This paper describes a methodology for applying natural language parsing (NLP) technologies, originally developed for analyzing biomedical journal articles, to the monitoring of emergency department patient charts for infectious diseases of interest.
To highlight the key role of Emergency Department syn-dromic surveillance in linking acute care and public health, thus enabling collaborative detection, monitoring and management of a local food borne outbreak.
Presented June 27, 2018.
During this presentation, researchers discussed how Outbreak Observatory, a pilot project initiated by the Johns Hopkins Center for Health Security, facilitated the conduct of real-time operational research during outbreak responses, helping to improve outbreak preparedness and response capabilities.
Presenters
Jennifer Nuzzo, DrPH, Senior Scholar
Matthew Shearer, MPH, Research Associate
Diane Meyer, RN, MPH, Research Associate
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.
The International Health Regulations (IHR) 2005, provides a framework that supports efforts to improve global health security and requires that, member states develop and strengthen systems and capacity for disease surveillance and detection and response to public health threats. To contribute to this global agenda, an international collaborative comprising of personnel from the Health Protection Agency, West Midlands, United Kingdom (HPA); the Indian Institute of Public Health (IIPH), Hyderabad, Andhra Pradesh (AP) state, India and the Department of Community Medicine, Rajarajeswari Medical College and Hospital (RRMCH), Bangalore, Karnataka state, India was established with funding from the HPA Global Health Fund to deliver the objectives stated above.
Objective:
This project aimed to contribute to ongoing efforts to improve the capability and capacity to undertake disease surveillance and Emergency Preparedness and Response (EPR) activities in India. The main outcome measure was to empower a cadre of trainers through the inter-related streams of training & education to enhance knowledge and skills and the development of collaborative networks in the regions.
Fusion Analytics is the data integration system developed by the Fusion Cell at the U.S. Department of Health and Human Services (HHS), Office of the Assistant Secretary for Preparedness and Response (ASPR). Fusion Analytics meaningfully augments traditional public and population health surveillance reporting by providing webbased data analysis and visualization tools.
Objective
The objective of this demonstration is to show conference attendees how they can integrate, analyze, and visualize diverse data type data from across a variety of systems by leveraging an off-the-shelf enterprise business intelligence (EBI) solution to support decisionmaking in disasters.
During the 2017 Houston floods Lauren Leining worked with the the American Red Cross to visit each disaster victim in a shelter to do bed evaluations, but learned it was a very common thing for people to refuse treatment for a variety of reasons. Many people didn’t want to walk to where the assessments were going on because it was often on one end of a giant convention center. Sometimes they just didn’t feel well enough – for example, they were in pain or their ankle hurt.
In late summer 2017, the United States endured two severe hurricanes back to back. On August 25, 2017, Hurricane Harvey made landfall in Texas and southwest Louisiana, dumping more than 19 trillion gallons of rain. On September 10, 2017, 20 days later, Hurricane Irma landed in Florida, leading residents across the Florida peninsula to evacuate inland and out of the path of the storm. Although Tennessee was far from the eye of the storms, state health officials knew residents from both states could choose to shelter in Tennessee.
Mass gatherings (e.g., concerts, festivals, sporting events, political rallies, and religious gatherings) pose unique challenges to public health officials. Risks associated with large events can vary and are influenced by factors such as crowd size and age (range, or average/mean), weather, event type and purpose, and use of alcohol or drugs. Often, the risk of injury increases. And not only do people in large crowds spread disease through close contact during an event, they can transport the disease when they leave. Healthcare resources can therefore be overwhelmed.
Hurricane Harvey made landfall along the Texas coast on August 25th, 2017 as a Category 4 storm. It is estimated that the ensuing rainfall caused record flooding of at least 18 inches in 70% of Harris County. Over 30,000 residents were displaced and 50 deaths occurred due to the devastation. At least 53 temporary refuge shelters opened in various parts of Harris County to accommodate displaced residents. On the evening of August 29th, Harris County and community partners set up a 10,000 bed mega-shelter at NRG Center, in efforts to centralize refuge efforts. Harris County Public Health (HCPH) was responsible for round-the-clock surveillance to monitor resident health status and prevent communicable disease outbreaks within the mega-shelter. This was accomplished through direct and indirect resident health assessments, along with coordinated prevention and disease control efforts. Despite HCPHâs 20-day active response, and identification of two relatively small but potentially worrisome communicable disease outbreaks, no large-scale disease outbreaks occurred within the NRG Center mega-shelter.
Objective:
1) Describe HCPHâs disease surveillance and prevention activities within the NRG Center mega-shelter;
2) Present surveillance findings with an emphasis on sharing tools that were developed and may be utilized for future disaster response efforts;
3) Discuss successes achieved, challenges encountered, and lessons learned from this emergency response.