Innovative Uses of Geographic Information Systems

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Innovative Uses of Geographic Information Systems

• Lance A. Waller
• Department of Biostatistics
• Rollins School of Public Health
• Emory University
• lwaller_at_sph.emory.edu

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Outline

• Why does the geography of immunization matter?
• What is GIS?
• What does GIS do?
• What data do I have?
• What questions can I answer with my data?

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Why geography?

• Is immunization coverage constant?
• If you know where coverage is low, can you do
  something?
• If you know where coverage is high, can you learn
  something?

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What is GIS?

• A geographic information system (GIS) links
• Geographic features
• Houses
• Census tracts
• Attribute measurements
• Immunized (yes/no)
• Age
• Sociodemographics

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Think of
Each cell contains an attribute value
linked with
Map (locations)
Table (attributes)
Objects on the map are features.
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What does a GIS do? Basic GIS operation 1

• Layering
• Non-compliers
• Health center
• cachement
• Compliers

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Basic GIS operation 2

• Buffering
• Find areas within a user-specified distance of
• points
• lines
• areas

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Famous public health map
!
Snow, J. (1949) Snow on Cholera. Oxford
University Press London.
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Wow! Can we do that?

• Many introductions to GIS and public health
  essentially say
• If John Snow could do it with shoe leather, ink,
  and paper, just imagine what we can do with a
  computer!

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Basic take-home figure

• The Whirling Vortex of GIS analysis

The question you want to answer
The question you can answer with those data
GIS
The data you need to answer that question
The data you can get
Original source Toxicologist EPA Region IV
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What kind of questions?

• Where is coverage the lowest?
• Where is coverage the highest?
• Outbreak size starting in high coverage area?
• Outbreak size starting in low coverage area?
• How could coverages impact the course of an
  outbreak?
• Best response to current outbreak?

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What kind of attributes?

• Compliers
• Residence location
• Census region counts
• Sociodemographic data
• Census summaries on age, race, sex, income of
  census region residents
• Some information on compliers sociodemographics

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Additional attributes

• Noncompliers
• Residence location
• Regional counts
• School data
• School district
• Health plan data
• Billing provides residence address
• ZIP codes?

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Basic location types

• Point data
• Latitude and longitude
• (Seems) precise
• Distance calculations
• Regional data
• Counts (cases/controls) from census regions

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Any complications?

• Maxcy (1926) Endemic typhus fever in Montgomery,
  AL
• Where is where?
• Which location for each case?

Maxcy, K.F. (1926) An epidemiological study of
endemic typhus (Brills disease) in the
Southeastern United States with special reference
to its mode of transmition. Public Health
Reports 41, 2967-2995.
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Residence
Employment
Lilienfeld, D.E. and Stolley, P.D. (1994)
Foundations of Epidemiology, Third Edition.
Oxford University Press New York, pp. 136-140.
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Complication Nonconstant population density
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Complications with regions

• Counts lose some resolution...

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Modifiable Areal Unit Problem

• Different aggregations can lead to different
  results.

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MAUP example John Snow
?
Monmonier, M (1991) How to Lie with Maps.
University of Chicago Press Chicago. p. 142.
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What questions can I ask?

• Point locations
• Interesting/uninteresting clusters
• Interesting clusters of non-compliers away from
  clusters of compliers
• Regional counts
• Interesting/uninteresting raised counts
• Interesting Less coverage than expected

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Point locations

• Treat locations as spatial point process
• Spatial intensity (average number of events per
  unit area)
• Think of intensity as a surface
• Compare intensity of compliers to intensity of
  non-compliers.
• Peaks and valleys in same places?

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Monte Carlo simulation

• Simulate data sets under null hypothesis (e.g.,
  constant coverage rate).
• See if observed data (actual compliers) appear
  unusual.
• To compare intensities, split all locations into
  compliers and non-compliers at random, find out
  how high peaks, how low valleys can get.
• Most GIS packages will not do this, but it is a
  very handy tool in spatial statistics.

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Regions

• Compare observed counts to expected counts.
• Some basic point process results extend to counts
  (counts of points in regions).
• Constant coverage rate (perhaps age-adjusted)
  again a common way of obtaining expected
  counts.
• Monte Carlo simulation for significance.

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Related work

• Cancer registries North American Association of
  Central Cancer Registries (NAACCR) report on GIS
  (Wiggins 2002)
• Birth outcome registries
• Public Health/Bioterrorism/Syndromic Surveillance
• Similarities
• Registry data
• Differences
• Infectious vs. chronic outcome
• Urgency of temporality

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Conclusion

• Best work a collaboration between
• Geographers
• GISers
• Epidemiologists
• Statisticians
• Get the best data you can to answer the questions
  you want.

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Handy references

• Wiggins L (Ed). Using Geographic Information
  Systems Technology in the Collection, Analysis,
  and Presentation of Cancer Registry Data A
  Handbook of Basic Practices. Springfield (IL)
  North American Association of Central Cancer
  Registries, October 2002, 68 pp.
• Cromley, E.K. and McLafferty, S.L. (2002) GIS and
  Public Health. The Guilford Press.
• Bailey and Gatrell (1995) Interactive Spatial
  Data Analysis. Longman.
• Waller and Crawford (2004) Applied Spatial
  Statistics for Public Health Data. Wiley.

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What kind of software?
Statistical Software(SAS, S Spatial
Stats)Spatially and/or visually
challenged Subject-specificSpaceStat/GeoDaSaTSc
an GSClusterSeerWinBUGS/GeoBUGSXGOBI/XGvisR
(many nice spatial modules, must write
code, quality control?)Link to GIS
S/ArcView 3.x SAS Bridge to ArcGIS 8.x
Commercial GIS Software(ArcView,
Mapinfo) Statistically challengedExtensions
(Analysts), limited capability Packages by
scientific user good, but basic Scripts and
MacrosUser-contributedOften do not give
numerical output