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Cluster Detection

Description

Scan statistics are highly successful for the evaluation of space-time clusters. Recently, concepts from the graph theory were applied to evaluate the set of potential clusters. Wieland et al. introduced a graph theoretical method for detecting arbitrarily shaped clusters on the basis of the Euclidean minimum spanning tree of cartogram transformed case locations, which is quite effective, but the cartogram construction step of this algorithm is computationally expensive and complicated.

 

Objective

We describe a method for prospective space-time cluster detection of point event data based on the scan statistic. Our aim is to detect as early as possible the appearance of an emerging cluster of syndromic individuals because of a real outbreak of disease amidst the heterogeneous population at risk.

Submitted by hparton on
Description

One objective of public health surveillance is detecting disease outbreaks by looking for changes in the disease occurrence, so that control measures can be implemented and the spread of disease minimized. For this purpose, the Florida Department of Health (FDOH) employs the Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE). The current problem was spawned by a laborintensive process at the FDOH: authentic outbreaks were detected by epidemiologists inspecting ESSENCE time series and derived event lists. The corresponding records indicated that patients arrived at an ED within a short interval, often less than 30minutes. The time-of-arrival (TOA) task was to develop and automate a capability to detect events with clustered patient arrival times at the hospital level for a list of subsyndrome categories of concern to the monitoring counties.

 

Objective

This presentation discusses the approach and results of collaboration to enable a solution of a hospital TOA monitoring problemin syndromic surveillance applied to public health data at the hospital level for county monitoring.

Submitted by hparton on
Description

The Electronic Integrated Disease Surveillance System (EIDSS) is a computer-based disease reporting application funded under the Cooperative Biological Engagement Program of the U.S. Defense Threat Reduction Agency. EIDSS deployment includes the Republics of Georgia (GG) and Azerbaijan (AJ) where personnel in the Ministry of Health and the Ministry of Agriculture in each country enter case-based disease reports. The potential benefits obtained through surveillance of infectious diseases across species have been widely discussed. A limitation of such practice has been the paucity of single applications that collect information about disease in both human and other animal populations (Scotch 2009). A unique feature of EIDSS is the use of a single platform to enter reports of disease in humans and other animals. Records are stored in a common database enabling ready access to information on multiple diseases and provide a quantitative linkage between human and animal data. An integrated analysis and reporting (AVR) module further supports timely investigation of disease events across the epizootic barrier.

Objective

We describe an electronic disease reporting system that integrates case-based disease information from humans and other animals in a single database and examine the utility for supporting disease surveillance functions through access to longitudinal case reports of multiple diseases across multiple species provided by the system.

Submitted by elamb on
Description

Data obtained through public health surveillance systems are used to detect and locate clusters of cases of diseases in space-time, which may indicate the occurrence of an outbreak or an epidemic. We present a methodology based on adaptive likelihood ratios to compare the null hypothesis (no outbreaks) against the alternative hypothesis (presence of an emerging disease cluster).

 

Objective

Disease surveillance is based on methodologies to detect outbreaks as soon as possible, given an acceptable false alarm rate. We present an adaptive likelihood ratio method based on the properties of the martingale structure which allows the determination of an upper limit for the false alarm rate.

Submitted by elamb on
Description

Influenza is a major cause of mortality. In developed countries, mortality is at its highest during winter months, not only as a result of deaths from influenza and pneumonia but also as a result of deaths attributed to other diseases (e.g. cardiovascular disease). Understandably, much of the surveillance of influenza follows predefined geographic regions (e.g. census regions or state boundaries). However, the spread of influenza and its resulting mortality does not respect such boundaries.

 

Objective

To cluster cities in the United States based on their levels of mortality from influenza and pneumonia.

Submitted by elamb on
Description

Time-of-arrival (TOA) surveillance methodology consists of identifying clusters of patients arriving to a hospital emergency department (ED) with similar complaints within a short temporal interval. TOA monitoring of ED visit data is currently conducted by the Florida Department of Health at the county level for multiple subsyndromes [1]. In 2011, North Carolina's NC DETECT system and CDC's Biosense Program collaborated to enhance and adapt this capability for 10 hospital-based Public Health Epidemiologists (PHEs), an ED-based monitoring group established in 2003, for North Carolina's largest hospital systems. At the present time, PHE hospital systems include coverage for approximately 44% of the statewide general/acute care hospital beds and 32% of all emergency department visits statewide. We present findings from TOA monitoring in one hospital system.

