bluuebunny/crossref_metadata_2025 huggingface dataset:
Augmented Reality Gaming Experience: Assessing Satisfaction Level Towards Post Event Behavior for MARA 1.0
Authors: Abdul Halim Abdul Hamid , Khir Masrur Mohd , Jee Hong Ryan Kang , Yoke Kei Natalie Loi
Published: September 2024

The proliferation of Augmented Reality (AR) technology has significantly transformed the gaming industry by merging digital content with the physical environment. This study focuses on assessing player satisfaction and subsequent behaviors in response to Mission AR Apocalypse (MARA) 1.0, an AR game designed to enhance educational engagement for Universiti Teknologi MARA (UiTM) students. Developed amidst the COVID-19 pandemic by UnBound Malaysia, this game aims to provide an enriching experience through the integration of educational content within an AR framework. This research investigates four primary factors influencing player satisfaction: immersion and visual experience, ease of use and controls, game content and storyline and technical performance. Utilizing a correlational research design, data were collected from 503 students across 15 UiTM branches via structured questionnaires. The study employs Net Promoter Score (NPS) to measure satisfaction levels and their impact on post-event behaviors, such as the intent to recommend and continue using the game. Findings indicate a high level of satisfaction with immersion and visual experience, ease of use, and game content, though technical performance showed mixed results. These results support the hypotheses that immersive, visually rich AR games with user-friendly controls and engaging content enhance player satisfaction and recommendation intentions. The study concludes that while the majority of players are satisfied, there are areas for improvement, particularly in technical performance, to ensure a more seamless and enjoyable gaming experience. These insights are crucial for developers and educators aiming to optimize AR applications for educational purposes.
Cultural Event Development Model for Quality Improvement of Tourism Event: Case of the 3rd Fishing Boat Festival in Sungsang Village IV, Banyuasin Regency, South Sumatra Province
Authors: Djohan Muhammad Iqbal , Wibowo Budi , Suroto Peni Zulandari
Published: September 2024

Tourism related to events is a representation of the selling power of the location where the event is held. Festival events have an important role in increasing the selling value of destinations, but this study found that the 3rd Fishing Boat Festival event has not been packaged properly and there is no target market database targeted as tourists. This study aims to create a model for the development of cultural festivals, based on 5 basic stages, namely research, design, planning, coordination, evaluation and compiling a template for providing a database of targets and target tourists using the definable, sizeable, reachable, relevant segmentation method. This study used a combination method or mixed method with a sequential explanatory design with a sample of 100 respondents with 3 data collection techniques, namely by direct observation, document study and interview. This research resulted in 5 stages consisting of 40 steps of festival event management, as well as the targeted tourist segmentation profile. The research contributes to the achievement of the goal of improving the quality of cultural events in tourism villages.
RNA m6A Modifikasyon Bölgelerinin Sınıflandırılması için Öznitelik Çıkarma ve Boyut Azaltma Yöntemlerinin Karşılaştırılması
Authors: Nuray Batuhan , Altuntaş Volkan
Published: September 2024

Bu çalışmada RNA’da sıklıkla meydana gelen N6-metiladenozin (m6A) modifikasyon bölgelerinin belirlenmesi ve gelecekte yapılacak çalışmalar için farklı öznitelik çıkarıcılar, öznitelik seçiciler ve boyut düşürme algoritmalarının, K-en yakın komşu sınıflandırma algoritması kullanılarak performanslarının karşılaştırılması amaçlanmıştır. 35 farklı öznitelik çıkarma algoritması ve 9 farklı boyut azaltma ve öznitelik seçici algoritma kullanılarak algoritmaların m6A modifikasyon bölgelerinin tanımlamasındaki performansları değerlendirilmiştir. Yapılan çalışmanın sonunda Nükleotidlerin kimyasal özelliklerini dikkate alarak öznitelik çıkarımı yapan NCP öznitelik çıkarma algoritması ile Ekstra Ağaçlar boyut azaltma yönteminin birlikte kullanılmasının m6A modifikasyon bölgelerinin belirlenmesinde yüksek performans gösterdiği görülmüştür.
Impacto de la Educación Sexual en la Salud Reproductiva de Adolescentes: Un análisis desde la perspectiva enfermería
Authors:   Universidad Técnica Estatal de Quevedo, Macías Sánchez Karen Gabriel , Quijije Chávez Vilma Rocío ,   Universidad Técnica Estatal de Quevedo, Verdezoto Michuy Carmen Lisbeth ,   Universidad Técnica Estatal de Quevedo, Betancourt Zambrano Shirley Vanessa ,   Universidad Técnica Estatal de Quevedo
Published: September 2024

