Improving opportunities for data linkage within Children Looked After administrative records in Wales

Bailey, Grace; Lee, Alexandra; Ahmed, Saira; Scanlon, Ieuan; Cowley, Laura; Stuart, Amy; Farr, Ian; Brooks, Caroline; North, Laura; Griffiths, Lucy

doi:10.23889/ijpds.v10i1.2383

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Improving opportunities for data linkage within Children Looked After administrative records in Wales

Grace Bailey

, Alexandra Lee, Saira Ahmed, Ieuan Scanlon, Laura Cowley, Amy Stuart, Ian Farr, Caroline Brooks

, Laura North, Lucy Griffiths

International Journal of Population Data Science, Volume: 10, Issue: 1

Swansea University Authors: Grace Bailey , Alexandra Lee, Saira Ahmed, Ieuan Scanlon, Laura Cowley, Ian Farr, Caroline Brooks , Laura North, Lucy Griffiths

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DOI (Published version): 10.23889/ijpds.v10i1.2383

Abstract

IntroductionLinkage of population-based administrative data is a powerful tool for studying important public issues. To overcome confidentiality and disclosure issues, records are de-identified and allocated a unique identifier. Within the Secure Anonymised Information Linkage (SAIL) Databank, these...

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Published in:	International Journal of Population Data Science
ISSN:	2399-4908
Published:	Swansea University 2025
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa68946

