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Prevalence of ALS in all 50 states in the United States, data from the National ALS Registry, 2011-2018.

Paul MehtaJaime RaymondTheresa NairMoon Kwon HanReshma PunjaniTheodore C LarsonJasmine BerrySuraya MohidulD Kevin Horton
Published in: Amyotrophic lateral sclerosis & frontotemporal degeneration (2024)
Objective: To summarize the prevalence of ALS in all 50 states and Washington, DC in the United States from 2011 to 2018 using data collected and analyzed by the National ALS Registry. In October 2010, the federal Agency for Toxic Substances and Disease Registry (ATSDR) launched the congressionally mandated Registry to determine the incidence and prevalence of ALS within the USA, characterize the demographics of persons with ALS, and identify the potential risk factors for the disease. This is the first analysis of state-level ALS prevalence estimates. Methods: ALS is not a notifiable disease in the USA, so the Registry uses a two-pronged approach to identify cases. The first approach uses existing national administrative databases (Medicare, Veterans Health Administration, and Veterans Benefits Administration). The second method uses a secure web portal to gather voluntary participant data and identify cases not included in the national administrative databases. Results: State-level age-adjusted average prevalence from 2011-2018 ranged from 2.6 per 100,000 persons (Hawaii) to 7.8 per 100,000 persons (Vermont), with an average of 4.4 per 100,000 persons in the US. New England and Midwest regions had higher prevalence rates than the national average. Conclusions: These findings summarize the prevalence of ALS for all 50 states from 2011 to 2018. This is a continuing effort to identify ALS cases on a national population basis. The establishment of the National ALS Registry has allowed for epidemiological trends of this disease and the assessment of potential risk factors that could cause ALS.
Keyphrases
  • risk factors
  • amyotrophic lateral sclerosis
  • quality improvement
  • big data
  • electronic health record
  • healthcare
  • public health
  • machine learning
  • climate change
  • health information