1. Thomas Talbot Chief, Environmental Health Surveillance Section NYS Department of Health April 18, 2013

2. W State health departments and federal agencies such as NCI and CDC provide county level health indicators. Stakeholders want the data at a finer geographic scale.

3. Health data can be shown at different geographic scales Residential address Census blocks, and tracts ZIP codes Towns

4. Concerns about release of small area data Risk of disclosure of confidential information. Rates of disease are unreliable due to small numbers.

5. Rate maps with small numbers provide very little information. Rates are suppressed due to confidentiality or are unstable.

6. Disclosure of confidential information Census Blocks

7. Geographic Smoothed or Aggregated Count & Rate Maps Protect Confidentiality so data can be shared. Reduce random fluctuations in rates due to small numbers.

8. Smoothed Rate Maps Borrow data from neighboring areas to provide more stable rates of disease. –Shareware tools available –Empirical or Hierarchal Bayesian approaches –Adaptive Spatial Filters –Head banging –etc.

10. from Talbot et al., Statistics in Medicine, 2000

11. Problems with smoothing Does not provide counts & rates for defined geographic areas. Not clear how to link risk factor data with smoothed health data. Methods are sometimes difficult to understand - “black boxes” May not meet requirements of some policies or legislation.

12. Environmental Facilities & Cancer Incidence Map Law, 2008 § 3-0317 Plot cancer cases by census block, except in cases where such plotting could make it possible to identify any cancer patient. Census blocks shall be aggregated to protect confidentiality.

13. Environmental Justice & Permitting NYSDEC Commissioner Policy 29 Incorporate existing human health data into the environmental review process. Data will be made available at a fine geographic scale.

14. Public Health Support for Brownfield/Land Reuse in the Areas of Concern for the Great Lakes CDC-RFA-TS10-1003 Identification of community health status indicators for areas of concern –Environmental data –Community health concerns –Public health data –Pre and post development

15. Aggregation Consider geographic scale Consider zone

16. In the following example I randomly placed points on a map with on average 10 points for each grid cell. The observed number of points vs. the expected number of points changes as we move the grid or if we change the scale by combining grids.

17. Talbot

20. Aggregated Count or Rate Maps Merge small areas with neighboring areas to provide more stable rates of disease and/or protect confidentiality. –Aggregation can be done manually. –Existing automated tools were difficult to use.

21. Original ZIP Codes 3 Years Low Birth Weight Incidence Ratios

22. Aggregated to 250 Births per ZIP Code Group

23. Goal Aggregate small areas into larger ones. User decides how much aggregation is needed. –Based on cases and/or underlying population –Example 250 births and at least 3 low birth weight births Works with various levels of geography. – C ensus blocks, tracts, towns, ZIP codes etc. – Can nest one level of geography in another Uses open source free software. Can output results for use in mapping programs. NYSDOH Geographic Aggregation Tool

24. Aggregation Tool C14 B20 A13 RegionCases Original Block Data † Regions SAS or R Tool 6 9 4 3 8 3 2 11 2 CasesBlock 103202/2002 103202/2001 014500/3010 014500/3009 014500/3008 014500/3007 014500/3005 122300/2005 122300/2004 6 9 4 3 8 3 2 11 2 CasesRegionBlock C103202/2002 C103202/2001 B014500/3010 B014500/3009 B014500/3008 B014500/3007 B014500/3005 A122300/2005 A122300/2004

25. The Aggregation Process: goals Should form a large number of areas The areas should be reasonably compact The areas have minimum values as defined by the user.

26. The Aggregation Process: method Pair-wise merges Merge until the areas have minimum values. –Cases and/or population –Expected numbers.

