Informatics

The NNDSS is a nationwide collaboration that enables all levels of public health (local, state, territorial, federal and international) to monitor, control, and prevent the occurrence and spread of state-reportable and nationally notifiable diseases and conditions. The NNDSS data are a critical source of information for monitoring disease trends, effectiveness of prevention and control programs, and policy development. To provide timely NNDSS data, state and territorial health departments voluntarily report notifiable disease incidence data to CDC when they become aware of these cases and as per recommended national notification timeframes. These provisional data are published each week in Morbidity and Mortality Weekly Report (MMWR). Great strides have been made exploring and exploiting new and different sources of disease surveillance data and developing robust statistical methods for analyzing the collected data. However, there have been fewer efforts in the area of online dissemination of surveillance data. Appropriate dissemination of surveillance data is important to maximize the utility of collected data.

Objective

The purpose of this study was to identify ideas for an enhanced dissemination of the US National Notifiable Diseases Surveillance System (NNDSS) provisional data.

Typical approaches to monitoring ED data classify cases into pre-defined syndromes and then monitor syndrome counts for anomalies. However, syndromes cannot be created to identify every possible cluster of cases of relevance to public health. To address this limitation, NC DETECT’s approach clusters cases by arrival times and monitors the textual chief complaint data associated with each identified cluster for relevant similarities [1]. This approach is time consuming and limited in its ability to detect emerging outbreaks that are dispersed across time. A new method is needed to automatically identify clusters of interest that would not be detected by existing syndromes. Clusters may be based on symptoms, events, place names, arrival time, or hospital location. The NC DPH dataset describes 198,511 de-identified ED visits over one year at 3 North Carolina hospitals. The data include chief complaint, altered date and time of arrival, hospital A/B/C, and age group. About 40 simulated outbreaks were injected into the data set by the NC DETECT team. For example, an inject cluster might consist of 4 patients who report getting sick after eating at a particular restaurant.

Objective

We apply a novel semantic scan statistic approach to solve a problem posed by the NC DETECT team, North Carolina Division of Public Health (NC DPH) and UNC Department of Emergency Medicine Carolina Center for Health Informatics, and facilitated by the ISDS Technical Conventions Committee. This use case identifies a need for methodology that detects emerging, potentially novel outbreaks in free-text emergency department (ED) chief complaint data.

 

Since their introduction to the US market in 2007, electronic cigarettes (e-cigarettes) have posed considerable challenges to both public health authorities and government regulators, especially given the debate – in both the scientific world and the community at large – regarding the potential advantages (e.g. helping individuals quit smoking) and disadvantages (e.g. renormalizing smoking) associated with the product1. Similarly, hookah – a kind of waterpipe used to smoke flavored tobacco – has increased in popularity in recent years, is known to be particularly popular among younger people, and has prompted a range of regulatory responses2. One important – and currently largely unexplored – area of research involves exploring consumer perceptions and experiences of these emerging tobacco products. In this work, we use online health discussion forums in conjunction with text mining and novel data visualization techniques to investigate consumer perceptions and experiences of e-cigarettes and hookah, focusing on the automatic identification of symptoms associated with each product, and consumer motivations for product use. Previous related research has focused on using text-mining to analyze e-cigarette or hookah related Twitter posts3,4 and on the qualitative identification of e-cigarette related symptoms from online discussion forums5. The research reported in this abstract is – to the best of our knowledge – the first time that text mining techniques have been used with online health forums to understand e-cigarette or hookah use.

Objective

Our aim in this work is to apply text mining and novel visualization techniques to textual data derived from online health discussion forums in order to better understand consumers’ experiences and perceptions of electronic cigarettes and hookah.

