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  2. Abstract


Health outcomes in rheumatoid arthritis (RA) have improved significantly over the past 2 decades. However, research suggests that disparities exist by race/ethnicity and socioeconomic status, with certain vulnerable populations remaining understudied. Our objective was to assess disparities in disease activity and function by race/ethnicity and explore the impact of language and immigrant status at clinics serving diverse populations.


We examined a cross-sectional study of 498 adults with confirmed RA at 2 rheumatology clinics: a university hospital clinic and a public county hospital clinic. Outcomes included the Disease Activity Score in 28 joints (DAS28) and its components, and the Health Assessment Questionnaire (HAQ), a measure of function. We estimated multivariable linear regression models including interaction terms for race/ethnicity and clinic site.


After adjusting for age, sex, education, disease duration, rheumatoid factor status, and medication use, clinically meaningful and statistically significant differences in DAS28 and HAQ scores were seen by race/ethnicity, language, and immigrant status. Lower disease activity and better function was observed among whites compared to nonwhites at the university hospital. This same pattern was observed for disease activity by language (English compared to non-English) and immigrant status (US-born compared to immigrant) at the university clinic. No significant differences in outcomes were found at the county clinic.


The relationship between social determinants and RA disease activity varied significantly across clinic setting with pronounced variation at the university, but not at the county clinic. These disparities may be a result of events that preceded access to subspecialty care, poor adherence, or health care delivery system differences.


  1. Top of page
  2. Abstract

Over the past 2 decades, health outcomes for persons with rheumatoid arthritis (RA) have improved significantly (1) due to the introduction of biologic therapies (2) and aggressive treatment with combination therapy (3). Biologic therapies (e.g., tumor necrosis factor inhibitors) reduce clinical symptoms and radiographic erosions, as well as improve quality of life and function (3–9). Despite this progress, worse outcomes have been observed among certain groups, such as African Americans and Hispanics, relative to their white counterparts, and among those of lower socioeconomic status (SES) (10–13).

Studies from a variety of populations have shown that RA is more prevalent, the burden of disease greater, and the risk of mortality greater among individuals of low SES (10–14). Time to initiate disease-modifying antirheumatic drugs (DMARDs) has been shown to be prolonged for minority populations, as well as for patients in public hospital clinics as compared to private settings (15). Observed variation in practice settings may conflate differences by race/ethnicity, which may be due to socioeconomic factors, language, or immigrant status.

Significant gaps in our knowledge of the disparities in this chronic, disabling condition remain. Specifically, variation in disease activity and function among expanding segments of the US population, such as Asians/Pacific Islanders, Hispanics, immigrants, and those with limited English language proficiency (LEP), is largely unknown. In the present study, we investigated associations of race/ethnicity with disease activity and function in a diverse RA cohort, including a significant proportion of Asians/Pacific Islanders, immigrants, and those with LEP. The diversity of our sample and the inclusion of 2 clinic sites allowed us to take into account a greater range of vulnerable populations, differences in clinical sites (university versus public hospital clinics), and treatment differences that could confound or mediate the relationship between race/ethnicity and health outcomes in RA.

Significance & Innovations

  • In multivariable analyses, lower disease activity and better function was observed among whites with rheumatoid arthritis (RA) compared to nonwhites at the university hospital.

  • This same pattern was observed for disease activity by language (English compared to non-English) and immigrant status (US-born compared to immigrant) at the university clinic.

  • No significant differences in outcomes were found by race/ethnicity, language, or immigrant status at the county clinic.

  • This is the first US study to examine whether variation in disease activity among diverse racial/ethnic groups with RA is moderated by clinic setting.


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  2. Abstract

Data source.

The data source was the University of California, San Francisco (UCSF) RA cohort, a dual-site observational cohort. Beginning in October 2006 (16, 17), existing patients were consecutively enrolled from 2 clinics staffed by UCSF faculty and fellows: the RA clinic at a county hospital and a university-based arthritis clinic. At time of enrollment, patients had to be age ≥18 years and meet the 1987 American College of Rheumatology criteria for RA (18). Enrollment in the cohort is ongoing; as of June 2010, there were 498 patients in the cohort. The research protocol was approved by the UCSF Committee on Human Research.

Data for the cohort were obtained from patients and physicians at the time of each regular clinical visit and integrated with laboratory and radiology test results.

Primary outcome.

The primary outcome was the Disease Activity Score in 28 joints (DAS28), an extensively validated composite measure of disease activity in RA (19, 20). The DAS28 is a continuous scale from 0–9.4 with established cutoffs: low (≤3.2), moderate (>3.2–5.1), or high (>5.1). An absolute disease activity level of ≤3.2 is considered a clinically meaningful goal for therapeutic intervention (21). The European League Against Rheumatism response criteria define an improvement in DAS28 of >1.2 as “good,” and >0.6 and ≤1.2 as “moderate” (22).

The 4 components of the DAS28 are tender joint count (0–28), swollen joint count (0–28), patient global assessment of disease activity (0–100 mm, visual analog scale), and an erythrocyte sedimentation rate (ESR). Physicians recorded joint counts at each visit. The patient completed the visual analog scale for disease activity (0 = no activity and 100 = maximal activity) before each visit in her or his preferred language (English, Spanish, or Chinese). An ESR, measured according to standard Westergren techniques, was drawn at the end of the clinical encounter or within 14 days of the visit. Due to the laboratory component of the score, it was not possible to calculate a DAS28 for each visit. Accordingly, we selected the first visit per patient with a complete DAS28 score. The interval between enrollment into the cohort and the first DAS28 score averaged 5 months, and more than 60% of the scores were obtained within 1 month of enrollment.

