Insights from linking police domestic abuse data and health data in South Wales, UK: a linked routine data analysis using decision tree classification

Kennedy, Tash; Win, Tint Lwin; Bandyopadhyay, Amrita; Kennedy, Jonathan; Rowe, Benjamin; McNerney, Cynthia; Evans, Julie; Hughes, Karen; Bellis, Mark A; Jones, Angela; Harrington, Karen; Moore, Simon; Brophy, Sinead

doi:10.1016/s2468-2667(23)00126-3

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Insights from linking police domestic abuse data and health data in South Wales, UK: a linked routine data analysis using decision tree classification

Tash Kennedy

, Tint Lwin Win, Amrita Bandyopadhyay, Jonathan Kennedy, Benjamin Rowe, Cynthia McNerney, Julie Evans, Karen Hughes, Mark A Bellis, Angela Jones, Karen Harrington, Simon Moore, Sinead Brophy

The Lancet Public Health, Volume: 8, Issue: 8, Pages: e629 - e638

Swansea University Authors: Tash Kennedy , Amrita Bandyopadhyay, Jonathan Kennedy, Cynthia McNerney, Sinead Brophy

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DOI (Published version): 10.1016/s2468-2667(23)00126-3

Abstract

Background: Exposure to domestic abuse can lead to long-term negative impacts on the victim's physical and psychological wellbeing. The 1998 Crime and Disorder Act requires agencies to collaborate on crime reduction strategies, including data sharing. Although data sharing is feasible for indiv...

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Published in:	The Lancet Public Health
ISSN:	2468-2667 2468-2667
Published:	Elsevier BV 2023
Online Access:	Check full text
URI:	https://cronfa.swan.ac.uk/Record/cronfa64522

