bluuebunny/crossref_metadata_2025 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.
Does the timing of special needs identification have an impact on children’s educational attainment, attendance, and exclusions?
Authors: Keating Jennifer , Sandu Alexandra , Huxley Katy , French Rob
Published: September 2024

Previous research has established an association between diagnosis of neurodevelopmental conditions and poorer academic outcomes. Many education policies advocate for early diagnosis and special education needs (SEN) support, however little is known about the association between timing of SEN identification and education outcomes.
To address this, we used Welsh administrative education data to identify the age at which a child first received SEN support. Using data from 90,271 children across 5 cohorts, logistic and linear regressions were run to examine the association between SEN identification and children’s attainment, exclusions, and attendance at age 16. In the latest cohort, we show that timing of SEN identification significantly associated with attainment, exclusions, and attendance. Children identified in their first year of school were 8 times less likely (than those without SEN) to meet national expectations for attainment, while those identified at age 16 were three times less likely, (X^2= 41.7, p<.001). The opposite pattern held true for exclusions and attendance: those identified in their final year were five times more likely to be excluded, compared to twice as likely for those identified in their first year (X^2= 7.3, p=.007). Those identified in final year were 2.52 times more likely to be persistently absent (missing >10% sessions), while those identified in their first year were 1.60 times more likely, (X^2= 8.9, p=.003). We show how these associations vary by gender, type of need, and timing of ending support. Early diagnosis is important across all outcomes, but most critical for attainment.
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.
Exploring UK-wide public perceptions of the 'public good' use of data for research and statistics
Authors: Kashef Shayda , Cowan Mary
Published: September 2024

In the UK, access to sensitive public data for research or statistics must serve the public good. These uses of data underpin the creation of better evidence for decision-making, however, decisions about what public good means rarely involve the public.
We carried out a UK-wide public dialogue to better understand public perceptions of ‘public good’ use of sensitive data for research and statistics within a UK context. This included deliberative in-person and online workshops with 68 members of the general public.
24 hours of deliberative discussions were analysed and distilled into five main findings, which demonstrate that participants want the public to have a say in how the public good is defined in the context of decision-making on accessing sensitive data for research or statistics. The participants strongly expressed that the use of data for research or statistics should address inequalities in society and minimise harms; both of which could be partly achieved through meaningful public engagement. Participants also suggested that they would like to see more communication around the benefits of the use of data for research and statistics. Finally, they were broadly supportive of more data sharing under best practice safeguards, over and above existing legal frameworks.
Insights from deliberative discussions with members of the public on their perceptions of public good use and access of sensitive data is reciprocally beneficial. It demonstrates trustworthiness to the public by meaningfully involving them in decisions about their data and ensures organisations can maximise the public benefit of their work.
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.
A Cohort Tree-based Representation and a Multimorbidity Progression Across Pregnancies in Scotland
Authors: Pedro Luciana , Azcoaga-Lorenzo Amaya , McCowan Colin
Published: September 2024

Pregnancy-related research using routinely collected data is increasing. Electronic Health Records (EHR) are a robust resource because they can provide information on a large population of pregnant women covering prolonged periods at a reasonable cost. Moreover, information is collected prospectively and is thus free from recall bias. However, these data sources also present challenges. Information is gathered for clinical purposes and often recorded in different datasets (prescriptions, hospital admission, demography, birth records, etc.). Therefore, a significant data management effort must be made to convert all the information into structured and meaningful datasets in which identifying the timing of conception, outcomes of interest, multiple pregnancies, and interpregnancy periods can easily be done.
In this study, we linked Scottish Maternity Records between 2007 and 2021 (1,575,153 pregnancies, 493,629 women) to demographics data, datasets with International Classification of Diseases (ICD) diagnostic codes for all inpatient stays and day cases in hospitals, and Prescribing Information System (PIS) for data on all medications from primary care. After combining these datasets using R codes, we developed a simple tree-based algorithm that visually represents each woman at each pregnancy and the related health conditions. Presenting data in this way helps identify relevant clinical questions and the temporal relation of different pregnancies. As an example, we identified the progression of multimorbidity through pregnancies and characterised how multimorbidity changes from one pregnancy to another, considering the woman's health conditions and social class.
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.
Projecting patterns of health inequalities in England up to 2040 using linked health data
Authors: Raymond Ann , Watt Toby , Rachet-Jacquet Laurie , Douglas Hannah-Rose , Head Anna , Kypridemos Chris
Published: September 2024

