Using logic regression to characterize extreme heat exposures and their health associations: a time-series study of emergency department visits in Atlanta.

ABSTRACT:

Background

Short-term associations between extreme heat events and adverse health outcomes are well-established in epidemiologic studies. However, the use of different exposure definitions across studies has limited our understanding of extreme heat characteristics that are most important for specific health outcomes or subpopulations.

Methods

Logic regression is a statistical learning method for constructing decision trees based on Boolean combinations of binary predictors. We describe how logic regression can be utilized as a data-driven approach to identify extreme heat exposure definitions using health outcome data. We evaluated the performance of the proposed algorithm in a simulation study, as well as in a 20-year time-series analysis of extreme heat and emergency depa

SUBMITTER: Jiang S 

PROVIDER: S-EPMC8077733 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature