ISDS Conference

Malaria control programs suffer from weak and fragmented surveillance of the wide range of information required to manage the disease effectively and efficiently. A computational framework to manage, integrate, analyze, and visualize the data resources, a cyberenvironment, can improve the surveillance and the outcomes.

Objective

This paper presents an ontology of a cyberenvironment for malaria surveillance. The ontology encapsulates a comprehensive natural language enumeration of the requirements of the cyberenvironment using a structured terminology. It can be used to systematically analyze and prioritize the functions of the cyberenvironment. It will help the medical, individual, environmental, and strategic management of malaria.

The Ontario Telehealth Telephone Helpline (henceforth referred to as “Telehealth”) was implemented in Ontario in 2001. It is administered by Clinidata, a private contractor hired by the Ontario Ministry of Health and Long-Term Care, 24 hours a day, 7 days a week, including holidays, at no cost to the caller. The calls are answered by registered nurses in both official languages from four calling centres that use identical decision rules (algorithms) and store all call information into one centralized data repository. The calls are usually approximately 10-minutes, patient based, and are directed by a nurse-operated electronic clinical support system.

Objective

Following the lead established by the UK’s NHS Direct Syndromic Surveillance system as well as the SARS Report’s desire to “broaden the information collection capacity of Telehealth as a syndromic surveillance tool,” we are retrospectively evaluating the value of Ontario’s Telehealth’s health helpline as a syndromic surveillance system. To date, there have been no published descriptions of Telehealth. This article endeavours to address this lacuna.

We hypothesize that epidemics around their onset tend to affect primarily a well-defined subgroup of the overall population that is for some reason particularly susceptible. While the vulnerable cohort is often well described for many human diseases, this is not the case for instance when we wish to detect a novel computer virus. Clustering may be used to define the subgroups that will be tested for over-density of symptom occurrence. The clustering slowly changes in response to changes in the population.

Objective

This paper describes a method of detecting a slowlygrowing signal in a large population, based on clustering the population into subgroups more homogeneous in their infectious agent susceptibility.

Infectious disease surveillance is important for disease control as well as to inform prevention and treatment [1]. While influenza surveillance data coverage and quality has improved significantly in recent years due to resource investments and advances in information technology, the need remains for improvements in data dissemination to the wider community.

Objective

This paper describes a review of modes and styles of the online dissemination of national influenza surveillance data.

In the past year, three major health care organizations – the American Veterinary Medical Association, the American Medical Association and the Society for Tropical Veterinary Medicine – have officially endorsed the concept of “One Health” recognizing the continuum of communicable infectious disease from humans to animals and animals to humans. Further, there is widespread recognition that continuous robust surveillance of animals is beneficial not only to animal health but to food safety for humans and for early warning of naturally-occurring novel diseases (all of significance have been zoonotic for the past 20 years in the US and elsewhere) and for detecting bioterrorism events (with only one exception, all human bioterrorism agents are animal diseases.)

Regional poison control centers (RPC) receive calls about a variety of poisoning exposures. Callers’ symptoms may not otherwise enter traditional public health (PH) surveillance systems. I report a 16-week pilot study of a new tool to enable the RPC to analyze and integrate call data with the PH, to augment ongoing disease surveillance efforts.

Objective

A new tool allowing analysis of poison control center data and integration of that data into public health surveillance efforts is described.

Recognizing the threat of pandemic influenza and new or emerging disease such as SARS, the U.S. Department of Health and Human Services has recommended that schools work in partnership with their local health departments “to develop a surveillance system that would alert the local health department to substantial increases in absenteeism among students.”3 Tarrant County’s pilot project system meets that need and transcends absenteeism data; it seeks to quantify ILI in schools and lets school nurses view daily maps of changing disease patterns, access flu prevention resources, and receive and respond to action items suggested by TCPH. While the focus is on seasonal flu, best practices for mitigating seasonal flu also apply to pandemic flu. Because the system uses open source software4 , it’s affordable and replicable for other public health agencies seeking to strengthen their school partnerships as well as their local or regional biosurveillance capabilities.

Objective

This oral presentation will share key findings and next steps following the first year of a pilot project in which Tarrant County, Texas schools used a Web-based system to share their daily health data with Tarrant County Public Health (TCPH) epidemiologists, who can use ESSENCE1 to analyze the data. The projectís ongoing goal is to reduce the magnitude of flu outbreaks by focusing on school-aged children and youth, where infectious diseases often emerge first and spread rapidly.2

The North Dakota Veterinary Diagnostic Laboratory (NDVDL) manages animal disease laboratory tests, results and diagnostic services using the software VetStar Animal Disease Diagnostic System (VADDS) (Advanced Technology Corporation, Ramsey, NJ). The North Dakota State Board of Animal Health with the Department of Agriculture, in collaboration with the North Dakota Department of Health (NDDoH), has developed an electronic laboratory reporting system using data streams exported from the VADDS system for statewide animal health and public health surveillance.

Objective

 To describe the North Dakota Electronic Animal Health Surveillance System and data analysis using the CDC EARS V4r5.

NC BEIPS is a system designed and developed by the NC Division of Public Health (DPH) for early detection of disease and bioterrorism outbreaks or events. It analyzes emergency department (ED) data on a daily basis from 33 (29%) EDs in North Carolina. With a new mandate requiring the submission of ED data to DPH, NC BEIPS will soon have data from all 114 EDs. NC BEIPS also receives data on a daily basis from the Carolinas Poison Center, the Prehospital Medical Information System and the Piedmont Wildlife Center, although syndromic surveillance output from these data sources is still in testing.

Objective

 This paper describes the North Carolina Bioterrorism and Emerging Infection Prevention System (NC BEIPS). NC BEIPS is the syndromic surveillance arm of NC PHIN.

The Texas Department of State Health Services (DSHS) Health Service Region 8 (HSR 8) encompasses 28 counties in South Central Texas. Of these, 5 counties are covered by a local health department syndromic surveillance system while the remaining counties fall under HSR 8 syndromic surveillance coverage. Of the 23 counties covered by HSR 8, 15 have hospitals with emergency departments. HSR 8 began receiving emergency department data from 3 hospitals for RedBat® syndromic surveillance monitoring in May of 2006. Four syndromes are monitored daily; Influenza-like Illness, Gastrointestinal Illness (GI), Rash-Illness, and Neurologic-Toxicologic Illness. Aberrations are detected by the Gustav algorithm using RedBat’s ‘Automatic Threshold Alert’ feature. The Gustav algorithm [patent pending], developed by ICPA, Inc., is an advanced variation of the cumulative sum method commonly used for aberration detection. The Gustav algorithm does not require an extended baseline level of illness and is very sensitive to small outbreaks; the algorithm also adjusts for weekly periodicity of medical visits.

Objective

This abstract describes the use of syndromic surveillance at a regional health department to detect an outbreak of norovirus in a nursing home facility.