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Geographic Aggregation GIS & Public Health Class

Geographic Aggregation GIS & Public Health Class. Thomas Talbot Chief, Environmental Health Surveillance Section NYS Department of Health April 18, 2013. W. State health departments and federal agencies such as NCI and CDC provide county level health indicators.

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Geographic Aggregation GIS & Public Health Class

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  1. Geographic AggregationGIS & Public Health Class 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.

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

  10. 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.

  11. 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.

  12. 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.

  13. Public Health Support for Brownfield/Land Reuse in the Areas of Concern for the Great LakesCDC-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

  14. Aggregation • Consider geographic scale • Consider zone

  15. 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.

  16. Talbot

  17. 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.

  18. Original ZIP Codes3 Years Low Birth Weight Incidence Ratios

  19. Aggregated to 250 Births per ZIP Code Group

  20. Goal NYSDOH Geographic Aggregation Tool • 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. • Census 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.

  21. Block Cases Region Block Cases 122300/2004 2 A 122300/2004 2 122300/2005 11 A 122300/2005 11 014500/3005 2 B 014500/3005 2 014500/3007 3 B 014500/3007 3 014500/3008 8 B 014500/3008 8 014500/3009 3 B 014500/3009 3 014500/3010 4 B 014500/3010 4 103202/2001 9 C 103202/2001 9 103202/2002 6 103202/2002 6 C Cases Region 13 A 20 B 14 C Aggregation Tool Regions Original Block Data † SAS or R Tool

  22. 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.

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

  24. 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 LBW births Low birth weight counts Total births

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

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

  27. Water

  28. The Pairwise merge: two methods to choose 2nd 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

  29. 9 Cases 98 Population † Simulated data

  30. 9 Cases 98 Population † Simulated data

  31. 9 Cases 98 Population † Simulated data

  32. 9 Cases 98 Population † Simulated data

  33. 9 Cases 98 Population † Simulated data

  34. 9 Cases 98 Population † Simulated data

  35. 9 Cases 98 Population † Simulated data

  36. 9 Cases 98 Population † Simulated data

  37. 9 Cases 98 Population † Simulated data

  38. 9 Cases 98 Population † Simulated data

  39. 9 Cases 98 Population † Simulated data

  40. 9 Cases 98 Population † Simulated data

  41. 9 Cases 98 Population † Simulated data

  42. 9 Cases 98 Population † Simulated data

  43. 9 Cases 98 Population † Simulated data

  44. 9 Cases 98 Population † Simulated data

  45. 9 Cases 98 Population † Simulated data

  46. New Regions: Level of Aggregation Statistic (calculated using populated regions only)‏ Original Census Blocks 6 cases 12 cases 24 cases Number of regions 225,167 39,748 21,525 11,381 Median Population 39 385 770 1,467 Median number of cases 1 10 20 38 Median number of blocks 1 4 7 14 New York StateDescriptive StatisticsYear 2000 populated census blocks NYS number of cases (5 yrs) 470,000 NYS population 2000 18,976,457 Note: The range in the census block populations is 0 - 23,373 Persons

  47. 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

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