Dataset Information

Racial Differences in Multimorbidity and Comorbidities in Rheumatoid Arthritis.

ABSTRACT:

Objective

To identify differences in multimorbidity and individual comorbidities among individuals with rheumatoid arthritis (RA), separated by race and ethnicity.

Methods

This case-control study within OptumLabs Data Warehouse from 2010 to 2019 matched RA cases (defined by 2 codes plus prescription of an RA drug) to non-RA controls 1:1 on age, sex, race and ethnicity, region, index date of RA, and insurance coverage duration. We defined multimorbidity as the presence of ≥2 or ≥5 validated comorbidities. Logistic regression models calculated adjusted odds of multimorbidity with 95% confidence intervals (95% CIs) within each race and ethnicity.

Results

We identified 154,391 RA cases and 154,391 controls (mean age 59.6, 76% female). Black enrollees had the most multimorbidity ≥2/≥5 (73.1%, 34.3%); Asian enrollees had the least (52.4%, 17.3%). Adjusted odds of multimorbidity ≥2 and ≥5 in RA cases versus controls was 2.19 (95% CI 2.16-2.23) and 2.06 (95% CI 2.02-2.09), respectively. This increase was similar across race and ethnicity. However, we observed elevated occurrence of certain comorbidities by race and ethnicity versus controls (P < 0.001), including renal disease in White enrollees (4.7% versus 3.2%) and valvular heart disease in Black and White enrollees (3.2% and 2.8% versus 2.6% and 2.2%).

Conclusion

Multimorbidity is a problem for all RA patients. Targeted identification of certain comorbidities by race and ethnicity may be a helpful approach to mitigate multimorbidity.

SUBMITTER: Kronzer VL

PROVIDER: S-EPMC9797440 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Racial Differences in Multimorbidity and Comorbidities in Rheumatoid Arthritis.

Kronzer Vanessa L VL Dykhoff Hayley J HJ Stevens Maria A MA Myasoedova Elena E Davis John M JM Crowson Cynthia S CS

Arthritis care & research 20221031 1

<h4>Objective</h4>To identify differences in multimorbidity and individual comorbidities among individuals with rheumatoid arthritis (RA), separated by race and ethnicity.<h4>Methods</h4>This case-control study within OptumLabs Data Warehouse from 2010 to 2019 matched RA cases (defined by 2 codes plus prescription of an RA drug) to non-RA controls 1:1 on age, sex, race and ethnicity, region, index date of RA, and insurance coverage duration. We defined multimorbidity as the presence of ≥2 or ≥5 ...[more]

PMID: 36094853

Similar Datasets

Project description:ObjectiveRacial and ethnic disparities in rheumatoid arthritis (RA) disease activity measures have been documented. We compared racial and ethnic differences in disease activity using multiple composite measures, including an objective measure, the multi-biochemical disease activity (MBDA) score.MethodsData are derived from the University of California, San Francisco RA Cohort, a longitudinal observational cohort. Participants with at least one MBDA measure and self-reported race and ethnicity were included. Multivariable linear regression evaluated the association between race and ethnicity groups and mean MBDA score, adjusting for potential confounders, including symptom duration and medication use. Sensitivity analyses substituted the Clinical Disease Activity Index (CDAI) and the Disease Activity Score-28 joints with erythrocyte sedimentation rate (DAS28-ESR) for the MBDA in multivariable models.ResultsWe included 267 participants (86% female, mean age 52.7 ± 13.3 years). The majority were Latinx (n = 137; 51%), followed by Asian (n = 91; 34%). After adjustment, Latinx participants had the highest mean MBDA score (40.6 ± 2.1) compared with White participants at (32.8 ± 6.7). Black participants had the second highest mean MBDA score, followed by Asian participants (36.3 ± 5.3, 36.0 ± 2.7, respectively), although neither were significantly different from White participants. The trends observed for the CDAI and DAS28-ESR were similar to those for the MBDA.ConclusionWe found significantly higher disease activity measured by the MBDA and DAS28-ESR in Latinx participants compared with White participants. We also found significantly higher disease activity in Asian participants compared with White participants with the DAS28-ESR. Our findings, although limited by the small number of White participants in the referent group, suggest that RA disease activity measures may be influenced by external factors that have differential impacts by racial and ethnic group.

