Pub Date : 2022-08-25DOI: 10.23889/ijpds.v7i3.2057
J. Cooper, D. O’Reilly, Richard Kirk, Trish Kelly, Rachel Gibbs, M. Donnelly
A project designed to examine, for the first time, the health records of adult prisoners in Northern Ireland and their linkage to other available health data: the test case of prisoner post-release mortality risk ObjectivesThe linkage of routinely collected administrative data for research purposes has the potential to improve knowledge and public benefit. We describe a novel data linkage study between the Northern Ireland (NI) Healthcare in Prisons and Business Services Organisation (BSO). This work is undertaken within the Administrative Data Research Centre-NI (ADRC-NI). ApproachThis joint project between ADRC-NI Queen’s University Belfast and NI Healthcare in Prisons (South Eastern Health and Social Care Trust) will test linkage of prisoner health records to health data held in the BSO and the potential to generate a population-based cohort for a retrospective analysis of prisoner health (2012-2021) that will attempt to characterise prisoners according to socio-demographic, health and committal factors, compare post-release mortality rates with a reference group from the NI population using indirect standardisation and estimate post-release mortality risk using Cox proportional hazards models. ResultsUsing novel data-linkages, a dataset will be created to examine the health of prisoners (and former prisoners) in NI. Ethics and governance approvals are in place for this data-linkage. The linkage will be undertaken via the Honest Broker Service (HBS) in NI and the dataset will be accessed in the safe setting at the BSO. The processes involved, experiences including significant delays or difficulties, and recommendations for future data-linkage studies will be discussed. In addition, a key deliverable of this project will be an assessment of access and linkage capabilities of the prisoner health data, with metadata created and made available to future researchers. In addition, we plan to present preliminary results relating to the test research question. ConclusionWe will describe the processes involved and first-hand research experience in the development of a novel data-linkage project, in addition we will detail access and linkage capabilities in relation to this new dataset to examine health in prisoners (and former prisoners) in NI.
{"title":"A project designed to examine, for the first time, the health records of adult prisoners in Northern Ireland and their linkage to other available health data: the test case of prisoner post-release mortality risk.","authors":"J. Cooper, D. O’Reilly, Richard Kirk, Trish Kelly, Rachel Gibbs, M. Donnelly","doi":"10.23889/ijpds.v7i3.2057","DOIUrl":"https://doi.org/10.23889/ijpds.v7i3.2057","url":null,"abstract":"A project designed to examine, for the first time, the health records of adult prisoners in Northern Ireland and their linkage to other available health data: the test case of prisoner post-release mortality risk \u0000ObjectivesThe linkage of routinely collected administrative data for research purposes has the potential to improve knowledge and public benefit. We describe a novel data linkage study between the Northern Ireland (NI) Healthcare in Prisons and Business Services Organisation (BSO). This work is undertaken within the Administrative Data Research Centre-NI (ADRC-NI). \u0000ApproachThis joint project between ADRC-NI Queen’s University Belfast and NI Healthcare in Prisons (South Eastern Health and Social Care Trust) will test linkage of prisoner health records to health data held in the BSO and the potential to generate a population-based cohort for a retrospective analysis of prisoner health (2012-2021) that will attempt to characterise prisoners according to socio-demographic, health and committal factors, compare post-release mortality rates with a reference group from the NI population using indirect standardisation and estimate post-release mortality risk using Cox proportional hazards models. \u0000ResultsUsing novel data-linkages, a dataset will be created to examine the health of prisoners (and former prisoners) in NI. Ethics and governance approvals are in place for this data-linkage. The linkage will be undertaken via the Honest Broker Service (HBS) in NI and the dataset will be accessed in the safe setting at the BSO. The processes involved, experiences including significant delays or difficulties, and recommendations for future data-linkage studies will be discussed. In addition, a key deliverable of this project will be an assessment of access and linkage capabilities of the prisoner health data, with metadata created and made available to future researchers. In addition, we plan to present preliminary results relating to the test research question. \u0000ConclusionWe will describe the processes involved and first-hand research experience in the development of a novel data-linkage project, in addition we will detail access and linkage capabilities in relation to this new dataset to examine health in prisoners (and former prisoners) in NI.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49534857","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 : 2022-08-25DOI: 10.23889/ijpds.v7i3.1956
Yang Lu
IntroductionSharing aggregated electronic health records (EHRs) for integrated health care and public health studies is increasingly demanded. Patient privacy demands that anonymisation procedures are in place for data sharing. ObjectiveTraditional methods such as k-anonymity and its derivations are often overgeneralising resulting in lower data accuracy. To tackle this issue, we proposed the Semantic Linkage K-Anonymity (SLKA) approach to balance the privacy and utility preservation through detecting risky combinations hidden in the record linkage releases. ApproachK-anonymity processing quasi-identifiers of data may lead to ‘over generalisation’ when dealing with linkage data sets. As most linkage cases do not include all local patients and thus not all modifying data for privacy-preserving purposes needs to be used, we proposed the linkage k-anonymity (LKA) by which only obfuscated individuals in a released linkage set are required to be indistinguishable from at least k-1 other individuals in the local dataset. Considering the inference disclosure issue, we further designed the semantic-based linkage k-anonymity (SLKA) method through extending with a semantic-rule base for automatic detection of (and ruling out) risky associations from previous linked data releases. Specially, associations identified from the “previous releases” of the linkage dataset can become the input of semantic reasoning for the “next release”. ResultsThe approach is evaluated based on a linkage scenario where researchers apply to link data from an Australia-wide national type-1 diabetes platform with survey results from 25,000+ Victorians about their health and wellbeing. In comparing the information loss of three methods, we find that extra cost can be incurred in SLKA for dealing with risky individuals, e.g., 13.7% vs 5.9% (LKA, k=4) however it performs much better than k-anonymity, which can cause 24% information loss (k=4). Besides, the k values can affect the level of distortion in SLKA, such as 11.5% (k=2) vs 12.9% (k=3). ConclusionThe SLKA framework provides dynamic protection for repeated linkage releases while preserving data utility by avoiding unnecessary generalisation as typified by k-anonymity.
{"title":"Semantic-based Privacy-preserving Record Linkage.","authors":"Yang Lu","doi":"10.23889/ijpds.v7i3.1956","DOIUrl":"https://doi.org/10.23889/ijpds.v7i3.1956","url":null,"abstract":"IntroductionSharing aggregated electronic health records (EHRs) for integrated health care and public health studies is increasingly demanded. Patient privacy demands that anonymisation procedures are in place for data sharing. \u0000ObjectiveTraditional methods such as k-anonymity and its derivations are often overgeneralising resulting in lower data accuracy. To tackle this issue, we proposed the Semantic Linkage K-Anonymity (SLKA) approach to balance the privacy and utility preservation through detecting risky combinations hidden in the record linkage releases. \u0000ApproachK-anonymity processing quasi-identifiers of data may lead to ‘over generalisation’ when dealing with linkage data sets. As most linkage cases do not include all local patients and thus not all modifying data for privacy-preserving purposes needs to be used, we proposed the linkage k-anonymity (LKA) by which only obfuscated individuals in a released linkage set are required to be indistinguishable from at least k-1 other individuals in the local dataset. Considering the inference disclosure issue, we further designed the semantic-based linkage k-anonymity (SLKA) method through extending with a semantic-rule base for automatic detection of (and ruling out) risky associations from previous linked data releases. Specially, associations identified from the “previous releases” of the linkage dataset can become the input of semantic reasoning for the “next release”. \u0000ResultsThe approach is evaluated based on a linkage scenario where researchers apply to link data from an Australia-wide national type-1 diabetes platform with survey results from 25,000+ Victorians about their health and wellbeing. In comparing the information loss of three methods, we find that extra cost can be incurred in SLKA for dealing with risky individuals, e.g., 13.7% vs 5.9% (LKA, k=4) however it performs much better than k-anonymity, which can cause 24% information loss (k=4). Besides, the k values can affect the level of distortion in SLKA, such as 11.5% (k=2) vs 12.9% (k=3). \u0000ConclusionThe SLKA framework provides dynamic protection for repeated linkage releases while preserving data utility by avoiding unnecessary generalisation as typified by k-anonymity.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45845651","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 : 2022-08-25DOI: 10.23889/ijpds.v7i3.1963
P. Nair, Michael Smith, M. Theochari
ObjectiveDevelop a digital solution for automated data ingestion and rapid update of the large-scale Human Services Dataset (HSDS) which brings together data from across government to take a powerful view of the service usage to improve outcomes of communities. ApproachThe Centre for Health Record Linkage (CHeReL) hosts a secure, high-performing data linkage system, including a Master Linkage Key (MLK) of administrative health datasets, and generates linked data to inform policy decisions. Since 2018, CHeReL has also been annually linking over 70 frontline datasets to create a large-scale longitudinal linked dataset of over 2.5 billion records. Over the course of 2021, the CHeReL led a project to incrementally improve the currency of the HSDS in compressed timeframes. This provided opportunity to assess value and feasibility of more frequent updates to the dataset within the evaluation and investment context. ResultsThe automated data Ingestion and validation led to a significant reduction in the data processing timeframes for the Accelerated linkage. We observed 80% reduction in Data ingestion and 75% reduction in data validation. The digital solution also allows asset owners to register and approve new data providers, monitor their data provision in real-time and report on data sourcing. This provides transparency to the Asset Owner and reduces the need for time-intensive and manual processes to jointly monitor data provision with the Data Linkage Centre. The digital solution also has the capability to support Data Providers automate their data feeds and provide on a regular basis through a secure non- touch process. This reduces on-going workload and ensures on-time provision. ConclusionThe process requires a systematic change in the upstream data source, and we requested participating agencies to send us data in an agreed format. The receipt of files in standard format is pivotal for reducing the overall timeframes of HSDS creation and leverage it for policy and investment purpose.
