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Cost pressures and the need to demonstrate cost-effectiveness of new interventions require consideration of the costs of treating disease. This study presents analyses of resource use data covering 199 postmenopausal women who experienced a breast cancer recurrent event between 1991 and 2004 and were treated at the Western General Hospital, Edinburgh. Aggregate (5-year) treatment costs for alternative recurrent events were estimated, as well as the annual costs incurred by patients experiencing contralateral, locoregional, or distant recurrence, who remained alive without further recurrence for a year. The 95% confidence intervals for the 5-year costs of recurrence ranged from pounds 10,000 to pounds 37,000 for locoregional recurrence, and pounds 14,500- pounds 20,000 for distant recurrence. No evidence of significant variations in these costs across time periods between 1991 and 2004 was identified. Annual costs for patients remaining in the same health state showed high initial costs for contralateral and locoregional recurrence, with low costs in subsequent years, while costs associated with distant recurrence declined at a slower rate and plateaued at 4-5 years post-diagnosis. The cost estimates presented in this paper not only inform the magnitude of the resource consequences of breast cancer recurrences, but they are also better suited to informing cost-effectiveness analyses, which have a far greater role in allocating health-care resources. [ABSTRACT FROM AUTHOR]

Purpose: To assess the diagnostic accuracy of the Edinburgh visual loss algorithm.Methods: This was a prospective study. Patients referred to the Edinburgh Eye Pavilion with visual loss were assessed using the Edinburgh Visual Loss Algorithm by either a medical student, an inexperienced ophthalmology trainee or an optometrist in the Lothian Optometry Treat and Teach clinic. Accuracy of this 'algorithm-assisted' diagnosis was then compared with the 'gold-standard' diagnosis, made by an experienced ophthalmologist. Accuracy of the pre-algorithm diagnosis, made by the referrer, was also compared with the algorithm-assisted diagnosis.Results: All patients referred with visual loss were eligible for inclusion. Seventy patients were assessed; two were excluded. Pre-algorithm accuracy of referral of patients with visual loss was 51% (30/59). Overall, the algorithm-assisted diagnosis was correct 84% (57/68) of the time. The algorithm correctly diagnosed: retina in 71% of cases (5/7), macula in 86% (25/29), peripheral retina in 100% (2/2), optic nerve in 71% (5/7), media opacity in 89% (16/18), post chiasmal in 100% (4/4), and refractive error in 0% (0/1). Accuracy of diagnosis was similar for each algorithm user; medical student 81%, inexperienced ophthalmology trainee 84% and optometrist 92%.Discussion: The baseline diagnostic accuracy of clinicians who are inexperienced in ophthalmology rose from 51 to 84% when patients were assessed using the algorithm. This algorithm significantly improves the diagnostic accuracy of referrals to the hospital eye service, regardless of the user's previous ophthalmic experience. We hope we have demonstrated its potential as a learning tool for inexperienced clinicians. [ABSTRACT FROM AUTHOR]