first_indexed	2023-09-13T09:54:22Z
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The 1998 Crime and Disorder Act requires agencies to collaborate on crime reduction strategies, including data sharing. Although data sharing is feasible for individuals, rarely are whole-agency data linked. This study aimed to examine the knowledge obtained by integrating information from police and health-care datasets through data linkage and analyse associated risk factor clusters. Methods: This retrospective cohort study analyses data from residents of South Wales who were victims of domestic abuse resulting in a Public Protection Notification (PPN) submission between Aug 12, 2015 and March 31, 2020. The study links these data with the victims’ health records, collated within the Secure Anonymised Information Linkage databank, to examine factors associated with the outcome of an Emergency Department attendance, emergency hospital admission, or death within 12 months of the PPN submission. To assess the time to outcome for domestic abuse victims after the index PPN submission, we used Kaplan-Meier survival analysis. We used multivariable Cox regression models to identify which factors contributed the highest risk of experiencing an outcome after the index PPN submission. Finally, we created decision trees to describe specific groups of individuals who are at risk of experiencing a domestic abuse incident and subsequent outcome. Findings: After excluding individuals with multiple PPN records, duplicates, and records with a poor matching score or missing fields, the resulting clean dataset consisted of 8709 domestic abuse victims, of whom 6257 (71·8%) were female. Within a year of a domestic abuse incident, 3650 (41·9%) individuals had an outcome. Factors associated with experiencing an outcome within 12 months of the PPN included younger victim age (hazard ratio 1·183 [95% CI 1·053–1·329], p=0·0048), further PPN submissions after the initial referral (1·383 [1·295–1·476]; p<0·0001), injury at the scene (1·484 [1·368–1·609]; p<0·0001), assessed high risk (1·600 [1·444–1·773]; p<0·0001), referral to other agencies (1·518 [1·358–1·697]; p<0·0001), history of violence (1·229 [1·134–1·333]; p<0·0001), attempted strangulation (1·311 [1·148–1·497]; p<0·0001), and pregnancy (1·372 [1·142–1·648]; p=0·0007). Health-care data before the index PPN established that previous Emergency Department and hospital admissions, smoking, smoking cessation advice, obstetric codes, and prescription of antidepressants and antibiotics were associated with having a future outcome following a domestic abuse incident. Interpretation: The results indicate that vulnerable individuals are detectable in multiple datasets before and after involvement of the police. Operationalising these findings could reduce police callouts and future Emergency Department or hospital admissions, and improve outcomes for those who are vulnerable. Strategies include querying previous Emergency Department and hospital admissions, giving a high-risk assessment for a pregnant victim, and facilitating data linkage to identify vulnerable individuals.</abstract><type>Journal Article</type><journal>The Lancet Public Health</journal><volume>8</volume><journalNumber>8</journalNumber><paginationStart>e629</paginationStart><paginationEnd>e638</paginationEnd><publisher>Elsevier BV</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>2468-2667</issnPrint><issnElectronic>2468-2667</issnElectronic><keywords>Domestic abuse, police data, health data, decision tree classification, SAIL databank</keywords><publishedDay>31</publishedDay><publishedMonth>8</publishedMonth><publishedYear>2023</publishedYear><publishedDate>2023-08-31</publishedDate><doi>10.1016/s2468-2667(23)00126-3</doi><url>http://dx.doi.org/10.1016/s2468-2667(23)00126-3</url><notes/><college>COLLEGE NANME</college><department>Medical School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MEDS</DepartmentCode><institution>Swansea University</institution><apcterm/><funders>This research was funded by the National Institute for Health Research, Public Health Research Board (reference number NIHR133680: Unlocking Data to Inform Public Health Policy and Practice). The study was also supported by Health Care Research Wales through the National Centre for Population Health and Wellbeing Research, supported by ESRC through Administrative Data Research Wales, and received infrastructure support through Health Data Research UK. This study makes use of anonymised data held in the SAIL databank. We would like to acknowledge all the data providers who make anonymised data available for research.</funders><projectreference/><lastEdited>2023-10-02T13:34:34.0117273</lastEdited><Created>2023-09-13T10:47:56.0553968</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>Tash</firstname><surname>Kennedy</surname><orcid>0000-0002-1500-7112</orcid><order>1</order></author><author><firstname>Tint Lwin</firstname><surname>Win</surname><order>2</order></author><author><firstname>Amrita</firstname><surname>Bandyopadhyay</surname><orcid/><order>3</order></author><author><firstname>Jonathan</firstname><surname>Kennedy</surname><orcid/><order>4</order></author><author><firstname>Benjamin</firstname><surname>Rowe</surname><order>5</order></author><author><firstname>Cynthia</firstname><surname>McNerney</surname><orcid/><order>6</order></author><author><firstname>Julie</firstname><surname>Evans</surname><order>7</order></author><author><firstname>Karen</firstname><surname>Hughes</surname><order>8</order></author><author><firstname>Mark A</firstname><surname>Bellis</surname><order>9</order></author><author><firstname>Angela</firstname><surname>Jones</surname><order>10</order></author><author><firstname>Karen</firstname><surname>Harrington</surname><order>11</order></author><author><firstname>Simon</firstname><surname>Moore</surname><order>12</order></author><author><firstname>Sinead</firstname><surname>Brophy</surname><orcid>0000-0001-7417-2858</orcid><order>13</order></author></authors><documents><document><filename>64522__28524__1350dded3384401186e4194e458d87be.pdf</filename><originalFilename>64522.VOR.pdf</originalFilename><uploaded>2023-09-13T11:00:54.8488235</uploaded><type>Output</type><contentLength>585443</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>© 2023 The Author(s). 