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To overcome confidentiality and disclosure issues, records are de-identified and allocated a unique identifier. Within the Secure Anonymised Information Linkage (SAIL) Databank, these are known as Anonymised Linking Fields (ALFs). Assignment of an ALF enables linkage of individuals across multiple routinely collected datasets. Within the Children Looked After (CLA) Wales dataset, only 37% of the children have an ALF, limiting linkage to other datasets and, as a result, potential research. There are also other known data issues, including discrepancies with the week of births, duplicate identifiers and year-on-year changes in identifiers.ObjectivesTo improve accuracy and availability of the ALFs in the CLA dataset, and overall research quality.MethodsUsing several datasets within the SAIL Databank, we developed a six-step CLA matching algorithm to improve the ALF matching rate and correct for data errors. To assess the performance of our algorithm, we benchmarked against routine ALFs already identified via the algorithm currently used by SAIL.ResultsOur algorithm increased ALF matching by 25%, assigning 61% of individuals an ALF. Inconsistent weeks of birth, and incorrect and duplicate identifiers were resolved. When benchmarking against the current ALF-assigning algorithm used by SAIL, our algorithm had an overall sensitivity of 90%.ConclusionWe have developed an algorithm which demonstrates comparable ALF matching performance to the current algorithm used within SAIL, and which greatly improves the ALF matching in the CLA dataset. This algorithm may help to overcome potential bias due to missing data, and increases the potential for linkage to other datasets. Further development and refinement could result in the algorithm being applied to other datasets in SAIL.</abstract><type>Journal Article</type><journal>International Journal of Population Data Science</journal><volume>10</volume><journalNumber>1</journalNumber><paginationStart/><paginationEnd/><publisher>Swansea University</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>2399-4908</issnElectronic><keywords>administrative data linkage; children looked after; SAIL Databank</keywords><publishedDay>19</publishedDay><publishedMonth>2</publishedMonth><publishedYear>2025</publishedYear><publishedDate>2025-02-19</publishedDate><doi>10.23889/ijpds.v10i1.2383</doi><url/><notes/><college>COLLEGE NANME</college><department>Medical School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MEDS</DepartmentCode><institution>Swansea University</institution><apcterm>Other</apcterm><funders>This work was supported by Health and Care ResearchWales and Administrative Data Research (ADR) Wales. LJGis a member of the Children’s Social Care Research andDevelopment Centre (CASCADE) partnership, which receivesinfrastructure funding from Health and Care Research Wales(HCRW) (517199). LEC is a research fellow, funded by Healthand Care Research Wales (SCF-22-07).</funders><projectreference/><lastEdited>2025-03-12T15:15:32.6460714</lastEdited><Created>2025-02-23T15:22:43.3531424</Created><path><level id="1">Faculty of Medicine, Health and Life Sciences</level><level id="2">Swansea University Medical School - Health Data Science</level></path><authors><author><firstname>Grace</firstname><surname>Bailey</surname><orcid>0000-0003-4646-3134</orcid><order>1</order></author><author><firstname>Alexandra</firstname><surname>Lee</surname><order>2</order></author><author><firstname>Saira</firstname><surname>Ahmed</surname><order>3</order></author><author><firstname>Ieuan</firstname><surname>Scanlon</surname><order>4</order></author><author><firstname>Laura</firstname><surname>Cowley</surname><order>5</order></author><author><firstname>Amy</firstname><surname>Stuart</surname><order>6</order></author><author><firstname>Ian</firstname><surname>Farr</surname><order>7</order></author><author><firstname>Caroline</firstname><surname>Brooks</surname><orcid>0000-0002-4612-5867</orcid><order>8</order></author><author><firstname>Laura</firstname><surname>North</surname><order>9</order></author><author><firstname>Lucy</firstname><surname>Griffiths</surname><orcid>0000-0001-9230-624X</orcid><order>10</order></author></authors><documents><document><filename>68946__33801__003651af64bc447b8db94f8d6d7c2d5b.pdf</filename><originalFilename>68946.VoR.pdf</originalFilename><uploaded>2025-03-12T15:14:10.8451184</uploaded><type>Output</type><contentLength>1881729</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>© The Authors. 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spelling	2025-03-12T15:15:32.6460714 v2 68946 2025-02-23 Improving opportunities for data linkage within Children Looked After administrative records in Wales 1e09a407fca9e8047e7738b18d381130 0000-0003-4646-3134 Grace Bailey Grace Bailey true false 7c6dc217555b0fea264ff0dd7d0aa374 Alexandra Lee Alexandra Lee true false 2bf49b38ca1517d326228b3e8fdf6e78 Saira Ahmed Saira Ahmed true false 9fcb224c6bd804a4d41a2a8570a71185 Ieuan Scanlon Ieuan Scanlon true false a80501f280e89fee276510b25fc68e77 Laura Cowley Laura Cowley true false 3c02e7e9c2b064ee3e96e83b9777dde4 Ian Farr Ian Farr true false ac99c6134cf75b4c3e5f63cbb1a149ee 0000-0002-4612-5867 Caroline Brooks Caroline Brooks true false a255822cf77a0184cb6922e9fbea39e9 Laura North Laura North true false e35ea6ea4b429e812ef204b048131d93 0000-0001-9230-624X Lucy Griffiths Lucy Griffiths true false 2025-02-23 MEDS IntroductionLinkage of population-based administrative data is a powerful tool for studying important public issues. To overcome confidentiality and disclosure issues, records are de-identified and allocated a unique identifier. Within the Secure Anonymised Information Linkage (SAIL) Databank, these are known as Anonymised Linking Fields (ALFs). Assignment of an ALF enables linkage of individuals across multiple routinely collected datasets. Within the Children Looked After (CLA) Wales dataset, only 37% of the children have an ALF, limiting linkage to other datasets and, as a result, potential research. There are also other known data issues, including discrepancies with the week of births, duplicate identifiers and year-on-year changes in identifiers.ObjectivesTo improve accuracy and availability of the ALFs in the CLA dataset, and overall research quality.MethodsUsing several datasets within the SAIL Databank, we developed a six-step CLA matching algorithm to improve the ALF matching rate and correct for data errors. To assess the performance of our algorithm, we benchmarked against routine ALFs already identified via the algorithm currently used by SAIL.ResultsOur algorithm increased ALF matching by 25%, assigning 61% of individuals an ALF. Inconsistent