International Journal of Population Data Science最新文献

{"title":"Child Health Research Using linked Multi-domain Canadian Administrative Data: A Scoping Review.","authors":"Yen My Vuu, Marcelo L Urquia, Lisa M Lix, Amani F Hamad","doi":"10.23889/ijpds.v11i1.3407","DOIUrl":"10.23889/ijpds.v11i1.3407","url":null,"abstract":"<p><strong>Introduction: </strong>Linked administrative data integrating health and non-health information can support population-based research about biological and contextual environmental factors that influence child health. Database linkage studies leverage existing data to provide more comprehensive information than would be available from any single source. However, it is unknown the extent by which child health studies capitalise on linked multi-domain Canadian administrative data.</p><p><strong>Objective: </strong>This scoping review aims to describe Canadian population-based child health studies that used linked multi-domain (i.e., health and non-health) administrative data.</p><p><strong>Methods: </strong>A systematic search was conducted of MEDLINE, Embase, Scopus and Global Health from inception until March 12, 2025. Articles were included if they focused on children (birth to 18 years), used Canadian administrative data, and linked health with non-health data. Two reviewers independently screened titles/abstracts and full texts; a pilot test ensured consistency. Article characteristics, province/territory, parental linkage, and non-health variables, were collected using an extraction form.</p><p><strong>Results: </strong>The search yielded 4,437 articles, of which 42 met inclusion criteria. Most articles were conducted in Manitoba (45%) and Ontario (36%). Maternal linkage was common, whereas paternal linkage was limited to Manitoba and British Columbia. Immigration status was the most common non-health variable. Health service use, particularly preventive care, such as screening and vaccination coverage, was a common research theme. No multi-jurisdictional studies were identified.</p><p><strong>Conclusions: </strong>Multi-domain administrative data linkage studies remain concentrated in a few provinces. Expanding parental linkage, integrating non-health variables, and strengthening multi-jurisdictional studies are crucial for improving population-based understanding of child health influences across Canada.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 1","pages":"3407"},"PeriodicalIF":2.2,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13359102/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148438357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exposure to Ambient Air Pollution and Onset of Dementia.","authors":"Babak Jahanshahi, Duncan McVicar, Neil Rowland","doi":"10.23889/ijpds.v11i5.3644","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3644","url":null,"abstract":"<p><p>This study used Census data from Northern Ireland linked to administrative data on prescriptions between 2010 and 2016, to examine the association between ambient air pollution exposure and the risk of dementia onset. It contributes to this literature by providing new evidence in a comparatively low pollution context, using rich and nationally representative cohort data with comprehensive information on dementia disease medications dispensed over an extended period. We estimated Cox Proportional Hazards models for the association between pollution exposure and dementia onset as proxied by first receipt of dementia medication, controlling extensively for potentially confounding factors. Estimates are presented in the form of hazard ratios for the effect of long-term PM2.5 and NO2 exposure (define as 5-year moving average exposure in the primary model) on the risk of dementia onset. There was a clear unadjusted and adjusted association between long-term exposure to ambient pollution and the risk of developing dementia, with those experiencing higher exposures being at greater risk. There was evidence of a positive association with both PM2.5 and NO2 exposure for subsamples by age and sex with more tentative associations for those under 70 years of age. This study contributes to an emerging literature examining the association between ambient PM2.5 and NO2 pollution and onset of dementia. We found strong evidence for positive associations even in the relatively low-pollution context of Northern Ireland.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3644"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426819/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Collaboration to Impact: Insights from the DARE UK Community Building programme.","authors":"Najmeh Modarres, Elizabeth Waind, Katie Porter, Cassandra Gould Van Praag","doi":"10.23889/ijpds.v11i5.3733","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3733","url":null,"abstract":"<p><p>How can effective community building transform a fragmented Trusted Research Environment (TRE) ecosystem into a connected, standardised one? And how can it elevate the work of data custodians, data scientists and others across the sector, while effectively involving the public? These are some of the questions Data and Analytics Research Environments UK (DARE UK) has explored as it reflects on the development of its community groups. DARE UK's community groups are collaborative initiatives that bring together partners from across the UK data research landscape to co-create work aligned with the DARE UK mission to establish a safe and collaborative UK network of Trusted Research Environments (TREs). Ahead of the 2026 IPDLN Conference, DARE UK has funded nine Interest Groups - open-ended groups focused on broad challenges within the scope of the DARE UK programme - and four Working Groups, which are short-term collaborative efforts typically linked to an Interest Group and focused on specific pieces of work. Six of these groups have now been active for over a year. In this session, we will share case studies from selected active community groups and lessons learned about what it takes to establish a community that can sustain and govern itself. We will discuss the processes involved in developing a charter and code of conduct, defining what a successful community looks like, determining effective funding models based on our experience, and most importantly, meaningfully engaging and involving the public throughout.