Citation

Zhang K, Li Y, Schwartz JD, O׳Neill MS. Environ. Res. 2014; 132C: 350-359.

Affiliation

Departments of Environmental Health Sciences and Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, USA.

Copyright

(Copyright © 2014, Elsevier Publishing)

DOI

10.1016/j.envres.2014.04.004

PMID

24834832

Abstract

Hot weather increases risk of mortality. Previous studies used different sets of weather variables to characterize heat stress, resulting in variation in heat-mortality associations depending on the metric used. We employed a statistical learning method - random forests - to examine which of the various weather variables had the greatest impact on heat-related mortality. We compiled a summertime daily weather and mortality counts dataset from four U.S. cities (Chicago, IL; Detroit, MI; Philadelphia, PA; and Phoenix, AZ) from 1998 to 2006. A variety of weather variables were ranked in predicting deviation from typical daily all-cause and cause-specific death counts. Ranks of weather variables varied with city and health outcome. Apparent temperature appeared to be the most important predictor of heat-related mortality for all-cause mortality. Absolute humidity was, on average, most frequently selected as one of the top variables for all-cause mortality and seven cause-specific mortality categories. Our analysis affirms that apparent temperature is a reasonable variable for activating heat alerts and warnings, which are commonly based on predictions of total mortality in next few days. Additionally, absolute humidity should be included in future heat-health studies. Finally, random forests can be used to guide the choice of weather variables in heat epidemiology studies.

Language: en