of how they 

are financed. Their corresponding price deflators are then 

also based on total expenditures. In addition, there is an 

out-of-pocket scope covering only expenditures that are 

financed directly from consumers’ disposable income. 

Medicare, Medicaid, and private insurance reimbursements are included in measured medical expenditures 

under the total-expenditure scope, but are not included in 

that expenditure category under the out-of-pocket scope. 

Different expenditures scopes generate different prices. 

For the total-expenditure scope, the price is the total 

price, regardless of the source of financing, whereas for the 

out-of-pocket scope, the price is merely the out-of-pocket 

price that the consumer pays directly. The BLS scope is a 

hybrid between the total-expenditure scope and the outof-pocket scope: all out-of-pocket payments are included, 

and the portion of both public and private insurance reimbursement that is attributed to the consumer’s out-ofpocket payments for premiums also is included. So, too, 

are all employee contributions to employer-sponsored 

plans, as well as the individual’s payment of the Parts B

and D Medicare insurance premium. In what follows, indexes are generated for the total-expenditure scope, the 

out-of-pocket scope, and the BLS scope.
Monthly Labor Review • February 2010  25

mon comorbidities.

The following tabulation lists the aggregate medical indexes 

based on the different methods outlined in this article for the 

period from 1999 to 2004 (because of rounding, differences of 

columns may not exactly equal the resulting number shown): 

 

 (2) (3)

 (1) Treatment Adjusted

 Goods with for 

 and updated comorScope services utilization bidities (2) – (1) (3) – (2)

Total

 expenditures...... 1.3585 1.3342 1.3091 –0.0243 –0.0251

Out of pocket

 only................... 1.2831 1.3163 1.3057 .0332 –.0106

BLS scope ............ 1.3032 1.3055 1.2881 .0024 –.0175

Column 1 lists the results obtained from the treatment concept, in which utilizations are updated annually. Column 2 

lists the results for indexes computed by the goods-and-services concept, for which there is no utilization update. Column 3 lists the indexes computed under the treatment concept by prorating comorbidities such that if a service treated 

more than one disease, the utilization of that service would 

be prorated across the diseases treated. Under the total-expenditure scope, accounting for utilization changes results in 

a 2.43-percent drop in the cumulative index, compared with 

computing no utilization adjustment. When utilizations are 

prorated for comorbidities, there is a further 2.51-percent 

Results

Table 4 lists the number of diagnoses for each major disease 

category for the United States. The endocrine and nutritional 

disease category, which includes all diabetes diagnoses and 

confirms the rapid growth in type II diabetes in the Nation, 

grew the most rapidly between 1998 and 2004, increasing 

nearly 61 percent. The challenge here is that diabetes leads 

to additional comorbidities and is in part the reason for the 

growth in comorbidities depicted in table 1. Growth in the 

number of visits for one disease increased 12.3 percent between 1998 and 2004, while the growth rates in the number 

of visits for two and three diseases increased 77.2 percent and 

73.5 percent, respectively. The increase in diabetes is perhaps 

also part of the reason for the 33.2-percent increase in the 

incidence of circulatory system diseases between 1998 and 

2004, given that diabetes and circulatory problems are comIncidence of unreimbursed emergency room 

visits, 1998–2004

Year and status of patient Percent of visits 

unreimbursed

1998

All ..............................................................................................

Privately insured..........................................................

Publicly insured............................................................

Uninsured.......................................................................

7.14

4.34

6.87

24.32

1999

All ..............................................................................................

Privately insured..........................................................

Publicly insured............................................................

Uninsured.......................................................................

7.54

4.13

8.17

28.33

2000

All ..............................................................................................

Privately insured..........................................................

Publicly insured............................................................

Uninsured.......................................................................

8.72

5.75

7.38

31.12

2001

All ..............................................................................................

Privately insured..........................................................

Publicly insured............................................................

Uninsured.......................................................................

9.21

6.67

8.15

27.74

2002

All ..............................................................................................

Privately insured..........................................................

Publicly insured............................................................

Uninsured.......................................................................

6.48

4.01

5.67

26.16

2003

All ..............................................................................................

Privately insured..........................................................

