Pub Date : 2023-09-18DOI: 10.23889/ijpds.v8i3.2279
Nina Di Cara, Natalie Zelenka, Oliver Davis, Claire Haworth
Introduction & BackgroundHealth research using digital footprint data often involves the collection and use of large datasets that contain deeply personal information to make inferences about the course and onset of illness. In this context, innovating responsibly is essential for the field to develop safe, trustworthy and, ultimately, ethical research.
The inherent interdisciplinarity of digital footprints research can be a challenge to this aim, with different fields having different ethical norms and standards. As well as this, there has been a strong focus to date on traditional ethical issues such as privacy, which do not necessarily account for the breadth of issues that arise in data science and internet-based work.
Objectives & ApproachData Hazards is an open-source project that aims to provide a controlled vocabulary of ethical risks (Data Hazards) that can arise from data science research and its implementation. This vocabulary is presented as a set of 11 Hazard labels (v1.0) each with a visual icon and a set of safety precautions.
Over three events in 2021-2022 we invited feedback from researchers who volunteered to take part in a Data Hazards workshop (N=15). They varied from PhD students to professors and worked across a range of disciplines, and were asked to discuss the case of mental health prediction from Twitter.
Relevance to Digital FootprintsSince digital footprint technologies have great potential to pave the way for earlier and more personal medical treatment, it is important for researchers to be able to innovate whilst considering and communicating risk. We can then collaborate to establish effective safety precautions that allow us to maintain research momentum, without compromising safety or trust.
ResultsBased on discussion at the workshops and surveys completed by participants, four main Data Hazards were raised for consideration by the digital footprint research community. These were: 'Lack of Community Involvement' relating to the need to further involve those with lived experience in the development of new technologies; 'Reinforces Existing Bias' due to the potential for automated labelling of ground-truth data to bias training datasets; 'Privacy' given the potential disclosure of sensitive information without consent; and 'Danger of Misuse' due to strong potential for malicious use of such technologies.
Other considerations included the potential psychological risk to those labelling suicide and self-harm content with limited support.
Conclusions & ImplicationsThe Data Hazards identified provide a means of communicating and clarifying ethical concerns so that they can be more easily addressed in this complex and multidisciplinary field. Further collaboration by the research community to develop and agree appropriate safety precautions would help to build trust in these new technologies before they are deployed in practice.
{"title":"Using Data Hazards to support safe and ethical digital footprint research","authors":"Nina Di Cara, Natalie Zelenka, Oliver Davis, Claire Haworth","doi":"10.23889/ijpds.v8i3.2279","DOIUrl":"https://doi.org/10.23889/ijpds.v8i3.2279","url":null,"abstract":"Introduction & BackgroundHealth research using digital footprint data often involves the collection and use of large datasets that contain deeply personal information to make inferences about the course and onset of illness. In this context, innovating responsibly is essential for the field to develop safe, trustworthy and, ultimately, ethical research.
 The inherent interdisciplinarity of digital footprints research can be a challenge to this aim, with different fields having different ethical norms and standards. As well as this, there has been a strong focus to date on traditional ethical issues such as privacy, which do not necessarily account for the breadth of issues that arise in data science and internet-based work.
 Objectives & ApproachData Hazards is an open-source project that aims to provide a controlled vocabulary of ethical risks (Data Hazards) that can arise from data science research and its implementation. This vocabulary is presented as a set of 11 Hazard labels (v1.0) each with a visual icon and a set of safety precautions.
 Over three events in 2021-2022 we invited feedback from researchers who volunteered to take part in a Data Hazards workshop (N=15). They varied from PhD students to professors and worked across a range of disciplines, and were asked to discuss the case of mental health prediction from Twitter.
 Relevance to Digital FootprintsSince digital footprint technologies have great potential to pave the way for earlier and more personal medical treatment, it is important for researchers to be able to innovate whilst considering and communicating risk. We can then collaborate to establish effective safety precautions that allow us to maintain research momentum, without compromising safety or trust.
 ResultsBased on discussion at the workshops and surveys completed by participants, four main Data Hazards were raised for consideration by the digital footprint research community. These were: 'Lack of Community Involvement' relating to the need to further involve those with lived experience in the development of new technologies; 'Reinforces Existing Bias' due to the potential for automated labelling of ground-truth data to bias training datasets; 'Privacy' given the potential disclosure of sensitive information without consent; and 'Danger of Misuse' due to strong potential for malicious use of such technologies.
 Other considerations included the potential psychological risk to those labelling suicide and self-harm content with limited support.
 Conclusions & ImplicationsThe Data Hazards identified provide a means of communicating and clarifying ethical concerns so that they can be more easily addressed in this complex and multidisciplinary field. Further collaboration by the research community to develop and agree appropriate safety precautions would help to build trust in these new technologies before they are deployed in practice.","PeriodicalId":132937,"journal":{"name":"International Journal for Population Data Science","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135154083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-18DOI: 10.23889/ijpds.v8i3.2275
Tamara Garcia del Toro, Francesca Pontin, Rachel Oldroyd, Stephen Clark, Nik Lomax
Introduction & BackgroundCurrent research in people’s diet habits has been very focused in the food environment: the different contexts in which people engage with the food system. Originally, this concept referred to the physical presence of food in a person’s surroundings, which affects their ability to access different foods.
