@article{ref1,
title="What weather variables are important in predicting heat-related mortality? A new application of statistical learning methods",
journal="Environmental research",
year="2014",
author="Zhang, Kai and Li, Yun and Schwartz, Joel D. and O׳Neill, Marie S.",
volume="132C",
number="",
pages="350-359",
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.<p /> <p>Language: en</p>",
language="en",
issn="0013-9351",
doi="10.1016/j.envres.2014.04.004",
url="http://dx.doi.org/10.1016/j.envres.2014.04.004"
}