Varied approaches have been used by syndromic surveillance systems for aberration detection. However, the performance of these methods has been evaluated only across a small range of epidemic characteristics.
Objective
We conducted a large simulation study to evaluate the detection properties of 6 different algorithms across a range of outbreak characteristics.
In the fall of 2001, the Bioterrorism Preparedness and Response (BT P&R) Unit initiated a syndromic surveillance system utilizing chief complaint data collected from Emergency Departments throughout Los Angeles County (LAC). Chief complaint data were organized into four syndromes (gastrointestinal, neurological, rash and respiratory) based on key words/phrases that appear in the patient’s record. Syndrome data are analyzed daily; counts for each syndrome are calculated and compared to a threshold to determine if a “signal” or aberration has occurred (EARS algorithm). A signal is defined as a case count elevated above threshold for a particular syndrome at an individual hospital.
Objective
To describe the methods used by LAC, Department of Health Services, BT P&R Unit in determining the response to unusual disease/syndromic activity in LAC hospitals.
On October 24, 2005, Hurricane Wilma made landfall on the southwest coast of Florida as a category 3 storm. The storm moved toward the northeast and passed through Palm Beach and Broward Counties before entering the Atlantic Ocean. Hurricane force winds and rain caused extensive damage to electrical infrastructure and traffic lights, and temporarily displaced thousands of residents. Power outages in Broward County affected over 90% of its 1.8 million residents, with some outages lasting >2 weeks. Boil water notices were declared for much of the county. Acute care hospitals remained open during this time, although services provided by health care providers in other settings were interrupted due to structural damage and power outages.
Objective
We used the syndromic surveillance system ESSENCE to describe the morbidity after Hurricane Wilma in Broward County, Florida.
The University of Washington has been working since 2000 with partners in Washington State to advance bioterrorism (BT) detection and preparedness. This project collects data on patients presenting with influenza-like illnesses and other potentially BT-related syndromes at emergency departments and primary care clinics (Kitsap, Clallam, and Jefferson counties) using a secure automated informatics approach. Local health jurisdiction epidemiologists use a web-based interface to view de-identified data and use a version of CDC’s EARS disease detection algorithms to watch for variances in patterns of diagnoses, volume, time and space as part of the public health real-time disease surveillance system. This processed hospital data is also made available back to the officials and administrators at the reporting hospital.
Objective
To understand GIS issues in a rural-tourban setting and demonstrate limitations of ZIPcode-only approaches compared to census tract and block approaches.
Syndromic surveillance systems use residential zip codes for spatial analysis to identify disease clusters. However, the use of emergency medical services can be influenced by geographic proximity, specialty services, and severity of illness. We evaluated zip codes reported to the Boston Public Health Commission’s syndromic surveillance system from 10 Boston emergency departments (EDs).
Objective
To examine the distribution of residential zip codes among patients in Boston EDs over a two month period to better understand how this type of spatial analysis may affect the sensitivity of syndromic surveillance.
There are multiple sources of influenza and influenza-like illness (ILI) surveillance data within the state of Georgia. These include laboratory surveillance for influenza viruses, sentinel providers that report ILI, pneumonia and influenza mortality, influenza-associated hospitalizations, and influenza-associated pediatric deaths. The usefulness of emergency department-based (ED) syndromic surveillance (SS) data as an additional source of ILI surveillance data is currently being evaluated at national, state, and local levels.
Objective
To describe Georgia’s experience using ED-based SS as a source of influenza-like illness surveillance data.
Most of the time, health consequences of heat waves are serious. Heat wave response plans were developed for reducing health effects but even if they are very efficient it is not possible to eliminate all health consequences. It is therefore necessary to develop a flexible health surveillance system capable of rapidly identifying the population health burden of elevated temperature. This study focused on the Year 2006 summer heat wave, which resulted in 2,000 deaths in a 2 week period. This study represents the first opportunity to test the capabilities of a syndromic surveillance system to provide pertinent information and define appropriate indicators.
Objective
The objective of the study is to evaluate the value of a syndromic surveillance system during a heat wave and propose pertinent indicators.
When a chemical or biological agent with public health implications is detected in the City of Houston, analysis of syndromic surveillance data is an important tool for investigating the authenticity of the alert, as well as providing information regarding the extent of contamination.
Syndromic surveillance data in Houston is currently provided by the Real-Time Outbreak Disease Surveillance, which collects and synthesizes real-time chief complaint data from 34 area hospitals, representing approximately 70% coverage of licensed ER beds in Harris County. Data collected for each complaint includes patient home and work zip codes, allowing for geographic analysis of the data in the case of a localized environmental contamination.
Historically, when alerted to a contaminant in the Houston area, the Houston Department of Health and Human Services (HDHHS) has analyzed health data for each zip code in the geographic area of interest separately, a time-intensive process.
Recognizing the need for a more accurate and timely response to an environmental alert, HDHHS proposes aggregating zip codes into zones, based on coverage of population and areas of high risk. These “Surveillance Zones” will be used to quickly reference syndromic data in the event of a chemical or biological event.
Objective
This paper discusses the development of zones within the City of Houston in order to more quickly and accurately reference surveillance data in the case of chemical or biological events.
Four waves of pandemic influenza from 1918-1920 in New York City caused ~40,000 deaths, primarily of young-adults and children. The explosiveness of the autumn 1918 wave has led many to believe that in the event of a similar pandemic today early detection and intervention strategies may not be effective. Recent historical studies of the 1918 pandemic, however, provide evidence of controllable transmissibility, of a limited early wave4, and of social distancing measures significantly reducing pandemic impact in many US cities. Importantly, mitigation efforts initiated after the beginning of community-wide transmission (even up to the point of 3-6% of a population being infected) significantly reduced the total impact in 1918.
Objective
In response to an Institute of Medicine report recommending community-based pandemic influenza mitigation strategies be informed by surveillance and disease modeling, we aimed to assess the feasibility of using emergency department data to identify model derived threshold triggers for initiating intervention efforts in the event of a 1918-like pandemic.
The Automated Hospital Emergency Department Data System is designed to detect early indicators of bioterrorism events and naturally occurring public health threats. Four investigatory tools have been developed with drill-down detail reporting: 1. Syndromic Alerting, 2. Chief Complaint Data Mining, 3. ICD9 Code Disease, and 4. Influenza-Like-Illness Tracking.
All analysis processing runs on the server in seconds using ORACLE PL/SQL stored procedures and arrays.
Objective
This paper details the development of electronic surveillance tools by Communicable Disease Surveillance, which have increased detection and investigation capabilities.