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Medical care expenditures and earnings losses of persons with arthritis and other rheumatic conditions in the United States in 1997Total and incremental estimates.

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ARTHRITIS & RHEUMATISM
Vol. 50, No. 7, July 2004, pp 2317–2326
DOI 10.1002/art.20298
© 2004, American College of Rheumatology
Medical Care Expenditures and Earnings Losses of
Persons With Arthritis and Other Rheumatic Conditions in
the United States in 1997
Total and Incremental Estimates
Edward Yelin,1 Miriam G. Cisternas,2 David J. Pasta,3 Laura Trupin,1 Louise Murphy,4
and Charles G. Helmick4
lion). The largest components of these expenditures
were inpatient care (39%), ambulatory care (29%), and
prescriptions (14%). The mean increment in medical
care expenditures specifically attributable to AORC
among those ages 18 years and older was $1,391 (total
⬃$51.1 billion). Persons with AORC ages 18–64 years
earned $3,812 less on average than did other persons of
these ages (total $82.4 billion). Of this average, $1,579
was attributable to the AORC (total $35.1 billion).
Conclusion. In 1997, persons with AORC incurred direct and indirect costs of $269.3 billion, of
which $86.2 billion was attributable to these conditions.
Objective. To provide estimates of the total medical care expenditures and earnings losses associated
with arthritis and other rheumatic conditions (AORC),
as well as the increment in such costs specifically
attributable to these conditions, in the US in 1997.
Methods. The estimates were derived from the
1997 Medical Expenditures Panel Survey (MEPS), a
national probability sample of 14,147 households including 34,551 persons, of whom 4,776 self-reported
arthritis. After weighting, those who self-reported
AORC represent 38.4 million persons. We tabulated all
medical care expenditures of the adult MEPS respondents, stratified by arthritis and comorbidity status,
and then used regression techniques to estimate the
increment in health care expenditures attributable to
AORC, after taking comorbidity, demographic characteristics, and insurance status into account. Using the
same methods, we also estimated the magnitude of the
earnings losses sustained by persons of working ages
(18–64 years) who had AORC.
Results. Persons with AORC incurred mean total
medical care expenditures of $4,865 (total $186.9 bil-
Information about the economic impact of illness
has become central to health policy debates, affecting
the allocation of research funding among conditions and
the choice of therapies for specific conditions. Because
of the growing importance of cost of illness studies in
health policy, the literature concerning the cost of all
forms of musculoskeletal conditions for the US as a
whole (1–8) and for other nations (9–13), as well as the
cost of specific musculoskeletal conditions, including
various forms of arthritis and other rheumatic conditions
(AORC) (14–37), has been expanding rapidly.
In general, the studies of the economic impact of
all forms of musculoskeletal conditions have been derived from population-based surveys, but not directly
observed individual-level medical care expenditures or
employment status and earnings. In contrast, studies
that have tracked the actual costs or expenditures for
individuals have used clinical-based samples and are,
thus, not representative of the general population. In the
present study, we used the results of the Medical Expen-
Supported by grants from the Arthritis Foundation and from
the Arthritis Program of the Health Care and Aging Studies Branch,
Centers for Disease Control and Prevention.
1
Edward Yelin, PhD, Laura Trupin, MPH: University of
California, San Francisco; 2Miriam G. Cisternas, MA: MGC Data
Services, Carlsbad, California; 3David J. Pasta, MS: DMA Corporation, Palo Alto, California; 4Louise Murphy, BSc, Charles G. Helmick,
MD: Centers for Disease Control and Prevention, Atlanta, Georgia.
Address correspondence and reprint requests to Edward
Yelin, PhD, Rosalind Russell Medical Research Center for Arthritis,
UCSF, Box 0920, San Francisco, CA 94143-0920. E-mail:
yelin2@itsa.ucsf.edu.
Submitted for publication August 21, 2003; accepted in
revised form March 1, 2004.
2317
2318
YELIN ET AL
ditures Panel Survey (MEPS), which melds the methods
of the 2 kinds of studies by using a population-based
sampling frame and then prospectively tracking actual
expenditures and employment and earnings among respondents.
The specific goals of the study were as follows: 1)
to provide estimates of all medical care expenditures on
behalf of all persons with any form of AORC in the US
in 1997, estimates of the increment in expenditures that
was specifically attributable to the AORC among persons ages 18 years and older, and estimates of the
fraction of total medical care expenditures attributable
to the AORC and 2) to provide estimates of lost
earnings among persons with AORC ages 18–64 years
and estimates of the increment in lost earnings attributable to the AORC among persons of these ages.
A method developed by the investigators in an
earlier study concerning all forms of musculoskeletal
disease (7) was used to estimate the increment in
medical care expenditures attributable to AORC. An
expansion of that method was used to estimate the
increment in earnings losses.
