The National Pediatric Readiness Quality Initiative: Empowering Emergency Departments to Improve Pediatric Care

Abstract: Over 80% of all pediatric emergency visits occur in non-pediatric emergency departments (EDs). Continuous quality improvement (QI) is linked to higher pediatric readiness and improved patient outcomes, and, consequently, can lead to a 76% decreased mortality risk for critically ill and injured children. However, fewer than one third of EDs use quality indicators to evaluate and improve pediatric care delivery. Major barriers to QI in non-pediatric EDs include the absence of performance standards; challenges with standardized data collection, analysis, and benchmarking; and limited institutional resources to enhance pediatric emergency care. The authors describe the National Pediatric Readiness Quality Initiative (NPRQI), a free and secure platform that provides ED-based health care teams with real-time, crucial insights into the quality of pediatric emergency care delivery to empower providers to engage in pediatric QI. The NPRQI overcomes the aforementioned barriers to QI by providing (1) the first nationally vetted pediatric quality measures that apply to all types of EDs, including resource-restricted, low-volume EDs; (2) standardized data collection, performance visualization, and benchmarking for participating EDs; and (3) a free, secure web-based direct-to-provider interface for rapid interpretation and action by health care teams. Since its launch in 2023, the NPRQI has engaged more than 100 diverse EDs across 26 states. The NPRQI seeks to transform pediatric emergency care by shortening the time to adoption of and adherence to evidence-derived clinical processes through data transparency and benchmarking. Furthermore, by capturing pediatric care delivery nationally across all types of EDs, the NPRQI is poised to establish the first set of national standards for pediatric emergency care following the model of standards established for stroke and cardiac centers.

NEJM catalyst innovations in care delivery

Humans
Emergency Service, Hospital
Quality Improvement
Child
Pediatrics
Quality Indicators, Health Care
United States
Benchmarking


Laparoscopic training in porcine models: a tool for continuous professional skill enhancement for paediatric surgeons

Abstract: Laparoscopic training is an established component of minimally invasive surgery education, and involves different tools: virtual reality, training box and in vivo models. The use of porcine models is the most similar to human anatomy. Its role is well known during the residency program, but its specific impact on paediatric surgery consultants performance is poorly explored. Six consultant surgeons attempted a consecutive 4-h a week laparoscopic training on porcine models for a total of 12 weeks, focusing on refinement of fine dissection, intracorporeal suturing, and management of intraoperative complications. Participants underwent pre- and post-training (respectively Period A and B) assessment of surgical effectiveness by assessment of mean operative time, intraoperative and postoperative complications on three commonly performed procedures: inguinal hernia repair, varicocelectomy and laparoscopic orchiopexy. 210 procedures were analysed, 109 before and 101 after the training. We observed a significant overall reduction in operative time for all procedures (p = 0.007); more precisely, inguinal hernia repair and varicocelectomy showed a significant reduction in operative time (p = 0.028 and p = 0.04 respectively), while no significant reduction in operative time for laparoscopic orchidopexy was observed (p = 0.2668). Mean age at surgical intervention was significantly reduced in period B for herniorrhaphy. Laparoscopic training in porcine models could be considered an effective tool to improve skills of paediatric surgery consultants. Its use could allow the improvement of laparoscopic confidence and effectiveness also in surgeons with an already acquired learning curve, supporting the systematic and continuous integration of such programmes into the professional development.

Pensabene M
Patti M
Baldanza F
Grasso F
Cicero L
Sergio M
Di Pace MR

Animals
Laparoscopy
Swine
Clinical Competence
Pediatrics
Male
Hernia, Inguinal
Humans
Models, Animal
Orchiopexy
Herniorrhaphy
Varicocele
Operative Time


