No Cover Image

E-Thesis 34 views 5 downloads

What works to protect disadvantaged children and their families: a linked routine data approach / AMRITA BANDYOPADHYAY

Swansea University Author: AMRITA BANDYOPADHYAY

  • 2026_Bandyopadhyay_A.final.71745.pdf

    PDF | E-Thesis – open access

    Copyright: the author, Amrita Bandyopadhyay, 2026. Copyright: the author, Amrita Bandyopadhyay, 2026

    Download (8.42MB)

DOI (Published version): 10.23889/SUThesis.71745

Abstract

Aim: This thesis conducts a data-driven, population-level investigation into risk factors of early-life vulnerabilities using linked routine administrative data, integrated and harmonised with health, education and socio-economic records.Method: The primary areas of vulnerability examined in this th...

Full description

Published: Swansea 2026
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
Supervisor: Kennedy, N.
URI: https://cronfa.swan.ac.uk/Record/cronfa71745
first_indexed 2026-04-15T10:16:32Z
last_indexed 2026-04-17T03:55:23Z
id cronfa71745
recordtype RisThesis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2026-04-15T11:34:20.6533729</datestamp><bib-version>v2</bib-version><id>71745</id><entry>2026-04-15</entry><title>What works to protect disadvantaged children and their families: a linked routine data approach</title><swanseaauthors><author><sid>897b285b5396f86a1b45602fd823f297</sid><firstname>AMRITA</firstname><surname>BANDYOPADHYAY</surname><name>AMRITA BANDYOPADHYAY</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2026-04-15</date><abstract>Aim: This thesis conducts a data-driven, population-level investigation into risk factors of early-life vulnerabilities using linked routine administrative data, integrated and harmonised with health, education and socio-economic records.Method: The primary areas of vulnerability examined in this thesis include low birth weight, low school readiness, living in deprived areas, exposure to domestic abuse, early alcohol use, injury risk and mental health challenges. Data-driven models using advanced statistical methods (logistic regression, negative binomial regression, Cox hazard regression) and machine learning techniques (feature selection and decision trees) are employed to identify signi&#xFB01;cant risk factors and their association with vulnerabilities. The Wales Electronic Cohort for Children Phase 4 has been established through this research, compiling health, education and social care data of children born or growing up in Wales.Results: Consistent risk factors for low birth weight, low school readiness or poor academic outcomes include children living in deprivation, and poor maternal mental and physical health. Lifestyle issues such as maternal smoking, clinically signi&#xFB01;cant alcohol use and substance abuse within families further exacerbate these vulnerabilities.Results reveal that children at risk of adverse outcomes, including early alcohol use and domestic abuse exposure, have fewer routine primary care contacts and more frequent emergency healthcare interactions, indicating neglect and challenging family circumstances for these children.Conclusion: The &#xFB01;ndings demonstrate that data-driven methods can identify the signs of neglect and the associated vulnerable population from linked routine data early on in their life. This research has led to nine published papers, contributing to a strong evidence base for policies and practices aimed at improving the life chances of disadvantaged children and shaping their life trajectories.</abstract><type>E-Thesis</type><journal/><volume/><journalNumber/><paginationStart/><paginationEnd/><publisher/><placeOfPublication>Swansea</placeOfPublication><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic/><keywords>Early Years, Vulnerability, Data linkage, Data-driven, Machine Learning, Routine administrative data, Disadvantages children and families</keywords><publishedDay>6</publishedDay><publishedMonth>3</publishedMonth><publishedYear>2026</publishedYear><publishedDate>2026-03-06</publishedDate><doi>10.23889/SUThesis.71745</doi><url/><notes/><college>COLLEGE NANME</college><CollegeCode>COLLEGE CODE</CollegeCode><institution>Swansea University</institution><supervisor>Kennedy, N.</supervisor><degreelevel>Doctoral</degreelevel><degreename>Ph.D</degreename><apcterm/><funders/><projectreference/><lastEdited>2026-04-15T11:34:20.6533729</lastEdited><Created>2026-04-15T10:52:39.9132966</Created><path><level id="1">Faculty of Medicine, Health and Life Sciences</level><level id="2">Swansea University Medical School - Biomedical Science</level></path><authors><author><firstname>AMRITA</firstname><surname>BANDYOPADHYAY</surname><order>1</order></author></authors><documents><document><filename>71745__36512__0d0e4b37978d4e4c94a0313f3739498c.pdf</filename><originalFilename>2026_Bandyopadhyay_A.final.71745.pdf</originalFilename><uploaded>2026-04-15T11:11:58.8294054</uploaded><type>Output</type><contentLength>8826172</contentLength><contentType>application/pdf</contentType><version>E-Thesis &#x2013; open access</version><cronfaStatus>true</cronfaStatus><documentNotes>Copyright: the author, Amrita Bandyopadhyay, 2026. Copyright: the author, Amrita Bandyopadhyay, 2026</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>https://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2026-04-15T11:34:20.6533729 v2 71745 2026-04-15 What works to protect disadvantaged children and their families: a linked routine data approach 897b285b5396f86a1b45602fd823f297 AMRITA BANDYOPADHYAY AMRITA BANDYOPADHYAY true false 2026-04-15 Aim: This thesis conducts a data-driven, population-level investigation into risk factors of early-life vulnerabilities using linked routine administrative data, integrated and harmonised with health, education and