International Journal of Population Data Science最新文献

{"title":"Automating User Approval and Onboarding in Trusted Research Environments.","authors":"Justin Biddle, Simon Elwood-Thompson","doi":"10.23889/ijpds.v11i5.3518","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3518","url":null,"abstract":"<p><p>At SeRP, we provide a suite of software and hardware that enables Trusted Research Environments (TREs) to securely manage access to their data. A critical aspect of this is user approval and onboarding. When a new user requests access to a TRE, the environment typically requires a series of checks-such as completion of researcher training, signing of data access agreements, and director approval. Individual projects may also have additional, project-specific requirements. Traditionally, this process is manual, involving document requests, recording checks, and periodic verification of continuing approvals. To address this, we have developed a modular solution that leverages Camunda for customizable workflows and DocuSeal for document and signature management. Camunda workflows not only request documents and approvals, but also verify whether users meet general and project-specific access criteria, allowing permissions to be adjusted automatically. The system performs regular checks to ensure user and membership validity, and custom workflows can be configured for each TRE or project. This approach significantly automates the onboarding process, reduces administrative overhead, and strengthens security while maintaining flexibility for diverse TRE requirements.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3518"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426626/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654643","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":"Using machine learning to classify pediatric intensive care unit episodes in hospital admissions data.","authors":"Vincent Nguyen, Ofran Almossawi, Kate Lewis, Ania Zylbersztejn, Katie Harron","doi":"10.23889/ijpds.v11i5.3585","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3585","url":null,"abstract":"<p><strong>Introduction: </strong>Paediatric critical care (PIC) episodes, unlike adult critical care, are not routinely recorded in administrative hospital records in England (Hospital Episode Statistics; HES). Previous studies have developed indicators for high dependency care and intensive care using critical care activities, but have under ascertained PIC episodes compared to national audit data (PICANet). We used linked PICANet to HES to develop and validate indicators of PIC episodes in HES Admitted Patient Care (APC).</p><p><strong>Methods: </strong>We used data from HES APC between April 2010 and March 2011 for children aged <=16, excluding providers with no PIC units. PIC status was generated through linkage to PICANet. We matched each PIC episode with four age-provider-matched non-PIC episodes. Predictors included (but were not limited to) treatment specialty, diagnoses, operations, healthcare resource group, and admission method. Decision trees were used to identify PIC episodes in HES APC. We evaluated sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) with an 80:20 train-test hold-out validation.</p><p><strong>Results: </strong>We matched 94,337 PIC episodes with 377,348 non-PICU episodes. The decision tree's main predictors were healthcare resource group and treatment specialty (SUSHRG, SUSCOREGHRG and TREATSPEF variables). Using this algorithm, we were able to identify 76% of PIC episodes (sensitivity). Specificity was 0.78, and the AUC was 0.85.</p><p><strong>Conclusions: </strong>We demonstrate a proof-of-concept machine learning model to discriminate between PIC and non-PIC hospital episodes. This algorithm could be used by researchers to further understand the trajectories of children in PIC, when linkage to external data is not possible.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3585"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426593/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654647","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":"Using Linked Administrative Health Data to Examine Maternal Preconception Diabetes and Hypertension and Child Growth in Ontario, Canada: A Longitudinal Cohort Study.","authors":"Xuedi Li, Larissa Chomka, Charles Keown-Stoneman, Christopher Allen, Laura Anderson, Esmot Ara Begum, Danielle D'Annunzio, Sharon Thadani, Jonathon Maguire, Catherine Birken","doi":"10.23889/ijpds.v11i5.3570","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3570","url":null,"abstract":"<p><strong>Background: </strong>Research on whether maternal diabetes and hypertension prior to conception influence their children's health is limited. In Ontario, Canada, ICES is an independent research institute that securely houses population-based administrative health data, including diagnostic information, for individuals covered under the province's universal health insurance program.</p><p><strong>Objectives: </strong>To determine whether maternal preconception diabetes and hypertension were associated with child age- and sex- standardized body mass index (zBMI).