Publicly insured............................................................

Uninsured.......................................................................

7.60

5.04

6.15

27.34

2004

All ..............................................................................................

Privately insured..........................................................

Publicly insured............................................................

Uninsured.......................................................................

8.39

5.73

5.98

33.34

Table 2. Average prices for emergency room visits, 

1998–2004

Year and type of visit Price 

per visit

Standard 

error

Yearly price 

growth

1998

All visits.......................................

Reimbursed visits....................

$381.38 6.4 ...

410.69 6.5 ...

1999

All visits.......................................

Reimbursed visits....................

399.60 9.1 4.78

432.21 9.4 5.24

2000

All visits.......................................

Reimbursed visits....................

410.21 8.2 2.65

449.39 8.5 3.97

2001

All visits.......................................

Reimbursed visits....................

463.82 9.1 13.07

510.85 9.5 13.68

2002

All visits.......................................

Reimbursed visits....................

493.93 9.1 6.49

528.16 9.4 3.39

2003

All visits.......................................

Reimbursed visits....................

524.84 8.2 6.26

567.98 8.4 7.54

2004

All visits.......................................

Reimbursed visits....................

646.73 14.7 23.22

705.99 15.3 24.30

Table 3.
Medical Consumer Price Indexes

26  Monthly Labor Review • February 2010

drop in the cumulative index, reflecting the effect of growing comorbidities on the productivity of medical services. 

Both differences are statistically significant.

When an out-of-pocket scope is used, the results differ. 

Here, utilization adjustment actually increases the index 

by a statistically significant 3.32 percent. There are two 

major reasons for this difference. First, most of the savings 

that occur are the result of shifting from inpatient hospital 

services to outpatient services. The share of total medical expenditures that finance inpatient services is much 

higher than the out-of-pocket counterpart. Therefore, the 

savings from the inpatient-to-outpatient shift are higher 

for the total-expenditure approach. Adjusting for comorbidity then yields a drop in the index; for example, under 

the BLS scope, the drop is a statistically significant 1.75 

percent.

Table 5 lists the ratio of out-of-pocket payments to 

total payments for various services from 1998 to 2004. In 

2004, out-of-pocket payments were 1.8 percent for inpatient facilities and 6.7 percent of total payments for outpatient facilities. Suppose that there was a shift in 2004 

from inpatient to outpatient facilities that resulted in a 

50-percent saving for total expenditures. Then, given the 

preceding ratios, consumer out-of-pocket payments would 

still have risen 86 percent, because their rate of insurance 

reimbursement on outpatient services was less than their 

rate of reimbursements on inpatient services. A second 

reason that the utilization-adjusted out-of-pocket index 

is higher than the unadjusted indexes is that the utilization intensity of pharmaceutical products has increased, 

disproportionately affecting out-of-pocket payments.

Because the BLS scope is a hybrid of the total-expenditure and out-of-pocket scopes, the results are mixed. 

There is no statistically significant difference in the indexes between adjusting and not adjusting for utilization. 

Note, however, that table 5 covers only the 1998–2004 

period, and another period might produce differences that 

are statistically significant. Accounting for comorbidities 

does create a significant 1.75-percent drop in the index. 

The savings from the substitution toward less expensive 

inputs have been concentrated in several disease categories that have relatively large expenditure shares—such as 

neoplasms, mental disorders, and pregnancies—for which 

inpatient utilization has dropped dramatically.

THE BLS RESPONSE TO CNSTAT’S RECOMMENDATION

that the BLS construct disease-based consumer medical 

price indexes has produced mixed results. With the totalexpenditure scope, adjusting for utilizations under the 

treatment concept results in a drop in the rate of medical 

price growth for the 1999–2004 period. But this drop does 

not extend to all diseases and all scopes. Most of the savings accrues to insurance benefit payments; the consumer 

sees no drop in either out-of-pocket payments or lower 

insurance premiums. Thus, using an out-of-pocket scope 

actually results in an increase in the index when utilization 

changes are taken into account. During the 1999–2004 

period, had the BLS kept its expenditure scope and shifted 

Number of diagnoses for major categories of disease, 1998–2004

[In millions]

