MacLean CH, Louie R, Leake B, McCaffrey DF, Paulus HE, Brook RH, Shekelle PG. Quality of Care for Patients With Rheumatoid Arthritis. JAMA. 2000;284(8):984-992. doi:10.1001/jama.284.8.984
Author Affiliations: Department of Medicine (Drs MacLean, Paulus, and Brook), School of Public Health (Drs MacLean, Leake, and Brook), and School of Nursing (Dr Leake), University of California, Los Angeles; RAND Health Program, Santa Monica, Calif (Drs MacLean, McCaffrey, Brook, Shekelle, and Ms Louie); Greater Los Angeles VA Health Care System and Veterans Affairs Health Services Research and Development Service, Los Angeles, Calif (Dr Shekelle).
Context Patients with rheumatoid arthritis are at risk for substantial morbidity
because of their arthritis and premature mortality due to comorbid diseases.
However, little is known about the quality of the health care that these patients
Objective To assess the quality of the health care that rheumatoid arthritis patients
receive for their arthritis, comorbid diseases, and health care maintenance
and to determine the effect of patterns of specialty care on quality.
Design, Setting, and Participants Historical cohort study of 1355 adult rheumatoid arthritis patients
enrolled in the fee-for-service or discounted fee-for-service plans of a nationwide
US insurance company. Patients were identified and followed up through administrative
data between 1991 and 1995.
Main Outcome Measures Quality scores for arthritis, comorbid disease, and health care maintenance
were developed from performance on explicit process measures that related
to each of these domains and described the percentage of indicated health
care processes performed within each domain during each person-year of the
Results During 4598 person-years of follow-up, quality scores were 62% (95%
confidence interval [CI], 61%-64%) for arthritis care, 52% (95% CI, 49%-55%)
for comorbid disease care, and 42% (95% CI, 40%-43%) for health care maintenance.
Across domains, care patterns including relevant specialists yielded performance
scores 30% to 187% higher than those that did not (P<.001)
and 45% to 67% of person-years were associated with patterns of care that
did not include a relevant specialist. Presence of primary care without specialty
care yielded health care maintenance scores that were 43% higher than those
for patterns that included neither primary nor relevant specialty care (P<.001).
Conclusions In this population, health care quality appears to be suboptimal for
arthritis, comorbid disease, and health care maintenance. Patterns of care
that included relevant specialists were associated with substantially higher
quality across all domains. Patterns that included generalists were associated
with substantially higher quality health care maintenance than patterns that
included neither a generalist nor a relevant specialist. The optimal roles
of primary care physicians and specialists in the care of patients with complex
conditions should be reassessed.
Rheumatoid arthritis is a chronic disabling condition that affects 1%
of US adults.1 It causes substantial morbidity2- 4 and is associated with
a 5- to 15-year reduction in life expectancy.5- 11
Early death stems not from the arthritis per se, but rather from comorbid
diseases that commonly affect the general population.12
Several factors may contribute to these premature deaths. First, pathophysiologic
features of rheumatoid arthritis may accelerate the courses of other diseases.13- 15 Next, medications
used to treat rheumatoid arthritis may cause or exacerbate other diseases.16,17 Finally, patients with rheumatoid
arthritis may not receive adequate treatment for comorbid diseases, perhaps
because the attention of patients and/or physicians is focused on the arthritis.
The few studies that have assessed comorbid disease care among patients with
specific underlying chronic diseases suggest that the quality of health care
for patients with comorbid diseases may be poor.18- 21
However, no studies have examined how physician specialty affects quality
of care across a spectrum of diseases in patients with a single chronic condition,
such as rheumatoid arthritis.
We tested the following 3 hypotheses about the quality of care received
by patients with rheumatoid arthritis: (1) patients with rheumatoid arthritis
receive higher quality care for that disease than for prevention or treatment
of comorbid diseases; (2) patients who see a specialist receive higher quality
arthritis care, comorbid disease care, and health care maintenance; and (3)
patients treated by a combination of specialists and primary care physicians
receive the highest quality care.
Our primary data source was administrative data from a national health
insurance company. During the study period (1991-1995), the company had an
average annual enrollment of 250,000 individuals in either fee-for-service
or discounted-fee-for-service plans. Enrollees lived throughout the United
States, although the Southeast and Midwest were overrepresented. The administrative
data consisted of 3 files. The patient eligibility file included information
on patient demographics and health insurance characteristics. The claims file
documented all submitted claims for inpatient and outpatient services, including
diagnoses, procedures, and physician specialty associated with each claim.
