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Int. J. Environ. Res. Public Health 2016, 13(3), 267; doi:10.3390/ijerph13030267

Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella

1
National Climate Predictions and Projections platform (NCPP), NCAR RAL CSAP, 3450 Mitchell Lane, Boulder, CO 80301, USA
2
Department Atmospheric, Oceanic and Space Sciences, University of Michigan, 525 Space Research Building, Ann Arbor, MI 48109-2143, USA
3
CIRES—NOAA/University of Colorado, 325 Broadway, Boulder, CO 80305-3328, USA
4
Department of Global Health, School of Public Health, University of Washington, 1959 NE Pacific Street, Health Sciences Building, Seattle, WA 98195, USA
5
Department of Biostatistics, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109-2029, USA
6
Department of Epidemiology, University of Michigan School of Public Health, 1415 Washington Heights, Ann Arbor, MI 48109-2029, USA
7
NCAR JNT RAL, 3450 Mitchell Lane, Boulder, CO 80301, USA
8
NESII—NOAA/ESRL, 325 Broadway, Boulder, CO 80305-3328, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Jan C. Semenza
Received: 19 August 2015 / Revised: 7 February 2016 / Accepted: 17 February 2016 / Published: 29 February 2016
(This article belongs to the Collection Climate Change and Human Health)
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Abstract

Foodborne diseases have large economic and societal impacts worldwide. To evaluate how the risks of foodborne diseases might change in response to climate change, credible and usable climate information tailored to the specific application question is needed. Global Climate Model (GCM) data generally need to, both, be downscaled to the scales of the application to be usable, and represent, well, the key characteristics that inflict health impacts. This study presents an evaluation of temperature-based heat indices for the Washington D.C. area derived from statistically downscaled GCM simulations for 1971–2000—a necessary step in establishing the credibility of these data. The indices approximate high weekly mean temperatures linked previously to occurrences of Salmonella infections. Due to bias-correction, included in the Asynchronous Regional Regression Model (ARRM) and the Bias Correction Constructed Analogs (BCCA) downscaling methods, the observed 30-year means of the heat indices were reproduced reasonably well. In April and May, however, some of the statistically downscaled data misrepresent the increase in the number of hot days towards the summer months. This study demonstrates the dependence of the outcomes to the selection of downscaled climate data and the potential for misinterpretation of future estimates of Salmonella infections. View Full-Text
Keywords: foodborne disease; Salmonella infections; evaluation; temperature-based heat indices; ARRM and BCCA statistical downscaling methods; Washington D.C. foodborne disease; Salmonella infections; evaluation; temperature-based heat indices; ARRM and BCCA statistical downscaling methods; Washington D.C.
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Guentchev, G.S.; Rood, R.B.; Ammann, C.M.; Barsugli, J.J.; Ebi, K.; Berrocal, V.; O’Neill, M.S.; Gronlund, C.J.; Vigh, J.L.; Koziol, B.; Cinquini, L. Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella. Int. J. Environ. Res. Public Health 2016, 13, 267.

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