The food environment has been transformed in the past decade, with the development of new services such as online grocery and take away delivery services. Alongside a shift towards more out-of-home-food consumption and the unique current historical context (COVID-19 pandemic and the cost-of-living crisis).
Previous work carried out by Keeble et al (2021) has looked at association between area outlet availability, online delivery platform usage and area deprivation, showing a positive association between number of food outlets available only, online delivery service usage, and area deprivation using scraped and self-reported data. However, no work to date has been able to look at transaction record to validate these results and better understand the demographic characteristics of ordering populations.
Objectives & ApproachTo better understand consumer habits around takeaway purchasing, and how the growth of online food delivery services has shaped new behaviours, we have partnered with a large online takeaway delivery platform to use their transaction data in order to shed light on how changing customer habits are shaping the food environment.
Over 5 million rows of transaction data for online food purchasing were provided by the data partner, a large online food delivery service. The data included anonymised customer reference id, location and order information, as well as food outlet details. Data were accessed through the retailer’s own secure platforms. Data analysis was carried out in two phases: an exploration of the locational characteristics of these classifications and distribution across UK geography, and exploration of fitted linear regression models to explain median basket price per output area.
Geodemographic data was sourced from the 2011 and 2021 census at the Output Area Level (approximately 125 households) and retailer data were matched using postcode information.
Model performance was estimated using the adjusted R2 coefficient and p-value for statistical significance, and further diagnostics tests included different residuals plots.
Relevance to Digital FootprintsSelf-reported nutrition data has been notoriously difficult to work with due to unreliability of memory and stigma.
Understanding people's eating habits is important if we are to understand how nutrition impacts health outcomes, how people interact with the food environment, which interventions are working, and to identify vulnerable populations.
Much research using digital footprints data to carry out nutrition research has focused around supermarket transaction data, which is limit
{"title":"Customer trends in take-away purchasing: Geospatial patterns of online food delivery platform usage in UK output areas","authors":"Tamara Garcia del Toro, Francesca Pontin, Rachel Oldroyd, Stephen Clark, Nik Lomax","doi":"10.23889/ijpds.v8i3.2275","DOIUrl":"https://doi.org/10.23889/ijpds.v8i3.2275","url":null,"abstract":"Introduction & BackgroundCurrent research in people’s diet habits has been very focused in the food environment: the different contexts in which people engage with the food system. Originally, this concept referred to the physical presence of food in a person’s surroundings, which affects their ability to access different foods.
 The food environment has been transformed in the past decade, with the development of new services such as online grocery and take away delivery services. Alongside a shift towards more out-of-home-food consumption and the unique current historical context (COVID-19 pandemic and the cost-of-living crisis).
 Previous work carried out by Keeble et al (2021) has looked at association between area outlet availability, online delivery platform usage and area deprivation, showing a positive association between number of food outlets available only, online delivery service usage, and area deprivation using scraped and self-reported data. However, no work to date has been able to look at transaction record to validate these results and better understand the demographic characteristics of ordering populations.
 Objectives & ApproachTo better understand consumer habits around takeaway purchasing, and how the growth of online food delivery services has shaped new behaviours, we have partnered with a large online takeaway delivery platform to use their transaction data in order to shed light on how changing customer habits are shaping the food environment.
 Over 5 million rows of transaction data for online food purchasing were provided by the data partner, a large online food delivery service. The data included anonymised customer reference id, location and order information, as well as food outlet details. Data were accessed through the retailer’s own secure platforms. Data analysis was carried out in two phases: an exploration of the locational characteristics of these classifications and distribution across UK geography, and exploration of fitted linear regression models to explain median basket price per output area.
 Geodemographic data was sourced from the 2011 and 2021 census at the Output Area Level (approximately 125 households) and retailer data were matched using postcode information.
 Model performance was estimated using the adjusted R2 coefficient and p-value for statistical significance, and further diagnostics tests included different residuals plots.
 Relevance to Digital FootprintsSelf-reported nutrition data has been notoriously difficult to work with due to unreliability of memory and stigma.
 Understanding people's eating habits is important if we are to understand how nutrition impacts health outcomes, how people interact with the food environment, which interventions are working, and to identify vulnerable populations.
 Much research using digital footprints data to carry out nutrition research has focused around supermarket transaction data, which is limit","PeriodicalId":132937,"journal":{"name":"International Journal for Population Data Science","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135154392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-14DOI: 10.23889/ijpds.v8i2.2226
Abigail Brake, Dan Birks, Mark Mon-Williams, Sam Relins
Linking administrative data from Yorkshire Ambulance Service with primary health care data, this research project aims to answer the question, “What can YAS data tell us about how vulnerable populations interact with the service in Bradford?”
