Algorithms developed from health administrative data are sensitive and specific for identifying older adults with AD-RD.

Objective. To assess risk and risk factors for serious infections in seniors with rheumatoid arthritis (RA) using a case-control study nested within an RA cohort. Methods. We assembled a retrospective RA cohort age >66 years from Ontario health administrative data across 1992-2010. Nested case-control analyses were done, comparing RA patients with a primary diagnosis of infection (based on hospital or emergency department records) to matched RA controls. We assessed independent effects of drugs, adjusting for demographics, comorbidity, and markers of RA severity. Results. A total of 86,039 seniors with RA experienced 20,575 infections, for a rate of 46.4 events/1,000 person-years. The most frequently occurring events included respiratory infections, herpes zoster, and skin/soft tissue infections. Factors associated with infection included higher comorbidity, rural residence, markers of disease severity, and history of previous infection. In addition, anti-tumor necrosis factor agents and disease-modifying antirheumatic drugs were associated with a several-fold increase in infections, with an adjusted odds ratio (OR) ranging from 1.2-3.5. The drug category with the greatest effect estimate was glucocorticoids, which exhibited a clear dose response with an OR ranging from 4.0 at low doses to 7.6 at high doses. Conclusion. Seniors with RA have significant morbidity related to serious infections, which exceeds previous reports among younger RA populations. Rural residence, higher comorbidity, markers of disease severity, and previous infection were associated with serious infections in seniors with RA. Our results emphasize that many RA drugs may increase the risk of infection, but glucocorticoids appear to confer a particular risk.

Objective. Health administrative data can be a valuable tool for disease surveillance and research. Few studies have rigorously evaluated the accuracy of administrative databases for identifying rheumatoid arthritis (RA) patients. Our aim was to validate administrative data algorithms to identify RA patients in Ontario, Canada. Methods. We performed a retrospective review of a random sample of 450 patients from 18 rheumatology clinics. Using rheumatologist-reported diagnosis as the reference standard, we tested and validated different combinations of physician billing, hospitalization, and pharmacy data. Results. One hundred forty-nine rheumatology patients were classified as having RA and 301 were classified as not having RA based on our reference standard definition (study RA prevalence 33%). Overall, algorithms that included physician billings had excellent sensitivity (range 94 -100%). Specificity and positive predictive value (PPV) were modest to excellent and increased when algorithms included multiple physician claims or specialist claims. The addition of RA medications did not significantly improve algorithm performance. The algorithm of "(1 hospitalization RA code ever) OR (3 physician RA diagnosis codes [claims] with >1 by a specialist in a 2-year period)" had a sensitivity of 97%, specificity of 85%, PPV of 76%, and negative predictive value of 98%. Most RA patients (84%) had an RA diagnosis code present in the administrative data within ؎1 year of a rheumatologist's documented diagnosis date. Conclusion. We demonstrated that administrative data can be used to identify RA patients with a high degree of accuracy. RA diagnosis date and disease duration are fairly well estimated from administrative data in jurisdictions of universal health care insurance.

Objective. To evaluate the quality of the methods and reporting of published studies that validate administrative database algorithms for rheumatic disease case ascertainment. Methods. We systematically searched MEDLINE, Embase, and the reference lists of articles published from 1980 to 2011. We included studies that validated administrative data algorithms for rheumatic disease case ascertainment using medical record or patient-reported diagnoses as the reference standard. Each study was evaluated using published standards for the reporting and quality assessment of diagnostic accuracy, which informed the development of a methodologic framework to help critically appraise and guide research in this area. Results. Twenty-three studies met the inclusion criteria. Administrative database algorithms to identify cases were most frequently validated against diagnoses in medical records (83%). Almost two-thirds of the studies (61%) used diagnosis codes in administrative data to identify potential cases and then reviewed medical records to confirm the diagnoses. The remaining studies did the reverse, identifying patients using a reference standard and then testing algorithms to identify cases in administrative data. Many authors (61%) described the patient population, but few (26%) reported key measures of diagnostic accuracy (sensitivity, specificity, and positive and negative predictive values). Only one-third of studies reported disease prevalence in the validation study sample. Conclusion. The methods used in administrative data validation studies of rheumatic diseases are highly variable. Few studies reported key measures of diagnostic accuracy despite their importance for drawing conclusions about the validity of administrative database algorithms. We developed a methodologic framework and recommendations for validation study conduct and reporting.

The prevalence and incidence of psoriasis and PsA in Ontario are similar to those observed in Europe and the United States. The steady increase in the prevalence of psoriasis and PsA over the past decade may be due to a combination of population aging, population growth, and increasing life expectancy. This article is protected by copyright. All rights reserved.