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 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

Overview of GIS

• GIS are computer-based systems for integrating and analyzing geographic data

• Three mains functionsSpatial database managementVisualization and mappingSpatial analysis

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

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

Spatial Basis of Evidence

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

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

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?

Views of UConn Campus

Raster data (imagery)

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Views of UConn Campus

Raster data (classified land use/cover)

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Views of UConn Campus"

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"Vector data (points, lines, polygons)

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Views of UConn Campus

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Cadaster data (property parcels)

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Views of UConn Campus

09013881100

0901388120009013881300

09013881500

• Administrative

• 09013881200 is a census tract from the 2000 Census

• Group quarters populationUniversity of Connecticut campus

Town Population With UConn Tract

4000 3000 2000 1000 0 1000 2000 3000

<5

15 to 19

30 to 34

45 to 49

60 to 64

75 to 79

Female

Male

Town Population Without UConn Tract

4000 3000 2000 1000 0 1000 2000 3000

<5

15 to 19

30 to 34

45 to 49

60 to 64

75 to 79

Female

Male

Stability of areas (seasonal change)

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

Perceived Neighborhood

• Paper map of Hartford on which person drew neighborhood boundary

• Paper map scanned and imported to GIS application, georeferenced, and screen digitized

Comparisons of Areas

Perceived neighborhood issplit across two census tracts

Perceived neighborhood coincides with Sheldon Charter Oak Neighborhood

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

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

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)

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

One Mile Grid

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

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?

Implementing Health Programs

• Monitoring sampling and enrollment patterns

• Using GIS to support field operations

Aggregating Data Spatially

Aggregation units like political/administrative boundaries arbitrarily partition the underlying patterns we are trying to uncover and understand

Monitoring Sampling

• Team defined a group of 66 clusters of residences in 3 study communities

• Select a random sample of clusters

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

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

Supporting Field Operations

Network Databasewith Stops

Shortest Path with Reordered Stops

Shortest Path with Barrier

Evaluating Health Programs

• Spatial meta-analysis

• Documenting spatially varying processes using local statistics

Which programs work where?

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

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?

Example

• Spatial error model

• GeoDa softwaregeodatacenter.asu.edu

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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

Global and Local Statistics Global Statistics Local Statistics Summarize data for entire regions Summarize data for individual places

within entire regions Single statistic Multiple statistics, one for each place Uninteresting when mapped (aspatial) Interesting when mapped (spatial) Provide misleading interpretations of local relationships

Useful for exploratory data analysis, confirmatory analysis, and building more accurate global models

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

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

Example

• Geographically Weighted Regression

• GWR softwarencg.nuim.ie/ncg/GWR/software.htmand bundled in some GIS softwareincluding ArcGIS 9.3

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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

OppNet Issues

• Collaboration across disciplines• Massive amounts of data• Lifecourse development• Understanding processes

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

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