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

Shan Jiang,Matthew J Strickland,Howard H Chang,Joshua L Warren,Andrew J Newman,Shannon E Moss,Yong Chen,Lyndsey A Darrow,Noah Scovronick,Stefanie T Ebelt

doi:10.1186/s12874-021-01278-x

Abstract

BackgroundShort-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.MethodsLogic 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.ResultsFor 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.ConclusionLogic 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.

Highlights

Extreme heat events have significant public health impacts as demonstrated, for example, by historical heat waves in Chicago [1, 2] and Europe [3, 4]
We examine the use of logic regression [27,28,29], a machine learning method, to help identify characteristics of extreme heat events that are associated with adverse health outcomes
We evaluated the performance of the approach in simulation studies and applied the method to a 20-year time-series analysis of daily emergency department (ED) visits in Atlanta, Georgia

Summary

Introduction

Extreme heat events have significant public health impacts as demonstrated, for example, by historical heat waves in Chicago [1, 2] and Europe [3, 4]. Recent epidemiology studies have found consistent shortterm associations between extreme heat events and various cause-specific mortality [5,6,7,8,9] and morbidity outcomes [10,11,12]. Previous studies have defined heat waves as a period where temperature exceeds the 98th percentile over two or more consecutive days [17,18,19]. Many other relative (or absolute) thresholds and durations have been used to define extreme heat in health studies [20]. Short-term associations between extreme heat events and adverse health outcomes are wellestablished in epidemiologic studies. 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

Results

Conclusion