ISDS Conference

The impetus for the development of many first syndromic surveillance systems was the hope of detecting infectious disease outbreaks earlier than with traditional surveillance. Various data sources have been suggested as potential disease indicators. Researchers have analyzed many of these, including those resulting from behaviors that change due to illness, such as purchasing medications, missing school or work, and using health care call centers or the internet to obtain health information. To define the prodromal behavior of patients presenting for care of acute illnesses, we initiated a pilot survey in the emergency room and acute care clinics at Walter Reed Army Medical Center.

Objective

This study describes the results of a survey given to patients to determine if any changes occurred in their behavior secondary to the illness that could potentially be tracked and used to detect a disease outbreak.

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.

West Nile Virus (WNV) is a mosquito-borne virus that can cause meningitis and encephalitis. Since its discovery in New York City during an encephalitis outbreak in 1999, WNV has become endemic in North America. In the United States, 16,000 human WNV disease cases (including West Nile fever, meningitis, encephalitis, and unspecified clinical illness) and over 600 WNV-related deaths have been reported to the Centers for Disease Control from 46 states. Perennial WNV epidemics occur during summer months, peaking during late August. BioSense Early Event Detection and Situation Awareness System receives daily laboratory test order data feed in HL7 from Laboratory Corporation of America. In this study, test orders were studied for their correlation with WNV activity.

Objective

To determine the feasibility of using BioSense laboratory test order data for West Nile disease surveillance in the United States. 

Heat related illness is the number one cause of human death in relation to extreme weather events in the United States, resulting in an average of 400 deaths per year over the past few decades. It is also expected that both the duration and intensity of these events will increase. The temperature of the surface is measurable from a number of space borne satellites and can be derived using a number of available algorithms. This type of data can be compared to census collected variables to determine the number of persons at risk for heat related morbidity and mortality within urban environments.

Objective

This paper describes a method of determining areas at risk during extreme urban heat events using remote sensing technologies, geographical information systems and artificial neural networks.

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.

Major challenges in syndromic surveillance today include lack of standardization in syndrome definitions and limited ability to detect outbreaks of specific and rare diseases. To generate situational awareness surveillance results across various regions must be comparable and epidemiologically well defined. In addition, the high cost of obtaining and maintaining powerful computing resources (e.g., parallel computers) needed for data processing and analysis, and absence of a protocol for data sharing, highlight some of the obstacles to achieving situational awareness.

Cloud computing is an enabling technology that can overcome these challenges and facilitate new and novel approaches to surveillance.

Objective

We present a Cloud Computing based approach to disease surveillance that facilitates efficient data collection, processing and storage, as well as new concepts for data sharing and data fusion, disease search and situational awareness.

In Connecticut, several syndromic surveillance systems have been established to detect and monitor potential public health threats: 1) the hospital admissions syndromic surveillance (HASS) system in 2001; and 2) the emergency department syndromic surveillance (EDSS) system in 2004. For the HASS, hospitals manually categorize unscheduled admissions into 11 syndrome categories and report these aggregate counts through an internet-based system daily to DPH; all 32 hospitals participate. For the EDSS, hospitals electronically report deidentified emergency department chief complaint data to DPH, and using a computerized algorithm, DPH categorizes this data into 8 syndrome categories; currently 17 hospitals participate. As part of pandemic influenza planning, there has been an increased focus on situational awareness at the state and national level; Connecticut would likely rely on these two systems for this purpose.

Objective

To evaluate the performance of the HASS and EDSS systems in reflecting seasonal influenza activity in Connecticut and, thus, their possible utility during a pandemic.

Every year the United States generates close to 300 million scrap tires. Due to their high energygenerating capacity, tires can be used as a fuel source (tire-derived fuel, or TDF). In 2006 a paper mill located less than 3 miles from the Vermont border received a permit to conduct a 2-week test burn of TDF to evaluate its potential to replace oil as a source of fuel. Simulations and data from other mills suggested that tires may release metal emissions and fine particulates when they are burned. The Vermont Department of Health (VDH) conducted surveillance in the population living closest to the paper mill because metal emissions and fine particulates have been associated with adverse health effects.

Objective

The VDH established a short term surveillance system to track health effects related to a test burn of tire-derived fuel.

Early and reliable detection of anomalies is a critical challenge in disease surveillance. Most surveillance systems collect data from multiple data streams but the majority of monitoring is performed at univariate time series level. Purely statistical methods used in disease surveillance look at each time series separately and tend to generate a large number of false alarms. Support Vector Machines can be used to develop rich multivariate models that allow detecting abnormal relationships between different time series leading to greatly reduced number of false alarms.

Objective

This paper depicts a novel method for reliable detection of disease outbreaks. The methodology and initial results obtained on ESSENCE data are presented.