SUBJECTS AND METHODS
Data source. Data from the MEPS, a joint endeavor of
the Agency for Healthcare Research and Quality and the
National Center for Health Statistics, was used in the present
study. MEPS is designed to provide data on health care use,
medical care expenditures, sources of payment, and insurance
coverage for a representative sample of the civilian noninstitutionalized population of all ages in the US. MEPS also tracks
the employment status and earnings of that sample.
The full MEPS data include survey responses from this
sample of households (MEPS-H), information from their
providers about their medical conditions, information from
their healthcare plans about the specifics of these plans, and a
separate sample of nursing home residents (38). The MEPS-H
sample is derived from the previous year’s National Health
Interview Survey respondents, who are, in turn, derived from a
clustered, random sample of the civilian noninstitutionalized
population, with an oversample of African Americans and
Hispanics. Data for nursing home residents were not included
in the analyses. In the present study, we used data from the
1997 MEPS-H, which included 14,147 households with 34,551
persons (39).
MEPS-H data are collected through 6 rounds of interviews over a 2.5-year period. The first 3 interviews, which cover
expenditures, employment, and earnings over an entire year,
provided the data used in this analysis. The interviews are used
to collect information on health and functional status, health
care utilization and expenditures, employment and earnings, as
well as basic demographic information. The health status
section elicits data on the specific medical conditions each
respondent has. The information from the self-report of the
respondents is then used to code conditions to 3-digit levels
using the International Classification of Diseases, Ninth Revision (ICD-9) system. The utilization and expenditures and the
employment and earnings sections elicit information about the
previous 4 months in order to improve the reliability of
responses.
In the MEPS, expenditure data derive from a combination of the MEPS-H interviews and information obtained
from providers. Expenditures in MEPS are defined as the
actual expenditures for the medical care services used, regardless of the source of payment (38–40). In contrast to studies of
the costs of illness in which costs are tabulated even if
uncompensated care is provided, on the assumption that
resources are being consumed regardless of payment, in studies of medical care expenditures, the analyst is studying the
actual exchange of money. Because MEPS is based on expenditures rather than costs, there are health care encounters
enumerated in the survey for which no expenditures were
made.
In an entirely fee-for-service system, one could track
all expenditures among respondents. However, in many forms
of managed care, charges are not rendered when services are
provided and, hence, there are no expenditures specific to the
medical care encounters. Accordingly, in such instances,
MEPS-H imputes expenditures based on the charges incurred
within the fee-for-service sector for similar services provided to
similar individuals.
Analyses. Data partitions. We first partitioned the 1997
MEPS-H data into the following chronic condition groups,
based on ICD-9 codes: persons with only AORC (ICD-9 codes
274, 354, 390, 391, 443, 446, 710–716, 719–721, and 725–729),
persons with both AORC and non-AORC chronic conditions,
persons with 1 non-AORC condition, persons with ⱖ2 nonAORC chronic conditions, and persons with no chronic conditions. The definition of AORC is based on a standard
developed by the National Arthritis Data Workgroup (6).
Because MEPS-H data used only 3-digit ICD-9 codes, we
could only use the Workgroup’s 3-digit ICD-9 codes. For a few
Workgroup ICD-9 codes for which the presence of a fourth
digit is normally necessary, we used the corresponding 3-digit
ICD-9 code if we thought it would be composed primarily of
the Workgroup’s targeted condition. Chronic conditions in
general are defined by the protocol devised by Hoffman et al
(41), which was designed to provide a conservative estimate of
prevalence. Of all 34,551 MEPS respondents, 4,776 met the
criteria for any form of AORC.
General considerations. Because MEPS-H is based on a
2-stage cluster sample rather than a simple random sample of
the noninstitutionalized population, it is necessary to weight
the data to make inferences for the US population. In
MEPS-H, the sampling weights also take into account nonresponse in the households targeted for inclusion and attrition
among respondents after completion of the first interview (42).
We used SUDAAN software to account for the cluster sampling design in all analyses requiring the calculation of the
standard error of estimates (43).
Description of expenditures. We began by enumerating
medical care expenditures on behalf of persons of all ages by
condition group and by category of health services, as well as
the distribution of total health care expenditures. In the
foregoing analysis, we tabulated all expenditures among persons in the condition groups, regardless of whether the condi-
MEDICAL CARE EXPENDITURES AND EARNINGS LOSSES IN ARTHRITIS, 1997
tion in question accounted for the expenditures. In the tables
for these analyses, we indicate the estimates that had low
statistical reliability, that is, a relative standard error ⬎30%.
Analysis of increment in health care expenditures. In
order to assess the incremental contribution of AORC to
health care expenditures, we estimated a series of regressions
separately for adults with and without AORC. We then
simulated the expected level of expenditures for the AORC
group as if they did not have the AORC by applying the
parameter estimates derived from the group without the
condition to the data for the group with the condition. The
increment was then calculated as the difference between these
2 values (44).
