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Seminar Series

Presenter:

Nishant Jain

Date:

12-05-2019

Time:

3:30pm-4:30pm

Location:

Leadership Auditorium

Evaluation of chronic disease education & health information quality using online social networks & communities

Chronic diseases such as diabetes, cancer and mental illness are the leading causes of morbidity and disability. The total cost in the United States was $327 billion for diagnosed diabetes, 80.2 billion for cancer and $193.2 billion for serious mental illness. Chronic diseases rely a great deal on patient education and self-management and social media is an important tool for information dissemination in that regard.  Diabetes, cancer and mental illness are among the top 10 searched diseases on social media, which is among the newly emerging Consumer to Consumer (C2C) tools. For instance, Twitter is increasingly becoming a space for online conversations about chronic diseases, including healthy behaviors, drugs & treatments. This study hypothesizes that public perception about health topics can be influenced by the quality of social networks around a topic. A combination of methods will be used to analyze online C2C channels such as social media and publicly available health information resources. We will use various search themes based on medical topics including but not limited to emerging behavioral guidelines and standard terminology for chronic diseases. The primary goal is to visualize the nature and shape of communities and find cues about information sharing behaviors among online chronic disease communities. The hope is that this information can be exploited for educational, promotional or interventional strategies and policies. The eventual goal is to develop an informatics framework to systematically determine the quality and reliability of health information on specific medical topics.

MUIDSI Comprehensive Exam

Presenter:

Tim Haithcoat

Date:

11-08-2019

Time:

3:15PM-4:15PM

Location:

240 Naka Hall

Geospatial Context Big Table (GeoCBT): Facilitating Geospatial Analysis in Health Research

A consistent finding across health literature is that location-matters. Cancer incidence varies across scales from blocks to neighborhoods to regions. As well, a complex myriad of factors that can affect health outcomes also exists. In rural contexts, aging populations, health care access, sparse populations, environmental exposures, and infrastructure are components. In urban contexts, food-deserts, stress, and pollution (air, water, light, and noise) play possible roles. What is the interaction of all these factors? At what scale(s) is the context and association important? The collection, integration, and use of varied data are foundational to health research. However, time is wasted and effort duplicated by compiling, re-formatting, and integrating the same public sources of information at various geographic levels. In this intervening time, diseases continue to flourish and lives are potentially lost.
Geographic context is an integral component of health research. It is paramount to understand the nature of the ‘environment’ in which individuals are located in order to explore the ways that race, ethnicity, accessibility, contaminants, or other contextual characteristics affect disease incidence and outcomes. This project focuses on benefits of developing a Geospatial Context Big Table (GeoCBT) with 318 million systematic locations (rows), each with a myriad of attributes compiled from public data sources across multiple scales, geographies, and times in a queriable spatial context. There are potentially tens of thousands of attributes (columns) containing functional sociodemographic, environmental, infrastructure, cultural, economic, as well as geospatially derived data (isolation, accessibility, etc.) to provide richer context.
The ability to integrate health research data and information with spatially enabled big data within a common framework has the potential to transform health research. The GeoCBT can catalyze complex health research, broaden geospatial data use and analytics, and enable more cost-effective research.Tim Haith