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Enhancing spatial detection accuracy for syndromic surveillance with street level incidence data.

Savory DJ, Cox KL, Emch M, Alemi F, Pattie DC - Int J Health Geogr (2010)

Bottom Line: This study focuses on spatial detection and attempts to improve the effectiveness of the ESSENCE implementation of the spatial scan statistic by increasing the spatial resolution of incidence data from zip codes to street address level.Spatial detection accuracy improved notably with regard to both location and size when incidents were geocoded to street addresses rather than zip code centroids.Since street address geocoding success rates were only 73.5%, zip codes were still used for more than one quarter of ILI cases.

View Article: PubMed Central - HTML - PubMed

Affiliation: Planned Systems International, Inc, Falls Church, VA 22041, USA.

ABSTRACT

Background: The Department of Defense Military Health System operates a syndromic surveillance system that monitors medical records at more than 450 non-combat Military Treatment Facilities (MTF) worldwide. The Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE) uses both temporal and spatial algorithms to detect disease outbreaks. This study focuses on spatial detection and attempts to improve the effectiveness of the ESSENCE implementation of the spatial scan statistic by increasing the spatial resolution of incidence data from zip codes to street address level.

Methods: Influenza-Like Illness (ILI) was used as a test syndrome to develop methods to improve the spatial accuracy of detected alerts. Simulated incident clusters of various sizes were superimposed on real ILI incidents from the 2008/2009 influenza season. Clusters were detected using the spatial scan statistic and their displacement from simulated loci was measured. Detected cluster size distributions were also evaluated for compliance with simulated cluster sizes.

Results: Relative to the ESSENCE zip code based method, clusters detected using street level incidents were displaced on average 65% less for 2 and 5 mile radius clusters and 31% less for 10 mile radius clusters. Detected cluster size distributions for the street address method were quasi normal and sizes tended to slightly exceed simulated radii. ESSENCE methods yielded fragmented distributions and had high rates of zero radius and oversized clusters.

Conclusions: Spatial detection accuracy improved notably with regard to both location and size when incidents were geocoded to street addresses rather than zip code centroids. Since street address geocoding success rates were only 73.5%, zip codes were still used for more than one quarter of ILI cases. Thus, further advances in spatial detection accuracy are dependant on systematic improvements in the collection of individual address information.

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Related in: MedlinePlus

Cluster Placement Example: 10 Miles West of Beale Air Force Base.
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Figure 2: Cluster Placement Example: 10 Miles West of Beale Air Force Base.

Mentions: For purposes of this study, detection accuracy is a measure of how close a detected cluster is to the "true" location of the outbreak. To implement this, detection of simulated incident clusters was performed against the backdrop of real incidents. Since the true center (loci) of simulated clusters is known, the distance to loci from detected clusters can be measured. The CHIP Spatial/Temporal Cluster Generator [11], an open source software application, was used to generate the synthesized clusters consisting of spatially randomized locations. Clusters were positioned within the beneficiary catchment area (40 mile radius) of 10 prominent MTFs representing a variety of geographies within the study area. In large metropolitan areas such as Los Angeles or San Diego, the cluster loci was placed 10 miles due east of the MTF. Otherwise, the loci was placed 10 miles from the MTF in the cardinal direction (i.e., N, NE, etc.) of the most densely populated locality (Figure 2). Such placement adds rigor to the detection testing by ensuring that simulated incidents intermingle with actual ILI cases. Though the number and size of zip codes intersected by simulated clusters impacts the outcome, no attempt was made to influence this factor.


Enhancing spatial detection accuracy for syndromic surveillance with street level incidence data.

Savory DJ, Cox KL, Emch M, Alemi F, Pattie DC - Int J Health Geogr (2010)

Cluster Placement Example: 10 Miles West of Beale Air Force Base.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2819064&req=5

Figure 2: Cluster Placement Example: 10 Miles West of Beale Air Force Base.
Mentions: For purposes of this study, detection accuracy is a measure of how close a detected cluster is to the "true" location of the outbreak. To implement this, detection of simulated incident clusters was performed against the backdrop of real incidents. Since the true center (loci) of simulated clusters is known, the distance to loci from detected clusters can be measured. The CHIP Spatial/Temporal Cluster Generator [11], an open source software application, was used to generate the synthesized clusters consisting of spatially randomized locations. Clusters were positioned within the beneficiary catchment area (40 mile radius) of 10 prominent MTFs representing a variety of geographies within the study area. In large metropolitan areas such as Los Angeles or San Diego, the cluster loci was placed 10 miles due east of the MTF. Otherwise, the loci was placed 10 miles from the MTF in the cardinal direction (i.e., N, NE, etc.) of the most densely populated locality (Figure 2). Such placement adds rigor to the detection testing by ensuring that simulated incidents intermingle with actual ILI cases. Though the number and size of zip codes intersected by simulated clusters impacts the outcome, no attempt was made to influence this factor.

Bottom Line: This study focuses on spatial detection and attempts to improve the effectiveness of the ESSENCE implementation of the spatial scan statistic by increasing the spatial resolution of incidence data from zip codes to street address level.Spatial detection accuracy improved notably with regard to both location and size when incidents were geocoded to street addresses rather than zip code centroids.Since street address geocoding success rates were only 73.5%, zip codes were still used for more than one quarter of ILI cases.

View Article: PubMed Central - HTML - PubMed

Affiliation: Planned Systems International, Inc, Falls Church, VA 22041, USA.

ABSTRACT

Background: The Department of Defense Military Health System operates a syndromic surveillance system that monitors medical records at more than 450 non-combat Military Treatment Facilities (MTF) worldwide. The Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE) uses both temporal and spatial algorithms to detect disease outbreaks. This study focuses on spatial detection and attempts to improve the effectiveness of the ESSENCE implementation of the spatial scan statistic by increasing the spatial resolution of incidence data from zip codes to street address level.

Methods: Influenza-Like Illness (ILI) was used as a test syndrome to develop methods to improve the spatial accuracy of detected alerts. Simulated incident clusters of various sizes were superimposed on real ILI incidents from the 2008/2009 influenza season. Clusters were detected using the spatial scan statistic and their displacement from simulated loci was measured. Detected cluster size distributions were also evaluated for compliance with simulated cluster sizes.

Results: Relative to the ESSENCE zip code based method, clusters detected using street level incidents were displaced on average 65% less for 2 and 5 mile radius clusters and 31% less for 10 mile radius clusters. Detected cluster size distributions for the street address method were quasi normal and sizes tended to slightly exceed simulated radii. ESSENCE methods yielded fragmented distributions and had high rates of zero radius and oversized clusters.

Conclusions: Spatial detection accuracy improved notably with regard to both location and size when incidents were geocoded to street addresses rather than zip code centroids. Since street address geocoding success rates were only 73.5%, zip codes were still used for more than one quarter of ILI cases. Thus, further advances in spatial detection accuracy are dependant on systematic improvements in the collection of individual address information.

Show MeSH
Related in: MedlinePlus