Secondary outcome.

At clinic visits, patients completed a self-report measure of function, the Health Assessment Questionnaire (HAQ) (23), which was administered by a bilingual research assistant. HAQ scores range from 0 (no disability) to 3 (severe disability); a minimum clinically important difference is defined as 0.22 (23, 24). The HAQ score was obtained approximately every 6 months. Because HAQ scores are relatively stable over time (25), we took the score closest to, but within 1 year of, the first DAS28 score. All but 26 of the patients had a HAQ score within that time frame.

Primary predictor.

Disparities were first assessed based on self-reported race/ethnicity (Hispanic, Asian/Pacific Islander, African American, and non-Hispanic white). Because of the possible effects of nativity and language on variation in outcomes, we also explored disparities by immigrant status (non–US-born versus US-born) and preferred language (English versus other) in separate models.

Other covariates.

The patient's age, sex, disease duration, rheumatoid factor (RF) status, and clinic site were recorded at enrollment. Education level (less than high school, high school graduate, and any college education) was included as a measure of SES. Medication use as reported by the patient was recorded by the physician in the chart at each visit. Disease-modifying medications were dichotomized into 2 groups: nonbiologic DMARDs (methotrexate, hydroxychloroquine, leflunomide, minocycline, and sulfasalazine) and biologic DMARDs (etanercept, infliximab, adalimumab, rituximab, and abatacept). Corticosteroids and nonsteroidal antiinflammatory drugs were not categorized as DMARDs (26). Corticosteroids were recorded separately, and categorized as none, low dose (<7.5 mg of prednisone or equivalent), or high dose (≥7.5 mg) (27). Medication use was always obtained from the visit with the first DAS28.

Handling missing data.

Except for education, sociodemographic measures were available for all patients. Educational attainment was collected for 364 (73%) of the patients. The only other variables with any missing data were the HAQ (5%) and disease duration (9%). To reduce possible bias and loss of power from using only a subset of the data, we performed multiple imputations (MIs) to estimate nonreported values and their variability. Using the method of Rubin (28) and Schafer (29), each missing value was estimated 20 times from a Bayesian Markov Chain Monte Carlo model; all analyses were then conducted separately on the resulting data sets and combined using the formulae of Rubin (28) to yield the results presented here. The MI model included all variables associated either with the study outcomes or with having missing values; it also included interaction terms for the site with race/ethnicity, language, and immigrant status. Sensitivity analyses including the 330 patients with complete case data showed no substantial differences from the imputed results presented here.

Statistical analysis.

We first examined sample characteristics of the cohort by clinic site. We tested the differences by site, using t-tests for continuous variables and chi-square tests for categorical variables. Additionally, we explored the relationships of race/ethnicity and language with site and medication use, calculating chi-square tests for the difference in nonbiologic and biologic DMARD use by groups defined by site and either race/ethnicity or language.

To examine differences in DAS28, HAQ, and the 4 components of the DAS28 (tender and swollen joint counts, ESR, and patient global status), we estimated bivariable linear regression models including race/ethnicity. Covariates for multivariable regressions included age, sex, disease duration, education level, RF status, and medication use (including biologic and nonbiologic DMARDs and corticosteroids). Because enrollment into the cohort may have coincided with heightened disease activity, the models for DAS28 and its components also included a variable for the number of months between enrollment and the date of the DAS28 score. For each outcome measure, we also estimated bivariable and multivariable regression models in which we replaced race/ethnicity as the primary predictor first with language and, in a separate model, immigrant status. Regression models were examined using standard colinearity and influence diagnostics; no problems were detected. Squared multiple correlation coefficients (R2) ranged from 0.20–0.35 for these models. All statistical analyses were conducted using SAS statistical software, version 9.2.

Exploratory analyses.

We ran 2 additional multivariable models for the DAS28 and HAQ: one adding language and another adding immigrant status to the models with race/ethnicity. The purpose of these exploratory analyses was to examine whether language or immigrant status could explain any variation in disease activity or function by race/ethnicity.

Addressing distributional differences in clinic sites.

Although physicians from the university staff both clinics, the 2 clinic sites differed in populations served, in terms of both sociodemographics and disease status. Additionally, there were significant interactions of clinic site with the sociodemographic effects for the various outcomes under study. Therefore, we included interaction terms for site and the sociodemographic effects for all models and present results for the sociodemographic variables separately for each clinic site.


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  2. Abstract

Most of the 498 patients included in this study were women (84%), with a mean ± SD age of 54 ± 14 years and a mean ± SD disease duration of 10 ± 11 years (Table 1). Patients were almost evenly divided between the 2 clinic sites. Approximately one-third of patients were non-Hispanic white, 34% Hispanic, 23% Asian/Pacific Islander, 10% African American, and 1% American Indian. A majority (56%) were immigrants. Seventy-six percent were RF positive with a mean ± SD DAS28 score of 4.0 ± 1.5 and a mean ± SD HAQ score of 1.2 ± 0.8, signifying moderate disease activity and disability. Sixty-nine percent of the patients were on at least 1 nonbiologic DMARD, 31% were on a biologic DMARD, and 22% were on no DMARD.

