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

Environmental Research

Volumn 132, Issue , 2014, Pages 350-359

  Zhang, Kai ^a   Li, Yun ^b   Schwartz, Joel D ^c   O'Neill, Marie S ^d  

^a UNIVERSITY OF TEXAS SCHOOL OF PUBLIC HEALTH   (United States)

^b UNIVERSITY OF MICHIGAN   (United States)

^c HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

^d UNIVERSITY OF MICHIGAN SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Absolute humidity; Heat; Mortality; Random forests; Temperature; Weather

Indexed keywords

CLIMATE EFFECT; EPIDEMIOLOGY; HEALTH IMPACT; HEALTH RISK; HISTORICAL RECORD; MORTALITY; STATISTICAL ANALYSIS; URBAN AREA;

ARTICLE; CARDIOVASCULAR DISEASE; CAUSE OF DEATH; CEREBROVASCULAR ACCIDENT; CHRONIC OBSTRUCTIVE LUNG DISEASE; CITY; CONGESTIVE HEART FAILURE; CONTROLLED STUDY; ENVIRONMENTAL TEMPERATURE; HEART INFARCTION; HEAT; HUMAN; HUMIDITY; MORTALITY; PNEUMONIA; PREDICTION; PRIORITY JOURNAL; RANDOM FOREST; RESPIRATORY TRACT DISEASE; UNITED STATES; WEATHER;

UNITED STATES;

ABSOLUTE HUMIDITY; HEAT; MORTALITY; RANDOM FORESTS; TEMPERATURE; WEATHER;

CHICAGO; CITIES; HOT TEMPERATURE; HUMANS; MORTALITY; PHILADELPHIA; STATISTICS AS TOPIC;

EID: 84900403486     PISSN: 00139351     EISSN: 10960953     Source Type: Journal    
DOI: 10.1016/j.envres.2014.04.004     Document Type: Article

References (20)

1. Barnett A.G., Tong S., Clements A.C. What measure of temperature is the best predictor of mortality?. Environ. Res. 2010, 110(6):604-611.
2. Bobb J.F., Dominici F., Peng R.D. A Bayesian model averaging approach for estimating the relative risk of mortality associated with heat waves in 105 U.S. cities. Biometrics 2011, 67(4):1605-1616.
3. Breiman L. Random forests. Mach. Learn. 2001, 45(1):5-32.
4. Coull B.A., Wellenius G.A., Gonzalez-Flecha B., Diaz E., Koutrakis P., Godleski J.J. The toxicological evaluation of realistic emissions of source aerosols study: statistical methods. Inhal. Toxicol. 2011, 23:31-41.
5. Faraway J.J. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models 2006, Chapman & Hall/CRC, Boca Raton, FL.
6. Hajat S., Sheridan S.C., Allen M.J., Pascal M., Laaidi K., Yagouti A., et al. Heat-health warning systems: a comparison of the predictive capacity of different approaches to identifying dangerously hot days. Am. J. Public Health 2010, 100(6):1137-1144.
7. Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2009, Springer Science+Business Media, LLC, New York, NY. second edition.
8. Klepeis N.E., Nelson W.C., Ott W.R., Robinson J.P., Tsang A.M., Switzer P., et al. The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants. J. Expo. Anal. Environ. Epidemiol. 2001, 11(3):231-252.
9. Kovats R.S., Hajat S. Heat stress and public health: a critical review. Annu. Rev. Public Health 2007, 29:41-55.
10. Liaw A., Wiener M. Classification and regression by randomForest. R News 2002, 2(3):18-22.
11. Meehl G.A., Tebaldi C. More intense, more frequent, and longer lasting heat waves in the 21st century. Science 2004, 305:994-997.
12. R: A Language and Environment for Statistical Computing 2012, R Foundation for Statistical Computing, Vienna, Austria, (Available, [accessed 5 October 2012]). http://www.R-project.org.
13. Shaman J., Kohn M. Absolute humidity modulates influenza survival, transmission, and seasonality. Proc. Natl. Acad. Sci. USA 2009, 106(9):3243-3248.
14. Steadman R.G. The assessment of sultriness. Part I: a temperature humidity index based on human physiology and clothing science. J. Appl. Meteorol. 1979, 18:861-873.
15. Steadman R.G. The assessment of sultriness. Part II: effects of wind, extra radiation and barometric pressure on apparent temperature. J. Appl. Meteorol. 1979, 18:874-885.
16. Steadman R.G. A universal scale of apparent temperature. J. Climate Appl. Meteorol. 1984, 23:1674-1687.
17. Strobl C., Boulesteix A.-L., Zeileis A., Hothorn T. Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinform. 2007, 8(1):25.
18. Wood S.N. Fast stable direct fitting and smoothness selection for generalized additive models. J. R. Stat. Soc. Series B (Stat. Methodol.) 2008, 70:495-518.
19. Zanobetti A., Schwartz J. Temperature and mortality in nine US cities. Epidemiology 2008, 19(4):563-570.
20. Zhang K., Rood R.B., Michailidis G., Oswald E.M., Schwartz J.D., Zanobetti A., et al. Comparing exposure metrics for classifying 'dangerous heat' in heat wave and health warning systems. Environ. Int. 2012, 46:23-29.