Background: Various methodologies have been reported to assess the real-world epidemiology of amyotrophic lateral sclerosis (ALS) in the United States. The aim of this study was to estimate the prevalence, incidence, and geographical distribution of ALS using administrative claims data and to model future trends in ALS epidemiology. Methods: We performed a retrospective analysis of deidentified administrative claims data for >100 million patients, using 2 separate databases (IBM MarketScan Research Databases and Symphony Health Integrated DataVerse [IDV]), to identify patients with ALS. We evaluated disease prevalence, annual incidence, age- and population-controlled geographical distribution, and expected future trends. Results: From 2013 to 2017, we identified 7,316 and 35,208 ALS patients from the MarketScan databases and IDV, respectively. Average annual incidence estimates were 1.48 and 1.37 per 100,000 and point prevalence estimates were 6.85 and 5.16 per 100,000 and in the United States for the MarketScan databases and IDV, respectively. Predictive modeling estimates are reported out to the year 2060 and demonstrate an increasing trend in both incident and prevalent cases. Conclusions: This study provides incidence and prevalence estimates as well as geographical distribution for what the authors believe to be the largest ALS population studied to date. By using 2 separate administrative claims data sets, confidence in our estimates is increased. Future projections based on either database demonstrate an increase in ALS cases, which has also been seen in other large-scale ALS studies. These results can be used to help improve the allocation of healthcare resources in the future.

Keywords:
Clinical neurology, Amyotrophic lateral sclerosis, Administrative claims, Big data analytics, Future projections
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© 2021 S. Karger AG, Basel
2021
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