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Alleviating Environmental Health Disparities Through Community Science and Data Integration
Environmental contamination is a fundamental determinant of health and well-being, and when the environment is compromised, vulnerabilities are generated. The complex challenges associated with environmental health and food security are influenced by current and emerging political, social, economic,...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9165534/ https://www.ncbi.nlm.nih.gov/pubmed/35664667 http://dx.doi.org/10.3389/fsufs.2021.620470 |
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author | Ramírez-Andreotta, Mónica D. Walls, Ramona Youens-Clark, Ken Blumberg, Kai Isaacs, Katherine E. Kaufmann, Dorsey Maier, Raina M. |
author_facet | Ramírez-Andreotta, Mónica D. Walls, Ramona Youens-Clark, Ken Blumberg, Kai Isaacs, Katherine E. Kaufmann, Dorsey Maier, Raina M. |
author_sort | Ramírez-Andreotta, Mónica D. |
collection | PubMed |
description | Environmental contamination is a fundamental determinant of health and well-being, and when the environment is compromised, vulnerabilities are generated. The complex challenges associated with environmental health and food security are influenced by current and emerging political, social, economic, and environmental contexts. To solve these “wicked” dilemmas, disparate public health surveillance efforts are conducted by local, state, and federal agencies. More recently, citizen/community science (CS) monitoring efforts are providing site-specific data. One of the biggest challenges in using these government datasets, let alone incorporating CS data, for a holistic assessment of environmental exposure is data management and interoperability. To facilitate a more holistic perspective and approach to solution generation, we have developed a method to provide a common data model that will allow environmental health researchers working at different scales and research domains to exchange data and ask new questions. We anticipate that this method will help to address environmental health disparities, which are unjust and avoidable, while ensuring CS datasets are ethically integrated to achieve environmental justice. Specifically, we used a transdisciplinary research framework to develop a methodology to integrate CS data with existing governmental environmental monitoring and social attribute data (vulnerability and resilience variables) that span across 10 different federal and state agencies. A key challenge in integrating such different datasets is the lack of widely adopted ontologies for vulnerability and resiliency factors. In addition to following the best practice of submitting new term requests to existing ontologies to fill gaps, we have also created an application ontology, the Superfund Research Project Data Interface Ontology (SRPDIO). |
format | Online Article Text |
id | pubmed-9165534 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
record_format | MEDLINE/PubMed |
spelling | pubmed-91655342022-06-04 Alleviating Environmental Health Disparities Through Community Science and Data Integration Ramírez-Andreotta, Mónica D. Walls, Ramona Youens-Clark, Ken Blumberg, Kai Isaacs, Katherine E. Kaufmann, Dorsey Maier, Raina M. Front Sustain Food Syst Article Environmental contamination is a fundamental determinant of health and well-being, and when the environment is compromised, vulnerabilities are generated. The complex challenges associated with environmental health and food security are influenced by current and emerging political, social, economic, and environmental contexts. To solve these “wicked” dilemmas, disparate public health surveillance efforts are conducted by local, state, and federal agencies. More recently, citizen/community science (CS) monitoring efforts are providing site-specific data. One of the biggest challenges in using these government datasets, let alone incorporating CS data, for a holistic assessment of environmental exposure is data management and interoperability. To facilitate a more holistic perspective and approach to solution generation, we have developed a method to provide a common data model that will allow environmental health researchers working at different scales and research domains to exchange data and ask new questions. We anticipate that this method will help to address environmental health disparities, which are unjust and avoidable, while ensuring CS datasets are ethically integrated to achieve environmental justice. Specifically, we used a transdisciplinary research framework to develop a methodology to integrate CS data with existing governmental environmental monitoring and social attribute data (vulnerability and resilience variables) that span across 10 different federal and state agencies. A key challenge in integrating such different datasets is the lack of widely adopted ontologies for vulnerability and resiliency factors. In addition to following the best practice of submitting new term requests to existing ontologies to fill gaps, we have also created an application ontology, the Superfund Research Project Data Interface Ontology (SRPDIO). 2021-06 2021-06-10 /pmc/articles/PMC9165534/ /pubmed/35664667 http://dx.doi.org/10.3389/fsufs.2021.620470 Text en https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Article Ramírez-Andreotta, Mónica D. Walls, Ramona Youens-Clark, Ken Blumberg, Kai Isaacs, Katherine E. Kaufmann, Dorsey Maier, Raina M. Alleviating Environmental Health Disparities Through Community Science and Data Integration |
title | Alleviating Environmental Health Disparities Through Community Science and Data Integration |
title_full | Alleviating Environmental Health Disparities Through Community Science and Data Integration |
title_fullStr | Alleviating Environmental Health Disparities Through Community Science and Data Integration |
title_full_unstemmed | Alleviating Environmental Health Disparities Through Community Science and Data Integration |
title_short | Alleviating Environmental Health Disparities Through Community Science and Data Integration |
title_sort | alleviating environmental health disparities through community science and data integration |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9165534/ https://www.ncbi.nlm.nih.gov/pubmed/35664667 http://dx.doi.org/10.3389/fsufs.2021.620470 |
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