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Cluster Detection Comparison in Syndromic Surveillance

Cluster Detection Comparison in Syndromic Surveillance. MGIS Capstone Project Proposal Tuesday, July 8 th , 2008. Background. Syndromic Surveillance.

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Cluster Detection Comparison in Syndromic Surveillance

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  1. Cluster Detection Comparison in Syndromic Surveillance MGIS Capstone Project Proposal Tuesday, July 8th, 2008

  2. Background

  3. Syndromic Surveillance • Syndromic Surveillance is the application of health-related data to identify signals of potential events or outbreaks before they would otherwise be detected through standard means. • A key method of syndromic surveillance involves the detection of statistically significant spatial signals using SaTScan. • In NYC, data is analyzed daily from Emergency Department (ED) visits. The date and time of visit, age, sex, home ZIP Code and free-text chief complaint of each patient is recorded.

  4. Goals and Objectives

  5. Goals and Objectives • While syndromic surveillance has been around for a number of years, there are geographic considerations that may influence the accuracy of the spatial analysis being performed by SaTScan. • One program being evaluated, FleXScan, may provide for fewer false signals in our system, and provide a more accurate picture of a cluster. • Combining such programs with additional geographic information analysis may provide the most accurate spatial assessment of clusters.

  6. Characterizing Geography of Input Files Input files to both SaTScan and FleXScan are important to construct accurate analysis. This project considers how important those considerations actually are: • If centroids reflect characteristics of an underlying population, they should not be arbitrary. • How do differences in coordinate / measurement systems affect accuracy? • How do natural / man-made boundaries impact adjacency?

  7. Detecting Spatial Clusters: SaTScan • NYC presently uses SaTScan to detect spatial clusters in space and time. • SaTScan is good at picking out clusters that are circular in nature. • A known limitationis that many clusters are notcircular in nature.

  8. Detecting Spatial Clusters: FleXScan • FleXScan employs a flexible scan statistic using a matrix design. • The matrix identifies neighboring areas, and may better detect non-circular clusters.

  9. Methodology

  10. How do geographic considerations impact final results? SaTScan and FleXScan will be evaluated using preferable characteristics of software packages that perform spatial exploratory analysis (Anselin, 2004): • Effective data input • Handling of distance and spatial weighting • Provides descriptive statistics • Performs point pattern analysis • Spatial autocorrelation analysis (LISA) • Modeling spatial variation in events and linking to explanatory variables • Mapping and visualization of results

  11. How effective is each program at detecting syndromic signals? FleXScan and SaTScan output will be compared using the following criteria*: 1. Most likely cluster (primary cluster) identified 2. Secondary clusters identified 3. Location and radius or area of identified cluster 4. P-value, log-likelihood ratio 5. Recurrence interval * Takahashi et al’s “A flexibly shaped space-time scan statistic for disease detection and monitoring”, April 2008, can be used as a guide for correctly comparing SaTScan with FleXScan.

  12. Proposed Framework • Purely Spatial Analysis completed using SaTScan and FleXScan against ED daily intake data • Results compared / analyzed • Fine-tuning of input files using geographic considerations (centroid weighting, natural boundaries, true adjacency) • Space-Time Analysis applied (as time allows)

  13. Data: Emergency Department (ED) Daily Intake Data • Project will initially investigate six key datasets:

  14. Detecting Spatial Clusters: FleXScan Original SaTScan Cluster FleXScan Cluster

  15. Syndromic Data How do natural / man-made boundaries impact adjacency?

  16. Assigning Connectivity: Matrix File • GeoDa used to create a matrix file. • Connectivity becomes an issue, as it is defined across boundaries and distance.

  17. Adjacency, Natural and Man-Made Boundaries • Comparingcluster detection accuracy across boundaries.

  18. ZIP Code Centroid Weighting

  19. Coordinate Systems • Compare cluster detection behaviors between a set of geographic (unprojected) coordinates and a set of projected coordinates. • If measurements vary and thereby change results of analysis, the input coordinate system weighs significantly on the output of the program. DiBiase, David (1999-2006) The Nature of Geographic Data, Lesson 2: Scales and Transformations. The Pennsylvania State University World Campus Certificate Program in GIS. Accessed May 2006.

  20. Proposed Timeline

  21. Proposed Timeline To date: • 09 / 07 – GEOG586 Quarter Project, “Improving Syndromic Surveillance Signal Detection Using GIA” • 12 / 07 – Application to NSF / EAPSI Program, “Cluster Detection Comparison in Syndromic Surveillance: FleXScan and SaTScan” • 02 / 08 – Notice of NSF Award • 06 / 08 – GEOG596A Capstone Proposal, Work begins at NIPH – Wako-shi, Saitama, Japan Planned: • 08 / 08 – NSF research period ends • 12 / 08 - Seventh Annual International Society for Disease Surveillance Conference, Raleigh, NC

  22. Thank you… Chris Goranson, GISP Director GIS Center Bureau of Epi Services 125 Worth Street, Room 315, CN-6 New York, NY 10013 212-788-4334 cgoranso@health.nyc.gov

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