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Medicaid and access to care among persons with systemic lupus erythematosus.

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Arthritis & Rheumatism (Arthritis Care & Research)
Vol. 57, No. 4, May 15, 2007, pp 601– 607
DOI 10.1002/art.22671
© 2007, American College of Rheumatology
Medicaid and Access to Care Among Persons With
Systemic Lupus Erythematosus
Objective. To evaluate the associations between Medicaid insurance and distance traveled by patients to treating
physicians and health care utilization for patients with systemic lupus erythematosus (SLE).
Methods. A total of 982 adults with SLE were recruited between 2002 and 2004. We calculated the distance between
patient homes and physicians using Mapquest, an Internet mapping program. We then assessed the association between
Medicaid status and distance traveled to the primary SLE provider, presence of >1 physician visits, and the number of
all physician visits, with and without adjustment for demographic and medical covariates.
Results. On an unadjusted basis, Medicaid patients traveled longer distances to see their primary SLE provider. This
effect was pronounced for patients under the care of a rheumatologist. Adjustment reduced, but did not eliminate, these
differences. With adjustment for covariates, Medicaid patients were equally as likely to see a rheumatologist as
non-Medicaid patients. However, Medicaid patients were more likely to be seen by a general practitioner or in the
emergency room for their SLE, and reported more visits to general practitioners and the emergency room for SLE.
Conclusion. Medicaid patients with SLE traveled longer distances to see an SLE physician, especially rheumatologists.
They also reported a different pattern of health care utilization. These results suggest that Medicaid patients may face
barriers in obtaining comprehensive medical services in proximity to their residences.
KEY WORDS. Access to care; Lupus; Medicaid.
Medicaid, one of the nation’s largest insurance programs,
covers approximately 55 million persons at an annual cost
of ⬃$300 billion. Established in 1965, Medicaid aims to
provide equal access to health care for those in poverty.
Because Drs. Yelin and Katz are Editors of Arthritis Care
& Research, review of this article was handled by the Editor
of Arthritis & Rhehumatism.
Supported by the Arthritis Foundation’s State of California Lupus Fund, the Rosalind Russell Medical Research
Center for Arthritis, and a grant from the US Public Health
Service’s National Center for Research Resources (5-M01RR-00079). Dr. Yelin’s work was supported by a grant from
the Agency for Healthcare Research and Quality/National
Institute of Arthritis and Musculoskeletal and Skin Diseases
(1-R01-HS-013893). Dr. Criswell’s work was supported by
grants from the NIH (K24-AR-02175, and R01-AR-44804).
JoAnn Zell Gillis, MD, Jinoos Yazdany, MD, MPH, Laura
Trupin, MPH, Laura Julian, PhD, Pantelis Panopalis, MD,
Lindsey A. Criswell, MD, MPH, Patricia Katz, PhD, Edward
Yelin, PhD: University of California, San Francisco.
Address correspondence to JoAnn Zell Gillis, MD, Rosalind Russell Medical Research Center for Arthritis, University of California, San Francisco, 3333 California, San Francisco, CA 94143-0920. E-mail:
Submitted for publication August 16, 2006; accepted in
revised form November 30, 2006.
However, some studies suggest declining physician participation in the program, thereby limiting access to enrolled
patients, especially for those requiring specialists (1). In
2003, Bindman et al demonstrated that in California there
were 9 specialists per 100,000 Medicaid patients as compared with 25 specialists per 100,000 patients with other
types of insurance (2). Similarly, a 2001 California report
showed that only 55% of medical specialists reported
treating Medicaid patients in their practice and less than
half stated that they would accept a new patient with
Medicaid insurance (3). The effects of Medicaid status on
access may be particularly pronounced for those with
chronic illnesses who depend on the care of a specialist.
Four studies have examined access to specialty care in
the Medicaid population and all suggest a disadvantage for
those with Medicaid coverage. In 2001, a study by Skaggs
et al conducted in Los Angeles County found that a timely
orthopedic appointment was provided to 100% of children with private insurance and only 2% of those with
Medicaid insurance (4). In 2006, a nationwide version of
the study by Skaggs et al found that 38% of orthopedic
practices provided limited or no access for Medicaid patients (5). These authors also noted that lack of access
correlated with low physician reimbursement (5). In 2004,
Wang et al (6) surveyed 100 California otolaryngologists in
a clinical scenario and found that 97% would offer an
appointment to a child with private insurance compared
with 27% if the child was covered by Medicaid. Reasons
provided for not offering an appointment included excessive paperwork and/or administrative burdens (97%) and
low monetary reimbursement for the office visit (87%).
