作者
Douglas S Lee, Chloe X Wang, Finlay A McAlister, Shihao Ma, Anna Chu, Paula A Rochon, Padma Kaul, Peter C Austin, Xuesong Wang, Sunil V Kalmady, Jacob A Udell, Michael J Schull, Barry B Rubin, Bo Wang
发表日期
2022/2/1
期刊
The Lancet Regional Health–Americas
卷号
6
出版商
Elsevier
简介
Background
SARS-Cov-2 infection rates are high among residents of long-term care (LTC) homes. We used machine learning to identify resident and community characteristics predictive of SARS-Cov-2 infection.
Methods
We linked 26 population-based health and administrative databases to identify the population of all LTC residents tested for SARS-Cov-2 infection in Ontario, Canada. Using ensemble-based algorithms, we examined 484 factors, including individual-level demographics, healthcare use, comorbidities, functional status, and laboratory results; and community-level characteristics to identify factors predictive of infection. Analyses were performed separately for January to April (early wave 1) and May to August (late wave 1).
Findings
Among 80,784 LTC residents, 64,757 (80.2%) were tested for SARS-Cov-2 (median age 86 (78–91) years, 30.6% male), of whom 10.2% of 33,519 and 5.2% of 31,238 …
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DS Lee, CX Wang, FA McAlister, S Ma, A Chu… - The Lancet Regional Health–Americas, 2022