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.
Using New York CityÃs dead bird surveillance for West Nile Virus (WNV), this paper presents two explorations of the spatial cluster detection problem in which lagged test results are available for a random subset of observations. First, we establish a framework for the direct evaluation of methods and identify the optimal parameterization over a large family of models. We then investigate ways in which the lagged test results and other covariates might be used prospectively to extend the family of models by refining the baseline.
To determine whether preprocessing chief complaints before automatically classifying them into syndromic categories improves classification performance.
The prompt detection of disease outbreaks is a major concern to public health as it has the potential to reduce morbidity and mortality (1). Real-time syndromic surveillance uses existing non-traditional data for timely analysis and feedback to those responsible for investigations and follow-up of potential outbreaks (2). Recent studies have suggested that integrating multiple data sources can significantly improve detection accuracy of syndromic surveillance systems, but more work is needed to explore the most effective means of said integration and what types of data streams give the greatest benefit (3;4).
Objective:
This paper will examine the temporal relationship between OntarioÃs emergency department (ED) visits and telephone health line (Telehealth Ontario) call volume for respiratory illnesses, in an effort to test the feasibility of using Telehealth OntarioÃs system for real-time surveillance.
A significant amount of resources and effort have been recently invested in syndromic surveillance systems. However, how these systems complement or compare with traditional public health surveillance systems, such as outbreak reporting, is not clear.
Objective:
The purpose of this paper is to describe the value of outbreak and syndromic surveillance data from the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) syndromic surveillance system to monitor respiratory illness activity in Maryland.
There is a need for regular evaluation of surveillance strategies. The emergence of new diagnostic tests and new sources of data, changes in the spatio-temporal distribution of diseases and other factors must be periodically assessed to guarantee that the objectives of the surveillance effort are met. Underlying this evaluation process is the need to increase the efficient use of resources.
OBJECTIVE
We have developed a flexible model which can evaluate surveillance strategies at different hierarchical levels. It identifies key elements in the performance of the surveillance and recommends optimal sampling designs.
Recent extreme weather events have caused serious health and social problems across Europe. During the summer heat waves of 2003 across Europe, France recorded an excess of over 14,000 deaths contributed to heat-related causes. Other countries such as Italy and Portugal experienced over 3,000 and over 2,000 excess deaths respectively. The extreme rises in mortality were initially unobserved by traditional public health surveillance techniques; morbidity related to heat-related exposures also went initially unnoticed by public health authorities.
Real-time monitoring of clinical data has been proposed as one method of surveillance that may be used to alert public health authorities during extreme weather conditions when heat-related morbidity may be higher than expected. Previous studies have shown increased ambulance calls during heat alert conditions in Canada. These potential data sources, including electronic medical records for emergency department visits, are already in existence in many of the countires affected by the heat waves of 2003. Syndromic surveillance methods such as those described by Mandl et al could be applied to these data to help detect when heat-related morbidity and possibly heat-related mortality begins to rise.
Objective
The specific objectives of the study are to evaluate the usefulness of syndromic surveillance data to monitor heat-related morbidity and mortality during extreme weather conditions. During such conditions, real time data monitoring could potentially help drive interventions to reduce morbidity and mortality.
To evaluate the robustness of a spatial anonymization algorithm for syndromic surveillance data against a triangulation vulnerability attack. `BACKGROUND We have published an anonymization algorithm that takes precise point locations for patients and moves them a randomized distance according to a 2D Gaussian distribution that is inversely adjusted by the underlying population density. Before such algorithms can be integrated into live systems, assurances are needed so that patients cannot be reidentified through systematic vulnerabilities. Here we investigate the ease with which a spatial anonymization algorithm can be compromised by triangulating the original points with multiple repeated data requests. Obfuscative and cryptographic algorithms may be susceptible to weakening when it is possible for an adversary to produce output from the algorithm according to adversary-provided input. Under this threat model, an adversary could use a syndromic surveillance system to request anonymized patient data from a RHIO or other health network several different times. If the anonymized results are produced each time they are requested, triangulation of original addresses may be possible or the anonymity afforded by the algorithm may be reduced.
Malaria, major leading cause of morbidity and mortality in third world countries has been successfully eliminated from Jamaica since 1965. This, however, is being constantly challenged by lack of sustained vector control activities increased movement of global travellers to and from endemic countries to Jamaica given that the presence of vector “anopheles mosquitoes” that transmit malaria parasites. On December 2006 the first locally transmitted case of malaria was identified in Kingston, the capital city of Jamaica. Due to the impending threat to the country’s economy, such as travel advisory as Jamaica’s main foreign income comes from tourism especially in the western Jamaica, and to health care system. The Ministry of Health stepped up the prevention and control of malaria program. The objectives of the program are (a) early detection of cases and (b) prompt treatment of cases identified.
Objective
This paper evaluates the effectiveness of “active fever surveillance” during malaria outbreak (from December 2006 to June 2007) in western Jamaica.
Methods for locating spatial clusters of diseases are typically variations of the circular scan statistic method. They restrict the number of potential clusters by considering all circular, rectangular, or elliptical regions, and then apply a likelihood ratio test to evaluate the statistical significance of each potential cluster. Because disease outbreaks may have variable shapes, there has been recent interest in developing methods to detect irregularly-shaped clusters. Starting with a neighborhood graph of the administrative regions in the study area, certain sub-graphs are evaluated. These include all connected subgraphs within a circular window and sub-graphs of the minimum spanning tree of a weighted neighborhood graph formed by deleting one edge. These methods restrict the maximum cluster size or identify large clusters having greater likelihood ratios than true clusters in the data, suggesting a limitation of using the likelihood ratio to detect arbitrarily-shaped clusters.
Objective
A method for detecting spatial clusters of diseases of any shape based on the Euclidean minimum spanning tree is described and compared to the circular scan statistic.