We selected 9 primary callout reasons as recorded in the data that could be vulnerability-related, and explored patterns of these both spatially and temporally, with comparison to all other callout reasons. The data also includes a pseudonymised NHS number which allows linkage with other datasets for which the patient has shared this identifier. In this case, we took their home LSOA to create a rudimentary gravity model visualising flows of people from their home location to their ambulance incident location.
Key findings include that vulnerability-related callouts were more frequent in the evenings and overnight on weekends, and concentrated on specific areas, both in terms of where incidents occur and areas from which callers originate. In terms of the individuals behind the calls, we found that while callers from both subsets were more likely to be female, the average age of callers for vulnerability-related incidents was almost 20 years younger than callers for all other reasons. Additionally, we discovered which callout reasons were most likely to see individuals requiring an ambulance multiple times.
This research provides valuable policy-relevant insights into emergency service demand relating to vulnerable populations in the Bradford region, highlighting the importance of understanding the needs of vulnerable populations to ensure that emergency services are allocated effectively and efficiently.
{"title":"What insights can ambulance data provide on vulnerable groups?","authors":"Abigail Brake, Dan Birks, Mark Mon-Williams, Sam Relins","doi":"10.23889/ijpds.v8i2.2226","DOIUrl":"https://doi.org/10.23889/ijpds.v8i2.2226","url":null,"abstract":"Linking administrative data from Yorkshire Ambulance Service with primary health care data, this research project aims to answer the question, “What can YAS data tell us about how vulnerable populations interact with the service in Bradford?”
 We selected 9 primary callout reasons as recorded in the data that could be vulnerability-related, and explored patterns of these both spatially and temporally, with comparison to all other callout reasons. The data also includes a pseudonymised NHS number which allows linkage with other datasets for which the patient has shared this identifier. In this case, we took their home LSOA to create a rudimentary gravity model visualising flows of people from their home location to their ambulance incident location.
 Key findings include that vulnerability-related callouts were more frequent in the evenings and overnight on weekends, and concentrated on specific areas, both in terms of where incidents occur and areas from which callers originate. In terms of the individuals behind the calls, we found that while callers from both subsets were more likely to be female, the average age of callers for vulnerability-related incidents was almost 20 years younger than callers for all other reasons. Additionally, we discovered which callout reasons were most likely to see individuals requiring an ambulance multiple times.
 This research provides valuable policy-relevant insights into emergency service demand relating to vulnerable populations in the Bradford region, highlighting the importance of understanding the needs of vulnerable populations to ensure that emergency services are allocated effectively and efficiently.","PeriodicalId":132937,"journal":{"name":"International Journal for Population Data Science","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134913015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-14DOI: 10.23889/ijpds.v8i2.2310
Iain Atherton, Michelle Jamieson
ObjectivesTo ascertain geographical differences in retention of nurses and midwives across the United Kingdom using registrant data.
MethodsThe Nursing and Midwifery Council (NMC) are responsible for holding a register of all nurses and midwives in the United Kingdom. Registrants are required to revalidate every three years. Linking together resulting data creates a longitudinal dataset that follows registrants over time. The NMC is providing anonymised data through the ONS Safe Researcher Service (SRS). Data sharing agreements have been signed off and data is in process of being ingested by ONS. Initial analysis will focus on geographical differences in retention by for nurses by field of practice (adult, mental health, children, and learning disability) and midwifery.
ResultsThere are estimated to be around 750 thousand nurses and midwives currently registered. Processes used to take this work forward will be described including public and stakeholder engagement. Early findings will be presented comparing demographic profiles of the professions and, for nursing, fields of practice in 2018 and 2021. Cox proportional hazard models will enable comparison of geographical differences in retention between England, Scotland, Wales, and Northern Ireland.
ConclusionRegistrant data provides a basis that can inform policy. This is especially important given current challenges with regard to recruitment and retention in the nursing and midwifery professions. Future work will be outlined that will utilise registrant data including linkage to other administrative and census data sources.
{"title":"The dynamics of the nursing and midwifery professions: Initial findings from analysis of longitudinal registrant data","authors":"Iain Atherton, Michelle Jamieson","doi":"10.23889/ijpds.v8i2.2310","DOIUrl":"https://doi.org/10.23889/ijpds.v8i2.2310","url":null,"abstract":"ObjectivesTo ascertain geographical differences in retention of nurses and midwives across the United Kingdom using registrant data.
 MethodsThe Nursing and Midwifery Council (NMC) are responsible for holding a register of all nurses and midwives in the United Kingdom. Registrants are required to revalidate every three years. Linking together resulting data creates a longitudinal dataset that follows registrants over time. The NMC is providing anonymised data through the ONS Safe Researcher Service (SRS). Data sharing agreements have been signed off and data is in process of being ingested by ONS. Initial analysis will focus on geographical differences in retention by for nurses by field of practice (adult, mental health, children, and learning disability) and midwifery.
 ResultsThere are estimated to be around 750 thousand nurses and midwives currently registered. Processes used to take this work forward will be described including public and stakeholder engagement. Early findings will be presented comparing demographic profiles of the professions and, for nursing, fields of practice in 2018 and 2021. Cox proportional hazard models will enable comparison of geographical differences in retention between England, Scotland, Wales, and Northern Ireland.