ObjectiveThe existence of wide inequalities in self-reported health across England is well-documented. Our research adds to this evidence by describing current patterns and projecting future patterns of inequality in diagnosed illness by deprivation.
ApproachWe used a microsimulation model which combines individual-level data on demographics, health and mortality from linked data for primary and secondary care with survey responses on leading modifiable risk factors and epidemiological evidence on the associations between risk factors and chronic illness.
We used the Cambridge Multimorbidity Score to measure multimorbidity. This assigns a weight to 20 common long-term conditions based on individuals’ healthcare use and their likelihood of death. We further focus on “major illness” which corresponds to a score greater than 1.5.
ResultsWe project that health inequalities are not projected to improve between 2019 and 2040. In 2040, we project the difference in the average time spent without major illness between the most and least deprived 10% of areas in England to be 10.7 years. This is largely unchanged from 10.4 years in 2019.
In 2040, we project there will be more working age adults living with major illness in the most deprived areas, more than double the rate in the least deprived areas (15.2% versus 6.8%). These rates remain largely unchanged from 2019 at 14.6% and 6.3% respectively.
Conclusion and ImplicationsOn current trends, health inequalities are projected to persist into the future. This has significant implications not just for population health but for labour supply and wider economic growth.
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.
Content, controls, audience: contextualizing de-identification as a novel approach to enabling cross-sectoral data linkages
Authors: Cartagena Rosario , Smith Michael , Li Kathy , Lewandoski Ash-Lei
Published: September 2024

BackgroundCanadian privacy laws often distinguish personal health information (PHI) from non-health data. A recent legislative designation permits the Institute for Clinical Evaluative Sciences (ICES) cross-sectoral data linkages and analyses of health and non-health data but requires a discrete environment for each data type and only one environment to conduct research and analytics. It further stipulates that only de-identified, non-health data may enter the PHI environment where research and analytics are conducted, an added complication since data linkages necessarily require some identifiability. In order to consolidate its health and non-health data, then, ICES requires novel approaches to data governance and de-identification.
ObjectiveTo establish a comprehensive data governance framework that optimizes cross-sectoral data linkages and classifies identifiability as existing on a spectrum dependent on content, controls and audience.
ApproachOperating under legislative and regulatory definitions of de-identification that contemplate what is “reasonably foreseeable in the circumstances,” ICES is working with its privacy regulator to create a data governance framework and de-identification guidelines for its health and non-health data.
ResultsWhile still awaiting regulatory approval, ICES will be a first-of-its-kind data integration unit whose data governance model is formally approved by its privacy regulator and an industry leader in novel approaches to de-identification and cross-sectoral data linkages.
ConclusionsIdentifiability is determined by context, and data may be simultaneously identifiable and de-identified, depending on its contextual factors.
ImplicationsICES’ approach to data governance and de-identification can offer important insights to other research and analytics-based organizations facing legislative barriers to cross-sectoral data linkages.
Using Integrated Administrative Data for Promoting Access to Early Childhood Education Programs
Authors: Kim Hannah , Rouse Heather
Published: September 2024

Early childhood education (ECE) can significantly mitigate the detrimental effects of poverty. Head Start (HS) is a federally funded program in U.S. designed to promote school readiness for children from low-income families. Nationwide estimates suggest that HS only reaches 41% of eligible children and programs are facing under-enrollment, which highlights the importance of addressing the evolving needs. This is a secondary analysis study that utilizes real-world data to explore (a) child, family, neighborhood, and program characteristics that predict HS non/enrollment and (b) HS eligible children’s use of other public ECE programs. It employs an individual child-level integrated dataset from Iowa’s Integrated Data System for Decision-making (I2D2), including data from the State Departments of Public Health and Human Services, Education, and HS recipients. Our data is unique as one of the first systems in the U.S. that includes child-level data from HS recipients statewide. We also integrated aggregated county- and program-level datasets to include multidimensional neighborhood conditions (i.e., poverty, employment) and comprehensive information about HS programs (i.e., staffing, transportation, partnerships). Our analytic sample is 30,048 potential HS eligible children who were born in Iowa and attended kindergarten in 2016-17 or 17-18. Preliminary results showed that only 12% of eligible children were enrolled in HS the year before kindergarten. We are testing logistic and multinomial logistic regression models, and the results will be ready by the presentation. Findings will inform HS programs on improving equitable access and recruiting strategies to meet the needs of children who are most in need.
Development of a Enhanced System Review for Public Agency Multi-Sectoral Integrated Data Systems
Authors: Rodriguez Baron
Published: September 2024

While traditional security assessments (such as mandated external security reviews which include a penetration test tied to a security framework) are a critical component to securing your data infrastructure, the complexities and governance structures around integrated data systems that involve multiple public sector agencies require a more comprehensive framework to address comprehensive risk, regulatory, and governance considerations that are critical to the confidentiality of the data within these systems.
In January of 2024, the Data Integration Support Center (DISC) created and piloted an enhanced system review that includes components that are often overlooked or oversimplified when working with today's technologies such as cloud-based secure enclaves. By following up on a mandated security review, an enhanced system review can help a maturing Integrated Data System (IDS) address both security and privacy risks as it moves forward. An enhanced system review looks primarily, though not necessarily exclusively, at the following aspects of an IDS, principally in the interest of ensuring privacy:

