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Assessing data linkage quality in cohort studies

Background: Linkage of administrative data sources provides an efficient means of collecting detailed data on how individuals interact with cross-sectoral services, society, and the environment. These data can be used to supplement conventional cohort studies, or to create population-level electroni...

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Autores principales: Harron, Katie, Doidge, James C., Goldstein, Harvey
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7261400/
https://www.ncbi.nlm.nih.gov/pubmed/32429765
http://dx.doi.org/10.1080/03014460.2020.1742379
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author Harron, Katie
Doidge, James C.
Goldstein, Harvey
author_facet Harron, Katie
Doidge, James C.
Goldstein, Harvey
author_sort Harron, Katie
collection PubMed
description Background: Linkage of administrative data sources provides an efficient means of collecting detailed data on how individuals interact with cross-sectoral services, society, and the environment. These data can be used to supplement conventional cohort studies, or to create population-level electronic cohorts generated solely from administrative data. However, errors occurring during linkage (false matches/missed matches) can lead to bias in results from linked data. Aim: This paper provides guidance on evaluating linkage quality in cohort studies. Methods: We provide an overview of methods for linkage, describe mechanisms by which linkage error can introduce bias, and draw on real-world examples to demonstrate methods for evaluating linkage quality. Results: Methods for evaluating linkage quality described in this paper provide guidance on (i) estimating linkage error rates, (ii) understanding the mechanisms by which linkage error might bias results, and (iii) information that should be shared between data providers, linkers and users, so that approaches to handling linkage error in analysis can be implemented. Conclusion: Linked administrative data can enhance conventional cohorts and offers the ability to answer questions that require large sample sizes or hard-to-reach populations. Care needs to be taken to evaluate linkage quality in order to provide robust results.
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spelling pubmed-72614002020-06-11 Assessing data linkage quality in cohort studies Harron, Katie Doidge, James C. Goldstein, Harvey Ann Hum Biol Research Paper Background: Linkage of administrative data sources provides an efficient means of collecting detailed data on how individuals interact with cross-sectoral services, society, and the environment. These data can be used to supplement conventional cohort studies, or to create population-level electronic cohorts generated solely from administrative data. However, errors occurring during linkage (false matches/missed matches) can lead to bias in results from linked data. Aim: This paper provides guidance on evaluating linkage quality in cohort studies. Methods: We provide an overview of methods for linkage, describe mechanisms by which linkage error can introduce bias, and draw on real-world examples to demonstrate methods for evaluating linkage quality. Results: Methods for evaluating linkage quality described in this paper provide guidance on (i) estimating linkage error rates, (ii) understanding the mechanisms by which linkage error might bias results, and (iii) information that should be shared between data providers, linkers and users, so that approaches to handling linkage error in analysis can be implemented. Conclusion: Linked administrative data can enhance conventional cohorts and offers the ability to answer questions that require large sample sizes or hard-to-reach populations. Care needs to be taken to evaluate linkage quality in order to provide robust results. Taylor & Francis 2020-05-20 /pmc/articles/PMC7261400/ /pubmed/32429765 http://dx.doi.org/10.1080/03014460.2020.1742379 Text en © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Paper
Harron, Katie
Doidge, James C.
Goldstein, Harvey
Assessing data linkage quality in cohort studies
title Assessing data linkage quality in cohort studies
title_full Assessing data linkage quality in cohort studies
title_fullStr Assessing data linkage quality in cohort studies
title_full_unstemmed Assessing data linkage quality in cohort studies
title_short Assessing data linkage quality in cohort studies
title_sort assessing data linkage quality in cohort studies
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7261400/
https://www.ncbi.nlm.nih.gov/pubmed/32429765
http://dx.doi.org/10.1080/03014460.2020.1742379
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