期刊
ENVIRONMENTAL RESEARCH
卷 132, 期 -, 页码 350-359出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.envres.2014.04.004
关键词
Absolute humidity; Heat; Mortality; Random forests; Temperature; Weather
资金
- Graham Environmental Sustainability Institute at the University of Michigan
- U.S. Environmental Protection Agency Science to Achieve Results (STAR [R832752010]
- U.S. Centers for Disease Control and Prevention [R18 EH 000348]
- National Institute for Environmental Health Sciences [R01 ES-016932, R21 ES-020695]
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. (C) 2014 Elsevier Inc. All rights reserved.
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