Finally, Resneck et al (7), in a national sample, found that
Medicaid patients were less likely to receive a dermatology appointment (32%) compared with patients with
Medicare (85%) or other insurance carriers (87%). Resneck et al also noted that wait time until scheduled appointments was longer (50 days versus 37).
Systemic lupus erythematosus (SLE) is a chronic autoimmune condition of unknown etiology that primarily
affects women of childbearing age. No cure exists for SLE,
and patients face lifelong periods of disease flares with
resultant inflammation of numerous organ systems. Although somewhat uncommon in the general population (1
in 2,000 persons in the US), SLE is more common in
minority populations such as African Americans and Hispanics. These ethnic groups are disproportionally represented among those eligible for Medicaid on the basis of
income even in the absence of active disease. In addition,
many patients with SLE can meet the income eligibility
criterion by “spending down” their resources as a result of
high medical costs.
Prior research in rheumatoid arthritis demonstrates that
care by a rheumatologist may result in reduced disability,
less pain, and better overall functional status compared
with care by generalists (8,9). Although similar studies
have not been completed in SLE, the complexity of the
disease may necessitate care by a rheumatologist or by
another health care professional experienced in dealing
with the complex array of manifestations.
Patients with SLE who have Medicaid coverage may face
additional barriers to access if they live in a nonurban
location. Prior literature suggests that patients in rural
areas may be less likely to be seen by a rheumatologist for
their musculoskeletal symptoms (10). In one Veteran’s Administration study not specific to SLE (11), longer distance
to care was correlated with increased utilization of primary care and emergency services, perhaps indicating that
lack of geographic access may promote substitution of
available care for specialty care.
Few studies have examined the effects of insurance status on access to appropriate care for rheumatology patients
(10,12–14), and, to our knowledge, no studies have examined access to care for patients with SLE. In our study, we
calculated the distance to primary lupus providers in a
large established cohort of patients with SLE and examined the association of Medicaid insurance with distance
traveled to care. We hypothesized that the low physician
enrollment in Medicaid may require Medicaid patients
with lupus to travel longer distances to obtain appropriate
specialty medical care. We also compared the likelihood of
an outpatient visit within the past year and the average
number of outpatient visits between participants with
Medicaid insurance alone and those with other types of
coverage to determine how distance to care may affect
patterns of health care utilization.
Zell Gillis et al
Patients. For this analysis, we used data from the first
year of the University of California San Francisco (UCSF)
Lupus Outcomes Study (LOS), an ongoing longitudinal
survey of 982 English-speaking patients with SLE. Details
regarding creation of the cohort are described elsewhere
(15,16); however, relevant aspects are summarized here.
Approximately two-thirds of the initial LOS cohort were
derived from nonclinical sources such as lupus support
groups and conferences, newsletters, Web sites, and other
forms of publicity. Twenty-two percent of patients were
recruited from academic rheumatology offices, and 11%
from community rheumatology offices. Two-thirds of the
participants reside in California, whereas the remainder
reside in 40 states in the US. All LOS participants were
confirmed to have SLE after chart review completed by a
rheumatologist or a registered nurse working under a rheumatologist’s supervision. The UCSF Committee on Human
Research approved the study protocol. All participants
provided informed consent prior to the interview.
Data. The principal source of data for the LOS is a
structured 1-hour telephone survey conducted by trained
interviewers. The survey includes well-validated items
covering the following domains: demographics and socioeconomic status, status of SLE, disability, general health
status and social functioning, employment status, psychological and cognitive status, health care utilization, medications, and health insurance coverage (15).
