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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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Size Distribution of Detected 10 Mile Radius Clusters.
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Figure 8: Size Distribution of Detected 10 Mile Radius Clusters.

Mentions: Cluster size distributions varied widely for tested detection scenarios as did the rate of zero radius clusters. Figures 6, 7, &8 depict the size distributions for the ESSENCE-Zip and Bernoulli-Street scenarios and Table 6 gives the rates of zero radius clusters for all scenarios. Although the results for the Bernoulli-Zip scenario were more positive than for ESSENCE-ZIP, they were less favorable than Bernoulli-Street and therefore have been excluded from the histograms in the interest of clarity. In general, distributions were not normally distributed so measures of central tendency have been omitted in lieu of visual analysis of histograms. At 2 miles radius Bernoulli-Street clusters were normally distributed mainly between 2 and 5 miles radius (Figure 6). In contrast, 78% of ESSENCE-ZIP generated clusters had a zero radius. For 5 miles radius, Bernoulli-Street clusters were bi-modally distributed between 3 and 13 mile radius (Figure 7). For ESSENCE-ZIP, although roughly 40% of clusters were in the same range, there were 36% zero radius and 24% with radii of 15 miles or larger. Finally at 10 miles radius, Bernoulli-Street clusters maintained a bimodal but cohesive spread between 8 and 20 miles radius (Figure 8). ESSENCE-ZIP maintained its unacceptable rate of zero radius clusters at 29% with the remainder widely scattered.


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)

Size Distribution of Detected 10 Mile Radius Clusters.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 8: Size Distribution of Detected 10 Mile Radius Clusters.
Mentions: Cluster size distributions varied widely for tested detection scenarios as did the rate of zero radius clusters. Figures 6, 7, &8 depict the size distributions for the ESSENCE-Zip and Bernoulli-Street scenarios and Table 6 gives the rates of zero radius clusters for all scenarios. Although the results for the Bernoulli-Zip scenario were more positive than for ESSENCE-ZIP, they were less favorable than Bernoulli-Street and therefore have been excluded from the histograms in the interest of clarity. In general, distributions were not normally distributed so measures of central tendency have been omitted in lieu of visual analysis of histograms. At 2 miles radius Bernoulli-Street clusters were normally distributed mainly between 2 and 5 miles radius (Figure 6). In contrast, 78% of ESSENCE-ZIP generated clusters had a zero radius. For 5 miles radius, Bernoulli-Street clusters were bi-modally distributed between 3 and 13 mile radius (Figure 7). For ESSENCE-ZIP, although roughly 40% of clusters were in the same range, there were 36% zero radius and 24% with radii of 15 miles or larger. Finally at 10 miles radius, Bernoulli-Street clusters maintained a bimodal but cohesive spread between 8 and 20 miles radius (Figure 8). ESSENCE-ZIP maintained its unacceptable rate of zero radius clusters at 29% with the remainder widely scattered.

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