The pharmacy file documented all pharmacy claims, including the specific drug
prescribed, the quantity, the number of days for which drugs were supplied,
and the Drug Enforcement Agency number of the prescribing physician.
Other data sources for the study were the American Medical Association
(AMA) Master File and the 1990 US Census file. The AMA Masterfile includes
demographic and specialty data (that have been validated elsewhere22,23) about all practicing physicians
in the United States. The US Census file includes demographic information
about the US population including median household income.
The study population consisted of patients 18 years or older with rheumatoid
arthritis. Patients were classified with rheumatoid arthritis and entered
the study cohort if between 1991 and 1993 they had a minimum of 2 claims,
at least 2 months apart, for a physician visit specifying a diagnosis of rheumatoid
arthritis (International Classification of Diseases, Ninth
Revision [ICD-9] codes 714, 714.0, 714.1, 714.2, 714.4, 714.8, 714.81,
714.89). We required 2 visits to increase the reliability of the diagnosis
of rheumatoid arthritis. We required a period between claims to ensure that
individuals who were initially evaluated, but subsequently ruled out for rheumatoid
arthritis, were not classified as having rheumatoid arthritis. Patients remained
in the cohort until the end of the study period or until they disenrolled
from the insurance plan, whichever came first.
We obtained the age, sex, ZIP code, and type of health insurance for
each subject from the eligibility claims file. Median household income was
estimated by matching the patient's ZIP code with ZIP codes in the 1990 US
Census file that were linked to median household income. For each patient,
we constructed a chronological claims history that included claims for physician
visits, procedures, and drugs. We reviewed a 5% sample of these constructed
claims histories, and on implicit review they appeared to describe logical
histories of health care. We considered health histories to be logical if
the drugs prescribed for a patient were consistent with the diagnostic codes
for that patient and if the chronological sequence of diagnostic, procedure,
and drug claims was consistent with what occurs in clinical practice. For
example, patients with claims for cardiac procedures and cardiac medications
also had diagnostic codes for cardiac disease, and physician visits with diagnostic
codes for acute infectious diseases were followed by prescriptions for antimicrobial
We used process rather than outcome to measure quality because process
measures are more sensitive to differences in quality.24,25
The process measures we used have links to outcomes that are either directly
supported by randomized clinical trial evidence or by a combination of indirect
evidence and expert consensus26- 37
(Table 1). We selected measures
to assess quality for arthritis care, comorbid disease care, and health care
maintenance (Table 1, Table 2, and Table 3).
We selected diabetes mellitus, heart disease, and gastrointestinal bleeding
as the comorbid diseases for 2 reasons. First, individuals with rheumatoid
arthritis are at increased risk for these conditions as a consequence of the
disease itself or of drugs used to treat it. Second, they were sufficiently
common to give us statistical power to distinguish quality of care by physician
specialty, and they have process of care that can be assessed using administrative
data. Health care maintenance indicators included screening for breast, cervical,
and colorectal cancer.
Overview of Our Approach. We developed a system for determining an overall performance score for
each process measure, then aggregated these scores to obtain a score for each
domain of care. This score represented the percentage of recommended procedures
in the domain that an eligible patient received during a person-year. We also
analyzed the scores for each process measure and the scores for each domain
according to the patterns of specialty care for each patient.
Scores for Individual Measures. Each process measure had a specified period during which the process
should be performed in eligible patients. For each individual measure, we
determined the number of patients eligible to receive a process and the number
of periods in which they were eligible. Next, we determined the number of
eligibility periods during which the process was completed. Scores for individual
measures were calculated by dividing the number of periods during which the
measure was completed by the total number of eligible periods. We used eligibility
periods as the unit of analysis because individuals were potentially eligible
for each process measure more than once, and certain processes could occur
more than once in a year. For example, an individual with diabetes who was
enrolled in the study for 3 years would have 3 eligibility periods for the
assessment of annual glycosylated hemoglobin. An individual taking methotrexate
for 40 weeks during the study would have 5 eligibility periods that could
occur within 1 year for the process measure of assessing white blood cell
counts every 8 weeks. Patients were eligible for individual process measures
only if they were enrolled in the plan for the entire eligibility period of
Scores for Each Domain. We used scores on individual process measures to develop aggregate scores
for each of the 3 domains, using the person-year as the unit of analysis.
