Projections of temperature-attributable premature deaths in 209 U.S. cities using a cluster-based Poisson approach. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Schwartz, Joel; Lee, Mihye; Kinney, Patrick; Yang, Suijia; Mills, David; Sarofim, Marcus; Jones, Russell; Streeter, Richard; Juliana, Alexis; Peers, Jennifer; Horton, Radley
• Is Part Of: Environmental health. Volume 14:Issue 1(2015)
• Page Start: 1
• Page End: 15
• Publication Date: 2015-12
• Subjects: Temperature-attributable premature mortality -- United States -- Climate change; Environmentally induced diseases -- Periodicals; Epidemiology -- Periodicals; Occupational diseases -- Periodicals; Toxicology -- Periodicals; 616.98005
• DOI: 10.1186/s12940-015-0071-2
• Languages: English
• ISSNs: 1476-069X

Abstract

Abstract Background A warming climate will affect future temperature-attributable premature deaths. This analysis is the first to project these deaths at a near national scale for the United States using city and month-specific temperature-mortality relationships. Methods We used Poisson regressions to model temperature-attributable premature mortality as a function of daily average temperature in 209 U.S. cities by month. We used climate data to group cities into clusters and applied an Empirical Bayes adjustment to improve model stability and calculate cluster-based month-specific temperature-mortality functions. Using data from two climate models, we calculated future daily average temperatures in each city under Representative Concentration Pathway 6.0. Holding population constant at 2010 levels, we combined the temperature data and cluster-based temperature-mortality functions to project city-specific temperature-attributable premature deaths for multiple future years which correspond to a single reporting year. Results within the reporting periods are then averaged to account for potential climate variability and reported as a change from a 1990 baseline in the future reporting years of 2030, 2050 and 2100. Results We found temperature-mortality relationships that vary by location and time of year. In general, the largest mortality response during hotter months (April – September) was in July in cities with cooler average conditions. The largest mortality response during colder months (October–March) was at the beginning (October) and end (March) of the period. Using data from two global climate models, we projected a net increase in premature deaths, aggregated across all 209 cities, in all future periods compared to 1990. However, the magnitude and sign of the change varied by cluster and city. Conclusions We found increasing future premature deaths across the 209 modeled U.S. cities using two climate model projections, based on constant temperature-mortality relationships from 1997 to 2006 without any future adaptation. However, results varied by location, with some locations showing net reductions in premature temperature-attributable deaths with climate change.