bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of out-of-home care on children’s outcomes: A longitudinal cohort study using linked administrative data from Manitoba, Canada
Authors: Brownell Marni , Frank Kayla , Nickel Nathan , Enns Jennifer , Flaten Lisa , Sinclair Stephanie , Decaire Elizabeth , Chartrand Jennifer , Sinclair Scott , Mayer Teresa , Nash Chris , Pfau Jamie , Durksen Anita , Scatliff Colette , Hunter Mikayla , Han Soomin , Patrick Stephaney , Casidsid Hera , Amjad Sana , Bennett Marlyn , Casiano Hygiea , Houston James , Bowler Laura , Stukel Therese 
Published: September 2024

ObjectiveTo provide rigorous quantitative evidence about the impact of out-of-home care on children’s outcomes.
ApproachGuided by an Advisory Circle of First Nations (FN) Knowledge Keepers and conducted in partnership with FN researchers, we used linked administrative data to identify all Manitoba children served by Child Protection Services (CPS) at FN agencies (FNA) and other agencies (OA) (2007-2018). We compared health, education, and legal system outcomes of those in care (FNA n=10,856; OA n=8,468) to those with protection concerns but not in care (FNA n=12,896; OA n=14,394). Using instrumental variable analysis with CPS agency rate of out-of-home care as the instrument, outcomes between groups were compared using 2-stage multivariate probit regressions adjusted for child and family factors.
ResultsOdds of teen pregnancy (FNA OR 3.69, 95% CI 1.40-9.77; OA 5.10 (1.83-14.25)), teen birth (FNA 3.23 (1.10-9.49); OA 5.06 (1.70-15.03)), and positive STI tests (FNA ns; OA 7.21 (3.63-14.32)) were higher and odds of vaccination at age 2 (FNA ns; OA 0.49 (0.29-0.80)) were lower for children in care than children not in care. Odds of being accused (FNA ns; OA 2.71 (1.27-5.75)), victim (FNA ns; OA 1.68 (1.10-2.56)), or charged with a crime (FNA ns; OA 2.68 (1.21-5.96)), and odds of being incarcerated (FNA 3.64 (1.95-6.80); OA 1.19 (1.19-8.04)) were higher for children in care.
ConclusionsOut-of-home care negatively affects children’s health, social and legal system outcomes.
ImplicationsCPS agencies should work with FN leaders to develop and implement strategies to reduce the number of children taken into care.
An innovative approach to developing practice informed evidence in the absence of RCTs for a Family-led peer to peer support program
Authors: Montgomerie Alicia , Maiorano Vita , Abbott Danielle , Lynch John , Pilkington Rhiannon 
Published: September 2024

ObjectiveTo illustrate how whole-population linked data can be used to understand a system perspective of client complexity, and build robust evidence of Family by Family program impact.
MethodsFamily by Family program (the program) participant data were linked into the Better Evidence Better Outcomes Linked Data (BEBOLD) Platform. BEBOLD is a whole-of-population linked de-identified administrative data platform for all South Australian children born 1991 onwards (n~500,000), as well as their parents including data spanning health, education, and social services.
We descriptively analysed parental child protection history, emergency department presentations, hospitalisations, homelessness and justice system contact in the 24 months prior to and post program commencement. We emulate a trial using the ‘target trial’ causal inference framework to evaluate the program effect on a range of child outcomes using targeted maximum likelihood with a set of over 20 confounders.
Results There were 361 families and 841 children in the program included in analysis. Selected results follow: Prior to the program, 35.8% of children were in a family where at least one parent had their own child protection history and 8% had a parent who experienced out-of-home care. Nearly 40% of children had at least one parent with a mental health related emergency department and/or hospitalisation, while 22% of children were in a family with specialist homelessness service contact. Program impact results will be presented at the conference.
ConclusionThis research-practice partnership illustrates how bringing together program and linked-administrative data generates new evidence about client complexity and program impact.
Data as infrastructure: Systematic data curation addressing fundamental data content differences across the UK
Authors: Orton Chris , Edwards Lara , Seymour David , Jones Monica , Quinlan Philip , Thompson Simon , Goble Carole , Quint Jennifer , Sheikh Aziz 
Published: September 2024

