Jiang S, Warren JL, Scovronick N, Moss SE, Darrow LA, Strickland MJ, Newman AJ, Chen Y, Ebelt ST, Chang HH. Using logic regression to characterize extreme heat exposures and their health associations: a time-series study of emergency department visits in Atlanta.
BMC Med Res Methodol 2021;
21:87. [PMID:
33902463 PMCID:
PMC8077733 DOI:
10.1186/s12874-021-01278-x]
[Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 04/12/2021] [Indexed: 11/13/2022] Open
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 department visits for 12 outcomes in the Atlanta metropolitan area.
Results
For the Atlanta case study, our novel application of logic regression identified extreme heat exposure definitions that were associated with several heat-sensitive disease outcomes (e.g., fluid and electrolyte imbalance, renal diseases, ischemic stroke, and hypertension). Exposures were often characterized by extreme apparent minimum temperature or maximum temperature over multiple days. The simulation study also demonstrated that logic regression can successfully identify exposures of different lags and duration structures when statistical power is sufficient.
Conclusion
Logic regression is a useful tool for identifying important characteristics of extreme heat exposures for adverse health outcomes, which may help improve future heat warning systems and response plans.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12874-021-01278-x.
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