Solomon et al. Arthritis Research & Therapy 2013, 15:R43
http://arthritis-research.com/content/15/2/R43
RESEARCH ARTICLE
Open Access
Treatment of rheumatoid arthritis in the Medicare
Current Beneficiary Survey
Daniel H Solomon1,2*, Edward Yelin3, Jeffrey N Katz1,4, Bing Lu1, Tamara Shaykevich1 and John Z Ayanian5
Abstract
Introduction: Numerous studies across different health systems have documented that many patients with
rheumatoid arthritis (RA) do not receive disease-modifying anti-rheumatic drugs (DMARDs). Relatively little is known
about correlates of DMARD use and whether there are socioeconomic and demographic disparities. We examined
DMARD use during 2001 to 2006 in the Medicare Current Beneficiary Survey (MCBS), a longitudinal US survey of
randomly selected Medicare beneficiaries.
Methods: Participants in MCBS with RA were included in the analyses, and DMARD use was based on an in-home
assessment of all medications. Variables included as potential correlates of DMARD use in weighted regression
models included race/ethnicity, insurance, income, education, rheumatology visit, region, age, gender, comorbidity
index, and calendar year.
Results: The cohort consisted of 509 MCBS participants with a diagnosis code for RA. Their median age was 70
years, 72% were female, and 24% saw a rheumatologist. Rates of DMARD use ranged from 37% among those <75
years of age to 25% of those age 75 to 84 and 4% of those age 85 and older. The multivariable adjusted
predictors of DMARD use include: visit with a rheumatologist in the prior year (odds ratio, OR, 7.74, 95% CI, 5.37,
11.1) and older patient age (compared with <75 years, ages 75 to 84, OR 0.58, 95% CI 0.37, 0.92, and 85 and over,
OR 0.09, 95% CI 0.02, 0.31). In those without a rheumatology visit, lower income and older age were associated
with a significantly reduced probability of DMARD use; no association of DMARD use with income or age was
observed for subjects seen by rheumatologists. Race and ethnicity were not significantly associated with receipt of
DMARDs.
Conclusions: Among individuals not seeing rheumatologists, lower income and older age were associated with a
reduced probability of DMARD use.
Introduction
Rheumatic disease experts and their professional societies widely embrace the importance of early and sustained use of disease-modifying antirheumatic drugs
(DMARDs) for rheumatoid arthritis (RA) [1,2]. Despite
this recommendation, several population-based and
community-based studies demonstrate under-use of
these agents. Several large studies of community-based
practice suggest that rates of DMARD use may be no
higher than 50% [3-5], with some evidence of recent
improvement [6,7]. A more complete understanding of
* Correspondence: dsolomon@partners.org
1
Division of Rheumatology, Immunology, and Allergy, Brigham and Women’s
Hospital, 75 Francis Street, Boston MA, 02115, USA
Full list of author information is available at the end of the article
patterns of DMARD use could inform potential quality
improvement interventions.
Studies that have assessed correlates of under-use have
identified several important variables. A prior study
within the Medicare population and another using data
from health plans suggested that specific race/ethnicity
groups, such as black race, was associated with underuse
[4,6]. The largest study of this topic used data from
Medicare Managed Care plans enrolled in the National
Committee on Quality Assurance’s Healthcare Effectiveness Data and Information Set (HEDIS) program [6].
This study found several patient characteristics associated with lower DMARD use, including black race,
lower income, and older age. All studies that have
examined the inclusion of a rheumatologist in a patient’s
care have found this factor to be the strongest predictor
© 2013 Solomon et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.
Solomon et al. Arthritis Research & Therapy 2013, 15:R43
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of DMARD use, with a 2- to 7-fold increase in prescribing among patients seeing a rheumatologist [4,5,7,8].
These findings are consistent across various countries.
Just as with other health care interventions, including
total joint replacement, cardiac revascularization and
organ transplant, vulnerable populations (people at high
risk for disparities in health and health care delivery,
typically including groups categorized by age, gender,
race, ethnicity, geography, and disability status) appear
to receive DMARDs less often than the general population [9-11]. DMARDs are recommended as the standard
of care for RA and have been shown to improve function and reduce pain. Thus, differences in care can be
described as treatment disparities, which have been
defined as ‘racial or ethnic differences in the quality of
healthcare that are not due to access-related factors of
clinical needs, preferences, and appropriateness of intervention’ [12].
