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GIS and Health Promotion

GIS and Health Promotion. Ellen K. Cromley, Ph.D. Center for Health, Intervention, and Prevention December 2, 2010 Storrs, Connecticut. Purpose. Provide an overview of GIS G eographic I nformation S ystems

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GIS and Health Promotion

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  1. GIS and Health Promotion Ellen K. Cromley, Ph.D.Center for Health, Intervention, and Prevention December 2, 2010 Storrs, Connecticut

  2. Purpose • Provide an overview of GISGeographic Information Systems • Discuss the role of GIS in designing, implementing, and evaluating prevention programs • Consider examples from Connecticut and other places • Summarize challenges for the future

  3. Overview of GIS • GIS are computer-based systems for integrating and analyzing geographic data • Three mains functionsSpatial database managementVisualization and mappingSpatial analysis

  4. Role of GIS in Health Promotion • Designing health programsDelimiting study areaTargeting populations • Implementing health programsMonitoring sampling and enrollment patternsImproving operations in the field • Evaluating health programsSpatial meta-analysisDocumenting spatially varying processes

  5. Designing Health Programs • Where we look will affect what we observe • What is the spatial basis of evidence? • GIS and spatial analysis offer important support for study area delimitation and targeting populations

  6. Spatial Basis of Evidence Geographic distribution of black women 50 or older in Connecticut based on 2000 Census data at the town level.

  7. Study Area Delimitation • Communities are social, spatial, perceptual • Most studies benefit from a clear definition of study community boundaries • Key role for GIS in representing factors that help define study communities

  8. Factors to Consider • Existing regions Physical environmentBuilt environmentPolitical/administrative regions • Sociodemographic characteristics of population • Flows (based on distance) • Perceived communities (Lebel et al., 2005) • Stability of study community designations?

  9. Views of UConn Campus Raster data (imagery) HorsebarnHill

  10. Views of UConn Campus Raster data (classified land use/cover) HorsebarnHill

  11. Views of UConn Campus Vector data (points, lines, polygons) HorsebarnHill

  12. Views of UConn Campus Cadaster data (property parcels) HorsebarnHill

  13. Views of UConn Campus • Administrative • 09013881200 is a census tract from the 2000 Census • Group quarters populationUniversity of Connecticut campus

  14. Stability of areas (seasonal change)

  15. Perceived Areas • Ask people to identify their neighborhoods • Scan and register individual maps • Digitize individual neighborhood polygons • Union individual neighborhood polygons • Compare to political/administrative units

  16. Perceived Neighborhood • Paper map of Hartford on which person drew neighborhood boundary • Paper map scanned and imported to GIS application, georeferenced, and screen digitized

  17. Comparisons of Areas Perceived neighborhood is split across two census tracts Perceived neighborhood coincides with Sheldon Charter Oak Neighborhood

  18. Targeting Populations • Select sites for a Project Safe pilot program • Pilot program designed to streamline process of connecting clients who report serious substance abuse problems with treatment • Individual level data on people screened and their GAIN SS scores Geocoded 97% of the 11,939 individuals screened 7/07 – 11/09

  19. Approaches • Map individual level data by residential address and GAIN SS score • Calculate high GAIN SS score rates by town • Perform a spatial statistical analysis to detect significant clusters of high GAIN SS scores

  20. Issues • Mapping thousands of cases makes it difficult to see patterns of high GAIN SS scores • Calculating rates for administrative areas may be misleading if boundaries split clusters • Calculated rates for areas are not equally reliable; rates are less reliable in areas with small populations (the small numbers problem)

  21. Spatial Adaptive Filtering • Uses empirically identified geographic areas to show patterns of variation • Accounts for differences in population density and avoids problem of unstable rates • Can assess whether clusters are due to chance • DMAP IV(Disease Mapping and Analysis Program)www.uiowa.edu/~gishlth/DMAP4

  22. One Mile Grid

  23. Results • Significant clusters of high GAIN SS scores in three parts of the state, but not in major urban centers • Need to investigate to see whether these areas have a sufficient volume of screened individuals to support the pilot program

  24. Criteria • VolumeDo we select places with most cases? • ConcentrationDo we select places with the highest rates? • Spatial basis of evidenceHow do we aggregate individuals to count cases or calculate rates?

  25. Implementing Health Programs • Monitoring sampling and enrollment patterns • Using GIS to support field operations

  26. Aggregating Data Spatially Aggregation units like political/administrative boundaries arbitrarily partition the underlying patterns we are trying to uncover and understand

  27. Monitoring Sampling • Team defined a group of 66 clusters of residences in 3 study communities • Select a random sample of clusters

  28. Surveyed Clusters • Surveys not conducted in every cluster • Selected a random sample of 40 clusters • Reference map of sampled clusters shows that random sample covers all areas • Reference map used to monitor progress in completing surveys

  29. Monitoring Enrollment • Collect data on where study participants reside • Map data to see where there might be gaps in recruitment or out-of-area participants

  30. Supporting Field Operations Network Database with Stops Shortest Path with Reordered Stops Shortest Path with Barrier

  31. Evaluating Health Programs • Spatial meta-analysis • Documenting spatially varying processes using local statisticsWhich programs work where?

  32. Meta-Analysis • Combine the results of several studies addressing related research hypotheses • Enhance statistical power • Fixed- and random-effects regression models used to study effect sizes

  33. Spatial Meta-Analysis • Studies analyzed in meta-analyses were conducted in time and space • Are studies geographically clustered? • Are there spatial dependencies in effect sizes?

  34. Example • Spatial error model • GeoDa softwaregeodatacenter.asu.edu

  35. Requirements • Use GIS to georeference study locations • Use GIS to export data for input into GeoDa • Test for spatial autocorrelation in errors and perform spatial regression analysis

  36. Global and Local Statistics Adapted from A. Stewart Fotheringham, Chris Brunsdon, and Martin Charlton, 2002, Geographically Weighted Regression: The Analysis of Spatially Varying Relationships.

  37. Spatially Varying Processes • Example—We are interested in the relationship between sleep and health • Daylight affects sleep and daylight is spatially variable • The relationship between daylight and sleep may also be variable from place to placeGeographically Weighted Odds RatiosGeographically Weighted Regression

  38. Example • Geographically Weighted Regression • GWR softwarencg.nuim.ie/ncg/GWR/software.htmand bundled in some GIS softwareincluding ArcGIS 9.3

  39. Barriers to GIS Adoption • Difficulty of being a little bit spatial • Theoretical and methodological challengesUnderstanding behavior in time and spaceModeling spatial/temporal dependencies • Practical issuesDatabase acquisition and managementSoftware selection and trainingSystem design and application development

  40. OppNet Issues • Collaboration across disciplines • Massive amounts of data • Lifecourse development • Understanding processes

  41. Opportunities • DataMAGICmagic.lib.uconn.edu • Software and TrainingESRI GIS Site Licensewww.geography.uconn.edu/esri/ • Research and Educationwww.geog.uconn.eduUndergraduate and graduate coursesGraduate Certificate in GISOn-line Masters in GIScience in development

  42. Conclusion • Geospatial techniques are key to understanding processes and mechanisms affecting health • The field is shifting beyond using GIS to make maps • GIS are helping us design and implement more effective programs to promote health

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