Table 1. Sample description for 498 rheumatoid arthritis cohort members by clinic site*
CharacteristicAll patientsHospital clinic patients
County (n = 254)University (n = 244)
  • *

    Values are the number (percentage) unless otherwise indicated. DMARD = disease-modifying antirheumatic drug.

  • P < 0.05 for difference by site.

Age, mean ± SD (range) years54 ± 14 (19–86)52 ± 13 (19–82)56 ± 14 (21–86)
Women419 (84)221 (87)198 (81)
 African American48 (10)26 (10)22 (9)
 Hispanic171 (34)124 (49)47 (19)
 Asian/Pacific Islander117 (23)84 (33)33 (14)
 American Indian4 (1)1 (0)3 (1)
 White, non-Hispanic158 (32)19 (7)139 (57)
 Less than high school graduate158 (32)125 (49)32 (13)
 High school graduate103 (21)65 (26)37 (15)
 Any college education237 (48)63 (25)175 (72)
Immigrant278 (56)204 (80)74 (30)
 English287 (58)75 (30)212 (87)
 Spanish127 (25)107 (32)19 (8)
 Chinese70 (14)61 (24)9 (4)
 Other15 (3)11 (4)4 (2)
Disease duration, mean ± SD (range) years10 ± 11 (0–53)6 ± 11 (0–42)14 ± 18 (0–53)
Rheumatoid factor positive380 (76)209 (82)171 (70)
Medication use   
 Synthetic DMARD345 (69)186 (73)159 (65)
 Biologic agent152 (31)49 (19)103 (42)
 Both DMARD and biologic agent107 (21)40 (16)67 (27)
 Either DMARD or biologic agent390 (78)195 (77)195 (80)
  None198 (40)95 (37)103 (42)
  Low dose (<7.5 mg prednisone or equivalent)192 (39)98 (39)94 (39)
  High dose (≥7.5 mg prednisone or equivalent)108 (22)61 (24)47 (19)

Significant differences were observed between the 2 clinic sites by sociodemographic characteristics, although the university clinic patients were also racially/ethnically diverse (43% nonwhite). There were no significant differences by site in nonbiologic DMARDs (P = 0.05) or corticosteroid use (P = 0.40). In contrast, we observed differences in the use of biologic agents by site (19% at the county clinic compared to 42% at the university clinic; P < 0.001).

To further explore the relationships of sociodemographics, clinic site, and medication use, we compared nonbiologic and biologic DMARD use in groups of patients defined by both the sociodemographic characteristics and site (Table 2). Use of nonbiologic DMARDs did not vary by race/ethnicity or immigrant status within clinic sites, nor were there any substantial differences between the 2 sites when we controlled for either race/ethnicity (P = 0.08) or immigrant status (P = 0.13). Non-English speakers at the university hospital clinic were more likely to receive nonbiologic DMARDs (P = 0.04), a difference that was not apparent at the county hospital clinic (P = 0.96). By contrast, there were pronounced differences in the use of biologic DMARDs at the 2 clinic sites across all sociodemographic groups. Regardless of race/ethnicity, language, or immigrant status, patients at the county hospital were less likely to receive biologic therapies (P < 0.001).

Table 2. Treatments received, by sociodemographic characteristics and clinic site*
CharacteristicNonbiologic DMARD treatmentBiologic DMARD treatment
County hospitalUniversity hospitalCounty hospitalUniversity hospital
  • *

    Values are the percentage (95% confidence interval) unless otherwise indicated. DMARD = disease-modifying antirheumatic drug.

  • By chi-square test.

  • By chi-square test, controlling for sociodemographic characteristics.

 African American77 (60–94)55 (32–77)19 (3–35)32 (11–53)
 Hispanic69 (60–77)72 (59–86)23 (15–30)55 (41–70)
 Asian/Pacific Islander81 (72–90)52 (34–70)13 (6–20)36 (19–54)
 White, non-Hispanic63 (39–87)67 (59–75)21 (1–41)41 (33–49)
 P for within-site differences0.230.160.380.19
 P for between-site differences0.08 < 0.001 
 Immigrant75 (68–81)64 (52–75)19 (14–25)41 (29–52)
 Native68 (55–81)66 (59–73)68 (9–31)43 (35–50)
 P for within-site differences0.360.720.730.90
 P for between-site differences0.13 < 0.001 
 Other language73 (67–80)81 (67–96)18 (13–24)38 (20–55)
 English73 (63–84)63 (56–69)21 (12–31)43 (36–50)
 P for within-site differences0.960.040.610.56
 P for between-site differences0.39 < 0.001 

Sociodemographic and clinic site differences in disease activity.