We examined potential disparities in DMARD use for
RA in the Medicare Current Beneficiary Survey (MCBS), a
large representative survey of Medicare beneficiaries in the
US. The goal of these analyses was to identify potential
demographic and socioeconomic disparities in DMARD
use in the total cohort and within the sub-groups of individuals who did and did not see a rheumatologist.
Methods
Study cohort and design
The MCBS is conducted by the Center for Medicare and
Medicaid Services on a representative sample of Medicare
beneficiaries, excluding those exclusively in Medicare
Advantage Plans (a Medicare health plan offered by a private company that contracts with Medicare to provide all
physician and hospital benefits). Beneficiaries are visited in
their homes and instructed to keep all paperwork related
to medical services. Each year since 1992, approximately
4,000 to 6,000 Medicare beneficiaries have been recruited
and then surveyed annually for four consecutive years.
They assure its representativeness through a strategic sampling procedure. The MCBS sample is selected through a
three-stage process that results in a sample of individuals
who are more likely to live close to each other than a
random draw of individuals. In the first stage, primary
sampling units are selected consisting of metropolitan statistical areas or clusters of nonmetropolitan counties. In
the second stage, ZIP code clusters are sampled within the
primary sampling unit. In the third stage, beneficiaries are
sampled within the ZIP code clusters. The MCBS also
oversamples individuals under age 65 years (disabled) and
age 85 or older (the oldest old) to increase the precision of
estimates for these groups.
The survey includes sociodemographic information,
medical illnesses, insurance information, region of residence, and medication use. There are few missing data in
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the MCBS survey, with one recent study finding 2.7% of
all data elements missing [13]. In addition to the survey
data from MCBS, we also had linked Part A and Part B
health care claims from Medicare. This information
includes ambulatory and inpatient diagnoses, visits and
procedure codes. We used MCBS data from 2000 to
2006. Subjects entering in the last few years did not have
complete follow-up over the four years of the survey; we
included data from the available years through 2006.
Individuals with RA were required to meet two criteria:
1) report RA on the survey and 2) experience a health care
claim coded with a diagnosis of RA. Those who were in a
Medicare Advantage Plan must have had some period during which individual health care utilization claims were
reported. We began follow-up of the study cohort at the
first of these two criteria and continued through the end of
the four years of surveys, allowing each participant to
potentially contribute multiple years of follow-up. We
required at least one year of follow-up after qualifying with
a diagnosis of RA to determine subsequent DMARD use
(the study outcome); thus, our last cases entered the cohort
in 2005. Since several of our variables relied on data from
the 12 months prior to qualifying for the definition of RA,
our earliest cases entered the study cohort in 2001.
All of the MCBS data are publically available and do not
require subjects’ informed consent. Medicare performs the
linkage and de-identifies the dataset. The Partners Healthcare Institutional Review Board approved the study
protocol.
DMARD use endpoint
DMARD use was based on an in-home assessment of all
medications by MCBS interviewers. Participants were
asked to retain all medication vials and prescription
receipts. Detailed information on drug name, strength,
and number of tablets was collected at the in-home visits.
The medication data reported in the survey were assessed
for report of any non-biologic (azathioprine, cyclosporine,
d-penicillamine, hydroxychloroquine, leflunomide, methotrexate, and sulfasalazine) or biologic (abatacept, adalimumab, etanercept, infliximab, kineret, and rituximab)
DMARDs used during the study time period. We also
examined the medication reports for oral glucocorticoids,
but these were not included in the DMARD category.
Participants were followed over several years and may
have used DMARDs in certain years but not in others.
Thus, each year of follow-up was considered a separate
observation for the endpoint of DMARD use, allowing
subjects to contribute multiple endpoints.
Potential predictors of DMARD use
Sociodemographic
We defined several sociodemographic variables as
potential predictors of DMARD use. These included age
Solomon et al. Arthritis Research & Therapy 2013, 15:R43
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at the start of follow-up, gender, self-reported race/ethnicity, annual household income and educational attainment. Participants’ residences were coded to US Census
regions.
