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

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

Occupation Sex, Age Socioeconomic class Behavior (smoking) Race Time Space Adverse health events can cluster by:

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

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.

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:

Detecting Clusters Consider scale Consider zone Control for multiple testing

Talbot

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?

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

Nearest Neighbor Analysis Cuzick & 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.

Nearest Neighbor Analyses

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

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

Spatial Scan Statistic Martin 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

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.

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.

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)

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

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.

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

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.

Interactive Selections by rate, population and p value

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

Apparent cluster of low birth weights. NYSDOH Vital Statistics Data

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

Potential Birth Defect Clusters identified by Spatial Scan Statistic

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

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

SaTScan We will be using a beta version of SatScan in the next Lab. Download SaTScan from Talbot’s website to your Flash Drive. Launch Make sure you choose an installation path on your flash drive so you can run it in class from your flash drive.

Homework 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: Forand SP, Talbot TO, Druschel C, Cross PK. Data Quality and the Spatial Analysis of Disease Rates: Congenital Malformations in New York Health and Place. 2002, 8: Kuldorff M, National Cancer Institute. SatScan User Guide Cromley and McLafferty. GIS and Public Health, Chapter 5

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