An examination of disparities in cancer incidence in Texas using Bayesian random coefficient models
- Published
- Accepted
- Subject Areas
- Epidemiology, Public Health, Statistics
- Keywords
- health disparities, Bayesian modeling, cancer incidence, INLA
- Copyright
- © 2015 Sparks
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2015. An examination of disparities in cancer incidence in Texas using Bayesian random coefficient models. PeerJ PrePrints 3:e814v3 https://doi.org/10.7287/peerj.preprints.814v3
Abstract
Disparities in cancer risk exist between ethnic groups in the United States. These disparities often result from differential access to healthcare, differences in socioeconomic status and differential exposure to carcinogens. This study uses cancer incidence data from the population based Texas Cancer Registry to investigate the disparities in digestive and respiratory cancers from 2000 to 2008. A Bayesian hierarchical regression approach is used. All models are fit using the INLA method of Bayesian model estimation. Specifically, a spatially varying coefficient model of the disparity between Hispanic and Non-Hispanic incidence is used. Results suggest that a spatio-temporal heterogeneity model best accounts for the observed Hispanic disparity in cancer risk. Overall, there is a significant disadvantage for the Hispanic population of Texas with respect to both of these cancers, and this disparity varies significantly over space. The greatest disparities between Hispanics and Non-Hispanics in digestive and respiratory cancers occur in eastern Texas, with patterns emerging as early as 2000 and continuing until 2008.
Author Comment
This is a revision of the previous version, with some grammar correction, remade maps and new figures showing the significance of the spatio-temporal clusters.