To make these calculations with respect to expenditures for ambulatory care, inpatient care, prescription drugs,
and residual expenditures, we followed the 2-stage method
outlined by Duan et al (45). This method accounts for the
distribution of health expenditures in which many persons have
no health expenditures or relatively low expenditures, while a
small proportion has very high expenditures. In this method,
one first uses a logistic regression to estimate the probability
that an individual has any expenditures and then uses ordinary
least-squares regression on the logarithm of the expenditure to
estimate the level of expenditures among those with strictly
positive expenditures.
We estimated the incremental contribution of AORC
to total expenditures by a 4-stage model, which has also been
described by Duan et al (45). The first stage uses a logistic
regression to predict the probability of any medical expenditures. The second stage uses a separate logistic regression to
predict the probability of any hospital expenditures given the
presence of medical expenditures. The third stage uses an
ordinary least-squares regression to predict the logarithm of
the level of total costs among persons without hospitalizations,
and the fourth stage uses ordinary least-squares regression to
predict the logarithm of total costs among persons with
hospitalizations. Total expenditures include ambulatory and
inpatient care, prescription drugs, and a residual category that
comprises home healthcare, vision aids, dental visits, and
medical devices.
For both the 2-stage and 4-stage models, the log
transformation is used in the ordinary least-squares regressions to account for the skewed distribution of expenditures.
The resulting estimate, when transformed back to the original
units by exponentiation, is biased downward (that is, the
expected value of the estimate is lower than the expected value
of the population mean). Duan (46) has shown that a simple
adjustment called the smearing estimate can be used to adjust
for this bias. The smearing coefficient is the mean of the
exponentiated residuals from the regression on the logtransformed variable.
All the regression models included independent variables for the presence or absence of AORC and the following
9 other high-cost chronic conditions: hypertension, other forms
of heart disease, pulmonary disease, stroke, other neurologic
conditions, diabetes, cancer, mental illness, and musculoskeletal conditions other than AORC.
In addition to the indicator variables for conditions, we
included variables for age categories (45–64 years and ⱖ65
years, with 18–44 years as the reference category), sex (female
versus male), race (white versus nonwhite), ethnicity (Hispanic
2319
versus non-Hispanic), marital status (single, widowed, separated, and divorced, with currently married as the reference
category), level of formal education (high school graduate,
some college, college graduate, and some graduate school,
with less than high school as the reference category), and
health insurance status (only public insurance or no insurance,
with those having any private insurance as the reference
category).
For the 2-stage model, the final estimated expenditure
for each individual was obtained by exponentiating the predicted logarithm of expenditures, multiplying that value by the
smearing coefficient, and multiplying that, in turn, by the
predicted probability of a positive expenditure from the logistic
regression. The calculations for the 4-stage model were similar.
All of these analyses were limited to the 22,376 MEPS
respondents who were 18 years of age or older and who had
complete data for all covariates used in the multistage models,
of whom 4,457 met the study criteria for any form of AORC.
Calculation of attributable fractions. Attributable fractions, which are the proportion of all health care expenditures
in the entire population that would not occur if no one had
AORC, were calculated by dividing the increment in total
medical care expenditures attributable to AORC by the total
medical care expenditures for the entire population. We
cross-validated the estimates of attributable fractions in each
of 12 cells defined by age and sex. In the cross-validation
studies, we estimated 10 different values for the attributable
fraction within each cell. We estimated the models on ninetenths of the data and used that model to calculate the
attributable fraction in the other one-tenth of the data. The
largest range of estimates within a cell was 8 percentage points,
but in most of the cells, the range was ⱕ3 percentage points.
Estimation of employment and earnings losses. The
estimations of earnings losses were limited to persons ages
18–64 years with a history of ever being employed as recorded
in MEPS. Earnings losses were estimated only for persons who
were ages 18–64 years, both for the substantive reason that
labor force participation rates decline precipitously at age 65
years, as individuals reach the earliest age of eligibility for full
Social Security retirement benefits, and for the methodologic
reason that estimates among persons ages ⱖ65 years, based on
a relatively small sample size, would not be reliable.
To estimate earnings losses, we tabulated the actual
employment status of persons with and without AORC who
were between the ages of 18 years and 64 years, and, among
those who were employed, the hours of work. The absolute
level of lost wages was then calculated as the sum of 2 products.
The first product was the cost of lost wages among those who
were not working at all. This product is equal to the difference
in the employment rates of persons with and without AORC
times the number of persons with AORC times the mean wage
among all employed persons with AORC. The second product
was the cost of lost wages among those who continued to work.
This product is equal to the difference in the hours of work per
year by employed persons with and without AORC divided by
the mean hours of work of all persons (yielding the percentage
reduction in hours) times the number of persons with AORC
times the mean wage among all employed persons with such
conditions.
Analysis of increment in earnings losses. We then estimated the increment of earnings losses in a manner analogous
2320
YELIN ET AL
Table 1.