Measures. Distance to primary SLE provider. Our primary outcome in this analysis was the number of miles
traveled to see primary SLE-treating physicians. Patients
identified their primary SLE provider and indicated the
specialty of the physician. Patient mailing addresses as
well as physician addresses were provided. For patient
addresses with post office boxes, actual residence addresses were obtained for the purposes of geocoding and
mapping. We verified the address and specialty of each
primary SLE physician using the American College of
Rheumatology directory, Internet resources, and telephone
calls to physician practices. The distances between patient
homes and primary SLE physician were then calculated
using Mapquest, an Internet-based mapping tool that provides the most reasonable travel route (www.mapquest.
com). We excluded 47 persons from our analysis due to
inability to map this distance: no lupus provider/physician could be identified for 24 patients, patient or physician address could not be found for 13, and patient and
physician were located in noncontiguous states for 10. For
5 individuals with difficult to use addresses (Mapquest
could not locate the street address), zip code centroids
were used.
To control for distance to SLE provider in the utilization
analysis, we used the calculated distance variable. Because of the skewed nature of this variable, we truncated
the variable at the third standard deviation above the
mean. This limited the mileage for 21 patients (2.0%)
whose physicians were ⬎150 miles away from their resi-
Medicaid and Access to Care in SLE
dences. Distances were adjusted for 7 patients with Medicaid only and 14 participants in the comparison group.
Demographic/socioeconomic variables. Predictors of interest included socioeconomic and demographic characteristics, urban versus nonurban status, and SLE disease
status. The socioeconomic and demographic characteristics included age at interview (in years), sex, race/ethnicity
(white as referent, Hispanic, African American, and other),
and education (high school education or less as referent,
some college, college graduate, and postgraduate degree).
SLE-specific variables. SLE-specific variables included
duration of disease (in years), age at diagnosis (in years),
self-reported SLE flare in the last 3 months, and measure of
SLE activity (scale from 1 to 10, with 10 representing
more severe activity). We also created another measure of
severity from questionnaire items. For this measure, disease was considered severe if patients required major immunosuppressive medications (mycophenolate mofetil,
cyclophosphamide, methotrexate, intravenous steroids,
chlorambucil, or azathioprine) or experienced major endorgan manifestations within the 2 years prior to the interview (kidney disease requiring biopsy, dialysis, or transplantation; bronchial or open-lung biopsy; hemoptysis or
venous thromboembolism).
Health care utilization. The health care utilization section of the questionnaire asks participants about their
medical care over the past 12 months. It included an
enumeration of all health care practitioner visits by specialty and whether the visits were for SLE or another
indication. Similarly, the section also included information about emergency room use and notes if the visit was
for SLE or for another indication. To address the skewed
distribution of these variables, we truncated the values for
each variable at the third standard deviation above the
mean, resetting the upper limit of the variable, rather than
dropping these potential outliers from the analysis. To
ensure correct interpretation, we also completed these analyses using 1 ⫹ log of response and found the results to be
Health insurance. The insurance section, derived from
the Medical Expenditures Panel Survey (17), included
items regarding the type of health plan (health maintenance organization versus fee-for-service) and source of
coverage (employment based, individually purchased
plan, or public program). We chose to initially examine the
characteristics of 3 insurance groups: Medicaid only, dual
coverage (Medicare plus Medicaid), and other insurance.
However, to best investigate access for individuals with
only Medicaid insurance, for our analysis we chose to
dichotomize the health insurance variable to represent
those with only Medicaid insurance versus all other forms
of insurance (Medicare, Medicare plus Medicaid, employer based, independent, or Veteran’s Affairs) because
there is evidence that physicians accept patients with
Medicare coverage in a fashion similar to privately insured
patients (7). We excluded a small number of patients (n ⫽
15) who reported having no health insurance and 1 individual who did not respond to this item because of inadequate power to draw reliable conclusions from this group.
All patient addresses were geocoded by Sonoma Technology Inc. (Petaluma, CA), assigning each participant to a
census block. Consequently, information about neighbor-
hood characteristics could be obtained and used for analysis. Using the density of the census block group, we
designated each LOS participant’s residence as urban
(higher local density) or nonurban (lower local density).
Statistical analysis. Distance to care. We used linear
regression to assess the association between Medicaid status and distance traveled to the principal SLE provider,
with and without adjustment for age, ethnicity, education,
disease severity, and urban/nonurban locale. The same
model was completed for the subset of patients (⬃75%)
with a rheumatologist as their primary provider.
