Uncertainty Analysis Within the EU HEIMTSA (Health and Environment Integrated Methodology and Toolbox for Scenario Assessment) Project : Epidemiology

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Abstracts: ISEE 22nd Annual Conference, Seoul, Korea, 28 August–1 September 2010: Statistical Methods in Environmental Health Research

Uncertainty Analysis Within the EU HEIMTSA (Health and Environment Integrated Methodology and Toolbox for Scenario Assessment) Project

Sabel, Clive1; Shaddick, Gavin2; Blangiardo, Marta3; Salway, Ruth2; Zenie, Alex4; Denby, Bruce5; Gerharz, Lydia6

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Epidemiology 22(1):p S176-S177, January 2011. | DOI: 10.1097/01.ede.0000392218.51262.f2
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PP-31-183

Background/Aims:

Health and Environment Integrated Methodology and Toolbox for Scenario Assessment (HEIMTSA) is an Integrated Project funded under the EU Sixth Framework Programme—Priority 6.3 Global Change and Ecosystems. It aims to develop and apply new integrated approaches to the assessment of environmental health risks and their consequences to European policy in areas of transport, energy, agriculture, industry, household, and waste treatment and disposal. Within the project, 1 work package (WP1.2) is dedicated to uncertainty analysis.

Methods:

HEIMTSA is committed to considering the effects of uncertainty within a full-chain approach, where the effects of uncertainty at each stage in a process are considered, both qualitative and quantitative, where possible using Monte-Carlo simulation techniques and Bayesian methods. The techniques developed have been used in 5 case studies, designed to facilitate the analysis of the full chain approach in a practical context. These include the Health Impact Assessments of (i) outdoor air pollutants; (ii) indoor air contaminants; (iii) of complex pollutants with multi-pathway exposure, (iv) traffic noise, and (iv) the “mega” case study, which concerns the traditional environmental health impacts of policies designed to reduce greenhouse gas emissions in Europe, or to adapt to climate change.

Results:

The method of uncertainty analysis differs between the case studies in order to adapt to the different challenges they present, for example a qualitative characterization was used for the noise case study, whereas a Monte-Carlo approach was implemented in the complex pollutants case study. Due to the nature of the complexity of each stage of the full-chain, for any realistic example, the stages are considered separately by the different expert groups who use different methods and computer software. Visualizing the results of the uncertainty analysis is an integral part of the HEIMTSA spatial toolbox.

Conclusion:

This presentation presents details of the HEIMTSA uncertainty framework, gives recommendations, describes lessons learnt from its 5 case studies, and discusses possible future developments in uncertainty analysis.

© 2011 Lippincott Williams & Wilkins, Inc.