Noninstitutionalized population of all ages, by arthritic condition status, US, 1997
Chronic condition status
No. of persons
(millions)
% of total
population
% with
arthritic conditions
All arthritic conditions
Arthritic conditions only
Arthritic and nonarthritic conditions
All nonarthritic conditions
1 nonarthritic condition only
ⱖ2 nonarthritic conditions
No chronic/comorbid conditions
38.423
5.635
32.788
131.749
58.609
73.14
101.106
14.2
2.1
12.1
48.6
21.6
27.0
37.3
100.0
14.7
85.3
to the estimate of the increment in expenditures. Specifically,
we began by estimating separate logistic regressions of the
probability of employment among persons with and without
AORC and then estimated separate ordinary least-squares
regressions of the level of annual earnings among the members
of the 2 groups with earnings. The regressions included the
following independent variables: age (35–44 years, 45–54
years, and 55–64 years, with 18–34 years as a reference), sex,
race (white versus nonwhite), ethnicity (Hispanic versus nonHispanic), marital status (single, widowed, separated, and
divorced, with married as the reference category), education
level (high school graduate, some college, college graduate,
and some graduate school, with less than high school as the
reference category), and indicator variables for the same
comorbid conditions as described in the direct cost analyses
above.
After the regressions were completed, we applied the
parameter estimates derived from regressions on persons
without AORC to the data from those with such conditions to
simulate the expected level of earnings among such persons if
they did not have AORC. The increment in earnings losses was
then calculated as the difference between the AORC values
and simulated non-AORC values. In the ordinary leastsquares regressions, the natural logarithm of earnings was
used; the results were then transformed back to absolute dollar
amounts using separate smearing estimates calculated among
persons with and without AORC in a manner analogous to the
estimations of medical care expenditures described above.
Sensitivity analyses. In order to assess the extent to
which estimates of the prevalence and economic impact of
AORC were sensitive to the inclusion of potentially problematic ICD-9 codes within the AORC rubric, we eliminated the
following ICD-9 codes from the rubric: codes 354 (mononeuritis of the upper extremity; 85% of 255 sample cases were
carpal tunnel syndrome in later analyses), 390 and 391 (acute
rheumatic fever), and 443 (other peripheral vascular disease;
39% of 25 sample cases were Raynaud’s syndrome in later
analyses). The elimination of these conditions from the AORC
rubric reduced the prevalence of AORC by 3.1% (from 38.4 to
37.2 million) and reduced aggregate medical expenditures by
2.4% (from $186.9 billion to $182.4 billion), while increasing
the average expenditure level by 0.8% (from $4,865 to $4,902).
Because the results were relatively insensitive to the elimination of these codes and because relying on the definition of
AORC from the National Arthritis Data Workgroup uses a
standard that permits comparison with other studies (6,47), we
report below the estimates based only on that definition.
We also calculated the sensitivity of the estimate of the
increment in direct costs to the inclusion of different forms of
several of the independent variables, including age (inclusion
of age and age-squared), education (truncation of education at
“college graduate”), and comorbidity (inclusion of count of all
chronic conditions, rather than disease-specific indicator variables). In no instance did the estimate of the increment in
direct costs vary by more than 7.1%. In a similar manner, we
calculated the sensitivity of the estimate of the increment in
direct costs to the form of the comorbidity variable. Replacement of the chronic condition indicator variables with the
count of chronic conditions increased the estimate of the
increment in indirect costs by 14.6%.
RESULTS
Medical care expenditures. Distribution of conditions. In 1997, 38.423 million persons (14.2% of the
civilian noninstitutionalized population) reported any
form of AORC. Of those, 32.788 million (12.1% of the
entire population and 85.3% of all persons with AORC)
also reported having 1 or more non-AORC conditions
(Table 1). In that same year, 131.749 million persons
(48.6%) reported non-AORC conditions in the absence
of AORC and another 101.106 million (37.3%) reported
no chronic conditions whatsoever (all told, there were
232.855 million persons without AORC in 1997).
A higher proportion of persons with AORC than
without AORC were women and white, while a slightly
smaller percentage were of Hispanic origin. Such persons were also ⬎2 decades older on average, and
reflecting this, they were more likely to be widowed,
separated, or divorced and less likely to never have been
married (data on characteristics of persons with and
without AORC not presented in tables).
