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Usefulness of Syndromic Data Sources for Investigating Morbidity Resulting From a Severe Weather Event

Published online by Cambridge University Press:  08 April 2013

Atar Baer ,
Yevgeniy Elbert ,
Howard S. Burkom ,
Rekha Holtry ,
Joseph S. Lombardo  and
Jeffrey S. Duchin
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Abstract

Objective: We evaluated emergency department (ED) data, emergency medical services (EMS) data, and public utilities data for describing an outbreak of carbon monoxide (CO) poisoning following a windstorm.

Methods: Syndromic ED data were matched against previously collected chart abstraction data. We ran detection algorithms on selected time series derived from all 3 data sources to identify health events associated with the CO poisoning outbreak. We used spatial and spatiotemporal scan statistics to identify geographic areas that were most heavily affected by the CO poisoning event.

Results: Of the 241 CO cases confirmed by chart review, 190 (78.8%) were identified in the syndromic surveillance data as exact matches. Records from the ED and EMS data detected an increase in CO-consistent syndromes after the storm. The ED data identified significant clusters of CO-consistent syndromes, including zip codes that had widespread power outages. Weak temporal gastrointestinal (GI) signals, possibly resulting from ingestion of food spoiled by lack of refrigeration, were detected in the ED data but not in the EMS data. Spatial clustering of GI-based groupings in the ED data was not detected.

Conclusions: Data from this evaluation support the value of ED data for surveillance after natural disasters. Enhanced EMS data may be useful for monitoring a CO poisoning event, if these data are available to the health department promptly.

(Disaster Med Public Health Preparedness. 2011;5:37-45)

Keywords

weatherdisastersemergency service, hospitalemergency medical servicesCarbon Monoxide PoisoningData CollectionepidemiologyWashington Statediagnosisgastrointestinal diseasesinformation managementsyndromic surveillancecarbon monoxideepidemiologyKing County
Type
Original Research
Information
Disaster Medicine and Public Health Preparedness , Volume 5 , Issue 1 , March 2011 , pp. 37 - 45
Copyright
Copyright © Society for Disaster Medicine and Public Health, Inc. 2011

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References

REFERENCES

1.Washington State hazard mitigation plan. Washington State Emergency Management Division web site. www.emd.wa.gov/plans/documents/ehmp_5.7_severe_storm.pdf.Accessed April 26, 2010.Google Scholar
2.Gulati, RK, Kwan-Gett, TS, Hampson, NB.Carbon monoxide epidemic among immigrant populations: King County, Washington, 2006. Am J Public Health. 2009;99 (9):1687–1692.Google Scholar
3.Hampson, NB, Weaver, LK.Carbon monoxide poisoning: a new incidence for an old disease. Undersea Hyperb Med. 2007;34 (3):163–168.Google Scholar
4.Cukor, J, Restuccia, M.Carbon monoxide poisoning during natural disasters: the Hurricane Rita experience. J Emerg Med. 2007;33 (3):261–264.Google Scholar
5.Centers for Disease Control and Prevention (CDC). Carbon monoxide exposures after hurricane Ike - Texas, September 2008. MMWR Morb Mortal Wkly Rep. 2009;58 (31):845–849.Google Scholar
6.Van Sickle, D, Chertow, DS, Schulte, JM.Carbon monoxide poisoning in Florida during the 2004 hurricane season. Am J Prev Med. 2007;32 (4):340–346.Google Scholar
7.Heffernan, R, Mostashari, F, Das, D, Karpati, A, Kulldorff, M, Weiss, D.Syndromic surveillance in public health practice, New York City. Emerg Infect Dis. 2004;10 (5):858–864.CrossRefGoogle ScholarPubMed
8.Elbert, Y, Burkom, HS.Development and evaluation of a data-adaptive alerting algorithm for univariate temporal biosurveillance data. Stat Med. 2009;28 (26):3226–3248.Google Scholar
9.Kulldorff, M.SaTScan: software for the spatial, temporal, and space-time scan statistics. SaTScan web site. http://www.satscan.org/. Accessed April 26, 2010.Google Scholar
10.Xing, J, Burkom, H, Moniz, L, Edgerton, J, Leuze, M, Tokars, J.Evaluation of sliding baseline methods for spatial estimation for cluster detection in the biosurveillance system. Int J Health Geogr. 2009;8:4519615075 doi: 10.1186/1476-072X-8-45.Google Scholar
11.Abrams, A, Kulldorff, M, Kleinman, K.Empirical/asymptotic p -values for Monte Carlo-based hypothesis testing: an application to cluster detection using the scan statistic. Proceedings of the Fourth Conference on Extreme Value Analysis and Statistical Models and Their Applications: August 15, 2005; Gothenburg.Google Scholar
12.Centers for Disease Control and Prevention (CDC). Carbon monoxide–related deaths–United States, 1999-2004. MMWR Morb Mortal Wkly Rep. 2007;56 (50):1309–1312.Google Scholar
13.Varon, J, Marik, PE, Fromm, RE Jr, Gueler, A.Carbon monoxide poisoning: a review for clinicians. J Emerg Med. 1999;17 (1):87–93.Google Scholar
14.Marx, MA, Rodriguez, CV, Greenko, J.Diarrheal illness detected through syndromic surveillance after a massive power outage: New York City, August 2003. Am J Public Health. 2006;96 (3):547–553.Google Scholar
15.Centers for Disease Control and Prevention (CDC). Leveraging syndromic surveillance during the San Diego Wildfires, 2003. MMWR Morb Mortal Wkly Rep. 2005;54(suppl)190.Google Scholar
16.Centers for Disease Control and Prevention (CDC). Injury and illness surveillance in hospitals and acute-care facilities after Hurricanes Katrina And Rita--New Orleans area, Louisiana, September 25-October 15, 2005. MMWR Morb Mortal Wkly Rep. 2006;55 (2):35–38.Google Scholar
17.Jhung, MA, Shehab, N, Rohr-Allegrini, C.Chronic disease and disasters medication demands of Hurricane Katrina evacuees. Am J Prev Med. 2007;33 (3):207–210.Google Scholar