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A proposed de-identification framework for a cohort of children presenting at a health facility in Uganda
Data sharing has enormous potential to accelerate and improve the accuracy of research, strengthen collaborations, and restore trust in the clinical research enterprise. Nevertheless, there remains reluctancy to openly share raw data sets, in part due to concerns regarding research participant confi...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931294/ https://www.ncbi.nlm.nih.gov/pubmed/36812586 http://dx.doi.org/10.1371/journal.pdig.0000027 |
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author | Mawji, Alishah Longstaff, Holly Trawin, Jessica Dunsmuir, Dustin Komugisha, Clare Novakowski, Stefanie K. Wiens, Matthew O. Akech, Samuel Tagoola, Abner Kissoon, Niranjan Ansermino, J. Mark |
author_facet | Mawji, Alishah Longstaff, Holly Trawin, Jessica Dunsmuir, Dustin Komugisha, Clare Novakowski, Stefanie K. Wiens, Matthew O. Akech, Samuel Tagoola, Abner Kissoon, Niranjan Ansermino, J. Mark |
author_sort | Mawji, Alishah |
collection | PubMed |
description | Data sharing has enormous potential to accelerate and improve the accuracy of research, strengthen collaborations, and restore trust in the clinical research enterprise. Nevertheless, there remains reluctancy to openly share raw data sets, in part due to concerns regarding research participant confidentiality and privacy. Statistical data de-identification is an approach that can be used to preserve privacy and facilitate open data sharing. We have proposed a standardized framework for the de-identification of data generated from cohort studies in children in a low-and-middle income country. We applied a standardized de-identification framework to a data sets comprised of 241 health related variables collected from a cohort of 1750 children with acute infections from Jinja Regional Referral Hospital in Eastern Uganda. Variables were labeled as direct and quasi-identifiers based on conditions of replicability, distinguishability, and knowability with consensus from two independent evaluators. Direct identifiers were removed from the data sets, while a statistical risk-based de-identification approach using the k-anonymity model was applied to quasi-identifiers. Qualitative assessment of the level of privacy invasion associated with data set disclosure was used to determine an acceptable re-identification risk threshold, and corresponding k-anonymity requirement. A de-identification model using generalization, followed by suppression was applied using a logical stepwise approach to achieve k-anonymity. The utility of the de-identified data was demonstrated using a typical clinical regression example. The de-identified data sets was published on the Pediatric Sepsis Data CoLaboratory Dataverse which provides moderated data access. Researchers are faced with many challenges when providing access to clinical data. We provide a standardized de-identification framework that can be adapted and refined based on specific context and risks. This process will be combined with moderated access to foster coordination and collaboration in the clinical research community. |
format | Online Article Text |
id | pubmed-9931294 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-99312942023-02-16 A proposed de-identification framework for a cohort of children presenting at a health facility in Uganda Mawji, Alishah Longstaff, Holly Trawin, Jessica Dunsmuir, Dustin Komugisha, Clare Novakowski, Stefanie K. Wiens, Matthew O. Akech, Samuel Tagoola, Abner Kissoon, Niranjan Ansermino, J. Mark PLOS Digit Health Research Article Data sharing has enormous potential to accelerate and improve the accuracy of research, strengthen collaborations, and restore trust in the clinical research enterprise. Nevertheless, there remains reluctancy to openly share raw data sets, in part due to concerns regarding research participant confidentiality and privacy. Statistical data de-identification is an approach that can be used to preserve privacy and facilitate open data sharing. We have proposed a standardized framework for the de-identification of data generated from cohort studies in children in a low-and-middle income country. We applied a standardized de-identification framework to a data sets comprised of 241 health related variables collected from a cohort of 1750 children with acute infections from Jinja Regional Referral Hospital in Eastern Uganda. Variables were labeled as direct and quasi-identifiers based on conditions of replicability, distinguishability, and knowability with consensus from two independent evaluators. Direct identifiers were removed from the data sets, while a statistical risk-based de-identification approach using the k-anonymity model was applied to quasi-identifiers. Qualitative assessment of the level of privacy invasion associated with data set disclosure was used to determine an acceptable re-identification risk threshold, and corresponding k-anonymity requirement. A de-identification model using generalization, followed by suppression was applied using a logical stepwise approach to achieve k-anonymity. The utility of the de-identified data was demonstrated using a typical clinical regression example. The de-identified data sets was published on the Pediatric Sepsis Data CoLaboratory Dataverse which provides moderated data access. Researchers are faced with many challenges when providing access to clinical data. We provide a standardized de-identification framework that can be adapted and refined based on specific context and risks. This process will be combined with moderated access to foster coordination and collaboration in the clinical research community. Public Library of Science 2022-08-24 /pmc/articles/PMC9931294/ /pubmed/36812586 http://dx.doi.org/10.1371/journal.pdig.0000027 Text en © 2022 Mawji et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Mawji, Alishah Longstaff, Holly Trawin, Jessica Dunsmuir, Dustin Komugisha, Clare Novakowski, Stefanie K. Wiens, Matthew O. Akech, Samuel Tagoola, Abner Kissoon, Niranjan Ansermino, J. Mark A proposed de-identification framework for a cohort of children presenting at a health facility in Uganda |
title | A proposed de-identification framework for a cohort of children presenting at a health facility in Uganda |
title_full | A proposed de-identification framework for a cohort of children presenting at a health facility in Uganda |
title_fullStr | A proposed de-identification framework for a cohort of children presenting at a health facility in Uganda |
title_full_unstemmed | A proposed de-identification framework for a cohort of children presenting at a health facility in Uganda |
title_short | A proposed de-identification framework for a cohort of children presenting at a health facility in Uganda |
title_sort | proposed de-identification framework for a cohort of children presenting at a health facility in uganda |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9931294/ https://www.ncbi.nlm.nih.gov/pubmed/36812586 http://dx.doi.org/10.1371/journal.pdig.0000027 |
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