Surveillance Systems

HealthMap is a real-time disease epidemic intelligence tracking and visualization system that collects information from general news media, individual first-hand reports and public health sources around the world. Gaps in this effort clearly occur during times of crisis where traditional mechanisms may be dismantled. Clinical information gathered by deployed physicians can play a key role in providing early insight on emerging public health threats. We developed OutbreakMD to gather such information in real-time and combine with existing HealthMap informal and formal surveillance techniques. 

Objective

OutbreakMD is a mobile Web application that was piloted in post-earthquake Port-au-Prince, Haiti. The application is designed for collecting, organizing and visualizing clinical information from individual patients to better monitor emerging infectious disease in disaster situations, in situations with limited public health infrastructure and unreliable Internet connectivity

National and state surveillance systems for oral health have relied on sample-based screenings and self-reported surveys.1 Recent publications suggest the need and potential for use of data from syndromic surveillance systems and insurers to monitor indicators of oral health status, utilization of care, and costs of treatment.2,3 Few consensus indicators for oral health derived from these data sources exist, with the exception of a set of five ICD-9 codes comprising ambulatory care sensitive dental problems (ACS-DP).4 This paper describes North Carolina’s Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT) data analyzed within CDC’s BioSense System to report state and county population-based rates of hospital emergency department (ED) utilization for ACS dental conditions.

Objective

This paper describes use of national and state syndromic surveillance systems for monitoring and evaluating usage of hospital emergency departments for ambulatory care sensitive dental problems.

NC DETECT provides near-real-time statewide surveillance capacity to local, regional and state level users across NC with twice daily data feeds from 119 (99%) emergency departments (EDs), hourly updates from the statewide poison center, and daily feeds from statewide EMS runs, select urgent care centers and veterinary lab data. The NC DETECT Web Application provides access to aggregate and line listing analyses customized to users’ respective jurisdictions. Several reports are currently available to monitor the health effects of heat waves. Heat wave surveillance is essential as temperature extremes are expected to increase with climate change.

Objective

To examine the utilization of NC emergency departments for heat-related illness by age, disposition and cause based on chief complaint and triage note categorization.

A comprehensive electronic medical record (EMR) represents a rich source of information that can be harnessed for epidemic surveillance. At this time, however, we do not know how EMR-based data elements should be combined to improve the performance of surveillance systems. In a manual EMR review of over 15 000 outpatient encounters, we observed that two-thirds of the cases with an acute respiratory infection (ARI) were seen in the emergency room or other urgent care areas, but that these areas received only 15% of total outpatient visits.1 Because of this seemingly favorable signal-to-noise ratio, we hypothesized that an ARI surveillance system that focused on urgent visits would outperform one that monitored all outpatient visits.

The H1N1 outbreak in the spring of 2009 in NYC originated in a school in Queens before spreading to others nearby. Active surveillance established epidemiological links between students at the school and new cases at other schools through household connections. Such findings suggest that spatial cluster detection methods should be useful for identifying new influenza outbreaks in school-aged children. As school-to-school transmission should occur between those with high levels of interaction, existing cluster detection methods can be improved by accurately characterizing these links. We establish a prospective surveillance system that detects outbreaks in NYC schools using a flexible spatial scan statistic (FlexScan), with clusters identified on a network constructed from student interactions.

Objective

To improve cluster detection of influenza-like illness within New York City (NYC) public schools using school health and absenteeism data by characterizing the degree to which schools interact.

Absenteeism is regarded as an expedient and responsive marker of illness activity. It has been used as a health outcome measure for a wide spectrum of exposures and as an early indicator of influenza outbreaks.1 A handful of studies have described its validity compared with traditional ‘goldstandards’ for influenza and ILI.2,3 We sought to further quantify the relationship between ED ILI and school absenteeism because absenteeism, as it relates to illness, and subsequent loss in productivity and wages for parents, school staff and children, is an important public health outcome.

Objective

To describe the relationship between emergency department (ED) visits for influenza-like-illness (ILI) and absenteeism among school-aged children.

Prior work demonstrates the extent to which sampling strategies reduce the power to detect clusters.1 Additionally, the power to detect clusters can vary across space.2 A third, unexplored, effect is how much the sample size impacts the power of spatial cluster detection methods. This research examines this effect.

Objective

In syndromic surveillance settings, the use of samples may be unavoidable, as when only a part of the population reports flu-like symptoms to their physician. Taking samples from a complete population weakens the power of spatial cluster detection methods.1 This research examines the effectiveness of different sampling strategies and sample sizes on the power of cluster detection methods.

Prediction markets are a type of futures market in which users trade shares that pay off if the event to which they are connected occurs. They are used to aggregate knowledge on a large scale, as the prices of the various contracts can be interpreted as probabilities of their events. Since 2006, our group has been using prediction markets and testing their utility in predicting the spread and impact of diseases, including seasonal influenza, syphilis, and others on a market called the Iowa Electronic Health Markets (IEhM), found at http://iehm.uiowa.edu. For example, in 2009, a series of markets were run on novel influenza A (H1N1), which showed success in predicting the extent and duration of the outbreak.1 We currently plan to move into a new phase of development that will allow the community of users to submit proposals for new prediction markets, which will then be approved by site editors and referees. We call the new system Samos.

Objective

This poster presents a software system to provide a community-driven, user-generated, low-overhead, web-based prediction market system called Samos.

Emerging and re-emerging infectious diseases are a serious threat to global public health. The World Health Organization (WHO) has identified more than 1100 epidemic events worldwide in the last 5 years alone. Recently, the emergence of the novel 2009 influenza A (H1N1) virus and the SARS coronavirus has demonstrated how rapidly pathogens can spread worldwide. This infectious disease threat, combined with a concern over man-made biological or chemical events, spurred WHO to update their International Health Regulations (IHR) in 2005. The new 2005 IHR, a legally binding instrument for all 194 WHO member countries, significantly expanded the scope of reportable conditions, and are intended to help prevent and respond to global public health threats. SAGES aims to improve local public health surveillance and IHR compliance, with particular emphasis on resource-limited settings.

Objective

This paper describes the development of the Suite for Automated Global bioSurveillance (SAGES), a collection of freely available software tools intended to enhance electronic disease surveillance in resource-limited settings around the world.

Timeliness of information has a key role in disease reporting, and may be easily impaired by several factors affecting data entry and utilization.1 Regarding data entry, previous studies have shown that monitoring strategies, such as telephone reminders and supervision visits ensure reporting timeliness.2 Likewise, limited reporting infrastructure may prevent adequate reporting and effective data utilization.3,4 The Peruvian Air Force, in collaboration with the US Naval Medical Research Center Detachment in Lima, Peru, implemented in 2009 an electronic disease surveillance system with the objective of establishing near real-time baseline estimates of disease trends, and detecting disease outbreaks in a timely manner. This system has proven to perform well, although reporting sites vary in their reporting infrastructure. Therefore, we attempted to test the effect of a lack of monitoring on the performance of reporting sites, and explore the influence of other factors potentially affecting timeliness.

Objective

The objective of this paper is to describe the effect of close monitoring on performance of the electronic disease surveillance system of the Peru Air Force.