The Top 50 Cited Articles in Pediatric Neurology: A Bibliometric Analysis

Abstract: To identify and analyze the most influential articles within the field of pediatric neurology through a bibliometric lens. As pediatric neurology continues to expand across subspecialties, identifying landmark studies is critical for understanding clinical and research priorities. Bibliometric analysis enables the evaluation of citation trends to highlight impactful contributions that have shaped the field. A bibliometric analysis of the 50 most-cited pediatric neurology articles was conducted. Articles were assessed by citation metrics, study design, subspecialty, journal impact factor (JIF), and funding status. Correlation and regression analyses evaluated associations between variables. Citation counts ranged from 185 to 1,440 (median: 332). Epilepsy accounted for the largest proportion of studies (26%) but showed lower citation density (31.5 cites/year) compared with subspecialties such as neuroimmunology (45 cites/year). International collaboration was the strongest predictor of citation impact; each additional collaborating country increased total citations (β = 48.9, p = 0.002) and citation density (β = 5.3, p < 0.001), resulting in a 42% higher citation density than single-nation studies. While publication year negatively affected total citations (β = -22.4, p < 0.001), newer studies demonstrated faster citation accrual. Funding modestly increased citation count (β = 135.3, p = 0.026), whereas JIF showed no significant effect (p = 0.846). Neurodevelopmental topics had the highest mean citations, and open-access articles exhibited 38% greater citation density than paywalled publications. The most influential studies were collaborative, funded, and open access. Recent works, especially in neurogenetics and neurocritical care, are gaining rapid traction. International collaboration and topic selection drive scholarly impact. Future research should focus on high-impact and underrepresented specialties.

Alyazidi A
Muthaffar O
Bamaga A
Qashqari H
Alhithlool A
Alanezi A
Almehmadi S
Babiker M
Bashiri F

Bibliometrics
Neurology
Humans
Pediatrics
Journal Impact Factor
Periodicals as Topic


Perioperative Difficult Conversations With Guardians of Pediatric Patients: A Simulation-Based Workshop for Anesthesiology Practitioners Using the VitalTalk Framework

Abstract: Effective communication of adverse events is a critical skill for health care practitioners, particularly those in anesthesiology. Despite the importance of delivering bad news to guardians after complications, many anesthesia practitioners receive little to no formal training in this area. This curriculum aims to address this by introducing a learning activity that integrates the SPIKES and NURSE frameworks with simulated parent (SP) encounters. A randomized waitlist control design was used to evaluate this 3-hour simulation curriculum. Learners were randomly assigned to receive either immediate training in communication skills (a 1-hour didactic session on the SPIKES and NURSE framework, followed by SP deliberate practice) or delayed training after 1 month. Both groups completed a pretest, posttest, and delayed posttest. Communication skills were evaluated using a published checklist based on the SPIKES and NURSE frameworks. Course surveys were also administered to assess changes in learners' self-reported confidence and overall course satisfaction with the training. Twenty-one learners (15 attending anesthesiologists and 6 certified registered nurse anesthetists) participated in this educational activity. Both groups demonstrated significant improvements in checklist-scored communication skills and self-reported confidence following the simulation curriculum (P < .001). In the Early Intervention group, these improvements in simulated communication skills were sustained after a 1-month delay. Overall, course satisfaction was high, with a median score of 5 (IQR 5-5). Our findings demonstrate that a structured curriculum incorporating the SPIKES and NURSE frameworks, combined with SP deliberate practice, enhances communication skills for difficult perioperative conversations with pediatric guardians.

MedEdPORTAL : the journal of teaching and learning resources

Phillips ML
Yanko F
Vermylen JH
Ballard HA

Humans
Anesthesiology
Communication
Simulation Training
Curriculum
Female
Male
Surveys and Questionnaires
Pediatrics
Parents
Child


Photon minibeam-based LATTICE radiotherapy for small and medium-sized tumors: A dosimetric planning study

