To evaluate the added value of a syndromic surveillance system in detecting a large severe respiratory disease outbreak with a point-source we used the Legionnaires' disease (LD) outbreak of 1999 in the Netherlands as a case-study. We retrospectively simulated a prospective syndromic surveillance for space-time clusters of patients with pneumonia in hospital records to detect the LD outbreak.
Every year the United States generates close to 300 million scrap tires. Due to their high energygenerating capacity, tires can be used as a fuel source (tire-derived fuel, or TDF). In 2006 a paper mill located less than 3 miles from the Vermont border received a permit to conduct a 2-week test burn of TDF to evaluate its potential to replace oil as a source of fuel. Simulations and data from other mills suggested that tires may release metal emissions and fine particulates when they are burned. The Vermont Department of Health (VDH) conducted surveillance in the population living closest to the paper mill because metal emissions and fine particulates have been associated with adverse health effects.
Objective
The VDH established a short term surveillance system to track health effects related to a test burn of tire-derived fuel.
The Early Aberration Reporting System was developed at the Centers for Disease Control and Prevention to help assist local and state health officials to focus limited resources on appropriate activities of public health surveillance. Outbreaks of
infectious diseases are indicated in multiple spatial and temporal data sources, such as emergency department visits, drug store sales, and ambulatory clinic visits. Based on this premise, we provided correlated data sets and investigated disease clusters.
Objective
We present a pilot study of simulation of correlated outbreak signals for early aberration reporting and evaluating detection methods.
It has been noted since the era of Hippocrates that weather conditions at a specific location can influence the incidence of various infectious and noninfectious diseases. It has also been implied that variations in weather conditions influence the number of cases of infectious respiratory conditions. Syndromic surveillance was introduced in Athens, Greece, for the first time in July 2002 in the framework of increased preparedness for the Olympic Games of 2004. Our experience showed that the incidence of some syndromes parallels that of diseases surveyed by the mandatory notification system of the Hellenic Center for Diseases Control and Prevention that are known to have a strong seasonal pattern in their incidence e.g. influenza. Influenza incidence peaks at the same time with the “respiratory infection with fever” syndrome during spring. This study aimed at investigating possible relationships between the incidence of the “respiratory infection with fever” syndrome and meteorological parameters.
Objective
This study explores the possible impact of meteorological conditions on the incidence of clinical syndromes with an interest for public health in the basin of Athens, Greece.
Previously we developed an “Ngram” classifier for syndromic surveillance of emergency department (ED) chief complaints (CC) in Turkish for bioterrorism. The classifier is developed from a set of ED visits for which both the ICD diagnosis code and CC are available. A computer program calculates the associations of text fragments within the CC (e.g. 3 characters for a “3-gram”) with a syndromic group of ICD codes. The program then generates an algorithm which can be deployed to evaluate chief complaint data in real-time. However, the N-gram method differs from most other classifiers in that it assigns a probability that each visit falls within the syndrome rather than ruling the visit “in” or “out” of the syndrome. It is possible to dichotomize visits “in” or “out” using N-grams by choosing a cut-off sensitivity for the n-grams used, but this affects the specificity of the method. The effect of this trade-off is best measured by a receiveroperator curve.
Objective
Our objective was to determine the sensitivity and specificity of the Ngram CC classifier for individual ED visits. We also wish to compare these results to those obtained when we substituted anglicized characters for 6 problematic Turkish characters.
One of the most important goals of disease surveillance is to identify the "what" and "when" of an epidemic. Influenza surveillance is made difficult by inconsistent laboratory testing, deficiencies in testing techniques, and coding subjectivity in hospital records. We hypothesized that respiratory diseases other than influenza may serve as a useful proxy for this infection in pediatric populations, due to similarities in the seasonal characteristics of these illnesses.
Syndromic surveillance may be suited for detection of emerging respiratory disease elevations that could pass undiagnosed. The syndromes under surveillance should then retrospectively reflect known respiratory pathogen activity. To validate this for respiratory syndromes we analyzed dutch medical registration data from 1999-2003 (national hospital discharge diagnoses and causes of death). We assume that syndromes with a good reflection of pathogen activity have the potential ability to reflect unexpected respiratory pathogen activity in prospective surveillance.
Objective
As a validation for syndromic surveillance we studied whether respiratory syndromes indeed reflect the activity of respiratory pathogens. Therefore we retrospectively estimated the temporal trend of two respiratory syndromes by the seasonal dynamics of common respiratory pathogens.
Yearly epidemics of respiratory diseases occur in children. Early recognition of these and of unexpected epidemics due to new agents or as acts of biological/chemical terrorism is desirable. In this study, we evaluate the ordering of chest radiographs as a proxy for early identification of epidemics of lower respiratory tract disease. This has the potential to act as a sensitive real-time surveillance tool during such outbreaks.
Objective:
Create a tool for monitoring respiratory epidemics based on chest radiograph ordering patterns.
A number of different methods are currently used to classify patients into syndromic groups based on the patient’s chief complaint (CC). We previously reported results using an “Ngram” text processing program for building classifiers (adapted from business research technology at AT&T Labs). The method applies the ICD9 classifier to a training set of ED visits for which both the CC and ICD9 code are known. A computerized method is used to automatically generate a collection of CC substrings (or Ngrams), with associated probabilities, from the training data. We then generate a CC classifier from the collection of Ngrams and use it to find a classification probability for each patient. Previously, we presented data showing good correlation between daily volumes as measured by the Ngram and ICD9 classifiers.
Objective
Our objective was to determine the optimized values for the sensitivity and specificity of the Ngram CC classifier for individual visits using a ROC curve analysis. Points on the ROC curve correspond to different classification probability cutoffs.
Existing statistical methods can perform well in detecting simulated bioterrorism events. However, these methods have not been well-evaluated for detection of the type of respiratory and gastrointestinal events of greatest interest for routine public health practice. To assess whether a syndromic surveillance system can detect these outbreaks, we constructed simulated outbreaks based on public health interest and experience. We then inserted these outbreaks into real data. We assessed whether the simulated outbreaks could be detected using a battery of detection methods, including model-adjusted scan statistics and space-time permutation scan statistics.
Objective
We used simulation methods to assess the performance of two distinct anomaly-detection approaches, each under a variety of parameter settings, with respect to their ability to detect outbreaks of commonly occurring events of public health importance.