Distribution of expenditures. The 38.423 million
persons with any form of AORC had average total
medical care expenditures in 1997 of $4,865 ($1,074
among the 5.635 million without non-AORC, $5,516
among the 32.788 million with such conditions). This is
much higher than the average of $2,397 among persons
with non-AORC conditions or than the $500 among
those without chronic conditions (Table 2). The 3 largest
MEDICAL CARE EXPENDITURES AND EARNINGS LOSSES IN ARTHRITIS, 1997
2321
Table 2. Health care expenditures (by type) of the noninstitutionalized population of all ages, by arthritic condition status, US, 1997
Office-based and hospital
outpatient,
mean dollars (row %)
Condition status
Arthritic conditions
Arthritic conditions only
Arthritic and nonarthritic
conditions
Nonarthritic conditions
1 nonarthritic condition
ⱖ2 arthritic conditions
No chronic conditions
All Persons
ER and inpatient,
mean dollars (row %)
Inpatient¶
Home
healthcare
Prescriptions
filled
Other#
Total
expenditures,
mean dollars
103 (2)
35 (3)
115 (2)
1,905 (39)
320 (30)**
2,177 (39)
398 (8)
15 (1)**
464 (8)
687 (14)
81 (8)
792 (14)
377 (8)
210 (20)
406 (7)
4,865
1,074
5,516
82 (3)
59 (5)
101 (3)
30 (6)
66 (3)
951 (40)
379 (33)
1,409 (41)
149 (30)
787 (39)
99 (4)
29 (3)
155 (5)
9 (2)**
108 (5)
330 (14)
121 (11)
498 (15)
23 (5)
266 (13)
248 (10)
188 (16)
296 (9)
137 (27)
225 (11)
2,397
1,142
3,403
500
2,039
Physicianrelated*
Other†
Total‡
ER§
967 (20)
290 (27)
1,083 (20)
428 (9)
123 (11)
480 (9)
1,394 (29)
413 (38)
1,563 (28)
687 (29)
365 (32)
944 (28)
151 (30)
587 (29)
488 (20)
269 (24)
664 (20)
115 (23)
417 (20)
198 (8)
96 (8)
280 (8)
36 (7)
170 (8)
Other,
mean dollars (row %)
* Includes office-based visits to physicians, physician outpatient expenses billed separately, and associated outpatient facility expenses.
† Includes all nonphysician office-based and outpatient visits and associated facility expenses and unknown office-based and outpatient expenses.
‡ Includes office-based visits to physicians, physician outpatient expenses billed separately, associated outpatient facility expenses, all nonphysician
office-based and outpatient visits and associated facility expenses, and unknown office-based and outpatient expenses.
§ Emergency room (ER) includes facility and separately billed provider expenses.
¶ Includes facility and separately billed provider expenses.
# Includes dental visits and other medical supplies and equipment.
** Estimate has low statistical reliability (relative standard error ⬎30%).
components were inpatient costs (39%), ambulatory
care (29%), and prescription drugs (14%).
Overall, in 1997, persons with any form of AORC
accounted for $186.9 billion in expenditures, the equivalent of ⬃2.3% of the gross domestic product for the US
in that year, with all but $6.1 billion accounted for by
persons reporting AORC as well as non-AORC (Table
3). The 38.423 million persons with any form of AORC
represent 14.2% of the US population but account for
34% of all medical care expenditures in the MEPS.
Increment in expenditures and attributable fractions. The $186.9 billion in expenditures among persons
with AORC are only partially attributable to the AORC;
a significant proportion is due to the other chronic
conditions such persons have, as well as to their acute
care and wellness care. In Table 4, we present the results
of our analysis of the proportion of various kinds of
expenditures attributable to AORC among persons ages
ⱖ18 years. Overall, we estimated that all adults with
AORC had an increment in total expenditures of $1,391
per person beyond what would be expected of similar
persons without such conditions. This is equivalent to a
population attributable fraction of 10%, meaning that
eliminating AORC would reduce total health care expenditures by 10%. The attributable fraction rose with
age, from 6% among persons ages 18–44 years, to 12%
among those ages 45–64 years, and to 18% among those
ages ⱖ65 years, as befits a set of conditions associated
with aging.
