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Spatial scale in environmental risk mapping: A Valley fever case study

Heidi E. Brown, Wangshu Mu, Mohammed Khan, Clarisse Tsang, Jian Liu, Daoqin Tong
  • Wangshu Mu
    School of Geography and Development, University of Arizona, Tucson, AZ, United States
  • Mohammed Khan
    Office of Infectious Disease Services, Infectious Disease Epidemiology and Surveillance, Arizona Department of Health, Phoenix, AZ, United States
  • Clarisse Tsang
    Office of Infectious Disease Services, Infectious Disease Epidemiology and Surveillance, Arizona Department of Health, Phoenix, AZ, United States
  • Jian Liu
    Department of Engineering, University of Arizona, Tucson, AZ, United States
  • Daoqin Tong
    School of Geography and Development, University of Arizona, Tucson, AZ, United States

Abstract

Background. Valley fever is a fungal infection occurring in desert regions of the U.S. and Central and South America. Environmental risk mapping for this disease is hampered by challenges with detection, case reporting, and diagnostics as well as challenges common to spatial data handling.
Design and Methods. Using 12,349 individual cases in Arizona from 2006 to 2009, we analyzed risk factors at both the individual and area levels.
Results. Risk factors including elderly population, income status, soil organic carbon, and density of residential area were found to be positively associated with residence of Valley fever cases. A negative association was observed for distance to desert and pasture/ hay land cover. The association between incidence and two land cover variables (shrub and cultivated crop lands) varied depending on the spatial scale of the analysis.
Conclusions. The consistence of age, income, population density, and proximity to natural areas supports that these are important predictors of Valley fever risk. However, the inconsistency of the land cover variables across scales highlights the importance of how scale is treated in risk mapping.

Keywords

Risk mapping, GIS, uncertainty, Valley fever

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Submitted: 2017-03-21 18:26:10
Published: 2017-09-22 09:52:58
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Copyright (c) 2017 Heidi Brown, Wangshu Mu, Mohammed Khan, Clarisse Tsang, Jian Liu, Daoqin Tong

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