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

Increased risk of peripheral arterial disease in polymyalgia rheumatica: a population-based cohort study

INTRODUCTION: The present study was conducted to determine whether patients with polymyalgia rheumatica (PMR) are at an increased risk of peripheral arterial disease (PAD). METHODS: An inception cohort of all Olmsted County, Minnesota residents diagnosed with PMR between 1 January 1970 and 31 December 1999 was compared with non-PMR subjects (two for each PMR subject) from among residents. Both cohorts were followed longitudinally by complete medical record review from the incidence date of PMR (or index date for the non-PMR cohort) until death, incident PAD, migration, or 31 December 2006. PMR-related disease characteristics, traditional cardiovascular risk factors and diagnosis of PAD were abstracted from the medical record. Cumulative incidence of PAD was estimated using Kaplan–Meier methods. Cox proportional hazards models were used to assess the risk of PAD in PMR compared with non-PMR. RESULTS: A total of 353 PMR patients (mean age 73.3 years, 67% women) and 705 non-PMR subjects (mean age 73.2 years, 68% female) were followed for a median of 11.0 years. PAD developed in 38 patients (10-year cumulative incidence, 8.5%) with PMR and in 28 non-PMR subjects (10-year cumulative incidence, 4.1%) (hazard ratio (95% confidence interval), 2.40 (1.47, 3.92)). After adjusting for traditional cardiovascular risk factors, patients with PMR still had a significantly higher risk for PAD (hazard ratio, 2.50 (1.53, 4.08)) compared with controls. Giant cell arteritis occurred in 63 (18%) PMR patients but was not predictive of PAD (P = 0.15). There was no difference between mortality in PMR and the non-PMR cohorts nor in PMR patients with and those without PAD (P = 0.16). CONCLUSIONS: Patients with PMR appear to have an increased risk of PAD

Georgia Abortion Law and Our Commitment to Patients

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153775/1/art41143.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153775/2/art41143_am.pd

Impact of risk factors associated with cardiovascular outcomes in patients with rheumatoid arthritis

ObjectivesPatients with rheumatoid arthritis (RA) have an excess risk of cardiovascular disease (CVD). We aimed to assess the impact of CVD risk factors, including potential sex differences, and RA-specific variables on CVD outcome in a large, international cohort of patients with RA.MethodsIn 13 rheumatology centres, data on CVD risk factors and RA characteristics were collected at baseline. CVD outcomes (myocardial infarction, angina, revascularisation, stroke, peripheral vascular disease and CVD death) were collected using standardised definitions.Results5638 patients with RA and no prior CVD were included (mean age: 55.3 (SD: 14.0) years, 76% women). During mean follow-up of 5.8 (SD: 4.4) years, 148 men and 241 women developed a CVD event (10-year cumulative incidence 20.9% and 11.1%, respectively). Men had a higher burden of CVD risk factors, including increased blood pressure, higher total cholesterol and smoking prevalence than women (all p<0.001). Among the traditional CVD risk factors, smoking and hypertension had the highest population attributable risk (PAR) overall and among both sexes, followed by total cholesterol. The PAR for Disease Activity Score and for seropositivity were comparable in magnitude to the PAR for lipids. A total of 70% of CVD events were attributable to all CVD risk factors and RA characteristics combined (separately 49% CVD risk factors and 30% RA characteristics).ConclusionsIn a large, international cohort of patients with RA, 30% of CVD events were attributable to RA characteristics. This finding indicates that RA characteristics play an important role in efforts to reduce CVD risk among patients with RA

Prediction of cardiovascular events in rheumatoid arthritis using risk age calculations: evaluation of concordance across risk age models

Background: In younger individuals, low absolute risk of cardiovascular disease (CVD) may conceal an increased risk age and relative risk of CVD. Calculation of risk age is proposed as an adjuvant to absolute CVD risk estimation in European guidelines. We aimed to compare the discriminative ability of available risk age models in prediction of CVD in rheumatoid arthritis (RA). Secondly, we also evaluated the performance of risk age models in subgroups based on RA disease characteristics. Methods: RA patients aged 30?70 years were included from an international consortium named A Trans-Atlantic Cardiovascular Consortium for Rheumatoid Arthritis (ATACC-RA). Prior CVD and diabetes mellitus were exclusión criteria. The discriminatory ability of specific risk age models was evaluated using c-statistics and their standard errors after calculating time until fatal or non-fatal CVD or last follow-up. Results: A total of 1974 patients were included in the main analyses, and 144 events were observed during followup, the median follow-up being 5.0 years. The risk age models gave highly correlated results, demonstrating R2 values ranging from 0.87 to 0.97. However, risk age estimations differed > 5 years in 15?32% of patients. C-statistics ranged 0.68?0.72 with standard errors of approximately 0.03. Despite certain RA characteristics being associated with low c-indices, standard errors were high. Restricting analysis to European RA patients yielded similar results. Conclusions: The cardiovascular risk age and vascular age models have comparable performance in predicting CVD in RA patients. The influence of RA disease characteristics on the predictive ability of these prediction models remains inconclusive

Prediction of cardiovascular events in rheumatoid arthritis using risk age calculations: evaluation of concordance across risk age models

Background In younger individuals, low absolute risk of cardiovascular disease (CVD) may conceal an increased risk age and relative risk of CVD. Calculation of risk age is proposed as an adjuvant to absolute CVD risk estimation in European guidelines. We aimed to compare the discriminative ability of available risk age models in prediction of CVD in rheumatoid arthritis (RA). Secondly, we also evaluated the performance of risk age models in subgroups based on RA disease characteristics. Methods RA patients aged 30–70 years were included from an international consortium named A Trans-Atlantic Cardiovascular Consortium for Rheumatoid Arthritis (ATACC-RA). Prior CVD and diabetes mellitus were exclusion criteria. The discriminatory ability of specific risk age models was evaluated using c-statistics and their standard errors after calculating time until fatal or non-fatal CVD or last follow-up. Results A total of 1974 patients were included in the main analyses, and 144 events were observed during follow-up, the median follow-up being 5.0 years. The risk age models gave highly correlated results, demonstrating R 2 values ranging from 0.87 to 0.97. However, risk age estimations differed > 5 years in 15–32% of patients. C-statistics ranged 0.68–0.72 with standard errors of approximately 0.03. Despite certain RA characteristics being associated with low c-indices, standard errors were high. Restricting analysis to European RA patients yielded similar results. Conclusions The cardiovascular risk age and vascular age models have comparable performance in predicting CVD in RA patients. The influence of RA disease characteristics on the predictive ability of these prediction models remains inconclusive