Panel examines the role of data in public and environmental health

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Panelists and audience members participate in the South Big Data Hub roundtable on data analytics in environmental health both remotely and in person.

The South Big Data Hub Roundtable held on January 11 in Chapel Hill, NC, provided an open discussion forum with a focus on Translational Data Analytics for Environmental Health.

Ashok Krishnamurthy, PhD, moderated the discussion, which included panelists Andy May, PhD, an assistant professor in the department of civil, environmental, and geodetic engineering at The Ohio State University, Ayaz Hyder, PhD, assistant professor in the Division of Environmental Health Sciences at Ohio State’s College of Public Health, David Peden, PhD,  a distinguished professor at the UNC-Chapel Hill School of Medicine, and Paul Kizakevich, PhD, a senior research engineer in the bioinformatics program at RTI International.

May and Hyder talked about their projects funded by the National Science Foundation to establish a collaborative partnership with high schools in Ohio to utilize low-cost air quality sensors to monitor traffic-related air pollution. They also expressed their interest in working with the high school students and to analyze this data as a hands-on learning experience that will teach them about the importance of data analytics and how data can be used to understand health issues and improve public health. Hyder spoke about partnering with hospitals, health care providers, and local public health departments to deploy these sensors around their neighborhoods to get a better idea of environmental health disparities in very localized populations.

Peden addressed the need for data analytics to provide snapshots of a patient’s health data in real time at the point of care. For example, personal monitoring can be an extremely effective way to calculate a person’s activity level while also taking into account where the person is at any given time, their housing, and various health factors and personal habits. Spatial forecasting models—similar to the predictive models used in weather forecasting—were noted as an efficient tool to predict health events and to adjust patient treatment strategies to avoid health crises.

One such model is Kizakevich’s Personal Health Informatics Toolkit, which is a comprehensive data collection platform used to collect data and derive health information in real time so that doctors and patients are equipped with useful information for health or environmental interventions. The toolkit has mostly been used with the military, providing assessments, center-based data, and activities aimed at reducing symptoms of Post Traumatic Stress Disorder, such as providing PTSD sufferers with self-relaxation tools. The same platform, he said, could be used to collect environmental data and provide real-time feedback about environmental conditions and health conditions that might be influenced by the environment.

One of the concerns discussed by the panelists was duplication in sensor technology. Clinicians, medical researchers, scientists and environmentalists all want environmental health data, but if everyone builds sensors, efforts are duplicated. A collaborative funding model is one solution to this challenge. The panelists also agreed that empowering communities to learn about their environmental exposures through community outreach and engagement should be encouraged.

The discussion concluded with the speakers proposing that data analytics related to the environment shift from focusing on regulation to analytics that can provide insight into public health issues and progress in environmental health research.

-Deepti Kumra, data science intern, RENCI/UNC-Chapel Hill

 

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