Health care utilization. We used univariate and multivariate linear and logistic regression, where appropriate, to
assess the association between Medicaid status and various health care utilization measures, including whether
visits to a specific type of practitioner had occurred within
the last year and the number of visits within the last year,
with and without adjustment for the same covariates as were
used in distance to care analysis. To determine if the effect of
Medicaid only status differed by poverty status, educational
level, African American race, and Hispanic ethnicity, we
created interaction terms for Medicaid and each of these
characteristics. STATA software, version 8.0 (StataCorp, College Station, TX) was used for all statistical analyses.
A total of 58 patients (5.9%) reported Medicaid as the only
source of insurance coverage compared with 84 persons
(9.1%) with dual coverage (Medicare plus Medicaid) and
778 (85%) with other types of coverage (Table 1). The
majority of individuals identified a rheumatologist as the
primary SLE physician (74% of those with Medicaid insurance only, 68% with dual, and 81% with other insurance). As expected, because of Medicare coverage of endstage renal disease, patients with dual coverage were much
more likely to report a history of dialysis or transplant, and
consequently these patients were more likely than the
other groups to report a nephrologist as the primary SLE
physician (11% versus 7% with Medicaid only and 4%
with other insurance; P ⬍ 0.05).
Patients with only Medicaid insurance (hereafter referred to as Medicaid for simplicity) tended to be younger
than patients with dual coverage or with other insurance
(mean age 40.7 years, 46.7 years, and 46.4 years, respectively) and were more likely to live in a nonurban locale as
categorized by census data. As might be predicted, patients with Medicaid coverage and dual coverage were less
likely to be working and reported lower incomes than the
group with other insurance. In general, the Medicaid
group reported less formal education than the dual coverage group or the group with other insurance, although
37.9% of the Medicaid group reported attending trade
school, vocational school, or some college.
In unadjusted analysis, the Medicaid and dual coverage
groups reported higher SLE disease activity as measured
by flare in the last 3 months and higher patient global
assessments. Medicaid patients were more likely to have
received intravenous steroids within the past year, but the
3 groups were equally likely to be taking oral steroids, cyclophosphamide, mycophenolate mofetil, or azathioprine.
Zell Gillis et al
Table 1. Demographic and medical characteristics of study participants by insurance status*
Age, mean ⫾ SD years‡
Female sex
African American
Native American
Household income
High school or less‡
Vocational/trade/some college
Medical characteristics
Disease duration, mean years
Flare in last 3 months‡
SLE activity in last 3 months (1–10)‡
Severe disease
Taking oral steroids
Taking IV steroids in past year‡
IV cyclophosphamide in past year
Taking mycophenolate mofetil
Taking azathioprine
Organ manifestations
Kidney problems‡
Dialysis (ever)‡
Lung problems (ever)
Urban locale
Primary SLE physician is rheumatologist‡
Medicaid only
(n ⴝ 58, 5.9%)
Medicare plus Medicaid
(n ⴝ 84, 9.1%)
Other insurance
(n ⴝ 778, 85%)†
40.7 ⫾ 11.0
46.4 ⫾ 12.0
47.6 ⫾ 13.4
* Values are the percentage unless otherwise indicated. SLE ⫽ systemic lupus erythematosus; IV ⫽ intravenous.
† Includes employer based, independent, Medicare, and Veteran’s Affairs.
‡ P ⬍ 0.05.
In unadjusted analysis (Table 2), Medicaid patients reported traveling longer distances to see their primary SLE
provider than those with Medicare, Medicare plus Medicaid, or other types of insurance coverage (mean distance
41.9 miles, 23.8 miles, 20.3 miles, and 24.3 miles, respectively; P ⬍ 0.05). For those who identified their primary
SLE provider as a rheumatologist, Medicaid patients traveled longer distances than those with only Medicare,
Medicare plus Medicaid, or other insurance (54.1 miles,
28.3 miles, 18.5 miles, and 26.9 miles, respectively; P ⬍
0.05). Although the group with dual coverage more closely
resembled the Medicaid group with respect to sociodemographic characteristics, for our final analyses we merged
the dual coverage group and other insurance group to
specifically examine access issues for the group with Medicaid as their sole insurance.