{"title":"Accelerate the Creation of the cross agency Human Services Dataset.","authors":"P. Nair, Michael Smith, M. Theochari","doi":"10.23889/ijpds.v7i3.1963","DOIUrl":"https://doi.org/10.23889/ijpds.v7i3.1963","url":null,"abstract":"ObjectiveDevelop a digital solution for automated data ingestion and rapid update of the large-scale Human Services Dataset (HSDS) which brings together data from across government to take a powerful view of the service usage to improve outcomes of communities. \u0000ApproachThe Centre for Health Record Linkage (CHeReL) hosts a secure, high-performing data linkage system, including a Master Linkage Key (MLK) of administrative health datasets, and generates linked data to inform policy decisions. Since 2018, CHeReL has also been annually linking over 70 frontline datasets to create a large-scale longitudinal linked dataset of over 2.5 billion records. \u0000Over the course of 2021, the CHeReL led a project to incrementally improve the currency of the HSDS in compressed timeframes. This provided opportunity to assess value and feasibility of more frequent updates to the dataset within the evaluation and investment context. \u0000ResultsThe automated data Ingestion and validation led to a significant reduction in the data processing timeframes for the Accelerated linkage. We observed 80% reduction in Data ingestion and 75% reduction in data validation. \u0000The digital solution also allows asset owners to register and approve new data providers, monitor their data provision in real-time and report on data sourcing. This provides transparency to the Asset Owner and reduces the need for time-intensive and manual processes to jointly monitor data provision with the Data Linkage Centre. \u0000The digital solution also has the capability to support Data Providers automate their data feeds and provide on a regular basis through a secure non- touch process. This reduces on-going workload and ensures on-time provision. \u0000ConclusionThe process requires a systematic change in the upstream data source, and we requested participating agencies to send us data in an agreed format. The receipt of files in standard format is pivotal for reducing the overall timeframes of HSDS creation and leverage it for policy and investment purpose.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46019220","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 : 2022-08-25DOI: 10.23889/ijpds.v7i3.1953
R. Beare, Adam Morris, Tanya Ravipati, Elizabeth Le, T. Collyer, Helene Roberts, V. Srikanth, Nadine E. Andrew
ObjectivesTo develop a flexible platform for creating, reviewing and adjudicating annotation of unstructured text. Natural Language Processing models and statistical classifiers use the results for analysis of large databases of text, such as electronic health records, that are curated by the National Centre for Healthy Ageing (NCHA) Data Platform. ApproachAutomated approaches are essential for large scale extraction of structured data from unstructured documents. We applied the CogStack suite to annotate clinical text from hospital inpatient records based on the Unified Medical Language System (UMLS) for classifying dementia status. We trained a logistic regression classifier to determine dementia/non-dementia status within two cohorts based on frequency of occurrence of a set of terms provided by experts - one with confirmed dementia based on clinical assessment and the other confirmed non-dementia based on telephone cognitive interview. We used our annotation platform to review the accuracy of concepts assigned by CogStack. ResultsThere were 368 people with clinically confirmed dementia and 218 screen-negative for dementia. Of these, 259 with dementia and 195 without dementia had documents in the inpatient electronic health record system, 84045 inpatient documents 16950 for the dementia and non-dementia cohort respectively. A set of key words pertaining to dementia was generated by a specialist neurologist and a health information manager, and matched to UMLS concepts. The NCHA data platform holds a copy of the inpatient text records (>13million documents) that has been annotated using CogStack. Annotated documents corresponding to the study cohort were extracted. We tested true positive rates of annotation against 50 concepts judged by a neurologist and health information manager to be relevant to dementia patients by manually review of 100 documents. ConclusionAutomated annotations must be validated. The platform we have developed allows efficient review and correction of annotations to allow models to be trained further or provide confidence that accuracy is sufficient for subsequent analysis. Implementation within our linked NCHA data platform will allow incorporation of text based data at scale.
