I examine the nature and expression of the null hypothesis often used in spatial surveillance. I also show an example of how incorrect specification of the null can lead to excess signals without interesting outbreaks, and argue that this may be a cause of excess signals when using spatial surveillance in public health applications.
This paper evaluates the operating characteristics of limited baseline aberration detection methods using different lengths (7-28 days) and end dates (1-7 days prior to the current day) for the baseline period using simulated outbreaks added to real data and simulated data representative of real data.
BioSense is a national program designed to improve the nation’s capabilities for conducting disease detection, monitoring, and real-time situational awareness. Currently, BioSense receives near real-time data from non-federal hospitals, as well as national daily batched data from the Departments of Defense and Veteran’s Affairs facilities. These data are analyzed, visualized, and made simultaneously available to public health at local, state, and federal levels through the BioSense application.
Objective:
In this paper we present summary information on the non-federal hospitals currently sending data to the BioSense system and describe this distribution by hospital type, method of data delivery as well as patient class and patient health indicator.
Benchmarking of temporal surveillance techniques is a critical step in the development of an effective syndromic surveillance system. Unfortunately, holding “bakeoffs” to blindly compare approaches is a difficult and often fruitless enterprise, in part due to the parameters left to the final user for tuning. In this paper, we demonstrate how common analytical development and analysis may be coupled with realistic data sets to provide insight and robustness when selecting a surveillance technique.
OBJECTIVE
This paper compares the robustness and performance of three temporal surveillance techniques using a twofold approach: 1) a unifying statistical analysis to establish their common features and differences, and 2) a benchmarking on respiratory, influenza-like ill-nesses, upper GI, and lower GI complaint time series from the Harvard Pilgrim Health Care (HPHC).
Objective Cluster detection with a mechanism for reducing false alarms and increasing sensitivity.
Since July 2004 the BioSense program at the Centers for Disease Control and Prevention (CDC) has received data from DoD military and VA outpatient clinics (not in real time). In January 2006 real-time hospital data (e.g. chief complaints and diagnoses) was added. Various diagnoses from all sources are binned into one or more of 11 syndrome categories.
Objective
This paper's objective is to compare syndromic categorization of newly acquired real-time civilian hospital data with existing BioSense data sources.
Since we donÃt know when such a disaster may occur, we have to perform this syndromic surveillance routinely, and thus the system should be automatic. Namely, information is drawn from electronic medical records (EMR), and is statistical analyzed, aberrations are detected and then Results are reported by e-mail or HP. It is preferable that this system be fully automatic. Though many systems of this type have been developed in the US, they have not been well developed in Japan. So as to develop such a system, we made a prototype system and have been performing prospectively and evaluating the system.
This paper describes a new expectation-based scan statistic that is robust to outliers (individual anomalies at the store level that are not indicative of outbreaks). We apply this method to prospective monitoring of over-the-counter (OTC) drug sales data, and demonstrate that the robust statistic improves timeliness and specificity of outbreak detection.
Syndromic surveillance has traditionally been used by public health to supplement mandatory disease reporting. The use of chief complaints as a data source is common for early event detection. Though some public health syndromic surveillance systems allow individual hospitals to view their own data through a web interface, many ICPs have the experience and knowledge-base to conduct their own surveillance and analysis internally. Additionally, they often have interests specific to their hospital which may motivate them to conduct additional syndromic surveillance projects themselves. Lastly, in many cases, ICPs are better able to investigate problems with chief complaint syndrome categorization and aberrations within their own facility before notification of public health staff. A good understanding of the foundation of syndromic surveillance by hospital ICPs can be extremely beneficial when paired with public health to investigate possible cases and outbreaks. ICPs at Greenville Hospital System (GHS), composed of 1110 beds, a level I trauma center with an average of 85,000 visits per year plus three smaller outlying emergency rooms, has had interest in syndromic surveillance for many years and collected data manually for trend analysis using Microsoft Excel to monitor chief complaint data since August 2003.
Objective
Demonstrate the use and benefit to hospital-based infection control practitioners (ICP) of chief complaint data for syndromic surveillance in partnership with public health to assist with traditional public health disease investigations.
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.