Citations

BACKGROUND Previous epidemiologic studies of autoimmune diseases in the US have included a limited number of diseases or used metaanalyses that rely on different data collection methods and analyses for each disease. METHODS To estimate the prevalence of autoimmune diseases in the US, we used electronic health record data from 6 large medical systems in the US. We developed a software program using common methodology to compute the estimated prevalence of autoimmune diseases alone and in aggregate that can be readily used by other investigators to replicate or modify the analysis over time. RESULTS Our findings indicate that over 15 million people, or 4.6% of the US population, have been diagnosed with at least 1 autoimmune disease from January 1, 2011, to June 1, 2022, and 34% of those are diagnosed with more than 1 autoimmune disease. As expected, females (63% of those with autoimmune disease) were almost twice as likely as males to be diagnosed with an autoimmune disease. We identified the top 20 autoimmune diseases based on prevalence and according to sex and age. CONCLUSION Here, we provide, for what we believe to be the first time, a large-scale prevalence estimate of autoimmune disease in the US by sex and age. FUNDING Autoimmune Registry Inc., the National Heart Lung and Blood Institute, the National Center for Advancing Translational Sciences, the Intramural Research Program of the National Institute of Environmental Health Sciences.

ObjectiveApproximately one third of individuals worldwide have not received a COVID‐19 vaccine. Although studies have investigated risk factors linked to severe COVID‐19 among unvaccinated people with rheumatic diseases (RDs), we know less about whether these factors changed as the pandemic progressed. We aimed to identify risk factors associated with severe COVID‐19 in unvaccinated individuals in different pandemic epochs corresponding to major variants of concern.MethodsPatients with RDs and COVID‐19 were entered into the COVID‐19 Global Rheumatology Alliance Registry between March 2020 and June 2022. An ordinal logistic regression model (not hospitalized, hospitalized, and death) was used with date of COVID‐19 diagnosis, age, sex, race and/or ethnicity, comorbidities, RD activity, medications, and the human development index (HDI) as covariates. The main analysis included all unvaccinated patients across COVID‐19 pandemic epochs; subanalyses stratified patients according to RD types.ResultsAmong 19,256 unvaccinated people with RDs and COVID‐19, those who were older, male, had more comorbidities, used glucocorticoids, had higher disease activity, or lived in lower HDI regions had worse outcomes across epochs. For those with rheumatoid arthritis, sulfasalazine and B‐cell–depleting therapy were associated with worse outcomes, and tumor necrosis factor inhibitors were associated with improved outcomes. In those with connective tissue disease or vasculitis, B‐cell–depleting therapy was associated with worse outcomes.ConclusionRisk factors for severe COVID‐19 outcomes were similar throughout pandemic epochs in unvaccinated people with RDs. Ongoing efforts, including vaccination, are needed to reduce COVID‐19 severity in this population, particularly in those with medical and social vulnerabilities identified in this study.

ObjectiveTo determine the association between race/ethnicity and COVID‐19 outcomes in individuals with systemic lupus erythematosus (SLE).MethodsIndividuals with SLE from the US with data entered into the COVID‐19 Global Rheumatology Alliance registry between March 24, 2020 and August 27, 2021 were included. Variables included age, sex, race, and ethnicity (White, Black, Hispanic, other), comorbidities, disease activity, pandemic time period, glucocorticoid dose, antimalarials, and immunosuppressive drug use. The ordinal outcome categories were: not hospitalized, hospitalized with no oxygenation, hospitalized with any ventilation or oxygenation, and death. We constructed ordinal logistic regression models evaluating the relationship between race/ethnicity and COVID‐19 severity, adjusting for possible confounders.ResultsWe included 523 patients; 473 (90.4%) were female and the mean ± SD age was 46.6 ± 14.0 years. A total of 358 patients (74.6%) were not hospitalized; 40 patients (8.3%) were hospitalized without oxygen, 64 patients (13.3%) were hospitalized with any oxygenation, and 18 (3.8%) died. In a multivariable model, Black (odds ratio [OR] 2.73 [95% confidence interval (95% CI) 1.36–5.53]) and Hispanic (OR 2.76 [95% CI 1.34–5.69]) individuals had higher odds of more severe outcomes than White individuals.ConclusionBlack and Hispanic individuals with SLE experienced more severe COVID‐19 outcomes, which is consistent with findings in the US general population. These results likely reflect socioeconomic and health disparities and suggest that more aggressive efforts are needed to prevent and treat infection in this population.

