5242.0: Wednesday, November 15, 2000 - Board 4

Abstract #9911

Climate change and Coccidioidomycosis

Russell P. Lopez, MCRP, Environmental Health, Boston University School of Public Health, Talbot Building 2E, 715 Albany Street, Boston, MA 02118, 617 536-7575, rptlopez@bu.edu and Lolis Rodriguez, Department of Environmental Health, Boston University School of Public Health, Talbot Building 2E, 715 Albany Street, Boston, MA 02118.

Coccidioidomycosis (valley fever) was first identified over a century ago in Argentina and the San Joaquin valley region of California. A fungus that has a very limited ecological distribution annually infects approximately 200,000 people and can be fatal to those with a compromised immune system. Unlike other diseases potentially affected by global climate change, Coccidioidomycosis is directly contracted by breathing in fungus infected dust, no vectors are involved. Data from over 850 weather stations in California, Arizona, Nevada and Utah was obtained and analyzed to produce a model that predicted the geographical distribution of the disease.

Background climate data was adjusted to reflect various climate change scenarios for the four-state region. The results of the climate change scenarios were mapped on a GIS system to assist in the analysis. The scenarios predict that higher temperatures would interact with changes in precipitation. Higher rainfall or lower rainfall would mitigate some of the impacts of rising temperatures. If temperatures rose and rainfall fell, the effects would be the greatest. Steady temperatures and a drought would reduce the extent of the disease. Areas most at risk would vary according to scenario. If worse case projections were realized, Los Angeles and/or the Sacramento valley would be most at risk, raising the potential for millions of new people being exposed to the disease. Central Arizona and southern Nevada/Utah may also be affected. The desert regions would be most likely to have their exposures reduced if rainfall levels fell below a critical limit.

Learning Objectives: 1. How to access web-based climate data. 2. Understanding models of climate-disease interaction. 3. How to use GIS to map existing disease patterns and to predict the impact of differing climate change scenarios

Keywords: Climate, Geographic Information Systems

Presenting author's disclosure statement:
Organization/institution whose products or services will be discussed: None
I do not have any significant financial interest/arrangement or affiliation with any organization/institution whose products or services are being discussed in this session.

The 128th Annual Meeting of APHA