Research

Method for mapping population-based case-control studies: an application using generalized additive models

Thomas Webster¹ , Verónica Vieira¹ , Janice Weinberg² and Ann Aschengrau³

¹  Department of Environmental Health, Boston University School of Public Health, Talbot 2E, 715 Albany Street, Boston, MA 02118, USA

²  Department of Biostatistics, Boston University School of Public Health, Talbot 2E, 715 Albany Street, Boston, MA 02118, USA

³  Department of Epidemiology, Boston University School of Public Health, Talbot 2E, 715 Albany Street, Boston, MA 02118, USA

author email corresponding author email

International Journal of Health Geographics 2006, 5:26doi:10.1186/1476-072X-5-26

Published:	9 June 2006

Abstract

Background

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.