(provided by the investigator): The study objective is to describe the validity of influenza-like-illness (ILI) from emergency department (ED) data to predict laboratory-confirmed influenza in a pediatric population during epidemic periods in King County, Washington.
The specific aims are to:
Aim 1. a) Estimate and compare the accuracy of various influenza-like illness (ILI) syndromes for detecting laboratory-confirmed influenza;b) Estimate the accuracy of Respiratory Syndrome for detecting laboratory-confirmed influenza. Compare the accuracy of Respiratory with ILI;and c) (Exploratory Analysis) Develop a symptom prediction model for lab-confirmed influenza, which will be translated into an ILI classifier.
Aim 2. Describe and compare temporal trends of ILI visits with positive influenza isolates from laboratory surveillance. The impact of various ILI syndrome classifications on ecological accuracy, using laboratory surveillance as a gold-standard, will be described. A retrospective cohort of children <18 years of age who presented to the Seattle Children's Hospital ED during known influenza periods from 2000-2005 will be randomly selected to address Aim 1. Chief complaint and diagnoses fields will be used to categorize records as positive or negative for ILI and Respiratory Syndrome. ILI and Respiratory definitions tested will represent those currently used in operation across the U.S. The Principal Investigator will also test new definitions informed by exploratory analysis. Logistic regression will be used to model the probability of laboratory-confirmed influenza given presence of ILI/Respiratory Syndrome, administrative and demographic risk factors, and other respiratory infection. Logic regression, a method for identifying population subsets based on a Boolean combination of binary coded risk factors, will be used in the exploratory analysis. Using a simulated prospective analysis, optimal ILI definitions from Aim 1 will be applied to all ED visits presenting between 2005-2008. All visits will be coded as positive or negative for ILI syndrome. ILI positive visits will be aggregated, and frequencies estimated and compared with the frequency of positive influenza isolates from the UW Clinical Virology Laboratory using dynamic linear models, a form of time-series analysis. Aberrations, or signals, will be defined from ED data when observed values exceed expected values at some threshold. Signals from ED and laboratory data will be compared. The project is consistent with the increasing emphasis on improving surveillance systems for influenza and the unique role children play in its transmission. This study provides the first validation of a syndrome definition from syndromic data sources against a single, laboratory-confirmed disease.
actions in response to disease outbreaks are dependent on timely and accurate description of disease trends using surveillance systems. Traditional surveillance systems are hampered by incomplete and delayed reporting. Novel approaches to surveillance, including syndromic surveillance systems, minimize clinician workload and provide more complete and timely information. However, these systems present possible risks, including lower specificity for disease detection, potential for violation of privacy rights with the reporting of conditions that do not pose a threat to the health of others. Careful evaluations are needed before syndromic surveillance becomes accepted practice. This study will help characterize the existing value of syndromic data sources for public health surveillance of influenza and will help inform decisions regarding both seasonal and pandemic influenza surveillance system methods.
| Neill, Daniel B; Soetebier, Karl A (2011) International society for disease surveillance conference 2011: building the future of public health surveillance. Emerg Health Threats J 4:11702 |