 ConclusionRegistrant data provides a basis that can inform policy. This is especially important given current challenges with regard to recruitment and retention in the nursing and midwifery professions. Future work will be outlined that will utilise registrant data including linkage to other administrative and census data sources.","PeriodicalId":132937,"journal":{"name":"International Journal for Population Data Science","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134913018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-14DOI: 10.23889/ijpds.v8i2.2212
Janet Bowstead
ObjectivesFrom April 2013, responsibility for healthcare services for prisoners was shifted to NHS England. Administrative survey data from English prisons covering 2000-2021 was used to identify if this change affected detainees’ experiences of healthcare quality and/or access; as well as the association with other characteristics of prison or prisoner.
MethodsSince 2000, HM Inspectorate of Prisons (HMIP) has carried out surveys of detainees as part of its inspections. This presentation will highlight the potential of these datasets by presenting substantive results of analysis on detainees’ experience of healthcare in prison. Merging datasets over time provides continuity of some variables over the whole period, with responses from up to 95,000 individuals. Variables of detainees’ assessment of the ease of access to different healthcare professionals, as well as the quality of services provided, were analysed over time and in terms of association with different types of prison and demographics of prisoner.
ResultsThe HMIP data are used to inform inspections and reports, and an ESRC-funded project has now developed these administrative datasets for wider research use. With a timeframe of over 20 years, the data can be analysed on a range of policy-relevant prison issues, such as safety, preparation for release, support within prison, treatment of prisoners, and access to information, legal rights, education, exercise and healthcare. These can be associated with demographic characteristics of detainees, and functional types of prison; as well as the analysis presented here of trends over time and whether these can be aligned to distinct policy or practice changes. The policy change in healthcare provision in April 2013 is contextualised within trends of greater health needs of prisoners and differentials between prisoners.
ConclusionHMIP detainee survey data are now archived with the UK Data Service for research use. Cross-sectional analysis on a range of demographic factors shows differential healthcare needs and experiences, and analysis over time indicates both trends and the impact, or not, of policy changes on detainees’ experiences.
{"title":"Healthcare in prison: Does it matter how it is provided?","authors":"Janet Bowstead","doi":"10.23889/ijpds.v8i2.2212","DOIUrl":"https://doi.org/10.23889/ijpds.v8i2.2212","url":null,"abstract":"ObjectivesFrom April 2013, responsibility for healthcare services for prisoners was shifted to NHS England. Administrative survey data from English prisons covering 2000-2021 was used to identify if this change affected detainees’ experiences of healthcare quality and/or access; as well as the association with other characteristics of prison or prisoner.
 MethodsSince 2000, HM Inspectorate of Prisons (HMIP) has carried out surveys of detainees as part of its inspections. This presentation will highlight the potential of these datasets by presenting substantive results of analysis on detainees’ experience of healthcare in prison. Merging datasets over time provides continuity of some variables over the whole period, with responses from up to 95,000 individuals. Variables of detainees’ assessment of the ease of access to different healthcare professionals, as well as the quality of services provided, were analysed over time and in terms of association with different types of prison and demographics of prisoner.
 ResultsThe HMIP data are used to inform inspections and reports, and an ESRC-funded project has now developed these administrative datasets for wider research use. With a timeframe of over 20 years, the data can be analysed on a range of policy-relevant prison issues, such as safety, preparation for release, support within prison, treatment of prisoners, and access to information, legal rights, education, exercise and healthcare. These can be associated with demographic characteristics of detainees, and functional types of prison; as well as the analysis presented here of trends over time and whether these can be aligned to distinct policy or practice changes. The policy change in healthcare provision in April 2013 is contextualised within trends of greater health needs of prisoners and differentials between prisoners.
 ConclusionHMIP detainee survey data are now archived with the UK Data Service for research use. Cross-sectional analysis on a range of demographic factors shows differential healthcare needs and experiences, and analysis over time indicates both trends and the impact, or not, of policy changes on detainees’ experiences.","PeriodicalId":132937,"journal":{"name":"International Journal for Population Data Science","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134913233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-14DOI: 10.23889/ijpds.v8i2.2346
Maria Loane, Joan Morris, Ester Garne, None EUROlinkCAT Working Group
ObjectiveTo establish a linked European cohort of children with congenital anomalies (CAs) to evaluate mortality and morbidity outcomes of these children up to the age of 10 years.
MethodEUROlinkCAT supported 22 EUROCAT population-based congenital anomaly registries in 14 countries to link their data on children with CAs to mortality, vital statistics, hospital discharge and prescription databases. All live births with a CA born 1995-2014 recorded in the registries were followed up to age 10 years or to 31st December 2015. Each registry transformed their local mortality and morbidity data to a Common Data Model (CDM) and ran centrally created syntax scripts and produced tables/outputs in a standard form for meta-analysis. Analyses were performed on 100 different congenital anomaly subgroups for children <1 year,1-4 years, and 5-9 years.
