During this webinar, Michael Coletta and Aaron Kite-Powell (CDC’s National Syndromic Surveillance Program) presented analyses of syndromic data for the outbreak of lung injury associated with the use of e-cigarette, or vaping, products.
CALL RESOURCES
The New York City (NYC) Department of Health and Mental Hygiene monitors visits daily from 49 of 54 NYC emergency departments (EDs), capturing 95% of all ED visits. ED visits for influenza-like illness (ILI) have reflected influenza activity in NYC, better than the more broadly defined fever/flu and respiratory syndromes, but the correlation with H1N1 is unknown.
Laboratory-confirmed influenza and respiratory syncytial virus (RSV) were made reportable in NYC in February 2008. DOHMH receives electronic reports of positive tests.
As part of 2009–10 influenza surveillance, five hospitals were selected for ‘sentinel’ surveillance of hospitalized influenza cases, to test all patients with a respiratory condition for influenza. Sentinel hospitals ensured that patient medical record numbers were in the daily ED syndromic file and in the electronic laboratory reports.
Objective
To determine the correlation of the ILI syndrome with laboratory-confirmed H1N1 and RSV during the October 2009 to March 2010 H1N1 season in NYC.
Microorganisms resistant to antibiotics (ABX) increase the mortality, morbidity and costs of infections. In the absence of a drug development pipeline that can keep pace with the emerging resistancemechanisms, these organisms are expected to threaten public health for years to come. Because exposure to ABX promotes the development of bacterial resistance, health care providers have long been urged to avoid using antibiotics to treat conditions that they are unlikely to improve, including many uncomplicated acute respiratory infections. We asked if interposing clinical decision support software at the time of electronic order entry could adjust ABX utilization toward consensus guidelines for these conditions.
Monitoring trends of respiratory illnesses via syndromic surveillance in SC is performed on a daily basis. SC Syndromic Surveillance primarily utilizes emergency department data, and provides situational awareness regarding broad syndrome categories among hospitals in the state. Respiratory illnesses represent a significant public health burden, causing the second highest number of outbreaks reported in SC. Since syndromic surveillance can potentially serve as an earlier indicator of outbreaks,1 it is beneficial to assess seasonality of respiratory illnesses to identify illness clusters early to mobilize a rapid response.
Objective
To assess the temporal patterns of respiratory illnesses in South Carolina (SC) using syndromic surveillance emergency department (ED) data.
The threat of epidemics due to non-human strains of influenza A viruses is ever present1. Surveillance is a critical aspect of pandemic preparedness for early case detection2. Identification of the index cases of a pandemic virus can trigger public health mitigation efforts3. To develop an appropriate surveillance process, it is important to understand the two possibilities of pandemic evolution. A new pandemic may begin with mild cases, during which surveillance should be concentrated on work/school absenteeism and in physician offices. The other possibility begins with severe cases, characterized by sCAP, respiratory failure, and ICU admission. As the syndrome of pneumonia is not reportable to health agencies for public health surveillance, a year-round, hospital-based surveillance mechanism may be an important tool for early case detection in the event of an epidemic of sCAP. To fill these gaps, we developed a statewide, hospital-based surveillance network for sCAP surveillance in Kentucky.
Objective
To present the development and implementation of the SIPS project, a statewide, hospital-based surveillance system for severe community-acquired pneumonia (sCAP) in Kentucky.
Outbreaks of Avian influenza (AI) in poultry were first reported in Nigeria in 2006 (1). The only human case was reported in 2007 (1). The epizootics of AI among poultry and wild birds and subsequent risk to human health highlighted the need to detect influenza viruses with pandemic potential and for establishment of Influenza Sentinel Surveillance (ISS) System. This is to aid the description of the the epidemiology and burden of seasonal human influenza, to provide information for public health decision making, for program planning and preparedness and to serve as an early warning for outbreaks of Avian or pandemic flu. Also, to characterize and monitor trends in illnesses and deaths attributable to SARI (2). Lagos State University Teaching Hospital (LASUTH) is one of the 4 sites for ISS in Nigeria and started functioning in 2009.
Objective
To analyze the Lagos site of the National Influenza Sentinel Surveillance (NISS) system and to determine the viruses responsible for Influenza-like illnesses (ILI) and Severe Acute Respiratory Infection (SARI).
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.
Calls to NHS Direct (a national UK telephone health advice line) which may be indicative of infection show marked seasonal variation, often peaking during winter or early spring. This variation may be related to the seasonality of common viruses. There is currently no routine microbiological confirmation of the cause of illness in NHS Direct callers. Modelling trends in NHS Direct syndromic call data against laboratory data may help by attributing the likely cause of these calls the and surveillance ‘signals’ generated by syndromic surveillance.
Multiple linear regression has been used previously to estimate the contribution of rotavirus and RSV to hospital admission for infectious intestinal disease and lower respiratory tract infections respectively. We applied a similar regression model to NHS Direct syndromic surveillance data and laboratory reports.
Objective
To provide weekly estimates of the proportions of NHS Direct respiratory calls attributable to common infectious disease pathogens.
This project was established through the Border Infectious Disease Surveillance (BIDS) program in Arizona (AZ) to monitor infecting respiratory pathogens among hospitalized patients with Severe Acute Respiratory Infections (SARI) in the AZ border region from September 2010 to the present.
Objective
To present the epidemiology, clinical aspects, and laboratory results of AZ SARI case patients and to describe respiratory viruses in the AZ border region.
Objective
To study if syndromic surveillance would have an added value over existing surveillance systems, we retrospectively evaluated whether known trends in respiratory pathogens are reflected in medical registrations in the Netherlands when using respiratory syndrome groupings.