Adjustment for covariates such as SLE severity, age, ethnicity, urban status, and education reduced, but did not
eliminate, the difference in distances traveled (Table 3). For
all SLE providers, Medicaid patients traveled 11.5 more
miles than those with other insurance, and for those seeing a
rheumatologist, patients traveled 19.8 more miles (P ⬍ 0.05).
We used flare in the last 3 months as a surrogate for SLE
illness activity in our final model (Table 3). However, to
determine if the results were sensitive to the choice of
severity measure, we also modeled the results using our
constructed measure of disease severity and patient global
assessment (scale of 1 to 10) without significant alteration
of results (data not shown).
A significant interaction between Medicaid only status,
education, and distance to primary SLE provider was detected (P ⫽ 0.03). Specifically, Medicaid patients with
Medicaid and Access to Care in SLE
Table 2. Average distance in miles traveled to systemic lupus erythematosus (SLE) physician by Medicaid status*
Primary SLE physician is
Any SLE physician†
Medicaid only
Medicare plus Medicaid
Medicare only
Other insurance
No. of patients
Miles traveled
No. of patients
Miles traveled
41.9 ⫾ 61.7‡
20.3 ⫾ 40.1
23.8 ⫾ 44.0
24.3 ⫾ 37.7
54.1 ⫾ 68.2‡
18.5 ⫾ 19.4
28.3 ⫾ 50.7
26.9 ⫾ 40.0
* Values are the mean ⫾ SD unless otherwise indicated. Analysis of variance (ANOVA) for Medicare only, Medicaid and Medicare, and other
insurance: P ⫽ 0.68. ANOVA for Medicare only, Medicaid and Medicare, and other insurance for those with primary physician as rheumatologist: P ⫽
† Regardless of specialty (includes general practitioners).
‡ P ⬍ 0.05.
higher than a high school education traveled longer distances to receive SLE care than those of similar educational background without Medicaid status and those with
less than a high school education regardless of Medicaid
status. This interaction was also seen in our subset group
analysis of patients who reported a rheumatologist as their
primary provider (P ⫽ 0.07) (Table 3).
For health care utilization, Medicaid individuals were
equally as likely to have seen a rheumatologist within the
past year (Table 4) as those with other types of insurance,
and no significant difference existed in the number of
rheumatology visits between the 2 groups (mean excess
visits ⫽ 0.95 for Medicaid; 95% confidence interval [95%
CI] 0.02, 1.88). There was also no difference between
groups with regard to utilization of other health care practitioners such as nephrologists, gynecologists, and pulmonologists (data not shown). Although both groups were
equally as likely to have had ⱖ1 visits to a general practitioner in the past year, Medicaid participants were more
likely to see a general practitioner for SLE-related symptoms (odds ratio 2.63; 95% CI 1.50, 4.61) and reported a
mean of ⬃5 more visits to the general practitioner within
the past year for SLE-related issues (P ⬍ 0.05).
Similarly, Medicaid patients were more likely to have had
ⱖ1 visits to the emergency room for SLE within the last year
and reported more visits to the emergency room for SLE
(mean 0.89 more visits; P ⬍ 0.05). Our findings with respect
to general physician and emergency room visits persisted
when modeled for the subset of patients primarily cared for
by a rheumatologist (data not shown). In multivariate analysis, controlling for SLE disease activity, age, education, race/
ethnicity, urban status, and distance to SLE provider, the
utilization patterns remained significant (Table 4).
Equal access to health care for Americans remains an
implicit goal of public policy (18). Although prior studies
Table 3. Multivariate linear analysis of distance (in excess miles traveled) to primary systemic lupus erythematosus (SLE)
provider by Medicaid status*
All SLE providers
Medicaid/education interaction
⬎High school education/other insurance
⬎High school education/Medicaid
⬍High school education/other insurance
⬍High school education/Medicaid
Rheumatologist as primary SLE physician
Medicaid/education interaction
⬎High school education/other insurance
⬎High school education/Medicaid
⬍High school education/other insurance
⬍High school education/Medicaid
(main effect only)
18.1 (7.13, 29.0)‡
11.5 (0.63, 22.3)‡
Adjusted (with interaction
for education and
Medicaid status)
25.1 (10.0, 40.1)‡
2.72 (⫺4.01, 9.44)
0.79 (⫺13.4, 15.1)
27.6 (14.4, 40.9)‡
19.8 (6.92, 32.7)‡
* Values are the number of excess miles traveled (95% confidence interval).