27. The Pairwise Merge: 1st area Select those areas which require merging to meet minimum values. Example: 3 low birth weight babies, 250 births Of those, select those whose value is the highest percentage of the minimum value to merge first. –20>3, 8>3 these numbers not used –244/250>85/250 Low birth weight counts Total births LBW births

28. The Pairwise merge: 2 nd area Find the adjacent neighbors of the selected area If a boundary variable is used, select those neighbors that are within the boundary variable

29. The Pairwise Merge If there are no adjacent neighbors, choose the closest area (according to distance between centroids)

30. Water

31. The Pairwise merge: two methods to choose 2 nd area Choose the area whose centroid is closest to the first area Choose the area which has the smallest ratio of the aggregation variable to the minimum value. –e.g. 85/250

32. 9 Cases 98Population † Simulated data

33. 9 Cases 98Population † Simulated data

34. 9 Cases 98Population † Simulated data

35. 9 Cases 98Population † Simulated data

36. 9 Cases 98Population † Simulated data

37. 9 Cases 98Population † Simulated data

38. 9 Cases 98Population † Simulated data

39. 9 Cases 98Population † Simulated data

40. 9 Cases 98Population † Simulated data

41. 9 Cases 98Population † Simulated data

42. 9 Cases 98Population † Simulated data

43. 9 Cases 98Population † Simulated data

44. 9 Cases 98Population † Simulated data

45. 9 Cases 98Population † Simulated data

46. 9 Cases 98Population † Simulated data

47. 9 Cases 98Population † Simulated data

48. 9 Cases 98Population † Simulated data

49. New York State Descriptive Statistics Year 2000 populated census blocks 14741 Median number of blocks 3820101 Median number of cases 1,46777038539 Median Population 11,38121,52539,748225,167 Number of regions 24 cases12 cases6 cases Original Census Blocks Statistic (calculated using populated regions only)‏ New Regions: Level of Aggregation NYS number of cases (5 yrs) 470,000 NYS population 200018,976,457 Note: The range in the census block populations is 0 - 23,373 Persons

50. Performance Measures Compactness Homogeneity with respect to demographic factors (measured as index of dissimilarity) Similar population sizes. Number of aggregated areas. Aggregated zones are completely contained within larger areas. –e.g. blocks aggregation areas contained within tracts

52. Index of dissimilarity the percentage of one group that would have to move to a different area in order to have a even distribution b i = the minority population of the ith area, e.g. census tract B = the total minority population of the large geographic entity for which the index of dissimilarity is being calculated. w i = the non-minority population of the ith area W = the total non-minority population of the large geographic entity for which the index of dissimilarity is being calculated. Wikipedia

53. Follow-up Issues Scale and method of aggregation will impact map & correlation coefficients. Modifiable area unit problem Counties Aggregation Areas ZIP Codes

54. Compactness

56. GAT Outputs KML Files

57. What is R? A programming language A software environment Similar to S or S-plus Can do statistical computing Has graphics capabilities

58. Why R? It’s free Widely used and accepted Works on windows, MacOS, unix platforms Many user-developed packages that add functionality Can run script files

59. Viewing the Results ArcGIS MapInfo Google Maps Google Earth

61. Lab Exercise We will be trying out a beta version of GAT in the lab today. 5 years of simulated low birth weight data, NY State. 2003 ZIP Code Scale. Socio-economic variables for race, poverty and education. Detailed Instructions are provided for running the GAT Tool program in the “GAT v12 Manual” which is in the GAT R directory You will run the program “GAT vR12” batch program to aggregate zip codes into larger regions see next slide.

62. Geographic Aggregation Tool is available on Talbot web site

63. Spatial Aggregation Homework Assignment Use the Geographic Aggregation Tool to aggregate ZIP Codes from the testdata ZIP Code Shape File. Each aggregated area should have a minimum of 250 births (simbir0105) and 3 low birth weight births (Simlbw0105) 1.Make a thematic map of percent of low birth weight births for the original unaggregated ZIP codes. Make a second thematic map of percent of low birth weight births for your new aggregated boundaries. Each map should have at least 5 classes (categories). Use the same class breaks and colors for both maps. Make sure you include a legend and title on the map. Use ArcGIS or Indie Mapper to make the Thematic Maps. Attach a copy of your aggregation log file to your lab write-up along with the two thematic maps. 2.Open the aggregated boundary in Google Earth. Use the print screen feature in Windows to show that the file successfully opened in Google Earth. Add the screen shot to your write up. 3.Provide at least one suggestion on how the GAT-R program could be made more user friendly. 4.Provide at least one suggestion of how the User Guide could be made more useful or easier to understand. 5. Provide at least one suggestion of additional features that could be added to the program. Lab due May 2, 2013

64. GeoMasking Tool Randomly Moves Points within a user defined area

65. The End