{"title":"A configurable software platform for creating, reviewing and adjudicating annotation of unstructured text.","authors":"R. Beare, Adam Morris, Tanya Ravipati, Elizabeth Le, T. Collyer, Helene Roberts, V. Srikanth, Nadine E. Andrew","doi":"10.23889/ijpds.v7i3.1953","DOIUrl":"https://doi.org/10.23889/ijpds.v7i3.1953","url":null,"abstract":"ObjectivesTo develop a flexible platform for creating, reviewing and adjudicating annotation of unstructured text. Natural Language Processing models and statistical classifiers use the results for analysis of large databases of text, such as electronic health records, that are curated by the National Centre for Healthy Ageing (NCHA) Data Platform. \u0000ApproachAutomated approaches are essential for large scale extraction of structured data from unstructured documents. We applied the CogStack suite to annotate clinical text from hospital inpatient records based on the Unified Medical Language System (UMLS) for classifying dementia status. We trained a logistic regression classifier to determine dementia/non-dementia status within two cohorts based on frequency of occurrence of a set of terms provided by experts - one with confirmed dementia based on clinical assessment and the other confirmed non-dementia based on telephone cognitive interview. We used our annotation platform to review the accuracy of concepts assigned by CogStack. \u0000ResultsThere were 368 people with clinically confirmed dementia and 218 screen-negative for dementia. Of these, 259 with dementia and 195 without dementia had documents in the inpatient electronic health record system, 84045 inpatient documents 16950 for the dementia and non-dementia cohort respectively. A set of key words pertaining to dementia was generated by a specialist neurologist and a health information manager, and matched to UMLS concepts. The NCHA data platform holds a copy of the inpatient text records (>13million documents) that has been annotated using CogStack. Annotated documents corresponding to the study cohort were extracted. \u0000We tested true positive rates of annotation against 50 concepts judged by a neurologist and health information manager to be relevant to dementia patients by manually review of 100 documents. \u0000ConclusionAutomated annotations must be validated. The platform we have developed allows efficient review and correction of annotations to allow models to be trained further or provide confidence that accuracy is sufficient for subsequent analysis. Implementation within our linked NCHA data platform will allow incorporation of text based data at scale.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45739094","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 : 2022-08-25DOI: 10.23889/ijpds.v7i3.2051
E. Frymire, M. Green, R. Glazier, Shahriar Khan, Kamila Premji, I. Bayoumi, L. Jaakkimainen, T. Kiran, P. Gozdyra
ObjectivesTo produce open access Primary Care Data Reports using standard health administrative measures in primary care in conjunction with measures for attachment to a primary care provider. Illustrate the importance of incorporating patient attachment data as an essential component in Human Health Resource (HHR) planning. ApproachThis cohort study uses standard health administrative linked data in primary care in conjunction with measures of attachment to a primary care provider for the population of Ontario, Canada (14,632,575). Data includes attached and uncertainly attached patients stratified according to key demographics, patient characteristics, health care utilization and primary care indicators. We stratified based on health utilization characteristics and produced 6 priority populations of interest by region. ResultsThe factors most often utilized in informing human health resource planning were based on policy and practice users input and included:1.Patient enrolment model, 2.Attachment to a primary care provider, 3.Who does and does not receive care, 4.Continuity with regular source of care. Policy planners use the reports for improved understanding of the scope of issues in regions and improved understanding of primary care involvement with priority populations. Policy planners have used this report as a data support and measurement tool to identify supply (physician) and demand (patient) data essential in HHR planning. Health system reform initiatives can use this data to inform improvements in the quality of, and equitable access to, primary care services in specific jurisdictions. ConclusionsThese reports contain key physician and patient data characteristics that correspond to primary care attachment rates. This data is essential to HHR planning when the goal is improving access to primary care for both attached and uncertainly attached patients. Data visualization in the form of mapping is especially impactful for policy and practice stakeholders.