La educación sexual integral se ha establecido como una herramienta crucial para mejorar la salud reproductiva de los adolescentes, y su efectividad ha sido ampliamente documentada. Este artículo ofrece un análisis exhaustivo del impacto de la educación sexual en la salud reproductiva juvenil desde la perspectiva de la enfermería. A través de una revisión de la literatura reciente, se examina cómo los programas de educación sexual contribuyen a la reducción de comportamientos sexuales de riesgo, embarazos adolescentes y enfermedades de transmisión sexual (ETS). Los hallazgos indican que los programas educativos que ofrecen información completa y basada en evidencia son más efectivos en la prevención de estos problemas, al proporcionar a los adolescentes el conocimiento necesario para tomar decisiones informadas sobre su salud sexual. El estudio también resalta el papel fundamental de los profesionales de enfermería en la implementación y gestión de estos programas. Los enfermeros no solo facilitan la educación, sino que también crean un entorno de apoyo que mejora la comunicación y la receptividad de los jóvenes hacia la información proporcionada. Sin embargo, se identifican desafíos significativos, como barreras culturales y políticas, que afectan la efectividad de los programas. La falta de formación continua para los educadores también es una preocupación. Finalmente, el artículo subraya la necesidad de adaptar los programas a las necesidades locales y de superar los obstáculos existentes para maximizar el impacto positivo de la educación sexual en la salud reproductiva de los adolescentes.
Enfermedad de graves, signos, síntomas, diagnóstico y tratamiento
Authors:   Investigador Independiente, Llamuca Salazar Erika Paola , Lima Erréis Daniela del Cisne ,   Investigador Independiente, Salazar Santana Sandy Carola ,   Investigador Independiente, Guanotasig Guamba Katherine Dayanna ,   Investigador Independiente
Published: September 2024

La enfermedad de Graves es un trastorno autoinmune que se presenta con hipertiroidismo, bocio, enfermedad ocular orbitaria y, en ocasiones, mixedema localizado. La característica principal de la enfermedad de Graves es el hipertiroidismo, que afecta a casi todos los pacientes y está causado por anticuerpos que activan los receptores de la hormona estimulante de la tiroides (TSH). Estos anticuerpos estimulan la producción y liberación de hormona tiroidea, lo que provoca el crecimiento de la tiroides y el desarrollo de un bocio difuso. La presencia de estos anticuerpos en la sangre y la presencia de enfermedad ocular tiroidea ayudan a distinguir la enfermedad de Graves de otras causas de hipertiroidismo. Este artículo proporciona una descripción general de las opciones de tratamiento para el hipertiroidismo de Graves en adultos, la patogénesis de la enfermedad de Graves, las manifestaciones clínicas y el diagnóstico del hipertiroidismo.
Studies on the Bioactive Components and Antibacterial Activities of Kigelia Africana
Authors: IK* Uchendu
Published: September 2024

Objective: Nature has been a source of medicinal agents for thousands of years and an impressive number of modern drugs have been isolated from natural sources. K. africana is a plant which various part have been used as antibacterial agents, antiprotozoal agents, antineoplastic agents, CNS stimulants e.t.c. Method: Fresh leaves and stem bark of K. africana were collected from Egugwu Agbaja Izzi LGA, Ebonyi State. The plant was washed, soaked, filtered and air-dried to get the aqueous extract. Semi quantitative phytochemical identification, Gas chromatography/Mass spectrometry as well as antibacterial assays of the extracts against C. freundii, E. coli, K. pneumonia and S. aureus by agar well diffusion method were carried out. The aqueous extracts were re-extracted to obtain an ethanolic fraction. Result: The aqueous extract of the leaf had more inhibitory effects than that of the stem bark on the test organisms. The aqueous extract had more inhibitory effects against S. aureus, K. pneumonia and C. freundii as compared to E. coli. The ethanolic fraction had a very significant (P˂0.05) inhibitory effect against all the test organism. Phytochemical analysis of the crude extracts showed the presence of glycosides, tannins, carbohydrate resin and reducing sugar in all extract while saponin and flavonoid was noted only in the aqueous extract of K. africana stem bark. The GC/MS revealed the presence of acetic acid, hydroxy-ethyl ester e.t.c in the leaf aqueous extract whereas phenol, 3-methoxy-2,4,6-trimethyl was revealed in the stem bark. Conclusion: This study revealed that aqueous extract of K. africana contains high amount of phyoconstituents and it may be responsible for its good antibacterial activity.
Efficient Identification of Patients Eligible for Clinical Studies Using Case-Based Reasoning on The Scottish Health Research Register (SHARE)
Authors: Shi Wen , Kelsey Tom , Sullivan Frank
Published: September 2024

