Geostatistical software for the analysis of individual-level epidemiologic data

DESCRIPTION (provided by applicant): The overall objective of this project is to develop the first GIS-based software to offer tools that are specifically designed for the geostatistical analysis of both aggregated and individual-level health data, providing: description of spatial patterns of cancer incidence and mortality rates and identification of scales of variability, spatial interpolation of individual-level data to create isopleth maps of relative risk, identification of clusters and hotspots of significantly high or low incidence or mortality rates, detection of significant discrepancies between health outcomes for different ethnic or racial groups (health disparities), and visualization of changes in disparity through time. This product will allow the investigation of geographic and ethnic variations in cancer stage at diagnosis and survival data, and the exploration of relationships between health outcomes and potential factors, such as environmental and occupational exposures, socio-economic conditions, proximity to screening centers, leading to: (1) a better understanding of the causes underlying observed geographic and racial disparities in cancer incidence, mortality and survival, and (2) long-term quantification of the benefits of current strategies for reducing the disproportionate incidence of cancer morbidity and mortality among minorities and the medically underserved in the United States. Instructional materials will be developed to promote the use of this relatively new methodology among health scientists. Phase I of the project will: risk maps from individual-level data and statistical tests to detect significant differences in cancer rates among sub-populations and across space. into the software: TerraSeer Space-Time Intelligence System(tm) (STIS(tm)). geographical and racial disparities. The substantial benefit of this research is its utility in accessing, linking, mapping and analyzing diverse population-based and individual-level data including cancer vital events and measures of socioeconomic, demographic, behavioral and environmental conditions. The methods developed in this project will help understanding the causes underlying the disparities in cancer incidence, mortality and morbidity observed across space and among sub-populations, as well as lead to a better assessment of the benefits of current strategies for reducing these disparities.