A new method for estimating under-recruitment of a patient registry: a case study with the Ohio Registry of Amyotrophic Lateral Sclerosis

Key points

Describes an alternative method to capture-recapture to estimate missing cases in Ohio’s ALS Registry, 2016-2018. Authors propose spatial analysis as an alternative means for estimating missing cases in disease registries.

Screenshot of first two pages of paper

Affiliates

Meifang Li [1], Xun Shi [1], Jiang Gui [2], Chao Song [3], Angeline S. Andrew [4], Erik P. Pioro [4], Elijah W. Stommel [4], Maeve Tischbein [5] & Walter G. Bradley [6]

  1. Department of Geography, Dartmouth College, Hanover, NH, USA
  2. Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
  3. HEOA Group, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu City, Sichuan Province, China
  4. Department of Neurology, Geisel School of Medicine, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
  5. Section of ALS and Related Disorders, Cleveland Clinic, Cleveland, OH, USA
  6. Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA

Journal

Scientific Reports

Summary

This paper describes an alternative method to capture-recapture to estimate missing cases in Ohio’s ALS Registry. The team used statistical modeling and spatial adjustments to identify counties in Ohio between 2016-2018 with normal case-population relationships and from it built a methodology of identifying missing cases per county. Due to factors such as incomplete case ascertainment and the limitations inherent in capture-recapture, this paper proposes using spatial analysis as an alternative means for estimating missing cases in disease registries.

Link to Paper

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