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spelling	2023-10-02T13:34:34.0117273 v2 64522 2023-09-13 Insights from linking police domestic abuse data and health data in South Wales, UK: a linked routine data analysis using decision tree classification 3f6f07de33204db4c0ab665fb4b36367 0000-0002-1500-7112 Tash Kennedy Tash Kennedy true false 9f1e77f76a83746112ef45709bf83630 Amrita Bandyopadhyay Amrita Bandyopadhyay true false 08163d1f58d7fefcb1c695bcc2e0ef68 Jonathan Kennedy Jonathan Kennedy true false 72a863680d277585888649ae8e0bbeae Cynthia McNerney Cynthia McNerney true false 84f5661b35a729f55047f9e793d8798b 0000-0001-7417-2858 Sinead Brophy Sinead Brophy true false 2023-09-13 MEDS Background: Exposure to domestic abuse can lead to long-term negative impacts on the victim's physical and psychological wellbeing. The 1998 Crime and Disorder Act requires agencies to collaborate on crime reduction strategies, including data sharing. Although data sharing is feasible for individuals, rarely are whole-agency data linked. This study aimed to examine the knowledge obtained by integrating information from police and health-care datasets through data linkage and analyse associated risk factor clusters. Methods: This retrospective cohort study analyses data from residents of South Wales who were victims of domestic abuse resulting in a Public Protection Notification (PPN) submission between Aug 12, 2015 and March 31, 2020. The study links these data with the victims’ health records, collated within the Secure Anonymised Information Linkage databank, to examine factors associated with the outcome of an Emergency Department attendance, emergency hospital admission, or death within 12 months of the PPN submission. To assess the time to outcome for domestic abuse victims after the index PPN submission, we used Kaplan-Meier survival analysis. We used multivariable Cox regression models to identify which factors contributed the highest risk of experiencing an outcome after the index PPN submission. Finally, we created decision trees to describe specific groups of individuals who are at risk of experiencing a domestic abuse incident and subsequent outcome. Findings: After excluding individuals with multiple PPN records, duplicates, and records with a poor matching score or missing fields, the resulting clean dataset consisted of 8709 domestic abuse victims, of whom 6257 (71·8%) were female. Within a year of a domestic abuse incident, 3650 (41·9%) individuals had an outcome. Factors associated with experiencing an outcome within 12 months of the PPN included younger victim age (hazard ratio 1·183 [95% CI 1·053–1·329], p=0·0048), further PPN submissions after the initial referral (1·383 [1·295–1·476]; p<0·0001), injury at the scene (1·484 [1·368–1·609]; p<0·0001), assessed high risk (1·600 [1·444–1·773]; p<0·0001), referral to other agencies (1·518 [1·358–1·697]; p<0·0001), history of violence (1·229 [1·134–1·333]; p<0·0001), attempted strangulation (1·311 [1·148–1·497]; p<0·0001), and pregnancy (1·372 [1·142–1·648]; p=0·0007). Health-care data before the index PPN established that previous Emergency Department and hospital admissions, smoking, smoking cessation advice, obstetric codes, and prescription of antidepressants and antibiotics were associated with having a future outcome following a domestic abuse incident. Interpretation: The results indicate that vulnerable individuals are detectable in multiple datasets before and after involvement of the police. Operationalising these findings could reduce police callouts and future Emergency Department or hospital admissions, and improve outcomes for those who are vulnerable. Strategies include querying previous Emergency Department and hospital admissions, giving a high-risk assessment for a pregnant victim, and facilitating data linkage to identify vulnerable individuals. Journal Article The Lancet Public Health 8 8 e629 e638 Elsevier BV 2468-2667 2468-2667 Domestic abuse, police data, health data, decision tree classification, SAIL databank 31 8 2023 2023-08-31 10.1016/s2468-2667(23)00126-3 http://dx.doi.org/10.1016/s2468-2667(23)00126-3 COLLEGE NANME Medical School COLLEGE CODE MEDS Swansea University This research was funded by the National Institute for Health Research, Public Health Research Board (reference number NIHR133680: Unlocking Data to Inform Public Health Policy and Practice). The study was also supported by Health Care Research Wales through the National Centre for Population Health and Wellbeing Research, supported by ESRC through Administrative Data Research Wales, and received infrastructure support through Health Data Research UK. This study makes use of anonymised data held in the SAIL databank. We would like to acknowledge all the data providers who make anonymised data available for research. 2023-10-02T13:34:34.0117273 2023-09-13T10:47:56.0553968 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Health Data Science Tash Kennedy 0000-0002-1500-7112 1 Tint Lwin Win 2 Amrita Bandyopadhyay 3 Jonathan Kennedy 4 Benjamin Rowe 5 Cynthia McNerney 6 Julie Evans 7 Karen Hughes 8 Mark A Bellis 9 Angela Jones 10 Karen Harrington 11 Simon Moore 12 Sinead Brophy 0000-0001-7417-2858 13 64522__28524__1350dded3384401186e4194e458d87be.pdf 64522.VOR.pdf 2023-09-13T11:00:54.8488235 Output 585443 application/pdf Version of Record true © 2023 The Author(s). Published by Elsevier Ltd. Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0). true eng https://creativecommons.org/licenses/by/4.0/
