General - ISDS

The purposes of this study are to identify and characterize increases in emergency department (ED) visits for falls during the 2007-08 winter season.

 Following the development of an introductory Continuing Education (CME) course in syndromic surveillance, the Education and Training Committee of the International Society for Disease Surveillance recognized the need to educate future non-medical public health workers and reviewed courses offered by the top five public health schools recognized by US News and World Report1.  All public health schools offered courses that included information on public health practice and infectious disease epidemiology and few offered courses on spatial and disaster epidemiology with attention given to syndromic surveillance, but none of the schools offered a comprehensive course that integrated topics of public health practice, infectious disease surveillance, data management and analytic techniques, disaster preparedness, and syndromic surveillance2-6.  The development of the graduate school course builds on our existing CME slide set goals that teaches students about syndromic surveillance and presents the course in a free and easy to use format for all schools of public health.  The ISDS hopes the semester long course will be taught by ISDS members in each state to spread awareness and knowledge on the topic of syndromic surveillance.

Objective

The paper describes the development of a graduate-level course to teach future non-medical public health workers about syndromic surveillance.

Many heuristics were developed recently to find arbitrarily shaped clusters (see  review  [1]). The most popular statistic is the spatial scan  [2]. Nevertheless, even if all cluster solutions could be known, the problem  of selecting the best cluster is ill posed. This happens because other measures, such as geometric regularity  [3-5] or topology  [6] must be taken intoconsideration. Most cluster finding  methods does not address  this last problem. A genetic multi-objective algorithm was developed elsewhere to identify irregularlyshaped clusters [5]. That method conducts a search aiming to maximize two objectives, namely the scan  statistic and the regularity of shape (using the compactness concept).The solution presented is a Pareto-set, consisting of all the clusters found which are not simultaneously worse in both objectives. The significance evaluation is conducted in parallel for all the  clusters  in  the  Pareto-set  through a  Monte Carlo simulation, determining the best cluster solution.

Objective

Irregularly shaped clusters occur naturally in disease surveillance, but they are not well defined. The number of possible clusters increases exponentially with the number of regions in a map. This concurs to reduce the power of detection, motivating the utilization of some kind of penalty function to avoid excessive freedom of shape. We introduce a weak link based correction which penalizes inconsistent clusters, without forbidding the presence of the geographically interesting irregularly shaped ones.

This study describes the ability by which total volume of ED visits correlate with influenza and respiratory syncytial virus (RSV) activity in the community.

To assess the accuracy of community-based physician claims for identifying 5 syndromes: fever, gastrointestinal, neurological, rash, and respiratory.

To compare age-group-specific correlation of influenza-like syndrome (ILS) emergency department (ED) visits with influenza laboratory data in Boston and NYC using locally defined ILS definitions.

The purpose of this study is to depict a local county health departmentís analysis and dissemination algorithm of surveillance system (SS) aberration (alarm) to designated stakeholders within the community.

There is a great deal of interest in spatial patterns of infant mortality. However, small numbers can make spatial patterns difficult to discern and may mask areas of persistently high risk. This study investigates the spatial pattern of birthweight and gestation, two primary risk factors for infant mortality, using normal-distribution methods available in SaTScan and for which data is available in much greater quantity.

This study uses data on births in New York City between 2000-2005 to investigate the spatial pattern of birthweight and gestation, two primary risk factors for infant mortality. The analysis uses SatScan to perform normal-distribution cluster detection after controlling for individual-level demographic variables. While previous research has investigated neighborhood effects and spatial patterns of low birth weight and infant mortality, few studies have done so with individual-level information and continuous outcomes. The overarching goal is to develop a framework to better understand demographic and spatial patterns of infant mortality, birthweight, and gestation to inform public health practice.