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An external validation of coding for childhood maltreatment in routinely collected primary and secondary care data

Ann John Orcid Logo, Joanna McGregor Orcid Logo, Amanda Marchant Orcid Logo, Marcos del Pozo Banos Orcid Logo, Ian Farr, Ulugbek Nurmatov, Alison Kemp, Aideen Naughton

Scientific Reports, Volume: 13, Issue: 1

Swansea University Authors: Ann John Orcid Logo, Joanna McGregor Orcid Logo, Amanda Marchant Orcid Logo, Marcos del Pozo Banos Orcid Logo, Ian Farr

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DOI (Published version): 10.1038/s41598-023-34011-3

Abstract

Background: Validated methods of identifying childhood maltreatment (CM) in primary and secondary care data are needed. We aimed to create the first externally validated algorithm for identifying maltreatment using routinely collected healthcare data.Methods: Comprehensive code lists were created fo...

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Published in: Scientific Reports
ISSN: 2045-2322
Published: Springer Science and Business Media LLC 2023
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We aimed to create the first externally validated algorithm for identifying maltreatment using routinely collected healthcare data.Methods: Comprehensive code lists were created for use within GP and hospital admissions datasets in the SAIL Databank at Swansea University working with safeguarding clinicians and academics. These code lists build on and refine those previously published to include an exhaustive set of codes. Sensitivity, specificity and positive predictive value of previously published lists and the new algorithm were estimated against a clinically assessed cohort of CM cases from a child protection service secondary care-based setting-‘the gold standard’. We conducted sensitivity analyses to examine the utility of wider codes indicating Possible CM.Trends over time from 2004-2020 were calculated using Poisson regression modellingResults Our algorithm outperformed previously published lists identifying 43-72% of cases in primary care with a specificity &gt;=85%. Sensitivity of algorithms for identifying maltreatment in hospital admissions data was lower identifying between 9-28% of cases with high specificity (&gt;96%). Manual searching of records for those cases identified by the external dataset but not recorded in primary care suggest that this code list is exhaustive. Exploration of missed cases shows that hospital admissions data is often focused on the injury being treated rather than recording the presence of maltreatment. The absence of child protection or social care codes in hospital admissions data poses a limitation for identifying maltreatment in admissions data.Linking across GP and hospital admissions maximises the number of cases of maltreatment that can be accurately identified. Incidence of maltreatment in primary care using these code lists has increased over time.Conclusions:The updated algorithm has improved our ability to detect CM in routinely collected healthcare data. It is important to recognize the limitations of identifying maltreatment in individual healthcare datasets. The inclusion of child protection codes in primary care data makes this an important setting for identifying CM, whereas hospital admissions data is often focused on injuries with CM codes often absent. Implications and utility of algorithms for future research are discussed.</abstract><type>Journal Article</type><journal>Scientific Reports</journal><volume>13</volume><journalNumber>1</journalNumber><paginationStart/><paginationEnd/><publisher>Springer Science and Business Media LLC</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic>2045-2322</issnElectronic><keywords>Child maltreatment, Adverse childhood experiences, validation, incidence</keywords><publishedDay>19</publishedDay><publishedMonth>5</publishedMonth><publishedYear>2023</publishedYear><publishedDate>2023-05-19</publishedDate><doi>10.1038/s41598-023-34011-3</doi><url>http://dx.doi.org/10.1038/s41598-023-34011-3</url><notes/><college>COLLEGE NANME</college><department>Health Data Science</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>HDAT</DepartmentCode><institution>Swansea University</institution><apcterm>Another institution paid the OA fee</apcterm><funders>MQ, MRC Pathfinder, Wolfson, MRC and HDRUK though DATAMIND. 