socio-economic records.Method: The primary areas of vulnerability examined in this thesis include low birth weight, low school readiness, living in deprived areas, exposure to domestic abuse, early alcohol use, injury risk and mental health challenges. Data-driven models using advanced statistical methods (logistic regression, negative binomial regression, Cox hazard regression) and machine learning techniques (feature selection and decision trees) are employed to identify significant risk factors and their association with vulnerabilities. The Wales Electronic Cohort for Children Phase 4 has been established through this research, compiling health, education and social care data of children born or growing up in Wales.Results: Consistent risk factors for low birth weight, low school readiness or poor academic outcomes include children living in deprivation, and poor maternal mental and physical health. Lifestyle issues such as maternal smoking, clinically significant alcohol use and substance abuse within families further exacerbate these vulnerabilities.Results reveal that children at risk of adverse outcomes, including early alcohol use and domestic abuse exposure, have fewer routine primary care contacts and more frequent emergency healthcare interactions, indicating neglect and challenging family circumstances for these children.Conclusion: The findings demonstrate that data-driven methods can identify the signs of neglect and the associated vulnerable population from linked routine data early on in their life. This research has led to nine published papers, contributing to a strong evidence base for policies and practices aimed at improving the life chances of disadvantaged children and shaping their life trajectories. E-Thesis Swansea Early Years, Vulnerability, Data linkage, Data-driven, Machine Learning, Routine administrative data, Disadvantages children and families 6 3 2026 2026-03-06 10.23889/SUThesis.71745 COLLEGE NANME COLLEGE CODE Swansea University Kennedy, N. Doctoral Ph.D 2026-04-15T11:34:20.6533729 2026-04-15T10:52:39.9132966 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Biomedical Science AMRITA BANDYOPADHYAY 1 71745__36512__0d0e4b37978d4e4c94a0313f3739498c.pdf 2026_Bandyopadhyay_A.final.71745.pdf 2026-04-15T11:11:58.8294054 Output 8826172 application/pdf E-Thesis – open access true Copyright: the author, Amrita Bandyopadhyay, 2026. Copyright: the author, Amrita Bandyopadhyay, 2026 true eng https://creativecommons.org/licenses/by/4.0/
title What works to protect disadvantaged children and their families: a linked routine data approach
spellingShingle What works to protect disadvantaged children and their families: a linked routine data approach
AMRITA BANDYOPADHYAY
title_short What works to protect disadvantaged children and their families: a linked routine data approach
title_full What works to protect disadvantaged children and their families: a linked routine data approach
title_fullStr What works to protect disadvantaged children and their families: a linked routine data approach
title_full_unstemmed What works to protect disadvantaged children and their families: a linked routine data approach
title_sort What works to protect disadvantaged children and their families: a linked routine data approach
author_id_str_mv 897b285b5396f86a1b45602fd823f297
author_id_fullname_str_mv 897b285b5396f86a1b45602fd823f297_***_AMRITA BANDYOPADHYAY
author AMRITA BANDYOPADHYAY
author2 AMRITA BANDYOPADHYAY
format E-Thesis
publishDate 2026
institution Swansea University
doi_str_mv 10.23889/SUThesis.71745
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 - Biomedical Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Biomedical Science
document_store_str 1
active_str 0
description Aim: This thesis conducts a data-driven, population-level investigation into risk factors of early-life vulnerabilities using linked routine administrative data, integrated and harmonised with health, education and socio-economic records.Method: The primary areas of vulnerability examined in this thesis include low birth weight, low school readiness, living in deprived areas, exposure to domestic abuse, early alcohol use, injury risk and mental health challenges. Data-driven models using advanced statistical methods (logistic regression, negative binomial regression, Cox hazard regression) and machine learning techniques (feature selection and decision trees) are employed to identify significant risk factors and their association with vulnerabilities. The Wales Electronic Cohort for Children Phase 4 has been established through this research, compiling health, education and social care data of children born or growing up in Wales.Results: Consistent risk factors for low birth weight, low school readiness or poor academic outcomes include children living in deprivation, and poor maternal mental and physical health. Lifestyle issues such as maternal smoking, clinically significant alcohol use and substance abuse within families further exacerbate these vulnerabilities.Results reveal that children at risk of adverse outcomes, including early alcohol use and domestic abuse exposure, have fewer routine primary care contacts and more frequent emergency healthcare interactions, indicating neglect and challenging family circumstances for these children.Conclusion: The findings demonstrate that data-driven methods can identify the signs of neglect and the associated vulnerable population from linked routine data early on in their life. This research has led to nine published papers, contributing to a strong evidence base for policies and practices aimed at improving the life chances of disadvantaged children and shaping their life trajectories.
published_date 2026-03-06T07:40:16Z
_version_ 1862698812408070144
score 11.102298

Similar Items