Emergency Department (ED)

Collaborative relationships between academicians and public health practitioners are necessary to ensure that methodologies created in the research setting translate into practice. One barrier to forging these collaborations is restrictions on the sharing and availability of public health surveillance data; therefore, most academics with expertise in method development cannot access 'real world' surveillance data with which to evaluate their approaches. The ISDS Technical Conventions Committee was established in 2013 to facilitate and expedite the development, evaluation, and implementation of technical methods for public health surveillance. The purpose of the committee is to bridge a long-standing gap between technical challenges in public health practice and solution developers needing both understanding of these challenges and representative data.

Objective

The purpose of this panel is to facilitate the dissemination of surveillance-related use cases by public health practitioners with accompanying benchmark datasets to method developers. The panel will present practitioners' experiences with preparing patient-level emergency department data sets to accompany a use case submitted to the ISDS Technical Conventions Committee.

A common problem in syndromic surveillance using ED department data is temporary gaps in the data received from individual ED departments caused by delays in receiving the data.

Currently most syndromic surveillance systems provide information about the status of the data sources feeding into the system, for example on the home page of the system, but do not show the effects of any missing data sources on individual derived data elements (except in that graphs may show obvious drops in counts on days when data sources are missing).

Following an Oct 12-13, 2006 snowstorm, almost 400,000 homes in western New York lost power, some for up to 12 days. News reports said that emergency rooms saw many patients with CO exposure; 3 deaths were attributed to CO poisoning. As part of NYS DOH’s syndromic surveillance system, electronic ED records with a free-text CC field listing the symptoms reported by the patient are sent to NYS DOH daily. Each CC is searched for text strings indicating complaints in one or more of 6 syndromes (asthma, fever, gastrointestinal (GI), neurological, respiratory, rash). The system also allows nonroutine searches of CCs for complaints of interest. NYS hospitals also submit ED records to the Statewide Planning and Research Cooperative System (SPARCS) that include diagnostic codes assigned after evaluation of the patient (due within 30 days of each calendar quarter).

Objective

To assess the ability to identify cases of carbon monoxide (CO) poisoning from chief complaints (CC) in hospital emergency department (ED) records submitted daily to the New York State (NYS) Department of Health (DOH) Electronic Syndromic Surveillance System.

The New York City Department of Health and Mental Hygiene (NYC DOHMH) collects data daily from 50 of 61 (82%) emergency departments (EDs) in NYC representing 94% of all ED visits (avg daily visits ~10,000). The information collected includes the date and time of visit, age, sex, home zip code and chief complaint of each patient. Observations are assigned to syndromes based on the chief complaint field and are analyzed using SaTScan to identify statistically significant clusters of syndromes at the zip code and hospital level. SaTScan employs a circular spatial scan statistic and clusters that are not circular in nature may be more difficult to detect. FlexScan employs a flexible scan statistic using an adjacency matrix design.

Objective

To use the NYC DOHMH's ED syndromic surveillance data to evaluate FleXScan’s flexible scan statistic and compare it to results from the SaTScan circular scan. A second objective is to improve cluster detection in by improving geographic characteristics of the input files.

Many syndromic surveillance systems have been developed and are operational, yet lack concise guidelines for investigating and conducting followups on daily alarms. Daily emergency department visits from six reporting hospitals in the Duval County area are assessed and classified into a BioDefend (BD) system entry by triage personnel. Alarms are categorized into alerts, 3 SD above a 30 day rolling mean, or warnings, 2-3 SD above the mean. Signals are monitored and in response, public health investigations and recommended interventions are initiated.

Objective

To evaluate the protocol that the Duval County Health Department (DCHD) epidemiology staff uses to respond to BD syndromic surveillance system alarms. The response protocol utilizes all signals detected by BD and its secondary resources, within the DCHD jurisdiction.

Under a grant from the Centers for Disease Control and Prevention (CDC), the DC DOH established the Environmental Public Health Tracking Program (EPHTP) to monitor specific environmental and public health indicators and to investigate any potential links for the purpose of guiding policy development, resource allocation, and decision-making on disease prevention and treatment activities. This information improves understanding of the immediate and short-term effects of airborne pollutants on health care usage. In a collaborative project between JHU/APL and DC DOH, investigators explored and quantified correlations between ambient air quality measurements from five DC stations between October 2001 and March 2004 and DC hospital pediatric emergency department (ED) visits for asthma exacerbations. 

