Statistical approaches for detecting unexplained clusters of disease . Spatial Aggregation Thomas Talbot New York State

1. Statistical approaches for detecting unexplained clusters of disease. • Spatial Aggregation Thomas Talbot New York State Department of Health Environmental Health Surveillance Section Albany School of Public Health GIS & Public Health Class March 3, 2009

2. Cluster • A number of similar things grouped closely togetherWebster’s Dictionary • Unexplained concentrations of health eventsin space and/or time Public Health Definition

3. Occupation • Sex, Age • Socioeconomic class • Behavior (smoking) • Race • Time • Space

4. Spatial Autocorrelation “Everything is related to everything else, but near things are more related than distant things.” Negative autocorrelation - Tobler’s first law of geography Positive autocorrelation

5. Moran’s I • A test for spatial autocorrelation in disease rates. • Nearby areas tend to have similar rates of disease. Moran I is greater than 1, positive spatial autocorrelation. • When nearby areas are dissimilar Moran I is less than 1, negative spatial autocorrelation.

6. Detecting Clusters • Consider scale • Consider zone • Control for multiple testing

7. Talbot

14. Cluster Questions • Does a disease cluster in space? • Does a disease cluster in both time and space? • Where is the most likely cluster? • Where is the most likely cluster in both time and space?

15. More Cluster Questions • At what geographic or population scale do clusters appear? • Are cases of disease clustered in areas of high exposure?

16. Nearest Neighbor AnalysisCuzick & Edwards Method • Count the the number of cases whose nearest neighbors are cases and not controls. • When cases are clustered the nearest neighbor to a case will tend to be another case, and the test statistic will be large.

17. Nearest Neighbor Analyses

18. Advantages • Accounts for the geographic variation in population density • Accounts for confounders through judicious selection of controls • Can detect clustering with many small clusters

19. Disadvantages • Must have spatial locations of cases & controls • Doesn’t show location of the clusters

20. Spatial Scan StatisticMartin Kulldorff • Determines the location with elevated rate that is statistically significant. • Adjust for multiple testing of the many possible locations and area sizes of clusters. • Uses Monte Carlo testing techniques

29. The Space-Time Scan Statistic • Cylindrical window with a circular geographic base and a height corresponding to time. • Cylindrical window is moved in space and time. • P value for each cylinder calculated.

30. Knox Method test for space-time interaction • When space-time interaction is present cases near in space will be near in time, the test statistic will be large. • Test statistic: The number of pairs of cases that are near in both time and space.

31. Focal tests for clustering • Cross sectional or cohort approach: Is there a higher rate of disease in populations living in contaminated areas compared to populations in uncontaminated areas? (Relative risk) • Case/control approach: Are there more cases than controls living in a contaminated area? (Odds ratio)

32. Focal Case-Control Design 500 m. 250 m. Case Control

33. Regression Analysis • Control for know risk factors before analyzing for spatial clustering • Analyze for unexplained clusters. • Follow-up in areas with large regression residuals with traditional case-control or cohort studies • Obtain additional risk factor data to account for the large residuals.

37. At what geographic or population scale do clusters appear?Multiresolution mapping.

38. A cluster of cases in a neighborhood provides a different epidemiological meaning then a cluster of cases across several adjacent counties. Results can change dramatically with the scale of analysis.

39. 1995-1999

43. Interactive Selections by rate, population and p value

44. References • Talbot TO, Kulldorff M, Forand SP, and Haley VB. Evaluation of Spatial Filters to Create Smoothed Maps of Health Data.  Statistics in Medicine. 2000, 19:2451-2467 • Forand SP, Talbot TO, Druschel C, Cross PK. Data Quality and the Spatial Analysis of Disease Rates: Congenital Malformations in New York. 2002. Health and Place.  2002, 8:191-199 • Haley VB, Talbot TO. Geographic Analysis of Blood Lead Levels in New York State Children Born 1994-1997.  Environmental Health Perspectives 2004, 112(15):1577-1582 • Kuldorff M, National Cancer Institute. SatScan User Guide www.satscan.org

45. Geographic Aggregation of Health DatabyThomas TalbotNYS Department of HealthEnvironmental Health Surveillance Section

46. Health data can be shown at different geographic scales • Residential address • Census blocks, and tracts • Towns • Counties • State

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

48. Rate maps with small numbers provide very little information. http://www.nyhealth.gov/statistics/ny_asthma/hosp/zipcode/hamil_t2.htm http://www.nyhealth.gov/statistics/ny_asthma/hosp/zipcode/pdf/hamil_m2.pdf

49. Disclosure of confidential information Census Blocks

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