Development and validation of an administrative data algorithm to estimate the disease burden and epidemiology of multiple sclerosis in Ontario, Canada

Abstract

Background: Few studies have assessed the accuracy of administrative data for identifying multiple sclerosis (MS) patients. Objectives: To validate administrative data algorithms for MS, and describe the burden and epidemiology over time in Ontario, Canada. Methods: We employed a validated search strategy to identify all MS patients within electronic medical records, to identify patients with and without MS (reference standard). We then developed and validated different combinations of administrative data for algorithms. The most accurate algorithm was used to estimate the burden and epidemiology of MS over time. Results: The accuracy of the algorithm of one hospitalisation or five physician billings over 2 years provided both high sensitivity (84%) and positive predictive value (86%). Application of this algorithm to provincial data demonstrated an increasing cumulative burden of MS, from 13,326 patients (0.14%) in 2000 to 24,647 patients in 2010 (0.22%). Age-and-sex standardised prevalence increased from 133.9 to 207.3 MS patients per 100,000 persons in the population, from 2000 – 2010. During this same period, age-and-sex-standardised incidence varied from 17.9 to 19.4 patients per 100,000 persons. Conclusions: MS patients can be accurately identified from administrative data. Our findings illustrated a rising prevalence of MS over time. MS incidence rates also appear to be rising since 2009.