Emergency Department (ED)

We have previously shown that timeliness of detection is influenced both by the data source (e.g., ambulatory vs. emergency department) and demographic characteristics of patient populations (e.g., age). Because epidemic waves are thought to move outward from large cities, patient distance from an urban center also may affect disease susceptibility and hence timing of visits. Here, we describe spatial models of local respiratory illness spread across two major metropolitan areas and identify recurring early hotspots of risk. These models are based on methods that explicitly track illness as a traveling wave across local geography.

Objective

To characterize yearly spatial epidemic waves of respiratory illness to identify early hotspots of infection.

San Diego County Public Health has been conducting syndromic surveillance for the past few years. Currently, the system has become largely automated and processes and analyzes data from a variety of disparate sources including hospital emergency departments, 911 call centers, prehospital transports, and over-the-counter drug sales. What has remained constant since the system’s initial conceptualization is the local opinion that the data should be analyzed and interpreted in a variety of ways, in anticipation for the variety of contexts in which events that are of public health interest may unfold. Relatively small increases in volume that are sustained over time will likely be detected by methods designed to detect “small process shifts”, and include the CUSUM and EWMA methods. Larger increases in volume that are not sustained over time will likely be detected by other employed methods (P-Chart in the event of a non-proportional increase in volume, U-Chart in the event of a proportional increase in volume). A retrospective analysis was conducted on historical data from various data sources to determine the frequency of signals and detected events as well as the context within which the alert occurred (i.e., the “shape” of the data). Findings regarding several actual public health events will also be discussed.

Objective

This paper describes the frequency, various “shapes” and magnitudes of data anomalies, and varying ways actual public health events may present themselves in syndromic data.

While there has been some work to evaluate different data sources for syndromic surveillance of influenza, no one has yet assessed the utility of simultaneously restricting data to specific visit settings and patient age-groups using data drawn from a single source population. Furthermore, most studies have been limited to the emergency departments (ED), with few evaluating the timeliness of data from community-based primary care.

Objective

Using physician billing data from a single source population, we aimed to compare age-group and visit setting specific patterns in the timing of patients presenting to community-based healthcare settings and hospital ED for influenza-like-illnesses (ILI). We thus evaluate the utility of focusing on particular age-groups and care settings for syndromic surveillance of ILI in ambulatory care.

Of critical importance to the success of syndromic surveillance systems is the ability to collect data in a timely manner and thus ensure rapid detection of disease outbreaks. Most emergency department-based syndromic surveillance systems use information rou-tinely collected in patient care including patient chief complaints and physician diagnostic coding. These sources of data have been shown to have only limited sensitivities for the identification of cer-tain syndromes. Another potential source of information, which has not been previously studied, is the patient. Studies have shown that patients as well as parents can accurately report information about their own or their child’s illness. The value of of patient and parent self-reported informa-tion for disease surveillance systems has not been measured.

Objective

To determine whether patients and their families can directly provide medical information that enables syndrome classification.

In order to detect influenza outbreaks, the New York State Department of Health emergency department (ED) syndromic surveillance system uses patients’ chief complaint (CC) to assign visits to respiratory and fever syndromes. Recently, the CDC developed a more specific set of “sub-syndromes” including one that included only patients with a CC of flu or having a final ICD9 diagnosis of flu. Our own experience was that although flu may be a common presentation in the ED during the flu season, it is not commonly diagnosed as such. Emergency physicians usually use a symptomatic diagnosis in preference, probably because rapid testing is generally unavailable or may not change treatment. The flu subsyndrome is based on a specific ICD9 code for influenza. It is unknown whether patient visits that meet these restrictive criteria are sufficiently common to be of use, or whether patients who identify themselves as having the flu are correct.

Objective

Our objective was to examine the CC and ICD9 classifiers for the influenza sub-syndrome to assess the frequency of visits and the agreement between the CC, ICD9 code and chart review for these patient visits.

In 2004, the NSW Public Health Real-time Emergency Department Surveillance System operating in and around Sydney, Australia signalled a large-scale increase in Emergency Department (ED) visits for gastrointestinal illness (GI). A subsequent alarming state-wide rise in institutional gastroenteritis outbreaks was also seen through conventional outbreak surveillance.

Objectives

To examine the association between short-term variation in ED visits for GI with short-term variation in institutional gastroenteritis outbreaks and thus to evaluate whether syndromic surveillance of GI through EDs provides early warning for institutional gastroenteritis outbreaks.

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.

The Centers for Disease Control and Prevention BioSense has developed chief complaint (CC) and ICD9 sub syndrome classifiers for the major syndromes for early event detection and situational awareness. The prevalence of these sub-syndromes in the emergency department population and the performance of these CC classifiers have been little studied. Chart reviews have been used in the past to study this type of question but because of the large number of cases to review, the labor involved would be prohibitive. Therefore, we used an ICD9 code classifier for a syndrome as a surrogate by chart reviews to estimate the performance of a CC classifier.

Objective

To determine the prevalence of the sub-syndromes based on the ICD9 classifiers, and to determine the sensitivity, specificity, positive predictive value and negative predictive value of CC classifiers for the sub-syndromes associated with the respiratory and gastrointestinal syndromes using the ICD9 classifier as the criterion standard.

Syndromic surveillance of emergency department (ED) visit data is often based on computer algorithms which assign patient chief complaints (CC) and ICD code data to syndromes. The triage nurse note (NN) has also been used for surveillance. Previously we developed an “NGram” classifier for syndromic surveillance of ED CC in Italian for detection of natural outbreaks and bioterrorism. The classifier is developed from a set of ED visits for which both the ICD diagnosis code and CC are available by measuring the associations of text fragments within the CC (e.g. 3 characters for a “3-gram”) with a syndromic group of ICD codes. We found good correlation between daily volumes by the ICD10 classifier and estimated by NGrams. However, because the CC was limited to 23 options based on the pick list, it might be possible to obtain results as good as the NGram method or better using a simpler probabilistic approach. Also, in addition to the CC, the Italian data included a free-text NN note. We might be able achieve improved performance by applying the n-gram method to the NN or the CC supplemented by the NN.

Objective

Our objective was to compare the performance of the NGram CC classifier to two discrete classifiers based on probabilistic associations with the CC pick list items. Also, we wished to determine the performance of the NGram method applied to CC alone, NN alone, and CC plus NN.

Syndromic surveillance of emergency department (ED) visit data is often based on computer algorithms which assign patient chief complaints (CC) to syndromes. ICD9 code data may also be used to develop visit classifiers for syndromic surveillance but the ICD9 code is generally not available immediately, thus limiting its utility. However, ICD9 has the advantages that ICD9 classifiers may be created rapidly and precisely as a subset of existing ICD9 codes and that the ICD9 codes are independent of the spoken language. If a classifier based on ICD9 codes could be used to automatically create the code for a chief-complaint assignment algorithm then CC algorithms could be created and updated more rapidly and with less labor. They could also be created in multiple spoken languages. We had developed a method for doing this based on an “ngram” text processing program adapted from business research technology (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 with associated probabilities, and then generate a CC classifier program. The method includes specialized selection techniques and model pruning to automatically create a compact and efficient classifier.

Objective

Our objective was to determine how closely the performance of an ngram CC classifier for the gastrointestinal syndrome matched the performance of the ICD9 classifier.