Data Sharing

The One Health approach suggests that humans, animals, and the environment are closely tied together. Human interaction with wildlife and the environment contributes to increased risk for human, plant, and animal infectious disease outbreaks. Since human, animal, and ecosystem health are linked, interdisciplinary and holistic approaches are needed to prevent future infectious disease outbreaks. Despite the movement towards One Health, the software currently available to manage, analyze, and communicate the vast amount of One Health data is grossly inadequate. One Health data are continually growing in size and complexity, and new technologies must be developed to address the magnitude of the problem. Furthermore, the desire of single entities to control and leverage information for greater personal and organizational wealth and power directly opposes the goals of biosurveillance, One Health, and science. Open access and open source software are needed to address these complex One Health problems, and to improve data accessibility, interoperability, and information communication.

Objective

Mantle will be an open-source, cloud-compatible platform for storing, studying, and sharing data on infectious diseases across plants, animals, and humans. It will meet the needs of three groups of users: scientists, policymakers, and the general public. For scientists, Mantle will make datasets portable and connected. Scientists will be able to upload datasets to the Mantle website or collect data from the field using a mobile app. Users in Mantle will be able to easily make datasets entirely private, publicly accessible, or shared with specific users or groups. 

NBIC integrates, analyzes, and shares national biosurveillance information provided from capabilities distributed across public and private sectors. The integration of information enables early warning and shared situational awareness of nationally significant biological events to inform critical decisions directing response and recovery efforts.

The 2014-2015 HPAI H5 outbreak in the U.S. was the largest HPAI outbreak in the country’s history and resulted in the culling of millions of domestic birds and significant economic losses through loss wages, direct production losses, cost of recovery, consumer price increases, and trade restrictions.

NBIC worked closely with liaisons from USDA/APHIS and DOI/ NWHC over the course of the outbreak to integrate information from both agencies and open source reporting into reports and data sets providing early and sustained shared situational awareness to over 1400 federal, state, and local authorities.

Objective

The National Biosurveillance Integration Center (NBIC) coordinated information sharing with the U.S. Department of Agriculture (USDA/APHIS) and the Department of Interior (DOI/ NWHC) to integrate information and provide shared situational awareness of the 2014-2015 Highly Pathogenic Avian Influenza (HPAI) outbreak in the U.S. across all levels of government.

Sharing public health (PH) data and practices among PH authorities enhances epidemiological capacities and expands situational awareness at multiple levels. Ease of data sharing through the BioSense application, now part of the National Syndromic Surveillance Program (NSSP), and the increased use of SyS nationwide have provided opportunities for region-level sharing of SyS data. In addition, there is a need to build workforce competence in SyS given powerful new information technology that can improve surveillance system capacities. Peer-to-peer learning builds the relationships and trust among individuals and organizations that are required for inter jurisdictional data sharing.

Objective

Promote interjurisdictional syndromic surveillance (SyS) data sharing practices with a training model that engages participants in collaborative learning.

The outbreaks of Severe Acute Respiratory Syndrome (SARS) in 2003, influenza A (H1N1) in 2009 and Ebola in 2014 have shown increasingly that infectious diseases can spread globally in a short timeframe, affecting both high- and low-income countries. Taking action to mitigate the impact of future crises relies on sharing public health surveillance data across national borders in an efficient and effective way. However, data users, particularly in high-income countries, often use surveillance data, particularly from low- and middle-income countries, with little or no benefit to the data generator. As Indonesia’s refusal to share influenza virus sequences during the 2006 H5N1 outbreak illustrates, this imbalance increases reluctance to share and jeopardizes the global good that can be achieved. In order to share public health surveillance data internationally in an equitable way, technical, political, ethical, and legal issues need to be addressed. The Centre on Global Health Security at Chatham House is producing guidance that will address both the policy and technical issues with the aim of establishing new norms so that data can be shared in an open, transparent and equitable way.

Objective

To address both the policy and technical issues of sharing public health surveillance data across national borders with the aim of establishing new norms so that data can be shared in an open, transparent and equitable way.

Flu Near You allows individuals to volunteer to be a sentinel node of the syndromic surveillance (SyS) network. The platform has the potential to provide insight into the spread of influenza-like illness (ILI). CDC’s ILINet is the gold standard for tracking ILI at the national level, but does not track into the local level. Local health departments (LHD) frequently express a need for granular data specific to their jurisdictions. FNY attempts to meet this need by collecting and sharing data at the zip code level. Knowing how well FNY data correlates to ILINet data will give local health departments an important tool to communicate the arrival of influenza to their jurisdiction. However, there is significant skepticism at the quality of FNY data as compared to validated datasets.

Objective

Our objective is to provide evidence for the data quality of Flu Near You (FNY) by evaluating the national and Houston datasets against CDC ILI data.

There are currently over 25 installations of ESSENCE across the US. Among these, there are 3 instances of multi-jurisdictional implementations. These include a centralized regional system in the National Capital Region for MD, DC, and VA, a Missouri system that includes hospitals and users from the St. Louis area in Illinois, and soon the National Syndrome Surveillance Program (NSSP) version of ESSENCE which will centralize data from many jurisdictions. While each of these systems provides valid ways to share data across jurisdictions, they require data to be sent to another jurisdiction. There are some jurisdictions which have legal or philosophical or technical issues with these types of data sharing arrangements. Programs like Distribute attempted to solve this by only sharing pre-aggregated data. This caused issues though for surveillance of new and emerging issues that requires a more ad-hoc query capability. This gap can be filled with a locally-ran system that has the ability to perform queries into remote systems and perform a federated query across other jurisdictions.

Objective

The objective of this presentation is to describe the new federated query capability in ESSENCE and describe how this could affect public health practice in the future. Specifically, this presentation will describe how a federated set of disease surveillance systems across the country could help improve national disease surveillance situational awareness along with its potential to connect non-ESSENCE systems in the future for even more complete coverage. It will also describe how this capability is different than other data sharing projects that attempt to centralize data, but how there is room for both to benefit from each other. 

The introduction of electronic health systems has led to easier collation, compilation, and analysis of data as well as easier access. For data to be put to be impactful use, it must be shared both for research and decision making purposes. Data sharing and release should neither compromise privacy nor lead to wrong conclusions. The need to share information that guides policies and decision making should be balanced with the need for the data to be reliable. The aim was to produce a data release policy to be used as a baseline tool to guide the practice of data release and sharing across programs and with outside requesters.

Objective

To describe the process of producing a universal data release policy for use by different programs in a state health department.