ResultsSixteen registries linked their data on children with CAs to mortality databases, eleven to regional/national hospital databases, and six to prescription databases. Data on children without a CA born during the same time-period and from the same population area (reference population) were available for seven registries linking to hospital databases and for all six registries linking to prescription databases. For the mortality studies, linked information on survival was available for 96% of children recorded in the anomaly registries (180,00 live births). For the morbidity studies, 89% of children with a CA (n=99,000) and 95% of reference children (n=2 million) were linked. For the prescription studies, 95% of children with a CA (n=60,000) and 95% of reference children (n= 1,700,000) were linked.
ConclusionThe EUROlinkCAT project was successful in creating a linked cohort of children with and without CAs in Western Europe. More efforts are needed to support data linkage in Eastern European countries. We have developed a set of recommendations for data linkage studies based on our experiences in establishing this cohort.
{"title":"EUROlinkCAT: A linked European cohort of children with congenital anomalies","authors":"Maria Loane, Joan Morris, Ester Garne, None EUROlinkCAT Working Group","doi":"10.23889/ijpds.v8i2.2346","DOIUrl":"https://doi.org/10.23889/ijpds.v8i2.2346","url":null,"abstract":"ObjectiveTo establish a linked European cohort of children with congenital anomalies (CAs) to evaluate mortality and morbidity outcomes of these children up to the age of 10 years.
 MethodEUROlinkCAT supported 22 EUROCAT population-based congenital anomaly registries in 14 countries to link their data on children with CAs to mortality, vital statistics, hospital discharge and prescription databases. All live births with a CA born 1995-2014 recorded in the registries were followed up to age 10 years or to 31st December 2015. Each registry transformed their local mortality and morbidity data to a Common Data Model (CDM) and ran centrally created syntax scripts and produced tables/outputs in a standard form for meta-analysis. Analyses were performed on 100 different congenital anomaly subgroups for children <1 year,1-4 years, and 5-9 years.
 ResultsSixteen registries linked their data on children with CAs to mortality databases, eleven to regional/national hospital databases, and six to prescription databases. Data on children without a CA born during the same time-period and from the same population area (reference population) were available for seven registries linking to hospital databases and for all six registries linking to prescription databases. For the mortality studies, linked information on survival was available for 96% of children recorded in the anomaly registries (180,00 live births). For the morbidity studies, 89% of children with a CA (n=99,000) and 95% of reference children (n=2 million) were linked. For the prescription studies, 95% of children with a CA (n=60,000) and 95% of reference children (n= 1,700,000) were linked.
 ConclusionThe EUROlinkCAT project was successful in creating a linked cohort of children with and without CAs in Western Europe. More efforts are needed to support data linkage in Eastern European countries. We have developed a set of recommendations for data linkage studies based on our experiences in establishing this cohort.","PeriodicalId":132937,"journal":{"name":"International Journal for Population Data Science","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134913240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-14DOI: 10.23889/ijpds.v8i2.2319
Georgina Eaton, Eke Bont
ObjectivesAs people move through the courts and other justice services a wealth of administrative data is created which can provide critical new insights on justice system users, their pathways, and outcomes. Data linkage and widening access can maximise its value for research in the public good and to inform policy.
MethodData linkage has, for the first time, matched parties involved in family and civil law to criminal justice, enabling cross-cutting research opportunities. This data is available to researchers via Trusted Research Environments and these partnerships can build our capacity to derive policy-relevant findings. The administrative data from the family courts in England and Wales provides a joined-up picture of people involved in family law cases such as public law, private law, adoption, Family Law Act, and divorce. The team have published research showcasing the potential of this data and the presentation will primarily focus on this work.
ResultsThe family court dataset has enabled, for the first time, the extent and nature of repeat users to be explored at scale for research. This analysis provides better understanding of the stability of outcomes for children where courts make decisions about their care. We have conducted exploratory analysis of which parties in family law cases in 2011 returned over the following decade. The research investigates the frequency of return to court following involvement in different case types and roles, and transitions between case types. Locality-based analysis highlights important insights into varied patterns across England and Wales, which highlights an over-representation of family court users in some case types and roles residing in the most deprived areas in England and Wales compared to the general population.
ConclusionLinked administrative data can drive new insights into justice system use. Initial exploration has delivered new evidence on family justice that advances our understanding of real-world patterns, but also raises more questions. Collaboration across sectors can ensure this rich resource informs the evidence base for government policy and practice.
{"title":"Data First: Family courts data - An exploratory analysis of the nature and extent of repeat use of the family courts from 2011 to 2020 in England and Wales","authors":"Georgina Eaton, Eke Bont","doi":"10.23889/ijpds.v8i2.2319","DOIUrl":"https://doi.org/10.23889/ijpds.v8i2.2319","url":null,"abstract":"ObjectivesAs people move through the courts and other justice services a wealth of administrative data is created which can provide critical new insights on justice system users, their pathways, and outcomes. Data linkage and widening access can maximise its value for research in the public good and to inform policy.
