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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

Displacement of Detected 2 Mile Radius Simulated Clusters by MTF.
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Figure 3: Displacement of Detected 2 Mile Radius Simulated Clusters by MTF.

Mentions: Differences in accuracy results for individual MTFs across the range of simulated cluster sizes can be compared in Figures 3, 4, &5. MTFs in the bar charts are arranged in increasing catchment area beneficiary population from left to right. For 2 and 5 mile clusters, Bernoulli-Street displayed notably better accuracy at nearly all installations. For the larger 10 mile radius clusters street level improvements at many MTFs are less pronounced. The less consistent improvements reflect the influence of distant unassociated cases. Greater displacements may be expected in less populated rural areas due to their typically larger zip code areas. Additionally, military personnel tend to live on the base in rural areas and may be more prone to submitting a non geocode-able installation address during triage. Vandenberg Air Force Base and Lemoore Naval Hospital are two such areas where street level geocoding yielded mixed results, possibly due to these factors. On the right side of the bar chart, the urban based MTFs tended to have more consistent improvements from street level geocoding resolution.


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)

Displacement of Detected 2 Mile Radius Simulated Clusters by MTF.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Displacement of Detected 2 Mile Radius Simulated Clusters by MTF.
Mentions: Differences in accuracy results for individual MTFs across the range of simulated cluster sizes can be compared in Figures 3, 4, &5. MTFs in the bar charts are arranged in increasing catchment area beneficiary population from left to right. For 2 and 5 mile clusters, Bernoulli-Street displayed notably better accuracy at nearly all installations. For the larger 10 mile radius clusters street level improvements at many MTFs are less pronounced. The less consistent improvements reflect the influence of distant unassociated cases. Greater displacements may be expected in less populated rural areas due to their typically larger zip code areas. Additionally, military personnel tend to live on the base in rural areas and may be more prone to submitting a non geocode-able installation address during triage. Vandenberg Air Force Base and Lemoore Naval Hospital are two such areas where street level geocoding yielded mixed results, possibly due to these factors. On the right side of the bar chart, the urban based MTFs tended to have more consistent improvements from street level geocoding resolution.

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