Other variables
Several other variables of interest are available in MCBS
and were assessed as potential predictors of DMARD
use. We assessed three aspects of insurance status. All
subjects had health care insurance through Medicare,
but some also had supplemental private insurance.
Some had drug insurance during the period prior to
2006, when all Medicare beneficiaries became eligible
for Medicare Part D drug insurance. Finally, some noted
being enrolled in a Medicare managed care plan. Other
variables of interest included a visit to a rheumatologist
based on provider types provided in Medicare claims
data, the number of self-reported comorbid conditions
[14] and the calendar year.
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statistical software version 10.0.1 (Research Triangle
Institute, Durham, NC, USA).
Results
From a total of 66,897 participants in MCBS during 2001
to 2006, we found 509 (0.76%) who fulfilled our definition of RA and were included in these analyses. The characteristics of the cohort are given in Table 1 and reflect
the composition of Medicare beneficiaries with a median
age of 70 years. As expected in an RA cohort, 72% were
female. The majority was white and non-Hispanic and
about one-third of the cohort had an annual income level
Table 1 Characteristics of 509 subjects from the MCBS
(2001 to 2006) included in the rheumatoid arthritis
cohort
N (%) or median
(IQR)
Age at beginning of index round, years
Statistical analysis
Female
We first examined the characteristics of all participants
meeting the definition of RA, based on information collected for the year that they entered the RA cohort.
Participants were then followed from the first survey
year when they received a diagnosis code of RA through
the end of their time in the MCBS, usually spanning
one to three years. During each year, medication information was examined for the use of any DMARD as
well as biologic DMARDs and oral glucocorticoids.
We then analyzed predictors of DMARD use in multivariate logistic regression models. All variables were considered a priori as potentially related to DMARD use and
thus were included in the final models. Because of the
repeated measures of DMARD use, each year of followup was considered as a separate observation with adjustment for within subject correlation using generalized
estimating equations [15]. The primary model included
all subjects with RA and all years of follow-up. Since seeing a rheumatologist was known to be an important factor in DMARD use, secondary models were developed
that included only years of follow-up during which a
rheumatologist was seen and the converse, models
including only years of follow-up during which no rheumatologist was seen. Since we found that seeing a rheumatologist was an important predictor of DMARD use,
we also constructed a multivariate logistic regression
model that predicted seeing a rheumatologist as the
dependent variable.
We used the MCBS sampling weights to account for
the complex survey design and to calculate nationally
representative estimates. Analyses were conducted using
SAS 9.0 (SAS Institute Inc, Cary, NC, USA), except the
weighted analyses which were conducted using SUDAAN
Race/ethnicity
70.1 (IQR 68.0 to 74.9)
366 (71.9%)
Non-Hispanic, white
389 (76.4%)
Non-Hispanic, black
58 (11.4%)
Hispanic
Non-Hispanic, Other
Region (at index round)
44 (8.6%)
18 (3.5%)
Northeast
108 (21.2%)
Midwest
120 (23.6%)
South
176 (34.6%)
West
96 (18.9%)
Missing
9 (1.8%)
Years of education at index round
≤ high school
Beyond high school
343 (67.4%)
166 (32.6%)
Annual income (US dollars)
>50,000
26 (5.1%)
>30,000 to 50,000
103 (20.2%)
>20,000 to 30,000
110 (21.6%)
>15,000 to 20,000
64 (12.6%)
≤ 15,000
187 (36.7%)
Missing
Rheumatology visit during prior year
Number of comorbid conditions during index
round
0 comorbid conditions
19 (3.7%)
120 (23.6%)
19 (3.7%)
1 comorbid condition
183 (36.0%)
2+ comorbid conditions
307 (60.3%)
Insurance status
Private insurance
315 (61.9%)
Managed care
55 (10.8%)
Drug insurance
382 (75.1%)
All variables were measured at the survey round when individuals entered the
rheumatoid arthritis study cohort. MCBS, Medicare Current Beneficiary Survey;
IQR, interquartile range.
Solomon et al. Arthritis Research & Therapy 2013, 15:R43
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≤ $15,000. Private insurance in addition to Medicare was
common (61.9%), few were in Medicare managed care
(10.8%) and during this time period most (75.1%) had
drug insurance. The average follow-up after entry into
the RA cohort was 2.75 years.
