Show simple item record

dc.contributor.authorWebster, Thomasen_US
dc.contributor.authorVieira, Verónicaen_US
dc.contributor.authorWeinberg, Janiceen_US
dc.contributor.authorAschengrau, Annen_US
dc.date.accessioned2012-01-11T15:51:16Z
dc.date.available2012-01-11T15:51:16Z
dc.date.copyright2006en_US
dc.date.issued2006-6-9en_US
dc.identifier.citationWebster, Thomas, Verónica Vieira, Janice Weinberg, Ann Aschengrau. "Method for mapping population-based case-control studies: an application using generalized additive models" International Journal of Health Geographics 5:26. (2006)en_US
dc.identifier.issn1476-072Xen_US
dc.identifier.urihttp://hdl.handle.net/2144/3088
dc.description.abstractBACKGROUND. Mapping spatial distributions of disease occurrence and risk can serve as a useful tool for identifying exposures of public health concern. Disease registry data are often mapped by town or county of diagnosis and contain limited data on covariates. These maps often possess poor spatial resolution, the potential for spatial confounding, and the inability to consider latency. Population-based case-control studies can provide detailed information on residential history and covariates. RESULTS. Generalized additive models (GAMs) provide a useful framework for mapping point-based epidemiologic data. Smoothing on location while controlling for covariates produces adjusted maps. We generate maps of odds ratios using the entire study area as a reference. We smooth using a locally weighted regression smoother (loess), a method that combines the advantages of nearest neighbor and kernel methods. We choose an optimal degree of smoothing by minimizing Akaike's Information Criterion. We use a deviance-based test to assess the overall importance of location in the model and pointwise permutation tests to locate regions of significantly increased or decreased risk. The method is illustrated with synthetic data and data from a population-based case-control study, using S-Plus and ArcView software. CONCLUSION. Our goal is to develop practical methods for mapping population-based case-control and cohort studies. The method described here performs well for our synthetic data, reproducing important features of the data and adequately controlling the covariate. When applied to the population-based case-control data set, the method suggests spatial confounding and identifies statistically significant areas of increased and decreased odds ratios.en_US
dc.description.sponsorshipNational Institute of Enviornmental Health (5P42ES007381)en_US
dc.language.isoenen_US
dc.publisherBioMed Centralen_US
dc.rightsCopyright 2006 Webster et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/2.0en_US
dc.titleMethod for Mapping Population-Based Case-Control Studies: An Application Using Generalized Additive Modelsen_US
dc.typearticleen_US
dc.identifier.doi10.1186/1476-072X-5-26en_US
dc.identifier.pubmedid16764727en_US
dc.identifier.pmcid1526437en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Copyright 2006 Webster et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as Copyright 2006 Webster et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.