Disease 1998 1999 2000 2001 2002 2003 2004

Infectious diseases............................................................................... 25.1 23.8 24.5 26.2 26.1 26.0 23.9

Neoplasms.............................................................................................. 17.2 16.9 17.2 18.9 20.7 20.6 20.1

Endocrine, nutritional, and related diseases.............................. 47.1 50.2 55.0 60.8 64.7 67.7 75.6

Diseases of the blood......................................................................... 3.1 3.3 3.9 4.2 4.2 4.1 4.2

Mental disorders.................................................................................. 40.7 38.2 39.8 45.7 54.5 56.0 59.7

Diseases of the nervous system..................................................... 85.5 79.1 76.9 81.7 82.6 86.6 88.2

Diseases of the circulatory system ................................................ 65.7 65.1 68.8 72.4 80.0 83.6 87.5

Diseases of the respiratory system ............................................... 175.6 172.7 168.9 183.2 179.1 184.4 177.4

Diseases of the digestive system ................................................... 79.1 82.1 82.7 83.4 90.4 93.8 92.2

Diseases of the genitourinary system ......................................... 34.7 35.3 38.0 40.8 41.3 41.8 41.3

Complications of pregnancy............................................................ 13.7 14.6 16.9 18.4 18.0 19.0 18.8

Diseases of the skin............................................................................. 27.4 25.8 28.2 31.4 31.6 30.9 29.2

Diseases of the musculoskeletal system ..................................... 75.9 75.8 76.4 86.3 96.6 99.6 102.6

Congenital anomalies........................................................................ 2.3 1.6 1.6 1.7 1.7 1.8 1.9

Certain conditions in the prenatal period................................... .4 .5 .8 .8 .9 1.1 .9

Injury and poisoning .......................................................................... 64.3 60.1 60.8 64.7 66.1 68.0 68.5

Other conditions ................................................................................. 64.2 66.6 71.3 79.2 81.7 83.4 83.7

Table 4.
Monthly Labor Review • February 2010  27

from pricing services directly to pricing diseases, there 

would have been little change to the medical CPI.

Unlike the study by Song and colleagues, most of the 

results presented here are statistically significant. Significance was achieved by computing indexes for broad disease categories, rather than randomly selecting 40 disease 

categories from a narrowly defined classification system. 

This approach resulted in more degrees of freedom and 

reduced the variance of the parameter estimates. One 

might argue that there is little homogeneity within these 

broadly defined groups and that, consequently, overall 

disease severity could vary widely. The proper reply to 

this critique is that, although it is true that there is much 

within-group variance in the broad categories used in this 

article, it is evident that narrowing the categories will not 

substantially reduce that variance. 

The results presented here are likely more representative of U.S. consumers than are the results obtained in 

Song and colleagues’ study, because the sample used herein is representative of the entire U.S. civilian noninstitutional population. By contrast, Song and colleagues used a 

private claims database that perhaps is not representative 

of the privately insured population, and the scope of the 

study was limited to three metropolitan areas.

Even if more narrowly defined disease categories were 

used here, the within-category variance would still be 

large. Bradley computed summary statistics for utilizations within a more narrowly defined clinical classification 

system than the one used in this study.16 Even under that 

system, the standard deviations were large relative to their 

means. For example, the number of hospital nights used 

to treat an episode of acute myocardial infarction ranged 

from 0 to 325. The diagnosis can give only limited information about the overall severity of the disease and therefore only limited information about the resources used to 

treat the disease. Other factors, such as age and stage of 

the disease, play key roles. Perhaps the use of reporting reforms recommended in the next paragraph would reduce 

some of the variance.

As is true of any medical care statistic, the accuracy of 

the disease-based index depends on the accuracy of the 

records kept by the medical system. If physicians do not 

diagnose patients accurately or do not report their diagnoses accurately, then the resulting indexes will contain 

measurement error. Oftentimes, the physician cannot 

immediately diagnose an ailment, and the recordkeeping 

system must allow for this possibility. If a physician makes 

a misdiagnosis, there needs to be a process by which both 

the misdiagnosis and the corrected diagnosis can be reported. If misdiagnoses are not reported, then it is not 

possible to estimate the true quantity of services used to 

treat a disease.