IntroductionTrials often struggle to achieve their target sample size with only half doing so. Some researchers have turned to Electronic Health Records (EHRs), seeking a more efficient way of recruitment. The Scottish Health Research Register (SHARE) obtained patients’ consent for their EHRs to be used as a searching base from which researchers can find potential participants. However, due to the fact that EHR data is not complete, sufficient or accurate, a database search strategy may not generate the best case-finding result.
Objectives and ApproachA retrospective study was conducted to evaluate the performance of a case-based reasoning method in identifying participants for population-based clinical studies which had recruited through SHARE. A case-based reasoning framework was applied to nine studies with 119 total participants using two-fold cross-validation. Records of 30,000 random individuals were also merged with each test set to simulate the real-world recruitment setting. A prediction score for study participation was generated for each one in the test set through comparison of their diagnosis, procedure, pharmaceutical prescription, and laboratory test results attributes and those of the participants of a particular study. Evaluation was conducted by calculating Area Under the ROC Curve and information retrieval metrics for the ranking list of the test set by prediction score. We also compared the most likely participants as identified by searching a database to those ranked highest by our model.
ResultsThe average ROCAUC for nine projects was 81% indicating strong predictive ability. However, the derived ranking lists showed lower predictive performance. 21% of the persons ranked within top 50 positions being the same as identified by searching databases.
Conclusion / ImplicationsCase-based reasoning may be more effective than database search strategy for participant identification. This hypothesis requires a prospective study for further validation. The lower performance of ranking lists suggests improvements are needed in the collection and curation of EHRs.
The Link Between Early Involvement with Child Protection System and Youth Offending: A Multi-Agency Data-Linkage Study in Northern Territory, Australia
Authors: He Vincent Yaofeng
Published: September 2024

IntroductionNumerous studies have reported a link between child maltreatment and subsequent youth offending leading to calls for early intervention programs focussed on children in contact with child protection services (CPS) as a strategy to reduce the risk offending. While the overlap between child maltreatment and youth offending is well recognised, there have been few studies of the dimensions of CPS involvement that can inform early intervention programs. Whole-of-population linked data provides the opportunity to better understand the characteristics of CPS involvement that predict the risk of offending.
Objectives and ApproachThis retrospective cohort study investigated the association between the level of CPS involvement (no CPS contact through to out-of-home care) for children, before age 10, with youth offending. The study used linked, individual-level records from multiple agencies including birth records, CPS records and youth justice (court) data for 10,438 Aboriginal children born in the NT between 1999 and 2006.
ResultsThere was a gradient of increasing risk of youth offending as the level of CPS involvement increased from ‘no report of maltreatment’ to ‘out-of-home care’. There was a similar risk of offending for children with a record of either ‘unsubstantiated’ or ‘substantiated notifications’. Timing of maltreatment (at age 0-4, age 5-9 or both periods) was more strongly associated with offending than whether a notification was substantiated or unsubstantiated.
Conclusion / ImplicationsThe increased risk of offending associated with different levels of involvement with CPS provides early opportunities for differentiated interventions aimed at reducing the risk of youth offending. Although children with unsubstantiated reports of child maltreatment do not meet the criteria for a statutory response for child protection the increased risk of subsequent youth offending among these children indicates the need to engage families in preventive interventions.
Assessing antidepressant exposure and response using UK electronic health record data
Authors: Iveson Matthew , Ball Emily , Casey Arlene , Falis Matus , McIntosh Andrew , Whalley Heather
Published: September 2024

Objective and ApproachMany people do not respond to antidepressants and our poor mechanistic understanding of antidepressant response has hampered efforts to personalise treatment. Understanding how and when antidepressants work at an individual level requires real-world antidepressant exposure and response data, at scale.
To date, studies have rarely had access to sufficiently detailed clinical data at the scales needed to derive such phenotypes and track them longitudinally. Indeed, many studies use research-based measures and phenotypes that are not commonly used in clinical settings, making translation to clinically-applicable tools difficult.
This study aims to develop novel measures of antidepressant treatment exposure and response by capitalising on the variety and volume of UK clinical records. We identify cases of depression from national primary and secondary care records, and derive longitudinal antidepressant treatment episodes from national community dispensing data.
ResultsFocusing on the Generation Scotland cohort (N = 21,000), we describe common antidepressant treatment trajectories and identify changes in medication, dose and strength. We examine the frequency of drug switching, augmentation, and drug stability, contrasting these to other UK linked cohorts. We also describe the demographic characteristics of those defined as treatment resistant and those identified as in drug maintenance.
ConclusionsLinked electronic records provide the opportunity to track antidepressant exposure and response longitudinally. Changes in treatment can provide new measures that help to better understand treatment resistance and maintenance.
ImplicationsBy better tracking antidepressant response, the present study helps inform mechanistic research to understand treatment effectiveness and ultimately to develop personalised treatments.
Rates and causes of death after release from incarceration: an individual participant data meta-analysis from 1,471,526 people in eight high- and middle-income countries
Authors: Borschmann Rohan , Kinner Stuart , MARIC team
Published: September 2024

BackgroundFormerly incarcerated people have exceptionally poor health profiles and are at increased risk of preventable mortality when compared to their general population peers. However, not enough is known about the epidemiology of mortality in this population – specifically the rates, causes, and timing in specific subgroups and regions – to inform the development of targeted, evidence-based responses.
MethodsWe examined mortality outcomes for 1,471,526 people released from incarceration in eight countries from 1980-2018, across 10,534,441 person-years of follow-up (range: 0-24 years per person). We combined data from 18 cohort studies using two-step individual participant data meta-analyses to estimate pooled all-cause and cause-specific crude mortality rates (CMRs) per 100,000 person-years, with 95% confidence intervals (95%CI), for specific time periods after release, overall and stratified by age, sex, and region. 
Findings75,427 deaths were recorded. The all-cause CMR was highest during the first week. The highest cause-specific rates of death during the first week were due to alcohol and other drug poisoning, suicide, and cardiovascular disease. We observed considerable variation in cause-specific CMRs over time since release and across regions. Pooled all-cause CMRs were similar between males and females, and were higher in older age groups. 
InterpretationThe markedly elevated rate of death in the first week post-release underscores an urgent need for investment in evidence-based, coordinated transitional healthcare, including treatment for mental illness and substance use disorders. Variations in both rates and causes of death over time highlight the need for routine monitoring of post-release mortality.
Patient Identity Management in The Global Health Context
Authors: Ong Toan , Pittman Mary , Biondich Paul , Mamlin Burke , Onions Brett , Suleman Martin , Shivers Jennifer
Published: September 2024