Objective and ApproachHealth Data Research UK, the UK national institute for health data science, is coordinating efforts alongside national academic partners to streamline data curation at disease, population, and data structure level to enhance data offerings and provide networked data infrastructure supporting whole-UK research. Due to clinical, coding, and system differences across the constituent countries of the UK, data is often not standardised for whole-UK analyses, creating burden on research teams and leading to long data preparation times in order to run even distributed analyses.
The approach to solve this is multi-faceted, including deploying data curation and cohort creation algorithms into health data providers’ environments, and through the novel integration of federated analytics solutions (such as those piloted through recent national infrastructure programmes) improving data access and research deployment efficiency.
ResultsStandardising data through clinical and structural data curation directly deployed to health data providers creates a framework for whole-UK studies to be readily achievable, and provide the data infrastructure base to integrate new technical federated analytics solutions to deploy and reproduce analytics without unnecessary large scale data migration. Conclusions and ImplicationsSystematic curation of health data within national data providing organisations provides flexibility and choice for researchers in terms of the data they will apply for to answer vital research questions affecting the UK populace. Such advances will improve the quality and efficiency of research for all corners of the UK, and create a community of practice in terms of developing data from health systems to research environments.
GDPR-anonymous population overlap estimation
Authors: Baier Patrick 
Published: September 2024

The feasibility of many collaborative, data-driven research projects in Public Health and Social Sciences depends on the size of the overlap between the populations of contributing data partners, that is, the number of individuals for whom all parties can provide data. Where the identity of respective population members is subject to privacy protections, the overlap is traditionally estimated using de-identified cryptographic tokens to represent individuals. This does however not meet the strict standard of anonymity under GDPR and potentially runs counter to emerging privacy laws of US States, and may thus impose significant regulatory and governance burdens on research institutions, even at the early scoping stage.
We present a novel method for overlap estimation, where two data partners construct a single, anonymous Bloom filter for their populations from which the overlap can be estimated. Two new techniques -- (a) the randomization of the contribution of individual population members to the Bloom filter and (b) an interactive bit-flipping protocol between data partners -- are presented which ensure that the Bloom filters are safe against dictionary attack by one party against the other party. We discuss the security aspects of this method, present the required mathematical formulas for calculating the overlap estimate, and report on the practical performance in terms of computational effort and resulting accuracy.
The goal of the method is to facilitate new research by reducing the cost and complexity of initial planning and by making the overlap estimate GDPR-anonymous, and thus safer and subject to less onerous compliance requirements.
Sensitive Data Flagging within Data Quality reports: R and Regex Integration for Effective Text Flagging in Large Datasets
Authors: Coldea Alex-Ioan , Elmessary Muhammad A , Thayer Daniel S , Scanlon Ieuan , Davies Hannah 
Published: September 2024

ObjectiveWe’re introducing a comprehensive approach to enhance sensitive data flagging within our Data Quality (DQ) tool. Within de-identified health records, sensitive data such as user ids, post codes, phone numbers, and addresses pose significant privacy risks if exposed in their raw form. To address this challenge, we propose using all necessary regex patterns and free text field checks for sensitive data flagging with the objective of efficient detection within large datasets and ultimately reporting such data.
ApproachA regex map has been developed along free text field thresholds, distribution counts and percentage checks to allow a generic approach. This ensures that any new regexes can be added to the flagging process without significant code changes.
Once identified, sensitive data instances, including fields that contain free text are flagged and displayed in an html report that is human readable by DQ reviewers.
ResultsThrough a combination of SQL and R regex based text processing, our approach allows a seamless identification and flagging of sensitive data within datasets. Sensitive data for large tables of +25 million records with over 50 columns are getting flagged in less than 150 seconds (approx. 2 minutes).
ConclusionOur development offers a practical solution for sensitive data flagging in complex datasets where team members can implement robust sensitive data flagging by adding more regex formulas in the codebase.
ImplicationsDQ reviewers can inspect the sensitive flagged fields in a human readable way, providing an extra measure of confidence when determining the quality of de-identified health records.
Risk factors and trajectories of self-harm, neurodevelopmental disorders, and mental health conditions in pupils Educated in Other Than At School (EOTAS) in Wales: a retrospective nationwide electronic cohort study
Authors: Rouquette Olivier Y. , Del Pozo Banos Marcos , Lee Sze Chim , John Ann 
Published: September 2024