Another area of reform centers around the documentation of treatments. Usually, it is the responsibility of the 

primary physician to organize and record all treatments, 

including the use of any additional physician specialties. 

However, when physicians submit their claims to insurers, 

they often do not give the insurer this information, so the 

insurer must use a “grouper” to try to determine which treatments the physician actually used when he or she treated a 

particular disease. Bradley found that the groupers utilized 

by insurers generally fail to link all the goods and services 

that are used to treat a particular disease.17 Frequently, there 

are treatments that cannot be assigned a diagnosis, and this 

generates what is called an “orphan” record. Consequently, for 

many diagnoses, utilizations are underreported. For instance, 

if an expenditure for Glucophage does not have a diagnosis 

linked to it, then there is a diagnosis (most likely, diabetes) for 

which the total amount of money spent on Glucophage by 

prescription will be underreported. This situation can introduce a systematic downward bias in disease-based indexes. 

At other times, there are diagnoses that do not have links 

to all the treatments used to treat the disease in question. 

Both the MEPS database and claims data have records of 

Ratio of out-of-pocket payments to total payments, selected services, 1998–2004

[In percent]

Year

Total 

expenditures

Emergency 

room 

facilities

Emergency 

room 

physicians

Outpatient 

facilities

Outpatient 

physician

Inpatient 

facilities

Inpatient 

physician

Office based 

visits Prescriptions

1998 .......... 19.3 15.7 13.0 8.1 6.4 2.7 4.4 18.2 48.0

1999 .......... 19.2 14.7 10.4 5.1 6.2 2.6 3.7 18.0 46.2

2000 .......... 19.4 11.7 14.6 8.1 5.5 2.0 3.5 16.8 46.1

2001........... 19.7 11.6 13.6 6.8 7.0 1.8 5.6 15.2 44.0

2002 .......... 19.1 11.0 13.1 5.9 8.1 2.0 5.1 16.0 42.3

2003 .......... 19.6 12.5 11.0 5.9 7.6 1.9 3.7 15.2 44.9

2004 .......... 19.0 11.5 13.1 6.7 7.6 1.8 5.1 14.1 42.2

Table 5.
Medical Consumer Price Indexes

28  Monthly Labor Review • February 2010

acute myocardial infarction diagnoses that have no physician 

office visit assigned to them,18 yet, in order to establish the 

diagnosis, there had to be at least one such visit.

Finally, improved outcomes have not been factored into 

these indexes. Whether or not the BLS publishes diseasebased indexes, accounting for improvements in outcomes 

will continue to be a deficiency. At this point in time, it is 

difficult to estimate a reliable value that a consumer places 

on an outcome. Using an approach such as that of Cutler 

and colleagues,19 in which a dollar value is placed on an 

additional “quality-adjusted life year,” is likely too controversial to incorporate into a monthly published index.

The findings presented in this study show that there 

have been both productivity gains and substitutions toward less expensive services that have reduced the total 

price of health care. However, it is also evident that these 

price reductions have not “trickled down” to patient outof-pocket payments. Nor have they led to any significant 

reduction in premiums. In another study, Bradley constructed a cost-of-living index that directly prices health 

insurance and that accounts for increases in productivity.20 However, the main conclusion drawn by Bradley was 

no different from that presented in this article: although 

these savings from substituting toward less expensive 

inputs generated savings in insurance benefit payments, 

they did not induce reductions in premiums.

Notes

1 A full description of how the CPI measures medical care price 

movement can be found in “Consumer Price Index: Measuring Price 

Change for Medical Care in the CPI” (Bureau of Labor Statistics, Feb. 

23, 2010), on the Internet at www.bls.gov/cpi/cpifact4.htm (visited 

Feb. 28, 2010).

2 A Report to the President on Medical Prices (U.S. Department of 

Health, Education, and Welfare, 1967), p. 13.

3 Matthew D. Shapiro and David Wilcox, “Mismeasurement in the 

Consumer Price Index: An Evaluation,” NBER Macroeconomics Annual, 

December 1996, pp. 93–142.