 MethodData linkage has, for the first time, matched parties involved in family and civil law to criminal justice, enabling cross-cutting research opportunities. This data is available to researchers via Trusted Research Environments and these partnerships can build our capacity to derive policy-relevant findings. The administrative data from the family courts in England and Wales provides a joined-up picture of people involved in family law cases such as public law, private law, adoption, Family Law Act, and divorce. The team have published research showcasing the potential of this data and the presentation will primarily focus on this work.
 ResultsThe family court dataset has enabled, for the first time, the extent and nature of repeat users to be explored at scale for research. This analysis provides better understanding of the stability of outcomes for children where courts make decisions about their care. We have conducted exploratory analysis of which parties in family law cases in 2011 returned over the following decade. The research investigates the frequency of return to court following involvement in different case types and roles, and transitions between case types. Locality-based analysis highlights important insights into varied patterns across England and Wales, which highlights an over-representation of family court users in some case types and roles residing in the most deprived areas in England and Wales compared to the general population.
 ConclusionLinked administrative data can drive new insights into justice system use. Initial exploration has delivered new evidence on family justice that advances our understanding of real-world patterns, but also raises more questions. Collaboration across sectors can ensure this rich resource informs the evidence base for government policy and practice.","PeriodicalId":132937,"journal":{"name":"International Journal for Population Data Science","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134913320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-14DOI: 10.23889/ijpds.v8i2.2237
Siobhán Murphy, Dermot O'Reilly, Emma Ross, Aideen Maguire, Denise O'Hagan
ObjectivesA large proportion of those who die by suicide present to an Emergency Department (ED) with self-harm (SH) in the year before death. This study examines ‘does risk of death following ED presentation with SH vary according to hospital attended?’
MethodsThe Northern Ireland Self-Harm Registry provided data on SH presentations to 12 ED departments in NI between 2012-2019. Linkage to health and mortality records provided follow up to December 2019. Cox proportional hazards regression models were employed to assess mortality risk following presentation with SH among 12 ED departments in NI.
ResultsAnalysis of the 64,350 ED presentations for self-harm by 30,011 individuals confirmed a marked variation across EDs in proportion of patients receiving mental health assessment and likelihood of admission to general and psychiatric wards. There was a significant variation in suicide risk according to ED attended with the three-fold range between the lowest (HRadj 0.32 95%CIs 0.16, 0.67) and highest. These differences persisted even after adjustment for patient characteristics, variation in types of self-harm, and care management at the ED.
ConclusionManagement of SH cases in the ED is important, however, it is the availability, access and level of engagement with, care in the community rather than the immediate care at EDs that is most critical for patients presenting to ED with self-harm.
目的:在自杀死亡的患者中，有很大一部分人在死前一年曾有过自残(SH)。本研究探讨了“ED合并SH后的死亡风险是否因医院而异?”
方法:北爱尔兰自残登记处提供了2012-2019年北爱尔兰12个ED部门的自残报告数据。提供截至2019年12月的健康和死亡率记录的联系。采用Cox比例风险回归模型评估NI 12个急诊科的SH患者的死亡风险。
结果:对来自30,011个人的64,350份ED自残报告的分析证实，在接受心理健康评估的患者比例以及进入普通病房和精神病房的可能性方面，ED之间存在显著差异。参加ED的患者的自杀风险有显著差异，最低(HRadj = 0.32, 95% ci = 0.16, 0.67)和最高(HRadj = 0.32, 95% ci = 0.16, 0.67)之间有3倍的差异。即使在调整了患者特征、自我伤害类型的变化和急诊科的护理管理之后，这些差异仍然存在。
结论:急诊科对自残患者的管理很重要，然而，对急诊科自残患者来说，最关键的是社区护理的可得性、可及性和参与程度，而不是急诊科的即时护理。
{"title":"Suicide risk following Emergency Department presentation with self-harm varies by hospital","authors":"Siobhán Murphy, Dermot O'Reilly, Emma Ross, Aideen Maguire, Denise O'Hagan","doi":"10.23889/ijpds.v8i2.2237","DOIUrl":"https://doi.org/10.23889/ijpds.v8i2.2237","url":null,"abstract":"ObjectivesA large proportion of those who die by suicide present to an Emergency Department (ED) with self-harm (SH) in the year before death. This study examines ‘does risk of death following ED presentation with SH vary according to hospital attended?’
 MethodsThe Northern Ireland Self-Harm Registry provided data on SH presentations to 12 ED departments in NI between 2012-2019. Linkage to health and mortality records provided follow up to December 2019. Cox proportional hazards regression models were employed to assess mortality risk following presentation with SH among 12 ED departments in NI.
 ResultsAnalysis of the 64,350 ED presentations for self-harm by 30,011 individuals confirmed a marked variation across EDs in proportion of patients receiving mental health assessment and likelihood of admission to general and psychiatric wards. There was a significant variation in suicide risk according to ED attended with the three-fold range between the lowest (HRadj 0.32 95%CIs 0.16, 0.67) and highest. These differences persisted even after adjustment for patient characteristics, variation in types of self-harm, and care management at the ED.