† Adjusted for SLE disease activity, age, urban/nonurban locale, race/ethnicity, and education.
‡ P ⬍ 0.05.
31.6 (14.3, 48.9)
1.40 (⫺6.33, 9.11)
8.37 (⫺9.26, 26.0)
Zell Gillis et al
Table 4. Health care utilization differences between persons with and without Medicaid coverage*
Generalist for any indication
Generalist for SLE
Emergency room visits for any
Emergency room visits for SLE
>1 visits OR (95% CI)
Mean difference in no. of visits
(95% CI)
No. of visits,
mean (range)
3.64 (0–16)
4.98 (0–30)
3.65 (0–25)
1.19 (0–7)
1.14 (0.58, 2.24)
1.56 (0.75, 3.25)
2.63 (1.50, 4.61)‡
3.65 (1.98, 6.71)‡
0.71 (0.39, 1.49)
1.60 (0.74, 3.49)
3.78 (1.50, 5.17)‡
3.43 (1.80, 6.53)‡
0.95 (0.02, 1.88)
6.13 (4.39, 7.86)‡
5.01 (3.34, 6.67)‡
1.22 (0.73, 1.71)‡
0.36 (⫺0.58, 1.31)
5.54 (3.75, 7.43)‡
4.91 (3.20, 6.63)‡
1.09 (0.58, 1.60)‡
0.74 (0–5)
3.00 (1.77, 5.11)‡
2.40 (1.36, 4.24)‡
0.89 (0.50, 1.30)‡
0.79 (0.35, 1.17)‡
* OR ⫽ odds ratio; 95% CI ⫽ 95% confidence interval; SLE ⫽ systemic lupus erythematosus.
† Adjusted for distance (in miles) traveled to SLE physician, race/ethnicity, education, urban/non-urban locale, age, and SLE disease activity
‡ P ⬍ 0.05.
have established that Medicaid patients may face barriers
in obtaining health care, few studies have considered insurance status as a predictor for access to rheumatology
subspecialty services and none have done so for persons
with SLE. The present study was designed to assess
whether persons with Medicaid travel longer distances to
obtain care and whether they have different utilization
patterns than those with other forms of coverage.
We found that patients who reported Medicaid insurance traveled longer distances to obtain SLE care by any
health professional and especially when the primary SLE
physician was a rheumatologist. Even though geocoding of
patient addresses demonstrated that Medicaid patients
were more likely to live in a nonurban locale, our results
persisted when controlling for this potential confounder.
Although patients with dual coverage (Medicare plus
Medicaid) more closely resembled the Medicaid group
demographically, the addition of Medicare seemed to predict shorter distances to care. This finding helps highlight
the possible effect of insurance coverage as a predictor
above and beyond the effect of sociodemographic status.
Only a few (n ⫽ 15) individuals in our study were uninsured. Because the number was small, meaningful analysis
could not be performed in this group alone. However, it is
likely that this group also faces difficulty in obtaining care.
Although Medicaid patients were as likely to have seen
a rheumatologist and reported a similar number of visits
per year as those with other insurance, Medicaid patients
reported more visits to general practitioners and to the
emergency room for SLE than other participants. However,
in multivariate analysis, distance to care did not fully
explain this finding. There are several possible explanations for this observed trend. For example, the Medicaid
group may in fact have less controlled disease that was not
fully accounted for in our analysis, or a larger burden of
comorbid illness. However, we believe a more complex
explanation may be needed. Perhaps Medicaid patients,
due to lack of social support or greater disease activity,
require increased access to rheumatologists to adequately
care for their SLE. Indeed, the increased utilization of
available services, such as the emergency room, may reflect this lack of ready access to specialty care.
