ISDS Conference

In 1911, Christophers developed an early-warning system for malaria epidemics in Punjab based on rainfall, fever-related deaths and wheat prices. Since that initial system, researchers and practitioners have continued to search for determinants of spatial and temporal variability of malaria to improve systems for forecasting disease burden. Malaria thrives in poor tropical and subtropical countries where resources are limited. Accurate disease prediction and early warning of increased disease burden can provide public health and clinical health services with the information needed to implement targeted approaches for malaria control and prevention that make effective use of limited resources. Malaria forecasting models do not typically consider clinical predictors, such as type of antimalarial treatment, in the forecasting models. 

Objective

The objective of the research was to identify the most accurate models for forecasting malaria at six different sentinel sites in Uganda, using environmental and clinical data sources.

Influenza epidemics occur seasonally but with spatiotemporal variations in peak incidence. Many modeling studies examine transmission dynamics [1], but relatively few have examined spatiotemporal prediction of future outbreaks [2]. Bootsma et al [3] examined past influenza epidemics and found that the timing of public health interventions strongly affected the morbidity and mortality. Being able to predict when and where high influenza incidence levels will occur before they happen would provide additional lead time for public health professionals to plan mitigation strategies. These predictions are especially valuable to them when the positive predictive value is high and subsequently false positives are infrequent.

Objective

Advanced techniques in data mining and integrating evidence from multiple sources are used to predict levels of influenza incidence several weeks in advance and display results on a map in order to help public health professionals prepare mitigation measures.

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.

The role of public health in preparing for, responding to, and recovering from emergencies has expanded as a result of the massive impact recent disasters have had on affected populations. Nearly every large-scale disaster carries substantial public health risk and requires a response that addresses immediate effects of the disaster on a population (e.g., mass casualties and severe injuries, lack of shelter in severe weather), as well as subsequent secondary physical effects (e.g., carbon monoxide poisoning due to improper operation or location of carbon monoxide-producing devices such as generators) and emotional effects (e.g., grief, anxiety, and post-traumatic stress disorder) caused by the disaster. Disaster epidemiology has been identified as an evolving field that integrates a variety of data sources and technological and geospatial resources to expedite reporting and to increase the accuracy of information collected and used by emergency planners and incident managers. As the national organization that supports the activities of applied epidemiologists in state, tribal, local, territorial, and federal public health agencies, the Council of State and Territorial Epidemiologists (CSTE) assembled a Disaster Epidemiology Subcommittee of public health experts and practitioners from diverse fields of applied epidemiology to discuss the use of epidemiologic methods in all phases of the disaster management cycle. In 2012, the Subcommittee assessed state-level disaster epidemiology capacity with a focus on surveillance. 

Objective

The panel will discuss the current status of disaster surveillance capabilities at local and state health departments in the United States and will provide an overview of current resources available to epidemiologists for surveillance.

The nature of Emergency Room services makes the patients' visits hard to predict and control and the services incur high costs. Chronic patients should not require urgent care to treat their chronic illness, if they were properly managed in primary care. We track frequency of emergency room visits by chronically ill when the primary complaint of record is their chronic condition. We use a record of institutional insurance claims collected in over 400 hospitals in California between 2006 and 2010. We identify dimensions of data that provide statistically significant differences of utilization between strata. We found particularly significant differences in resource utilization subject to type of insurance coverage carried by the patient, and subject to patient's age. We studied Diabetes, Asthma, and Arthritis patients from 8 age groups spanning ages 5 to 85, and 13 insurance payer types.

Objective

To study patterns of utilization of emergency care resources by chronically ill in order to identify efficiency and quality of care improvement opportunities.

BioSense 2.0 protects the health of the American people by providing timely insight into the health of communities, regions, and the nation by offering a variety of features to improve data collection, standardization, storage, analysis, and collaboration. BioSense 2.0 is the result of a partnership between the Centers for Disease Control and Prevention (CDC) and the public health community to track the health and well-being of communities across the country. As part of the redesign effort, new fat pipe system architecture has recently been implemented to improve the features and capabilities of the system.

Objective

The objective of this presentation is to provide an overview of the technical architecture of BioSense 2.0.

Respiratory infectious diseases are the most common diseases reported in rural China. Studies have suggested that the OTC retail sale data could be used to detect early outbreak (1, 2). However, few researches have performed to identify whether OTC retail sales data could also predict the outbreak in developing countries and resource poor settings. Here, we conducted a web-based syndromic surveillance system with OTC retail sales to detect respiratory epidemics in rural area in China.

Objective

To explore the feasibility of using OTC medication sales data for early detection of respiratory epidemics in rural China.

Fungal infections (FI) are a leading cause of morbidity and mortality among patients undergoing allo-SCT. The newer anti-fungal agents, the echinocandins and extended spectrum azoles, have offered alternatives to Amphotericin B and fluconazole. Data from large patient samples evaluating the magnitude of benefit with the newer anti-fungal agents are lacking.

Objective

We analyzed the Nationwide Inpt Sample (NIS) database from Healthcare Cost and Utilization project to evaluate the trends in the incidence of FI and to evaluate the potential impact of newer anti-fungal agents on in-hospital mortality (IHM) among allo-SCT recipients.

ORBiT is implemented as a distributed analytic platform consisting of a software stack atop of Hadoop and makes use of Titan, a distributed graph database as a backend for data storage. Data from each of the traditional and non-traditional sources are hosted as a massive linked structure, with extensible interfaces for each stream. The data from the linked structure is interfaced with streaming and graph-data analytic modules. The outputs from the analytic modules are interfaced with visualization tools that enable analysts to detect spatial and temporal patterns/correlations across multiple data sources.

Objective

Our objective is to provide 1) forecasting and early warning, and 2) an extensible data analytics platform for biosurveillance by enabling the use of traditional and non-traditional datasets, consisting of heterogeneous data types and modalities.

OpenMRS has global presence as an open source web based medical record system (MRS). It is built on an extensible, modular framework that allows the user to create a MRS that is as simple or complex as needed. OE is a multi-user network accessible analysis and visualization tool that enables users to monitor the populationÕs health from any computer connected to that network. OE was created as an open source solution using features and lessons learned from Enterprise ESSENCE, (Electronic Surveillance System for Early Notification of Community-Based Epidemics.) OE provides analyses, maps, graphs, charts, crosstabs, as well as detail tables and export functionality.

Objective

By pairing an open source medical record system, OpenMRS, with the Suite for Automated Global Electronic bioSurveillance (SAGES) disease surveillance tool, OpenESSENCE (OE), we have prototyped an open source solution for passive disease and program surveillance using an active medical record system.