The attributable fraction associated with AORC
was larger for men of all ages (11%) than for women of
all ages (9%). The attributable fraction among men ages
Table 3. Distribution of total health expenditures of the noninstitutionalized population of all ages, by arthritic condition status, US, 1997
Condition status
Minimum
5%
25%
Median
75%
95%
Maximum
Mean
Total,
billion dollars
(column %)
Arthritic conditions
Arthritic conditions only
Arthritic and nonmusculoskeletal
conditions
Nonarthritic conditions
1 nonarthritic condition
ⱖ2 arthritic conditions
No chronic conditions
All persons
0
0
0
60
0
154
575
98
799
1,644
349
2,004
4,306
910
5,114
20,041
3,703
22,332
191,297
65,316
191,297
4,865
1,074
5,516
186.9 (34)
6.1 (1)
180.9 (33)
0
0
0
0
0
0
0
78
0
0
223
108
424
0
83
635
337
1,068
89
394
1,847
854
2,870
319
1,415
9,644
4,494
13,735
1,958
8,478
351,156
337,262
351,156
136,109
351,156
2,397
1,142
3,403
500
2,039
315.8 (57)
66.9 (12)
248.9 (45)
50.5 (9)
553.2 (100)
Dollars
2,783
722
1,044
1,017
1,666
471
673
522
12,187
6,482
3,464
2,241
10,248
5,717
2,991
1,540
94,303
53,934
26,308
14,062
102,026
55,063
28,207
18,756
196,329
108,997
54,515
32,817
Total
Inpatient
Prescription drugs
Attributable fractions by expenditure type
Residual
Total
14,897
4,599
5,644
4,654
21,862
6,075
7,813
7,973
36,759
10,674
13,458
12,628
514
391
599
539
649
491
757
680
598
453
698
627
629
543
665
715
882
771
932
993
768
668
812
867
13
6
19
25
16
7
23
29
15
7
21
28
795
734
535
1,066
150
196
⫺22
310
401
407
195
605
1,050
899
927
1,248
1,045
902
942
1,248
1,042
895
930
1,241
12
7
12
28
3
2
⫺1
11
7
4
5
19
121
87
186
108
107
86
194
74
112
86
190
85
369
236
388
456
522
341
557
654
456
295
483
567
5
3
10
8
4
3
10
5
5
3
10
6
165
140
172
168
181
154
192
182
176
149
185
177
387
323
433
462
444
373
496
530
418
350
467
498
7
4
9
12
9
5
11
15
8
4
10
14
1,762
1,788
1,695
1,962
1,163
1,268
1,182
1,278
1,391
1,467
1,377
1,539
2,465
2,051
2,474
2,931
2,886
2,405
2,949
3,456
2,689
2,238
2,728
3,209
11
7
15
22
9
6
11
16
10
6
12
18
IncreIncreIncreIncreIncreWith
ment, Adjusted Attributable ment, Adjusted Attributable ment, Adjusted Attributable ment, Adjusted Attributable ment, Adjusted Attributable
arthritis
$†
cost, $‡ fraction, %
$†
cost, $‡ fraction, %
$†
cost, $‡ fraction, %
$†
cost, $‡ fraction, %
$†
cost, $‡ fraction, %
Outpatient
* MEPS ⫽ Medical Expenditures Panel Survey.
† Average increment per person with the condition.
‡ Adjusted cost per person whether or not they have the condition.
4,449
1,193
1,717
1,539
22,435
12,199
6,455
3,781
Total
With
arthritis
MEPS
respondents*
Estimated
population
(thousands)
Increment in expenditures and attributable fractions for arthritic conditions for the US population ages ⱖ18 years, 1997, by age and sex group and by expenditure type
Both sexes
All ages
18–44 years
45–64 years
ⱖ65 years
Females
All ages
18–44 years
45–64 years
ⱖ65 years
Males
All ages
18–44 years
45–64 years
ⱖ65 years
Sex, age
Table 4.
MEDICAL CARE EXPENDITURES AND EARNINGS LOSSES IN ARTHRITIS, 1997
Table 5. Individual and aggregate employment rates of persons with
arthritic conditions ages 18–64 years, raw differences and increments
attributable to the arthritic conditions, US, 1997
2323
Table 6. Individual and aggregate earnings losses of persons with
arthritic conditions ages 18–64 years, raw differences and increments
attributable to the arthritic conditions, US, 1997
Employment
Value
Earnings
Dollars
Employment rate of persons without arthritic
conditions, raw, %
Employment rate of persons with arthritic
conditions, raw, %
Individual employment rate gap, raw, %
Aggregate employment gap, raw, no. of
persons*
Individual incremental employment rate gap
attributable to arthritic conditions, %
Aggregate incremental employment gap
attributable to arthritic conditions, no. of
persons*
91.0
Aggregate lost earnings due to lower employment rates
among persons with arthritic conditions, raw, billions
Per capita earnings gap due to lower employment rates
among persons with arthritic conditions, raw
Aggregate lost earnings among employed persons with
arthritic conditions, raw, billions
Per capita lost earnings among employed persons with
arthritic conditions, raw
Net aggregate earnings gap, raw, billions
Net per capita earnings gap, raw
Aggregate incremental net earnings gap attributable to
arthritic conditions, billions
Per capita incremental net earnings gap attributable to
arthritic conditions
73.2
79.6
11.4
2,542,603
4.0
889,948
* Represents the number of affected individuals (⬃22.2 million) times
individual employment gap.
ⱖ65 years was particularly pronounced (22%), suggesting that the presence of AORC among men of these
ages adds dramatically to their overall medical care
expenditures.
Among all persons with AORC, the attributable
fraction associated with this group of conditions was
highest for outpatient services (15%) and lowest for
prescription medications (5%). The attributable fractions associated with inpatient services and the residual
category were 7% and 8%, respectively.
Although the attributable fraction for inpatient
services was only 7% overall, it was much higher for
men, regardless of age (12%), particularly men who
were ⱖ65 years old (28%). Presumably, the relatively
high attributable fraction among men these ages reflects
the growing prevalence of total joint replacement surgery.
