Emergency Department (ED)

In 2003, with the advent of SARS, the Ontario Ministry of Health and Long-Term Care (MOHLTC) released a document mandating the use of a clinical screening tool to detect patients at high risk for having a febrile respiratory illness (FRI), defined as a temperature of > 38ºC and a new or worsening cough or shortness of breath (1). The FRI screening tool is available in all Ontario Emergency Departments (ED), and is utilized in 86% of them (2). Any patient who meets all of the criteria is designated FRI positive, treated with droplet precautions and is instructed to wear a mask and undergo frequent hand-washing (1). The FRI screening tool was created as a response to the SARS outbreaks, and while it is used to identify any FRI, its sensitivity has not been documented. We attempt to determine the utility of FRI as a defining element of clinical influenza.

Objective

 (1) To determine if patients who are found to be positive for influenza or parainfluenza by culture or antigen detection are all detected by the Ontario Ministry of Health and Long-Term Care's Febrile Respiratory Illness (FRI) screening tool, and thereby treated with appropriate respiratory precautions to prevent spread. (2) To determine if syndromic surveillance or another clinical predictor would be a more effective screening tool than FRI.

Yearly epidemics of respiratory diseases occur in children. Early recognition of these and of unexpected epidemics due to new agents or as acts of biological/chemical terrorism is desirable. In this study, we evaluate the ordering of chest radiographs as a proxy for early identification of epidemics of lower respiratory tract disease. This has the potential to act as a sensitive real-time surveillance tool during such outbreaks.

Objective:

Create a tool for monitoring respiratory epidemics based on chest radiograph ordering patterns.

With the widespread deployment of near real time population health monitoring, there is increasing focus on spatial cluster detection for identifying disease outbreaks. These spatial epidemiologic methods rely on knowledge of patient location to detect unusual clusters. In hospital administrative data, patient location is collected as home address but use of this precise location raises privacy concerns. Regional locations, such as center points of zip codes, have been deployed in many existing systems. However, this practice could distort the geographic properties of the raw data and affect subsequent spatial analyses. The impact of location error due to centroid assignment on the statistical analyses underlying these systems requires study.

Objective

To investigate the impact of address precision (exact latitude and longitude versus the center points of zip codes) on spatial cluster detection.

Los Angeles County Department of Health Services is currently testing SaTScan’s space-time permutation model to assist in identifying aberrant illness activity in the community and determine it’s ability to detect outbreaks through analyzing real-time syndromic data. SaTScan could be useful especially due to its ability to provide geographic locations of outbreaks in the community.

Objective

To determine the usefulness of SaTScan as an outbreak and illness cluster detection tool in syndromic surveillance and to compare to a purely temporal CUSUM algorithm.

The North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT) is the early event detection system that serves public health users across North Carolina. One important data source for this system is North Carolina emergency department visits. ED data from hospitals across the state are downloaded, standardized, aggregated, and updated twice daily.

After hurricane Katrina devastated the Gulf Coast on August 29, 2005, federal officials evacuated two large groups of evacuees into Wake and Mecklenburg counties in North Carolina. In order to identify and monitor the hospital-based public health needs of these and other “unofficial” evacuees, NC state officials used both NC DETECT and hospital-based Public Health Epidemiologist reporting methods, along with other public health surveillance initiatives.

Objective

To compare two different methods of monitoring hurricane Katrina evacuees’ hospital visits in North Carolina.

The 2003 heat wave in France (15,000 extra deaths in 10 days) led the French institute for public health surveillance to modify its public health surveillance system. One of the major objectives of this program was a real time surveillance based on emergency departments (EDs). Trials experiments started in 2004 with a daily automatic data collection from 20 hospitals in the Paris area. The objectives of this new system were: 1) to detect early all threats for public health; and 2) to measure the impact of an identified phenomena.

In 2006 France was concerned by a new heat wave. It was the opportunity for recording health data during a hot period through this real time system.

Objective

This paper describes the performances of a syndromic surveillance system based on EDs during a heat wave.

The Centers for Disease Control and Prevention BioSense project has developed chief complaint (CC) and ICD9 sub-syndrome classifiers for the major syndromes for early event detection and situational awareness. This has the potential to expand the usefulness of syndromic surveillance, but little data exists evaluating this approach. The overall performance of classifiers can differ significantly among syndromes, and presumably among subsyndromes as well. Also, we had previously found that the seasonal pattern of diarrhea was different for patients < 60 months of age (younger) and for patients > 60 months of age (older).

Objective

Using chart review as the criterion standard to estimate the sensitivity, specificity, positive predictive value and negative predictive value of New York State hospital emergency department CC classifiers for patients < 60 months of age and > 60 months of age for the gastrointestinal (GI) syndrome and the following GI sub-syndromes: “abdominal pain”, “nausea-vomiting” and “diarrhea”.

Ideal anomaly detection algorithms should detect both sudden and gradual changes, while keeping the background false positive alert rate at a tolerable level. Further, the algorithm needs to perform well when the need is to detect small outbreaks in low-incidence diseases. For example, when surveillance is done based on the specific ICD9 diagnosis of flu rather than a larger syndromic grouping, the baseline counts will generally be low, in the range of 0 or 1 per day even in a large sample of EDs. 

Objective

Our goal was to determine the sensitivity of detection of various inserted outbreak sizes and shapes using a modified Holt-Winters detection algorithm applied to daily flu count data before the flu season and after its peak. We compare our results to C3 of EARS.

On December 14th, 2006, a severe windstorm in western Washington caused hundreds of thousands of residents to lose power. On December 15, 2006, there was a surge in emergency department (ED) visits for patients presenting with signs of acute carbon monoxide (CO) poisoning. A Public Health investigation was initiated following the storm to determine the extent of CO poisoning due to the windstorm. A retrospective analysis was later undertaken to evaluate how well our syndromic surveillance system was able to identify patients who presented to area EDs with carbon monoxide poisoning.

Objective

We evaluated the performance of our ED syndromic data for detecting visits associated with CO poisoning.

To recognize outbreaks so that early interventions can be applied, BioSense uses a modification of the EARS C2 method, stratifying days used to calculate the expected value by weekend vs weekday, and including a rate-based method that accounts for total visits. These modifications produce lower residuals (observed minus expected counts), but their effect on sensitivity has not been studied.

Objective

To evaluate several variations of a commonlyused control chart method for detecting injected signals in 2 BioSense System datasets.