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Development and Application of Syndromic Surveillance for Severe Weather Events Following Hurricane Sandy

Published online by Cambridge University Press:  05 May 2016

Stella Tsai ,
Teresa Hamby ,
Alvin Chu ,
Jessie A. Gleason ,
Gabrielle M. Goodrow ,
Hui Gu ,
Edward Lifshitz  and
Jerald A. Fagliano
Stella Tsai*
Affiliation:
Communicable Disease Service, New Jersey Department of Health, Trenton, New Jersey
Teresa Hamby
Affiliation:
Communicable Disease Service, New Jersey Department of Health, Trenton, New Jersey
Alvin Chu
Affiliation:
Communicable Disease Service, New Jersey Department of Health, Trenton, New Jersey
Jessie A. Gleason
Affiliation:
Environmental and Occupational Health Surveillance Program, New Jersey Department of Health, Trenton, New Jersey
Gabrielle M. Goodrow
Affiliation:
Brown University, Public Health Program, Providence, Rhode Island
Hui Gu
Affiliation:
Communicable Disease Service, New Jersey Department of Health, Trenton, New Jersey
Edward Lifshitz
Affiliation:
Communicable Disease Service, New Jersey Department of Health, Trenton, New Jersey
Jerald A. Fagliano
Affiliation:
Drexel University, Environmental and Occupational Health Program, Dornsife School of Public Health, Philadelphia, Pennsylvania.
*
Correspondence and reprint requests to Stella Tsai, PhD, CIH, PO Box 369, Trenton, NJ 08625-0369 (e-mail: stella.tsai@doh.nj.gov).
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Abstract

Objective

Following Hurricane Superstorm Sandy, the New Jersey Department of Health (NJDOH) developed indicators to enhance syndromic surveillance for extreme weather events in EpiCenter, an online system that collects and analyzes real-time chief complaint emergency department (ED) data and classifies each visit by indicator or syndrome.

Methods

These severe weather indicators were finalized by using 2 steps: (1) key word inclusion by review of chief complaints from cases where diagnostic codes met selection criteria and (2) key word exclusion by evaluating cases with key words of interest that lacked selected diagnostic codes.

Results

Graphs compared 1-month, 3-month, and 1-year periods of 8 Hurricane Sandy-related severe weather event indicators against the same period in the following year. Spikes in overall ED visits were observed immediately after the hurricane for carbon monoxide (CO) poisoning, the 3 disrupted outpatient medical care indicators, asthma, and methadone-related substance use. Zip code level scan statistics indicated clusters of CO poisoning and increased medicine refill needs during the 2 weeks after Hurricane Sandy. CO poisoning clusters were identified in areas with power outages of 4 days or longer.

Conclusions

This endeavor gave the NJDOH a clearer picture of the effects of Hurricane Sandy and yielded valuable state preparation information to monitor the effects of future severe weather events. (Disaster Med Public Health Preparedness. 2016;10:463–471)

Keywords

syndromic surveillancesevere weatherhurricanedisrupted outpatient medical carecarbon monoxide poisoning
Type
Original Research
Information
Disaster Medicine and Public Health Preparedness , Volume 10 , Special Issue 3: Superstorm Sandy , June 2016 , pp. 463 - 471
Copyright
Copyright © Society for Disaster Medicine and Public Health, Inc. 2016 