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3733"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426670/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing Equity, Diversity, and Inclusion in Data Research: Insights from a PEDRI Cross-Sector Roundtable.","authors":"Samaira Khan, Doreen Tembo, Katie Porter, Cassie Smith","doi":"10.23889/ijpds.v11i5.3606","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3606","url":null,"abstract":"<p><p>Persistent inequities in research datasets mean that marginalised communities are routinely under-represented. Engagement practices often unintentionally exclude the very groups most affected by data-driven decisions, while organisations frequently lack the training, resources, or guidance needed to address these gaps. Together, these issues risk reinforcing structural inequities and undermining the quality, relevance, and public trust of data research. To respond to these challenges, PEDRI convened a cross-sector roundtable in September 2025, bringing together twenty researchers, engagement practitioners, inclusion specialists, and public contributors to identify practical strategies for embedding EDI throughout the research lifecycle. Participants identified several critical barriers: exclusionary engagement practices, over-reliance on the same communities, inconsistent data collection standards, lack of funding for Public Involvement and engagement and insufficient EDI training for researchers and data managers. The group identified a range of solutions rooted in intersectional approaches and the need to embed meaningful public involvement at every research stage. Priority actions included expanding PEDRI's Resources Hub with tailored EDI guidance, strengthening its convening role, and working with partners across the sector nationally and internationally to connect researchers with diverse communities, reduce engagement burden, and align sector-wide EDI and PIE priorities. This presentation will share concrete findings, cross-sector solutions, and priority actions arising from the roundtable. By placing inclusion at the heart of data research, we can build not only better datasets, but a fairer, more trustworthy research ecosystem that serves all communities.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3606"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426833/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility research into using administrative data sources to produce multimorbidity scores for predicting general health statistics for England.","authors":"Tolu Adedire, Wajiha Munir, Charlotte Standeven, Matthew Minifie","doi":"10.23889/ijpds.v11i5.3495","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3495","url":null,"abstract":"<p><p>The current primary measure of general health in England relies on census and survey data, which are limited in timeliness and granularity. Developing an administrative data-based indicator offers the potential for more frequent, detailed insights to support research and policy. This paper sets out research to develop and evaluate a multimorbidity score derived from linked administrative health datasets, combining National Health Service (NHS) hospital records, NHS General Practice data, and the Office for National Statistics' death registrations, which are then joined to census information for residents of England. Moving beyond simple counts of conditions, or prescriptions, the multimorbidity score provides a structured measure of the burden and complexity of chronic illnesses at the individual level. We explore using this score as a key predictor of health-related outcomes, including self-reported general health status. Machine learning models were trained to predict the general health status responses from the 2021 Census. Given the subjective nature of census responses, we considered exposures on clinical indicators, healthcare utilisation metrics, demographic factors, social factors, and lifestyle factors. To ensure robustness, we experimented with multiple modelling approaches, including tree-based algorithms and regression-based methods, comparing their predictive performance across evaluation metrics. Model performance was assessed on an independent subsample using multiple evaluation metrics, and predicted probabilities were aggregated to produce breakdowns by key characteristics, which were compared against observed values. Finally, the paper considers the feasibility of generating a time series from 2015 onwards and discusses potential applications and limitations of these estimates for producing broader health-related statistics.