Data Analytics

The catchment area of a health-care facility is used to assess health service utilization and calculate population-based rates of disease. Current approaches for catchment definition have significant limitations such as being based solely on distance from the facility or using an arbitrary threshold for inclusion.

Objective

We propose a simple statistical method, the cumulative case ratio, for defining a catchment area using surveillance data.

Major depressive disorder has a lifetime prevalence of 16.6% in the United States. Social media platforms – e.g. Twitter, Facebook, Reddit – are potential resources for better understanding and monitoring population-level mental health status over time. Based on DSM-5 diagnostic criteria, our research aims to develop a natural language processing-based system for monitoring major depressive disorder at the population-level using public social media data.

Objective

We aim to develop an annotation scheme and corpus of depression-related tweets to serve as a test-bed for the development of natural language processing algorithms capable of automatically identifying depression-related symptoms from Twitter feeds.

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.

 

Public health departments need enhanced surveillance tools for population monitoring, and external researchers have expertise and methods to provide these tools. However, collaboration with potential solution developers and students in academia, industry, and government has not been sufficiently close or well informed for rapid progress. Many peer-reviewed papers on biosurveillance methods have been published by researchers, but few methods have been adopted in systems used by health departments. In a 2013 BioSense User Group survey with responses from users in more than 40 U.S. states, access to improved analytic methods was a top priority. Among the tools most desired by respondents were the ESSENCE biosurveillance system with multiple analytic tools and statistical software packages such as SAS. Multiple obstacles have slowed the progress of practitioners and developers who seek the development and implementation of useful analytic tools. First, the epidemiological challenges and associated operational constraints are not sufficiently understood among academic developers. Many health departments do not have the resources to hire such developers beyond maintenance of information technology, and the health monitors are typically too busy to publish in peer-reviewed journals. Second, data cannot be shared because of privacy and proprietary limitations with varying local rules. Data-sharing has posed difficult administrative problems, both within and external to health departments, in the course of ISDS Technical Conventions committee efforts to promote interactions through use case problems. Third, aspects of situational awareness vary widely among health monitors at different jurisdictional levels, so analytical challenges and constraints vary widely among potential users. Practitioners have pointed out that “surveillance is local”, but local operational and data environments vary widely. A fourth main issue is cross-cultural: Understaffed health departments must respond to successive crises and often lack the time for requirements analysis and technical publication. Such client work situations complicate interaction with academic environments of semester schedules and limited grants and transient student support. This panel brings together academic statisticians who have had successful direct relationships with public health departments to discuss how they have dealt with these challenges.

Objective

The session will explore past collaborations between the scientist panelists and public health departments to highlight approaches that have and have not been effective and to recommend effective, sustainable relationship strategies for the mutual advancement of practical disease surveillance and relevant academic research.