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References

1. Drewniak, M, Roberts, R. Christie Administration Releases Total Hurricane Sandy Damage Assessment of $36.9 Billion. State of New Jersey website. http://www.nj.gov/governor/news/news/552012/approved/20121128e.html. Published November 28, 2012. Accessed April 5, 2016.Google Scholar
2. Wade, TJ, Sandhu, SK, Levy, D, et al. Did a severe flood in the Midwest cause an increase in the incidence of gastrointestinal symptoms? Am J Epidemiol. 2004;159(4):398-405. http://dx.doi.org/10.1093/aje/kwh050.Google Scholar
3. Brandt, M, Brown, C, Burkhart, J, et al. Mold prevention strategies and possible health effects in the aftermath of hurricanes and major floods. MMWR Recomm Rep. 2006;55(RR-8):1-27.Google Scholar
4. Chen, BC, Shawn, LK, Connors, NJ, et al. Carbon monoxide exposures in New York City following Hurricane Sandy in 2012. Clin Toxicol (Phila). 2013;51(9):879-885. http://dx.doi.org/10.3109/15563650.2013.839030.Google Scholar
5. McFarlane, AC, Williams, R. Mental health services required after disasters: learning from the lasting effects of disasters. Depress Res Treat. 2012;2012:970194. http://dx.doi.org/10.1155/2012/970194.Google Scholar
6. Abramson, D. Sandy Child and Family Health Study. National Center for Disaster Preparedness, Earth Institute, Columbia University website. http://ncdp.columbia.edu/microsite-page/sandy-child-and-family-health-study/scafh-publications-reports/. Published 2015. Accessed April 5, 2016.Google Scholar
7. Münchener Rückversicherungs-Gesellschaft (Munich Re). Loss events worldwide 1980-2014. http://preventionweb.net/go/44281. Published January 2015. Accessed April 5, 2016.Google Scholar
8. Solomon, S, Qui, D, Manning, M, et al, eds. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, 2007. Cambridge, United Kingdom: Cambridge University Press; 2007.Google Scholar
9. Henning, KJ. Overview of syndromic surveillance: what is syndromic surveillance? MMWR Morb Mortal Wkly Rep. 2004;53(suppl):5-11.Google Scholar
10. Kman, NE, Bachmann, DJ. Biosurveillance: a review and update. Adv Prev Med. 2012;2012:301408. http://dx.doi.org/10.1155/2012/301408.Google Scholar
11. Hughes, HE, Morbey, R, Hughes, TC, et al. Using an emergency department syndromic surveillance system to investigate the impact of extreme cold weather events. Public Health. 2014;128(7):628-635. http://dx.doi.org/10.1016/j.puhe.2014.05.007.Google Scholar
12. Hiller, KM, Stoneking, L, Min, A, et al. Syndromic surveillance for influenza in the emergency department - a systematic review. PLOS ONE. 2013;8(9):e73832. http://dx.doi.org/10.1371/journal.pone.0073832.Google Scholar
13. Smith, GE, Bawa, Z, Macklin, Y, et al. Using real-time syndromic surveillance systems to help explore the acute impact of the air pollution incident of March/April 2014 in England. Environ Res. 2015;136:500-504. http://dx.doi.org/10.1016/j.envres.2014.09.028.Google Scholar
14. Elliot, AJ, Bone, A, Morbey, R, et al. Using real-time syndromic surveillance to assess the health impact of the 2013 heatwave in England. Environ Res. 2014;135:31-36. http://dx.doi.org/10.1016/j.envres.2014.08.031.Google Scholar
15. Chu, AF, Tsai, S, Hamby, T, et al. Development of mental health classification related to severe weather events. Online J Public Health Inform. 2015;7(1):e120.Google Scholar
16. Hoopes Halpin, SH. The Impact of Superstorm Sandy on New Jersey Towns and Households. Rutgers University School of Public Affairs and Administration. http://njdatabank.newark.rutgers.edu/sites/default/files/files/RutgersSandyImpact-FINAL-25Oct13.pdf. Published 2013. Accessed April 4, 2016.Google Scholar
17. Wang, L, Ramoni, MF, Mandl, KD, Sebastiani, P. Factors affecting automated syndromic surveillance. Artif Intell Med. 2005;34(3):269-278. http://dx.doi.org/10.1016/j.artmed.2004.11.002.Google Scholar
18. Vilain, P, Pages, F, Combes, X, et al. Health impact assessment of Cyclone Bejisa in reunion island (france) using syndromic surveillance. Prehosp Disaster Med. 2015;30(2):137-144. http://dx.doi.org/10.1017/S1049023X15000163.Google Scholar
19. Hansen, AL, Bi, P, Ryan, P, et al. The effect of heat waves on hospital admissions for renal disease in a temperate city of Australia. Int J Epidemiol. 2008;37(6):1359-1365. http://dx.doi.org/10.1093/ije/dyn165.Google Scholar
20. Ochi, S, Hodgson, S, Landeg, O, et al, Disaster-driven evacuation and medication loss: a systematic literature review. PLOS Currents Disasters. 2014 Jul 18. http://dx.doi.org/10.1371/currents.dis.fa417630b566a0c7dfdbf945910edd96.Google Scholar
21. Kolwaite, AR, Hlady, WG, Simon, MD, et al. Assessing functional needs sheltering in Pike County, Kentucky: using a community assessment for public health emergency response. Disaster Med Public Health Prep. 2013;7(6):597-602. http://dx.doi.org/10.1017/dmp.2013.110.Google Scholar
22. Chen, JH, Lauper, U, Pantea, C, et al. Carbon monoxide poisoning during Hurricane Sandy in affected New York State counties. Online J Public Health Inform. 2015;7(1):e119.Google Scholar
23. Wade, TJ, Lin, CJ, Jagai, JS, Hilborn, ED. Flooding and emergency room visits for gastrointestinal illness in Massachusetts: a case-crossover study. PLoS One. 2014;9(10):e110474. http://dx.doi.org/10.1371/journal.pone.0110474.Google Scholar
24. Costilla-Esquivel, A, Corona-Villavicencio, F, Velasco-Castanon, JG, et al. A relationship between acute respiratory illnesses and weather. Epidemiol Infect. 2014;142(07):1375-1383. http://dx.doi.org/10.1017/S0950268813001854.Google Scholar
25. Göksel, Ö, Çelik, GE, Öner Erkekol, F, et al. Triggers in adult asthma: are patients aware of triggers and doing right? Allergol Immunopathol (Madr). 2009;37(3):122-128. http://dx.doi.org/10.1016/S0301-0546(09)71723-9.Google Scholar