IDS alignment with its legal framework and other structuring documents (e.g., policies, procedures, interagency agreements)
Training to support privacy and security
Use of technology to prevent or mitigate human error

The results of the review are provided directly to the IDS leadership and generally include a list of recommendations in the areas of improved legal agreements, leveraging privacy enhancing technologies to mitigate or reduce errors, procedural/policy gap identification and/or architectural recommendations to improve the security.
Adapting to evidence users: accessibility and incentives
Authors: Lenczner Michael
Published: September 2024

Adapting infrastructure for evidence creation and dissemination involves taking new technological, social, and environmental realities into account. Adaptations should also respond to what hasn’t changed: structural disincentives to evidence use among intended users. This presentation addresses the understudied problem of how administrative data systems go beyond enabling access to information – how data linkages or other infrastructure impacts policymakers or other intended information users. It addresses data sharing and data use, as well as public engagement and involvement.
A recent practitioner convening examined how new administrative data infrastructure can catalyze evidence-building, and, specifically explored the incentives and disincentives for stakeholders' use of evidence from evaluations. Participants discussed the multiple functions of evaluations for nonprofits, funders, policymakers, and communities, and shared lessons in capacity building from the recent history of nonprofit & human services evaluation. Preliminary results from this convening will be discussed in terms of their broader implications for the use of information resulting from future large-scale data linkages.
Broadly, the convening validated the hypothesis that efforts to increase the availability of evidence often proceed without sufficient consideration of the history of non-uptake of evidence, and that there has been limited acknowledgement of this problem by practitioners of evidence generation. Much work is needed within the evaluation & evidence, and nonprofit sector to address this challenge. Key themes for this work that emerged during the convening will illustrate what might work and what won’t work to address the non-uptake of evidence more broadly.
A CONTEMPORANEIDADE DA AVICULTURA: DA PRÉ-HISTÓRIA À LIDERANÇA CONCORRENCIAL BRASILEIRA
Authors: Piccolo Erasmo Aparecido , Duval Henrique Carmona , Gallo Zildo , Ferraz José Maria Gusman
Published: September 2024

As aves despertam interesse seja pela sua beleza, seus hábitos e principalmente como fonte de alimento, a expansão das espécies ocorreu com o desaparecimento dos dinossauros e diminuiu com o crescimento dos mamíferos. Atualmente, está presente no cardápio alimentar praticamente em todo o planeta e o Brasil lidera as exportações mundiais. Objetivo: levantar a história da avicultura, bem como a contemporaneidade do ambiente concorrencial brasileiro. O referencial teórico ancorou-se na evolução da avicultura, agricultura, alimentação, animais, tecnologia, genética, cultura, sociedade, economia e história. Método: adotou abordagem qualitativa, tipo exploratório com perfil bibliográfico e a utilização da ferramenta timeline que permitiu o deslindar da pesquisa da pré-história à contemponaneidade. Resultados: a história da avicultura inicia-se pela expansão das aves após o final da era glacial com o fim dos dinossauros, mas elas tornaram-se presas fáceis aos mamíferos e resultou na expressiva diminuição no número de espécies. Na pré-história, ocorreu o processo de sedentarização humana, melhorias nas dinâmicas agrárias, domesticação dos animais, em especial, as galinhas em 5500 a.C. No mercantilismo as aves foram difundidas ao redor do mundo, chegou no Brasil em 1503, as implementações de políticas agrícolas nos anos 70 aliadas ao processo de globalização após os anos 90 contribuíram para que o Brasil chegasse ao ranking como o maior exportador de carne de frango em 2004. Atualmente, o país lidera as exportações para a Ásia, Oriente Médio, África e União Europeia, o Estados Unidos lidera na América do Norte e a União Europeia concorre significativamente somente na África.
Socio-Economic Determinants of Individual Muslim Zakat Payment Behavior in Indonesia: Analysis of Indonesia Family Life Survey (IFLS) Dataset
Authors: Durohman Hapid , Andrian Sutisna Fajar , Yuka Anugrah Muhammad , Baharuddin Andi Zamakhsyari
Published: September 2024