We developed the aggregate score for each domain in a 2-step process. First,
we determined for each person-year the number of eligibility periods during
which processes in that domain were performed. We divided these by the total
number of eligibility periods for those processes to calculate a performance
score for that person-year. For example, if during 1 year a subject was eligible
for and received 2 care processes pertaining to diabetes, and was eligible
for but did not receive 1 care process pertaining to ischemic heart disease,
the performance score for comorbid disease care for that person-year would
We averaged the domain-specific performance scores for each person-year
to determine the overall performance score for each domain. These scores describe
for each domain the average percentage of the process measures that were performed
for eligible populations during each person-year. With our method, patients
could contribute more than 1 eligibility period to each person-year and more
than 1 person-year to each domain score, which could result in clustering
or dependency of the scores within a patient. We adjusted for the effects
of possible clustering in the analysis.
Analysis by Pattern of Specialty Care. We also analyzed scores for each individual process measure and for
each domain of care by the following mutually exclusive patterns of care based
on whether a patient received (1) specialty care without primary care in which
a patient had 1 or more contacts with a relevant specialist, but no contacts
with primary care physicians; (2) specialty and primary care in which a patient
had 1 or more contacts with both relevant specialists and with a primary care
physician; (3) primary care without specialty care in which a patient had
contacts with a primary care physician, but not with relevant specialists;
and (4) neither primary care nor specialty care in which a patient had contacts
with neither relevant specialists nor a primary care physician.
We defined relevant specialists according to the specific disease and
domain of care being assessed (Table 4).
At the disease level, we required contact with any physician from the specialty
considered relevant for that disease to classify a patient as having had contact
with a relevant specialist. At the domain level, we classified patients in
specialist practice patterns if they had contacts with any of the relevant
specialists for that domain. For example, a patient with diabetes and myocardial
infarction who had contacts with a rheumatologist and cardiologist, but neither
a primary care physician nor endocrinologist, was classified as having had
contact with a relevant specialist but not with a primary care physician for
the arthritis and comorbid disease domains, and as having had contact with
neither a relevant specialist nor primary care physician for the health care
Physician contacts were defined both by physician visits and prescriptions
written by physicians. For physician visits, the physician specialty was ascertained
from the claims file. For prescriptions, we linked the Drug Enforcement Agency
number associated with each prescription to the AMA Master File to determine
the self-reported specialty of the prescribing physician.
We assigned each patient to 1 of the 4 patterns of care for each of
the diseases studied and for each domain during each person-year of enrollment.
Patterns of specialty care were assigned on the basis of person-years because
we reasoned that being seen by a given physician could affect a patient's
quality of care over some finite period, probably not longer than 1 year.
We assigned person-years for each individual consecutively from the time he/she
entered the study.
Process measures were assigned to person-years, and hence to pattern
of specialty care, based on when in time the eligibility period for the process
measure occurred relative to each patient's person-years. Process measures
with eligibility periods that were 1 year or less were assigned to the person-year
in which the eligibility period ended. For example, a patient with diabetes
enrolled in the study for 3 years would be eligible for annual glycosylated
hemoglobin testing during each of the 3 years of enrollment. Process measures
that spanned multiple years were assigned to the person-year in which the
process was completed, if it was completed. If the process was not completed,
we assigned the process measure to the last year of the eligibility period.
For example, a patient eligible for a Papanicolaou test every 3 years and
enrolled in the study for 3 years would have 1 eligibility period for this
process measure during the study period. This process measure would be assigned
to person-year 1, 2, or 3 if the Papanicolaou test were performed in year
1, 2, or 3, respectively. If the Papanicolaou test were not performed, the
process measure would be assigned to the practice pattern of year 3.
We expressed continuous variables as mean (SD). For individual process
scores, differences between physician pattern groups were assessed by using
logistic regression and a t test. At the domain level,
adjusted performance scores that took into account the effects of age, sex,
insurance characteristics (discounted vs nondiscounted fee-for-service reimbursement)
and median household income were calculated using binomial logistic regression.
We adjusted for these factors a priori based on potential correlates with
health care quality. We used a t test to assess for
differences between physician pattern groups for adjusted scores in each domain.
To adjust for possible clustering of outcomes from repeated observations from
the same individual, we used empirical SEs (the Huber correction).38
We identified 1355 patients with 4878 person-years of observation that
met our inclusion criteria; 1176 were eligible for at least 1 process measure
during each of 4598 person-years. The 179 patients who were not eligible had
claims for rheumatoid arthritis for less than 1 year of the study period.
