2. Innovative Uses of Geographic Information Systems Lance A. Waller Department of Biostatistics Rollins School of Public Health Emory University lwaller@sph.emory.edu

3. 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?

4. 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?

5. What is GIS? A “geographic information system” (GIS) links: Geographic features Houses Census tracts Attribute measurements Immunized (yes/no) Age Sociodemographics

6. Think of… Map (locations) Table (attributes) linked with Each cell contains an attribute value Objects on the map are features.

7. What does a GIS do? Basic GIS operation #1: Layering Non-compliers Health center cachement Compliers

8. Basic GIS operation # 2: Buffering Find areas within a user- specified distance of: points lines areas

9. Famous public health map ! Snow, J. (1949) Snow on Cholera. Oxford University Press: London.

10. 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!”

11. Basic take-home figure The Whirling Vortex of GIS analysis The question you want to answer The data you need to answer that question The data you can get The question you can answer with those data Original source: Toxicologist EPA Region IV GIS

12. 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?

13. 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

14. Additional attributes Noncompliers Residence location Regional counts School data School district Health plan data Billing provides residence address ZIP codes?

15. Basic location types Point data Latitude and longitude (Seems) precise Distance calculations Regional data Counts (cases/controls) from census regions

16. 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 (Brill’s disease) in the Southeastern United States with special reference to its mode of transmition.” Public Health Reports 41, 2967-2995.

17. Residence: Lilienfeld, D.E. and Stolley, P.D. (1994) Foundations of Epidemiology, Third Edition. Oxford University Press: New York, pp. 136-140. Employment:

18. Complication: Nonconstant population density

19. Complications with regions Counts lose some resolution... 4 1 2 1 1 2

20. Modifiable Areal Unit Problem Different aggregations can lead to different results. 4 1 2 1 1 2 2 0000 2 1 0 2 0 0 2 4

21. MAUP example: John Snow Monmonier, M (1991) How to Lie with Maps. University of Chicago Press: Chicago. p. 142. ?

22. 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”

23. 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?

24. 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.

25. 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.

26. 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

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

28. 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.

29. What kind of software? Statistical Software (SAS, S+ Spatial Stats) Spatially and/or visually challenged Subject-specific SpaceStat/GeoDa SaTScan GS+ ClusterSeer WinBUGS/GeoBUGS XGOBI/XGvis R (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 challenged Extensions (Analysts) $$$, limited capability Packages by scientific user good, but basic Scripts and Macros User-contributed Often do not give numerical output