Observed DAS28 scores were higher (worse) for patients at the county clinic than at the university hospital clinic (mean 4.4 versus 3.5; P < 0.001). Likewise, all nonwhite racial/ethnic groups had significantly higher mean DAS28 scores than whites. However, in a model of the DAS28 as a function of clinic site and race/ethnicity, there was significant interaction between these 2 variables (P = 0.04). Results for bivariable and multivariable models were similar; P values in the text that follows derive from the multivariable models. Significant and clinically meaningful differences in disease activity were seen by race/ethnicity in both bivariable and multivariable analyses at the university hospital clinic (Table 3). A higher mean DAS28 score was observed in all nonwhite ethnic groups compared to whites (P < 0.001). African Americans had an adjusted mean DAS28 score of 4.4 (95% confidence interval [95% CI] 3.8–4.9); Hispanics of 4.0 (95% CI 3.6–4.4), and Asians/Pacific Islanders of 4.2 (95% CI 3.7–4.7), compared to 3.3 (95% CI 3.0–3.6) for whites at the university hospital clinic. Statistically significant differences were also observed in separate models for nativity (P = 0.01) and language (P = 0.01) as reflected by higher mean DAS28 scores for immigrant and non-English language groups in both unadjusted and adjusted analyses at the university hospital clinic. Although observed disease activity, on average, was worse in the county than the university hospital clinic, there were no significant differences in DAS28 scores within the county clinic for any of these models (P = 0.18 for race/ethnicity, P = 0.95 for language, and P = 0.59 for nativity).

Table 3. Measures of disease activity and function by sociodemographic characteristics and clinic, with and without adjustment for covariates*
  • *

    Values are the mean (95% confidence interval) unless otherwise indicated. All results derived from models with an interaction term for site with the given sociodemographic characteristic. DAS28 = Disease Activity Score in 28 joints; HAQ = Health Assessment Questionnaire.

  • Adjusted for age, sex, education, disease duration, rheumatoid factor status, and medication use (including corticosteroids, disease-modifying antirheumatic drugs, and biologic agents).

  • Four subjects of American Indian ethnicity dropped from these models.

All participants4.0 (3.8–4.1) 1.2 (1.2–1.3) 
Model 1: race/ethnicity (n = 494)    
 County hospital clinic    
  African American4.1 (3.6–4.7)4.2 (3.6–4.7)1.4 (1.1–1.7)1.5 (1.2–1.8)
  Hispanic4.6 (4.4–4.9)4.3 (4.1–4.6)1.3 (1.2–1.5)1.3 (1.1–1.5)
  Asian/Pacific Islander4.3 (4.0–4.7)4.2 (3.9–4.5)1.3 (1.1–1.4)1.2 (1.1–1.4)
  White, non-Hispanic4.1 (3.4–4.7)4.1 (3.4–4.7)1.0 (0.7–1.4)1.1 (0.7–1.5)
  P for within-site differences0.180.470.490.41
 University hospital clinic    
  African American4.2 (3.6–4.8)4.4 (3.8–4.9)1.5 (1.1–1.8)1.5 (1.2–1.8)
  Hispanic4.1 (3.7–4.5)4.0 (3.6–4.4)1.5 (1.3–1.8)1.5 (1.2–1.7)
  Asian/Pacific Islander4.2 (3.7–4.6)4.2 (3.7–4.7)1.1 (0.9–1.4)1.1 (0.8–1.4)
  White, non-Hispanic3.0 (2.8–3.3)3.3 (3.0–3.6)1.0 (0.9–1.1)1.0 (0.9–1.2)
  P for within-site differences< 0.01< 0.01< 0.01< 0.01
  P for interaction term0.
Model 2: nativity    
 County hospital clinic    
  Immigrant4.5 (4.3–4.7)4.2 (4.0–4.5)1.3 (1.2–1.4)1.2 (1.1–1.4)
  Native4.2 (3.8–4.6)4.2 (3.9–4.6)1.3 (1.1–1.5)1.4 (1.2–1.6)
  P for within-site differences0.150.590.940.25
 University hospital clinic    
  Immigrant4.1 (3.8–4.4)4.0 (3.7–4.4)1.4 (1.2–1.6)1.3 (1.1–1.4)
  Native3.2 (3.0–3.5)3.6 (3.3–3.8)1.1 (0.9–1.2)1.2 (1.0–1.3)
  P for within-site differences< 0.010.01< 0.010.32
  P for interaction term0.
Model 3: language preference    
 County hospital clinic    
  Other language4.5 (4.3–4.7)4.2 (4.0–4.5)1.3 (1.2–1.4)1.2 (1.1–1.4)
  English4.2 (3.9–4.6)4.3 (4.0–4.6)1.3 (1.1–1.5)1.4 (1.2–1.6)
  P for within-site differences0.620.950.150.15
 University hospital clinic    
  Other language4.5 (4.0–5.0)4.2 (3.7–4.8)1.5 (1.2–1.7)1.2 (0.9–1.5)
  English3.3 (3.1–3.5)3.6 (3.4–3.8)1.1 (1.1–1.2)1.2 (1.1–1.3)
  P for within-site differences<
  P for interaction term< 0.01<

In the multivariable model of DAS28, not graduating from high school and high-dose corticosteroid use were both associated with higher disease activity, while both biologic and synthetic DMARD use was associated with lower disease activity. None of the other variables, including age, sex, disease duration, RF status, and time from enrollment to first DAS28 measurement, were associated with disease activity. The adjusted R2 for this model was 0.25.


At the university clinic, African Americans and Hispanics had clinically and statistically significant poorer function (0.5 point higher mean HAQ scores) when compared to non-Hispanic whites (P < 0.001) (Table 3). A similar but nonsignificant pattern was seen for the county hospital clinic (P = 0.49). Among university clinic patients, immigrants had significantly poorer function (1.4 [95% CI 1.2–1.6]) than US-born subjects (1.1 [95% CI 0.9–1.2]), but this difference was attenuated and no longer significant in the adjusted model (P = 0.81).

