Climate change-related temperature impacts on warm season heat mortality: a proof-of-concept methodology using BenMAP

Environ Sci Technol. 2011 Feb 15;45(4):1450-7. doi: 10.1021/es102820y. Epub 2011 Jan 19.

Abstract

Climate change is anticipated to raise overall temperatures and is likely to increase heat-related human health morbidity and mortality risks. The objective of this work was to develop a proof-of-concept approach for estimating excess heat-related premature deaths in the continental United States resulting from potential changes in future temperature using the BenMAP model. In this approach we adapt the methods and tools that the US Environmental Protection Agency uses to assess air pollution health impacts by incorporating temperature modeling and heat mortality health impact functions. This new method demonstrates the ability to apply the existing temperature-health literature to quantify prospective changes in climate-sensitive heat-related mortality. We compared estimates of future temperature with and without climate change and applied heat-mortality health functions to estimate relative changes in heat-related premature mortality. Using the A1B emissions scenario, we applied the GISS-II global circulation model downscaled to 36-km using MM5 and formatted using the Meteorology-Chemistry Interface Processor. For averaged temperatures derived from the 5 years 2048-2052 relative to 1999-2003 we estimated for the warm season May-September a national U.S. estimate of annual incidence of heat-related mortality to be 3700-3800 from all causes, 3500 from cardiovascular disease, and 21 000-27 000 from nonaccidental death, applying various health impact functions. Our estimates of mortality, produced to validate the application of a new methodology, suggest the importance of quantifying heat impacts in economic assessments of climate change.

MeSH terms

  • Cardiovascular Diseases / mortality*
  • Climate Change / mortality*
  • Forecasting
  • Hot Temperature / adverse effects*
  • Humans
  • Incidence
  • Models, Theoretical*
  • Prospective Studies
  • Seasons
  • United States / epidemiology