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spelling v2 63249 2023-04-26 An external validation of coding for childhood maltreatment in routinely collected primary and secondary care data ed8a9c37bd7b7235b762d941ef18ee55 0000-0002-5657-6995 Ann John Ann John true false caa651da7e3807cbeac8ec2f40643677 0000-0003-0242-4600 Joanna McGregor Joanna McGregor true false 0776f450dd575004ba7c69930c579cae 0000-0001-7013-6980 Amanda Marchant Amanda Marchant true false f141785b1c0ab9efe45665d35c081b84 0000-0003-1502-389X Marcos del Pozo Banos Marcos del Pozo Banos true false 3c02e7e9c2b064ee3e96e83b9777dde4 Ian Farr Ian Farr true false 2023-04-26 HDAT Background: Validated methods of identifying childhood maltreatment (CM) in primary and secondary care data are needed. We aimed to create the first externally validated algorithm for identifying maltreatment using routinely collected healthcare data.Methods: Comprehensive code lists were created for use within GP and hospital admissions datasets in the SAIL Databank at Swansea University working with safeguarding clinicians and academics. These code lists build on and refine those previously published to include an exhaustive set of codes. Sensitivity, specificity and positive predictive value of previously published lists and the new algorithm were estimated against a clinically assessed cohort of CM cases from a child protection service secondary care-based setting-‘the gold standard’. We conducted sensitivity analyses to examine the utility of wider codes indicating Possible CM.Trends over time from 2004-2020 were calculated using Poisson regression modellingResults Our algorithm outperformed previously published lists identifying 43-72% of cases in primary care with a specificity >=85%. Sensitivity of algorithms for identifying maltreatment in hospital admissions data was lower identifying between 9-28% of cases with high specificity (>96%). Manual searching of records for those cases identified by the external dataset but not recorded in primary care suggest that this code list is exhaustive. Exploration of missed cases shows that hospital admissions data is often focused on the injury being treated rather than recording the presence of maltreatment. The absence of child protection or social care codes in hospital admissions data poses a limitation for identifying maltreatment in admissions data.Linking across GP and hospital admissions maximises the number of cases of maltreatment that can be accurately identified. Incidence of maltreatment in primary care using these code lists has increased over time.Conclusions:The updated algorithm has improved our ability to detect CM in routinely collected healthcare data. It is important to recognize the limitations of identifying maltreatment in individual healthcare datasets. The inclusion of child protection codes in primary care data makes this an important setting for identifying CM, whereas hospital admissions data is often focused on injuries with CM codes often absent. Implications and utility of algorithms for future research are discussed. Journal Article Scientific Reports 13 1 Springer Science and Business Media LLC 2045-2322 Child maltreatment, Adverse childhood experiences, validation, incidence 19 5 2023 2023-05-19 10.1038/s41598-023-34011-3 http://dx.doi.org/10.1038/s41598-023-34011-3 COLLEGE NANME Health Data Science COLLEGE CODE HDAT Swansea University Another institution paid the OA fee MQ, MRC Pathfinder, Wolfson, MRC and HDRUK though DATAMIND. Grant information: MQBF/3ADP, MC_PC_17211, 517483, MR/W014386/1. MQBF/3ADP, MC_PC_17211, 517483, MR/W014386/1 2023-05-24T15:04:34.2414546 2023-04-26T07:58:41.9967552 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Health Data Science Ann John 0000-0002-5657-6995 1 Joanna McGregor 0000-0003-0242-4600 2 Amanda Marchant 0000-0001-7013-6980 3 Marcos del Pozo Banos 0000-0003-1502-389X 4 Ian Farr 5 Ulugbek Nurmatov 6 Alison Kemp 7 Aideen Naughton 8 63249__27556__f3798e64324f4c749f2cad4d57f9491c.pdf 63249.VOR.ScientificReports.pdf 2023-05-22T12:25:45.0250027 Output 1084188 application/pdf Version of Record true This article is licensed under a CC-BY Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. true eng http://creativecommons.org/licenses/by/4.0/
title An external validation of coding for childhood maltreatment in routinely collected primary and secondary care data