With respect to the individual DAS28 components (Table 4), the patient global assessment varied significantly by race/ethnicity with higher adjusted mean scores (worse disease activity) for African Americans compared to non-Hispanic whites at the university clinic (P = 0.001). We observed significant differences in adjusted analyses for ESR with mean ESR between 30–41 mm/hour for nonwhite ethnic groups compared to 23 mm/hour for non-Hispanic whites at the university clinic (P = 0.001); a similar but less pronounced pattern (P = 0.049) was seen at the county clinic. No significant differences were seen in patient global assessment or ESR for nativity or language. No significant differences were observed for joint counts at either clinic site (P = 0.58 for the university clinic, P = 0.09 for the county).

Table 4. Components of DAS28 by sociodemographic characteristics and clinic, controlling for covariates*
CharacteristicPatient global assessment (0–100)Erythrocyte sedimentation rateTender joint count (0–28)Swollen joint count (0–28)
  • *

    Values are the mean (95% confidence interval) unless otherwise indicated. Adjusted for age, sex, education, disease duration, rheumatoid factor status, and medication use (including corticosteroids, disease-modifying antirheumatic drugs, and biologic agents). DAS28 = Disease Activity Score in 28 joints.

  • Four subjects of American Indian ethnicity dropped from these models.

All participants45 (42–47)30 (28–32)4 (3–4)4 (4–5)
Race/ethnicity (n = 494)    
 County hospital clinic    
  African American51 (42–47)32 (23–41)3 (1–5)5 (3–7)
  Hispanic48 (42–47)34 (29–39)5 (4–6)5 (4–6)
  Asian/Pacific Islander47 (42–47)34 (29–40)4 (3–5)5 (4–6)
  White, non-Hispanic45 (42–47)18 (8–29)5 (2–7)5 (3–7)
  P for within-site differences0.770.0490.090.98
 University hospital clinic    
  African American56 (46–67)41 (31–50)4 (2–6)4 (2–6)
  Hispanic43 (36–51)30 (23–37)4 (3–6)4 (3–5)
  Asian/Pacific Islander49 (40–58)36 (28–44)3 (2–5)5 (3–6)
  White, non-Hispanic36 (31–41)23 (18–28)3 (2–4)4 (3–5)
  P for within-site differences< 0.01< 0.010.580.71
  P for interaction term0.170.300.760.46
 County hospital clinic    
  Immigrant47 (43–52)33 (29–36)4 (4–5)5 (4–6)
  Native47 (40–55)31 (24–38)4 (2–5)6 (4–7)
  P for within-site differences0.600.460.180.87
 University hospital clinic    
  Immigrant43 (37–49)33 (27–38)4 (2–5)4 (3–5)
  Native41 (36–46)26 (22–30)3 (2–4)4 (3–5)
  P for within-site differences0.450.030.410.28
  P for interaction term0.140.120.920.68
Language preference    
 County hospital clinic    
  Other language45 (41–50)33 (28–37)4 (4–5)5 (4–6)
  English51 (45–57)30 (25–36)4 (3–5)5 (4–6)
  P for within-site differences0.310.330.190.99
 University hospital clinic    
  Other language42 (33–52)28 (19–36)5 (3–6)5 (4–7)
  English42 (38–46)28 (24–32)3 (2–4)4 (3–4)
  P for within-site differences0.770.990.070.02
  P for interaction term0.020.660.100.08

Exploratory analyses.

To explore whether language or immigrant status explained any of the race/ethnic disparities in disease activity or function, we fit separate models that added each of these variables to the multivariable models already containing race/ethnicity. For both the DAS28 and the HAQ, there was no independent significant effect of either language or immigrant status, nor did the addition of either variable modify (i.e., confound) the associations of race/ethnicity with the outcomes. Therefore, neither language nor nativity was a mediator of the relationship between race/ethnicity and the DAS28 or HAQ.


  1. Top of page
  2. Abstract

In this study of an ethnically diverse population of 498 adults with RA, we found significant variation by race/ethnicity in disease activity and function. Most striking, the relationship between race/ethnicity and RA outcomes differed between 2 clinic settings, with significant racial and ethnic disparities in disease activity and function observed only at the university hospital clinic. Whites were observed to have less disease activity and better function than nonwhites, and English speakers compared to non-English speakers, as well as US-born patients compared to immigrants, also had less disease activity at the university hospital clinic; these differences were not observed at the county clinic.

This is the first US study to examine whether variation in disease activity among diverse racial/ethnic groups with RA is moderated by clinic setting. Our study population included a significant number of subjects of Asian/Pacific Islander ethnicity, and with non-English proficiency and immigrant status. The fact that clinically and statistically significant disparities in outcomes persisted even after adjusting for medication use indicates that variation in current treatment, an important indicator of quality of care, did not explain these differences.

Possible mechanisms for the clinic-level differences in outcomes observed in our sample include differences in patient characteristics and preceding events (e.g., residual effects of low SES, communication barriers such as health literacy and LEP, or genetic/biologic differences), patient behavior (e.g., adherence or preferences for medication), or delivery system structure (e.g., variation in time to initial rheumatology care and access to treatment). Research in other chronic diseases indicates that LEP can contribute to health disparities (30, 31), and ours is the first study to demonstrate this in RA. Of note, the addition of language to the race/ethnicity models in this study had no effect on the race/ethnicity outcomes associations, suggesting that language does not substantially explain the racial and ethnic disparities observed in our study. While there is significant language diversity in our cohort, the county hospital clinic is staffed with full-time in-person interpreters, which may substantially mitigate the contribution of language to disparities at that clinic site. Given the small numbers of non-Hispanic whites as well as the small numbers of English-speaking Hispanics or Asian/Pacific Islanders, our analysis may have been underpowered to distinguish between the effects of language and race/ethnicity at the county hospital.