Objective

To describe collaborations between North Carolina Division of Public Health and the Centers for Disease Control and Prevention (CDC) implementing time-of-arrival (TOA) surveillance to monitor for exposure-related visits to emergency departments (ED) in small groups of North Carolina hospitals.

Submitted by elamb on
Description

The spatial scan statistic [1] is the most used measure for cluster strenght. The evaluation of all possible subsets of regions in a large dataset is computationally infeasible. Many heuristics have appeared recently to compute approximate values that maximizes the logarithm of the likelihood ratio. The Fast Subset Scan [2] finds exactly the optimal irregularly spatial cluster; however, the solution may not be connected. The spatial cluster detection problem was formulated as the classic knapsack problem [3], and modeled as a bi-objective unconstrained combinatorial optimization problem. Dynamic programming relies on the principle that, in an optimal sequence of decisions or choices, each sub-sequence must also be optimal. During the search for a solution it avoids full enumeration by pruning early partial decision solutions that cannot possibly lead to optimal solutions.

Objective

We propose a fast, exact algorithm to make detection and inference of arbitrarily shaped connected spatial clusters in aggregated area maps based on constrained dynamic programming.

Submitted by elamb on
Description

The intrinsic variability that exists in the cases counting data for aggregated-area maps amounts to a corresponding uncertainty in the delineation of the most likely cluster found by methods based on the spatial scan statistics [3]. If this cluster turns out to be statistically significant it allows the characterization of a possible localized anomaly, dividing the areas in the map in two classes: those inside and outside the cluster. But, what about the areas that are outside the cluster but adjacent to it, sometimes sharing a physical border with an area inside the cluster? Should we simply discard them in a disease prevention program? Do all the areas inside the detected cluster have the same priority concerning public health actions? The intensity function [2], a recently introduced visualization method, answers those questions assigning a plausibility to each area of the study map to belong to the most likely cluster detected by the scan statistics. We use the intensity function to study cases of diabetes in Minas Gerais state, Brazil.

Objective

Cluster finder tools like SaTScan[1] usually do not assess the uncertainty in the location of spatial disease clusters. Using the nonparametric intensity function[2], a recently introduced visualization method of spatial clusters, we study the occurrence of several non-contageous diseases in Minas Gerais state, in Southeast Brazil.

Submitted by elamb on
Description

The Voronoi Based Scan (VBScan)[1] is a fast method for the detection and inference of point data set space-time disease clusters. A Voronoi diagram is built for points representing population individuals (cases and controls). The number of Voronoi cells boundaries intercepted by the line segment joining two cases points defines the Voronoi distance between those points. That distance is used to approximate the density of the heterogeneous population and build the Voronoi distance Minimum Spanning Tree (MST) linking the cases. The successive removal of its edges generates sub-trees which are the potential space-time clusters, which are evaluated through the scan statistic [2]. Monte Carlo replications of the original data are used to evaluate cluster significance. In the present work we modify VBScan to find the best partition dividing the map into multiple low and high risk regions.

Objective

We describe a method to determine the partition of a map consisting of point event data, identifying all the multiple significant anomalies, which may be of high or low risk, thus monitoring the existence of possible outbreaks.

Submitted by elamb on
Description

The New York State (NYS) Medicaid Program provides healthcare for 34% of the population in New York City (NYC) and 4%-20% in each of the 57 county populations up-state. Prescription data are collected through the sub-mission of claims forms to the Medicaid Program and transmitted daily to the NYS Syndromic Surveillance Program as summary counts by drug category and patient’s ZIP Code, age category, and sex. One of the 18 drug categories is influenza agents, which in-cludes rimantadine, oseltamivir, and zanamivir.

For surveillance of influenza-like illness (ILI) activity, the NYS and NYC Sentinel Physician Influenza Surveillance Program collects from sentinel physicians weekly reports of the total number of patients seen and the number of patients presenting with ILI (defined as temperature > 100 degrees F, presence of cough or sore throat, and absence of other known cause of these symptoms). Not all counties in NYS have sentinel physicians: in the 2003-2004 flu surveillance season (Week 40, in early October, 2003, to Week 20, in late May, 2004), 37 of 57 upstate counties and all 5 counties of NYC had sentinel physicians.

 

Objective

To evaluate the usefulness of daily counts of prescriptions for influenza agents charged to Medicaid insurance, by county of residence of the recipient, for detection of elevated ILI in NYS, currently monitored through physicians participating in the CDC Influenza Surveillance Program.

Submitted by elamb on