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APPLICATION OF VISUAL ANALYTIC TOOLS TO EVALUATE COMPLEX RELATIONSHIPS BETWEEN ENVIRONMENTAL FACTORS AND HEALTH OUTCOMES
Contact
email:
CCTE@epa.gov
Impact/Purpose:
Improving Linkages in the Source-to-Outcome Paradigm - Advanced Computational Toxicology Approaches to Improve Cumulative Risk Predictions
Description:
Characterizing cumulative risk and understanding the complex relationships between environmental exposures and human health outcomes requires collection and analysis of a wide range of data. Information on the characteristics of multiple stressors (chemical, physical, biological and psychosocial), the characteristics of the human receptor (genetics, health status, life stage, behaviors, social factors, etc.) at multiple levels of organization (individual, community, population), and the temporal and spatial patterns of exposures and outcomes must be combined to assess cumulative risk.
Current approaches for designing studies and evaluating collected data tend to focus on a limited number of environmental factors and/or measures of outcome. As such, it is difficult to understand the potential impacts of the full range of factors on environmental health. More holistic approaches for interrogating this multidimensional data space are required to identify potentially important relationships for further study and analysis. Application of emerging computational tools will allow us to optimize utility of collected data, improve understanding of complex exposure-outcome systems, and improve risk assessment.
Record Details:
Record Type:PROJECT
Projected Completion Date:09/30/2008
OMB Category:Other
Record ID:
149124
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Project Information:
Progress :To date some preliminary (and very conceptual) research has been conducted to consider how biomonitoring data can be used to characterize cumulative risk and how psychosocial factors can be incorporated into cumulative risk assessments. In addition, a novel data mining tool is being tested and visual analytic software packages are being evaluated using extant children’s exposure data to identify relationships between exposure factors and body burden for a selected set of chemical agents.Approach :In this project, we plan to use engineering principles to develop conceptual and mathematical models of the human-receptor, source-to-outcome system, and visual analytic tools to address the significant challenges associate with characterizing cumulative risks. First, we will apply a systems approach to develop a human-receptor based conceptual framework. We plan to adapt the strategy for creating conceptual models for complex ecological risk assessments presented by Suter (1999).
We will also identify and evaluate visual analytic tools required to address analysis needs for characterizing multi-factorial relationships between environmental factors and human health outcomes. Visual analytics is a new branch of visualization that merges scientific and information visualization and includes technologies from other fields, including information extraction, knowledge management, and statistical analysis. Visual analytics tools can be developed and applied to represent complex multidimensional data. Large, dynamic and complex data sets containing text, measurements, and images, can be effectively combined to reveal significant relationships and trends and to enhance discovery. Visual analytics can be used for outcome analysis and visualization, to find patterns and subtle relationships in data, and to infer rules that allow predictive analysis to prevent and mitigate environmental disease.
Finally, we will test and demonstrate VA tools using existing available data. We propose doing this demonstration using data from children’s cohort studies to explore the potential of visual analytics to facilitate evaluation of the effects of environmental exposures on child health and development. Another possible demonstration could involve using data collected for a community-based cumulative risk assessment.
Relevance :Across EPA, program and regional offices are being called on to assess cumulative risk resulting from real-world exposures. The Agency is also being required to identify vulnerable populations, characterize life-stage risks, and evaluate gene-environment interactions. Multi-factorial analyses of one form or another are required: to conduct national-scale regulatory-based risk assessments (program offices); to conduct community-based risk screening and remediation (regions and states); to support epidemiology studies investigating gene-environment interactions (interagency); and to characterize exposure and risk for public health tracking (all of the above). The approaches and tools developed through this research will help the Agency meet the increasingly complex needs for cumulative risk assessment. In addition, results of this effort may be used to develop concepts and tools for application to the Detroit Children’s Study, the North Carolina Cohort, and the National Children’s Study.
Project IDs:
ID Code :IIIE-2Project type :Partner Specific