Significant progress in the field of genetic epidemiology has revealed ethnic differences in the genetic predisposition to RA between persons of European and Asian ancestries (32). While genetics may be one mechanism by which variation in outcomes occur, it is unlikely to be the sole factor (33). Racial/ethnic differences in disease activity within the university setting, but not in the public hospital, do not support a genetic or biologic basis for observed racial/ethnic differences.

Patient preference is another possible mechanism by which disparities may occur (34, 35). In the 2003 report, Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care, the Institute of Medicine notes that patient preferences based on inaccurate understanding may be a source of racial disparity in care (36). Constantinescu et al found that African Americans with RA were more risk averse than whites (35). The difference in rates of biologic use across settings may also be a reflection of insurance coverage for biologic agents, a patient's ability to pay, or of sociodemographic differences within all racial/ethnic groups between patients at the university and county hospital clinics, such that university hospital clinic patients have increased access to subspecialty care and fewer barriers to obtaining biologic agents (e.g., lower rates of latent tuberculosis). Despite the fact that we found large differences in rates of biologic agents across clinic sites (more than twice the rate of use for all subgroups at the university hospital compared to the county clinic), differences in outcomes persisted in multivariable models controlling for current treatment, indicating that variation in preferences or access cannot fully explain the differences in RA outcomes.

Our study parallels findings from the UK where patients from socially deprived areas and those with lower individual-level SES had higher disease activity and poorer function (11, 37, 38). In a US study, Bruce et al observed worse function, pain, and global health among Hispanics and African Americans compared to non-Hispanic whites but only found statistically significant differences in pain after adjusting for age, sex, education, disease duration, comorbidities, and DMARDs (13). In contrast to our study, that sample was predominantly white and patients were cared for in numerous community and university clinics. A study by Yazici et al examined racial and ethnic differences in baseline clinical status measures in 118 DMARD-naive subjects with early RA (<3 years) at 1 site (14); Hispanics had statistically significantly higher (worse) HAQ scores, longer morning stiffness, and higher psychological distress scores compared to African Americans and whites adjusting for age and disease duration. While our study found racial/ethnic differences in patient-reported measures of function and patient global assessments similar to the study by Yazici et al, we also saw differences in the ESR and the DAS28 scores. Therefore, we conclude that racial/ethnic differences are observed in “objective” outcomes beyond those that are patient-reported. Prior examples of racial/ethnic differences in ESR have been reported. Del Rincon et al found higher ESR levels in Hispanic and African American RA patients compared to non-Hispanic whites from multiple clinical sites in Texas (33).

Our study has several limitations. The cross-sectional design does not allow inferences regarding causation. If minority RA patients with milder disease tend not to seek or receive subspecialty care, this could lead to an overestimation of disparities. By contrast, the public hospital clinic we studied is staffed with interpreters and routinely utilizes drug assistance programs for biologic therapies, which may mitigate disparities that could occur in other settings. Our covariates did not include potential confounders related to SES, including prior access to care, insurance status, and income. However, our main measure of SES was education level, previously shown to be a more reliable measure of long-term SES since it remains relatively constant throughout adult life (39). Given the main finding of our study, that whites at the university hospital clinic had significantly lower disease activity and better function than nonwhites, this may be explained in part by higher educational attainment among whites at the university. The pattern of educational attainment by race/ethnicity was the same at both clinic sites with whites having higher educational levels than other race/ethnic groups. We therefore performed additional analyses that categorized education into 4 levels rather than 3, and then further subdivided the top category of bachelor's degree or higher into college graduate and postgraduate education. The results presented herein were not affected by either change. Due to the limited number of non-Hispanic white subjects at the county hospital (n = 19), this study may have been underpowered to detect a true difference between the racial and ethnic groups at the county hospital. It should be noted, however, that the relationship between the other sociodemographic variables of language and immigrant status (which included greater numbers of subjects in each category) with disease activity and function at the county hospital were also not significant, which is consistent with the race/ethnicity finding. Another potential limitation is that given the cross-sectional design, we are unable to account for variation in disease activity among patients over time (early, potentially untreated or undertreated versus later disease, better controlled) and whether that may differ based on the length of time treated at either clinic. However, disease duration is included in the multivariable models, and descriptively, the majority of patients had longer disease duration with only 15% having less than 12 months and 50% with disease duration greater than 7 years. Lastly, we cannot determine whether disparities resulted from problems with delays in diagnosis or initial treatment, access, or self-management (e.g., adherence).

By including a diverse sample of patients who received care from a uniform set of university-employed rheumatologists, this study confirms prior research by demonstrating that racial/ethnic disparities exist in patient-reported and “objective” outcomes (e.g., inflammatory markers). It also extends prior research by demonstrating that disparities exist for Asians/Pacific Islanders as well as for immigrants and non-English speakers, and that clinic setting/context can influence overall disease severity and modify the relationships between race/ethnicity and RA outcomes. The next steps will be to explore whether these across-clinic differences occur as a result of differences in recognition, access, and quality of care in early disease, from the stress associated with disadvantaged circumstances, from challenges in self-management or communication barriers once specialty care is underway, or from some combination of these factors. An additional challenge is to discover how characteristics of patients, providers, and local policies associated with different health care systems can affect outcomes and either contribute to or eliminate disparities.