</p><p><strong>Methods: </strong>This longitudinal cohort study included children 0 to 12 years enrolled in the TARGet Kids! primary care practice-based cohort (www.targetkids.ca) in Ontario, Canada (2008-2021). Maternal diagnoses of diabetes and hypertension prior to conception were identified through provincial administrative datasets held at ICES, deterministically linked to TARGet Kids! data using individual health insurance numbers. Child zBMI was derived from directly measured height and weight and electronic medical records. Linear mixed effects models were used to assess associations adjusting for confounders.</p><p><strong>Results: </strong>Among 7,351 mothers (mean age at delivery 33 years), 308 (4.6%) had preconception diabetes (2.1%) or hypertension (2.9%). Children (n = 9,087; mean age 4.6 years; 48.4%female) contributed to 28,861 zBMI measures. Maternal preconception hypertension was associated with higher mean child zBMI. The adjusted estimated difference for children of mothers with preconception hypertension with the average duration of hypertension prior to conception was 0.29 units higher in zBMI (95%CI: 0.03;0.55) compared with children of mothers without hypertension.</p><p><strong>Conclusions: </strong>The association between preconception hypertension and higher child zBMI may indicate a potential maternal health target for the prevention of child overweight and obesity.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3570"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426783/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654656","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":"The young social care workforce in Wales: A longitudinal analysis of transitions into social care work using linked workforce and education data.","authors":"Katy Huxley","doi":"10.23889/ijpds.v11i5.3627","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3627","url":null,"abstract":"<p><p>The social care workforce in the UK is under strain, with significant recruitment and retention issues. This is highlighted by the English governments new commission into social care reform, and reforms in Scotland and Wales (such as registration and pay commitments). It is crucial to understand the pathways into social care jobs, who is and who isn't moving into social care roles, as the ageing population creates ever greater strains on an already fragile workforce. Using the social care workforce register (official records on registration of social care workers) and records on learners educated (from schools to higher education), we undertake unique longitudinal analysis for Wales. Data linkage and analysis enables us to explore issues pertinent to the future of this important sector, asking: 1) Who are the learners moving into social care jobs in Wales? 2) To what extent are the personal characteristics of learners and their education pathways associated with working in social care? 3) Are certain personal characteristics or educational pathways associated with different types of social care jobs? Descriptive findings show, overall: A significant continued gender division in social care work An underrepresentation of 'top' attainers and 'low' attainers Limited associations with socio-economic status Vocational (work-related) education is the more common education pathway Regression analysis shows associations between personal characteristics and educational pathways vary for different social care jobs (such as home care compared to social work). These findings are important for future social care workforce planning and recruitment.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3627"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426534/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654670","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":"QUAIL: Quality Analyser for Interpreting Linkage.","authors":"Jack Linaker, Leah Maizey, Gavin Thomson, Matthew Wray, Laszlo Antal, Tim Gammon","doi":"10.23889/ijpds.v11i5.3596","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3596","url":null,"abstract":"<p><p>Metrics, such as precision and recall, are required to understand the quality of a linked product, yet standardised methods of quality assurance are seldom adopted. This is particularly true of the approaches to sample size selection for error estimation, which can lead to uncertainty. To overcome these issues, we present QUAIL - the QUality Analyser for Interpreting Linkage - a project that aims to provide a package of recommended researched methodologies and tools to streamline the quality assurance process of linked data. We will highlight the key processes and considerations made in the development of QUAIL and its sub-modules. QUAIL incorporates methodological innovations to facilitate quality assurance. Areas of work include candidate link creation for the purpose of false positive and false negative estimation, stratification, sampling, clerical review, and the computation of key quality metrics, including precision and recall, as well as their corresponding confidence intervals. QUAIL enhances the efficiency and reliability of linkage quality assessment by embedding best-practice linkage methodologies into an automated and generalisable process. This reduces operational overhead, mitigates human error, and promotes standardisation across projects. By providing robust and transparent quality metrics, QUAIL supports informed decision-making about linkage strategies, data usability, and downstream analyses.