{"title":"Using Primary care data metrics to inform policy and practice: Human Health Resource implications.","authors":"E. Frymire, M. Green, R. Glazier, Shahriar Khan, Kamila Premji, I. Bayoumi, L. Jaakkimainen, T. Kiran, P. Gozdyra","doi":"10.23889/ijpds.v7i3.2051","DOIUrl":"https://doi.org/10.23889/ijpds.v7i3.2051","url":null,"abstract":"ObjectivesTo produce open access Primary Care Data Reports using standard health administrative measures in primary care in conjunction with measures for attachment to a primary care provider. Illustrate the importance of incorporating patient attachment data as an essential component in Human Health Resource (HHR) planning. \u0000ApproachThis cohort study uses standard health administrative linked data in primary care in conjunction with measures of attachment to a primary care provider for the population of Ontario, Canada (14,632,575). Data includes attached and uncertainly attached patients stratified according to key demographics, patient characteristics, health care utilization and primary care indicators. We stratified based on health utilization characteristics and produced 6 priority populations of interest by region. \u0000ResultsThe factors most often utilized in informing human health resource planning were based on policy and practice users input and included:1.Patient enrolment model, 2.Attachment to a primary care provider, 3.Who does and does not receive care, 4.Continuity with regular source of care. Policy planners use the reports for improved understanding of the scope of issues in regions and improved understanding of primary care involvement with priority populations. Policy planners have used this report as a data support and measurement tool to identify supply (physician) and demand (patient) data essential in HHR planning. Health system reform initiatives can use this data to inform improvements in the quality of, and equitable access to, primary care services in specific jurisdictions. \u0000ConclusionsThese reports contain key physician and patient data characteristics that correspond to primary care attachment rates. This data is essential to HHR planning when the goal is improving access to primary care for both attached and uncertainly attached patients. Data visualization in the form of mapping is especially impactful for policy and practice stakeholders.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46153634","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 : 2022-08-25DOI: 10.23889/ijpds.v7i3.1869
Jacqueline Caldwell, Robert Wallace, Carole Morris, Simon Fleming, Rob Baxter, Ruairidh Macleod, W. Kerr, Donald Scobbie, Simon Rogers, F. Ritchie, Esma Mansouri-Benssassi, Susan Krueger, E. Jefferson
ObjectivesThe Scottish Medical Imaging (SMI) service provides linkable, population based, “research-ready” real-world medical images for researchers to develop or validate AI algorithms within the Scottish National Safe Haven. The PICTURES research programme is developing novel methods to enhance the SMI service offering through research in cybersecurity and software/data/infrastructure engineering. ApproachAdditional technical and governance controls were required to enable safe access to medical images. The researcher is isolated from the rest of the trusted research environment (TRE) using a Project Private Zone (PPZ). This enables researchers to build and install their own software stack, and protects the TRE from malicious code. Guidelines are under development for researchers on the safe development of algorithms and the expected relationship between the size of the model and the training dataset. There is associated work on the statistical disclosure control of models to enable safe release of trained models from the TRE. ResultsA policy enabling the use of “Non-standard software” based on prior research, domain knowledge and experience gained from two contrasting research studies was developed. Additional clauses have been added to the legal control – the eDRIS User Agreement – signed by each researcher and their Head of Department. Penalties for attempting to import or use malware, remove data within models or any attempt to deceive or circumvent such controls are severe, and apply to both the individual and their institution. The process of building and deploying a PPZ has been developed allowing researchers to install their own software. No attempt has yet been made to add additional ethical controls; however, a future service development could be validating the performance of researchers’ algorithms on our training dataset. ConclusionThe availability to conduct research using images poses new challenges and risks for those commissioning and operating TREs. The Private Project Zone and our associated governance controls are a huge step towards supporting the needs of researchers in the 21st century.
{"title":"Scottish Medical Imaging Service - Technical and Governance controls.","authors":"Jacqueline Caldwell, Robert Wallace, Carole Morris, Simon Fleming, Rob Baxter, Ruairidh Macleod, W. Kerr, Donald Scobbie, Simon Rogers, F. Ritchie, Esma Mansouri-Benssassi, Susan Krueger, E. Jefferson","doi":"10.23889/ijpds.v7i3.1869","DOIUrl":"https://doi.org/10.23889/ijpds.v7i3.1869","url":null,"abstract":"ObjectivesThe Scottish Medical Imaging (SMI) service provides linkable, population based, “research-ready” real-world medical images for researchers to develop or validate AI algorithms within the Scottish National Safe Haven. The PICTURES research programme is developing novel methods to enhance the SMI service offering through research in cybersecurity and software/data/infrastructure engineering. \u0000ApproachAdditional technical and governance controls were required to enable safe access to medical images. \u0000The researcher is isolated from the rest of the trusted research environment (TRE) using a Project Private Zone (PPZ). This enables researchers to build and install their own software stack, and protects the TRE from malicious code. \u0000Guidelines are under development for researchers on the safe development of algorithms and the expected relationship between the size of the model and the training dataset. There is associated work on the statistical disclosure control of models to enable safe release of trained models from the TRE. \u0000ResultsA policy enabling the use of “Non-standard software” based on prior research, domain knowledge and experience gained from two contrasting research studies was developed. Additional clauses have been added to the legal control – the eDRIS User Agreement – signed by each researcher and their Head of Department. Penalties for attempting to import or use malware, remove data within models or any attempt to deceive or circumvent such controls are severe, and apply to both the individual and their institution. The process of building and deploying a PPZ has been developed allowing researchers to install their own software. \u0000No attempt has yet been made to add additional ethical controls; however, a future service development could be validating the performance of researchers’ algorithms on our training dataset. \u0000ConclusionThe availability to conduct research using images poses new challenges and risks for those commissioning and operating TREs. The Private Project Zone and our associated governance controls are a huge step towards supporting the needs of researchers in the 21st century.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46605876","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 : 2022-08-25DOI: 10.23889/ijpds.v7i3.2020
