2014.ISDS.Abstracts.Final.pdf


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ISDS 2014 Conference Abstracts

Carbon Monoxide Poisoning during Hurricane Sandy in 
Affected New York State Counties
Jian-Hua Chen*, Ursula Lauper, Cristian Pantea, Shao Lin and Hwa-Gan Chang

Division of Epidemiology, New York State Department of Health, Albany, NY, USA

Objective
1) To identify carbon monoxide (CO) poisoning in three most

affected New York State (NYS) counties (Nassau, Suffolk, and 
Westchester) during and immediately after Hurricane Sandy with 
hospital emergency department (ED) chief complaint data reported 
daily to the New York State Department of Health (NYSDOH). 2) To 
explore the relationship between power outage and the numbers of 
CO-related ED visits (CO-EDs).

Introduction
CO poisoning is a leading cause of mortality and morbidity in 

disaster and post-disaster situations, when widespread power outages 
most likely occur (1, 2). The NYSDOH Syndromic Surveillance 
System receives daily ED visit chief complaint data from 140 NYS 
(excluding New York City) hospitals. Daily power outage data are 
available from the NYS Department of Public Service (NYSDPS). 
These data can be used to estimate the risk of CO-EDs and provide 
useful information for public health situational awareness and 
emergency response management during disaster events.

Methods
This was a retrospective study in which the daily CO-EDs were 

identified by searching CO-related keywords in chief complaints 
of the ED visits from the affected three counties during the Sandy 
period (October 29 – November 27, 2012). Data on daily maximum 
number of households without power during the hurricane in each 
of the three affected counties were obtained from NYSDPS. The 
correlation between CO-EDs and maximum number of households 
without power was estimated with a log-transformed linear regression 
model. A negative binomial model was used to examine the risk of 
CO-EDs by the maximum households affected by power outages each 
day during the month following Hurricane Sandy. Data were analyzed 
using SAS 9.3 and the p-value for testing statistical significance was 
set at 0.05.

Results
A total of 188 CO-EDs were identified in the three affected counties 

during the study period. Overall, the number of CO-EDs increased 
during the hurricane period. Coinciding with the power outage in the 
areas, the CO-EDs increase peaked at 3-4 days after the hurricane 
made landfall in NYS on October 29 (Figure 1). Statistical analysis 
showed that there was a significant correlation between the maximum 
number of households without power and the number of CO- EDs 
such that 53% of the variance of CO-EDs can be explained by the 
power outage (p<0.0001). The risk ratio of CO poisoning was more 
than three times as great on days of significant power outage (i.e. 
when more than 200,000 households were without power), compared 
with days of insignificant power outage - defined as 10,000 or fewer 
households (Table 1). The higher the number of households without 
power, the higher the risk ratio of CO poisoning.

Conclusions
CO exposures can be identified with ED chief complaint data, 

and the risk of CO-EDs can be estimated using power outage data. 

If ED data and/or power outage data are available in a timely base, 
they can provide valuable CO exposure information for situational 
awareness and emergency management during and after disasters. 
The study results also support that power outage is an underlying 
cause that indirectly leads to CO exposures. Education on proper 
use of alternative power and heating sources should be part of 
public programs designed to prevent future disaster-associated CO 
poisoning.

Keywords
CO poisoning; event syndromic surveillance; disaster situational 
awareness; power outage; chief complaint

References

1. Hampson NB, Stock AL,. Storm-related carbon monoxide poisonings:
Lessons learned from recent epidemics. Undersea Hyperb Med 2006; 
33:257-63.

2. Iqbak S, Clower JH, Hernandez SA, Damon SA, Yip FY. A review
of disaster-related carbon monoxide poisoning: surveillance, 
epidemiology, and opportunities fro pervention. Am J Public Health 
2012;102:1957-63.

*Jian-Hua Chen
E-mail: jianhua.chen@health.ny.gov    

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * (1):e119, 201