Project description:ObjectiveTo determine racial/ethnic differences in the use of conventional synthetic or biologic disease-modifying anti-rheumatic drugs (csDMARDs or bDMARDs, respectively) and long-term glucocorticoids (GC) or opioids among beneficiaries of the Social Security Disability Insurance (SSDI) with rheumatoid arthritis (RA) and <65 years old.MethodsSerial cross-sectional analyses of Centers for Medicare and Medicaid Services claims data (2007, 2011, and 2014) for individuals <65 years old with RA receiving SSDI Medicare and Medicaid, no longer working because they were considered disabled. Generalized estimating equation models were used to determine whether the proportion of patients who used csDMARD, bDMARD, long-term GC, and long-term opioids differed by race/ethnicity.ResultsThere were 12,931; 15,033; and 15,599 participants in 2007, 2011, and 2014, respectively. The overall use of csDMARD without bDMARD among beneficiaries of the SSDI were 31.1%, 30.3%, and 29.2%; 50.2%, 51.7%, and 53.8% used bDMARDs; 37.6%, 36.1%, and 34.4% used long-term GC; and 61.1%, 63.8%, and 63.7% used long-term opioids in years 2007, 2011, and 2014 respectively. The use of csDMARDs without bDMARDs was higher and the use of bDMARDs was lower among Blacks compared to Whites (adjusted absolute difference: +3.0%, +5.0%, and +3.3% for csDMARDs without bDMARDs and -4.6%, -5.7%, and -4.0% for bDMARDs in 2007, 2011, and 2014, respectively; all p<0.05). The use of bDMARDs was higher among Hispanics compared to Whites (adjusted absolute difference: +7.1%, +7.3%, and +7.5% in 2007, 2011, and 2014, respectively; all p<0.05). Long-term GC use was lower among Hispanics than among Whites only in year 2014 (absolute percentage point difference of -4.2%); no other difference in long-term GC use was identified. Whites were the patients with the highest use of long-term opioids (more than two third in each calendar year).ConclusionRacial and ethnic differences exists in regards to the treatment of RA among beneficiaries of the SSDI. These findings suggest that this already vulnerable population of patients with RA can also have a racial and ethnic disparity that can contribute to additional disease burden and that should be examined in order to inform future interventions or even inform future policy changes to the SSDI.

Project description:BackgroundComorbid conditions are very common in rheumatoid arthritis (RA) and several prior studies have clustered them using machine learning (ML). We applied various ML algorithms to compare the clusters of comorbidities derived and to assess the value of the clusters for predicting future clinical outcomes.MethodsA large US-based RA registry, CorEvitas, was used to identify patients for the analysis. We assessed the presence of 24 comorbidities, and ML was used to derive clusters of patients with given comorbidities. K-mode, K-mean, regression-based, and hierarchical clustering were used. To assess the value of these clusters, we compared clusters across different ML algorithms in clinical outcome models predicting clinical disease activity index (CDAI) and health assessment questionnaire (HAQ-DI). We used data from the first 3 years of the 6-year study period to derive clusters and assess time-averaged values for CDAI and HAQ-DI during the latter 3 years. Model fit was assessed via adjusted R2 and root mean square error for a series of models that included clusters from ML clustering and each of the 24 comorbidities separately.Results11,883 patients with RA were included who had longitudinal data over 6 years. At baseline, patients were on average 59 (SD 12) years of age, 77% were women, CDAI was 11.3 (SD 11.9, moderate disease activity), HAQ-DI was 0.32 (SD 0.42), and disease duration was 10.8 (SD 9.9) years. During the 6 years of follow-up, the percentage of patients with various comorbidities increased. Using five clusters produced by each of the ML algorithms, multivariable regression models with time-averaged CDAI as an outcome found that the ML-derived comorbidity clusters produced similarly strong models as models with each of the 24 separate comorbidities entered individually. The same patterns were observed for HAQ-DI.ConclusionsClustering comorbidities using ML algorithms is not computationally complex but often results in clusters that are difficult to interpret from a clinical standpoint. While ML clustering is useful for modeling multi-omics, using clusters to predict clinical outcomes produces models with a similar fit as those with individual comorbidities.

Dataset Information

Racial Differences in Multimorbidity and Comorbidities in Rheumatoid Arthritis.

Objective

Methods

Results

Conclusion

Publications

Racial Differences in Multimorbidity and Comorbidities in Rheumatoid Arthritis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets