Geographic information systems for spatial disease cluster detection, spatio-temporal disease mapping, and health service planning
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Geographic information systems (GIS) are increasingly recognized as an effective and efficient tool to deal with geographic questions in health studies. The overarching research question of this dissertation asks how GIS and spatial analysis can be used to facilitate public health studies. Three aspects of health studies are included: spatial disease cluster detection, spatio-temporal disease mapping, and health service planning. New methods or models are proposed and implemented with GIS in this dissertation to address an important problem in each of the three aspects. First, a redesigned spatial scan statistic (RSScan) is proposed to quickly detect disease clusters in arbitrary shapes. The experimental results indicate that the improved RSScan method generally has higher power and accuracy than three existing methods for detecting the clusters in irregular shapes. Second, to explore the spatio-temporal patterns of lung cancer incidence risks in Georgia between 2000 and 2007, a total of seven hierarchical Bayesian models are developed and compared at the census tract level using a two-year time period as the temporal unit. The study shows the northwest region of Georgia has stably elevated lung cancer incidence risks for all the population groups by race and sex. It also shows that there are strong inverse relationships between socioeconomic status and lung cancer incidence risk in males and weak inverse relationships in females in Georgia. Finally, two transportation models that address the modular capacitated maximal covering location problem (MCMCLP) are proposed and used to optimally site ambulances for Emergency Medical Services (EMS) Region 10 in Georgia. As a component of the allocation-location problems for health service planning, spatial demand representation is discussed and three representation approaches are empirically compared in both problem complexity and representation error. Results of this dissertation contribute to the advancement of geospatial analysis in disease surveillance and health service decision making. Future research could include using GIS and spatial analysis to improve the accuracy of detected clusters, explore the environmental factors related to the spatio-temporal patterns of lung cancer incidence risks in Georgia, and integrate population movement in health service planning.