Data from: The prevalence of MS in the United States: a population-based estimate using health claims data

Objective: To generate a national multiple sclerosis (MS) prevalence
estimate for the United States by applying a validated algorithm to
multiple administrative health claims (AHC) datasets. Methods: A validated
algorithm was applied to private, military, and public AHC datasets to
identify adult cases of MS between 2008 and 2010. In each dataset, we
determined the 3-year cumulative prevalence overall and stratified by age,
sex, and census region. We applied insurance-specific and stratum-specific
estimates to the 2010 US Census data and pooled the findings to calculate
the 2010 prevalence of MS in the United States cumulated over 3 years. We
also estimated the 2010 prevalence cumulated over 10 years using 2 models
and extrapolated our estimate to 2017. Results: The estimated 2010
prevalence of MS in the US adult population cumulated over 10 years was
309.2 per 100,000 (95% confidence interval [CI] 308.1–310.1), representing
727,344 cases. During the same time period, the MS prevalence was 450.1
per 100,000 (95% CI 448.1–451.6) for women and 159.7 (95% CI 158.7–160.6)
for men (female:male ratio 2.8). The estimated 2010 prevalence of MS was
highest in the 55- to 64-year age group. A US north-south decreasing
prevalence gradient was identified. The estimated MS prevalence is also
presented for 2017. Conclusion: The estimated US national MS prevalence
for 2010 is the highest reported to date and provides evidence that the
north-south gradient persists. Our rigorous algorithm-based approach to
estimating prevalence is efficient and has the potential to be used for
other chronic neurologic conditions.