As would be expected among individuals with rheumatoid arthritis, 76%
of the population was female. Mean (SD) age was 52 (11) years; age ranged
from 18 to 113 years. Median (SD) household income for this insured population
was $30,000 ($11,000). On average, patients were followed up for 3.9 years.
Two hundred eighty-one patients (21%) had at least 1 comorbid condition;
65 patients (5%) had at least 2. One hundred twenty-seven (9%) had diabetes,
125 (9%) had ischemic heart disease, 40 (3%) had congestive heart failure,
and 85 (6%) had serious gastrointestinal tract bleeding. There were 527 patients
(38%) who were eligible for mammography, 671 (50%) eligible for Papanicolaou
tests, and 222 (16%) eligible for colorectal screening.
With respect to the arthritis care, comorbid disease care, and health
care maintenance, 329 patients were eligible to receive care in 1 domain of
care, 540 patients were eligible in 2, and 307 patients were eligible in 3.
For the most part, performance scores for individual processes of care
pertaining to arthritis care were higher than those for either comorbid diseases
or health care maintenance. For example, patients were more likely to receive
monitoring laboratory tests when treated with disease-modifying antirheumatic
drugs than to have annual glycosylated hemoglobin levels checked if they had
diabetes, or mammograms if they were eligible (Table 5; complete performance table available from C.H.M.).
For each of the 39 individual process measures we assessed and for which
patients were eligible, and across all 3 domains of care, the average percentage
of the measures performed was higher among patients who had contacts with
a relevant specialist than among patients who did not. The difference was
statistically significant for 19 process measures. For example, 83% of patients
with diabetes whose care pattern included an endocrinologist received an annual
test of glycosylated hemoglobin; the corresponding number was 20% for patients
with diabetes who had no contact with an endocrinologist (P<.05). Likewise, among women eligible for breast cancer screening,
69% with care patterns that included a gynecologist received mammograms compared
with 32% for women without contact with a gynecologist (P<.05).
The aggregate scores for each domain (Table 6) are consistent with the individual process scores discussed
above. Recommended processes were performed, on average, 62% of the time for
arthritis care during each person-year compared with 52% and 42% of the time
for comorbid disease care and health care maintenance, respectively (P<.001). Overall, across all domains, 57% of recommended
care was performed.
Across the arthritis care, comorbid disease care, and health care maintenance
domains, 45% to 67% of the person-years were associated with physician patterns
that did not include a relevant specialist. Within each of the domains (Table 6), patients that had relevant specialist
contacts had performance scores that were 30% to 187% higher than those who
did not (P<.001). There were no differences in
the performance scores of individuals who saw both a primary care physician
and a relevant specialist and those who saw a relevant specialist but not
a primary care physician (P≥.23 for all domains).
These results were essentially the same with and without adjustment for age,
sex, insurance characteristics, and median household income (Table 6).
In the arthritis and comorbid disease domains, patients who had contacts
with a primary care physician but no relevant specialist had performance scores
that were slightly higher than those of patients who had contacts with neither,
but these differences were not statistically significant (Table 6). In the health care maintenance domain, patients who had
contacts with a primary care physician but no relevant specialist had performance
scores that were 43% higher than those patients who did not have contact with
either (P<.001; Table 6).
We found that quality of care for rheumatoid arthritis patients, as
assessed by a number of process measures, fell far short of recommended guidelines
and varied as a function of both health care domain and pattern of specialty
Although performance on arthritis-specific process measures has not
been reported previously, our findings of suboptimal performance on individual
process measures in the comorbid disease and health care maintenance domains
are consistent with prior studies.39- 44
For example, the overall performance rates for annual glycosylated hemoglobin
and eye examinations among individuals with diabetes reported in this study
are 27% and 30%, respectively. Weiner et al39
have reported 16% and 46% performance among Medicare beneficiaries with diabetes
on the glycosylated hemoglobin and eye examination measures, respectively.
