Open AccessData silos undermine efforts to characterize, predict, and mitigate dementia-related missing person incidents
Author(s) -
Antonio Miguel-Cruz,
Samantha Dawn Marshall,
Christine Daum,
Hector Perez,
John P. Hirdes,
Lili Liu
Publication year - 2022
Publication title -
healthcare management forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 16
eISSN - 2352-3883
pISSN - 0840-4704
DOI - 10.1177/08404704221106156
Subject(s) - missing data , dementia , assisted living , data collection , population , medicine , gerontology , computer science , environmental health , disease , statistics , pathology , machine learning , mathematics
It is estimated that up to 60% of people living with dementia go missing at least once during the course of their disease. Databases on missing incidents involving people living with dementia are managed in silos with minimal or incomplete data. A national strategy for the collection of data on missing incidents of people living with dementia would optimize time and resources spent on police and search and rescue and enhance chances of saving lives of those who go missing. Such a strategy would be a first step toward developing strategies to prevent future missing person incidents among this population. The objectives of this manuscript are to: (1) describe the issues and challenges related to the lack of integrated data on people living with dementia at risk of going missing, and (2) propose directions to create a national database.