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  2. Abstract

All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be submitted for publication. Dr. Barton 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 conception and design. Barton, Trupin, Schillinger, Imboden, Yelin.

Acquisition of data. Barton, Schillinger, Margaretten, Chernitskiy, Graf, Imboden, Yelin.

Analysis and interpretation of data. Barton, Trupin, Schillinger, Gansky, Tonner, Imboden, Yelin.


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  2. Abstract
  • 1
    Uhlig T, Kvien TK. Is rheumatoid arthritis really getting less severe? Nat Rev Rheumatol 2009; 5: 4614.
  • 2
    Kekow J, Moots RJ, Emery P, Durez P, Koenig A, Singh A, et al. Patient-reported outcomes improve with etanercept plus methotrexate in active early rheumatoid arthritis and the improvement is strongly associated with remission: the COMET trial. Ann Rheum Dis 2010; 69: 2225.
  • 3
    Donahue KE, Gartlehner G, Jonas DE, Lux LJ, Thieda P, Jonas BL, et al. Systematic review: comparative effectiveness and harms of disease-modifying medications for rheumatoid arthritis. Ann Intern Med 2008; 148: 12434.
  • 4
    Kavanaugh A, Cohen S, Cush JJ. The evolving use of tumor necrosis factor inhibitors in rheumatoid arthritis. J Rheumatol 2004; 31: 18814.
  • 5
    Bathon JM, Martin RW, Fleischmann RM, Tesser JR, Schiff MH, Keystone EC, et al. A comparison of etanercept and methotrexate in patients with early rheumatoid arthritis. New Engl J Med 2000; 343: 158693.
  • 6
    Breedveld FC, Weisman MH, Kavanaugh AF, Cohen SB, Pavelka K, van Vollenhoven R, et al. The PREMIER study: a multicenter, randomized, double-blind clinical trial of combination therapy with adalimumab plus methotrexate versus methotrexate alone or adalimumab alone in patients with early, aggressive rheumatoid arthritis who had not had previous methotrexate treatment. Arthritis Rheum 2006; 54: 2637.
  • 7
    Klareskog L, van der Heijde D, de Jager JP, Gough A, Kalden J, Malaise M, et al. Therapeutic effect of the combination of etanercept and methotrexate compared with each treatment alone in patients with rheumatoid arthritis: double-blind randomised controlled trial. Lancet 2004; 363: 67581.
  • 8
    Van der Heijde D, Klareskog L, Rodriguez-Valverde V, Codreanu C, Bolosiu H, Melo-Gomes J, et al. Comparison of etanercept and methotrexate, alone and combined, in the treatment of rheumatoid arthritis: two-year clinical and radiographic results from the TEMPO study, a double-blind, randomized trial. Arthritis Rheum 2006; 54: 106374.
  • 9
    Van der Heijde D, Klareskog L, Singh A, Tornero J, Melo-Gomes J, Codreanu C, et al. Patient reported outcomes in a trial of combination therapy with etanercept and methotrexate for rheumatoid arthritis: the TEMPO trial. Ann Rheum Dis 2006; 65: 32834.
  • 10
    Jacobi CE, Mol GD, Boshuizen HC, Rupp I, Dinant HJ, van den Bos GA. Impact of socioeconomic status on the course of rheumatoid arthritis and on related use of health care services. Arthritis Rheum 2003; 49: 56773.
  • 11
    ERAS Study Group. Socioeconomic deprivation and rheumatoid disease: what lessons for the health service? Early Rheumatoid Arthritis Study. Ann Rheum Dis 2000; 59: 7949.
  • 12
    Marra CA, Lynd LD, Esdaile JM, Kopec J, Anis AH. The impact of low family income on self-reported health outcomes in patients with rheumatoid arthritis within a publicly funded health-care environment. Rheumatology (Oxford) 2004; 43: 13907.
  • 13
    Bruce B, Fries JF, Murtagh KN. Health status disparities in ethnic minority patients with rheumatoid arthritis: a cross-sectional study. J Rheumatol 2007; 34: 14759.
  • 14
    Yazici Y, Kautiainen H, Sokka T. Differences in clinical status measures in different ethnic/racial groups with early rheumatoid arthritis: implications for interpretation of clinical trial data. J Rheumatol 2007; 34: 3115.
  • 15
    Suarez-Almazor ME, Berrios-Rivera JP, Cox V, Janssen NM, Marcus DM, Sessoms S. Initiation of disease-modifying antirheumatic drug therapy in minority and disadvantaged patients with rheumatoid arthritis. J Rheumatol 2007; 34: 24007.
  • 16
    Margaretten M, Yelin E, Imboden J, Graf J, Barton J, Katz P, et al. Predictors of depression in a multiethnic cohort of patients with rheumatoid arthritis. Arthritis Rheum 2009; 61: 158691.
  • 17
    Graf J, Scherzer R, Grunfeld C, Imboden J. Levels of C-reactive protein associated with high and very high cardiovascular risk are prevalent in patients with rheumatoid arthritis. PLoS One 2009; 4: e6242.
  • 18