M. Fleming
ObjectivesLooked-after-children are defined as children who are in the care of their local authority. Previous studies have reported that looked-after-children have poorer mental and physical health, increased behavioural problems, and increased self-harm and mortality compared to peers. They also experience poorer educational outcomes yet population wide research into the latter is lacking, particularly in the UK. Education and health share a bidirectional relationship therefore it is important to dually investigate both outcomes. Our study aimed to compare educational and health outcomes for looked-after-children with peers, adjusting for sociodemographic, maternity and comorbidity confounders. ApproachLinkage of nine Scotland-wide databases, covering dispensed prescriptions, hospital admissions, maternity records, death certificates, annual pupil census, examinations, school absences/exclusions, unemployment, and looked-after-children provided retrospective data on 715,111 children attending Scottish schools between 2009 and 2012. ResultsCompared to peers, 13,898 (1.9%) looked-after-children were more likely to be absent and excluded from school, have special educational need and neurodevelopmental multimorbidity, achieve the lowest level of academic attainment, and be unemployed after leaving school. They were more likely to require treatment for epilepsy, attention deficit hyperactivity disorder and depression, be hospitalised overall, for injury and self-harm, and die prematurely. Compared to children looked after at home, children looked after away from home had less absenteeism, less exclusion, less unemployment, and better attainment. Therefore, amongst those in care, being cared for away from home appeared to be a protective factor resulting in better educational outcomes. ConclusionsLooked-after-children had poorer health and educational outcomes than peers independent of increased neurodevelopmental conditions and special educational need. Further work is required to understand whether poorer outcomes relate to reasons for entering care, including maltreatment and adverse childhood events, neurodevelopmental vulnerabilities, or characteristics of the care system.
{"title":"Educational and health outcomes of schoolchildren in local authority care in Scotland: a retrospective record linkage study.","authors":"M. Fleming","doi":"10.23889/ijpds.v7i3.2020","DOIUrl":"https://doi.org/10.23889/ijpds.v7i3.2020","url":null,"abstract":"ObjectivesLooked-after-children are defined as children who are in the care of their local authority. Previous studies have reported that looked-after-children have poorer mental and physical health, increased behavioural problems, and increased self-harm and mortality compared to peers. They also experience poorer educational outcomes yet population wide research into the latter is lacking, particularly in the UK. Education and health share a bidirectional relationship therefore it is important to dually investigate both outcomes. Our study aimed to compare educational and health outcomes for looked-after-children with peers, adjusting for sociodemographic, maternity and comorbidity confounders. \u0000ApproachLinkage of nine Scotland-wide databases, covering dispensed prescriptions, hospital admissions, maternity records, death certificates, annual pupil census, examinations, school absences/exclusions, unemployment, and looked-after-children provided retrospective data on 715,111 children attending Scottish schools between 2009 and 2012. \u0000ResultsCompared to peers, 13,898 (1.9%) looked-after-children were more likely to be absent and excluded from school, have special educational need and neurodevelopmental multimorbidity, achieve the lowest level of academic attainment, and be unemployed after leaving school. They were more likely to require treatment for epilepsy, attention deficit hyperactivity disorder and depression, be hospitalised overall, for injury and self-harm, and die prematurely. Compared to children looked after at home, children looked after away from home had less absenteeism, less exclusion, less unemployment, and better attainment. Therefore, amongst those in care, being cared for away from home appeared to be a protective factor resulting in better educational outcomes. \u0000ConclusionsLooked-after-children had poorer health and educational outcomes than peers independent of increased neurodevelopmental conditions and special educational need. Further work is required to understand whether poorer outcomes relate to reasons for entering care, including maltreatment and adverse childhood events, neurodevelopmental vulnerabilities, or characteristics of the care system.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44607312","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 : 2022-08-25DOI: 10.23889/ijpds.v7i3.1962
Yinshan Zhao, Mike Jarrett, Kimberlyn McGail, Brent Hills
ObjectivesPopulation Data BC (PopData) is an agency in British Columbia, Canada, that routinely performs linkages of various administrative and researcher-collected data to a population spine. We developed a linkage report template in order to increase transparency of linkage process and outcome for end users and data providers. ApproachPopData performs probabilistic and deterministic data linkage using an in-house software. A literature review identified existing guidelines and examples of linkage reporting. A survey collected input from a wide range of end users about their interest in receiving linkage reports and specific information that is important to their work. A draft template was developed by PopData’s linkage experts and data scientists which then was reviewed by PopData staff and external partners. Privacy requirements, mode of delivery, readability to the intended audience and operational feasibility were carefully considered. ResultsThe resulting template built on our existing internal linkage summaries. The report follows a framework suggested in the literature with three key components: 1) information on the data source and linkage fields, 2) data pre-processing and linkage methodology, and 3) linkage results, presented in tables and figures, including overall linkage rates, detail on matched fields, and the distribution of linkage weights of linked and unliked pairs. In addition, an appendix describes the linkage methods and population spine in detail, and supplementary notes will comment on unique issues related to the data, when those are applicable. Educational materials to aid understanding of linkage methodologies and reporting are also under development. ConclusionLinked data are increasingly used in research, making it important to provide information on linkage process and performance to the research community. Rigorous and standardized linkage reports produced by data centres can facilitate evaluation of the impact of linkage performance on research findings and enable transparent reporting in peer-reviewed research.