When examined year-by-year during follow-up, the
percentage of subjects receiving any DMARD, a nonbiologic DMARD or a biologic DMARD increased
slightly over time (see Table 2). Non-biologic DMARDs
use increased from 28% to 36% and biologic DMARD
use increased from 2% to 7% during this time period
(P-values for trend <0.05 for both). The percentage of
persons using any oral glucocorticoids (alone or in combination) ranged from 28% to 37%. As well, a sizeable
percentage (17% to 29%) used only oral glucocorticoids
(without a DMARD).
Table 3 demonstrates national estimates of DMARD
use based on sample weights. During the study period,
about two-thirds received any DMARD (non-biologic or
biologic) at some point. However, less than 5% of those
aged 85 and older received any DMARD, whereas
almost a quarter of those in this age group received oral
glucocorticoids. A smaller percentage of non-Hispanic
black and Hispanic patients received any DMARD than
white patients.
The multivariable adjusted predictors of DMARD use
are shown in Table 4. In the total cohort, the most
influential predictor of DMARD use was a visit with a
rheumatologist in the prior year (odds ratio, OR, 7.74,
95% CI, 5.37, 11.1). DMARD use declined significantly
at older ages (compared with <75 years: ages 75 to 84,
OR 0.58, 95% CI 0.37, 0.92, and age 85 and over, OR
0.09, 95% CI 0.02, 0.31).
In analyses restricted to follow-up years associated without a rheumatologist visit, low income categories (<
$30,000) were associated with significantly lower DMARD
use. Significant differences in DMARD use by income
were not observed among individuals who saw a rheumatologist in the current year, but those age 85 years and
over were much less likely to report DMARD use. Race
and ethnicity were not associated with DMARD use.
Since a rheumatologist visit was such a strong predictor
of DMARD use, we examined predictors of a rheumatology visit (see Table 5). We found that older individuals
were much less likely to see a rheumatologist (compared
with age <75 years: ages 75 to 84, OR 0.59, 95% CI 0.39,
0.90, and over 85 years, OR 0.22, 95% CI 0.11, 0.48). As
well, those with annual incomes less than $20,000 were
less likely to see a rheumatologist, but gender, race, and
ethnicity were not significant predictors of seeing a
rheumatologist.
Discussion
We studied DMARD use in a large nationally representative US cohort of older adult Medicare beneficiaries who
self-reported RA and had been diagnosed as having RA by
a health care provider. Similar to other community-based
studies, we found suboptimal DMARD use among MCBS
beneficiaries diagnosed with RA; approximately one-third
of follow-up time in this study cohort demonstrated any
Table 2 DMARDs and oral glucocorticoid use among subjects with rheumatoid arthritis in the MCBS, 2001 to 2006
2001
2002
2003
2004
2005
291
334
316
244
148
70
Any DMARDS
82 (28%)
83 (25%)
85 (27%)
88 (36%)
55(37%)
25 (36%)
Non-biologic DMARD
80 (27%)
82 (25%)
80 (25%)
85 (35%)
51 (35%)
22 (31%)
Methotrexate
49 (17%)
47 (14%)
42 (13%)
50 (20%)
31 (21%)
12 (17%)
Sulfasalazine
8 (3%)
5 (2%)
7 (2%)
5 (2%)
1 (0.5%)
1 (0.5%)
Hydroxychloroquine
25 (9%)
29 (9%)
29 (9%)
27 (11%)
15 (10%)
9 (13%)
Azathioprine
Leflunomide
5 (2%)
6 (2%)
3 (1%)
11 (3%)
2 (1%)
12 (4%)
3 (1%)
13 (5%)
2 (1%)
8 (5%)
0 (0%)
2 (3%)
Gold
0 (0%)
1 (0.3%)
2 (0.6%)
0 (0%)
0 (0%)
1 (1.4%)
Others
0 (0%)
0 (0%)
0 (0%)
1 (0.4%)
0 (0%)
0 (0%)
6 (2%)
5 (2%)
11 (3%)
10 (4%)
14 (9%)
5 (7%)
Number of patients
Biologic DMARD, any
2006
Etanercept
3 (1%)
4 (1%)
8 (3%)
5 (2%)
6 (4%)
3 (4%)
Adalimumab
0 (0%)
0 (0%)
2 (1%)
3 (1%)
7 (5%)
2 (3%)
3 (1%)
1 (0.3%)
1 (0.3%)
2 (0.8%)
1 (07%)
0 (0%)
Anakinra
Oral steroid use, any
Infliximab
0 (0%)
89 (31%)
1 (0.3%)
94 (28%)
0 (0%)
97 (31%)
0 (0%)
82 (34%)
0 (0%)
54(36%)
0 (0%)
26 (37%)
Oral steroid use, only
50 (17%)
60 (18%)
60 (21%)
50 (18%)
41 (28%)
20 (29%)
Results are presented as number (%) of patients. Percentages are based on the number for a given year. They do not add up to 100% because the rows are not
mutually exclusive categories. The sample size is greater than 509 because respondents were included in all years after they met the rheumatoid arthritis
definition. However, the sample size decreased over time because of the requirement for at least 12 months of follow-up after a diagnosis of rheumatoid
arthritis. DMARD, disease modifying anti-rheumatic drug; MCBS, Medicare Current Beneficiary Survey.