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3495"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426755/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AD-ARC (Administrative Data - Agricultural Research Collection): Linking Farms to Individual, Household and Business Data to create a Research-Ready Dataset for ADR UK.","authors":"Nathan O'Connor, Beth Allen, Esther Lewis, Sarah Cummins, Sian Morrison-Rees","doi":"10.23889/ijpds.v11i5.3682","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3682","url":null,"abstract":"<p><p>The aim of this project was to produce a complete record of farms in England to be linked to individual, household and business data, enabling the creation of a de-identified research-ready dataset (RRD) of farming individuals, households and businesses in England. Following lessons learned from the first phase of this project, a 'farm spine' was created using two farms data sources, 2021 June Survey of Agriculture and Horticulture and 2020-2022 Rural Payments Agency subsidy payment information. This spine utilised address and contact information at individual, farm holding, and business level, enhancing the matching potential when linking to census and business data. Deterministic linkage methods were used to match farms with associated households and businesses. The varied and nuanced nature of the data sources meant a non-standard approach was required when structuring the data for linkage, as well as ongoing feedback and collaboration between stakeholders to best deliver an optimised, high-quality bespoke dataset. The resulting dataset links geographic and farm activity data, subsidy payments, business ownership and turnover information, and characteristics of farming households. This presentation includes details of the data preparation, challenges encountered, and solutions utilised at each stage of building this complex dataset. To our knowledge, the AD|ARC RRDs are the first datasets linking agricultural data to individual and household-level data at a population level. They provide an invaluable resource that will inform policymakers seeking to support agricultural and rural communities. Its multi-organisational approach and use of varied data sources highlight lessons learned for future data linkage projects.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3682"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426674/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving community access to data and analytic services: an approach based on needs assessment and co-creation.","authors":"Elise Leong-Sit, Laura Ferreira-Legere, Bilal Khan, Susan Bronskill, J Michael Paterson, Lesley Plumptre, Minnie Ho","doi":"10.23889/ijpds.v11i5.3638","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3638","url":null,"abstract":"<p><strong>Objective: </strong>Communities who are the subject of administrative health and demographic data often encounter challenges accessing their own data and related analytic services, but achieving equitable access to data and analytic services is emerging as a crucial facet of ethical data stewardship, enabling communities to directly translate insights into real-world impact. As part of its mandate as a data institute, ICES offers organizations in Ontario (Canada) access to data analytics to support service planning and evaluation. Though community organizations are eligible, they accounted for only 2.4% of projects before 2023. To address this gap, we launched a community engagement and co-development process to increase service awareness and simplify access.</p><p><strong>Approach: </strong>We conducted a two-pronged needs assessment: environmental scan and seven key informant interviews based on purposive sampling. We then co-developed promotional materials with two other community members, and pilot-tested drafts with focus groups that included a mix of previous and new key informants (eight in total).</p><p><strong>Results: </strong>The needs assessment revealed the importance of developing materials using plain language, storytelling, messaging on impact, and trusted messengers. Products developed included a testimonial video showcasing previous community projects, a plain language webpage, and a 3-year dissemination strategy centered on building trusting relationships.</p><p><strong>Conclusion: </strong>Intentional needs assessment and embedded co-design approaches revealed the importance of narrative, plain, and impact-focused communication, leading to a novel suite of communication tools. Implications: Ongoing evaluation of these new approaches will be used to continue to refine promotion strategies as well as potentially tailor or expand future services.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3638"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426628/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-Context Integration in Population-Scale Social Networks and Its Association With Mortality.","authors":"Yue Li, Gerald Mollenhorst, Rense Corten, Marco Helbich","doi":"10.23889/ijpds.v11i5.3708","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3708","url":null,"abstract":"<p><p>Research on social networks and mortality has shown that structural characteristics of individuals' social environments are associated with health outcomes, yet most empirical work has relied on egocentric or survey-based measures that capture selected parts of people's networks. In this study, we use Dutch population registers to construct nationwide multilayer social networks that link individuals through family, household, workplace, educational, and neighborhood contexts. These data allow us to derive yearly individual-level indices-cross-context integration (excess closure across layers), density (mean embeddedness), closeness centrality, network heterogeneity, and network size-which reflect different dimensions of social structure. We link these indices to annual mortality records and estimate associations using time-varying discrete-time survival models with person-year observations. We also compare pre-COVID and COVID periods to explore whether associations vary under changing environmental risk conditions. By leveraging registry-based multilayer networks, this study provides new evidence on how multiple structural dimensions of social environments are patterned in relation to mortality risk at the population level.