Data Integration

New York State Department of Health (NYSDOH) implemented a Communicable Disease Electronic Surveillance System (CDESS), a single and secure application used by 57 local health departments (LHDs), hospital infection control programs and NYSDOH staff to collect, integrate, analyze, and report data for infectious disease surveillance. New York State Immunization Information System (NYSIIS) is a mandated application for providers to report all vaccinations of persons < 19 years old residing in New York State (excluding New York City). Currently, LHD staff must manually search NYSIIS for vaccine preventable disease case investigations and re-enter the immunization histories into CDESS. NYSIIS has built a HL7 query functionality which can be used to automate the data exchange between NYSIIS and CDESS.

Objective

To establish the infrastructure to provide a linkage between the immunization registry and disease surveillance system using standard for data exchange.

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.

Champaign County is one of the largest counties in central Illinois with a population of ~207,000 and is home to the University of Illinois at Urbana-Champaign which currently has 44,500 students. In the fall the university hosts Big Ten football games which have recently been drawing an average attendance of ~45,000 people, many traveling from Chicago or other parts of the Midwest. The twin cities host a number of community events and festivals throughout the Spring and Summer. Typically the community festivals have liquor licenses whereas no alcohol is served in the football stadium. Despite the lack of alcohol availability in the stadium many fans drink during tailgate parties before and after the game.

Objective

The objective for this research project was to see if there are predictable patterns for certain annual events in Champaign County, Illinois. The focus was on how alcohol intoxication effected the population and whether or not its effects were dangerous to the community at an alarming rate.

In 2012, half of all adults in the US had one or more chronic health conditions; at least 25% had two or more chronic health conditions. Seven of the top ten causes of death in 2010 were chronic diseases; two of the seven chronic diseases, heart disease and cancer, account almost for over 50% of all deaths. Chronic disease is one of the most costly contributors in healthcare expenditures; once diagnosed many patients must be followed for a lifetime. In lower-income countries chronic disease is now the biggest contributor to mortality. Socioeconomic inequalities are a major driver of the chronic disease epidemic. Chronic disease in the US, such as cancer, heart disease, renal end stage disease and diabetes are tracked in national datasets but are not linked. Chronic diseases share many risk factors, major risk factors, e.g. tobacco, diet, alcohol, and physical inactivity are already known, their interactions with comorbidities are important and clinical practice indicates that the chronic disease epidemic may be addressed more effectively using a holistic approach. However, this approach has not yet been implemented in disease surveillance activities as data collection is still disease specific. Data collection is still one disease at a time, without connecting our disease surveillance efforts to get better, more complete and encompassing data. Health inequities result in lower quality of healthcare, worse healthcare outcomes for minority racial/ ethnic populations and people with low socioeconomic status, increased direct and indirect healthcare costs, and decreased productivity.

Objective

Utilize existing data sets and data sources to address health equity and improve the management of chronic disease

Georgia Department of Public Health (DPH) epidemiologists have responded to multiple emergent outbreaks with diverse surveillance needs. During the 2009 H1N1 influenza response, it was necessary to electronically integrate multiple reporting sources and view population-level data, while during the 2014–2015 West African Ebola epidemic, it was necessary to easily collect and view individual level data from travelers to facilitate early detection of potential imported Ebola disease. DPH in-house information technology (IT) staff work closely with epidemiologists to understand and accommodate surveillance needs. Through this collaboration, IT created a robust electronic surveillance and outbreak management system (OMS) to accommodate routine reporting of notifiable diseases and outbreak investigations, and surveillance during emergent events.

Objective

To describe how flexible surveillance systems can be rapidly adapted and deployed, and increase the efficiency and accuracy of surveillance, during responses to outbreaks and all hazard emergent events.

ARIs have epidemic and pandemic potential. Prediction of presence of ARIs from individual signs and symptoms in existing studies have been based on clinically-sourced data. Clinical data generally represents the most severe cases, and those from locations with access to healthcare institutions. Thus, the viral information that comes from clinical sampling is insufficient to either capture disease incidence in general populations or its predictability from symptoms. Participatory data — information that individuals today can produce on their own — enabled by the ubiquity of digital tools, can help fill this gap by providing self-reported data from the community. Internet-based participatory efforts such as Flu Near You have augmented existing ARI surveillance through early and widespread detection of outbreaks and public health trends.

Objective

To evaluate prediction of laboratory diagnosis of acute respiratory infection (ARI) from participatory data using machine learning models

EpiCore draws on the knowledge of a global community of human, animal, and environmental health professionals to verify information on disease outbreaks in their geographic regions. By using innovative surveillance techniques and crowdsourcing these experts, EpiCore enables faster global outbreak detection, verification, and reporting

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.

Oregon Public Health Division (OPHD), in collaboration with the Johns Hopkins University Applied Physics Laboratory, implemented Oregon ESSENCE in 2012. Oregon ESSENCE is an automated, electronic syndromic surveillance system that captures emergency department data. To strengthen the capabilities of Oregon ESSENCE, OPHD sought other sources of health-outcome information, including Oregon Poison Center (OPC). In the past, Oregon’s surveillance staff manually monitored OPC data on the National Poison Data Service (NPDS) website. Although functional, it was not integrated into Oregon’s syndromic surveillance system and required epidemiologists to assess alerts on individual calls. To achieve data integration, OPHD pursued an automated solution to deliver OPC data into Oregon ESSENCE. OPHD’s growing interoperability infrastructure fostered development of a low-cost, reliable solution to automate the integration of these data sources. 

Objective

To enhance Oregon ESSENCE’s surveillance capabilities by incorporating data from the Oregon Poison Center using limited resources. 

Healthcare data, including emergency department (ED) and outpatient health visit data, are potentially useful to the public health community for multiple purposes, including programmatic and surveillance activities. These data are collected through several mechanisms, including administrative data sources [e.g., MarketScan claims data1; American Hospital Association (AHA) data2] andpublic health surveillance programs [e.g., the National Syndromic Surveillance Program (NSSP)3]. Administrative data typically become available months to years after healthcare encounters; however, data collected through NSSP provide near real time information not otherwise available to public health. To date, 46 state and 16 local health departments participate in NSSP, and the estimated nationalp ercentage of ED visits covered by the NSSP BioSense platform is 54%. NSSP’s new data visualization tool, ESSENCE, also includes additional types of healthcare visit (e.g., urgent care) data. Although NSSP is designed to support situational awareness and emergency response, potential expanded use of data collected through NSSP (i.e., by additional public health programs) would promote the utility, value, and long-term sustainability of NSSP and enhance surveillance at the local, state, regional, and national levels. On the other hand, studies using administrative data may help public health programs better understand how NSSP data could enhance their surveillance activities. Such studies could also inform the collection and utilizationof data reported to NSSP.

Objective

This roundtable will address how multiple data sources, including administrative and syndromic surveillance data, can enhance public health surveillance activities at the local, state, regional, and national levels. Provisional findings from three studies will be presented to promote discussion about the complementary uses, strengths and limitations, and value of these data sources to address public health priorities and surveillance strategies.