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Table 3 DMARD and glucocorticoid use by patient characteristics among MCBS respondents with rheumatoid arthritis
between 2001 and 2006, weighted to the US Medicare population
Total weighted number
(in thousands)
Any DMARD
Non-biologic DMARD
Biologic DMARD
Oral Glucocorticoid Use
N (%)*
Age, years
65 to 74
2,965
1,089 (37%)
1,021 (35%)
169 (6%)
1,010 (34%)
75 to 84
85+
1,338
353
328 (25%)
15 (4%)
323 (24%)
15 (4%)
31 (2%)
0
376 (28%)
80 (23%)
Female
3,440
1,071 (31%)
1,014 (30%)
173 (5%)
1,104 (32%)
Male
1,216
361 (30%)
346 (29%)
28 (2%)
361 (30%)
Non-Hispanic, white
2,996
1,170 (33%)
1,145 (32%)
130 (4%)
1,186 (33%)
Non-Hispanic, black
413
119 (25%)
111 (23%)
11 (2%)
141 (29%)
Hispanic
Non-Hispanic, other
364
133
90 (20%)
52 (33%)
52 (12%)
52 (33%)
60 (13%)
0
117 (26%)
22 (14%)
Gender
Race/ethnicity
Income (US dollars)
260
126 (49%)
126 (49%)
30 (12%)
98 (38%)
>30,000 to 50.000
>50,000
1,003
376 (38%)
353 (35%)
74 (7%)
353 (35%)
>20,000 to 30,000
992
309 (31%)
278 (28%)
65 (7%)
373 (38%)
>15,000 to 20,000
639
188 (30%)
175 (27%)
19 (3%)
175 (27%)
1,582
361 (23%)
358 (23%)
7 (0.5%)
392 (25%)
≤ 15,000
Total number = 4,656,213. *Percentage of the row total. DMARD, disease modifying anti-rheumatic drug use; MCBS, Medicare Current Beneficiary Survey.