Zakat is one of the pillars of Islam and holds significant potential in Indonesia. However, the collected amount remains below its potential due to the neglect and perceived insignificance of zakat payments by many Muslims. Therefore, understanding socio-economic behaviors and factors influencing individual decisions to pay zakat is crucial. This research investigates the determinants of socio-economic factors on individual zakat behavior, using data from the 2014 Indonesian Family Life Survey (IFLS) focusing on individuals aged 15 and above. Probit regression methodology is employed to examine individual decisions on zakat payments, utilizing STATA 17 for analysis. The findings indicate that individuals with higher socio-economic status are more likely to pay zakat, as evidenced by variables such as gender, household size, age, home ownership, loans, years of schooling, income, religiosity, and occupation significantly influencing contributions. However, variables like place of residence and marital status do not significantly impact payment decisions. Research implications highlight the need for targeted interventions and educational programs to enhance zakat awareness and compliance, especially among individuals with lower socio-economic status.
Factors Influencing Fashion Consciousness in Muslim Fashion Consumption among Zillennials
Authors: Ocktavia Annisa Kartika , Marenza Silvya Eka , Al-Ayubi Solahuddin , Maulana Huda Kurnia
Published: September 2024

This study aims to analyze the factors that influence awareness of Muslim fashion consumption among Generation Z in Indonesia. Data is collected using a questionnaire using the convenience sampling method. A total of 255 final data points can be used for data analysis using SmartPLS. The results show that the style of dressing, the motivation for fashion, the uniqueness of fashion, and the source of fashion knowledge positively influence fashion awareness and indirectly influence Muslim fashion consumption. The findings from this study provide valuable insights to stakeholders in the fashion industry, including designers, retailers, and entrepreneurs, in understanding Muslim market segments. Practitioners can develop Muslim fashion products that are sharia-friendly and effectively target Muslim consumer segments through appropriate marketing strategies.
The Decision to Use Halal Cosmetics: Influence of Quality, Benefits, Awareness, and Brand Image
Authors: Wandirah Ayu , Hudatil Atkiyan Fahrul , Juansha Andri , Ali Azhar Jeihan
Published: September 2024

The purpose of this research is to ascertain the influence of brand image-mediated product quality, product benefits, and awareness on travelers' decisions to use halal cosmetics while in Lombok, West Nusa Tenggara. This research is a quantitative study using primary data obtained from distributing questionnaires. A total of 305 respondents who have visited Lombok, NTB, made up the study's sample. The technique for data analysis is Structural Equation Modeling based on Partial Least Squares or SEM-PLS. It is known that product quality has no bearing on whether or not someone chooses to use halal cosmetics based on data analysis findings. The decision of Lombok NTB tourists to use halal cosmetics is, however, influenced positively by the benefits of the product, awareness, and brand image. Meanwhile, brand image is unable to mediate the relationship between product quality, benefits, and awareness of the choice to use halal cosmetics among Lombok NTB tourism.
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.
A decision support software system for cryptocurrency traders on the Trading View platform
Authors:   Lviv Polytechnic National University, Lviv, Ukraine, Bezsmolnyi Yu. V. , Seniv M. M. ,   Lviv Polytechnic National University, Lviv, Ukraine
Published: September 2024

The work carried out a comparative analysis of scientific publications regarding the possibility of predicting the direction of the cryptocurrency exchange rate using the data of open numerical indicators, based on the results of which it can be concluded that due to the volatility of the cryptocurrency market and the need for accurate forecasting, there is a need to create an aggregated indicator that will take into account the current price exchange rate asset, parameters of simple indicators, trading volume, etc. In addition, this indicator will be a parameter for the application of a multi-criteria analysis model in the process of supporting decision-making for cryptocurrency trading. A software decision support system for cryptocurrency traders on the Trading View platform has also been developed, which allows the cryptocurrency trader to get the value of the current situation of the cryptocurrency market in the form of a value using the method of weighting coefficients and selected indicators. Among the selected indicators: RSI, MA, CCI, Stochastic Oscillator, OBV, ADX, CMF to determine the moment of opening a position, and Fibonacci Retracement, Ichimoku Cloud to determine the closing of positions. Taking into account all the indicators and the coefficients determined for them, the obtained range of values is from 0 % to 100 %. If the value of the indicator exceeds the threshold of 20 %, it means that it is necessary to inform the trader about a possible entry point. That is, a value of 20 % to 40 % is weak performance, 40 % to 60 % is medium performance, 60 % to 80 % is strong performance, and a value greater than 80 % will not be overlapped by new pyramiding values for a better overall indicator success rate. The value of the indicator determines the potential effectiveness of opening positions, and thanks to the RSI indicator, the direction of opening positions is determined. The direction of the position is divided into long and short. An indicator has been developed for the TradingView platform, which, unlike existing simple indicators, collects data from open access and calculates a potential point for opening a position. Obtaining the numerical value of a single indicator saves the trader time to review and analyze a collection of indicators and time to decide on opening a position, as the cryptocurrency market is known for its sudden volatility, where a decision must be made quickly.