Early Detection

Shigella remains highly infectious in the United States and rapid detection of Shigella outbreaks is crucial for disease control and timely public health actions. The New York State Department of Health (NYSDOH) implemented a Communicable Disease Electronic Surveillance System (CDESS) for local health departments (LHDs) to collect clinical and laboratory testing information and supplement epidemiologic information for the patients from New York State, excluding New York City, with infectious diseases. The CDESS includes reported cases that are involved in outbreaks and which constituted the base for identifying any outbreak. The selection of a fitted outbreak detection method would play a critical role in enhancing disease surveillance.

Objective

To explore the possibility of using statistical methods to detect Shigella outbreaks, assess the effectiveness of the methods to signal real outbreaks, provide manageable information for follow-up activities and avoid unnecessary surveillance work.

In March-April, 2011, Salt Lake Valley Health Department (SLVHD) investigated an outbreak of measles (N=9) resulting from a single imported case from Europe. Syndromic surveillance was used to identify measles-like illness (MLI) and enhance early case detection, which is crucial for proper public health intervention [1].

Objective

To detect measles cases during an outbreak using syndromic surveillance.

Time-of-arrival (TOA) surveillance methodology consists of identifying clusters of patients arriving to a hospital emergency department (ED) with similar complaints within a short temporal interval. TOA monitoring of ED visit data is currently conducted by the Florida Department of Health at the county level for multiple subsyndromes [1]. In 2011, North Carolina's NC DETECT system and CDC's Biosense Program collaborated to enhance and adapt this capability for 10 hospital-based Public Health Epidemiologists (PHEs), an ED-based monitoring group established in 2003, for North Carolina's largest hospital systems. At the present time, PHE hospital systems include coverage for approximately 44% of the statewide general/acute care hospital beds and 32% of all emergency department visits statewide. We present findings from TOA monitoring in one hospital system.

Objective

To describe collaborations between North Carolina Division of Public Health and the Centers for Disease Control and Prevention (CDC) implementing time-of-arrival (TOA) surveillance to monitor for exposure-related visits to emergency departments (ED) in small groups of North Carolina hospitals.

The purpose of the National Collaborative for Bio-preparedness (NCB-P) is to enhance biosurveillance and situational awareness to better inform decision-making using a statewide approach. EMS represents a unique potential data source because it intersects with patients at the point of insult or injury, thus providing information on the timing and location of care. North Carolina uses a standardized EMS data collection system, the Prehospital Medical Information System (PreMIS), to collect information on EMS encounters across the state using the National EMS Information System (NEMSIS) template. Since NEMSIS is planned to be incorporated by EMS agencies in every state, an EMS-based approach to biosurveillance is extensible nationally.

Objective

To develop a statewide biosurveillance system based on emergency medical services (EMS) information which employs both symptom-based illness categorization and spatiotemporal analysis.

Influenza affects millions of people and causes about 36,000 deaths in the United States each winter. Pandemics of influenza emerge at irregular intervals. National influenza surveillance is used to detect the emergence and spread of influenza virus variants and to monitor influenza-related morbidity and mortality. Existing surveillance consists of seven data types, which are reported weekly. Newly available national electronic data sources created as part of the routine delivery of medical care might supplement current data sources. Nurse call data offer national coverage, are timely, and do not require any extra manual data entry. Using such data for influenza-like illness (ILI) surveillance may lead to earlier detection of ILI in the community, both because people with ILI may call a nurse line before seeking care at a health-care facility and because the data are more timely than existing weekly data.

Objective

Our purpose was to compare nurse call data for respiratory and ILI against CDC national influenza surveillance data from the 2004-2005 season by region to determine if the call data were informative and might allow earlier detection of influenza activity.

The first prototype syndromic surveillance in Japan was used during the G8 summit meeting in 2000 with two local prefectures involved. The second trial syndromic surveillance and the first internet-based surveillance used in 2002 for the Japan-Korea 2002 World Cup soccer games. Since 2002, surveillances on over-the-counter medications, ambulance call, and outpatient visits were explored as syndromic approach candidates for early detection. Internet-based events and case reporting frame work has been reviewed for outpatient visits daily reporting concurrently. Limited spread of electrical patient record and vast range of commercialized medical record formats posed obstacles to nationwide syndromic surveillance implementation.

Recent threats from bioterrorism and influenza pandemic empowered Japanese government introducing surveillance of rapid detection mechanism. In line with the revision of the Infection Control Law took place in 2007 April, national syndromic surveillance system was implemented.

Objective

This paper describes recent establishment of national surveillance system for early detection of infectious diseases in Japan. With diagnostic data fed from existed routine surveillance, newly introduced system is expected to provide timely information for control response. We aim to facilitate cross-informative regional surveillance by sharing our experience and system frame work.

Objective

To enable the early detection of pandemic influenza, we have designed a system to differentiate between severe and mild influenza outbreaks. Historic information about previous pandemics suggested the evaluation of two specific discriminants: (1) the rapid development of disease to pneumonia within 1-2 days and (2) patient age distribution, as the virus usually targets specific age groups. The system is based on the hypothesis that an increased number of diagnosed pneumonia cases offers an early indication of severe influenza outbreaks. This approach is based on the fact that pneumonia cases will appear promptly in a severe influenza outbreak and can be diagnosed immediately in a physician office visit, while a confirmed influenza diagnosis requires a laboratory test. Furthermore, laboratory tests are unlikely to be ordered outside of the expected influenza season.