Event Abstract Back to Event MapGAM: Mapping geographic disparities in health outcomes using individual-level epidemiologic data Veronica Vieira1* 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 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 30 Jul 2019; Published Online: 27 Sep 2019. * Correspondence: Dr. Veronica Vieira, University of California, Irvine, Irvine, United States, vvieira@uci.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Veronica Vieira Google Veronica Vieira Google Scholar Veronica Vieira PubMed Veronica Vieira Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.