Similarly, the Health Plan Employer Data and Information Set (HEDIS) has reported
41% to 46% rates for annual eye examinations among individuals with diabetes.40 Although the breast and cervical cancer screening
rates (each 41%) reported in this study are lower than those reported in HEDIS
(70% for each),40 they are within the 35% to
47% rates reported by other investigators41- 44
for breast cancer screening and the 15% to 76% rates reported for cervical
cancer screening.42- 44
Of note, prior studies that describe performance have been based on
samples of patients with either the specific disease for which the process
measures were relevant (eg, a sample of individuals with diabetes as opposed
to a sample of individuals with rheumatoid arthritis and diabetes) or represented
the general adult population. Our results demonstrate suboptimal quality of
care for comorbid diseases and health maintenance among individuals with a
specific underlying disease (ie, rheumatoid arthritis) that increases frequency
of visits to physicians and thus the opportunity to provide higher quality
care in these areas. Furthermore, the poorer overall scores for comorbid conditions
and health care maintenance, compared with those for arthritis, suggest that
patients with chronic rheumatoid arthritis may receive relatively better health
care for their arthritis than for the treatment and prevention of comorbid
Our study demonstrates that patients with rheumatoid arthritis who had
contact with a relevant specialist received substantially higher quality care
along all measures of health care quality than did patients who did not. This
finding is particularly noteworthy since nearly half of our study population
never saw a specialist. Seeing a primary care physician made no significant
difference in the quality scores for arthritis and comorbid diseases, but
modestly improved performance in the area of health care maintenance.
Our finding about the effects of physician specialty on quality are
consistent with numerous studies demonstrating that, in their areas of expertise,
specialists have a greater bank of knowledge on which to draw and provide
higher quality care, as measured by process and/or outcomes, than do generalists.45 This study demonstrates the same differences in performance
based on specialty patterns in care of patients with multiple diseases.
Our work has several limitations. First, the performance scores reported
for each domain are a function of, and limited by, both the diseases we chose
to assess and the specific performance measures assessed. Adding additional
measures or conditions could significantly change our conclusions. Furthermore,
this study does not assess health care quality as it pertains to individuals
with undiagnosed diseases because the process measures we used were only applied
to patients who had claims for, and hence had been diagnosed as having these
diseases. However, the conditions we assessed are particularly important to
individuals with rheumatoid arthritis and the measures we used cover a broad
spectrum of medical care. Although we can never measure all the dimensions
of quality, we did cover a range of dimensions and those that we did measure
are believed to be important to producing good outcomes.
Second, not all of the process measures used can be linked to outcomes
through randomized controlled trials. This is particularly true for the arthritis
measures. However, the measures that are not directly linked to outcomes have
indirect links to outcomes and are believed by experts to be important to
producing good outcomes.
Finally, this research is based on administrative data, which raises
2 issues of validity: whether specific diagnoses or procedures that have occurred
are coded, and whether recorded diagnostic and procedure codes represent truth.
The positive predictive value of using ICD
codes to identify patients with rheumatoid arthritis (ie, the probability
that a patient with a code for the disease actually has the disease) has been
reported as 57%46 and 95%47
in 2 different studies. We expect that the positive predictive value of our
selection method is much higher than that reported by the first study because
that study was based on encounter data rather than claims submitted for reimbursement.
Furthermore, that study reported the positive predictive value of a single ICD code for rheumatoid arthritis. We required at least
2 ICD codes for rheumatoid arthritis that were separated
in time by at least 1 month. Our selection method probably has a lower positive
predictive value than that reported in the second study because while that
study was based on claims submitted by rheumatologists, claims in our data
set were submitted by physicians of all specialties. Hence, our selection
method allowed some patients who do not have rheumatoid arthritis into the
The diagnostic codes used to define patients with comorbid diseases
in this study all have high specificity (>90%), but only moderate sensitivity.48- 51 Hence,
we did not identify and assess all subjects with the comorbidities we were
examining. It is possible that performance scores among patients we did not
identify could differ from those of our study group. It is also possible that
some processes actually occurred (eg, a specific laboratory test), but were
not coded, falsely lowering performance scores. However, we are reassured
that performance scores we report in this study are both accurate and generalizable
because our results are consistent with those of other studies, many of which
used data from medical records.39- 44,52- 56
Our study has several strengths. First, because our sample was large,
we were able to identify subsets of individuals with several different comorbid
diseases and assess the quality of health care for each. Second, we used many
well-established process measures that have randomized controlled trial data
and expert opinion supporting process-outcome links. Third, as a result of
our sampling method, our sample should be representative of the national fee-for-service
population with rheumatoid arthritis.
Our findings provide support to those who have recently called for reevaluation
of the optimal roles of generalists and specialists in the care of patients
with complex conditions.57- 60
Health care delivery models that use primary care physicians as overseers
for overall care may not be the best models for patients with rheumatoid arthritis
if primary care physicians restrict access to specialty care. Efforts are
needed to improve the quality of care for rheumatoid arthritis patients and
to increase physician awareness of comorbid diseases among patients with this