weeks of birth, and incorrect and duplicate identifiers were resolved. When benchmarking against the current ALF-assigning algorithm used by SAIL, our algorithm had an overall sensitivity of 90%.ConclusionWe have developed an algorithm which demonstrates comparable ALF matching performance to the current algorithm used within SAIL, and which greatly improves the ALF matching in the CLA dataset. This algorithm may help to overcome potential bias due to missing data, and increases the potential for linkage to other datasets. Further development and refinement could result in the algorithm being applied to other datasets in SAIL. Journal Article International Journal of Population Data Science 10 1 Swansea University 2399-4908 administrative data linkage; children looked after; SAIL Databank 19 2 2025 2025-02-19 10.23889/ijpds.v10i1.2383 COLLEGE NANME Medical School COLLEGE CODE MEDS Swansea University Other This work was supported by Health and Care ResearchWales and Administrative Data Research (ADR) Wales. LJGis a member of the Children’s Social Care Research andDevelopment Centre (CASCADE) partnership, which receivesinfrastructure funding from Health and Care Research Wales(HCRW) (517199). LEC is a research fellow, funded by Healthand Care Research Wales (SCF-22-07). 2025-03-12T15:15:32.6460714 2025-02-23T15:22:43.3531424 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Health Data Science Grace Bailey 0000-0003-4646-3134 1 Alexandra Lee 2 Saira Ahmed 3 Ieuan Scanlon 4 Laura Cowley 5 Amy Stuart 6 Ian Farr 7 Caroline Brooks 0000-0002-4612-5867 8 Laura North 9 Lucy Griffiths 0000-0001-9230-624X 10 68946__33801__003651af64bc447b8db94f8d6d7c2d5b.pdf 68946.VoR.pdf 2025-03-12T15:14:10.8451184 Output 1881729 application/pdf Version of Record true © The Authors. Open Access under CC BY 4.0. true eng https://creativecommons.org/licenses/by/4.0/deed.en
title	Improving opportunities for data linkage within Children Looked After administrative records in Wales
spellingShingle	Improving opportunities for data linkage within Children Looked After administrative records in Wales Grace Bailey Alexandra Lee Saira Ahmed Ieuan Scanlon Laura Cowley Ian Farr Caroline Brooks Laura North Lucy Griffiths
title_short	Improving opportunities for data linkage within Children Looked After administrative records in Wales
title_full	Improving opportunities for data linkage within Children Looked After administrative records in Wales
title_fullStr	Improving opportunities for data linkage within Children Looked After administrative records in Wales
title_full_unstemmed	Improving opportunities for data linkage within Children Looked After administrative records in Wales
title_sort	Improving opportunities for data linkage within Children Looked After administrative records in Wales
author_id_str_mv	1e09a407fca9e8047e7738b18d381130 7c6dc217555b0fea264ff0dd7d0aa374 2bf49b38ca1517d326228b3e8fdf6e78 9fcb224c6bd804a4d41a2a8570a71185 a80501f280e89fee276510b25fc68e77 3c02e7e9c2b064ee3e96e83b9777dde4 ac99c6134cf75b4c3e5f63cbb1a149ee a255822cf77a0184cb6922e9fbea39e9 e35ea6ea4b429e812ef204b048131d93
author_id_fullname_str_mv	1e09a407fca9e8047e7738b18d381130_*_Grace Bailey 7c6dc217555b0fea264ff0dd7d0aa374__Alexandra Lee 2bf49b38ca1517d326228b3e8fdf6e78__Saira Ahmed 9fcb224c6bd804a4d41a2a8570a71185__Ieuan Scanlon a80501f280e89fee276510b25fc68e77__Laura Cowley 3c02e7e9c2b064ee3e96e83b9777dde4__Ian Farr ac99c6134cf75b4c3e5f63cbb1a149ee__Caroline Brooks a255822cf77a0184cb6922e9fbea39e9__Laura North e35ea6ea4b429e812ef204b048131d93_**_Lucy Griffiths
author	Grace Bailey Alexandra Lee Saira Ahmed Ieuan Scanlon Laura Cowley Ian Farr Caroline Brooks Laura North Lucy Griffiths
author2	Grace Bailey Alexandra Lee Saira Ahmed Ieuan Scanlon Laura Cowley Amy Stuart Ian Farr Caroline Brooks Laura North Lucy Griffiths
format	Journal article
container_title	International Journal of Population Data Science
container_volume	10
container_issue	1
publishDate	2025
institution	Swansea University
issn	2399-4908
doi_str_mv	10.23889/ijpds.v10i1.2383
publisher	Swansea University
college_str	Faculty of Medicine, Health and Life Sciences
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hierarchy_top_title	Faculty of Medicine, Health and Life Sciences
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hierarchy_parent_title	Faculty of Medicine, Health and Life Sciences
department_str	Swansea University Medical School - Health Data Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Health Data Science
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description	IntroductionLinkage of population-based administrative data is a powerful tool for studying important public issues. To overcome confidentiality and disclosure issues, records are de-identified and allocated a unique identifier. Within the Secure Anonymised Information Linkage (SAIL) Databank, these are known as Anonymised Linking Fields (ALFs). Assignment of an ALF enables linkage of individuals across multiple routinely collected datasets. Within the Children Looked After (CLA) Wales dataset, only 37% of the children have an ALF, limiting linkage to other datasets and, as a result, potential research. There are also other known data issues, including discrepancies with the week of births, duplicate identifiers and year-on-year changes in identifiers.ObjectivesTo improve accuracy and availability of the ALFs in the CLA dataset, and overall research quality.MethodsUsing several datasets within the SAIL Databank, we developed a six-step CLA matching algorithm to improve the ALF matching rate and correct for data errors. To assess the performance of our algorithm, we benchmarked against routine ALFs already identified via the algorithm currently used by SAIL.ResultsOur algorithm increased ALF matching by 25%, assigning 61% of individuals an ALF. Inconsistent weeks of birth, and incorrect and duplicate identifiers were resolved. When benchmarking against the current ALF-assigning algorithm used by SAIL, our algorithm had an overall sensitivity of 90%.ConclusionWe have developed an algorithm which demonstrates comparable ALF matching performance to the current algorithm used within SAIL, and which greatly improves the ALF matching in the CLA dataset. This algorithm may help to overcome potential bias due to missing data, and increases the potential for linkage to other datasets. Further development and refinement could result in the algorithm being applied to other datasets in SAIL.
published_date	2025-02-19T08:18:42Z
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score	11.058331

Improving opportunities for data linkage within Children Looked After administrative records in Wales

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