What weather variables are important in predicting heat-related mortality? A new application of statistical learning methods.

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 predict

SUBMITTER: Zhang K 

PROVIDER: S-EPMC4091921 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature