ISDS Conference

On Monday, August 29, 2005, Hurricane Katrina struck the Gulf Coast. Outside of the affected areas of TX, LA, MS, and AL, GA received the largest number of these evacuees, approximately 125,000. By August 30, 2005, GA began receiving a total of approximately 1,300 NDMS patients from flights arriving at Dobbins Air Force Base. Within days, Georgia established 13 shelters for evacuees. Crowded shelters can increase the risk for communicable diseases. In addition, many evacuees left behind needed medications, thus increasing the risk for chronic disease exacerbations.

Objective

To assess public health needs among sheltered evacuees, the GA Department of Human Resources, Division of Public Health recommended daily surveillance.

Spatial scan finds the most anomalous region that has shown increase in observed counts when compared to the expected baseline. As there can be infinitely many regions to search for, most state-of-the-art algorithms assumes a specific shape of the attack region (circles for Kulldorff and rectangles for Ultra-Fast Spatial Scan Statistics). This assumption might reduce the detection power as real world attacks don't follow standard geometric shapes.

Objective

We propose discriminative random field approach for detecting a disease outbreak. Given observed data on a spatial grid, the goal is to label each node as being under attack and non-attack.

Traditionally Emergency Department syndromic surveillance methods have relied on ICD-9 codes and chief complaints. The implementation of electronic medical record keeping has made much more information available than can potentially be used for surveillance. For example, information such as vital signs, review of systems and physical exam data are being stored discreetly. These data have the potential to detect specific diseases or outbreaks in a community earlier that the traditionally used ICD-9 and chief complaint.

Objective

This paper describes the integration of novel data sets from an Emergency Department Electronic Medical Record into a syndromic surveillance application.

Surveillance strategies following major natural disasters have varied widely with respect to methods used to collect and analyze data. Following Hurricane Katrina, public health concerns included infectious disease outbreaks, injuries, mental health and exacerbation of preexisting chronic conditions resulting from unprecedented population displacement and disruption of public health services and health-care infrastructure.

Objective

This paper describes the public health surveillance response to hurricane Katrina in New Orleans and surrounding Parishes; particularly illustrating the methods, results, and lessons learned for implementing passive, active and electronic syndromic surveillance systems during a major disaster.

Current syndromic surveillance systems run multiple simultaneous univariate procedures, each focused on detecting an outbreak in a single data stream. Multivariate procedures have the potential to better detect some types of outbreaks, but most of the existing methods are directionally invariant and are thus less relevant to the problem of syndromic surveillance. This article develops two directionally sensitive multivariate procedures and compares the performance of these procedures both with the original directionally invariant procedures and with the application of multiple univariate procedures using both simulated and real syndromic surveillance data. The performance comparison is conducted using metrics and terminology from the statistical process control (SPC) literature with the intention of helping to bridge the SPC and syndromic surveillance literatures. This article also introduces a new metric, the average overlapping run length, developed to compare the performance of various procedures on limited actual syndromic surveillance data. Among the procedures compared, in the simulations the directionally sensitive multivariate cumulative sum (MCUSUM) procedure was preferred, whereas in the real data the multiple univariate CUSUMs and the MCUSUM performed similarly. This article concludes with a brief discussion of the choice of performance metrics used herein versus the metrics more commonly used in the syndromic surveillance literature (sensitivity, specificity, and timeliness), as well as some recommendations for future research.

58 medical licensure boards require between 12 and 50 hours of Continuing Medical Education (CME) for re-licensure of physicians. 28 states as well as Puerto Rico, the U.S. Virgin Islands, and the Mariana Islands, require continuing nursing education (CNE) for nursing re-licensure, with requirements varying from 5 hours per year to 45 hours every 3 years. Continuing education requirements may include self-directed educational programs, academic education, or research and professional activities. To the best of our knowledge, although there are online public health preparedness programs and journal articles that provide continuing education credits, there is no currently available online course on syndromic surveillance available for CME or CNE.

Objective

The Education and Training Committee of the International Society for Disease Surveillance is developing an introductory online CME curriculum in syndromic surveillance for physicians and other health practitioners. This curriculum would also be available for public health practitioners new to syndromic surveillance. The goal of the curriculum is to provide an introductory knowledge of syndromic surveillance for interested practitioners and stimulate healthcare provider cooperation and involvement with syndromic surveillance.

After the SARS outbreak in 2003, Beijing established Fever Clinics in major hospitals for the early detection of potential respiratory disease outbreaks. The data collection in Fever Clinics contains the basic patient information, body temperature, cough, and breath condition, as well as a primary diagnosis. Since the symptoms and diagnosis are mainly recorded in free text format, it is very difficult to use for data analysis. Because of the problems in data processing, the data collection has decreased.

Objective

This paper describes the methodology in the development of an Integrated Surveillance System for Beijing, China.

Numerous recent papers have evaluated algorithms for biosurveillance anomaly detection. Common essential problems in the disparate, evolving data environment include trends, day-of-week effects, and other systematic behavior. Public health monitors have expressed the need for modifiable case definitions, requiring monitoring of time series that cannot be modeled in advance. Thus, automated algorithm selection is required. Recent research showed superior predictive performance of the H-W forecasting method compared to regression based predictors applied to syndromic data. This effort discusses extension to a practical monitoring tool, including selection from parametric and initialization settings based on limited data history, selection criteria for routine updating, specification of confidence limits, and validation of the resulting algorithm.

Objective

The objective is to develop and evaluate an operational alerting algorithm appropriate for the variety of time series behavior observed in biosurveillance data. The Holt-Winters (H-W) implementation of generalized exponential smoothing, comparable to complex regression models in predictive capability and far easier to specify and adapt, is built into a robust detection method.

In May 2000 accidental contamination of the water supply led to an outbreak of severe gastroenteritis in Walkerton Ontario, Canada. Of 1346 cases associated with exposure to Walkerton water, 65 were admitted to hospital, 27 developed Hemolytic-Uremic Syndrome, and six died. Estimates that 42% of cases were unreported indicate that the actual number of cases was likely 2321.

Objective

This abstract reports preliminary results of a retrospective study of the effectives of ER syndromic surveillance in detecting this outbreak.

Electronic laboratory-based surveillance can significantly improve the diagnostic specificity and response time of traditional infectious disease surveillance. Under the project “Models of Infectious Disease Agent Study”, we wished to evaluate the application of space-time outbreak detection algorithms utilizing SaTScan to a national database of routinely collected microbiology laboratory data.

Objective

This paper describes the application of the WHONET software integrated with SaTScan to the detection of Shigella outbreaks in a national database using a space-time cluster detection algorithm in simulated real-time and comparison of findings to outbreaks reported to the Ministry of Health.