Table 4 Regression models predicting DMARD use among respondents with rheumatoid arthritis in the MCBS, 2001 to
2006
Total cohort
Among respondents without rheumatology visits Among respondents with rheumatology visits
Odds ratio (95% confidence interval)
Number
1,403
1,058
345
Age, years
65 to 74
1.00
1.00
1.00
75 to 84
0.59 (0.36, 0.95)
0.36 (0.17, 0.74)
0.95 (0.49, 1.84)
0.10 (0.03, 0.33)
1.14 (0.69, 1.88)
0.07 (0.01, 0.34)
0.99 (0.50, 1.94)
0.13 (0.02, 0.91)
1.24 (0.63, 2.41)
85+
Gender, female
Race
Non-Hispanic, white
1.00
1.00
1.00
Non-Hispanic, black
0.61 (0.30, 1.22)
0.70 (0.23, 2.11)
0.63 (0.25, 1.59)
Hispanic
0.48 (0.18, 1.31)
0.39 (0.11, 1.38)
0.68 (0.18, 2.55)
Non-Hispanic, other
1.31 (0.53, 3.29)
1.55 (0.43, 5.57)
1.22 (0.26, 5.59)
>50,000
>30,000 to 50.000
1.00
0.76 (0.28, 2.08)
1.00
0.40 (0.14, 1.09)
1.00
1.12 (0.34, 3.67)
>20,000 to 30,000
0.57 (0.21, 1.57)
0.23 (0.08, 0.65)
1.06 (0.32, 3.57)
>15,000 to 20,000
0.59 (0.19, 1.81)
0.24 (0.08, 0.74)
1.01 (0.23, 4.51)
≤ 15,000
0.49 (0.18, 1.34)
0.16 (0.05, 0.48)
1.08 (0.31, 3.69)
Rheumatology care
7.69 (5.00, 11.1)
NA
NA
Income (US dollars)
The sample size is greater than 509 because respondents were included in all years after they met the rheumatoid arthritis definition. Models were adjusted for
variables in the table plus education, region of residence (South, Midwest, Northeast, and West), insurance status (private, managed care, and drug insurance),
and number of comorbid conditions, as well as year of observation. These models were weighted based on the Medicare Current Beneficiary Survey (MCBS)
sampling strategy. NA, not applicable; DMARD, disease modifying anti-rheumatic drug.
Solomon et al. Arthritis Research & Therapy 2013, 15:R43
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Table 5 Predictors of seeing a rheumatologist among 509
individuals with rheumatoid arthritis in the MCBS, 2001
to 2006
Odds ratio
(95% confidence interval)
Age, years
65 to 74
1.00
75 to 84
0.59 (0.39, 0.90)
85+
0.22 (0.11, 0.48)
Gender, female
0.99 (0.70, 1.45)
Race
Non-Hispanic, white
Non-Hispanic, black
1.0
1.15 (0.35, 3.79)
Hispanic
1.33 (0.44, 3.97)
Non-Hispanic, other
1.34 (0.49, 3.67)
Income (US dollars)
>50,000
1.0
>30,000 to 50.000
0.55 (0.26, 1.16)
>20,000 to 30,000
0.47 (0.22, 1.01)
>15,000 to 20,000
≤ 15,000
0.32 (0.14, 0.74)
0.35 (0.16, 0.75)
Models adjusted for variables in the table plus education, region of residence
(South, Midwest, Northeast, and West), insurance status (private, managed
care, and drug insurance), and comorbidity index, as well as year of
observation. MCBS, Medicare Current Beneficiary.
DMARD use (see Table 2). Consistent with prior literature
[4], a recent rheumatology visit was the strongest predictor
of DMARD use. Older age was also significantly associated
with a reduced probability of DMARD use in the total
cohort. In addition, we found that those with low annual
incomes were also significantly less likely to report
DMARD use when they had not seen a rheumatologist.
However, the income differences were not apparent in the
groups seen by rheumatologists, but age 85 years and over
remained associated with a reduced probability of
DMARD use. In our primary analyses, race/ethnicity was
not associated with a reduced probability of DMARD use.
First, we found that lower income was associated with
a reduced probability of DMARD use among individuals
not seeing a rheumatologist. This finding agrees with
studies of treatment disparities in diabetes and hypertension [16,17] and is consistent with prior studies in
RA [5,6]. Reduced DMARD use among those with lower
annual income may be explained by difficulty affording
medications or other unmeasured factors, such as
reduced geographic access. The lack of DMARD among
those with a lower income may also be related to
patient attitudes, physician factors, or access issues.
Second, the reduced use of DMARDs in older patients
has been found in several other analyses of RA [4,6,7].
Older individuals may have comorbidities that would be
relative contraindications to DMARD use. We did control for a comorbidity index, but providers may perceive
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that certain diagnoses preclude the use of DMARDs.
Moreover, it is possible that providers believe that RA is
milder in older patients and thus less important to treat
with DMARDs [18]. Some studies suggest elderly onset
RA may be milder [18] and that older adults receive less
aggressive treatment [19]. Another possibility is that
more of the diagnosed RA in older adults is actually
osteoarthritis; thus, there is differential misclassification
by age explaining the DMARD finding.