Objective

The study objective was to provide the CDC results from the EPHTP on quantifying the relationship between air quality and pediatric ED visits for asthma among DC residents over a 3 year period. This effort also explored novel uses of traditional data to understand background disease patterns so that unexpected fluctuations could be better detected in community disease trends and thereby identify early disease outbreaks.

One of the first county-wide syndromic surveillance systems in the nation, the Syndromic Tracking and Reporting System (STARS) has been in operation since 11/01/2001, and now covers Hillsborough, Pinellas and Collier counties. STARS uses hospital emergency department visit data to detect aberrations of non-specific syndromes and serves as an earlier warning system for public health threats. Patient’s syndrome is collected upon arrival, separately from routine collection of clinical and administrative data; but in some hospitals the process is being streamlined with routine data collection. Aberration detection is done twice daily using the statistical system EARS developed by the CDC. Upon flagging of an aberration, follow-up investigation is conducted to verify cases, and identify source of exposure following a sequence of decision procedure. After several years of operation and some instituted enhancements, a systematic evaluation was called to (1) assess if STARS has met the operation specifications and (2) characterize system efficacy and effectiveness.

Objective

To evaluate STARS with respect to quality of syndrome diagnoses, timeliness and completeness of data collection and processing, performance of aberration detection methods, and aberration investigation.

ARIMA models use past values (autoregressive terms) and past forecasting errors (moving average terms) to generate future forecasts, making it a potential candidate method for modeling citywide time series of syndromic data [1]. While past research supports the use of ARIMA modeling as a detection algorithm in syndromic surveillance [2], there has been little evaluation of an ARIMA model's prospective outbreak detection capabilities. We built an ARIMA model to prospectively detect simulated outbreaks in ED syndromic data. This method is one of eight being formally evaluated as part of a grant from the Alfred P. Sloan Foundation.

Objective

To evaluate seasonal autoregressive integrated moving average (ARIMA) models for prospective analysis of New York City (NYC) emergency department (ED) syndromic data.

ESSENCE is a web-based syndromic surveillance system utilized by DHMH to detect and track outbreaks, suspicious patterns of illness, public health emergencies, and biological threats. ESSENCE ED chief complaint data is collected daily from 47 emergency departments in Maryland (all 45 acute care hospitals and 2 freestanding emergency medical facilities). A chief complaint in ESSENCE is a free text field that lists the patient’s reason for the ED visit upon arrival at the hospital. Chief complaint data may be comprehensive or abbreviated and may include a single reason or multiple reasons for the ED visit. Medical history may be included in chief complaint data, which can create low specificity (false positive cases). Chief complaint data alone may yield less accurate modeling and lower outbreak detection sensitivity. This analysis evaluates whether counts of chief complaints are appropriate indicators of disease burden for several specific illnesses, by comparing chief complaints to their corresponding discharge diagnoses.

Objective

The state of Maryland has incorporated chief complaint data from 100% of its acute care emergency departments (ED) into the Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE). In 2012, the Maryland Department of Health and Mental Hygiene (DHMH) began using this statewide disease surveillance system to track several specific disease measures including certain chronic diseases. The validity of using ESSENCE ED data to track and analyze these health outcomes was evaluated.

The CC text field is a rich source of information, but its current use for syndromic surveillance is limited to a fixed set of syndromes that are routine, suspected, expected, or discovered by chance. In addition to syndromes that are routinely monitored by the NYC Department of Health and Mental Hygiene (e.g., diarrhea, respiratory), additional syndromes are occasionally monitored when requested by outside sources or when expected to increase during emergencies. During Hurricane Sandy, we discovered by manual inspection of data for a few EDs an increase in certain words in the CC field (e.g., 'METHADONE', 'DIALYSIS', and 'OXYGEN') that led to the creation of a 'needs medication' syndrome. Current syndromic surveillance systems cannot detect unanticipated events that are not defined a priori by keywords. We describe a simple data-driven method that routinely scans the CC field for increases in word frequency that might trigger further investigation and/or temporary monitoring.

Objective

To detect sudden increases in word frequency in the Emergency Department (ED) syndromic chief complaint (CC) text field.