    Arnett FC, Edworthy SM, Bloch DA, McShane DJ, Fries JF, Cooper NS, et al. The American Rheumatism Association 1987 revised criteria for the classification of rheumatoid arthritis. Arthritis Rheum 1988; 31: 31524.
  • 19
    Fransen J, van Riel PL. The Disease Activity Score and the EULAR response criteria. Rheum Dis Clin North Am 2009; 35: 74557, vii–viii.
  • 20
    Prevoo ML, van 't Hof MA, Kuper HH, van Leeuwen MA, van de Putte LB, van Riel PL. Modified disease activity scores that include twenty-eight–joint counts: development and validation in a prospective longitudinal study of patients with rheumatoid arthritis. Arthritis Rheum 1995; 38: 448.
  • 21
    Wells G, Becker JC, Teng J, Dougados M, Schiff M, Smolen J, et al. Validation of the 28-joint Disease Activity Score (DAS28) and European League Against Rheumatism response criteria based on C-reactive protein against disease progression in patients with rheumatoid arthritis, and comparison with the DAS28 based on erythrocyte sedimentation rate. Ann Rheum Dis 2009; 68: 95460.
  • 22
    Van Gestel AM, Prevoo ML, van 't Hof MA, van Rijswijk MH, van de Putte LB, van Riel PL. Development and validation of the European League Against Rheumatism response criteria for rheumatoid arthritis: comparison with the preliminary American College of Rheumatology and the World Health Organization/International League Against Rheumatism Criteria. Arthritis Rheum 1996; 39: 3440.
  • 23
    Bruce B, Fries JF. The Stanford Health Assessment Questionnaire: a review of its history, issues, progress, and documentation. J Rheumatol 2003; 30: 16778.
  • 24
    Wells GA, Tugwell P, Kraag GR, Baker PR, Groh J, Redelmeier DA. Minimum important difference between patients with rheumatoid arthritis: the patient's perspective. J Rheumatol 1993; 20: 55760.
  • 25
    Sokka T, Kautiainen H, Hannonen P, Pincus T. Changes in Health Assessment Questionnaire disability scores over five years in patients with rheumatoid arthritis compared with the general population. Arthritis Rheum 2006; 54: 31138.
  • 26
    Saag KG, Teng GG, Patkar NM, Anuntiyo J, Finney C, Curtis JR, et al. American College of Rheumatology 2008 recommendations for the use of nonbiologic and biologic disease-modifying antirheumatic drugs in rheumatoid arthritis. Arthritis Rheum 2008; 59: 76284.
  • 27
    Van der Hooft CS, Heeringa J, Brusselle GG, Hofman A, Witteman JC, Kingma JH, et al. Corticosteroids and the risk of atrial fibrillation. Arch Intern Med 2006; 166: 101620.
  • 28
    Rubin D. Multiple imputation for non-response in surveys. New York: John Wiley & Sons; 1987.
  • 29
    Schafer JL. Analysis of incomplete multivariate data. London: Chapman & Hall; 1997.
  • 30
    Lopez-Quintero C, Berry EM, Neumark Y. Limited English proficiency is a barrier to receipt of advice about physical activity and diet among Hispanics with chronic diseases in the United States. J Am Diet Assoc 2009; 109: 176974.
  • 31
    Wisnivesky JP, Kattan M, Evans D, Leventhal H, Musumeci-Szabo TJ, McGinn T, et al. Assessing the relationship between language proficiency and asthma morbidity among inner-city asthmatics. Med Care 2009; 47: 2439.
  • 32
    Kochi Y, Suzuki A, Yamada R, Yamamoto K. Genetics of rheumatoid arthritis: underlying evidence of ethnic differences. J Autoimmun 2009; 32: 15862.
  • 33
    Del Rincon I, Battafarano DF, Arroyo RA, Murphy FT, Fischbach M, Escalante A. Ethnic variation in the clinical manifestations of rheumatoid arthritis: role of HLA–DRB1 alleles. Arthritis Rheum 2003; 49: 2008.
  • 34
    Constantinescu F, Goucher S, Weinstein A, Fraenkel L. Racial disparities in treatment preferences for rheumatoid arthritis. Med Care 2009; 47: 3505.
  • 35
    Constantinescu F, Goucher S, Weinstein A, Smith W, Fraenkel L. Understanding why rheumatoid arthritis patient treatment preferences differ by race. Arthritis Rheum 2009; 61: 4138.
  • 36
    Smedley BD, Stith AY, Nelson AR, editors. Unequal treatment: confronting racial and ethnic disparities in health care. Washington (DC): Institute of Medicine; 2003.
  • 37
    Harrison MJ, Tricker KJ, Davies L, Hassell A, Dawes P, Scott DL, et al. The relationship between social deprivation, disease outcome measures, and response to treatment in patients with stable, long-standing rheumatoid arthritis. J Rheumatol 2005; 32: 23306.
  • 38
    Harrison MJ, Farragher TM, Clarke AM, Manning SC, Bunn DK, Symmons DP. Association of functional outcome with both personal- and area-level socioeconomic inequalities in patients with inflammatory polyarthritis. Arthritis Rheum 2009; 61: 1297304.
  • 39
    Galobardes B, Shaw M, Lawlor DA, Lynch JW, Davey Smith G. Indicators of socioeconomic position (part 2). J Epidemiol Community Health 2006; 60: 95101.