title	Insights from linking police domestic abuse data and health data in South Wales, UK: a linked routine data analysis using decision tree classification
spellingShingle	Insights from linking police domestic abuse data and health data in South Wales, UK: a linked routine data analysis using decision tree classification Tash Kennedy Amrita Bandyopadhyay Jonathan Kennedy Cynthia McNerney Sinead Brophy
title_short	Insights from linking police domestic abuse data and health data in South Wales, UK: a linked routine data analysis using decision tree classification
title_full	Insights from linking police domestic abuse data and health data in South Wales, UK: a linked routine data analysis using decision tree classification
title_fullStr	Insights from linking police domestic abuse data and health data in South Wales, UK: a linked routine data analysis using decision tree classification
title_full_unstemmed	Insights from linking police domestic abuse data and health data in South Wales, UK: a linked routine data analysis using decision tree classification
title_sort	Insights from linking police domestic abuse data and health data in South Wales, UK: a linked routine data analysis using decision tree classification
author_id_str_mv	3f6f07de33204db4c0ab665fb4b36367 9f1e77f76a83746112ef45709bf83630 08163d1f58d7fefcb1c695bcc2e0ef68 72a863680d277585888649ae8e0bbeae 84f5661b35a729f55047f9e793d8798b
author_id_fullname_str_mv	3f6f07de33204db4c0ab665fb4b36367_*_Tash Kennedy 9f1e77f76a83746112ef45709bf83630__Amrita Bandyopadhyay 08163d1f58d7fefcb1c695bcc2e0ef68__Jonathan Kennedy 72a863680d277585888649ae8e0bbeae__Cynthia McNerney 84f5661b35a729f55047f9e793d8798b_**_Sinead Brophy
author	Tash Kennedy Amrita Bandyopadhyay Jonathan Kennedy Cynthia McNerney Sinead Brophy
author2	Tash Kennedy Tint Lwin Win Amrita Bandyopadhyay Jonathan Kennedy Benjamin Rowe Cynthia McNerney Julie Evans Karen Hughes Mark A Bellis Angela Jones Karen Harrington Simon Moore Sinead Brophy
format	Journal article
container_title	The Lancet Public Health
container_volume	8
container_issue	8
container_start_page	e629
publishDate	2023
institution	Swansea University
issn	2468-2667 2468-2667
doi_str_mv	10.1016/s2468-2667(23)00126-3
publisher	Elsevier BV
college_str	Faculty of Medicine, Health and Life Sciences
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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/10.1016/s2468-2667(23)00126-3
document_store_str	1
active_str	0
description	Background: Exposure to domestic abuse can lead to long-term negative impacts on the victim's physical and psychological wellbeing. The 1998 Crime and Disorder Act requires agencies to collaborate on crime reduction strategies, including data sharing. Although data sharing is feasible for individuals, rarely are whole-agency data linked. This study aimed to examine the knowledge obtained by integrating information from police and health-care datasets through data linkage and analyse associated risk factor clusters. Methods: This retrospective cohort study analyses data from residents of South Wales who were victims of domestic abuse resulting in a Public Protection Notification (PPN) submission between Aug 12, 2015 and March 31, 2020. The study links these data with the victims’ health records, collated within the Secure Anonymised Information Linkage databank, to examine factors associated with the outcome of an Emergency Department attendance, emergency hospital admission, or death within 12 months of the PPN submission. To assess the time to outcome for domestic abuse victims after the index PPN submission, we used Kaplan-Meier survival analysis. We used multivariable Cox regression models to identify which factors contributed the highest risk of experiencing an outcome after the index PPN submission. Finally, we created decision trees to describe specific groups of individuals who are at risk of experiencing a domestic abuse incident and subsequent outcome. Findings: After excluding individuals with multiple PPN records, duplicates, and records with a poor matching score or missing fields, the resulting clean dataset consisted of 8709 domestic abuse victims, of whom 6257 (71·8%) were female. Within a year of a domestic abuse incident, 3650 (41·9%) individuals had an outcome. Factors associated with experiencing an outcome within 12 months of the PPN included younger victim age (hazard ratio 1·183 [95% CI 1·053–1·329], p=0·0048), further PPN submissions after the initial referral (1·383 [1·295–1·476]; p<0·0001), injury at the scene (1·484 [1·368–1·609]; p<0·0001), assessed high risk (1·600 [1·444–1·773]; p<0·0001), referral to other agencies (1·518 [1·358–1·697]; p<0·0001), history of violence (1·229 [1·134–1·333]; p<0·0001), attempted strangulation (1·311 [1·148–1·497]; p<0·0001), and pregnancy (1·372 [1·142–1·648]; p=0·0007). Health-care data before the index PPN established that previous Emergency Department and hospital admissions, smoking, smoking cessation advice, obstetric codes, and prescription of antidepressants and antibiotics were associated with having a future outcome following a domestic abuse incident. Interpretation: The results indicate that vulnerable individuals are detectable in multiple datasets before and after involvement of the police. Operationalising these findings could reduce police callouts and future Emergency Department or hospital admissions, and improve outcomes for those who are vulnerable. Strategies include querying previous Emergency Department and hospital admissions, giving a high-risk assessment for a pregnant victim, and facilitating data linkage to identify vulnerable individuals.
published_date	2023-08-31T20:25:06Z
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Insights from linking police domestic abuse data and health data in South Wales, UK: a linked routine data analysis using decision tree classification

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