Emergency Preparedness

Infection Control Law in Japan has asked doctors to cooperate in syndromic surveillance for pandemic flu and smallpox since 2007. However, doctors have to report by typing the number of patients on the web site, or by sending a fax to local public health centers. It imposes the heavy burden of reporting, and thus it has not worked well yet. Therefore, we need an automatic system for routine syndromic surveillance.

Objective

We performed some syndromic surveillance system for the Hokkaido Toyako G8 summit meeting in July 2008 in Japan as a counter-measure to bioterrorism attack or other health emergency. This presentation shows the workable syndromic surveillance systems in Japan.

This paper describes lessons learned from a regional tabletop exercise (TTX) of the National Capital Region (NCR) Syndromic Surveillance Network, from the perspective of the Maryland Department of Health and Mental Hygiene (DHMH).

To estimate diagnostic demand in case of pandemic threat using trends in respiratory syndromes (as input for a laboratory preparedness program).

An N-gram is a sub-sequence of n items from a given sequence where n can be 1, 2,…, n and the items can be letters or words. N-gram models are widely used in statistical natural language processing [3]. In the syndromic surveillance context, N-grams can be used to cluster or classify natural language data.  They can also help in the design of kernels for machine learning algorithms such as support vector machines to learn from text data.  This work calculates the similarity percentages of ED or TH reasons to syndromic fingerprints using Ngrams. We define “reasons similarity” as the percentage of matched N-grams derived from the reasons field of an ED or TH record with the fingerprint of a syndrome. The fingerprint of a syndrome is a list of frequent N-grams related to this syndrome.  This fingerprint is constructed by collecting a large sample of classified reasons data for a particular syndrome, calculating all of the N-grams for this set and then selecting the most frequent N-grams to form a profile or fingerprint. N-gram generation may require extensive processing time especially for large files but this issue has been addressed by using parallel computation.

Objective

The objective of this work is to identify syndromic fingerprints in reasons for entering an emergency department (ED) or calling telehealth (TH). It also demonstrates that these fingerprints are valuable for classification.

This paper describes the spatial pattern of New York City (NYC) heat-related emergency medical services (EMS) ambulance dispatches and emergency department visits (ED) and explores how this information can be used in planning for and response to heat-related health events.

The Indiana Public Health Emergency Surveillance System (PHESS) currently receives approximately 5,000 near real-time chief complaint messages from 55 hospital emergency departments daily.  The ISDH partners with the Regenstrief Institute to process, batch, and transmit data every three hours.  The Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) tool is utilized to analyze these chief complaint data and visualize generated alerts.1   

 

The ISDH syndromic surveillance team discovered that certain chief complaints of interest were coded into the “other” syndrome and not visible in typical daily alert data.  Staff determined that even a single chief complaint containing keywords related to specific reportable diseases could be of significant public health value and should be made available to investigating epidemiologists.2 

 

In addition, data quality is critical to the success of the program and must be evaluated to ensure optimal system performance.  Metrics related to data flow and completeness were identified to serve as indicators of hospital connectivity or coding problems.  These measures included the percent change in daily admits and the proportion of chief complaints missing the patient address.

Objective

This paper describes the development of targeted query tools and processes designed to maximize the extraction of information from, and improve the quality of, the hospital emergency department chief complaint data stream utilized by the Indiana State Department of Health (ISDH) for syndromic surveillance.

On Monday, August 29, 2005, Hurricane Katrina struck the Gulf Coast. Outside of the affected areas of TX, LA, MS, and AL, GA received the largest number of these evacuees, approximately 125,000. By August 30, 2005, GA began receiving a total of approximately 1,300 NDMS patients from flights arriving at Dobbins Air Force Base. Within days, Georgia established 13 shelters for evacuees. Crowded shelters can increase the risk for communicable diseases. In addition, many evacuees left behind needed medications, thus increasing the risk for chronic disease exacerbations.

Objective

To assess public health needs among sheltered evacuees, the GA Department of Human Resources, Division of Public Health recommended daily surveillance.

Surveillance strategies following major natural disasters have varied widely with respect to methods used to collect and analyze data. Following Hurricane Katrina, public health concerns included infectious disease outbreaks, injuries, mental health and exacerbation of preexisting chronic conditions resulting from unprecedented population displacement and disruption of public health services and health-care infrastructure.

Objective

This paper describes the public health surveillance response to hurricane Katrina in New Orleans and surrounding Parishes; particularly illustrating the methods, results, and lessons learned for implementing passive, active and electronic syndromic surveillance systems during a major disaster.

The objectives of this consultation, supported by the International Society for Disease Surveillance (ISDS), were to develop expert, consensus-based recommendations to promote Canadian and U.S. collaboration in using syndromic surveillance (SS) to detect, assess, monitor, or respond to potential or actual public health threats. The consultation focused on the Great Lakes region of the Canadian-U.S. borderóa region where there is substantial flow of people and goods between the two nations, a potential for occurrence of public health emergencies that affect people in both countries. Despite prior advances achieved by participants in the Early Warning Infectious Disease Surveillance (EWIDS) program regarding cross-border collaboration in notifiable disease reporting and follow-up, the EWIDS deliberations had not substantially addressed the role and uses of syndromic surveillance as part of cross-border disease prevention and control efforts, particularly in the context of potential large-scale public health emergencies. Presentations addressed a mix of issues that define the context for cross- border collaboration, including updates on SS practice and development in jurisdictions in the region, shared methodological challenges, protocols for responding to SS alerts, health information privacy regulations, and policies concerning public health emergencies that may shape information sharing during a crisis. Potential legal barriers to information sharing centered on individual-level privacy concerns, as opposed to sharing of aggregate SS data or notices of statistical alerts based on SS data. The meeting provided an impetus and agenda for future, ongoing consideration of including syndromic surveillance as a key component within the broader context of the EWIDS process. Identified priorities included development of procedures to share information about SS alerts and alert response protocols within EWIDS, increased use of SS inputs in crossborder tabletop exercises for pandemic influenza, and further collaboration in development of mapping projects that use data inputs from both sides of the border. In addition, the participants recommended that annual ISDS conferences provide a forum to address challenges in cross-border collaboration in SS practice and research.

To construct and validate a prediction algorithm that detects early increases in laboratory reports of enteric illnesses on the basis of calls to a poison control center reporting suspected foodborne illnesses.