Seasonality

The use of syndromic surveillance systems to assist with the timely detection of unusual health events first occurred prior to the events of September 11, 2001. In the State of Michigan a pilot project with emergency departments began collecting syndromic data in 2004. Little research has been done in rural settings which have unique characteristics such as having one medical facility for a large geographic region. In addition to being rural, the community in which the following study was done is a resort com-munity where the population differs between the summer and winter months in number and composi-tion. Another unique factor in this study is that there is little published literature utilizing triage and dis-charge syndromic groups as a means for determining system sensitivity and specificity.

Objective

This paper describes the analysis of sensitivity and specificity of an ICD-9 based syndromic surveillance system in a rural emergency department located in Northern Lower Michigan.

If the next influenza pandemic emerges in Southeast Asia, the identification of early detection strategies in this region could enable public health officials to respond rapidly. Accurate, real-time influenza surveillance is therefore crucial. Novel approaches to the monitoring of infectious disease, especially respiratory disease, are increasingly under evaluation in an effort to avoid the cost- and timeintensive nature of active surveillance, as well as the processing time lag of traditional passive surveillance. In response to these issues, we have developed an indications and warning (I&W) taxonomy of pandemic influenza based on social disruption indicators reported in news media.

Objective

Our aim is to analyze news media for I&W of influenza to determine if the signals they create differ significantly between seasonal and pandemic influenza years.

BioSense is a CDC initiative to promote situational awareness through summarizing, analyzing, and presenting health related event information. Among the data sources collected and analyzed through the BioSense application are the Department of Defense and Department of Veterans Affairs ambulatory clinic care data. Clinical diagnoses and procedures are quantified, and analytic results are presented and categorized into 94 state and metropolitan areas.

Objective

Precise geographic location of health events is a challenging but critical component to determine the likely site of exposure for disease surveillance. This paper describes a method used by BioSense to develop and implement a reasonable set of rules in defining geographic locations of health events.

In the last decade, time series analysis has become one of the most important tools of surveillance systems. Understanding the nature of temporal fluctuations is essential for successful development of outbreak detection algorithms, aberration assessment, and to control for seasonal variations. Typically, in applying the time series methods to health outcomes collected over an extended period of time it is assumed that population profiles remain constant. In practice, such assumptions have been rarely tested. At best, the temporal analysis is performed using stratification by age or other discriminating factors if heterogeneity is suspected. Any community can experience population changes in various forms. Long-term trends of inflow/outflow migration and rapid transient fluctuations associated with specific events are typical examples of changes in population profile. Seasonality, as an intrinsic property of infectious diseases manifestation in a community, is typically attributed to periodic changes in transmissibility of pathogens. To some extent, seasonal fluctuations in the incidence of infectious diseases could also be associated with the changes in population profiles. The ability to detect and describe such changes would provide valuable clues into seasonally changing factors associated with an infection.

Objective

The objective of this communication is two-fold: 1) to introduce an analytical approach for assessing temporal changes in the surveillance reporting with respect to population profile; and 2) to demonstrate the utility of this method using laboratory-confirmed cases for four reportable enteric infections (cryptosporidiosis, giardiasis, shigellosis, and salmonellosis) recorded by the Massachusetts Department of Public Health over the last 12 years. This new approach for assessing seasonal changes is based on comparison of gender-specific single-year age distributions, which constitute population profiles.