{"title":"A proposed approach for standardized reporting of data linkage processes and results.","authors":"Yinshan Zhao, Mike Jarrett, Kimberlyn McGail, Brent Hills","doi":"10.23889/ijpds.v7i3.1962","DOIUrl":"https://doi.org/10.23889/ijpds.v7i3.1962","url":null,"abstract":"ObjectivesPopulation Data BC (PopData) is an agency in British Columbia, Canada, that routinely performs linkages of various administrative and researcher-collected data to a population spine. We developed a linkage report template in order to increase transparency of linkage process and outcome for end users and data providers. \u0000ApproachPopData performs probabilistic and deterministic data linkage using an in-house software. A literature review identified existing guidelines and examples of linkage reporting. A survey collected input from a wide range of end users about their interest in receiving linkage reports and specific information that is important to their work. A draft template was developed by PopData’s linkage experts and data scientists which then was reviewed by PopData staff and external partners. Privacy requirements, mode of delivery, readability to the intended audience and operational feasibility were carefully considered. \u0000ResultsThe resulting template built on our existing internal linkage summaries. The report follows a framework suggested in the literature with three key components: 1) information on the data source and linkage fields, 2) data pre-processing and linkage methodology, and 3) linkage results, presented in tables and figures, including overall linkage rates, detail on matched fields, and the distribution of linkage weights of linked and unliked pairs. In addition, an appendix describes the linkage methods and population spine in detail, and supplementary notes will comment on unique issues related to the data, when those are applicable. Educational materials to aid understanding of linkage methodologies and reporting are also under development. \u0000ConclusionLinked data are increasingly used in research, making it important to provide information on linkage process and performance to the research community. Rigorous and standardized linkage reports produced by data centres can facilitate evaluation of the impact of linkage performance on research findings and enable transparent reporting in peer-reviewed research.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43188260","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 : 2022-08-25DOI: 10.23889/ijpds.v7i3.2061
L. Williamson, C. Dibben
ObjectivesAims of this research, involving data linkage and health outcomes, is to gain a full understanding of the impact of both fertility histories and childlessness on health outcomes mid-life accounting for socio-economic background and area of residence. The research draws on and extends work on reproductive histories and life-course outcomes. ApproachWe aim to extend this area of research, specifically for Scotland, using Census data (1991-2011) from the Scottish Longitudinal Study (SLS) linked to health data. The Census health measures – including the 2011 Census health condition question on mental health - are the research outcomes and the explanatory information is from Census socio-economic data (captured around peak fertility for the research cohort in 1991), along with the SMR02 Maternity and SMR04 Mental Health datasets. The time-frame for available data allows 20 year follow-up from the 1991 Census to mid-life for specific female SLS birth cohorts (born 1959-1966, aged ~45-52 in 2011). ResultsFrom preliminary modelling we initially find, for this specific female research cohort, high birth parity to be an important factor in relation to self-reported mental health conditions at follow-up in 2011, but not once socio-economic and area-level variables are controlled for. ConclusionPreliminary modelling also highlights that relationship status – single, married or cohabiting – to be important over that of legal marital status as recorded at Census. For limiting long-term illness as a health outcome the findings are similar.
{"title":"Understanding the impact of fertility history on health outcomes in later life.","authors":"L. Williamson, C. Dibben","doi":"10.23889/ijpds.v7i3.2061","DOIUrl":"https://doi.org/10.23889/ijpds.v7i3.2061","url":null,"abstract":"ObjectivesAims of this research, involving data linkage and health outcomes, is to gain a full understanding of the impact of both fertility histories and childlessness on health outcomes mid-life accounting for socio-economic background and area of residence. The research draws on and extends work on reproductive histories and life-course outcomes. \u0000ApproachWe aim to extend this area of research, specifically for Scotland, using Census data (1991-2011) from the Scottish Longitudinal Study (SLS) linked to health data. The Census health measures – including the 2011 Census health condition question on mental health - are the research outcomes and the explanatory information is from Census socio-economic data (captured around peak fertility for the research cohort in 1991), along with the SMR02 Maternity and SMR04 Mental Health datasets. The time-frame for available data allows 20 year follow-up from the 1991 Census to mid-life for specific female SLS birth cohorts (born 1959-1966, aged ~45-52 in 2011). \u0000ResultsFrom preliminary modelling we initially find, for this specific female research cohort, high birth parity to be an important factor in relation to self-reported mental health conditions at follow-up in 2011, but not once socio-economic and area-level variables are controlled for. \u0000ConclusionPreliminary modelling also highlights that relationship status – single, married or cohabiting – to be important over that of legal marital status as recorded at Census. For limiting long-term illness as a health outcome the findings are similar.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46647192","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 : 2022-08-25DOI: 10.23889/ijpds.v7i3.1785
F. Grimm, D. Lewer, J. Craig, R. Rogans-Watson, J. Shand
ObjectivesOlder people and people with complex needs often require both health and social care services, but there is limited insight into individual journeys across these services. To help inform joint health and social care planning, we aimed to assess the relationship between hospital admissions and domiciliary care receipt. ApproachWe used an individually linked dataset of primary care activity, hospital admissions and local authority-held social care records for adults living in Barking and Dagenham, a borough in London, England, on 1 April 2018, and followed them up until 31 March 2020. The outcome was initiation of a new domiciliary care package. We estimated the rate of hospital-associated care package initiation, and of care packages unrelated to hospital admissions. We also described the characteristics of hospital admissions that preceded domiciliary care and examined which primary diagnoses codes were associated with receiving domiciliary care after discharge. ResultsIn our cohort, 1.4 of participants had a domiciliary care package during a median follow-up of 1.87 years. One in three domiciliary care packages were initiated during a hospital stay or within 7 days of discharge. The rate of new domiciliary care packages was 120 times greater (95% CI 110-130) during or after a hospital stay than at other times, and this association was present for all age groups. Primary admission reasons accounting for the largest number of domiciliary care packages were hip fracture, pneumonia, urinary tract infection, septicaemia, and exacerbations of long-term conditions (COPD and heart failure). Admission reasons with the greatest likelihood of a subsequent domiciliary care package were fractures and strokes. ConclusionHospitals are a major referral route into domiciliary care. While new and acute illnesses account for many domiciliary care packages, exacerbations of long-term conditions and age- and frailty-related conditions are also an important driver. National-level linked datasets are needed for a better understanding of the relationship between health and social care receipt.
{"title":"Using cross-sector data linkage to track patient journeys across health and social care.","authors":"F. Grimm, D. Lewer, J. Craig, R. Rogans-Watson, J. Shand","doi":"10.23889/ijpds.v7i3.1785","DOIUrl":"https://doi.org/10.23889/ijpds.v7i3.1785","url":null,"abstract":"ObjectivesOlder people and people with complex needs often require both health and social care services, but there is limited insight into individual journeys across these services. To help inform joint health and social care planning, we aimed to assess the relationship between hospital admissions and domiciliary care receipt. \u0000ApproachWe used an individually linked dataset of primary care activity, hospital admissions and local authority-held social care records for adults living in Barking and Dagenham, a borough in London, England, on 1 April 2018, and followed them up until 31 March 2020. The outcome was initiation of a new domiciliary care package. We estimated the rate of hospital-associated care package initiation, and of care packages unrelated to hospital admissions. We also described the characteristics of hospital admissions that preceded domiciliary care and examined which primary diagnoses codes were associated with receiving domiciliary care after discharge. \u0000ResultsIn our cohort, 1.4 of participants had a domiciliary care package during a median follow-up of 1.87 years. One in three domiciliary care packages were initiated during a hospital stay or within 7 days of discharge. The rate of new domiciliary care packages was 120 times greater (95% CI 110-130) during or after a hospital stay than at other times, and this association was present for all age groups. Primary admission reasons accounting for the largest number of domiciliary care packages were hip fracture, pneumonia, urinary tract infection, septicaemia, and exacerbations of long-term conditions (COPD and heart failure). Admission reasons with the greatest likelihood of a subsequent domiciliary care package were fractures and strokes. \u0000ConclusionHospitals are a major referral route into domiciliary care. While new and acute illnesses account for many domiciliary care packages, exacerbations of long-term conditions and age- and frailty-related conditions are also an important driver. National-level linked datasets are needed for a better understanding of the relationship between health and social care receipt.","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43684699","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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