ObjectiveEffective management of patient identity is crucial in population health and quality improvement. Patient identity management (PIM) in healthcare refers to the processes and technologies used to match, authenticate, and authorize patients within healthcare systems. Identifying duplicate patient records, however, is complicated by variations in how individual patients are identified within and across different systems. We aim to understand the PIM use cases, challenges, and solutions from mid and low-income countries in the Open Health Information Exchange (OpenHIE) communities to support knowledge sharing and collaboration with the goal of delivering safe and effective patient care.
ApproachData collection methods used at the OHIE 2023 held in Lilongwe, Malawi, from May 1st to May 5th, 2023, include:

Cognitive collaborative hackathon: facilitation of international exchange, with participants from diverse countries elucidating the necessity for PIM within their respective nations.
Unconferencing:  a novel and participatory approach to knowledge and information exchange prioritizes the specific interests of the attendees over a pre-established agenda.
One-on-one interview: Country-specific discussion on PIM

ResultIn various sessions at the conference, 12 countries shared their experiences on PIM activities related to linkage methods, biometrics, data security, privacy, and policy. Key challenges include lack of connectivity, interoperability, poor data quality and compatibility, high costs, and linkages of newborn data.
ConclusionsLow- and middle-income countries face unique challenges in developing and implementing robust PIM systems. Despite these challenges, countries in the OpenHIE community are actively working on implementing PIM systems, adopting various approaches tailored to their specific needs.
Approaches to Identifying health disparities by ethnicity using linked Australian Census data
Authors: Stanaway Fiona , Allen Liz , Zhu Lin , McDonald Bree , Yap Mei Ling , Dickson Michelle , Nassar Natasha
Published: September 2024

BackgroundEthnicity is not collected routinely in Australian health data. Ancestry data is collected in the Census which is now linked to health data. We aimed to develop an approach to using ancestry data to examine health disparities.
MethodsAn expert panel was formed with representatives from the Federation of Ethnic Communities’ Councils, Australian Bureau of Statistics (ABS) and researchers. The work was also informed by a community panel and yarning circles led by Aboriginal and Torres Strait Islander academics. We analysed 2016 Census data linked to death registrations for 2016-2021 in 20.9 million people and compared how different approaches to categorising ethnicity impacted on detection of health inequalities between groups.
ResultsWe developed the following approach: (1) Recategorise those reporting Australian only or New Zealand only ancestry from the Oceania to the European category; (2) For those reporting two ancestries: prioritise ethnic minority over national identities (e.g. Australian) and for the remainder create multiethnic groups based on the combined ancestries; (3) First analyse data at the maximum level of granularity, but then collapse up granularity where no disparities are identified to simplify results and messaging. Analysis of mortality data demonstrated that these approaches improved identification of ethnic mortality inequalities.
DiscussionWe developed a set of principles to use Australian Census Ancestry data to identify health disparities by ethnicity. The recommendations facilitate harnessing valuable Census data to identify important health disparities and better inform future health policy in Australia.
Improving Responsiveness: Our Journey from Manual Yearly Updates to Automated Linkage for Near Real-Time Understanding of Outcomes and Modelling Future Service Demand
Authors: Witowski Philip , Sipthorp Mark , Ismail Adam , Sulaiman Windra , Phillips Beverley , Williams Sharon
Published: September 2024

ObjectiveDemand for real-time data during the COVID-19 pandemic revealed a need to increase efficiencies in manual linkage processes to respond to events in near real-time. In response, our jurisdictional linkage agency transitioned from yearly to daily, weekly and monthly linkage practices through increasing automation and improving process flows.
ApproachOur linkage agency transitioned to a fully automated process utilising scalable cloud infrastructure. Source data is now provided directly to a common data platform.  This data is split into linkage and content, cleansed and quality assured in Python and set to automatically run via Azure data pipelines. The data is then linked via a combination of deterministic and probabilistic criteria, with data quality checks automatically performed along the way. Researchers can analyse this data in a secure virtual machine that only they can access and retrieve data from.
Results This infrastructure expedites the data linkage process allowing daily linkage results to select datasets, and enables advanced research such as a predictive micro-simulation model, which leverages the platform to predict and intervene on outcomes influenced by governmental policies. This model relies on timely administrative data to build targeted interventions for groups with poor future outcomes, tests these interventions, and monitors outcomes in near real-time.  
ConclusionOur linkage agency has transitioned from manual to automated linkage processes in response to increasing need for timely data. By embracing cloud infrastructure and leveraging automation, we have streamlined our operations, enabling responsive linkage depending on need and expediting the provision of de-identified, linked data to researchers.
Family Matters: Enhancing Insight in Linked Administrative Data Through Familial Linkage
Authors: Phillips Beverley , Witowski Philip , Ismail Adam , Sulaiman Windra , Sipthorp Mark , Williams Sharon
Published: September 2024

ObjectiveFamilial relationships can provide researchers with important insight into genetic, environmental, and social influences across many domains of research. While most administrative datasets do not collect information about relationships, familial linkage is an approach which seeks to identify such relationships among individuals within a linked data environment. We sought to develop a familial linkage resource which permits researchers access to relationship information otherwise not available in unlinked disparate administrative collections.
ApproachLeveraging birth and marriage registration data, we sought to identify relationships between Victorians. Familial links were formed by summarising relationships that were either explicit in the data (e.g. Parent and Child), or implied (e.g. A parent of a parent is a grandparent).  Resulting relationship data was stored in a data asset which interfaces with our linkage infrastructure for easy access and use by linked data end-users.
ResultsThrough familial linkage, we have identified over 8.8 million unique relationships for Victorians, spanning 24 relationship subtypes which include both biological and non-biological connections.  This data can be linked to all administrative datasets within our linkage environment, however representation varies across sources. Overall, the highest coverage of known relationships is found in datasets which specialise in child services, while older Victorians remain a gap.
Conclusion and ImplicationsFamilial linkage offers new dimensions of insight to researchers than what is accessible in source data alone. This information enables our data end-users to gain critical insights into the complex interplay between biological and social influences on Victorians’ health and well-being.
Developing a Framework for Safe AI Model Development on Sensitive Healthcare Data
Authors: Hotchkiss Lewis , Squires Emma , Rittman Timothy , Gallacher John , Thompson Simon
Published: September 2024

The Dementias Platform UK (DPUK) Data Portal holds over 60 cohort datasets from over 3 million participants with a range of multi-modal data including neuroimaging and genomics. This has meant we have seen an increasing interest in the development of AI models with the potential for clinical implementation. However, this presents a unique challenge to disclosure control when it comes to assessing these AI models for release due to the risk of attacks such as membership inference, model inversion or even just vulnerabilities in the models like overfitting. This is why we hosted a series of workshops bringing together members of the public, expert researchers, and data owners across the UK to build a framework for allowing the safe development and release of AI models trained on sensitive healthcare data. From this, we have put together recommendations and guidelines for the use of privacy-preserving techniques in AI models to protect patient privacy, and to allow the safe deployment of these models outside of trusted research environments. We also identified the unique challenges to privacy and implementation of privacy-preserving techniques in AI models regarding the use of complex data such as neuroimaging and genomics. This framework has created a path forward for supporting safe AI model development which takes into consideration rapidly evolving ethical, legal and privacy considerations.
Privacy Preserving Evaluation of Sensitive Population Record Linkage in the Absence of Ground Truth Data
Authors: Song Jie , Nanayakkara Charini , Christen Peter , Garrett Eilidh
Published: September 2024

ObjectivesLinkage of data containing personal information allows extensive studies in the health and social sciences for population level studies. However, real-world linkage applications often lack ground truth (GT) data, generally due to privacy concerns, which hinders linkage quality assessment. We propose a method to assess the linkage of sensitive population data in the absence of GT data.
ApproachOur method takes as input a set of sensitive and public population clusters. The sensitive clusters are generated with some record linkage algorithm, whereas the public population dataset, which is considered an approximation for the GT, is assumed to have a group identifier allowing generation of known clusters, such as households or families. We encode clusters as bit vectors and conduct cluster matching across the sensitive and public datasets using vector comparisons. Based on matched clusters, the population linkage quality of sensitive data is estimated.
ResultsWe conducted experiments on a sensitive Scottish birth data set containing 17,613 records from 1861 to 1901, and four successive publicly available Scottish census datasets from 1871 to 1901. Using the quality measures precision, recall and  the F-measure, we show that our estimations are close to GT quality, with a high correlation in F-measure values ranging from 88% to 99%.  
ConclusionWe proposed a method to estimate the linkage quality of sensitive population data for situations where public data with similar characteristics are available for approximating the GT. Our experimental evaluation showed that our method was effective in estimating true linkage quality with high accuracy.
Unravelling the Complexity of Homelessness: Investigating Reasons and Risk Factors for Chronic Homelessness
Authors: Mitchell Eileen , Murphy Siobhán , O'Reilly Dermot , O’Donnelly Michael
Published: September 2024

ObjectivesHomelessness is a complex and pressing issue affecting individuals and communities worldwide, including Northern Ireland. Understanding its root causes and associated risk factors is crucial for effective interventions. This study aims to identify primary contributors to homelessness and explore risk factors linked to chronic homelessness in Northern Ireland.
MethodsUtilizing an anonymized, linked dataset incorporating Northern Ireland Housing Executive (NIHE) and health and social care data, we will conduct a pioneering record linkage study. This analysis will focus on unravelling homelessness dynamics, particularly chronic homelessness, spanning from 2012 to 2022.
ResultsThrough our comprehensive analysis of using linked administrative datasets, we anticipate unveiling significant insights into the prevalence and persistence of chronic homelessness in Northern Ireland. By employing descriptive analyses, we will uncover nuanced patterns and trends, shedding light on the multifaceted dynamics of homelessness within the region. Moreover, our examination aims to identify key factors contributing to the persistence of chronic homelessness, providing crucial insights for developing targeted interventions and support systems. The outcomes of this analysis will not only contribute to a deeper understanding of the complexities surrounding homelessness but also serve as a foundation for evidence-based policymaking and community initiatives aimed at alleviating homelessness in Northern Ireland.
ConclusionThis study has the potential to increase our understanding about the characteristics, needs and outcomes of the homeless population in Northern Ireland. It is possible that the results may contribute to facilitating further cross-departmental integration and commitment to addressing vulnerable homeless households with complex needs.
Identifying hospitalization episodes of care among people with and without HIV in British Columbia, Canada
Authors: Emerson Scott , McLinden Taylor , Sereda Paul , Yonkman Amanda , Trigg Jason , Barrios Rolando , Hogg Robert
Published: September 2024

BackgroundHospitalizations are a resource-intensive form of healthcare use, particularly for persons with chronic conditions such as those with HIV. Interhospital transfers typically appear as separate records in Canadian databases; misclassifying transfers as independent hospitalizations can bias key metrics such as readmission rates. We examined approaches of combining sequential, related records into hospitalization episodes of care (HEoCs) among persons with and without HIV (PWH; PWoH) in British Columbia (BC), Canada.
MethodsBC hospitalization records (1992 to 2020) were sourced from the Comparative Outcomes and Service Utilization Trends (COAST) study, a data linkage that includes samples of PWH and PWoH. We constructed 8 HEoC definitions that varied by the: a) time gap between records, and b) transfer indication. Comparisons were informed by the proportion of multi-record HEoCs (mHEoCs; episodes with multiple hospitalization records) generated, and feasibility given data quality. 
ResultsWe analyzed 98,553 hospitalization records from 13,498 PWH, and 1,874,507 hospitalization records from 385,011 PWoH. Across the definitions, the proportion of mHEoCs varied from 2.46% to 5.27% for PWH and 2.73% to 4.18% for PWoH. Definitions requiring no transfer indication yielded the highest proportion of mHEoCs, whereas those requiring two-way agreement of hospital identifiers yielded the lowest proportion of mHEoCs. Patterns were comparable among PWH and PWoH. A pragmatic approach to defining HEoCs can be a reasonable option for general purposes – requiring at least one populated hospital identifier field, and ≤ 1 day gap between hospitalizations.
ConclusionsVarious approaches can be employed to combine sequential, related hospitalization records into HEoCs to help provide less biased estimates of hospitalization-related metrics.
Inequalities in severe COVID-19 outcomes in Wales, UK
Authors: Robinson Lucy , Curds Matthew , Akbari Ashley , Abbasizanjani Hoda , Bedston Stuart
Published: September 2024

ObjectiveResearch evidence shows that COVID-19 has disproportionately affected people from more deprived backgrounds. In Wales (UK), there is strong evidence of a socioeconomic gradient in total hospitalisations, critical care admissions and deaths.
This study focussed on a cohort of the Welsh population using anonymised individual-level linked population-scale data in the SAIL Databank.
ApproachThis analysis examines the impact of confounding/mediating factors on severe health outcomes. We used logistic regression, considering the Welsh Index of Multiple Deprivation (WIMD) 2019 as a predictor, to investigate how the coefficient for WIMD is changed when other variables (such as comorbidities, smoking status, Body Mass Index) are added, aiming to understand whether inequalities are mediated by observed characteristics or related to other unobserved factors.
ResultsInitial findings show that age-standardised rates of hospital admissions, Intensive Care Unit (ICU) admissions and deaths were roughly twice as high in the most deprived vs least deprived WIMD quintile in Wales. We estimated Years of Life Lost per 1,000 due to COVID-19 as 28.9, with variation between WIMD quintiles of 38.4 for the most deprived and 22.2 for the least deprived. The project also produced regression formulas to evaluate the contribution of the different confounding factors.
ConclusionThis study built on existing research findings and further explored the potential reasons for known socioeconomic inequalities in relation to COVID-19 outcomes. Insights from this study have broadened the knowledge around the impact of mediating factors and may contribute to future pandemic preparedness and the formation of future policies.
Adaptive Governance: Empowering Data Sharing for Public Health Impact - Insights from North Carolina Department of Health and Human Services
Authors: Hawn Nelson Amy , Hogle Paul , Zanti Sharon , Proescholdbell Scott , Tenenbaum Jessie
Published: September 2024

Objective and ApproachAt the 2022 IPDLN Conference, we presented work led by the North Carolina Department of Health and Human Services (NCDHSS), in partnership with university-based researchers, to build legal and ethical processes for routine data sharing. This session provides significant updates on the methods, results, and implications of these efforts–namely, that NCDHHS implemented an enterprise-wide data governance process and a legal framework that has enabled timely, impactful use of cross-sector data. We relied on Participatory Action Research and Deliberative Dialogue methods to engage a diverse range of partners in a data landscape overview and the co-creation of new data sharing processes that better enables the enterprise to adapt to a changing world.
ResultsFour key actions were taken as a result of the participatory research process: NCDHHS developed a data strategy, created a data sharing guidebook, staffed their Data Office, and implemented a new legal framework. In addition to describing how these actions support data use across a large US state health and human services agency, we provide three use cases demonstrating the impact of this work.
ConclusionsEstablishing routine data sharing presents legal, technical, and cultural challenges, particularly in large  agencies. Through a collaborative, participatory approach, the NCDHHS successfully established enterprise-wide data governance and a legal framework to support data-driven policymaking and, ultimately, improve health outcomes for residents.
ImplicationsThis research presents a successful, actionable, and replicable framework for developing and implementing processes to support intradepartmental data access, linkage, and use.
National Center for Health Statistics Data Linkage Program: Generating Synthetic Data to Support Tiered Access
Authors: Golden Cordel
Published: September 2024

The National Center for Health Statistics (NCHS) has a long-established data linkage program that links NCHS health survey data with vital and other administrative records to expand the analytic potential of both the survey and administrative data. Integrating survey and administrative data provides a powerful mechanism for creating policy-relevant data resources to support the examination of factors that can influence disability, chronic disease, health care utilization, morbidity, and mortality. However, data privacy concerns impact the accessibility of NCHS linked data and thus its utilization. Nearly all NCHS linked data files are available only through the NCHS and Federal Statistical Research Data Centers. To minimize this barrier to access, the NCHS Data Linkage Program is engaged in a pilot project to create NCHS' first public-use fully synthetic data files. This initiative supports Evidence Act requirements, including tiered access to federal data. The talk will include an overview of the steps taken to generate the synthetic linked data and provide a verification process to support its analytic utility. The talk will also include a summary of NCHS’ exploration of other privacy enhancing technologies to increase partnership and data access.
Towards Streamlined Transparent Data Linkage
Authors: Edwards Michael , Lee Alexandra , Thompson Simon
Published: September 2024

Linked data is a powerful resource within data analytics and population-level research. However, methods for linkage vary and the choice of approach can impact downstream usage of data by introducing assumptions and biases in resulting links. Selecting stringent linkage methods helps strengthen identified links at risk of missing links; meanwhile, lenient rules or ill-considered comparisons may introduce false positive links. Therefore, the approach is non-trivial, requiring careful selection of preprocessing steps, model development and quality review to ensure suitable outputs, which can require significant human expertise and insight.
Real-world population-scale linkage can benefit from automation and scalability offered within modern data centres, with many tasks eligible for pipelining, such as applying predefined cleaning routines, training defined models, and generating mapping tables. Despite this, there are still pinch points requiring human interaction, such as selecting appropriate linkage fields, blocking rules and comparison methods, and reviewing quality of predictions.
We present an approach to provide scalable automation in linkage pipelines, whilst retaining transparency of the linkage process for downstream users, providing them with a dataset’s life history.
The work output for a given dataset is a versioned catalogue documenting the dataset’s journey, with transparent reporting of data origin, linkage settings, routines, and privacy-preserving quality analysis for inspection. This gives researchers insight into how it may affect their data and provides confidence in data usage. These insights also work in both directions, allowing users to provide feedback and iteratively refine linkage approaches.
Investigating the quality of NHS number linkage of COVID 19 vaccines data to Census 2021.
Authors: O'Dair Hannah , Cummins Sarah , Huck Rachel
Published: September 2024

Objectives Due to the availability of data and the speed of delivery required, NHS number (Health ID) was used during the COVID-19 pandemic to join different health datasets to census 2021. This research sought to assess the quality of linkage done by NHS number, between COVID-19 vaccinations data and census 2021, by comparing to the quality that could be achieved with deterministic linkage using Personal Identifiable Information (PII). Specifically, we wanted to assure against concern that unlinked records may have been misclassified as unvaccinated due to inaccurate recording of NHS number at time of vaccination.   
Approach We used deterministic (rules-based) linkage to identify extra links that were not made on NHS number. We also investigated how many links were incongruent between NHS number linkage and PII deterministic linkage.  
Results We were able to link 98.5% of census records to vaccines data, with a precision of 98.94% and a recall of 99.71% This identified 1,833,626 additional links made using PII linkage. Furthermore, analysis of the linked data was re-run with the additional links. 
Conclusion We were able to assure that the NHS number linkage used to produce COVID-19 vaccinations statistics, was valid. Whilst 1,833,626 more links were made, the findings resulting from the linked data were unchanged. 
Implications Whilst this research demonstrates the utility of NHS number-based linkage, acquisition and use of PII on health datasets can increase the link rate and plays a vital role in estimating linkage error.
Socio-demographic variations in prostate cancer diagnosis recording: Primary care compared to the Cancer Registry in England
Authors: Somathilake Gayasha , Ford Elizabeth , Armes Jo , Moschoyiannis Sotiris , Otter Sophie , Lemanska Agnieszka
Published: September 2024