Abstract: Lattice radiotherapy (LRT) is a spatially fractionated technique that delivers three-dimensional high-dose vertices within tumors. However, conventional LRT is constrained by geometric limitations when applied to small and medium-sized tumor volumes, primarily due to the large beam sizes that restrict optimal lattice pattern formation. This study aims to investigate a photon minibeam-based LATTICE radiotherapy (mini-LRT) approach designed to overcome the geometric limitations of conventional LRT and enable effective spatially fractionated treatment for small and medium-sized tumors adjacent to critical organs. Three brain cases and three lung cases, with clinical target volumes ranging from 12.34 cc to 40.88 cc, were presented for treatment planning comparison between conventional LRT and mini-LRT. A multi-collimator delivery strategy employing shifted slit patterns was implemented to achieve complementary spatial coverage. Dose calculation and optimization were performed using Monte Carlo simulations and solved via an iterative convex relaxation algorithm. Reference stereotactic body radiation therapy (SBRT) plans were generated for all six cases, and setup-error robustness analysis was performed for two representative cases using nominal and shifted dose-influence matrix scenarios. Mini-LRT produced denser lattice structures in all cases, generating 5-11 vertices compared to only 1-3 vertices achievable with conventional LRT. Vertex diameters were reduced from 15.0 mm to 3.0-4.0 mm, and vertex-to-vertex distances decreased from 25.0 mm to 12.5 mm. The lattice volume ratio ranged from 0.68% to 1.07% with mini-LRT. Organs at risk (OARs) dose reductions were observed in the evaluated cases, including reduced mean dose to brainstem, heart, and esophageal. Furthermore, mini-LRT achieved a higher peak-to-valley dose ratio (PVDR) ranging from 2.19 to 2.94, compared to 1.75-2.34 for conventional LRT. Reference SBRT plans achieved clinical target volume (CTV) D95 = 50 Gy in all cases but showed elevated D0.03cc to adjacent OARs in selected anatomically constrained cases. In the representative robustness analysis, worst-case setup-error scenarios showed modest PVDR degradation. This proof-of-concept dosimetric planning study suggests that photon minibeam-based LATTICE radiotherapy can generate compact spatially fractionated dose distributions in selected small and medium-sized tumors. Compared with conventional LRT, mini-LRT increased vertex density, reduced lattice volume ratio, and improved OAR sparing in the evaluated cases. Additional experimental validation, motion assessment, and larger patient studies are required before clinical translation.

Wu W
Shinde N
Li J
Vanhaezebrouck I
Jiang K
Lin Y
Li Q
Gao H


Indirect calculation of metabolic power in soccer: a critical analysis of a popular model

Abstract: The Global Positioning System (GPS)-based metabolic power model for soccer, proposed by di Prampero and applied by Osgnach, rests on a fixed energy cost (EC) of running of 4.6 J·kg-1·m-1 and a biomechanical equivalence between accelerated level and constant-speed uphill running. A narrative review was conducted via PubMed, Scopus, and Web of Science using terms like metabolic power, EC of running, GPS soccer, sprint biomechanics, equivalent slope, and running economy (2005-2025). Running EC is relatively speed-independent across submaximal velocities typical of football (~6-18 km·h-1). The geometric principle underlying the accelerated-to-inclined running equivalence is accepted, and GPS metabolic power provides a useful metric for relative load comparisons. A universal fixed EC ignores interindividual variability (~20%), fatigue, surfaces, and drag. The equivalence model has vector orientation inconsistencies, employs the imprecise term 'equivalent mass', and derives its postural reference from sprint-start conditions unrepresentative of match-play accelerations (~2-4 m·s-2). Validation studies show systematic underestimations of 29%-85% relative to indirect calorimetry. Refinements exist but remain unimplemented in most commercial platforms. Individually calibrated EC values, inertial measurement unit integration, and hybrid models combining GPS kinematics with cardiorespiratory data are promising. Force-velocity profiling offers a biomechanically grounded alternative for individual sprint characterization. Priorities include calorimetric validation under match conditions; player-specific EC models incorporating fatigue, surface, and role; implementing model updates in commercial software; and comparing alternative load metrics for injury-risk prediction.

Humans
Soccer
Energy Metabolism
Running
Biomechanical Phenomena
Geographic Information Systems
Athletic Performance
Models, Biological


The Multicriteria Decision Analysis for Extended Reality (MCDA-XR) Governance Framework for Health Care Adoption: Mixed Methods Development Study

