Skip to main content Accessibility help

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

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


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)


Corresponding author

Correspondence: Atar Baer, PhD, Public Health–Seattle & King County, 401 Fifth Ave, Suite 900, Seattle, WA 98104 (e-mail:


Hide All
1.Washington State hazard mitigation plan. Washington State Emergency Management Division web site. April 26, 2010.
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):16871692.
3.Hampson, NB, Weaver, LK.Carbon monoxide poisoning: a new incidence for an old disease. Undersea Hyperb Med. 2007;34 (3):163168.
4.Cukor, J, Restuccia, M.Carbon monoxide poisoning during natural disasters: the Hurricane Rita experience. J Emerg Med. 2007;33 (3):261264.
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):845849.
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):340346.
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):858864.
8.Elbert, Y, Burkom, HS.Development and evaluation of a data-adaptive alerting algorithm for univariate temporal biosurveillance data. Stat Med. 2009;28 (26):32263248.
9.Kulldorff, M.SaTScan: software for the spatial, temporal, and space-time scan statistics. SaTScan web site. Accessed April 26, 2010.
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.
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.
12.Centers for Disease Control and Prevention (CDC). Carbon monoxide–related deaths–United States, 1999-2004. MMWR Morb Mortal Wkly Rep. 2007;56 (50):13091312.
13.Varon, J, Marik, PE, Fromm, RE Jr, Gueler, A.Carbon monoxide poisoning: a review for clinicians. J Emerg Med. 1999;17 (1):8793.
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):547553.
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.
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):3538.
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):207210.


Related content

Powered by UNSILO

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

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


Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed.