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

Zhang, Kai, Li, Yun, Schwartz, Joel D., O׳Neill, Marie S.
Environmental Research 2014 v.132 pp. 350-359
cities, data collection, death, epidemiology, heat, heat stress, humidity, learning, mortality, prediction, risk, summer, temperature, Arizona, Illinois, Michigan, Pennsylvania
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.