A Framework for Estimating the Burden of Chronic diseases: Design and Application in the Context of Multiple Sclerosis


Background: When population-based databases are unavailable, nationwide assessments of the disease burden of Multiple Sclerosis (MS) resort to clinical, administrative or convenience-sampled data sources, which may produce results of limited external validity. Our aim was to develop a framework for estimating measures of occurrence of chronic diseases, and more broadly disease burden, that mitigate these limitations and to apply this framework to estimate the prevalence of Multiple Sclerosis (MS) in Switzerland.
Methods: We developed a 7-step framework which implements the combination of several data sources together with a resampling and critical appraisal approach. The framework was applied to estimate the MS prevalence for 2016 in Switzerland, for which four distinct data sources (Swiss MS registry, Swiss national MS treatment registry, MediService database, and Swiss MS cohort study) were combined. Results were reviewed by disease experts and compared to earlier Swiss estimates and current prevalence estimates from other countries.
Results: We estimate that in the year 2016 between 14,650 and 15,700 persons with MS have been living in Switzerland, yielding a period prevalence of 174–187/100,000 inhabitants. Compared to the last estimate in 1986, we detected a substantial increase of MS diagnoses which coincides with a higher number of diagnoses in women below the age of 65.
Conclusions: Internationally, Switzerland is a high-prevalence country for MS, although estimates were somewhat lower than recent evaluations of Northern European countries. In addition, we corroborate previous reports that the prevalence increase coincides with a higher number of MS diagnoses among women. The proposed framework has wide applicability and the potential to place estimates of disease occurrence and burden with imperfect data availability on more solid grounds.

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(Originally posted by Stuart)
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Monday, 30 March 2020

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