Evaluation of Syndromic Surveillance

Hospital syndromic surveillance data may be a useful tool in detecting increases in influenza-like-illness (ILI) and for monitoring seasonal trends or pandemic activity on a local level. A previous comparison of hospital syndromic surveillance data with ILI surveillance data manually abstracted from emergency department notes revealed that the general respiratory category performed better than symptomspecific subcategories. However, only about half of all patients hospitalized for influenza meet the ILI criteria defined as fever and either cough or sore throat. Hospital discharge data are used retrospectively to determine disease burden, but is not of use for acute monitoring due to the substantial lag time. Knowing how accurately admission data reflect discharge data can assist with interpretation of real or near-real time data streams commonly used in syndromic surveillance systems.

Objective

Timely unplanned hospital admissions data in a general respiratory syndrome category and/or with a pneumonia or influenza admission diagnosis are compared with hospital discharge data to determine accuracy for prediction of influenza disease burden.

Sickness absence is particularly pronounced within health care organizations where job demands and work environment expose workers to an increased risk of illness and injury, potentially leading to an inability to attend work. Health Care Workers (HCWs), especially nurses who are primarily responsible for front-line patient care, are at high risk of acquiring infections from direct patient contact. In addition, there is greater risk of exposure to contaminated human blood and body fluids.

Objective

1) To identify and describe Occupational Health visits (overall and specific conditions) among full-time Kingston General Hospital employees, according to frequency, duration, workplace variables and seasonality. 2) To consider the association between absenteeism and HCW exposure risk to infectious diseases based on a proxy variable defining level of patient contact. 3) To examine the potential for integration of this occupational health data stream into an existing Emergency Department Syndromic Surveillance system.

The H5N1 avian influenza virus is now considered endemic in poultry in some parts of the world and the continued exposure in humans suggests that the risk of the virus evolving into a more transmissible agent in humans − a step towards worldwide pandemic – remains high. Universities, with large assembly of students and student movements determined by the class schedules and travel routes between classes, in addition to the faculty and staff located in close proximity, are extremely susceptible environments to the spread of pandemic events. Moreover, large universities in the U.S. often have a good proportion of international students, who commute to/from their home country within their study period. Therefore, a good surveillance system to detect disease outbreaks is essential to support a system that is robust to this high impact low probability disruptive event.

Objective

This paper describes a framework for an aberration detection method − change-point analysis for mean and variance − adapted for Poisson-distributed data, for syndromic surveillance in an academic environment.

Objective

Several authors have described ways to introduce artificial outbreaks into time series for the purpose of developing, testing, and evaluating the effectiveness and timeliness of anomaly detection algorithms, and more generally, early event detection systems. While the statistical anomaly detection methods take into account baseline characteristics of the time series, these simulated outbreaks are introduced on an ad hoc basis and do not take into account those baseline characteristics. Our objective was to develop statistical-based procedures to introduce artificial anomalies into time series, which thus would have wide applicability for evaluation of anomaly detection algorithms against widely different data streams.

To evaluate four algorithms with varying baseline periods and adjustment for day of week for anomaly detection in syndromic surveillance data.

To apply syndromic techniques in assessing whether the false-positive rate (FP rate) of a rapid oral HIV test, routinely used for screening in New York Cityís STD clinics, deviated from the manufacturerís claim; results of which have important implications for assessing clinical test performance.

In this paper we investigate the use of the CUSUM algorithm on retrospective MMR and Pentacel (DTaP-IPV-Hib) immunization data to determine if this type of surveillance tool is useful for measuring changes in immunization rates.

The pilot near real time surveillance system ASTER, which currently monitors the French Forces in Djibouti and French Guiana [1], has been especially designed for inter-allied interoperability. This paper briefly describes the rationale of this system's interoperability framework and components, and its results from a 4 years long experience.

This abstract describes a suite of software utilities that have been developed for systematically evaluating the detection performance and robustness of univariate temporal alerting algorithms used in syndromic surveillance systems.

Multiple surveillance activities have been conducted in Great Britain (GB) with the objective of estimating the occurrence of scrapie, a fatal neurological infectious disease of small ruminants: statutory reporting of clinical cases, annual surveys on sections of the population and occasional anonymous postal surveys. None of the surveillance sources is either unbiased or comprehensive and if the progress of control schemes is to be closely monitored, better estimates of disease occurrence are required. With this objective, the Department for Food, Environment and Rural Affairs (Defra) funded a project to: i)provide estimates of the frequency of scrapie that integrate currently available surveillance data; and ii)inform the most effective surveillance strategies that will result in sensitive systems for the detection of changes in disease prevalence in time. To make this review as comprehensive as possible it should also: i)consider clinical disease and infection at both individual animal and holding level; ii) subject to data availability, extend all analyses to the recently detected atypical form of scrapie and iii) in a context of scarce and competitive resources, approach the problem efficiently. The approaches used within this project, outlined below, describe the efficient use and integration of all existing sources to evaluate the surveillance effort. Three surveillance attributes were of particular interest in the evaluation process: sensitivity, representativeness and cost.