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 f...
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doaj-1841d052a4684261ba60e43f589fb6d52021-05-02T11:03:05ZengBMCBMC Medical Research Methodology1471-22882021-04-012111910.1186/s12874-021-01278-xUsing logic regression to characterize extreme heat exposures and their health associations: a time-series study of emergency department visits in AtlantaShan Jiang0Joshua L. Warren1Noah Scovronick2Shannon E. Moss3Lyndsey A. Darrow4Matthew J. Strickland5Andrew J. Newman6Yong Chen7Stefanie T. Ebelt8Howard H. Chang9Department of Biostatistics and Bioinformatics, Emory UniversityDepartment of Biostatistics, Yale UniversityGangarosa Department of Environmental Health, Emory UniversityDepartment of Biostatistics and Bioinformatics, Emory UniversitySchool of Community Health Sciences, University of Nevada RenoSchool of Community Health Sciences, University of Nevada RenoResearch Applications Laboratory, National Center for Atmospheric ResearchDepartment of Biostatistics, Epidemiology and Informatics, University of PennsylvaniaGangarosa Department of Environmental Health, Emory UniversityDepartment of Biostatistics and Bioinformatics, Emory UniversityAbstract 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.https://doi.org/10.1186/s12874-021-01278-x |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shan Jiang Joshua L. Warren Noah Scovronick Shannon E. Moss Lyndsey A. Darrow Matthew J. Strickland Andrew J. Newman Yong Chen Stefanie T. Ebelt Howard H. Chang |
spellingShingle |
Shan Jiang Joshua L. Warren Noah Scovronick Shannon E. Moss Lyndsey A. Darrow Matthew J. Strickland Andrew J. Newman Yong Chen Stefanie T. Ebelt Howard H. Chang Using logic regression to characterize extreme heat exposures and their health associations: a time-series study of emergency department visits in Atlanta BMC Medical Research Methodology |
author_facet |
Shan Jiang Joshua L. Warren Noah Scovronick Shannon E. Moss Lyndsey A. Darrow Matthew J. Strickland Andrew J. Newman Yong Chen Stefanie T. Ebelt Howard H. Chang |
author_sort |
Shan Jiang |
title |
Using logic regression to characterize extreme heat exposures and their health associations: a time-series study of emergency department visits in Atlanta |
title_short |
Using logic regression to characterize extreme heat exposures and their health associations: a time-series study of emergency department visits in Atlanta |
title_full |
Using logic regression to characterize extreme heat exposures and their health associations: a time-series study of emergency department visits in Atlanta |
title_fullStr |
Using logic regression to characterize extreme heat exposures and their health associations: a time-series study of emergency department visits in Atlanta |
title_full_unstemmed |
Using logic regression to characterize extreme heat exposures and their health associations: a time-series study of emergency department visits in Atlanta |
title_sort |
using logic regression to characterize extreme heat exposures and their health associations: a time-series study of emergency department visits in atlanta |
publisher |
BMC |
series |
BMC Medical Research Methodology |
issn |
1471-2288 |
publishDate |
2021-04-01 |
description |
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. |
url |
https://doi.org/10.1186/s12874-021-01278-x |
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