ObjectiveSome patients with rheumatic diseases might be at higher risk for coronavirus disease 2019 (COVID‐19) acute respiratory distress syndrome (ARDS). We aimed to develop a prediction model for COVID‐19 ARDS in this population and to create a simple risk score calculator for use in clinical settings.MethodsData were derived from the COVID‐19 Global Rheumatology Alliance Registry from March 24, 2020, to May 12, 2021. Seven machine learning classifiers were trained on ARDS outcomes using 83 variables obtained at COVID‐19 diagnosis. Predictive performance was assessed in a US test set and was validated in patients from four countries with independent registries using area under the curve (AUC), accuracy, sensitivity, and specificity. A simple risk score calculator was developed using a regression model incorporating the most influential predictors from the best performing classifier.ResultsThe study included 8633 patients from 74 countries, of whom 523 (6%) had ARDS. Gradient boosting had the highest mean AUC (0.78; 95% confidence interval [CI]: 0.67‐0.88) and was considered the top performing classifier. Ten predictors were identified as key risk factors and were included in a regression model. The regression model that predicted ARDS with 71% (95% CI: 61%‐83%) sensitivity in the test set, and with sensitivities ranging from 61% to 80% in countries with independent registries, was used to develop the risk score calculator.ConclusionWe were able to predict ARDS with good sensitivity using information readily available at COVID‐19 diagnosis. The proposed risk score calculator has the potential to guide risk stratification for treatments, such as monoclonal antibodies, that have potential to reduce COVID‐19 disease progression.

ObjectiveThe relative risk of SARS–CoV‐2 infection and COVID‐19 disease severity among people with rheumatic and musculoskeletal diseases (RMDs) compared to those without RMDs is unclear. This study was undertaken to quantify the risk of SARS–CoV‐2 infection in those with RMDs and describe clinical outcomes of COVID‐19 in these patients.MethodsWe conducted a systematic literature review using 14 databases from January 1, 2019 to February 13, 2021. We included observational studies and experimental trials in RMD patients that described comparative rates of SARS–CoV‐2 infection, hospitalization, oxygen supplementation/intensive care unit (ICU) admission/mechanical ventilation, or death attributed to COVID‐19. Methodologic quality was evaluated using the Joanna Briggs Institute critical appraisal tools or the Newcastle‐Ottawa scale. Risk ratios (RRs) and odds ratios (ORs) with 95% confidence intervals (95% CIs) were calculated, as applicable for each outcome, using the Mantel‐Haenszel formula with random effects models.ResultsOf the 5,799 abstracts screened, 100 studies met the criteria for inclusion in the systematic review, and 54 of 100 had a low risk of bias. Among the studies included in the meta‐analyses, we identified an increased prevalence of SARS–CoV‐2 infection in patients with an RMD (RR 1.53 [95% CI 1.16–2.01]) compared to the general population. The odds of hospitalization, ICU admission, and mechanical ventilation were similar in patients with and those without an RMD, whereas the mortality rate was increased in patients with RMDs (OR 1.74 [95% CI 1.08–2.80]). In a smaller number of studies, the adjusted risk of outcomes related to COVID‐19 was assessed, and the results varied; some studies demonstrated an increased risk while other studies showed no difference in risk in patients with an RMD compared to those without an RMD.ConclusionPatients with RMDs have higher rates of SARS–CoV‐2 infection and an increased mortality rate.