Another way to visualize the magnitude of the
costs attributable to AORC is by estimating the aggregate impact of the mean increment in expenditures.
Thus, the average increment of $1,391 among each of
the 36.759 million persons ages ⱖ18 years with AORC
aggregated to $51.1 billion, or ⬃0.6% of the gross
domestic product for 1997.
Employment/earnings losses. As a result of their
markedly lower employment rates (91.0% versus 79.6%;
difference 11.4%), ⬃2.5 million persons with AORC
who were not then employed would have been working
if they had had the same employment rate as persons
without such conditions (Table 5). However, after controlling for differences in demographic characteristics
and comorbidity between the 2 groups, the incremental
employment gap was 4.0%, or ⬃0.9 million persons.
3,293
9.2
519
82.4
3,812
35.1
1,579
The raw wage loss costs associated with the
forgone earnings of the ⬃2.5 million persons with
AORC who would have been working but were not was
$73.2 billion ($3,293 per working-age person) (Table 6).
The wage losses of the then currently employed persons
with AORC that were associated with their lower earnings as compared with persons without such conditions
was $9.2 billion ($519 per working-age person with
AORC), leaving an overall net loss associated with
Table 7. Factors associated with employment of persons with arthritic conditions ages 18–64 years, controlling for demographic characteristics and 9 comorbid conditions, US, 1997
Characteristic
Female
Age
18–34 years
35–44 years
45–54 years
55–64 years
Race
Nonwhite
White
Ethnicity
Non-Hispanic
Hispanic
Education
Less than high school
High school graduate
Some college
College graduate
Some graduate school
Marital status
Married
Widowed, separated, divorced
Never married
Odds
ratio
95% confidence
interval for
odds ratio
0.58
0.44–0.75
Reference
0.55
0.37
0.12
0.33–0.94
0.22–0.64
0.07–0.20
Reference
1.65
1.17–2.32
Reference
0.87
0.58–1.29
Reference
1.9
2.85
3.72
5.5
1.41–2.56
1.94–4.18
2.29–6.04
3.09–9.82
Reference
1.05
1.07
0.79–1.39
0.66–1.72
2324
YELIN ET AL
AORC of $82.4 billion ($3,812 per working-age person
with AORC). After controlling for demographic characteristics and comorbidity, the increment in the net
individual earnings gap attributable to AORC averaged
$1,579, for a total of $35.1 billion.
Demographic factors associated with employment status. Because almost all of the earnings losses
associated with AORC occurred among those who were
not working, stopping work loss is essential to containing
the earnings losses associated with this group of conditions. Table 7 shows the demographic factors associated
with employment status among persons ages 18–64 years
with AORC and a work history. The characteristics
associated with a significantly reduced odds of employment included female sex (odds ratio 0.58; 95% confidence interval 0.44–0.75) and increments of age, while
increasing increments of education and being white were
associated with an increased odds of employment.
DISCUSSION
We performed 2 kinds of estimates of both the
medical care expenditures and earnings losses associated
with arthritis and other rheumatic conditions. In the first
estimate of medical care expenditures, we recorded the
magnitude and distribution of all medical care expenditures among the 38.423 million persons with AORC, and
we found that such persons experienced mean total
expenditures of $4,865, or $186.9 billion overall. Hospital admissions accounted for 39% of these expenditures;
ambulatory care (29%) and prescription drugs (14%)
were the next 2 largest components. Although average
expenditures of $4,865 among persons with AORC were
substantial, most individuals avoided such high levels of
expenditures. Among persons with AORC alone, the
average expenditures were only $1,074 and, even at the
95th percentile, were relatively small ($3,703). Among
persons with both AORC and non-AORC chronic conditions, median expenditures were only $2,004 and only
rose to $5,114 at the 75th percentile.
Nevertheless, expenditures incurred on behalf of
persons with AORC represent a substantial drain on the
nation’s economy. In 1995, the National Arthritis Data
Workgroup reported that both direct and indirect costs
of all forms of musculoskeletal conditions, including
AORC, for 1988 was 2.5% of the gross national product
of the US (6). In the present study, however, we found
that total medical expenditures for AORC alone in 1997
were equivalent to ⬃2.3% of the gross domestic product
for that year (48), and would suggest that both the
accounting methods used in the MEPS and the aging of
the population may have contributed to the increase in
the estimated impact of these conditions.
In the second set of analyses of medical care
expenditures, we estimated the proportion of total expenditures attributable to AORC among persons ages
ⱖ18 years. Overall, AORC was associated with an
attributable fraction of 10% for medical expenditures, or
$1,391 per person with AORC, which aggregates to
$51.1 billion, the equivalent of about 0.6% of the gross
domestic product in 1997. Since in a recession, by
definition, the economy retrenches by ⱖ1% of the gross
domestic product for at least 2 consecutive quarters, the
increment in medical care expenditures attributable to
AORC has an impact slightly less than that of a small
recession, but unlike a recession, it occurs in perpetuity.
