期刊名称：Environmental Health - a Global Access Science Source
印刷版ISSN：1476-069X
电子版ISSN：1476-069X
出版年度：2012
卷号：11
期号：1
页码：88
DOI：10.1186/1476-069X-11-88
语种：English
出版社：BioMed Central
摘要：Although Integrated Environmental Health Monitoring (IEHM) is considered an essential tool to better understand complex environmental health issues, there is no consensus on how to develop such a programme. We reviewed four existing frameworks and eight monitoring programmes in the area of environmental health. We identified the DPSEEA (Driving Force-Pressure-State-Exposure-Effect-Action) framework as most suitable for developing an IEHM programme for environmental health impact assessment. Our review showed that most of the existing monitoring programmes have been designed for specific purposes, resulting in narrow scope and limited number of parameters. This therefore limits their relevance for studying complex environmental health topics. Other challenges include limited spatial and temporal data availability, limited development of data sharing mechanisms, heterogeneous data quality, a lack of adequate methodologies to link disparate data sources, and low level of interdisciplinary cooperation. To overcome some of these challenges, we propose a DPSEEA-based conceptual framework for an IEHM programme that would enable monitoring and measuring the impact of environmental changes on human health. We define IEHM as ‘a systemic process to measure, analyse and interpret the state and changes of natural-eco-anthropogenic systems and its related health impact over time at the same location with causative explanations across the various compartments of the cause-effect chain’. We develop a structural work process to integrate information that is based on existing environmental health monitoring programmes. Such a framework allows the development of combined monitoring systems that exhibit a large degree of compatibility between countries and regions.
关键词：European Union ; Geographical Information System ; Bayesian Belief Network ; Environmental Public Health ; Qualitative Uncertainty