4 David M. Cutler, Mark McClellan, Joseph P. Newhouse, and 

Dahlia Remler, “Are Medical Prices Declining? Evidence from Heart 

Attack Treatments,” Quarterly Journal of Economics, November 1998, 

pp. 991–1024.

5 See Ernst R. Berndt, Iain M. Cockburn, Zvi Griliches, Theodore 

E. Keeler, and Martin Neil Baily, “Pharmaceutical Innovations and 

Market Dynamics: Tracking Effects on Price Indexes on Anti-Depressant Drugs,” Brookings Papers on Economic Activity, Microeconomics 

(Washington, DC, Brookings Institution, 1996), pp. 133–99; and Ernst 

R. Berndt, Anupa Bir, Susan H. Busch, Richard G. Frank, and SharonLise T. Normand, “The Medical Treatment of Depression, 1991–1996: 

Productive Inefficiency, Expected Outcome Variations, and Price Indexes,” Journal of Health Economics, May 2002, pp. 373–96.

6 An exception is the substitution of less expensive generic drugs 

for brand-name drugs.

7 See Ana M. Aizcorbe, Bonnie A. Retus, and Shelly Smith, “BEA 

Briefing: Toward a Health Care Satellite Account,” Survey of Current 

Business, May 2008, pp. 24–30.

8 The Federal Medicare Part A program sets its schedule of reimbursement by diagnosis-related groups, which some private insurers 

use. However, reimbursement is for a particular medical service that 

treats a particular disease, not for all the treatments for a given diagnosis-related group.

9 See Ana Aizcorbe and Nicole Nestoriak, “The Importance of Pricing the Bundle of Treatments,” BEA working Paper no. 2008-04 (Bureau of Economic Analysis, July 2008), on the Internet at www.bea.

gov/papers/pdf/wp2008-04_bundle_treatments_paper.pdf (visited 

Mar. 4, 2010).

10 See Christopher J. Mackie and the National Research Council, 

Strategies for a BEA Satellite Health Care Account: Summary of a Workshop/Committee on National Statistics, Division of Behavioral and Social 

Sciences and Education, Christopher Mackie, Rapporteur (Washington, 

DC, National Academies Press, 2009).

11 See Charles Schultze and Christopher Mackie, eds., At What 

Price? Conceptualizing and Measuring Cost-of-Living and Price Indexes

(Washington, DC, National Academies, 2002). “ICD-9” is an abbreviation used in the medical field that stands for “International Classification of Diseases, ninth revision.” The ICD-9 provides a standard classification of diseases for the purpose of maintaining health records. The 

World Health Organization assigns, publishes, and uses the ICD-9 to 

classify diseases and to track mortality rates on the basis of death certificates and other vital health records. Medical conditions and diseases 

are translated into a single format by means of ICD-9 codes.

12 Xue Song, William Marder, William Houchens, John E. Conklin, and Ralph Bradley, “Can a Disease Based Price Index Improve the 

Estimation of the Medical CPI?” in Price Index Concepts and Measurement (NBER, 2008), pp. 329–72.

13 As a rule, patients move in and out of databases. Thus, when 

an increase in, for example, the number of patient visits used to treat 

diabetics is observed in a database, it is unclear whether the change was 

due to a less effective use of physicians’ services or an increase in the 

number of relatively less healthy patients.

14 In the medical field, the quantity (amount) of a service or good 

used to treat a disease is oftentimes referred to as utilization. In this 

article, the quantity of a service (that is, the number of hospital visits) 

is synonymous with the utilization of that service.

15 Additional information on the MEPS may be found on the Internet at www.meps.ahrq.gov/mepsweb (visited Feb. 28, 2010).

16 Ralph Bradley, “Issues in Computing Disease Based Price Indexes,” unpublished BLS manuscript, 2006.

17 Ibid.

18 Ibid.

19 Cutler, McClellan, Newhouse, and Remler, “Are Medical Prices 

Declining?”

20 Ralph Bradley, “The Effects of Health Insurance Prices on the 

Cost of Living Index: The Shadow Price of Worry,” unpublished BLS 

manuscript, 2008.