We observed an interaction between Medicaid status
and education when examining distance traveled to care;
those with Medicaid coverage and more than a high school
education traveled the longest distances to see any type of
SLE provider. Perhaps education afforded these individuals the ability to find housing outside of low-income urban
areas, but Medicaid status limited them to urban health
care providers such as public clinics or tertiary care facilities. Alternatively, Medicaid persons with SLE who have
higher levels of education may have greater ability to access specialty health care, but at the price of having to
travel longer distances.
Although the LOS provides extensive information on
one of the largest SLE cohorts, the nature of this data
source limits the generalizability of the results. Participants in the LOS are English speaking, generally insured
(only 15 patients lacked insurance), and may underrepresent certain ethnic minority groups, such as African Americans and Hispanics (8.5% and 10% in the studied cohort,
respectively), thereby limiting our ability to detect differential access based on ethnicity alone. Perhaps due to
these limitations in our cohort, the number of Medicaid
patients was likely lower than that within the true population of patients with SLE. Furthermore, although we
attempted to adjust for disease status using several patient
self-reported measures, this is likely an imperfect surrogate for detailed clinical information.
In addition, although recruitment for the LOS drew from
both clinical and nonclinical sources, Medicaid patients
were more likely to be recruited from academic practices
(47% versus 32% for patients with Medicare plus Medicaid and 20% for those with other insurance; P ⬍ 0.05).
This observation has several implications for the findings
in our study. Patients recruited from university settings
may be more likely to report specialist care, which may
limit our ability to detect differential access to specialty
care for Medicaid patients. In regards to distance traveled,
patients at these settings may choose to seek care from a
well-known institution or from a noted individual at the
expense of longer travel distance. However, we believe
that the most likely explanation of this finding is that
university centers may be more willing to accept patients
with Medicaid, and that patients face barriers to access
within local community settings.
The influence of variable referral patterns and patient
choice may also contribute to the findings in our study. In
Medicaid and Access to Care in SLE
2004, Losina et al (19) found that Medicare patients of low
socioeconomic status as well as suburban or rural residence were more likely to use low-volume hospitals for
elective total knee replacement surgery, even though highvolume centers were associated with better outcomes. In
their study, low socioeconomic status may have reflected
differential referral patterns for surgical care, and for individuals outside of urban areas choice of a low-volume
center may have been influenced by shorter travel distances; however, neither fully explained patient choice.
Although patient choice may contribute to the findings
in our study, our clinical experience supports the fact that
a limited number of rheumatologists routinely accept
Medicaid. However, we believe that studying this phenomenon in a rigorous fashion would be difficult because
some rheumatologists may only accept current patients
who become Medicaid enrollees or may only accept a new
Medicaid patient under certain circumstances.
In this study, we used a novel method to assess access to
care for rheumatology patients. Extensive data from our
large SLE cohort also allowed us to comment on the possible implications of differential access on health care
utilization. We found that Medicaid patients appear to
travel longer distances to seek SLE care than patients with
other types of insurance. This difference is particularly
pronounced when patients report a rheumatologist as their
primary SLE provider. The distances likely reflect a lack of
SLE practitioners accepting Medicaid insurance, requiring
individuals to look beyond local communities. We also
found that Medicaid patients reported higher rates of utilization of general practitioners and emergency rooms, especially for SLE-related concerns. Although this was not fully
explained by distance to care, we believe it may represent the
downstream effects of differential access to care.
Our results generate multiple hypotheses surrounding
access to care for Medicaid patients that can be approached in future studies. Although we demonstrated
that Medicaid patients in the LOS travel longer distances
for SLE care, future studies should investigate the potential downstream effects of differential access to care on
health care quality and clinical outcomes. The results of
such a study would enable further exploration of the relationship between socioeconomic status and clinical outcomes, and perhaps influence the development of public
policy promoting access to care for less advantaged populations.
Dr. Zell Gillis had full access to all of the data in the study and
takes responsibility for the integrity of the data and the accuracy
of the data analysis.
Study design. Zell Gillis, Yazdany, Trupin, Criswell, Yelin.
Acquisition of data. Trupin, Criswell, Yelin.
Analysis and interpretation of data. Zell Gillis, Yazdany, Trupin,
Panopalis, Criswell, Katz, Yelin.
