Data Integration

The National Collaborative for Bio-Preparedness (NCB-Prepared) was established in 2010 to create a biosurveillance resource to enhance situational awareness and emergency preparedness. This jointinstitutional effort has drawn on expertise from the University of North Carolina- Chapel Hill, North Carolina State University, and SAS Institute, leveraging North Carolina’s role as a leader in syndromic surveillance, technology development and health data standards. As an unprecedented public/private alliance, they bring the flexibility of the private sector to support the public sector. The project has developed a functioning prototype system for multiple states that will be scaled and made more robust for national adoption.

Objective:

Demonstrate the functionality of the National Collaborative for Bio-Preparedness system.

 

Accurately gauging the health status of a population during an event of public health significance (e.g. hurricanes, H1N1 2009 pandemic) in support of emergency response and situation awareness efforts can be a challenge for established public health surveillance systems in terms of geographic and population coverage as well as the appropriateness of health indicators. The demand for timely, accurate, and event-specific data can require the rapid development of new data assets to “fill-in” existing information gaps to better characterize the scope, scale, magnitude, and population health impact of a given event within a very narrow time-window. Such new data assets may be concurrently under development and evaluation while being used to support response efforts. Recent examples include the “drop-in” surveillance processes deployed at evacuation centers following Hurricane Katrina1 and the illness and injury surveillance systems established for response workers during the Deepwater Horizon Oil spill response. During the 2009 H1N1 pandemic response, CDC acquired access to data from several national-level health information systems that previously had been un-vetted as public health information sources. These sources provided data extracts from massive administrative or electronic medical records (EMR) based in hospital and primary care settings. It was hoped that such data could supplement existing influenza surveillance systems and aid in the characterization of the pandemic. Few of these new data sources had formal documentation or concise information on the underlying populations and geographies represented.

Objective

To describe data management and analytic processes undertaken to rapidly acquire and use previously unavailable data during a public health emergency response.

This work builds on a successful demonstration project and expands its data linkage capacity to new community partners. Presently, a national non-fatal injury reporting system does not exist for the Fire Service. In order to tell the story of all injuries within a fire department, state, or on a national scale, we must utilize data that are available from multiple sources that do not naturally talk to each other. In this panel, we will describe the purpose of the project, its goals, and the success of its model to public health surveillance.

Objective

The purpose of this panel is to describe the process of using data to develop firefighter nonfatal injury surveillance systems in the city of Philadelphia and the state of Florida through the linkage of data from workers’ compensation, inpatient and emergency department hospitalizations, human resources, and continuing education/training registries.

Production animal health syndromic surveillance (PAHSyS) data are varied: there may be standardized ratios, proportions, counts of adverse events, categorical data and even qualitative ‘intelligence’ that may need to be aggregated up a hierarchy. PAHSyS provides some unique challenges for event detection. Livestock populations are made up of many subpopulations which are constantly moving around between farms and markets to slaughter. Pathogen expression often varies across production types and rearing-intensity levels. The complexity of animal production systems necessitates monitoring many time series; and makes the investigation of statistical signals imperative and at the same time difficult and resource intensive. Having multivariate surveillance methods that can work across multiple data streams to increase both sensitivity and specificity are much needed.

Objective

The question of how to aggregate animal health information derived from multiple data streams that vary in their specificity, scale, and behaviour is not trivial. Our view is that outbreak detection in a multivariate context should be viewed as a probabilistic prediction problem.

There is growing recognition that an inability to access timely health indicators can hamper both the design and the effective implementation of infectious diseases control interventions. In malaria control, the global use of standard interventions has driven down the burden of disease in many regions. Further gains in high transmission areas and elimination in lower transmission settings, however, will require an enhanced understanding of malaria epidemiology, population characteristics, and efficacy of clinical and public health programs at the local level. Currently, there is a dearth of information available to fine-tune malaria control interventions at the local level. A key obstacle is the fragmentation of data into silos, as existing data cannot be brought together to estimate accurate and timely health metrics.

Objective

Driven by the need to bring malaria surveillance data from different sources together to support evidence-based decision making, we are conducting the “Scalable Data Integration for Disease Surveillance” (SDIDS) project. This project aims to foster the integration of existing surveillance data to support evidence-based decision-making in malaria control and demonstrate a model applicable to other diseases. Central to this initiative is collaboration between academia, governmental and NGO sectors.

While HCV infections are associated with substantial morbidity and mortality in the United States, deaths due to HCV may not be detected well in Utah’s surveillance system. New interferon-free drugs for HCV can result in virologic cure with limited side effects, but treatment is expensive. It will therefore be increasingly important that public health accurately document the prevalence of HCV and outcomes, such as death, to inform policy makers and others who are responsible for allocating resources. A previous analysis conducted in Utah determined that a two-step methodology electronically linking death certificate data to HIV surveillance data was effective at ascertaining previously unreported deaths and cases in the HIVinfected population. Similarly, linkage to death certificate records may also provide an important avenue to identify deaths among the chronic HCV cases included in surveillance data and identify cases of HCV not previously reported to public health in Utah.

Objective

To evaluate the ascertainment of deaths among hepatitis C virus (HCV)-infected persons reported to public health and to identify additional HCV cases not reported to public health in Utah through review of death certificate data.

As interest in One Health (OH) continues to grow, alternative surveillance infrastructure may be needed to support it. Since most population health surveillance is domain specific; as opposed to OH which crosses multiple domains, changes to surveillance infrastructure may be required to optimize OH practice. For change to occur there must be a strong motivation that propagates from a perceived need. Since the purpose of surveillance is to produce information to support decision making, the motivation for change should relate to a lack of surveillance information needed to make OH decisions, or a gap in the surveillance infrastructure required to produce the information.

Objective

The primary purpose of this study was to explore the attitudes of surveillance stakeholders from different domains to:

-determine whether there is a perceived need for OHS

-identify significant surveillance gaps

-assess the motivation to change (fill the gaps)

A secondary purpose was to gather a group of surveillance stakeholders to identify and prioritize strategies to move One Health Surveillance forward.

From June 4-8, 2015, the New York City (NYC) syndromic surveillance system detected five one-day citywide signals in sales of over-the-counter (OTC) antidiarrheal medications using the CUSUM method with a 56-day moving baseline. The OTC system monitors sales of two classes of antidiarrheal medications, products with loperamide or bismuth, from two NYC pharmacy chains. To determine if this increase reflected a concerning cluster of diarrheal illness, we examined multiple communicable disease surveillance data systems.

Objective

To investigate a communicable disease syndromic surveillance signal using multiple data sources.