Applied Spatial Analysis in Epidemiology COURSE DURATION Course material will be available from: June 1- June 30, 2018 INSTRUCTOR Rena Jones, PhD MS rena.jones@yale.edu COURSE DESCRIPTION This course will provide students an overview of the application of spatial analysis in epidemiologic research, including analytic approaches that integrate Geographic Information Systems (GIS) and standard statistical analyses to enhance epidemiologic assessments. The course will cover common approaches for identifying spatial clustering, spatial interpolation and imputation methods, geographically weighted and land use regression, and basic spatial statistics useful for studying the spatial distribution of exposure and disease. Concepts will be applied in each module to a range of content areas in epidemiology, with current examples of population health research questions addressed using spatial analytic approaches. Students will conduct spatial analyses on actual datasets to reinforce the learning objectives. PREREQUISITES Previous theoretical training in statistics or biostatistics and experience with statistical programming software (e.g., SAS, STATA, R) and conducting epidemiologic analyses (e.g., regression) is strongly recommended. An introductory-level theoretical understanding of GIS and its applications in health research is preferred, as the course emphasizes application of GIS methods to epidemiologic data. However, hands-on experience using GIS software (e.g., ArcGIS, Mapinfo) is not necessary. Please note: This course is designed to maximize accessibility of content and is therefore not centered on use of specific analytic software (e.g., R). Most demonstrations and practice exercises will be conducted using free or online-based software programs. Students will need access to a computer with high-speed internet access and the following software: Spreadsheet software (any kind Windows Excel, OpenOffice Calc, etc.) GeoDa: Available for free download at this link: http://geodacenter.github.io/ SatScan: Available for free download at this link: http://www.satscan.org/ SAS, STATA, SPSS, R, or other data analysis software of your preference Google Earth: Free version available at this link: https://www.google.com/earth/ COURSE LEARNING OBJECTIVES The primary objective of this course is to provide students with the tools to apply spatial analytic methods in the conduct of epidemiologic analysis. To reach that objective, students will gain
knowledge in the underlying concepts of spatial analysis and gain facility in the use of tools to conduct spatial analyses of epidemiologic data. By the end of the course, students will be able to: Identify spatial analytic methods to address common epidemiologic study needs Implement appropriate statistical tools to evaluate spatial patterns in disease and risk factors Conduct spatial interpolation, imputation, and regression Construct a geography-based exposure assessment Apply spatial analytic techniques to address an epidemiologic research question COURSE READINGS The following textbook is recommended: Spatial Analysis in Epidemiology. Pfieffer and Robinson, eds. Oxford University Press, 2008. Further recommended readings: Gleason J.A., et al. Analysis of population-level determinants of legionellosis: spatial and geovisual methods for enhancing classification of high-risk areas. Int J Health Geogr. 2017; 16:45. Jones, R.R. et al. Accuracy of residential geocoding in the Agricultural Health Study. Int J Health Geogr. 2014; 13:37. Zirkle KW, et al. Assessing the relationship between groundwater nitrate and animal feeding operations in Iowa (USA). Sci Tot Environ, 2016; 566-567:1062-1068. Curriero, F.C. et al. Using imputation to provide location information for nongeocoded addresses. PLoS One. 2010. 10;5(2): 8998. Francis S.S. et al. Unusual space-time patterning of the Fallon, Nevada leukemia cluster: Evidence of an infectious etiology. Chemico-Biol Interact. 2012. 196; 102-109. Jerrett M. et al. Spatial analysis of air pollution and mortality in California. Am J Respir Crit Care Med. 2013. 188(5);593-9. Whitworth, K.W. et al. Kriged and modeled ambient air levels of benzene in an urban environment: an exposure assessment study. Environ Health. 2011. 21: 10-21. Salje, H. et al. Revealing the microscale spatial signature of dengue transmission and immunity in an urban population. Proc Natl Acad Sci. 2012. 109(24); 9535-8. Additional material for the course is drawn from the following sources:
Lawson, A.B. Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, 2nd Edition. 2013. Chapman & Hall/CRC Interdisciplinary Statistics. Waller, L.A. and Gotway, C.A. Applied Spatial Statistics for Public Health Data. 2004. Wiley. Wong, D.W.S. and Lee, J. A. Statistical Analysis of Geographic Information with ArcView GIS and ArcGIS. 2005. Wiley. COURSE STRUCTURE The on-line version of this course is meant to be a highly self-directed learning style that enables greater flexibility for course participants to complete the course objectives at the times and pace most conducive to the respective schedules of participants. This course utilizes the learning management software, Canvas: https://canvas.instructure.com/login To get started all registrants will receive and e-mail inviting them to join on the first day of the course offering. Upon receiving the e-mail, participants should follow the instructions to get signed up for a Canvas account. This course is anchored around 12 recorded lectures organized into five modules. The recorded lectures are audio and screen recordings that will enable the instructor to teach by moving between a PowerPoint lecture setting and the other software environment with relative ease so that conceptual points can be made while also embedding applied examples of course concepts using various mapping and spatial analysis tools. Data: There will also be several datasets provided to participants so they can replicate the examples shown in the lessons and also experiment on their own. The data sets can be found on-line at the Canvas course site. A few of the datasets used for in-class examples are pseudo datasets that are created for teaching purposes. Several other datasets are publicly available and sources will be provided. COURSE SCHEDULE (On-line modules and recommended timeline for completion) Session 1 Introduction to Spatial Analysis for Epidemiology June 1-5 -Spatial effects in the context of epidemiology -Mapping/cartography basics, including types of spatial data, common tools, and key terminology -Examples of spatial analyses and their application in diverse epidemiologic research settings Lessons: #1 #2 Exercises: #1
Recommended Reading: Pfeiffer et al., Chapters 1-3; Jones et al., 2014; Curriero et al., 2010 Session 2 Spatial Clustering June 6-10 -Concept of clustered events -Disease cluster investigation approaches -Difference between global versus local clustering of epidemiologic data -Distinction between clustering methods for aggregated versus point data Lessons: #3 #5 Exercises: #2-3 Recommended Reading: Pfeiffer et al., Chapters 4-5; Gleason et al., 2017; Francis et al., 2012 Session 3 Spatial Variation, Interpolation, and Imputation June 11-15 -Importance of underlying spatial patterns in health/ risk factor data -Spatial smoothing approaches and implications for identifying variation in risk or risk factors -Spatial interpolation methods, their assumptions and limitations - Geo-spatial imputation methods and their application Lessons: #6-8 Exercises: #4
Recommended Reading: Pfeiffer et al., Chapter 6; Curriero et al., 2010; Whitworth et al., 2011; Jerrett et al., 2013. Session 4 Risk factors, Risk assessment, and Disease Management June 16-20 -Spatiotemporal dynamics of disease -Application of common epidemiologic regression approaches to hypothesis testing with spatial data -Contrast between frequentist versus Bayesian approaches; trend and discriminant analysis -How results can be used for disease control on the population level Lessons: #9-10 Exercises: #5 Recommended Reading: Pfeiffer et al., Chapters 7-8; Zirkle et al; 2016; Gleason et al., 2017 Session 5 Application: Mapping, Assessing, and Interpreting Spatial Data June 21-30 -Review key concepts of clustering, spatial variation, and risk assessment -Mapping approaches to assess data and develop an epidemiologic hypothesis -How to design a spatial approach to address an epidemiologic question -Application of knowledge of disease mechanisms or risk factors to interpret findings from spatial analysis Lessons: #11-12 Exercises: #6-7 *This last module will serve as a culminating session to apply and integrate course concepts into the evaluation of several case studies. Recommended Reading: Saljie et al., 2012