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What weather variables are important in predicting heat-related mortality? A new application of statistical learning methods
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Details:
  • Pubmed ID:
    24834832
  • Pubmed Central ID:
    PMC4091921
  • Description:
    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.

  • Document Type:
  • Collection(s):
  • Funding:
    P30 ES000002/ES/NIEHS NIH HHS/United States
    R01 ES-016932/ES/NIEHS NIH HHS/United States
    R01 ES015172/ES/NIEHS NIH HHS/United States
    R01 ES016932/ES/NIEHS NIH HHS/United States
    R18 EH 000348/EH/NCEH CDC HHS/United States
    R21 ES-020695/ES/NIEHS NIH HHS/United States
    R21 ES020695/ES/NIEHS NIH HHS/United States
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