 ConclusionManagement of SH cases in the ED is important, however, it is the availability, access and level of engagement with, care in the community rather than the immediate care at EDs that is most critical for patients presenting to ED with self-harm.","PeriodicalId":132937,"journal":{"name":"International Journal for Population Data Science","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134913328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-14DOI: 10.23889/ijpds.v8i2.2327
Meghan Visnick, Jill Pell, Daniel Mackay, David Clark, Albert King, Michael Fleming
ObjectivesTraumatic brain injury (TBI) is a leading cause of death and disability among young children and adolescents and the effects can be lifelong and wide-reaching. This study aimed to compare the educational and employment outcomes of Scottish schoolchildren previously hospitalised for TBI with their peers.
MethodsA retrospective, record-linkage population cohort study was conducted using linkage of health and education administrative records. The cohort comprised all 766,244 singleton children born in Scotland and aged between 4 and 18 years who attended Scottish schools at some point between 2009 and 2013. Outcomes included special educational need (SEN), examination attainment, school absence and exclusion, and unemployment. Logistic regression models and generalised estimating equation (GEE) models were run unadjusted and then adjusted for sociodemographic and maternity confounders.
ResultsOf the 766,244 children in the cohort, 4,788 (0.6%) had a history of hospitalisation for TBI. Following adjustment for potential confounders, previous TBI was associated with SEN (OR 1.28, CI 1.18 to 1.39, p < 0.001), absenteeism (IRR 1.09, CI 1.06 to 1.12, p < 0.001), exclusion (IRR 1.33, CI 1.15 to 1.55, p < 0.001), and low attainment (OR 1.30, CI 1.11 to 1.51, p < 0.001). There was no significant association with unemployment 6 months after leaving school (OR 1.03, CI 0.92 to 1.16, p = 0.61). Excluding hospitalisations coded as concussion strengthened the associations.
ConclusionChildhood TBI, sufficiently severe to warrant hospitalisation, was associated with a range of adverse educational outcomes. These findings reinforce the importance of preventing TBI where possible. Where not possible, children with a history of TBI should be supported to minimise the adverse impacts on their education.
目的创伤性脑损伤(TBI)是幼儿和青少年死亡和残疾的主要原因，其影响可能是终身的和广泛的。本研究旨在比较苏格兰学童因创伤性脑损伤住院治疗与同龄人的教育和就业结果。方法采用卫生教育行政档案联系法进行回顾性、档案联系法人群队列研究。研究对象包括在2009年至2013年期间就读于苏格兰学校的766,244名在苏格兰出生、年龄在4至18岁之间的独生子女。结果包括特殊教育需要(SEN)、考试成绩、缺勤和排斥以及失业。Logistic回归模型和广义估计方程(GEE)模型在未调整的情况下运行，然后根据社会人口统计学和生育混杂因素进行调整。
结果在该队列的766,244名儿童中，4,788名(0.6%)有TBI住院史。调整潜在混杂因素后，既往TBI与SEN相关(OR 1.28, CI 1.18至1.39,p <0.001)，旷工(内部比1.09，可信区间1.06 ~ 1.12,p <0.001)，排除(IRR 1.33, CI 1.15 ~ 1.55, p <0.001)和低成就(OR 1.30, CI 1.11 ~ 1.51, p <0.001)。与毕业后6个月的失业率无显著相关性(OR 1.03, CI 0.92 ~ 1.16, p = 0.61)。排除编码为脑震荡的住院治疗强化了这种关联。
结论:儿童TBI严重到需要住院治疗，与一系列不良教育结果相关。这些发现强化了尽可能预防脑外伤的重要性。在不可能的情况下，应该支持有创伤性脑损伤史的儿童，以尽量减少对他们教育的不利影响。
{"title":"Educational and employment outcomes associated with childhood traumatic brain injury in Scotland: A population-based record-linkage cohort study","authors":"Meghan Visnick, Jill Pell, Daniel Mackay, David Clark, Albert King, Michael Fleming","doi":"10.23889/ijpds.v8i2.2327","DOIUrl":"https://doi.org/10.23889/ijpds.v8i2.2327","url":null,"abstract":"ObjectivesTraumatic brain injury (TBI) is a leading cause of death and disability among young children and adolescents and the effects can be lifelong and wide-reaching. This study aimed to compare the educational and employment outcomes of Scottish schoolchildren previously hospitalised for TBI with their peers.
 MethodsA retrospective, record-linkage population cohort study was conducted using linkage of health and education administrative records. The cohort comprised all 766,244 singleton children born in Scotland and aged between 4 and 18 years who attended Scottish schools at some point between 2009 and 2013. Outcomes included special educational need (SEN), examination attainment, school absence and exclusion, and unemployment. Logistic regression models and generalised estimating equation (GEE) models were run unadjusted and then adjusted for sociodemographic and maternity confounders.
