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Geospatial Linkage to Public Health Asthma Outcome

      Background: In 2003, Canada and the United States unveiled a joint strategy aimed at improving border air quality and addressing related health concerns. This proposed international, multi-disciplinary, and multi-institutional study builds upon geospatial models developed at the University of Windsor to identify and predict environmental influences on health outcomes in Detroit and Windsor.
      Purpose: The overall aim of this research is to develop spatial-temporal models using geographic information systems (GIS) to identify and predict environmentally induced health conditions in adults and children (5 years and older) in and across Detroit and Windsor. This study specifically measures spatial variability of airborne contaminants including NO2, sulfur dioxide (SO2), particulate matter (PM), volatile organic compounds (VOCs), and polycyclic aromatic hydrocarbons (PAHs) and their impact on the public health outcomes of asthma.
      Methodology: Our approach involved an international, multi-institutional, and multi-disciplinary team that is (1) collecting and modeling air quality data in Detroit, Michigan and Windsor, Ontario; (2) collecting and evaluating asthma health outcome information from Henry Ford Hospital, Windsor Health System, and Health Canada databases; (3) integrating the environmental and health outcome data into a GIS framework. The spatial relationships between environmental and health information will be analyzed to evaluate the study hypotheses.
      Findings: Air quality in Detroit and Windsor was sampled in September 2008 and June 2009. Health data related to asthma morbidity is currently being collected from databases in Detroit and Windsor and will be presented.
      Summary Concluding Statement: Our central hypothesis is that there are correlations among mappable environmental attributes and health indicators that can be used to understand and improve urban community health outcomes. Our findings will help in developing and applying spatial models of air quality that predict asthma morbidity in the Detroit and Windsor areas.