Epidemiological studies in incidence, prevalence, mortality, and comorbidity of the rheumatic diseases

Epidemiology is the study of the distribution and determinants of disease in human populations. Over the past decade there has been considerable progress in our understanding of the fundamental descriptive epidemiology (levels of disease frequency: incidence and prevalence, comorbidity, mortality, trends over time, geographic distributions, and clinical characteristics) of the rheumatic diseases. This progress is reviewed for the following major rheumatic diseases: rheumatoid arthritis (RA), juvenile rheumatoid arthritis, psoriatic arthritis, osteoarthritis, systemic lupus erythematosus, giant cell arteritis, polymyalgia rheumatica, gout, Sjögren's syndrome, and ankylosing spondylitis. These findings demonstrate the dynamic nature of the incidence and prevalence of these conditions – a reflection of the impact of genetic and environmental factors. The past decade has also brought new insights regarding the comorbidity associated with rheumatic diseases. Strong evidence now shows that persons with RA are at a high risk for developing several comorbid disorders, that these conditions may have atypical features and thus may be difficult to diagnose, and that persons with RA experience poorer outcomes after comorbidity compared with the general population. Taken together, these findings underscore the complexity of the rheumatic diseases and highlight the key role of epidemiological research in understanding these intriguing conditions

Patterns of Tumor Necrosis Factor Inhibitor (TNFi) Biosimilar Use Across United States Rheumatology Practices.

ObjectiveIt is unclear if biosimilars of biologics for inflammatory arthritis are realizing their promise to increase competition and improve accessibility. This study evaluates biosimilar tumor necrosis factor inhibitor (TNFi) utilization across rheumatology practices in the United States and compares whether patients initiating biosimilars remain on these treatments at least as long as new initiators of bio-originators.MethodsWe identified a cohort of patients initiating a TNFi biosimilar between January 2017 and September 2018 from an electronic health record registry containing data from 218 rheumatology practices and over 1 million rheumatology patients in the United States. We also identified a cohort of patients who initiated the bio-originator TNFi during the same period. We calculated the proportion of biosimilar prescriptions compared with other TNFi's and compared persistence on these therapies, adjusting for age, sex, diagnoses codes, and insurance type.ResultsWe identified 909 patients prescribed the biosimilar infliximab-dyyb, the only biosimilar prescribed, and 4413 patients with a new prescription for the bio-originator infliximab. Biosimilar patients tended to be older, have a diagnosis code for rheumatoid arthritis, and covered by Medicare insurance. Over the study period, biosimilar prescriptions reached a maximum of 3.5% of all TNFi prescriptions. Patients persisted on the biosimilar at least as long as the bio-originator infliximab (hazard ratio [HR] 0.83, P = 0.07).ConclusionThe uptake of biosimilars in the United States remains low despite persistence on infliximab-dyyb being similar to the infliximab bio-originator. These results add to clinical studies that should provide greater confidence to patients and physicians regarding biosimilar use

Updating and Validating the Rheumatic Disease Comorbidity Index to ICD-10-CM

Background/Objective: Comorbidities can contribute to increased risk for mortality and disability in individuals with rheumatoid arthritis (RA)1,2. The Rheumatic Disease Comorbidity Index (RDCI) assesses 11 comorbidities and produces a weighted score (0-9) that accurately predicts several health outcomes3. The RDCI was developed with self-report data and later validated with ICD-9-CM codes collected from administrative data3,4. On October 1, 2015, the U.S. transitioned to ICD-10-CM, resulting in a nearly five-fold increase in the number of codes available to classify conditions5. Our objective was to update the RDCI by translating it into ICD-10-CM. Methods: We defined an ICD-9-CM cohort and an ICD-10-CM cohort using patient data from the Veterans Affairs Rheumatoid Arthritis Registry (VARA). ICD-10-CM codes were generated by converting ICD-9-CM codes using tools that provide suggested crosswalks, and the codes were reviewed by a physician to assess clinical relevance. Comorbidities were collected from national VA administrative data over a two-year period in both cohorts (ICD-9-CM: October 1, 2013 to September 30, 2015; ICD-10-CM: January 1, 2016 to December 31, 2017). Comorbidity frequencies were compared using Cohen’s Kappa, and RDCI scores were compared using Intraclass Correlation Coefficients (ICC). Results: Both the ICD-9-CM cohort (n=1,082) and ICD-10-CM cohort (n=1,446) were predominantly male (ICD-9-CM: 89%; ICD-10-CM: 87%), Caucasian (ICD-9-CM: 76%; ICD-10-CM: 73%), and middle to old-aged (ICD-9-CM: 67.3 ± 10.2 years; ICD-10-CM: 68.2 ± 10.0 years). Prevalence of comorbidities were similar between coding systems, with absolute differences less than 4% (range: 0.28 to 3.91). Myocardial infarction, hypertension, diabetes mellitus, depression, stroke, other cardiovascular, lung disease, and cancer had moderate agreement or higher (range κ: 0.47 to 0.84), while fracture and ulcer/stomach problem had slight and fair agreement, respectively (κ = 0.13; κ = 0.27)6,7. The RDCI scores were 2.95 ± 1.73 (mean ± SD) for the ICD-9-CM cohort and 2.93 ± 1.75 for the ICD-10-CM cohort. RDCI scores had moderate agreement (ICC: 0.71; 95% CI: 0.68-0.74)8 among individuals who were observed during both the ICD-9-CM and ICD-10-CM eras. Conclusion: We have mapped the RDCI from ICD-9-CM to ICD-10-CM codes, generating comparable RDCI scores in a large RA registry. Individual comorbidity agreement varied, with more chronic conditions such as diabetes and hypertension having higher agreement and more acute conditions such as fractures and ulcer/stomach problems having lower agreement. The updated RDCI can be used in clinical outcomes research with ICD-10-CM era patient data.https://digitalcommons.unmc.edu/surp2021/1043/thumbnail.jp

The relationship between EQ-5D, HAQ and pain in patients with rheumatoid arthritis: further validation and development of the limited dependent variable, mixture model approach

Objective: To provide robust estimates of EQ-5D as a function of the Health Assessment Questionnaire (HAQ) and pain in patients with rheumatoid arthritis. Method: Repeated observations of patients diagnosed with RA in a US observational cohort (n=100,398 observations) who provided data on HAQ, pain on a visual analogue scale and the EQ-5D questionnaire. We use a bespoke mixture modelling approach to appropriately reflect the characteristics of the EQ-5D instrument and compare this to results from linear regression. Results: The addition of pain alongside HAQ as an explanatory variable substantially improves explanatory power. The preferred model is a four component mixture. Unlike the linear regression it exhibits very good fit to the data, does not suffer from problems of bias or predict values outside the feasible range. Conclusions: It is appropriate to model the relationship between HAQ and EQ-5D but only if suitable statistical methods are applied. Linear models underestimate the QALY benefits, and therefore the cost effectiveness, of therapies. The bespoke mixture model approach outlined here overcomes this problem. The addition of pain as an explanatory variable greatly improves the estimates