IntroductionIn the UK, primary care data are often used for cancer-related research, but the accuracy of cancer information is uncertain.
ObjectiveWe investigated socio-demographic variation based on the recording date of prostate cancer diagnosis between primary care and the National Cancer Registry (CR).
ApproachWe utilised a data extract of 1,600,000 patients over 65 years from Clinical Practice Research Datalink (CPRD). We extracted prostate cancer diagnoses using Read and SNOMED-CT codes from primary care, and ICD-10 from CR. Initial code entry determined diagnosis dates. We categorised recording timing differences as earlier, same-day or later in primary care than CR and used the chi-squared test and logistic regression (adjusted for recording year) to compare these discrepancies across age, deprivation, and ethnicity.
ResultsWe included 26,875 men with prostate cancer diagnoses commonly recorded in both sources during 2000-2016 (1,030 excluded with missing ethnicity). Compared to CR, 1,747 (7%) had diagnoses recorded on the same day in primary care, while 20,615 (77%) had later recordings with a median delay of 21 days (IQR: 13-38). Age at diagnosis was associated with recording discrepancies (p<0.001); older men were more likely to have earlier/same-day recordings. Adjusted ORs for age groups were 1.4 (95%CI: 1.3-1.5) for 60-69, 1.3 (1.2-1.5) for 70-79, and 0.9 (0.8-1.0) for ≥80 compared to <60. No associations were found between deprivation (p=0.096) or ethnicity (p=0.067) and recording differences.
Conclusion/ ImplicationsThe discrepancy in prostate cancer information between primary care and CR underscores potential biases in studies relying solely on one data source.
Federating governance, access and infrastructure to support researcher use of synthetic data
Authors: O'Sullivan Katherine , Caldwell Jackie , Cole Christian , Harrison Kathy , Mayor Charlie , Chaliou Antonietta , Dymiter Jaroslaw , Dawson Stacey , Brown Diane , Wilde Katie
Published: September 2024

Synthetic Data has the potential to improve efficiency of data analysis for researchers. However, there is no standard approach to synthetic data governance, access controls or infrastructure requirements, and researchers may face inconsistencies in how they can access or use synthetic data across trusted research environments. We present a federated solution taken by the Scottish Safe Haven Network to address these barriers to facilitate researcher use of synthetic data. We documented and evaluated existing governance pathways, access controls and infrastructure design for non-synthetic data across the Network, recognising uniformity and establishing equivalence using the 5 Safes framework, ISO27001 standards and the SATRE TRE specification. We also interviewed current and potential researchers using our trusted research environments to identify common use cases for accessing synthetic data. We then mapped researcher requirements against the documented equivalencies, validating with current and prospective users. We identified several use cases: to undertake feasibility studies, to understand dataset structure and format and to write analysis code whilst waiting on the project-specific data to be provided. By mapping the use cases onto existing governance and access processes and infrastructure designs, we were able to agree to a standard application process, access control mechanism, and infrastructure platform across the Network to provide a consistent process for researchers. A federated approach to synthetic data access will improve the speed at which research can be conducted as well as improving the transparency and consistency of data governance and access across organisations, ultimately improving the experience for researchers using TREs.
Disabilities in children receiving care and support in Wales and risk factors associated with being placed in care: A population-based data linkage study
Authors: Childs Jeanne , Griffiths Lucy , Hodges Helen , Bailey Grace , Elliott Martin , Cowley Laura
Published: September 2024

ObjectiveTo determine the prevalence of functional impairments in children with disabilities receiving care and support in Wales between 2016 and 2022, and to investigate risk factors associated with being placed in care amongst these children. 
MethodThis study used Children Receiving Care and Support (CRCS) Census records held in the Secure Anonymised Information Linkage Databank. We examined the proportion of children with specific impairments by their looked-after status, using bar charts and Upset plots. CRCS records were linked to demographic datasets to obtain information on area-level deprivation, and we conducted multivariable logistic regression analyses to model risk factors associated with being placed in care for children with disabilities. 
ResultsOf 38,165 children receiving care and support, 10,075 (26%) had a disability, and 3,360 (33%) of these children were placed in care. Older children were less likely to be placed in care than infants (e.g. for age group 1‒4: OR 0.37 (95% CI 0.24‒0.56)). Children of mixed ethnicity were more likely to be placed in care compared to White children (2.17 (1.65‒2.85)). Child mental health problems were associated with being placed in care (1.82 (1.60‒2.07)) as were parental mental health problems (1.44 (1.29‒1.60)), parental substance/alcohol misuse (3.16 (2.81‒3.56)), parental learning disability (2.92 (2.51‒3.38)), and domestic abuse (1.48 (1.31‒1.66)). 
ConclusionsThis new evidence may assist in the planning and provision of appropriate ongoing care and support for these children and their families to help prevent entry into the care system.