spellingShingle An external validation of coding for childhood maltreatment in routinely collected primary and secondary care data
Ann John
Joanna McGregor
Amanda Marchant
Marcos del Pozo Banos
Ian Farr
title_short An external validation of coding for childhood maltreatment in routinely collected primary and secondary care data
title_full An external validation of coding for childhood maltreatment in routinely collected primary and secondary care data
title_fullStr An external validation of coding for childhood maltreatment in routinely collected primary and secondary care data
title_full_unstemmed An external validation of coding for childhood maltreatment in routinely collected primary and secondary care data
title_sort An external validation of coding for childhood maltreatment in routinely collected primary and secondary care data
author_id_str_mv ed8a9c37bd7b7235b762d941ef18ee55
caa651da7e3807cbeac8ec2f40643677
0776f450dd575004ba7c69930c579cae
f141785b1c0ab9efe45665d35c081b84
3c02e7e9c2b064ee3e96e83b9777dde4
author_id_fullname_str_mv ed8a9c37bd7b7235b762d941ef18ee55_***_Ann John
caa651da7e3807cbeac8ec2f40643677_***_Joanna McGregor
0776f450dd575004ba7c69930c579cae_***_Amanda Marchant
f141785b1c0ab9efe45665d35c081b84_***_Marcos del Pozo Banos
3c02e7e9c2b064ee3e96e83b9777dde4_***_Ian Farr
author Ann John
Joanna McGregor
Amanda Marchant
Marcos del Pozo Banos
Ian Farr
author2 Ann John
Joanna McGregor
Amanda Marchant
Marcos del Pozo Banos
Ian Farr
Ulugbek Nurmatov
Alison Kemp
Aideen Naughton
format Journal article
container_title Scientific Reports
container_volume 13
container_issue 1
publishDate 2023
institution Swansea University
issn 2045-2322
doi_str_mv 10.1038/s41598-023-34011-3
publisher Springer Science and Business Media LLC
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
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
url http://dx.doi.org/10.1038/s41598-023-34011-3
document_store_str 1
active_str 0
description Background: Validated methods of identifying childhood maltreatment (CM) in primary and secondary care data are needed. We aimed to create the first externally validated algorithm for identifying maltreatment using routinely collected healthcare data.Methods: Comprehensive code lists were created for use within GP and hospital admissions datasets in the SAIL Databank at Swansea University working with safeguarding clinicians and academics. These code lists build on and refine those previously published to include an exhaustive set of codes. Sensitivity, specificity and positive predictive value of previously published lists and the new algorithm were estimated against a clinically assessed cohort of CM cases from a child protection service secondary care-based setting-‘the gold standard’. We conducted sensitivity analyses to examine the utility of wider codes indicating Possible CM.Trends over time from 2004-2020 were calculated using Poisson regression modellingResults Our algorithm outperformed previously published lists identifying 43-72% of cases in primary care with a specificity >=85%. Sensitivity of algorithms for identifying maltreatment in hospital admissions data was lower identifying between 9-28% of cases with high specificity (>96%). Manual searching of records for those cases identified by the external dataset but not recorded in primary care suggest that this code list is exhaustive. Exploration of missed cases shows that hospital admissions data is often focused on the injury being treated rather than recording the presence of maltreatment. The absence of child protection or social care codes in hospital admissions data poses a limitation for identifying maltreatment in admissions data.Linking across GP and hospital admissions maximises the number of cases of maltreatment that can be accurately identified. Incidence of maltreatment in primary care using these code lists has increased over time.Conclusions:The updated algorithm has improved our ability to detect CM in routinely collected healthcare data. It is important to recognize the limitations of identifying maltreatment in individual healthcare datasets. The inclusion of child protection codes in primary care data makes this an important setting for identifying CM, whereas hospital admissions data is often focused on injuries with CM codes often absent. Implications and utility of algorithms for future research are discussed.
published_date 2023-05-19T15:04:33Z
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