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Alerts

Description

The EPA Water Security initiative contamination warning system (CWS) detection strategy involves the use of multiple monitoring and surveillance components for timely detection of drinking water contamination in the distribution system. The public health surveillance (PHS) component of the contamination warning system involves the analysis of health-related data to identify disease events that may stem from drinking water contamination. Public health data include hospital admission reports, infectious disease surveillance, emergency medical service reports, 911 calls and poison control center (PCC) calls. Automated analysis of these data streams results in alerts, which are investigated by health department epidemiologists. A comprehensive operational strategy was developed to describe the processes and procedures involved in the the initial investigation and validation of a PHS alert. The operational strategy established specific roles and responsibilities, and detailed procedural flow descriptions. The procedural flow concluded with the determination of whether or not an alert generated from surveillance of public health data streams is indicative of a possible water contamination incident.

Objective

To develop standard operating procedures (SOPs) to identify or rule out possible water contamination as a cause for a syndromic surveillance alarm.

Submitted by teresa.hamby@d… on
Description

The National Syndromic Surveillance Program's (NSSP) instance of ESSENCE* in the BioSense Platform generates about 35,000 statistical alerts each week. Local ESSENCE instances can generate as many as 5,000 statistical alerts each week. While some states have well-coordinated processes for delegating data and statistical alerts to local public health jurisdictions for review, many do not have adequate resources. By design, statistical alerts should indicate potential clusters that warrant a syndromic surveillance practitioner's time and focus. However, practitioners frequently ignore statistical alerts altogether because of the overwhelming volume of data and alerts. In 2008, staff in the Virginia Department of Health experimented with rules that could be used to rank the statistical output generated in ESSENCE alert lists. Results were shared with Johns Hopkins University Applied Physics Lab (JHU/APL), the developer of ESSENCE, and were early inputs into what is now known as "myAlerts," an ESSENCE function that syndromic surveillance practitioners can use to customize alerting and sort through statistical noise. NSSP ESSENCE produces a shared alert list by syndrome, county, and age-group strata, which generates an unwieldy but rich data set that can be studied to learn more about the importance of these statistical alerts. Ultimately, guidance can be developed to help syndromic surveillance practitioners set up meaningful ESSENCE myAlerts effective in identifying clusters with public health importance.

Objective: Find practical ways to sort through statistical noise in syndromic data and make use of alerts most likely to have public health importance.

Submitted by elamb on
Description

An increase in tuberculosis (TB) among homeless men residing in Marion County, Indiana was noticed in the summer of 2008. The Marion County Public Health Department (MCPHD) hosted screening events at homeless shelters in hopes of finding unidentified cases. To locate men who had a presumptive positive screen, the MCPHD partnered with researchers at Regenstrief Institute (RI) to create an alert for health care providers who use the Gopher patient management system in one of the city's busiest emergency departments. A similar process was used at this facility to impact prescription behavior.[1] A similar method was also used at the New York City Department of Health and Mental Hygiene.[2]

Submitted by elamb on
Description

Biosurveillance in resource-limited settings is essential because of both enhanced risk of diseases rarely seen elsewhere (e.g. cholera) and pandemic threats (e.g. avian influenza). However, access to care and laboratory test capability are typically inadequate in such areas, amplifying the importance of syndromic surveillance. Such surveillance in turn may be a challenge because of insufficient data history and systematic or seasonal behavior. The Suite for Automated Global Electronic bioSurveillance (SAGES) is a collection of modular, freely-available software tools to enable electronic surveillance in these settings. These tools require statistical alerting methods appropriate for SAGES data, and development of such methods is the subject of this effort. We evaluated alerting methods for two main uses: weekly surveillance for seasonal outbreaks such as dengue fever and influenza, and daily syndromic data for settings where monitoring and response on a daily basis are practical. The latter situation has the added complication that day-of-week clinical visit patterns differ widely, (e.g. clinic closure on Sundays and Thursdays) and may evolve over time.

Objective

The authors develop open-source temporal alerting algorithms for data environments characteristic of resource-limited geographic settings and recommend appropriate usage of each.

Submitted by knowledge_repo… on
Description

Influenza is an important public health problem associated with considerable morbidity and mortality. A disease traditionally monitored via legally mandated reporting, researchers have identified alternative data sources for influenza surveillance. The hospital environment presents a unique opportunity for comparative studies of biosurveillance data with high quality and various level of clinical information ranging from provisional diagnoses to laboratory confirmed cases. This study investigated the alert times achievable from hospital-based sources relative to reporting of influenza cases. The earlier detection of influenza could potentially provide more advanced warning for the medical community and the early implementation of precautionary measures in vulnerable populations.

 

Objective

To determine the relative alert time of influenza surveillance based on hospital data sources compared to notifiable disease reporting.

Submitted by elamb on
Description

The Automated Hospital Emergency Department Data System is designed to detect early indicators of bioterrorism events and naturally occurring public health threats. Four investigatory tools have been developed with drill-down detail reporting: 1. Syndromic Alerting, 2. Chief Complaint Data Mining, 3. ICD9 Code Disease, and 4. Influenza-Like-Illness Tracking.

All analysis processing runs on the server in seconds using ORACLE PL/SQL stored procedures and arrays.

 

Objective

This paper details the development of electronic surveillance tools by Communicable Disease Surveillance, which have increased detection and investigation capabilities.

Submitted by elamb on
Description

Washoe County District Health Department (WCDHD) is a local health district serving nearly 400,000 residents in Washoe County including cities of Reno and Sparks, the second largest urban area in Nevada. To enhance overall public health surveillance capacities in the agency, WCDHD officially implemented National Retail Data Monitor (NRDM) in September 2004, Real-time Outbreak & Disease Surveillance (RODS) in July 2005, and FirstWatch in August 2005. These three systems monitor over-the-counter sales for medications and healthcare products, chief complaints at emergency room visits, and 911 calls, respectively. Preliminary evaluation of NRDM suggested the usefulness of system. The addition of RODS and FirstWatch also demonstrated the utility in assisting outbreak investigation during the past few months. Unfortunately no written protocols are in place to guide program staff to manage alerts in a standardized fashion and make appropriate responses. Such guidelines from federal or state level are not yet available as we are aware, however, such protocol is highly needed.

 

Objective

The objective of this paper is to describe the standard operation procedures for three existing syndromic surveillance systems in WCDHD, Nevada.

Submitted by elamb on
Description

Although many syndromic surveillance (SS) systems have been developed and implemented, few have included response protocols to guide local health jurisdictions when alerts occur [1,2]. SS was first implemented in GA during the 2004 G-8 Summit. Six EDs in the Coastal Public Health District (PHD), 1 of 18 GA PHDs (Figure 1), conducted SS during that “national security special event.” Since that time, EDs in other PHDs have been actively recruited to participate in GA’s SS system. In GA, the PHD has the responsibility for monitoring SS data. Likewise, the PHD responds to alerts and initiates public health investigations and interventions; the state Division of Public Health (DPH) assists, if requested. To address these responsibilities, the Coastal PHD informally developed their own response practices.

Objective

To develop a template protocol to guide local response to syndromic surveillance alerts generated through analyses of emergency department (ED) visit data.

Submitted by elamb on
Description

To develop an automated system which examines Poison Control Center data and provides (1) early recognition of events, both man-made and naturally-occurring, which may pose a threat to public health, and (2) real-time notification to Poison Specialists, the on-site experts who evaluate those alerts.

Submitted by elamb on