Teresa Hamby

Development and application of syndromic surveillance for severe weather events following Hurricane Sandy

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).

Game Plan: Communicable Disease Surveillance for Super Bowl XLVIII– New Jersey, 2014.

This oral presentation will describe the surveillance planning and activities for a large-scale event (Super Bowl XLVIII) using New Jersey syndromic surveillance system (EpiCenter).

Paralysis Analysis: Investigating Paralysis Visit Anomalies in New Jersey

Objective To describe the investigation of a statewide anomaly detected by a newly established state syndromic surveillance system and usage of that system. Introduction On July 11, 2012, New Jersey Department of Health (DOH) Communicable Disease Service (CDS) surveillance staff received email notification of a statewide anomaly in EpiCenter for Paralysis. Two additional anomalies followed within three hours. Since Paralysis Anomalies are uncommon, staff initiated an investigation to determine if there was an outbreak or other event of concern taking place. Also at question was whether receipt of multiple anomalies in such a short time span was statistically or epidemiologically significant. Methods In New Jersey, 68 of 81 total acute care and satellite Emergency Departments (EDs) are connected to EpiCenter, an online syndromic surveillance system developed by Health Monitoring Systems, Inc (HMS) that incorporates statistical management and analytical techniques to process health-related data in real time. Chief complaint text is classified, using text recognition methods, into various public health-related and other categories. Anomalies occur when any of several statistical methods detect increases in incoming data that are outside of established thresholds. After receiving three anomaly notifications related to Paralysis in a 4-hour time period, NJDOH surveillance data staff enlisted CDS and local epidemiologist colleagues to review the data and determine if there was an infectious cause. Results The first EpiCenter anomaly notification was received on July 11, 2012 at 1:22 pm as a result of increased ED visits classified as Paralysis based on facility location for the period beginning at noon on July 10, 2012. Using Cusum EMA analysis, 76 reported interactions exceeded the predicted value of 50.49 and the threshold of 70.72. The second anomaly, also based on facility location, was received at 3:20 pm and the third anomaly notification, based on home location, was received at 4:32 pm. Cusum EMA and Exponential Moving Average analysis methods detected these anomalies. Table 1 describes the anomalies in more detail. Compiled data from all anomalies were reviewed by CDS epidemiology and surveillance staff to determine whether there was a public health event taking place. A total of 89 patients were seen in 39 (57%) of the 68 NJ facilities reporting to EpiCenter with no geographic centralization. Age and gender of patients were reviewed with no clear pattern discerned. Figure 1 shows the time distribution of these visits. Upon further investigation, it was determined that a moderate increase in Paralysis visits over a relatively short time span was sufficient to create an anomaly under the default threshold for those visits. Multiple analysis methods created multiple anomalies which gave an impression the event was of greater significance compared to a single anomaly. To follow up, NJDOH requested that local epidemiologists investigate within their jurisdictions by contacting hospitals directly where EpiCenter data proved inconclusive. Their reports confirmed NJDOH’s findings that the anomalies did not signal an event of public health concern. Conclusions This investigation of three Paralysis anomalies is an important introduction to the newly implemented system’s capabilities in anomaly detection, and also to anomaly investigation procedures developed by NJDOH for local surveillance staff. As a result of this experience, these anomaly investigation procedures are being fine-tuned. The fact that these sequential anomalies resulted in an investigation being undertaken highlights the importance in setting investigation- generating alert thresholds within EpiCenter at a level that will minimize “false” positives without risking the missing of “true” positives.