Elevator Pitch
Using QGIS and Maxent we explore a rich multi-year antibiotic resistant bacterial infection population health dataset covering Atlanta, Georgia. We used Maxent to model potential staphylococcal bacteria locations and QGIS to find trends in the data with different data visualization techniques.
Description
Socio environmental risks and specific bacterial strains have defined the movement and development of antibiotic resistant bacterial infections in the US. Use of geocoded electronic health record (EHR) data to prevent spread of disease is limited in public health and health service research. Using FOSS4G tools, we explore population health data from 2002-2010, the impact of specific strains of antibiotic resistant staphylococcal bacteria on the development of community-acquired infections in children living in Atlanta, Georgia. Specifically, we demonstrate how FOSS4G tools can be used to identify risk factors for the development of antibiotic resistant bacterial infections. These are are tied to place-based social and health determinants and otherwise would not be uncovered using traditional electronic health record data analyses. For spatial analyses, 2010 US Census block group variables were used along with patients’ unique street addresses. Publicly available datasets on specific place-based neighborhood characteristics are used to create a weighted spatial statistical model. For example, data on neighborhood daycare centers and healthcare providers are available for the Atlanta metropolitan statistical area, which can be combined with individual patient demographics, and relevant census data at the block group level. This is the first in depth exploration of how open source geographic information system tools can be applied to public health surveillance and measures to stop the spread of the antibiotic resistant bacterial infections among our pediatric population.
Notes
This talk is intended for anyone interested in applications of FOSS4G software for public health applications. The co-authors are content experts in different domains, but collectively, they are developing a prediction, spatially driven, statistical model to prevent the spread of antibiotic resistance bacterial infections from a public health perspective. How to access and use open source GIS tools and publicly available data are exemplified in this talk.
Co-Authors: Kurt Menke. A former archaeologist, Kurt Menke is a GIS specialist based out of Albuquerque, New Mexico, USA. He runs his own GIS consulting business Bird’s Eye View. He has a broad skillset. He is a spatial analyst, cartographer, web map developer, trainer/teacher and author. He has used Maxent to model a variety of phenomena. He is an open source GIS authority and has authored 3 QGIS books: 2 editions of Mastering QGIS, 2 editions of Discover QGIS, and QGIS for Hydrologic Applications (in development).
Lilly Immergluck. A general pediatrician and pediatric infectious disease specialist, Dr. Immergluck is a population health service researcher based in Atlanta, Georgia. Dr. Immergluck has studied the issues surrounding antibiotic resistant bacterial infections, particularly those which originate from community settings for more than two decades. Her goal is to merge her clinical expertise with her research interest to improve healthcare delivery. Specifically, she is working to develop a predictive, place-based spatial statistical model to prevent the further spread of antibiotic resistant bacterial infections; a problem which continues to increase nationally and worldwide. She is learning how to apply open source GIS tools to achieve her goal.
Traci Leong, PhD is a Research Assistant Professor in the Department of Biostatistics and Bioinformatics at Emory University School of Public Health. Dr. Leong has longstanding collaborations with research faculty in the Emory Department of Pediatrics and Children’s Healthcare of Atlanta as a biostatistician for the Pediatrics Biostatistics core and is the sole biostatistician for the Division of Hospital Medicine at Emory University Hospital. Areas of research include clinical informatics with a particular focus on spatial statistics and geographic information systems. She is also the Director of the Atlantic Pediatric Device Consortium (APDC)Biostatistics Core(an FDA-funded consortium) where she has advised pediatric medical devices (including many POC’s) from concept to FDA submission. Designing clinical, pre-clinical and animal studies and analyzing/reporting the data have also been a part of her responsibilities.
Lance Waller.
Professor at Emory Rollins School of Public Health and former Department Chair for Biostatistics and Bioinformatics, Dr. Waller has been working with Dr. Immergluck in developing the spatial statistical model demonstrated in this talk. Dr. Waller received his PhD in Operations Research from Cornell University in 1992. His interests involve statistical analysis of spatially referenced data. Examples include tests of spatial clustering of disease cases, for example around a hazardous waste site; small area estimation; hierarchical models with spatially structured random effects; and spatial point process models. Recent applications include spatiotemporal mapping of disease rates, statistical methods for assessing environmental justice, the analysis of spatial trends in Lyme disease incidence and reporting, spatial modelling of the spread of raccoon rabies, and point process analysis of sea turtle nesting locations in Florida. He is interested in both the statistical methodology, and the environmental and epidemiologic models involved in the analysis of this type of data. He teaches courses in spatial biostatistics, applied linear models, and Geographic Information Systems (GIS) in Public Health.