Objective and ApproachPupils ‘Educated in Other Than At School’ (EOTAS) are some of the most vulnerable learners, who, for reasons such as mental health or behavioural difficulties, do not attend a mainstream school. We linked population data from Education Wales (EDUW) between 2010-19 to primary and secondary healthcare records. Individuals included in the EOTAS dataset aged from 7 to 18 years were identified as cases. Controls were pseudo-randomly selected based on equivalent age distribution. We used regression approach to compare characteristics of EOTAS pupils with controls and evaluate their risks of self-harm and mental health conditions before and after being in EOTAS provision.
ResultsBeing male, high deprivation levels, and previous records childhood maltreatment, self-harm, and mental health conditions resulted in higher odds of being in EOTAS provision. Female in EOTAS provision entered at a later age, and with higher proportion of co-morbidities. Pupils in EOTAS provision had increased incidence of self-harm and mental health conditions from one year after entering EOTAS provision up to 24 years of age than controls with similar characteristics.
ConclusionsAn important proportion of pupils in EOTAS provision are from the most deprived areas in Wales, with complex life trajectories, social, emotional, and mental health needs.
ImplicationsEOTAS provision is not sufficient to attend the social, emotional, and mental health needs of their pupils. Long-term support and a better integration with social care, primary and specialist mental health services is required.
The Challenges and Strategies for Cleaning the Routinely Collected Data for Total Knee Replacements (TKR) and Total Hip Replacements (THR) in the Kingdom of Fife
Authors: Pedro Luciana , Akhtar Muhammad , Winata Arthur , McCowan Colin 
Published: September 2024

Knee and hip replacement surgeries have revolutionised orthopaedic medicine by providing individuals with surgeries resulting in pain relief, improved mobility and a better quality of life. NHS Fife has been collecting routine data since 1998 on patient demographics, type of surgery, recovery time, follow-ups, revision, and outcomes, including complications. This work reports the challenges and strategies to clean the collected data.
Data was available for 12,313 patients, totalising 14,524 THR and TKR surgeries performed in NHS Fife between 1998 and 2021. The original dataset consisted of 34 unique-structured files covering distinct periods and representing five categories: primary surgery, follow-up, infection, mortality, and revision. Data wrangling was done using the programming language R, transforming the raw data into a more functional form. The main elements of the data-cleaning procedure were combining variables representing the same information and converting void/corrupted/purposeless values into missing data, providing a standardisation for the collected information. Then, a rigorous data merging resulted in reshaping this complex collection of files into one single streamlined file for each of the five categories and type of surgery. Patients who had multiple surgeries were also cross-identifying, enabling the characterisation of the epidemiology of multiple lower limb joint replacements.
The structured files can now be linked to studies across different datasets like social care, prescriptions, and primary care. It can also be used to develop future machine learning models to assess the risk of multiple primary and revision knee and hip replacements and their outcomes.
Comparing Methods for Missing Paternal Linkages in Administrative Data
Authors: Hamad Amani , Monchka Barret , Plana-Ripoll Oleguer , Ayilara Olawale , Lix Lisa 
Published: September 2024