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3596"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426788/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654695","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":"Real World Use of EHR data and Public Data Sets to Inform Public Health and Population Health Research and Surveillance.","authors":"Andrew Hamilton, Shelly Sital, Kyra VanDoren","doi":"10.23889/ijpds.v11i5.3715","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3715","url":null,"abstract":"<p><p>Across the United States, linking electronic health record (EHR) data with public health and other publicly available datasets remains a significant challenge due to the lack of a universal patient ID and limited interoperability. While technology exists to overcome these barriers, silos between primary care, tertiary care, and public health (PH) entities continue. AllianceChicago (AC), a network of Federally Qualified Health Centers (FQHCs), participates in two key initiatives, Multi-State EHR-based Network for Disease Surveillance (MENDS) and CAPriCORN, a Federated Data Network, to connect clinical data for public health surveillance, research, and care continuity. AC is working to bridge these efforts to strengthen integration between healthcare delivery and public health systems. A recent project completed using CAPriCORN related to Youth Suicide Prevention analyzed care utilization patterns linking a primary care cohort of youth (age ≤24) hospitalized for suicide attempt or ideation to tertiary care data. Findings revealed gaps in timely follow-up care, with only 16% receiving a primary care visit and 41% a psychiatric visit within 30 days of discharge. These insights informed the development of solutions to improve referral pathways between social and behavioral services and healthcare for care continuity. Similarly, MENDS, leverages standardized EHR data for chronic disease surveillance. AC contributes de-identified data from 17 FQHCs and collaborates with PH agencies using vetted tools to identify geographic risk hotspots and inform program planning. Emerging use cases demonstrate that improved data linkage and infrastructure can enhance PH surveillance and care coordination, paving the way for more effective health systems.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3715"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426787/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654702","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":"The Illusion of an Automated German Register Census.","authors":"Rainer Schnell, Severin V Weiand","doi":"10.23889/ijpds.v11i5.3713","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3713","url":null,"abstract":"<p><p>Currently, the German Federal Statistical Office is working on the transition from a register-enhanced to a fully register-based census. Deduplicating and linking multiple registers using record-linkage methods for census purposes is challenging because no universal national identifier is available.To evaluate the expected results, we simulated every procedural step of the register census. The simulation is based on a synthetic base population of about 80 million records that reflects the joint distribution of personal attributes such as household structure, region, and age. Furthermore, we conditioned the distributions of last names and first names on age, nationality, sex, and region to account for cohort, ethnic, and regional variation. All previous studies neglected these dependencies, leading to an overestimation of the performance of automatic record-linkage methods.The most important databases of the census are the about 9500 resident registers, which may contain duplicate records. Therefore, we estimate the deduplication rate of the resident registers by adding noise (errors in identifiers and population dynamics, such as regional mobility or demographic processes) based on previous research on errors in administrative data, and then deduplicating the registers using various record-linkage methods, such as probabilistic matching and multiple matchkeys.The results indicate that only a small percentage of the duplicates can be detected automatically. This fact is due to the limited number of available identifiers, not to algorithmic problems. Given the current set of identifiers, a register-based census will require an unreasonable additional amount of clerical work.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3713"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426689/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654775","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":"The Enriched Data Dictionary.","authors":"Cassia Smith","doi":"10.23889/ijpds.v11i5.3677","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3677","url":null,"abstract":"<p><p>As the research data community turns its attention to federated systems, data dictionaries and variable-level metadata become increasingly important. Not only do variables need to be identified for their value to various linkage methods, but the breadth and depth of available data needs to be easily navigable by characteristics ranging from collection timeframe to coding matrix. In a moment when the data dictionary is poised to engage systems far more complex than a spreadsheet or a codebook, it's worth considering the ways this data documentation format can be expanded to assist both researchers and data providers in their navigation of the wider federated landscape. Three areas in particular are worth considering. First, domain-appropriate metadata systems are one