MapGAM: Mapping geographic disparities in health outcomes using individual-level epidemiologic data
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1
University of California, Irvine, United States
As location is often an important predictor of disease risk, epidemiological studies and disease registries now routinely collect residential histories. Growing public health concerns of geographic disparities in health outcomes has prompted the need for flexible approaches to investigate the role of risk factors in spatial variation. MapGAM is a user-friendly R package that provides researchers and practitioners with a unified methodology for spatially analyzing individual-level geographic data and mapping the resulting point estimates and confidence intervals within R. MapGAM uses generalized additive models (GAMs) with a non-parametric bivariate smooth term of location to analyze geographic patterns in common epidemiologic datasets including case-control and survival data. The association between health outcomes and location can be estimated while simultaneously assessing the contribution from spatially-varying predictors such as socioeconomic characteristics or environmental exposures. MapGAM also includes convenient functions for efficient control sampling over space, optimal selection of smoothing parameters, and prediction of spatial associations for a continuous study area. We demonstrate the utility of MapGAM in different epidemiologic settings where the role of environmental exposures on geographic variation is assessed. The package is freely available under a GNU General Public License from the Comprehensive R Archive Network at https://CRAN.R-project.org/package=MapGAM.
Keywords:
Epidemiology,
Disease cluster,
Additive modeling,
Residential histories,
Spatio-temperal pattern
Conference:
GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data, Davis, United States, 8 Oct - 10 Oct, 2019.
Presentation Type:
Keynote
Topic:
Emerging GIS, data science and sensor technologies adapted to animal, plant and human health, including precision medicine and precision farming
Citation:
Vieira
V
(2019). MapGAM: Mapping geographic disparities in health outcomes using individual-level epidemiologic data.
Front. Vet. Sci.
Conference Abstract:
GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data.
doi: 10.3389/conf.fvets.2019.05.00001
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Received:
30 Jul 2019;
Published Online:
27 Sep 2019.
*
Correspondence:
Dr. Veronica Vieira, University of California, Irvine, Irvine, United States, vvieira@uci.edu