ObjectiveAdministrative data often lacks complete family linkages, particularly to fathers, hindering familial health research. We compared methods to address missing paternal linkages in research investigating the familial transfer of mental disorders.
ApproachA population-based cohort study of Manitoba (Canada) born adults, +18 years old between 1977 and 2017 with maternal linkages. Three methods were used to address missing paternal linkages: indicator category, complete case, and multiple imputation, which identified three candidate fathers for each individual within -4 to +7 years of mother’s age with same postal code. For each method, the association of maternal and paternal history with mental disorder risk during follow-up was tested using multivariable logistic regression models adjusted for demographics and comorbidities.
ResultsThe cohort included 142,549 individuals; 22.6% lacked paternal linkages. Using indicator category, maternal and paternal histories were associated with mental disorder risk, with odds ratio (OR) 1.49, 95% confidence interval (CI): 1.45-1.52 and OR 1.34, 95% CI: 1.30-1.37, respectively. Similar results were obtained in the complete case analysis (OR 1.49, 95% CI: 1.45-1.53 and OR 1.33, 95% CI: 1.30-1.37, for maternal and paternal history, respectively). With multiple imputation, maternal history’s association with mental disorder risk remained consistent with the other methods (pooled OR 1.52, 95% CI: 1.49-1.56); paternal history was associated with a smaller risk (pooled OR 1.25 95% CI: 1.22-1.29).
Conclusions and ImplicationsThe three methods for addressing missing paternal linkages produced similar findings about the familial transfer of mental disorders. Future familial studies could incorporate multiple methods to demonstrate robustness of findings.
Trends in Pregnancy-Associated Opioid Toxicity and Mortality
Authors: Camden Andi , Gomes Tara , Brown Hilary , Schmidt Rose , Patrikar Aditi , Lu Hong , Guttmann Astrid 
Published: September 2024

ObjectivesTo examine trends in pregnancy-associated non-fatal and fatal opioid toxicity and all-cause mortality, and identify associated factors.
ApproachWe conducted a population-based study of 1,555,370 pregnancies in Ontario, Canada, 2013-2022. We analyzed linked administrative datasets, including coroner data, and calculated pregnancy-associated (during pregnancy or within one-year post-pregnancy) non-fatal/fatal opioid toxicity and all-cause mortality ratios per 100,000 livebirths by year and timing (pregnancy, post-pregnancy). Poisson regression models analyzed trends in outcomes and generated adjusted relative risks (aRR) of opioid toxicity by socio-demographic and clinical factors.
ResultsPregnancy-associated non-fatal opioid toxicity increased 220% between 2013 and 2020 (45.5-145.4/100,000 livebirths) before declining by 30% in 2021. Over the study period, fatal opioid toxicity increased 150% (6.8-17.5/100,000) and all-cause mortality increased 120% (32.8-71.2/100,000). Our methods did not identify any opioid toxicity deaths in pregnancy, and most non-fatal (66.6%) and fatal (88.9%) opioid toxicity and all-cause mortality (73.9%) occurred 43-365 days post-pregnancy. The percent of deaths attributed to opioids increased from 12.7% in 2015 to 25.0% in 2020. Substance use disorder (aRR 19.52, 95% CI 16.87-22.58), pre-pregnancy opioid toxicity (aRR 4.69, 3.81-5.78), mental illness (aRR 2.01, 1.75-2.29), high neighbourhood deprivation (aRR 1.45, 1.28-1.64), and social disadvantage (aRR 3.21, 2.77-3.71) were associated with elevated risk of opioid toxicity.
ConclusionsPregnancy-associated opioid toxicity and mortality have increased substantially. In 2020, 1 in 4 pregnancy-associated deaths involved opioids.
ImplicationsFindings highlight the need for comprehensive care and perinatal harm reduction services. System-level improvements to reduce poor outcomes must include complete data capture of all pregnancy-associated deaths.
Generation Victoria (GenV): Creating a large consented longitudinal infrastructure for discovery and intervention research
Authors: Wake Melissa , Goldfeld Sharon , Mohal Jatender , Siero William , Hughes Elizabeth , Clifford Susan , Hall Simon , Schwarz Naomi , Frugier Tony , Wang Jing , Mavoa Suzanne , Zaritski Natasha , Saffery Richard 
Published: September 2024