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3708"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426676/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility research into using multiple administrative data sources and predictive modelling to produce disability status estimates for England.","authors":"Matthew Minifie, Charlotte Standeven, Jesse Ransley, Sarah Wood","doi":"10.23889/ijpds.v11i5.3485","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3485","url":null,"abstract":"<p><p>Official estimates of population by disability status for England and Wales are produced as part of the census once every ten years, but there is a pressing user need for more frequent, robust disability statistics. This presentation outlines ongoing novel research that responds to that challenge by examining the feasibility of producing population-level estimates for disability status in England using a predictive model applied to a suite of linked administrative data. We have linked nine administrative data sources from different government departments, including benefits, education, employment and health datasets. The de-identified data are linked via a population spine (the England-based residents on the Office for National Statistics' 2021 Statistical Population Dataset with a valid disability status from Census 2021), with a sample size of 48.9 million. Machine learning algorithms were then used to train a model on a subsample of data. The binary outcome variable is Census 2021 disability status (non-disabled or disabled). Predictor variables include age, sex, geography and various benefit, education, employment and health variables from the administrative data. The performance of the model was tested and evaluated on a different subsample of the data to that on which the model was trained. Various evaluation metrics were employed to assess the performance of the model, along with predicted and observed disability prevalence rates by socio-demographic factors to further assess the coherence between the predictive model and Census 2021. Additionally, the predictive model was applied to earlier years for limited analysis of a short time series.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3485"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426549/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementing a Scalable, Secure Genomics Ingest and Processing Pipeline in a Trusted Research Environment for Dementia Research.","authors":"Alieyeh Saraband Moghaddam, Lewis Hotchkiss, Emma Squires, Simon Thompson","doi":"10.23889/ijpds.v11i5.3709","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3709","url":null,"abstract":"<p><p>The integration of genomics data into dementia research offers transformative potential for understanding disease mechanisms and enabling precision medicine. However, its utility is constrained by significant challenges in data sharing, privacy, standardization, and computational scale. To address these barriers, we designed and implemented a secure, novel and scalable genomics ingest and processing pipeline within the Dementias Platform UK (DPUK) Trusted Research Environment. Our comprehensive, end-to-end framework encompasses a seven-stage process, including cohort discovery via an interactive metadata matrix, rigorous quality and privacy controls using bioinformatic tools (e.g. PLINK, VCFtools, etc.), a genomic data organization standard for scalability and balance between interoperability and flexibility, as well as secure provisioning within a Five Safes governance model. A key innovation is the use of a MinIO-based object storage architecture with custom metadata tagging, enabling efficient, privacy-preserving data access at large scale. We also operationalized a standardized polygenic risk score (PRS) pipeline to generate validated, derived datasets. This integrated system now facilitates secure research access to harmonized genomic data across 15 diverse cohorts, including population-based, clinical, and family studies, significantly expanding the resources available for dementia research. This work presents a replicable and governance-first model for the responsible management of complex, high-volume linked data. Looking ahead, we plan to migrate key computational modules, particularly the PRS generation and quality control workflows, to Nextflow. This migration will enhance computational reproducibility, enable portable and parallelized execution across high-performance environments, and further standardize analytical processes, directly advancing the methodological innovation and scalability goals of population data science.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3709"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426568/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}