Monitoring environmental quality is effective for an IEHM if it represent human exposure directly (e.g., noise) or indirectly (e.g., in drinking water via soil and ground water).
Scientific research should clarify the relationships between environmental changes and their health impacts by documenting the relevant exposure pathways, their magnitudes, and by investigating exposure-dose–response relationships.
Effective policies and measures reduce exposure and dose, both by improving environmental quality
and by identifying populations from areas with poor quality of life and high exposure to pollution [51,52].
Understanding how exposures are embedded within the exposure-dose–response relationship is essential, particularly in an IEHM programme.
Monitoring natural-eco-anthropogenic systems components
Human health is perceived as the integrated outcome of its ecological, socio-cultural, economic and institutional determinants at various spatial-temporal scales.
It can be seen as a high-level integrated index that reflects the environmental state and, in the long-term, the sustainability of our natural and socio-economic environment [53].
Therefore, the IEHM framework (Figure 1) should be based on descriptions of the natural-eco-anthropogenic system rather than on its individual components.
The IEHM framework (Figure 1) includes four subsystems: natural, man-made, ecological, and human.
The human system is incorporated into the IEHM framework as it is the most important factor in determining environmental changes [51].
With human health playing a central role, the IEHM addresses the interconnections between natural-eco-anthropogenic systems through four categories of monitoring (Figure 1): (i) environmental
monitoring; (ii) eco-surveillance; (iii) biomonitoring; and (iv) health surveillance.
The importance of linking environment monitoring and health surveillance with policy making has led to the addition of a fifth categoryof information: (v) the governance of policy intervention Linking linear DPSEEA operational framework andnatural-eco-anthropogenic systems components
The IEHM refers to simultaneous measurements of natural-eco-anthropogenic systems [51,53] and their health impacts over time at the same location with causative explanations [48-52].
This addresses the links between components of the monitoring systems and the E&H operational framework (Figure 1).
In practice, the IEHM can be divided into a number of sub-programmes(e.g., environmental monitoring, eco-surveillance, biomonitoring, health surveillance, etc.) which are linked by the use of the same parameters (monitoring systems components) and/or geographical location (E&H operational framework).
The E&H operational framework and monitoring systems components connectivity in an IEHM programme can provide the web of causation within the complex real natural-eco-anthropogenic systems with human health, and help us to improve our knowledge on the relationships between changes of natural-eco-anthropogenic systems and human health.
It views humans and natural-eco-anthropogenic systems as one interacting system.
And while it is not necessarily just cause and effect, it is more than exposure and response in an IEHM programme.
Keeping in mind the end goal of helping decision-making In recent years, it has become apparent that many of the health risks facing society are systemic in nature – these are complex risks, set within wider social, economic and environmental contexts.
Reflecting this, policy-making has become more wide-ranging in scope, more collaborative
and more precautionary in approach [31,54].
Therefore, science needs first to anticipate, understand, assess, and reduce risks to human health and our environment to support governmental programmes to protect human health and safeguard the environment.
This requires a consensus on the need to integrate data and an analytical methodology for monitoring and assessment [31,55].
The aim of an IEHM programme is to identify complex environmental health issues in a systematic
and cause-effect chain approach to provide data useful for policy decisions on investments and resource allocation. However, the full integration and entire systems analyses in an IEHM may not necessarily be needed in all decision-making contexts.
Particularly at strategic policy level such qualities are suitable, but at operational policy level specialized data or partial systems analyses can be more relevant.
Integrated data from existing environmental health monitoring programmesInstead of creating a completely new IEHM programme, a reasonable approach is to integrate data from existing E&H monitoring programmes.
This approach is fully in line with the EU’s goal to make better use of existing environmental
health data, for example with directives such as the Infrastructure for Spatial Information in the
European Community (INSPIRE) [56].
However, this creates the challenge of how to integrate data from multiplemonitoring programmes.
Here, we first propose a structural work process (Figure 2) to: (i) overcome this challenge; (ii) create new ‘services’ based on the existing data, and (iii) identify data and knowledge gaps.
This structural work process includes the following steps: Step 0: define the goal or ‘service’ of data integration; Step 1: set up an integrated plan that helps identify the required databases; Step 2: provide common access to collect meta-data from each individual database; Step 3: analyse data from each individual database and develop common meta-data information, including definition of
data characteristics, format and process data, and assess data usefulness and quality; Step 4: retrieve and analyseintegrated data; Step 5: statistical analysis, data presentation and report, and Step 6: recommendations. In order to link data from existing environmental health monitoring programmes, we need methodologies on data integration.
Three data linking methods that are currently used in E&H fields are:
-Stochastic-mechanistic models for linking exposure and dose data, for example, the physiologicallybasedpharmacokinetic (PBPK) [57] and the biologically-based pharmacokinetic (BBPK) models [58].
PBPKs are powerful tools to link exposure to a parent compounds and/or active metabolites at the
target sites of toxicity. BBPKs are increasingly used in risk assessment of environmental chemicals. In addition, other tools of hazard identification (e.g., Hazard analysis (HazAn), Hazard and operability (HazOp)) [59,60] and exposure assessment (e.g., Probabilistic risk assessment (PRA)) [60] can also be used to link exposure and dose data.
-Multiple empirical-statistical tools for linking doseand health effect data, including tools on dose–
response assessment (e.g., biologically-based dose– response (BBDR) and mode-of-action (MOA)) [61,62] and risk characterization (e.g., Probabilistic exposure assessment (PEA), the Area under the curve (AUC) and the Joint probability curves (JPCs)) [60,63].
For instance, dose and risk calculation software (DCAL) [64] is comprehensive software for calculation of tissue dose and subsequent health risk from intake of pollutants or exposure to pollutants present in environmental media.
- Multiple systematic tools for linking exposure, dose and health effect data, such as Geographical
Information Systems (GIS) [65], Bayesian belief networks (BBN) [66] and multiple lines and levels of evidence (MLLE) tools [67].
GIS links the indicators from environmental monitoring, biomonitoring and health surveillance in a visual way.
These links might be represented as different layers where each layer holds data about a particular kind of health related environmental features.
Each feature is linked to a position on the graphical image on a map and a record in an attributed table. Besides simply plotting environmental monitoring data and morbidity/mortality information on a map, GIS also offer important opportunities for interpolation or extrapolation of monitoring and modelling data [65].