Designed to help solve complex problems facing children and adults, Generation Victoria is Australia’s largest-ever birth cohort, and the only such cohort mounted internationally during the COVID-19 pandemic. Here, we describe progress in bringing together this ‘cell-to-society’ consented cohort with diverse existing data and biosamples assets.
GenV is a whole-of-state consented cohort open to all Victorian children born October 2021-October 2023 and their parents. With over 117,000 participants to date (babies, mothers, fathers and non-binary parents), GenV closely reflects Australia's diverse demographics. At recruitment parents consent to GenV accessing (a) government managed and routinely collected administrative datasets across health, primary care, education, social care and geospatial and clinical datasets, (b) residual biosamples, (c) clinical and other service records and (d) spatial data.
To date, GenV has developed protocols, governance and technical requirements for linking birthing hospitals clinical data, state based administrative datasets and residual samples from statewide pathology labs. In pilot studies we have achieved high linkages rate (over 98%) with the state-based linkage spine and with clinical records from one birthing hospital. Universal newborn blood spot linkage is nearing completion. We are developing methods to include our natural and built environment exposome platform, antenatal imaging and trials data.
While challenging, GenV has encountered no significant barriers in creating an enduring, linked and secure data resource to advance future evidence-based decision making for child, family and adult outcomes. Our collaborative approach welcomes partnerships to co-build and enhance the muti-sectoral data and its utility.
Understanding medicine related adverse events in people with multiple long-term conditions and polypharmacy
Authors: Rafferty James , Lyons Jane , Lyons Ronan , Owen Rhiannon 
Published: September 2024

Background and ObjectivesPolypharmacy is increasingly common in people living with multiple long-term conditions (MLTC), but clinical evidence often does not consider medicine interactions beyond two or three treatments at a time. The aim of this research is to develop statistical models to better understand interactions between treatments leading to adverse events for people living with MLTC.
ApproachWe will develop models of risk of adverse events in people with MLTC and polypharmacy using data in the Secure Anonymised Information Linkage (SAIL) Databank - a trusted research environment with linked electronic health record data at the individual-level for the population of Wales, UK. Taking a flexible Bayesian joint modelling approach, where the posterior risk is the product of several sub-models, will enable the characterisation of this complex system in a way that is interpretable for healthcare decision-makers, leading to improved understanding of risk factors for adverse events.
ResultsThe Welsh Multimorbidity e-Cohort (WMC) includes 2.9 million people alive and living in Wales on 1st Jan 2000 with follow-up for 20 years. Individuals in the cohort have 932,552,061 medication items prescribed and 277,279 coded adverse events recorded in primary care data. Modelling approaches will focus on feasibility of fitting models to population-level datasets, and the interpretability of the model results for healthcare decision-making.
Conclusions and ImplicationsResults have the potential to inform healthcare policy and practice for the management of people living with MLTC and polypharmacy, thus improving patient outcomes and making the best use of limited healthcare resources.
Navigating Generosity: Exploring Determinants of Zakat, Infaq, and Sadaqah among the Hijrah Community in the COVID-19 Pandemic
Authors: Azhar Neng Zulfa , Nofrianto Nofrianto , Khan Abdul Karim , Chantarungsri Chariyada 
Published: September 2024

This study investigates the factors influencing the hijrah community's decision to contribute to Zakat, Infaq, and Sadaqah (ZIS) during the COVID-19 pandemic in Indonesia. Drawing upon the Theory of Planned Behavior (TPB), we examine the impact of religiosity, altruism, subjective norms, behavioral control, and institutional image on ZIS payment decisions. Data were collected through a questionnaire survey of 255 members of the hijrah community and analyzed using ordinal logistic regression. Results indicate that religiosity, altruism, and subjective norms significantly influence the decision to pay ZIS, while behavioral control and institutional image do not. This suggests that individuals with strong religious convictions, driven by empathy and a sense of social responsibility, and influenced by their social networks, are more likely to contribute to ZIS, regardless of perceived ease or institutional factors. This preference for direct giving, motivated by trust and a desire for tangible impact, further explains the limited influence of institutional image. This study contributes to the understanding of ZIS giving behavior within the increasingly prominent hijrah community, particularly during times of crisis, and offers valuable insights for Islamic philanthropic institutions seeking to maximize ZIS collection by tailoring their strategies to align with the motivations and values of this growing segment of donors. While this study focuses specifically on the hijrah community in Indonesia, future research should explore these factors among diverse Muslim populations in various socio-cultural contexts to enhance the generalizability of the findings.