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Methodology
Julien Caudeville
INERIS (French National Institute for Industrial Environmnet and Risks)
Corresponding Author
ORCiD: https://orcid.org/0000-0001-8939-9369
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
At a regional or continental scale, the characterization of environmental health inequalities (EHI) expresses the idea that populations are not equal in the face of pollution. It implies the analysis in order to identifying and managing areas at risk of overexposure where increasing risk to human health is suspected. The development of methods is a prerequisite for the implementation of public health actions aimed at the protection of populations.
Methods
This paper presents the methodological framework developed by INERIS (French national institute for industrial environment and risks) to identify a common framework for conceptualizing and operationalizing environmental exposures as an important step towards articulating a science of EHI. We develop an integrated exposure assessment approach capable to integrate the multiplicity of exposure pathways from various sources, through a series of models up to the internal exposure.
Results
Measured data from environmental networks reflecting the actual contamination of the environment are reused to characterize the population's exposure. Sophisticated methods of spatial analysis are applied to include additional information and take benefit from spatial and inter-variable correlation to improve data representativeness and characterize associated uncertainty. Integrated approaches bring together all information necessary for assessing the source-to-human-dose continuum using Geographic Information System, multimedia exposure and toxicokinetic model.
Conclusion
This framework could be used for many purposes, such as mapping EHI, identifying vulnerable populations and determinants of exposure to manage and plan remedial actions and assessing spatial relationships between health and environmental to identify factors that influence the variability of disease patterns.
Keywords
Inequalities, spatial, exposure, modeling, integrated
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Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 12 Nov, 2020
No community comments so far
Editorial decision: Major revision
On 18 Jan, 2021
Review #3 received
Received 04 Jan, 2021
Review #2 received
Received 02 Jan, 2021
Review #1 received
Received 26 Dec, 2020
Reviewer #3 agreed
On 22 Dec, 2020
Reviewer #2 agreed
On 20 Dec, 2020
Reviewer #1 agreed
On 19 Dec, 2020
Reviewers invited
Invitations sent on 12 Dec, 2020
Editor assigned
On 04 Nov, 2020
Submission checks complete
On 04 Nov, 2020
Editor invited
On 04 Nov, 2020
First submitted
On 03 Nov, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
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A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
This preprint is under consideration at Environmental Health. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Methodology
Julien Caudeville
INERIS (French National Institute for Industrial Environmnet and Risks)
Corresponding Author
ORCiD: https://orcid.org/0000-0001-8939-9369
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 12 Nov, 2020
No community comments so far
Editorial decision: Major revision
On 18 Jan, 2021
Review #3 received
Received 04 Jan, 2021
Review #2 received
Received 02 Jan, 2021
Review #1 received
Received 26 Dec, 2020
Reviewer #3 agreed
On 22 Dec, 2020
Reviewer #2 agreed
On 20 Dec, 2020
Reviewer #1 agreed
On 19 Dec, 2020
Reviewers invited
Invitations sent on 12 Dec, 2020
Editor assigned
On 04 Nov, 2020
Submission checks complete
On 04 Nov, 2020
Editor invited
On 04 Nov, 2020
First submitted
On 03 Nov, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
At a regional or continental scale, the characterization of environmental health inequalities (EHI) expresses the idea that populations are not equal in the face of pollution. It implies the analysis in order to identifying and managing areas at risk of overexposure where increasing risk to human health is suspected. The development of methods is a prerequisite for the implementation of public health actions aimed at the protection of populations.
Methods
This paper presents the methodological framework developed by INERIS (French national institute for industrial environment and risks) to identify a common framework for conceptualizing and operationalizing environmental exposures as an important step towards articulating a science of EHI. We develop an integrated exposure assessment approach capable to integrate the multiplicity of exposure pathways from various sources, through a series of models up to the internal exposure.
Results
Measured data from environmental networks reflecting the actual contamination of the environment are reused to characterize the population's exposure. Sophisticated methods of spatial analysis are applied to include additional information and take benefit from spatial and inter-variable correlation to improve data representativeness and characterize associated uncertainty. Integrated approaches bring together all information necessary for assessing the source-to-human-dose continuum using Geographic Information System, multimedia exposure and toxicokinetic model.
Conclusion
This framework could be used for many purposes, such as mapping EHI, identifying vulnerable populations and determinants of exposure to manage and plan remedial actions and assessing spatial relationships between health and environmental to identify factors that influence the variability of disease patterns.
Figure 1
Figure 1
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