Int J Health Geogr - Enhancing spatial detection accuracy for syndromic surveillance with street level incidence data.

Tópicos

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Resumo

CKGROUND: 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.

Resumo Limpo

ckground depart defens militari health system oper syndrom surveil system monitor medic record noncombat militari treatment facil mtf worldwid electron surveil system earli notif communitybas epidem essenc use tempor spatial algorithm detect diseas outbreak studi focus spatial detect attempt improv effect essenc implement spatial scan statist increas spatial resolut incid data zip code street address levelmethod influenzalik ill ili use test syndrom develop method improv spatial accuraci detect alert simul incid cluster various size superimpos real ili incid influenza season cluster detect use spatial scan statist displac simul loci measur detect cluster size distribut also evalu complianc simul cluster sizesresult relat essenc zip code base method cluster detect use street level incid displac averag less mile radius cluster less mile radius cluster detect cluster size distribut street address method quasi normal size tend slight exceed simul radii essenc method yield fragment distribut high rate zero radius overs clustersconclus spatial detect accuraci improv notabl regard locat size incid geocod street address rather zip code centroid sinc street address geocod success rate zip code still use one quarter ili case thus advanc spatial detect accuraci depend systemat improv collect individu address inform

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