We report here on the use of the North Carolina Bioterrorism and Emerging Infection Prevention System (NC BEIPS, www.ncbeips.org) to reverse engineer a syndrome definition of influenza for the purpose of influenza surveillance.
In 2003, with the advent of SARS, the Ontario Ministry of Health and Long-Term Care (MOHLTC) released a document mandating the use of a clinical screening tool to detect patients at high risk for having a febrile respiratory illness (FRI), defined as a temperature of > 38ºC and a new or worsening cough or shortness of breath (1). The FRI screening tool is available in all Ontario Emergency Departments (ED), and is utilized in 86% of them (2). Any patient who meets all of the criteria is designated FRI positive, treated with droplet precautions and is instructed to wear a mask and undergo frequent hand-washing (1). The FRI screening tool was created as a response to the SARS outbreaks, and while it is used to identify any FRI, its sensitivity has not been documented. We attempt to determine the utility of FRI as a defining element of clinical influenza.
Objective
(1) To determine if patients who are found to be positive for influenza or parainfluenza by culture or antigen detection are all detected by the Ontario Ministry of Health and Long-Term Care's Febrile Respiratory Illness (FRI) screening tool, and thereby treated with appropriate respiratory precautions to prevent spread. (2) To determine if syndromic surveillance or another clinical predictor would be a more effective screening tool than FRI.
In response to increasing reports of avian influenza being identified throughout the eastern hemisphere, the U.S. Homeland Security Council, the Infectious Disease Society of America, and others have called for expansion of enhanced, real-time electronic syndromic and other advanced surveillance systems to supplement the traditional surveillance systems recommended in U.S. Department of Health & Human Services pandemic influenza preparedness plan guidance. Like many states, the Connecticut Department of Public Health, has updated its own Pandemic Influenza Response Plan to reflect its expanding arsenal of surveillance systems. These systems include a syndromic surveillance system, known as the Hospital Admissions Surveillance System (HASS), developed in September 2001 to monitor for possible bioterrorism events and emerging infections. HASS data has been utilized to supplement information received from laboratoryconfirmed influenza test results, influenza-like-illness reporting, and pneumonia influenza mortality to track seasonal influenza since 2003.
Objective
This paper summarizes the results of a continued review of state pandemic influenza preparedness plans and compares various approaches for routine influenza surveillance during interpandemic periods with approaches for enhanced surveillance during pandemic alerts. The increased reliance of syndromic and other advanced surveillance systems by U.S. states for seasonal influenza tracking and pandemic preparedness planning is documented.
Aerial transmission and direct contact are important factors for flu. Consequently, close contact with large groups of people, such as during mass transit, present opportunities for transmission. One protective method that decreases the probabilities of becoming ill is vaccination. The potential health impact of erminating subway service during a flu epidemic depends on both the potential for transmission and vaccination rates among riders. Mass transit, a major method of transit in NYC, exhibits a non-random distribution of riders based on demographics and socio-economic status. There is also a trend in vaccination rates by demography and socio-economic status. This analysis uses individual-based data on vaccination and ridership to separately predict vaccination and ridership for inclusion in agent-based models that can be used to assess impact of public health interventions.
Objective
Agent-based models (ABMs) have been developed to simulate epidemics including smallpox and pandemic flu. The ABM approach is an effective method to assess the potential impact of interventions on disease spread. Integrating the ABM approach with syndromic surveillance data provides potential benefits such as permitting a realistic specification of some critical model contact parameters, and permitting synthetic outbreaks to be generated with extremely fine resolution (e.g., age, gender, and address). This would provide the ability to test various clustering detection algorithms – a key component of syndromic surveillance methods. RTI International (the Models of Infectious Disease Agent Study (MIDAS) informatics group) and NYC DOHMH (a premier syndromic surveillance research center) collaborated to create a NYC-ABM of flu transmission. This poster describes implementation of several features required for accurate model specification, including assigning immunization rates and subway ridership. Incorporating subway ridership is of great interest, because a large subway system, like the NYC system, has never been investigated as a contributor of disease spread.
Ideal anomaly detection algorithms should detect both sudden and gradual changes, while keeping the background false positive alert rate at a tolerable level. Further, the algorithm needs to perform well when the need is to detect small outbreaks in low-incidence diseases. For example, when surveillance is done based on the specific ICD9 diagnosis of flu rather than a larger syndromic grouping, the baseline counts will generally be low, in the range of 0 or 1 per day even in a large sample of EDs.
