Cited by 152Climate Change and Health ImpactsThermoregulation and physiological responsesAir Quality and Health Impacts

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

Open access
Zhang, Kai, Li, Yun, Schwartz, Joel D., O׳Neill, Marie S.·2014·Environmental Research

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.

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Crossref · 10.1016/j.envres.2014.04.004

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  • Workers felt more uncomfortable in a hot and humid environment than in a hot and dry environment, with heat stress further exacerbated by heavy workload, personal protective equipment (PPE), among other factors.

    “Workers felt more uncomfortable in a hot and humid environment than in a hot and dry environment, with heat stress further exacerbated by heavy workload, personal protective equipment (PPE), among other factors [9].”

    From Assessing Heat Stress and Health among Construction Workers in a Changing Climate: A Review (2018) · cites this paper as [9]

    The Discussion (page unavailable) says absolute humidity may reflect physiologically stressful heat exposure. It does not compare workers’ discomfort in hot-humid and hot-dry conditions or discuss heavy workload or PPE as factors that exacerbate heat stress. (LLM, conf=0.80)

    Experimental — accuracy not yet measured
  • Several studies in environmental epidemiology concluded that the 'best measure to heat stress varied with populations and regions'.

    “Several studies in environmental epidemiology that have examined the associations between heat and mortality concluded that the ‘best measure to heat stress varied with populations and regions’ [9].”

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Research works printed but not linked: 12

  1. Barnett AG Tong S Clements AC 2010 What measure of temperature is the best predictor of mortality? Environ Research 110 6 604 611 20519131
  2. Bobb JF Dominici F Peng RD 2011 A Bayesian model averaging approach for estimating the relative risk of mortality associated with heat waves in 105 U.S. cities Biometrics 67 4 1605 16 21447046 PMC3128186
  3. Coull BA Wellenius GA Gonzalez-Flecha B Diaz E Koutrakis P Godleski JJ 2011 The toxicological evaluation of realistic emissions of source aerosols study: Statistical methods Inhal Toxicol 23 31 41 21913820 PMC3641936
  4. Hajat S Sheridan SC Allen MJ Pascal M Laaidi K Yagouti A 2010 Heat-health warning systems: A comparison of the predictive capacity of different approaches to identifying dangerously hot days Am J Public Health 100 6 1137 44 20395585 PMC2866620
  5. Hastie T Tibshirani R Friedman J 2009 The Elements of Statistical Learning: Data Mining, Inference, and Prediction Second New York, NY Springer Science+Business Media, LLC
  6. Klepeis NE Nelson WC Ott WR Robinson JP Tsang AM Switzer P 2001 The National Human Activity Pattern Survey (NHAPS): A resource for assessing exposure to environmental pollutants J Expo Anal Environ Epidemiol 11 3 231 252 11477521
  7. Kovats RS Hajat S 2007 Heat stress and public health: A critical review Annu Rev Public Health 29 41 55 18031221
  8. Metzger KB Ito K Matte TD 2010 Summer heat and mortality in New York City: How hot is too hot? Environ Health Perspect 118 1 80 86 20056571 PMC2831972
  9. Shaman J Kohn M 2009 Absolute humidity modulates influenza survival, transmission, and seasonality Proc Natl Acad Sci 106 9 3243 3248 19204283 PMC2651255
  10. Strobl C Boulesteix AL Zeileis A Hothorn T 2007 Bias in random forest variable importance measures: Illustrations, sources and a solution BMC Bioinformatics 8 1 25 17254353 PMC1796903
  11. Zanobetti A Schwartz J 2008 Temperature and mortality in nine US cities Epidemiology 19 4 563 570 18467963 PMC3722554
  12. Zhang K Rood RB Michailidis G Oswald EM Schwartz JD Zanobetti A 2012 Comparing exposure metrics for classifying ‘dangerous heat’ in heat wave and health warning systems Environ Int 46 23 29 22673187 PMC3401591

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Authors
Zhang, K., Li, Y., Schwartz, J. D., & O׳Neill, M. S.
Affiliations
  • The University of Texas Health Science Center at Houston (US)
  • University of Michigan (US)
  • Harvard University (US)
Corresponding author
Zhang, Kai
Venue
Environmental Research
Publisher
Elsevier BV
Published
2014-07
Volume
132
Pages
350-359
ISSN
0013-9351 (ISSN-L)
OpenAlex
W2050948329
Cited by
152
References
1
Citations by year
2026: 11 · 2025: 21 · 2024: 18 · 2023: 10 · 2022: 20 · 2021: 14 · 2020: 10 · 2019: 6 · 2018: 10 · 2017: 8 · 2016: 12 · 2015: 9 · 2014: 3
FWCI
7.01
Type
journal article
Language
en
Open access
Locations
  • Environmental Research (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • U.S. Environmental Protection Agency
  • Star Scientific Foundation (R832752010, R18 EH 000348)
  • National Institute of Environmental Health Sciences (R01 ES-016932, R21 ES-020695)
  • University of Michigan
Topics
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Climate Change and Health Impacts
  • Health Sciences › Medicine › Physiology › Thermoregulation and physiological responses
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Air Quality and Health Impacts
Keywords
heat-related mortality, heat stress, meteorological parameters, apparent temperature, absolute humidity, random forest, heat alerts, cause-specific mortality, all-cause mortality, urban heat
MeSH terms
Chicago, Cities, Hot Temperature, Humans, Mortality, Statistics as Topic, Philadelphia

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