Factors associated with SARS-CoV-2 test positivity in long-term care homes: A population-based cohort analysis using machine learning.

ABSTRACT:

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,

SUBMITTER: Lee DS 

PROVIDER: S-EPMC8763396 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature