Data Sharing

Electronic data that could be used for global health surveillance are fragmented across diseases, organizations, and countries. This fragmentation frustrates efforts to analyze data and limits the amount of information available to guide disease control actions. In fields such as biology, semantic or knowledge-based methods are used extensively to integrate a wide range of electronically available data sources, thereby rapidly accelerating the pace of data analysis. Recognizing the potential of these semantic methods for global health surveillance, we have developed the Scalable Data Integration for Disease Surveillance (SDIDS) software platform. SDIDS is a knowledge-based system designed to enable the integration and analysis of data across multiple scales to support global health decision-making. A ‘proof of concept’ version of SDIDS is currently focused on data sources related to malaria surveillance in Uganda.

Objective

To develop a scalable software platform for integrating existing global health surveillance data and to implement the platform for malaria surveillance in Uganda.

The Joint Incentive Fund (JIF) Authorization creates innovative DoD/VA sharing initiatives. In 2009, DoD and VA commenced a biosurveillance JIF project whose principle objectives include improved situational awareness of combined VA/ DoD populations 1 and determining the optimal business model allowing both agency biosurveillance programs to operate more efficiently by: 1) consolidating information technology assets; 2) targeting enhanced collaboration for improved public health outcomes; and 3) improving buying power, and return on investment. We analyzed various interoperability models aimed at biosurveillance data sharing, asset consolidation and enhanced collaboration. Potential end states to be evaluated include maintaining separate Departmental systems, bidirectional exchange of data to separately managed systems, consolidation of data within one Department and shared access to a common system, consolidation of data in a neutral repository accessed by separately run legacy systems, or a custom developed biosurveillance solution utilizing a common data repository.

Objective

Determine an optimal course of action for achieving a more mission and cost-effective model for implementing combined or collaborative biosurveillance across the Departments of Veterans Affairs (VA) and Defense (DoD).

The DoD provides daily outpatient and emergency room data feeds to the BioSense Platform within NSSP, maintained by the Centers for Disease Control and Prevention. This data includes demographic characteristics and diagnosis codes for health encounter visits of Military Health System beneficiaries, including active duty, active duty family members, retirees, and retiree family members. NSSP functions through collaboration with local, state, and federal public health partners utilizing the BioSense Platform, an electronic health information system.

Objective

The Department of Defense data is available to National Syndromic Surveillance Program (NSSP) users to conduct syndromic surveillance. This report summarizes the demographic characteristics of DoD health encounter visits.

Most countries do not report national notifiable disease data in a machine-readable format. Data are often in the form of a file that contains text, tables and graphs summarizing weekly or monthly disease counts. This presents a problem when information is needed for more data intensive approaches to epidemiology, biosurveillance and public health as exemplified by the Biosurveillance Ecosystem (BSVE). While most nations do likely store their data in a machine-readable format, the governments are often hesitant to share data openly for a variety of reasons that include technical, political, economic, and motivational issues. For example, an attempt by LANL to obtain a weekly version of openly available monthly data, reported by the Australian government, resulted in an onerous bureaucratic reply. The obstacles to obtaining data included: paperwork to request data from each of the Australian states and territories, a long delay to obtain data (up to 3 months) and extensive limitations on the data’s use that prohibit collaboration and sharing. This type of experience when attempting to contact public health departments or ministries of health for data is not uncommon. A survey conducted by LANL of notifiable disease data reporting in 52 countries identified only 10 as being machine-readable and 42 being reported in pdf files on a regular basis. Within the 42 nations that report in pdf files, 32 report in a structured, tabular format and 10 in a non-structured way. As a result, LANL has developed a tool-Epi Archive (formerly known as EPIC)-to automatically and continuously collect global notifiable disease data and make it readily accesible.

Objective

LANL has built a software program that automatically collects global notifiable disease data—particularly data stored in files—and makes it available and shareable within the Biosurveillance Ecosystem (BSVE) as a new data source. This will improve the prediction and early warning of disease events and other applications.

Under the CDC STD Surveillance Network (SSuN) Part B grant, WA DOH is testing electronic case reporting (eCR) of sexually transmitted infections (STI) from a clinical partner.

Objective

We reviewed CCDs (a type of consolidated clinical data architecture (C-CDA) document) shared by our clinical partner, Planned Parenthood of the Great Northwest and Hawaiian Islands (PPGNHI) since October, 2015. Analyses focuses on:

-Completeness

-Degree to which the CCD matches program area information needs

-Differences in EHR generation methods

-Presence and location of triggers (based on the Reportable Conditions Trigger Codes) that would initiate CCD generation.

A variety of government reports have cited challenges in coordinating national biosurveillance efforts at strategic and tactical levels. The General Accountability Office (GAO), an independent nonpartisan agency that investigates how the federal government funding and performs analysis at the request of congressional committees or by public mandate, has published 64 reports on biosurveillance since 2005. The aim of this project is to better characterize these issues by collecting and analyzing a sample of publicly documented biosurveillance systems, and making our data and results available for the public health community to review and evaluate. This study openly publishes the data files of information collected (i.e. CSV, XLS), the Python NLP scripts, and a freely available web-based application developed in R Shiny that filters against the 227 biosurveillance systems and activities to promote a more transparent understanding of how public health practitioners conduct surveillance activities.

Objective

The objective of this project is to advance the science of biosurveillance by providing a user curated cataloging system, to be used across health department and other users, that advances daily surveillance operations by better characterizing three key issues in available surveillance systems: duplication in biosurveillance activities; differing perspectives and analyses of the same data; and inadequate information sharing.