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

Affiliate

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

Nature

Summary

This paper proposes an alternative statistical method to capture-recapture in order to estimate under-ascertainment in the collection of ALS data for a disease registry in Ohio. The study utilized three statistical methods (z-score, straight section in a series, and Jenk’s natural breaks) to identify reference counties in Ohio with normal case-population relationships to build a model to estimate case counts in target counties with unrecruited cases. The researchers believe that this method has advantages over capture-recapture that allow it to be used to identify disease hotspots and associations between ALS and environment.

Link to Paper

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