In the unadjusted analysis, persons with AORC
ages 18–64 years who were not working were estimated
to be responsible for $73.2 billion in earnings losses,
while those who were employed were responsible for
losses of $9.2 billion, resulting in an estimate of net
earnings losses of $82.4 billion. The finding that those
who were not employed were responsible for the bulk of
the lost income is consistent with clinical studies in
specific rheumatic conditions that have shown a total
loss of employment to be more common than a reduction in hours or a change in work activities (49). After
controlling for demographic characteristics and comorbidity, the incremental value of the aggregate net earnings gap attributable to AORC was ⬃$35.1 billion, or
$1,579 per person with AORC ages 18–64 years.
In a recent estimate of the economic burden of
musculoskeletal conditions, Rice and colleagues (1)
calculated that the direct costs of AORC in 1995 were
$21.7 billion, or about $22.8 billion in 1997 dollars. In
the present study, we estimated that the increment in
medical care expenditures attributable to AORC was
$51.1 billion in 1997 dollars. Thus, the direct accounting
of medical care expenditures made possible by the
development of the MEPS may have resulted in a
substantial increase in the estimate of the direct costs
attributable to AORC. In the same study by Rice, it was
estimated that the indirect costs of AORC among
persons ages 18–64 years were $60.8 billion in 1995, or
about $64.0 billion in 1997 dollars. Thus, the estimate of
the earnings losses attributable to AORC in the current
study ($35.1 billion) is considerably smaller than the
estimate made by Rice and colleagues. The ability to
control for differences in characteristics other than the
AORC between persons with and without AORC may
MEDICAL CARE EXPENDITURES AND EARNINGS LOSSES IN ARTHRITIS, 1997
account for the lower estimate in the present study. The
estimate of earnings losses, however, is within the range
of estimates reported by Dunlop and colleagues (8).
Some AORC conditions were excluded from the
analysis because 4-digit ICD-9 data that would identify
these conditions were not available. This underestimation of cost was likely balanced by the presence of some
non-AORC conditions that were included in the analysis
because 3-digit ICD-9 codes are less specific.
There are 4 reasons why medical care expenditures may have been underestimated in this study. First,
the study sample was limited to the US civilian noninstitutionalized population and, thus, expenditures by
institutionalized and military populations were not included. Second, the MEPS measures only medical expenditures and does not capture services for which there
is no exchange of money. Third, we did not include in
this analysis expenditures for complementary and alternative medicine, which is becoming an increasingly
common treatment modality, especially among individuals with AORC (50,51). Fourth, the data from the
current study precede the introduction of biologics for
the treatment of rheumatoid arthritis, more-aggressive
testing for the presence and treatment of osteoporosis,
and the growth of the coxibs and/or the use of protonpump inhibitors with traditional nonsteroidal antiinflammatory drugs. However, it should be pointed out
that the relatively low prevalence of RA means that the
introduction of biologics would add, at most, several
billion dollars to the estimate of the aggregate increment
of ⬃$51 billion that was attributable to all forms of
AORC. Moreover, although it is becoming increasingly
common to prescribe bone densitometry for women at
risk of developing osteoporosis, the added costs of such
testing would be balanced by the reduction in the
number of women receiving estrogen replacement therapy. Nevertheless, all of these developments have undoubtedly increased the incremental costs associated
with AORC since 1997, making the estimates of the
present study relatively conservative.
There are some limitations that would apply to
the estimates of the increment in direct and indirect
costs. The MEPS survey provides no information with
which to estimate whether persons with AORC differ
from the rest of the population in their level of perseverance in the face of medical symptoms with respect to
the kinds of medical care services procured or their
willingness to stay at work.
These 1997 cost estimates, and the 1996 expenditures presented by Dunlop et al (8), are the first
2325
national population–based AORC cost estimates based
on directly observed individual-level expenditure and
employment data. This study is the first to provide
attributable fractions for medical care expenditures due
to AORC. These expenditure estimates are an essential
component of the ongoing effort by the Centers for
Disease Control and Prevention to characterize the
burden of AORC in the US for a single year, 1997. Thus
far, the impact of AORC on hospitals (52) and ambulatory care (47,53) has been reported, and these data will
soon extend to characterizing the burden of AORC in
nursing homes and assessing mortality rates among
persons with AORC.
The results reported here indicate that a larger
proportion of the indirect costs (41% of all $82.4 billion
in indirect costs, or $35.1 billion) than of the direct costs
(29% of all $186.9 billion, or $51.5 billion) of AORC is
attributable to the AORC. This finding from a
population-based study of persons with all forms of
AORC is consistent with studies of persons with discrete
rheumatic conditions (19) and indicates that the prevention of work loss and the resultant earnings losses should
be central to public health policy aimed at reducing the
impact of AORC, even as the cost of medical care
continues to garner a disproportionate amount of the
public’s attention.
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