Manuscript preparation. Zell Gillis, Yazdany, Trupin, Julian,
Panopalis, Criswell, Katz, Yelin.
Statistical analysis. Zell Gillis, Yazdany, Trupin.
1. Bindman AB, Huen W, Vranizan K, Yoon J, Grumbach K,
Street L. Physician participation in Medi-Cal, 1996 –1998.
Oakland (CA): Medi-Cal Policy Institute; 2002. URL: http://
Bindman AB, Yoon J, Grumbach K. Trends in physician participation in Medicaid: the California experience. J Ambul
Care Manage 2003;26:334 – 43.
Bindman AB, Yoon J, Grumbach K, Street L. Physician participation in Medi-Cal, 2001. Oakland (CA): Medi-Cal Policy
Institute; 2003. URL:
Skaggs DL, Clemens SM, Vitale MG, Femino JD, Kay RM.
Access to orthopedic care for children with Medicaid versus private insurance in California. Pediatrics 2001;107:
1405– 8.
Skaggs DL, Lehmann CL, Rice C, Killelea BK, Bauer RM, Kay
RM, et al. Access to orthopaedic care for children with medicaid versus private insurance: results of a national survey.
J Pediatr Orthop 2006;26:400 – 4.
Wang EC, Choe MC, Meara JG, Koempel JA. Inequality of
access to surgical specialty health care: why children with
government-funded insurance have less access than those
with private insurance in Southern California. Pediatrics
2004;114:e584 –90.
Resneck J Jr, Pletcher MJ, Lozano N. Medicare, Medicaid, and
access to dermatologists: the effect of patient insurance on
appointment access and wait times. J Am Acad Dermatol
Ward MM, Leigh JP, Fries JF. Progression of functional disability in patients with rheumatoid arthritis: associations with
rheumatology subspecialty care. Arch Intern Med 1993;153:
2229 –37.
Yelin EH, Such CL, Criswell LA, Epstein WV. Outcomes for
persons with rheumatoid arthritis with a rheumatologist versus a non-rheumatologist as the main physician for this condition. Med Care 1998;36:513–22.
Mikuls TR, Mudano AS, Pulley L, Saag KG. The association of
race/ethnicity with the receipt of traditional and alternative
arthritis-specific health care. Med Care 2003;41:1233–9.
Weeks WB, Bott DM, Lamkin RP, Wright SM. Veterans Health
Administration and Medicare outpatient health care utilization by older rural and urban New England veterans. J Rural
Health 2005;21:167–71.
Yelin E, Bernhard G, Pflugrad D. Access to medical care
among persons with musculoskeletal conditions: a study using a random sample of households in San Mateo County,
California. Arthritis Rheum 1995;38:1128 –33.
Saag KG, Doebbeling BN, Rohrer JE, Kolluri S, Mitchell TA,
Wallace RB. Arthritis health service utilization among the
elderly: the role of urban-rural residence and other utilization
factors. Arthritis Care Res 1998;11:177– 85.
Katz JN, Barrett J, Liang MH, Kaplan H, Roberts WN, Baron JA.
Utilization of rheumatology physician services by the elderly.
Am J Med 1998;105:312– 8.
Yelin E, Trupin L, Katz P, Criswell L, Yazdany J, Gillis J, et al.
Work dynamics among persons with systemic lupus erythematosus. Arthritis Rheum 2007;57:56 – 63.
Yazdany J, Gillis JZ, Trupin L, Katz P, Panopalis P, Criswell L,
et al. Association of socioeconomic and demographic factors
with utilization of rheumatology subspecialty care in systemic lupus erythematosus. Arthritis Rheum 2007;57:593–
Cohen JW, Monheit AC, Beauregard KM, Cohen SB, Lefkowitz
DC, Potter DE, et al. The Medical Expenditure Panel
Survey: a national information resource. Inquiry 1996;33:
373– 89.
Smedley B, Stith A, Nelson A. Unequal treatment: confronting racial and ethnic disparities in health care. Washington,
DC: The National Academies Press; 2003.
Losina E, Barrett J, Baron JA, Levy M, Phillips CB, Katz JN.
Utilization of low-volume hospitals for total hip replacement.
Arthritis Rheum 2004;51:836 – 42.
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