Statistical approaches for detecting clusters of disease . Feb. 26, 2013 Thomas Talbot

1. Statistical approaches for detecting clusters of disease. Feb. 26, 2013 Thomas Talbot New York State Department of Health Bureau of Environmental and Occupational Epidemiology Geographic Research and Analysis Section

2. Cluster • A number of similar things grouped closely togetherWebster’s Dictionary • Researchers are often interested in unexplained concentrations of health events in space and/or time.

3. Adverse health events can cluster by: • 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. GeoDA Overview • GeoDA is a tool for exploratory analysis of geographic data. • Primarily analyzes polygon data, but can also do some things with point data • Some useful functions. • creates spatial weights matrices • histograms, scatter plots • calculates and maps local Indices of spatial association (local Moran’s I). • Multiple regression full diagnostics for spatial effects • ArcGIS not required, but requires a shapefile for data input. • Download site:http://geodacenter.asu.edu/projects/opengeoda

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

8. Talbot

15. 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?

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

17. 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.

18. Nearest Neighbor Analyses

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

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

21. 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

30. 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.

31. 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.

32. 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)

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

34. 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.

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

39. 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.

40. 1995-1999

44. Interactive Selections by rate, population and p value

45. Apparent Spatial Clustering of Health Events is Often due to Data Quality Issues

46. Apparent cluster of low birth weights.NYSDOH Vital Statistics Data

47. Remove out-of-state births & cluster disappears.Rutland Hospital data coded in wrong weight units.

48. Potential Birth Defect Clusters identified bySpatial Scan Statistic

49. Hospital reporting rates presented on a map. Hospitals with poor reporting represented by blue & yellow circles

50. Remove NYC from analysis and clusters disappear.Conclusion: Reporting problems in NYC lead to the clusters