Evaluation

On 12 January 2010 at 1652 hours local time, an earthquake measuring 7.0 on the Richter scale struck near the Haitian capital of Port-au-Prince and created enormous devastation. Florida, as the state closest to Haiti, became an initial focal point for assisting the federal repatriation and humanitarian parolee efforts. Florida supported shipments of personnel and relief supplies into Haiti, and served as the point of entry for repatriated U.S. citizens and those evacuated from Haiti for medical care. As of 9 February 2010, there were 22,500 arrivals in Florida from Haiti related to repatriation and medical humanitarian missions. These patients primarily arrived in Miami, Tampa and Orlando areas. Approximately 650 people arrived in Florida during this period as medical evacuees, and were transported to and often directly admitted to hospitals for treatment of severe or complicated injuries. Some of these patients also generated emergency department (ED) visits on arrival.

Objective

To describe the public health surveillance efforts designed to measure syndromic and disease-specific conditions in patients who were in Haiti during or after the earthquake, and evacuated to Florida.

PHIN-MS can send and receive data securely and automatically. It is used by many hospitals in the state(s) to send data to the South Carolina Department of Health and Environmental Control (SC DHEC) for both our National Electronic Disease Surveillance System and our South Carolina Early Aberration Alerting Network syndromic surveillance system.

Objective

The objective of this presentation is to review and evaluate the use of Public Health Information Network (PHIN) Messaging Service (PHIN-MS)2 for the data transfer of the syndromic data between hospitals and public health. Included is an overview of the methodology used for PHIN-MS, and a review of the usage, adoption, benefits, and challenges within the hospitals and public health agencies in South Carolina. A formal survey is planned with results discussed during the presentation of this manuscript.

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.

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.

Current practices of automated case detection fall into the extremes of diagnostic accuracy and timeliness. In regards to diagnostic accuracy, electronic laboratory reporting (ELR) is at one extreme and syndromic surveillance is at the other. In regards to timeliness, syndromic surveillance can be immediate, and ELR is delayed 7 days from initial patient visit. A plausible solution, a middle way, to the extremes of diagnostic precision and timeliness in current case detection practices is an automated Bayesian diagnostic system that uses all available data types, for example, freetext ED reports, radiology reports, and laboratory reports.We have built such a solution - Bayesian case detection (BCD). As a probabilistic system, BCD operates across the spectrum of diagnostic accuracy, that is, it outputs the degree of certainty for every diagnosis. In addition, BCD incorporates multiple data types as they appear during the course of a patient encounter or lifetime, with no degradation in the ability to perform diagnosis.

Objective

This paper describes the architecture and evaluation of our recently developed automated BCD system.

The 2010 NATO DSS experiment was the second deployment of the French ‘Alerte et Surveillance en Temps Re´el’ (ASTER) system within a multinational armed task force in real operational conditions. This experiment was scheduled within the ASTER evaluation program, as constructed by French and NATO Armed Forces after several previous works.

Objective

The new NATO Disease Surveillance System (DSS) was deployed for the second time in Kosovo within the multinational armed forces in 2010 for a 3 days experiment. The objective of the survey was to continue the development of real-time disease surveillance capability for NATO forces, in parallel with the implementation of the NATO Deployment Health Surveillance Centre in Munich in 2010.