The overall objective of this project is to develop the first GIS-based software to offer tools that are specifically designed for the space-time analysis and detection of cancer disparities, providing: description of spatial patterns of cancer incidence and mortality rates and identification of scales of variability, spatial smoothing and filtering to correct for statistical instability caused by the smaller size of minority populations, detection of clusters and hotspots of significantly high or low health disparities, and visualization of changes in disparity through time. This product will allow the investigation and visualization of relationships between health disparity data and potential factors, such as environmental or occupational exposures, socio-economic conditions, leading to: (1) a better understanding of the causes underlying observed racial disparities in cancer incidence, mortality and morbidity (i.e. cancer epidemiology), 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: a) Build on a firm foundation of completed simulation studies to further demonstrate and evaluate the ability of geostatistics to filter spatially varying noise, in particular for minority populations that commonly display drastic variations among geographical units. b) Develop statistical tests to detect significant differences in cancer rates among sub-populations, and use Local Indicators of Spatial Autocorrelation (LISA) to identify clusters and local anomalies of health disparities. c) Develop and test software prototype to perform the small number correction and detection of health disparities.
| Goovaerts, Pierre (2006) Geostatistical analysis of disease data: visualization and propagation of spatial uncertainty in cancer mortality risk using Poisson kriging and p-field simulation. Int J Health Geogr 5:7 |
| Goovaerts, Pierre (2005) Geostatistical analysis of disease data: estimation of cancer mortality risk from empirical frequencies using Poisson kriging. Int J Health Geogr 4:31 |