 

Public health surveillance guides efforts to detect and monitor disease and injuries, assess the impact of interventions and assist in the management of and recovery from large-scale public health incidents. Today’s ever-present, media-hungry environment pressures public health scientists, researchers and frontline practitioners to provide information, on an almost instantaneous basis, responsive to public and policy maker concerns about specific geographies and specific populations. Actions informed by surveillance information take many forms, such as policy changes, new program interventions, public communications and investments in research. Local, state and federal public health professionals, government leaders, public health partners and the public are dependent on high quality, timely and actionable public health surveillance data. With a charge from the CDC Director, this Surveillance Strategy aims to improve CDC’s overall surveillance capabilities, and by extension those of the public health system at large. The Strategy guides efforts to make essential surveillance systems more adaptable to the rapidly changing technology landscape, more versatile in meeting demands for expanding knowledge about evolving threats to health, and more able to meet the demands for timely and populationspecific and geographically specific surveillance information. The Strategy will also facilitate work to consolidate systems, eliminate unnecessary redundancies in reporting, and reduce reporting burden. These expectations compel this strategy and argue for CDC to lead the public health system in improving the timeliness and availability, as well as the quality and specificity of surveillance data to CDC programs, STLT agencies, and other stakeholders.

Objective

This presentation aims to share the CDC Surveillance Strategy’s goals, initiatives and activities. The surveillance strategy describes how CDC will: 

• enhance accountability, resource use, workforce and innovation for surveillance by establishing a Surveillance Leadership Board, a surveillance workforce plan, and an innovation consortium;
•  accelerate the utilization of emerging tools and approaches to improve the availability, quality, and timeliness of surveillance data by establishing enhanced HIT policy engagement, HIT vendor forums, and informatics innovation projects; and 
• initiate four cross cutting surveillance system initiatives to improve surveillance by addressing data availability, system usability, redundancies, and incorporation of new information technologies

A variety of big data analytics, techniques and tools including social media analytics, open source visualizations, statistical anomaly detection, use of Application Programming Interfaces (APIs), and geospatial mapping, are used for infectious disease biosurveillance. Using these methodologies, policy makers and practitioners detect and monitor outbreaks across the world near real time, in multiple languages, 24/7. The non-infectious disease community, namely critical care, injury, and trauma stakeholders, currently lack this level of sophistication. To respond to most MCIs like a terrorist bombing, validated, real-time information is typically available via closed radio channels and limited to a specific set of emergency responders. Health care workers, policy makers, and citizens reach for news, radio, and Internet sources to characterize casualties and hazards, and increasingly social media. During the Boston Marathon bombing, witnesses began posting tweets seconds after the bombing and 15 seconds before CNN reported the incident. Current trauma data sets are unhelpful for real time response, including trauma registries that are used for hospital performance after an incident, and disaster databases consist of secondary reporting used for academic research purposes.

Objective

Discuss how different big-data analytics, techniques, and tools including open source platforms, cloud analytics, social media, crowdsourcing, and geospatial visualization can be used to quickly achieve situational awareness within seconds of a MCI, for use by pre-hospital responders, healthcare workers, and policy makers.

 

Whole-genome sequencing of disease-causing organisms provides an unabridged examination of the genetic content of individual pathogen isolates, enabling public health laboratories to benefit from comparative analyses of total genetic content. Combining this information with sample metadata such as temporal, geospatial, morbidity, and mortality can greatly increase the efficacy of genomics analysis. However, with the vast amount of data generated by such techniques, meaningful, rapid, and accurate analysis that interprets and correlates nucleotide polymorphisms for public health practice presents many challenges. To this end we have created a modular genomics analysis toolkit that can easily integrate diverse data streams and couple analysis with an array of visualization platforms.

Objective

To develop a modular approach to infectious disease genomic analysis that can easily integrate with public health analytics systems. Using dynamic approaches to genomic sequence analysis, relevant whole genome data can be quickly and accurately visualized and correlated, using a minimum of computational resources. We propose to develop visualization modules that integrate disparate data sources including integrate geospatial location metadata with associated epidemiological factors to enable faster outbreak identification and enhance surveillance.