Strengths of this study include the use of a large representative national sample as well as the breadth of potential predictors included in the MCBS. However, the
findings must be interpreted in light of some important
limitations. The definition of RA in the MCBS has not
been validated. We required self-report and a physician
diagnosis on a health care claim. Similar definitions have
been used and found to be reasonably accurate [20],
however there are very likely some individuals included
in the RA cohort who do not have RA and may have
other systemic rheumatic diseases (that is, polymyalgia
rheumatica). This may partly explain the relatively low
rates of DMARD use in our study. Differential misclassification of RA by patient characteristics could explain
some of our findings. As well, average follow-up was 2.76
years; longer follow-up may have yielded different results.
Furthermore, infusible DMARDs may not have been uniformly included in the in-home medication assessment.
However, during the study period, only infliximab was
given by infusion for RA. Another important limitation is
that this study included only Medicare beneficiaries who
are typically over 65 years of age and all have health
insurance. This limits the generalizability of our findings
but should not compromise internal validity. Additionally, when restricting the MCBS to RA patients, the sampling strategy may not yield a representative cohort of
Medicare beneficiaries. Our dataset also contains no
information about RA disease severity. Finally, it has
been noted before that income is under-estimated in the
MCBS [21].
Conclusions
In conclusion, we found relatively low rates of DMARD
use among individuals with a diagnosis code for RA in
MCBS. Disparities in the use of DMARDs were pronounced among those without a rheumatology visit. In
this group, older age and lower income were associated
with a reduced likelihood of DMARD use. However,
race/ethnicity did not predict DMARD use. Among those
seeing a rheumatologist, older age remained significant
but income was no longer associated with DMARD use.
As in several other studies, seeing a rheumatologist is the
strongest predictor of DMARD use. While this study did
not focus on methods to improve quality of DMARD
prescribing, our results suggest that quality improvement
Solomon et al. Arthritis Research & Therapy 2013, 15:R43
http://arthritis-research.com/content/15/2/R43
interventions focused on increasing appropriate DMARD
use and improving access to rheumatologists are warranted. Another recent study found that a clinic specializing in care of medically vulnerable populations was able
to reduce disparities in disease activity and function
among persons with RA compared to a university tertiary
care clinic [22]. This prior study and the present one suggest that disparities in utilization of suggested therapies
and outcomes may be reduced by the way health care is
organized, including appropriate referral to specialists
and perhaps a special orientation to at-risk populations.
Abbreviations
DMARD: disease-modifying antirheumatic drug; MCBS: Medicare Current
Beneficiary Survey; OR: odds ratio; RA: rheumatoid arthritis.
Authors’ contributions
DHS designed the study, conducted the analyses, drafted the manuscript
and received funding for the study. EY designed the study, conducted the
analyses, and revised the manuscript. JNK designed the study, conducted
the analyses, and revised the manuscript. TS analyzed the data and revised
the manuscript. JZA designed the study, conducted the analyses, and
revised the manuscript. All authors have read and approved the manuscript
for publication.
Page 7 of 7
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Competing interests
There are no direct competing interests to declare. Dr Solomon has received
research grants from Abbott, Amgen and Lilly unrelated to the current work.
He has also served in unpaid roles on two Pfizer trials not related to the
current work.
Acknowledgements
Support: National Institutes of Health (NIAMS AR 056215)
Author details
1
Division of Rheumatology, Immunology, and Allergy, Brigham and Women’s
Hospital, 75 Francis Street, Boston MA, 02115, USA. 2Division of
Pharmacoepidemiology, Brigham and Women’s Hospital, 75 Francis Street,
Boston MA, 02115, USA. 3Rosalind Russell Medical Research Center for
Arthritis, University of California, San Francisco, California 94143-0920, USA.
4
Department of Orthopedic Surgery, Brigham and Women’s Hospital, 75
Francis Street, Boston MA, 02115, USA. 5Division of General Internal
Medicine, Brigham and Women’s Hospital, 75 Francis Street, Boston MA,
02115, USA.
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Received: 18 June 2012 Revised: 14 December 2012
Accepted: 14 February 2013 Published: 18 March 2013
20.
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Cite this article as: Solomon et al.: Treatment of rheumatoid arthritis in
the Medicare Current Beneficiary Survey. Arthritis Research & Therapy
2013 15:R43.