 ResultsOf the 766,244 children in the cohort, 4,788 (0.6%) had a history of hospitalisation for TBI. Following adjustment for potential confounders, previous TBI was associated with SEN (OR 1.28, CI 1.18 to 1.39, p < 0.001), absenteeism (IRR 1.09, CI 1.06 to 1.12, p < 0.001), exclusion (IRR 1.33, CI 1.15 to 1.55, p < 0.001), and low attainment (OR 1.30, CI 1.11 to 1.51, p < 0.001). There was no significant association with unemployment 6 months after leaving school (OR 1.03, CI 0.92 to 1.16, p = 0.61). Excluding hospitalisations coded as concussion strengthened the associations.
 ConclusionChildhood TBI, sufficiently severe to warrant hospitalisation, was associated with a range of adverse educational outcomes. These findings reinforce the importance of preventing TBI where possible. Where not possible, children with a history of TBI should be supported to minimise the adverse impacts on their education.","PeriodicalId":132937,"journal":{"name":"International Journal for Population Data Science","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134913333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-14DOI: 10.23889/ijpds.v8i2.2299
Andy Boyd, Robin Flaig, Jacqui Oakley, Kirsteen Campbell, Katharine Evans, Stela McLachlan, Richard Thomas, Emma Turner
ObjectivesOur Trusted Research Environment (TRE) provides a centralised infrastructure to pool Longitudinal Population Studies’ (LPS) data and systematically link participants’ routine health, administrative and environmental records. All data are held in a centralised research resource which is now certified by UK Statistics Authority as meeting the Digital Economy Act standard.
ApproachWe have created an unprecedented infrastructure integrating data from interdisciplinary and pan-UK LPS linked to participants’ NHS England records with delegated access responsibilities. Integrated and curated data are made available for pooled analysis within a functionally anonymous DEA and ISO 27001 accredited TRE. We developed a bespoke governance and data curation framework with LPS data managers and Public/participant contributors. New data pipelines are being built with partners at ADRUK and the Office of National Statistics to link non-health records. Our design supports long-term sustainability, linkage accuracy and the ability to link data at both an individual and household level.
ResultsThis organisation is a collaboration of >24 LPS with ~280,000 participants. Participants' data are linked to NHS records and geo-coded environmental exposures. This resource is now accessible for public benefit research for bona fide UK researchers. Administrative data including tax, work and pensions, and education are being added to the resource. This data flow is enabled by: (1) a model where TTP processes participant identifiers for many different data owners; (2) creation of a novel longitudinal data pipeline, enabling linkage, data extraction and update of records over time; (3) an access framework where Linked Data Access Panel considers applications on behalf of data owners (e.g., the NHS), with review by a Public Panel and distributing applications to LPS for approval of appropriate data use.
ConclusionOur organisation provides a strategic research-ready platform for longitudinal research. We are extending linkages of LPS participants to previously inaccessible datasets. The research resource is positioned to allow researchers to investigate cross-cutting themes such as understanding health and social inequalities, health-social-environmental interactions, and managing the COVID-19 recovery.
{"title":"The UK Longitudinal Linkage Collaboration: A trusted research environment for the longitudinal research community","authors":"Andy Boyd, Robin Flaig, Jacqui Oakley, Kirsteen Campbell, Katharine Evans, Stela McLachlan, Richard Thomas, Emma Turner","doi":"10.23889/ijpds.v8i2.2299","DOIUrl":"https://doi.org/10.23889/ijpds.v8i2.2299","url":null,"abstract":"ObjectivesOur Trusted Research Environment (TRE) provides a centralised infrastructure to pool Longitudinal Population Studies’ (LPS) data and systematically link participants’ routine health, administrative and environmental records. All data are held in a centralised research resource which is now certified by UK Statistics Authority as meeting the Digital Economy Act standard.
 ApproachWe have created an unprecedented infrastructure integrating data from interdisciplinary and pan-UK LPS linked to participants’ NHS England records with delegated access responsibilities. Integrated and curated data are made available for pooled analysis within a functionally anonymous DEA and ISO 27001 accredited TRE. We developed a bespoke governance and data curation framework with LPS data managers and Public/participant contributors. New data pipelines are being built with partners at ADRUK and the Office of National Statistics to link non-health records. Our design supports long-term sustainability, linkage accuracy and the ability to link data at both an individual and household level.
 ResultsThis organisation is a collaboration of >24 LPS with ~280,000 participants. Participants' data are linked to NHS records and geo-coded environmental exposures. This resource is now accessible for public benefit research for bona fide UK researchers. Administrative data including tax, work and pensions, and education are being added to the resource. This data flow is enabled by: (1) a model where TTP processes participant identifiers for many different data owners; (2) creation of a novel longitudinal data pipeline, enabling linkage, data extraction and update of records over time; (3) an access framework where Linked Data Access Panel considers applications on behalf of data owners (e.g., the NHS), with review by a Public Panel and distributing applications to LPS for approval of appropriate data use.
 ConclusionOur organisation provides a strategic research-ready platform for longitudinal research. We are extending linkages of LPS participants to previously inaccessible datasets. The research resource is positioned to allow researchers to investigate cross-cutting themes such as understanding health and social inequalities, health-social-environmental interactions, and managing the COVID-19 recovery.","PeriodicalId":132937,"journal":{"name":"International Journal for Population Data Science","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134913335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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