The prevalence of giant cell arteritis and polymyalgia rheumatica in a UK primary care population. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Yates, Max; Graham, Karly; Watts, Richard; MacGregor, Alexander
• Is Part Of: BMC musculoskeletal disorders. Volume 17:Issue 1(2016)
• Page Start: 1
• Page End: 9
• Publication Date: 2016-12
• Subjects: Musculoskeletal system -- Diseases -- Periodicals; 616.705
• DOI: 10.1186/s12891-016-1127-3
• Languages: English
• ISSNs: 1471-2474

Abstract

Abstract Background To update community-based prevalence values for Polymyalgia Rheumatic (PMR) and Giant Cell Arteritis (GCA) using case record review supplemented by population survey and subsequent clinical review. Methods Clinical data were obtained from case records of a large primary care practice in Norfolk, UK and reviewed for diagnoses of GCA and PMR. In addition postal survey was carried out to capture potentially undiagnosed cases within the practice population. Those screening positive for potential diagnoses of GCA and PMR were invited for clinical review. A cumulative prevalence estimate was subsequently calculated on those diagnosed within the GP practice and subsequently on those fulfilling the various published classification criteria sets. The date of the database lock and mail merge was March 2013. Results Through detailed systematic review of 5, 159 GP case records, 21 patients had a recorded diagnosis of GCA and 117 had PMR.No new cases were identified among 2, 227 completed questionnaires returned from the population survey of a sample of 4, 728. The resulting cumulative prevalence estimate in those aged ≥ 55 years meeting the ACR classification criteria set for GCA was 0.25 % (95 % CI 0.11 to 0.39 %) and for five published criteria sets for PMR ranged from 0.91 to 1.53 % (95 % CI ranges 0.65 %, 1.87 %). The prevalence of both conditions was higher in women than in men and in older age groups. Conclusion This study provides the first UK prevalence estimate of GCA and PMR in over 30 years and is the first to apply classification criteria sets.