Surveillance Systems

On a day to day basis, farmers and their veterinarians deal with many diseases without the benefit of surveillance for early outbreak detection, or coordinated outbreak responses. Without this support, highly contagious pathogens such as Porcine Epidemic Diarrhea Virus (PEDv) can spread quickly and potentially cause significant harm. The purpose of this project was to develop a surveillance system to help Canadian swine farmers and veterinarians to deal more effectively with diseases

Objective

To improve swine farmers and veterinarians ability to manage disease

Health surveillance systems provide important functionalities to detect, monitor, respond, prevent, and report on a variety of conditions across multiple owners. They offer important capabilities, with some of the most fundamental including data warehousing and transfer, descriptive statistics, geographic analysis, and data mining and querying. We observe that while there is significant variety among surveillance systems, many may still report duplicative data sources, use basic forms of analysis, and provide rudimentary functionality.

Objective

To identify analytic gaps and duplication across U.S. government, international agencies, non-profit and academic health surveillance systems, programs, and initiatives in four areas: Analytics, Data Sources, Statistics, and System Requirements.

 

In response to the rise in obesity rates and obesity-related healthcare costs over the past several decades, numerous organizations have implemented obesity prevention programs. The current method for evaluating the success of these programs relies largely on annual surveys such as the Centers for Disease Control and Prevention’s Behavioral Risk Factor Surveillance System (BRFSS) which provides state-by-state obesity rates. As a result, public health policy makers lack the fine-grained evaluation data needed to make timely decisions about the success of their obesity prevention programs and to allocate resources more efficiently.

Objective

We developed Persistent Health Assessment Tools, PHAT, to equip public health policy makers with more precise tools and timely information for measuring the success of obesity prevention programs. PHAT monitors social media to supplement traditional surveillance by making real-time estimates based on observations of obesity-relevant behaviors.

 

To review current trends and issues in the development and use of mobile apps for public health surveillance and decision making in settings with different resource availability and technological development. The panel discussion will address cross-cutting issues of general interest, including timeliness, recruitment, validation, and engagement by presenting innovative examples of apps conceived for various uses in human and animal surveillance.

Introduction

An increasing number of mobile applications available for download provide biosurveillance capabilities using new and traditional data streams. Biosurveillance apps span a wide range of settings, uses, technologies, and resource capacities that provide health analysts rapid and efficient means of data collection, visualization, and analyses. However, this technological “heaven” is not free from the challenges of traditional biosurveillance applications, namely validation to inform specificity and recruitment and engagement to ensure representativeness. This panel will provide guidance for future development and utility of mobile apps by illustrating how these matters are addressed in field tested mobile applications.

To describe an R package that was designed to provide ready implementation of veterinary syndromic surveillance systems, from classified data to the generation of alerts and an html interface.

Introduction

Introduction

The field of veterinary syndromic surveillance (VSS) is developing fast, with countries exploring a great variety of data sources. After implementing two VSS systems we have demonstrated that the steps from classified data to full system implementation can be streamlined, and published a guideline for implementation. All the steps described have been made available in an R package (https:// github.com/nandadorea/vetsyn). We aim to demonstrate the utility and potential of this streamlined approach.

Veterinary syndromic surveillance (VSS) is a fast growing field, but development has been limited by the limited use of standards in recording animal health events and thus their categorization into syndromes. The adoption of syndromic classification standards would allow comparability of outputs from systems using a variety of animal health data sources (clinical data, laboratory tests, slaughterhouse records, rendering plants data, etc), in addition to improving the ability to compare outputs among countries. The project “Standardising Syndromic Classification in Animal Health Data” (SSynCAHD) aims to standardize the classification of animal health records into syndromes.

Objective

To develop an ontology for the classification of animal health data into syndromes with application to syndromic surveillance.

 

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.

Syndromic surveillance generally refers to the monitoring of disease related events, sets of clinical features (i.e. syndromes), or other indicators in a population. Originally conceived as a tool for the early detection of potential bioterrorism outbreaks, syndromic surveillance is also used by health departments as a tool for monitoring seasonal illness, evaluating health interventions, and other health surveillance activities. Over the past decade, the Tennessee Department of Health (TDH) has utilized syndromic surveillance at the jurisdictional level. These standalone, jurisdictional systems utilized chief complaint data from local emergency departments (EDs) and the Early Aberration Reporting System (EARS) developed by CDC. Some jurisdictions integrated other local data for analysis in EARS including 911 call center data, over the counter drug sales, and other non-traditional data sources. The analyses conducted on the data varied from jurisdiction to jurisdiction. CDC dismantled the EARS program in 2011, prompting the need for a complete syndromic surveillance overhaul. TDH decided to implement a centralized, statewide system that would maintain all the capabilities that jurisdictions currently had while allowing for statewide data analysis and aggregation. During this implementation process, TDH has been balancing the short term goal of supporting and maintaining the existing jurisdictional systems while moving forward with acquiring a statewide syndromic surveillance solution and establishing the infrastructure to support it.

Objective

To share lessons learned in Tennessee during its transition from a jurisdictional syndromic surveillance system to a state-wide, centralized system.

 

The Syndromic Surveillance Program (SSP) of the Georgia Department of Public Health collects chief complaint data from hospitals to characterize health trends in near real time. These data were critical for situational awareness during the 2009 H1N1 pandemic. In 2012, SSP and the Effingham County Schools began a project to collect syndromic surveillance data from school clinics. The hypothesis was that these data may be used to inform interventions during a pandemic, guide school health programs, elucidate health priorities in school-age populations, and quantify nursing staff needs in schools. Analysis of data from the first two pilot years has provided a novel look at the disparate burden of disease among students across schools in the county.

Objective

This project was designed to demonstrate the feasibility of schoolbased nurse clinic visit syndromic surveillance. Additional objectives include using clinic visit data to identify opportunities for health interventions at participating schools and to characterize the type and number of student visits to the school nurses. An electronic module was developed in the State Electronic Notifiable Disease Surveillance System (SendSS) to facilitate data entry by participating school nurses and data management by the Georgia Department of Public Health.

 

In Louisiana, information contained on electronic laboratory reports is not able to identify the pregnancy status for the majority of HIV-infected women. Laboratories have access to ICD9/ICD10 codes which could provide information about pregnancy status, but few laboratories provide these codes to Health Departments. In some areas, such as New York City, the reporting of pregnancy status, if available, is required. This study quantifies the opportunities for reducing perinatal HIV transmission if pregnancy status was available on laboratory reports and determines if this information would have been useful for targeting these pregnancies for follow up from Disease Intervention Specialists (DIS). If pregnancy status is found to be useful, states should require pregnancy status in their laboratory reporting guidelines.

Objective

Quantify the opportunities for reducing perinatal HIV transmission risk if pregnancy status was available on electronic laboratory reporting in Louisiana.