obvious way to promote federation by presenting data in a consistent and meaningful format. Complications arise when considering which format to adopt, but they are not insurmountable. Second, as DOI assignment becomes standard for research datasets, data dictionaries and metadata catalogues in general provide a possible route into data citation concepts that can help data providers track usage and researchers clearly identify the data they need to request. Third, creating linkages between data dictionaries and research outputs can both demonstrate the value of the data to funders and spark creativity among researchers. With over ten years of experience in metadata management and a current project developing a small federated data dictionary for a suite of longitudinal population studies, the presenter aims to spark conversation around this overlooked form of data documentation.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3677"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426782/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654803","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":"Guiding principles for the provision of synthetic data.","authors":"Emily Oliver","doi":"10.23889/ijpds.v11i5.3704","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3704","url":null,"abstract":"<p><p>Synthetic data is an emerging tool for those learning to use and/or planning to use secure datasets, and for collaborators on secure data projects. However, there is a lack of consistency in its provision, and this affects both trust in its use and appetite to routinely produce it as a resource for researchers. There are important considerations for data owners and providers to ensure they provide synthetic data that meets the needs of users, remains compliant, and has overall positive impact. As the synthetic data field is still relatively new, norms and precedents are yet to fully emerge and develop, and standards and consistent approaches are lacking. We collaborated with experts across a range of sectors and drew on recent research with stakeholders to unpack the key facets of synthetic data provision, including worries and realities. Starting with a focus on synthetic data with low disclosure risk (typically low fidelity), we created a structured workflow that involves planning, creating, validating and releasing a synthetic dataset. We then used it to develop guiding principles for providers framed around a 'Plan, Do, Check, Act' model. Each stage includes specific steps to ensure clarity, consistency, quality and effective risk control. By sharing the principles and the steps to support implementation, we anticipate that this approach can be helpful in informing organisational policies on synthetic data provision. We also hope that further discussion, debate and testing will lead to increased engagement, trust and a more consistent approach to creating and sharing synthetic data.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3704"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426589/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148653762","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":"The influence of ethnic name characteristics on string similarities when linking large heterogeneous population databases.","authors":"Yashithi Dharmawimala, Peter Christen, Sumayya Ziyad, Anushka Vidanage, Joseph Lam, Rainer Schnell","doi":"10.23889/ijpds.v11i5.3756","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3756","url":null,"abstract":"<p><p>Comparing names is at the core of many data linkage applications. Names can be recorded with variations and errors (typographical, phonetic, or scanning, depending on how names have been captured). Furthermore, names can change over time. Therefore, approximate string comparison functions are commonly employed to calculate similarities between names. There are many such comparison functions, with popular ones including Jaro-Winkler, edit distance, and q-gram based techniques. Which ones are suitable for a given data linkage application is a topic that has not been explored in much detail. Selecting a suitable string comparison function is, however, crucial in the context of bias and fairness when linking population databases that can contain ethnically diverse names, as recent research has shown (for example, see Lam et al,. IPDLN 2024).In this work, we investigate the following question: Do different string comparison functions result in different levels of bias when names from different ethnic groups are being compared? We conducted extensive experiments on a large public population database where the ethnicity of each record is available. By comparing name variations of the same person using multiple string comparison functions, we show that some of these functions exhibit larger similarity ranges than others, while very different similarities are obtained for the same name pair depending upon which comparison function is employed. Our results provide important insights into how practical data linkage of large population-level databases should be conducted in order to limit linkage bias with regard to ethnic groups by employing suitable string comparison functions.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3756"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426649/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654038","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}