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.
The use of spatially-based methods and algorithms in epidemiology and surveillance presents privacy challenges for researchers and public health agencies. We describe a novel method for anonymizing individuals in public health datasets, by transposing their spatial locations through a process informed by the underlying population density. Further, we measure the impact of blurring patient locations on detection of spatial clustering as measured by the SaTScan purely-spatial Bernoulli scanning statistic.
The traditional SaTScan algorithm[1],[2] uses the euclidean dis- tance between centroids of the regions in a map to assemble a con- nected (in the sense that two connected regions share a physical border) sets of regions. According to the value of the respective log- arithm of the likelihood ratio (LLR) a connected set of regions can be classified as a statistically significant detected cluster. Considering the study of events like contagious diseases or homicides we con- sider using the flow of people between two regions in order to build up a set of regions (zone) with high incidence of cases of the event. In this sense the regions will be closer as the greater the flow of peo- ple between them. In a cluster of regions formed according to the cri- terion of proximity due to the flow of people, the regions will be not necessarily connected to each other.
Objective
We present a new approach to the circular scan method [1] that uses the flow of people to detect and infer clusters of regions with high incidence of some event randomly distributed in a map. We use a real database of homicides cases in Minas Gerais state, in south- east Brazil to compare our proposed method with the original circu- lar scan method in a study of simulated clusters and the real situation.
Multiple data sources are essential to provide reliable information regarding the emergence of potential health threats, compared to single source methods [1,2]. Spatial Scan Statistics have been adapted to analyze multivariate data sources [1]. In this context, only ad hoc procedures have been devised to address the problem of selecting the most likely cluster and computing its significance. A multi-objective scan was proposed to detect clusters for a single data source [3].
Objective:
To incorporate information from multiple data streams of disease surveillance to achieve more coherent spatial cluster detection using statistical tools from multi-criteria analysis.
The Joint VA/DoD BioSurveillance System for Emerging Biological Threats project seeks to improve situational awareness of the health of VA/DoD populations by combining their respective data. Each system uses a version of the Electronic Surveillance System for Early Notification of Community-Based Epidemics (ESSENCE); a combined version is being tested. The current effort investigated combining the datasets for disease cluster detection. We compared results of retrospective cluster detection studies using both separate and joined data. — Does combining datasets worsen the rate of background cluster determination?
— Does combining mask clusters detected on the separate datasets?
— Does combining find clusters that the separate datasets alone would miss?
Objective:
We examined the utility of combining surveillance data from the Departments of Defense (DoD) and Veterans Affairs (VA) for spatial cluster detection.
The NYC Department of Health and Mental Hygiene (DOHMH) ED syndromic surveillance system receives data from 95% of all ED visits in NYC totaling 4 million visits each year. The data include residential ZIP code as reported by the patient. ZIP code information has been used by the DOHMH to separate visits into NYC and nonNYC for analysis; and, a closer examination of non-NYC visits may further inform disease surveillance.
Objective
To classify visits to NYC emergency departments (ED) into NYC residential, NYC PO Box or commercial building, commuters to NYC, and out-of-town visitors. To describe patterns in each group, to evaluate how they differ, and to consider how the differences can affect syndromic surveillance analyses and results.
The re-emergence of an infectious disease is dependent on social, political, behavioral, and disease-specific factors. Global disease surveillance is a requisite of early detection that facilitates coordinated interventions to these events. Novel informatics tools developed from publicly available data are constantly evolving with the incorporation of new data streams. Re-emerging Infectious Disease (RED) Alert is an open-source tool designed to help analysts develop a contextual framework when planning for future events, given what has occurred in the past. Geospatial methods assist researchers in making informed decisions by incorporating the power of place to better explain the relationships between variables.
Objective:
The application of spatial analysis to improve the awareness and use of surveillance data.
Absenteeism has great advantages in promoting the early detection of epidemics1. Since August 2011, an integrated syndromic surveillance project (ISSC) has been implemented in China2. Distribution of the absenteeism generally are asymmetry, zero inflation, truncation and non-independence3. For handling these encumbrances, we should apply the Zero-inflated Mixed Model (ZIMM).
Objective
To describe and explore the spatial and temporal variability via ZIMM for absenteeism surveillance in primary school for early detection of infectious disease outbreak in rural China.
Epidemiological surveillance is used to monitor time trends in diseases and the distribution of the diseases in the population. To streamline the process of identifying outbreaks, and notification of disease, syndromic surveillance has emerged as a method to report and analyze health data. Rather than report data by disease status (ie disease/no disease), clinical symptoms are used to detect outbreaks as early as possible.
Currently, only data collected via active surveillance (notifiable disease investigations) are usable for identifying communities that require attention. Therefore, any interventions performed using said data is reactive in nature. Syndromic surveillance systems must be disaggregated to enable proactive health promotion, and responses.
Furthermore, a common method must be established to assess the overall impact of syndromes. Diseases are not equal; some have a greater impact on health, and life. To address this issue, the World Health Organization (WHO) has created disability weights to be used in calculating disability adjusted life years (DALY). DALYs are effective in calculating the overall impact of disease in a community. DALYs estimate the burden of disease, not syndromes; therefore, it is reactive tool. To create a more effective syndromic surveillance system, syndromes must be associated with an overall impact weight.
Objective
The justification for address based syndromic surveillance systems, and building syndrome weighting mechanisms.
Since the release of anthrax in October of 2001, there has been increased interest in developing efficient prospective disease surveillance schemes. Poisson CUSUM is a control chart-based method that has been widely used to detect aberrations in disease counts in a single region collected over fixed time intervals. Over the past few years, different methods have been proposed to extend Poisson CUSUM charts to capture the spatial association among several regions simultaneously. In the proposed method, we extend an algorithm in industrial process control using multiple Poisson CUSUM charts to the spatial setting. The spatial association among regions is captured using the method proposed by Raubertas, which has been successfully applied in several prospective surveillance schemes. Also, to improve the power of the traditional multiple Poisson CUSUM charts, Poisson CUSUM charts were used along with fault discovery rate (FDR) control techniques.
Objective
To develop a computationally simple and fast algorithm for rapid detection of outbreaks producing easily interpretable results.