Clinical data captured in electronic health records (EHR) for patient health care could be used for chronic disease surveillance, helping to inform and prioritize interventions at a state or community level. While there has been significant progress in the collection of clinical information such as immunizations for public health purposes, greater attention could be paid to the collection of data on chronic illness. Obesity is a chronic disease that affects over a third of the US adult population1 , making it an important public health concern. Both HL7 v.2.5.12 and Clinical Document Architecture (CDA) messages3 can be used to facilitate the collection of HW EHR data. These standards include anthropometric and demographic information along with the option to transmit behavioral, continuity of care, community resource identification and care plan information. We worked with vendors participating in the Integrating the Healthcare Enterprise initiative (IHE) in developing, testing and showcasing scenarios to facilitate system development, increase the visibility of HW standards and demonstrate potential usages of obesity-related information.

Objective

To demonstrate the feasibility of using healthy weight (HW) IT standards in public health surveillance through the collection and visualization of patient height, weight and behavioral data.

The advent of Meaningful Use (MU) has allowed for the expansion of data collected at the hospital level and received by public health for syndromic surveillance. The triage note, a free text expansion on the chief complaint, is one of the many variables that are becoming commonplace in syndromic surveillance data feeds. Triage notes are readily available in many ED information systems, including, but not limited to, Allscripts, Cerner, EPIC, HMS, MedHost, Meditech, and T-System. North Carolina’s syndromic surveillance system, NC DETECT, currently collects triage notes from 33 out of 122 hospitals in the State (27%), and this number is likely to increase.

Objective

This roundtable will provide a forum for the ISDS community to discuss the use of emergency department (ED) triage notes in syndromic surveillance. It will be an opportunity to discuss both the benefits of having this variable included in syndromic surveillance data feeds, as well as the drawbacks and challenges associated with working with such a detailed data field.

Health care reform and the use of electronic health record systems is dramatically changing the health care landscape creating both challenges and opportunities for public health. High adoption of health information technology among Minnesota’s health care providers has created an opportunity to advance e-health by collecting and using these data to improve population health. It has been demonstrated that interoperable clinical data repositories can serve surveillance needs to support both public health and clinical care. Additionally, health reform is fostering the need for the collection of data to manage population health, compare and share data locally and across states for care coordination, and monitor cohorts and attributed populations. This project will provide a critical understanding of the status, challenges, and opportunities for leveraging the substantial investment in health care data systems to better support public health prevention programs, epidemiology, and surveillance to improve population health, address health disparities, and advance health equity.

Objective

This project describes the informatics characteristics of clinical data repositories among Minnesota health systems and their opportunities and readiness to support public health practice. The focus of the study is the use of these data for public health prevention programs and surveillance, including the opportunities to address health disparities. We examine technical, organization, and process readiness of repositories in support of epidemiology and other key public health programs, and how these data can be used as a statewide public health resource. 

Statutory veterinary disease surveillance generally focuses on food animals with only minimal resources committed to companion animals. However, the close contact between owners and pets suggests that disease surveillance in these species could benefit both animal and human health.

Following a successful pilot, SAVSNET Ltd. was set up as a joint venture between the University of Liverpool (UoL) and the British Small Animal Veterinary Association (BSAVA) to deliver companion animal health data for research and surveillance. SAVSNET consists of two projects: the first collates results from commercial diagnostic laboratories whilst the second collects data from enrolled veterinary practices for consultations where owners have provided consent by opt-out. Both projects have been approved by the UoL’s Research Ethics Committee and the aims are supported by the Royal College of Veterinary Surgeons (RCVS), the UK’s regulatory body for the veterinary profession.

Applications to use the data are encouraged and are assessed by a panel consisting of BSAVA, UoL and independent members. Data access attracts a nominal fee that is used for long-term sustainability. Currently, SAVSNET data is being used for a wide range of projects by academic collaborators, PhD researchers, undergraduate students and commercial companies.

Objective

SAVSNET—the Small Animal Veterinary Surveillance Network—collects and collates real-time data from veterinary diagnostic laboratories and veterinary practices across the UK to support research and disease surveillance in companion animals.