Data from: A new way to estimate neurologic disease prevalence in the United States
Nelson, Lorene M. et al. (2019), Data from: A new way to estimate neurologic disease prevalence in the United States, Dryad, Dataset, https://doi.org/10.5061/dryad.t1k42p8
Objective: Considerable gaps exist in knowledge regarding the prevalence of neurologic diseases, such as multiple sclerosis (MS), in the United States. Therefore, the MS Prevalence Working Group sought to review and evaluate alternative methods for obtaining a scientifically valid estimate of national MS prevalence in the current health care era. Methods: We carried out a strengths, weaknesses, opportunities, and threats (SWOT) analysis for 3 approaches to estimate MS prevalence: population-based MS registries, national probability health surveys, and analysis of administrative health claims databases. We reviewed MS prevalence studies conducted in the United States and critically examined possible methods for estimating national MS prevalence. Results: We developed a new 4-step approach for estimating MS prevalence in the United States. First, identify administrative health claim databases covering publicly and privately insured populations in the United States. Second, develop and validate a highly accurate MS case-finding algorithm that can be standardly applied in all databases. Third, apply a case definition algorithm to estimate MS prevalence in each population. Fourth, combine MS prevalence estimates into a single estimate of US prevalence, weighted according to the number of insured persons in each health insurance segment. Conclusions: By addressing methodologic challenges and proposing a new approach for measuring the prevalence of MS in the United States, we hope that our work will benefit scientists who study neurologic and other chronic conditions for which national prevalence estimates do not exist.