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Enabling data sharing and utilization for African population health data using OHDSI tools with an OMOP-common data model

The COVID-19 pandemic has spurred the use of AI and DS innovations in data collection and aggregation. Extensive data on many aspects of the COVID-19 has been collected and used to optimize public health response to the pandemic and to manage the recovery of patients in Sub-Saharan Africa. However,...

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Autores principales: Kiwuwa-Muyingo, Sylvia, Todd, Jim, Bhattacharjee, Tathagata, Taylor, Amelia, Greenfield, Jay
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10287979/
https://www.ncbi.nlm.nih.gov/pubmed/37361151
http://dx.doi.org/10.3389/fpubh.2023.1116682
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author Kiwuwa-Muyingo, Sylvia
Todd, Jim
Bhattacharjee, Tathagata
Taylor, Amelia
Greenfield, Jay
author_facet Kiwuwa-Muyingo, Sylvia
Todd, Jim
Bhattacharjee, Tathagata
Taylor, Amelia
Greenfield, Jay
author_sort Kiwuwa-Muyingo, Sylvia
collection PubMed
description The COVID-19 pandemic has spurred the use of AI and DS innovations in data collection and aggregation. Extensive data on many aspects of the COVID-19 has been collected and used to optimize public health response to the pandemic and to manage the recovery of patients in Sub-Saharan Africa. However, there is no standard mechanism for collecting, documenting and disseminating COVID-19 related data or metadata, which makes the use and reuse a challenge. INSPIRE utilizes the Observational Medical Outcomes Partnership (OMOP) as the Common Data Model (CDM) implemented in the cloud as a Platform as a Service (PaaS) for COVID-19 data. The INSPIRE PaaS for COVID-19 data leverages the cloud gateway for both individual research organizations and for data networks. Individual research institutions may choose to use the PaaS to access the FAIR data management, data analysis and data sharing capabilities which come with the OMOP CDM. Network data hubs may be interested in harmonizing data across localities using the CDM conditioned by the data ownership and data sharing agreements available under OMOP's federated model. The INSPIRE platform for evaluation of COVID-19 Harmonized data (PEACH) harmonizes data from Kenya and Malawi. Data sharing platforms must remain trusted digital spaces that protect human rights and foster citizens' participation is vital in an era where information overload from the internet exists. The channel for sharing data between localities is included in the PaaS and is based on data sharing agreements provided by the data producer. This allows the data producers to retain control over how their data are used, which can be further protected through the use of the federated CDM. Federated regional OMOP-CDM are based on the PaaS instances and analysis workbenches in INSPIRE-PEACH with harmonized analysis powered by the AI technologies in OMOP. These AI technologies can be used to discover and evaluate pathways that COVID-19 cohorts take through public health interventions and treatments. By using both the data mapping and terminology mapping, we construct ETLs that populate the data and/or metadata elements of the CDM, making the hub both a central model and a distributed model.
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spelling pubmed-102879792023-06-24 Enabling data sharing and utilization for African population health data using OHDSI tools with an OMOP-common data model Kiwuwa-Muyingo, Sylvia Todd, Jim Bhattacharjee, Tathagata Taylor, Amelia Greenfield, Jay Front Public Health Public Health The COVID-19 pandemic has spurred the use of AI and DS innovations in data collection and aggregation. Extensive data on many aspects of the COVID-19 has been collected and used to optimize public health response to the pandemic and to manage the recovery of patients in Sub-Saharan Africa. However, there is no standard mechanism for collecting, documenting and disseminating COVID-19 related data or metadata, which makes the use and reuse a challenge. INSPIRE utilizes the Observational Medical Outcomes Partnership (OMOP) as the Common Data Model (CDM) implemented in the cloud as a Platform as a Service (PaaS) for COVID-19 data. The INSPIRE PaaS for COVID-19 data leverages the cloud gateway for both individual research organizations and for data networks. Individual research institutions may choose to use the PaaS to access the FAIR data management, data analysis and data sharing capabilities which come with the OMOP CDM. Network data hubs may be interested in harmonizing data across localities using the CDM conditioned by the data ownership and data sharing agreements available under OMOP's federated model. The INSPIRE platform for evaluation of COVID-19 Harmonized data (PEACH) harmonizes data from Kenya and Malawi. Data sharing platforms must remain trusted digital spaces that protect human rights and foster citizens' participation is vital in an era where information overload from the internet exists. The channel for sharing data between localities is included in the PaaS and is based on data sharing agreements provided by the data producer. This allows the data producers to retain control over how their data are used, which can be further protected through the use of the federated CDM. Federated regional OMOP-CDM are based on the PaaS instances and analysis workbenches in INSPIRE-PEACH with harmonized analysis powered by the AI technologies in OMOP. These AI technologies can be used to discover and evaluate pathways that COVID-19 cohorts take through public health interventions and treatments. By using both the data mapping and terminology mapping, we construct ETLs that populate the data and/or metadata elements of the CDM, making the hub both a central model and a distributed model. Frontiers Media S.A. 2023-06-09 /pmc/articles/PMC10287979/ /pubmed/37361151 http://dx.doi.org/10.3389/fpubh.2023.1116682 Text en Copyright © 2023 Kiwuwa-Muyingo, Todd, Bhattacharjee, Taylor and Greenfield. 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 Public Health
Kiwuwa-Muyingo, Sylvia
Todd, Jim
Bhattacharjee, Tathagata
Taylor, Amelia
Greenfield, Jay
Enabling data sharing and utilization for African population health data using OHDSI tools with an OMOP-common data model
title Enabling data sharing and utilization for African population health data using OHDSI tools with an OMOP-common data model
title_full Enabling data sharing and utilization for African population health data using OHDSI tools with an OMOP-common data model
title_fullStr Enabling data sharing and utilization for African population health data using OHDSI tools with an OMOP-common data model
title_full_unstemmed Enabling data sharing and utilization for African population health data using OHDSI tools with an OMOP-common data model
title_short Enabling data sharing and utilization for African population health data using OHDSI tools with an OMOP-common data model
title_sort enabling data sharing and utilization for african population health data using ohdsi tools with an omop-common data model
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10287979/
https://www.ncbi.nlm.nih.gov/pubmed/37361151
http://dx.doi.org/10.3389/fpubh.2023.1116682
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