Analyzing Geographic Patterns of Disease Incidence: Rates of Late-Stage Colorectal Cancer in Iowa

Abstract

This study, using geocodes of the locations of residence of newly diagnosed colorectal cancer patients from the Iowa Cancer Registry, computed continuous spatial patterns of late-stage rates of colorectal cancer in Iowa. Variations in rates in intrahospital service regions were as great as interhospital service regions, shown by analysis of variance tests. Some of the spatial variations observed could be explained, using a general linear regression model on individual-level data, by spatial variations in attributes of individuals and their relationships to health resources. We show how this source of variation can be removed from the original map leaving a new map showing the remaining variation in late-stage rate not explained by these relationships. We argue that it would be more appropriate to organize prevention and control activities targeted at the areas with higher than expected late-stage rates shown on this map. The originality of this approach is in the integration of geocoded data from a cancer registry with methods of spatial analysis that provide considerable geographic detail in the cancer rate while controlling for rate stabilization and reliability due to the small number problem.

Modelling spatial concordance between Rocky Mountain spotted fever disease incidence and habitat probability of its vector Dermacentor variabilis (American dog tick)

“…Maps based on this approach are known as kernel density estimation maps. Using kernel density estimation allows the normalization of incidence rates that would otherwise be over-or under-estimated, creating a more accurate representation of disease incidence (Rushton et al, 2004;Tiwari and Rushton, 2005). More detailed discussions of spatial filters are available (Brillinger, 1994;Kafadar, 1994), and several illustrations of the method can be found (Lai et al, 2004;Yang et al, 2006).…”

Spatial Analysis of Colorectal Cancer Cases in Kuala Lumpur

“…GIS helped to identify two areas where the observed number of late-stage cancers was different than the expected numbers from the distribution in the rest of the state. This would help in the organization of prevention and control activities targeted at the areas with higher than expected late-stage colorectal cancer rates shown through GIS (Rushton et al, 2004).…”

Characteristics of Mammography Facility Locations and Stage of Breast Cancer at Diagnosis in Chicago

“…Following the example of other investigators, we used stage as a surrogate measure of periodic screening mammography utilization. [11][12][13][14][15] Our central hypothesis was that factors such as travel distance and public transportation service, safety of neighborhoods surrounding facilities, and the degree to which those neighborhoods are socially and economically similar to one's own neighborhood influence likelihood of utilizing the service and, accordingly, stage of cancer at the time of diagnosis. Though not directly tested here, these factors may alter the perceived risk/benefit ratio or desirability of the service and, together with other factors such as insurance status and receiving a physician's recommendation, influence likelihood of obtaining periodic screening.…”