Objective
Our goal was to determine the sensitivity of detection of various inserted outbreak sizes and shapes using a modified Holt-Winters detection algorithm applied to daily flu count data before the flu season and after its peak. We compare our results to C3 of EARS.
The Georgia Power Corporation (GPC) provides power to 155 (97.5%) of the 159 counties in Georgia (GA), and employs 9,600 people throughout the state. GPC is engaged in preparing for pandemic influenza, and committed to protecting the critical infrastructure and ensuring its continuity of operations. The GPC employee “Crisis Absence Reporting Tool” (CART) was designed to provide the Georgia Syndromic
Surveillance (GA SS) Program with employee absentee/ reason to inform Public Health and GPC leadership about health events occurring in their employees statewide.
The GA SS Program has been implemented in 13 (72%) of the 18 Health Districts. In each of these locations, data are transferred from an ED, ambulatory care center, or school district to the Georgia Division of Public Health (GDPH) for analysis and dissemination of results to all stakeholders. GDPH wanted to collaborate with a large corporation with a statewide employee base to conduct absentee and reason for absence SS to provide an additional perspective to the existing data streams used by GA SS.
In GA, the LHD are responsible for organizing pandemic planning committees comprised of community partners to discuss continuity of basic services and maintenance of the critical infrastructure at the local level during an influenza pandemic. Increasing SS capacity is an important component of Local Health District (LHD) pandemic planning strategies in GA.
Objective
To create a non-traditional partnership between the GPC and the GDPH to aid in adverse health event detection and response activities during an influenza pandemic or other health emergency. This will include augmenting CART with SS data from the GA SS Program. These data will be analyzed by GA SS and results disseminated to LHDs, who monitor and respond to SS data in their jurisdictions. Analyses will also be provided to GPC to aid in resource allocation to ensure the continuity of services in GA during emergencies.
The Ontario Telehealth Telephone Helpline (henceforth referred to as “Telehealth”) was implemented in Ontario in 2001. It is administered by Clinidata, a private contractor hired by the Ontario Ministry of Health and Long-Term Care, 24 hours a day, 7 days a week, including holidays, at no cost to the caller. The calls are answered by registered nurses in both official languages from four calling centres that use identical decision rules (algorithms) and store all call information into one centralized data repository. The calls are usually approximately 10-minutes, patient based, and are directed by a nurse-operated electronic clinical support system.
Objective
Following the lead established by the UK’s NHS Direct Syndromic Surveillance system as well as the SARS Report’s desire to “broaden the information collection capacity of Telehealth as a syndromic surveillance tool,” we are retrospectively evaluating the value of Ontario’s Telehealth’s health helpline as a syndromic surveillance system. To date, there have been no published descriptions of Telehealth. This article endeavours to address this lacuna.
Infectious disease surveillance is important for disease control as well as to inform prevention and treatment [1]. While influenza surveillance data coverage and quality has improved significantly in recent years due to resource investments and advances in information technology, the need remains for improvements in data dissemination to the wider community.
Objective
This paper describes a review of modes and styles of the online dissemination of national influenza surveillance data.
Methicillin resistant staphylococcus aureus (MRSA) is a leading cause of skin and soft tissue infections (SSTI). Until recently, S. aureus pneumonia has been considered primarily a nosocomial infection, and was reported infrequently as a cause of severe community-acquired pneumonia. In recent years, there have been several reports of MRSA community-acquired pneumonia cases associated with influenza among healthy individuals resulting in hospitalization or death. During the 2007-08 influenza season, the WA DOH received reports of necrotizing staphylococcus pneumonia associated with flu-like illness and confirmed flu; these included severe cases of pneumonia caused by MRSA. We examined data from our biosurveillance system to describe trends in staphylococcus infection among ED patients and patients hospitalized with pneumonia or influenza in King County, WA.
Objective
We used our biosurveillance system to describe trends in emergency department visits for SSTI as well as staphylococcus pneumonia hospitalization trends.
One of the standard approaches to public health surveillance for influenza is to monitor the percent of visits to about 2000 sentinel physicians for influenza-like illness (%ILI; fever plus cough or sore throat). The BioSense System currently receives (among other data) ICD-9 discharge diagnoses from Veteran’s Affairs (VA) and Department of Defense (DOD) outpatient clinics. A literature review found that, in addition to ICD-9 code 487 (the code specific for influenza), 29 other codes have been used previously to monitor influenza. We evaluated the utility of ICD-9 codes reported to BioSense for their utility in monitoring influenza.
Objective
To determine the utility of current CDC BioSense data sources in monitoring influenza activity at the national and state levels.