Abstract: Health services increasingly face decisions about how to integrate immersive technologies into routine practice. International guidance highlights the need for structured governance in digital health, yet extended reality (XR) initiatives are often launched through isolated pilots without a clear assessment of organizational readiness or implementation risk. Although factors influencing XR adoption are well documented, health care organizations and system-level decision-makers still lack practical, governance-oriented tools to translate these determinants into structured strategic decisions made before implementation. This study aims to develop multicriteria decision analysis for extended reality (MCDA-XR), a strategic governance framework that translates behavioral, organizational, and technical implementation determinants into a structured decision-support process for health care organizations. The study followed a sequential mixed methods design covering the first 2 phases of a 3-stage framework development and validation project. Phase 1 (identification) defined strategic criteria by integrating theoretical perspectives on organizational complexity, behavior change, technology acceptance, and immersive safety, together with a targeted review of XR implementation evidence. Phase 2 (construction) refined the framework through participatory sessions. A multidisciplinary group of 33 stakeholders, including professionals and managers from hospital and primary care settings, and postgraduate students, evaluated the proposed criteria for strategic relevance and operational clarity. This process resulted in a refined 10-criterion structure and the establishment of a dual-score assessment logic. Phase 3 (validation), planned as a subsequent step, will examine how the framework performs when applied prospectively in clinical settings. The development process yielded a framework comprising 10 operational criteria grouped into 3 conceptual domains (human, organizational, and technical). Stakeholder ratings indicated high strategic relevance across all criteria, with mean scores ranging from 4.03 (SD 0.95) for workflow integration to 4.61 (SD 0.56) for safety and comfort. The final instrument applies a dual-assessment approach in which each criterion is rated separately for strategic importance and organizational readiness. Mapping these dimensions enables organizations to identify priority gaps, particularly areas of high importance and low readiness, and to distinguish between manageable constraints and critical barriers requiring targeted preparatory action prior to implementation. MCDA-XR addresses a key governance gap in XR implementation by providing a structured way to align adoption decisions with institutional priorities and operational constraints. Rather than relying on descriptive feasibility assessments, the framework is intended to support explicit prioritization and action-oriented decision-making at the organizational level. MCDA-XR is positioned for Phase 3 evaluation, which will examine the practical utility, interpretability, and implementation relevance of the framework when applied prospectively in real-world clinical deployments.

Ferrer Costa J
Armayones Ruiz M
Bourdin-Kreitz P

Humans
Decision Support Techniques
Delivery of Health Care
Digital Health


Structure-preserving Image-quality Enhancement for 3D Synthetic FLAIR Using a 3D U-Net with Content and Style Losses

Abstract: Synthetic MRI can generate multiple contrasts from a single acquisition, yet synthetic fluid-attenuated inversion recovery (FLAIR) generally shows lower quality than conventional FLAIR. We aimed to improve 3D synthetic FLAIR image quality using deep learning, without losing the scan-time advantage of synthetic MRI. We studied 55 adults with inflammatory demyelinating diseases who underwent 3T MRI. For each participant, five 3D quantification using an interleaved Look-Locker acquisition sequence with T2 preparation (3D-QALAS) source images and a conventional 3D-FLAIR image were acquired, and a synthetic FLAIR image was generated from the 3D-QALAS data. We trained a deep learning model in which a 3D U-shaped convolutional network (U-Net)-based attention network predicted voxel-wise weights for generating FLAIR images from the five 3D-QALAS source images, using conventional 3D-FLAIR images as the reference. The model was trained with a combined loss function of mean squared error, content loss, and style loss. Agreement with the reference was assessed using image similarity and error metrics, lesion overlap using the Dice similarity coefficient, and overall image quality and focal lesion visibility using blinded radiologist ratings. Synthetic FLAIR and the deep learning-generated FLAIR images were compared using 2-sided Wilcoxon signed-rank tests; P < 0.05 was considered statistically significant. The deep learning-generated FLAIR images showed significantly higher agreement with the reference image than synthetic FLAIR images (all P < 0.001). Lesion overlap was higher with the deep learning-generated FLAIR images (median [interquartile range]: 0.642 [0.528-0.711] versus 0.487 [0.344-0.641]). Qualitatively, the deep learning-generated FLAIR images improved overall image quality and focal lesion visibility, but reader scores remained lower than those for the reference conventional 3D-FLAIR images (all P < 0.001). A deep learning-based approach applied to five 3D-QALAS source images improved the image quality of 3D synthetic FLAIR. These improvements may increase the clinical utility of 3D synthetic MRI for neuroradiologic assessment.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine

Kamio S
Hagiwara A
Otsuka Y
Nakaya M
Hosoi R
Hoshino Y
Tomizawa Y
Tsukamoto Y
Hara N
Kikuta J
Andica C
Akashi T
Wada A
Kusahara H
Yano R
Hatano T
Abe O
Kamagata K

Humans
Imaging, Three-Dimensional
Magnetic Resonance Imaging
Image Enhancement
Deep Learning
Adult
Female
Male
Middle Aged
Demyelinating Diseases
Image Interpretation, Computer-Assisted
Aged


Temporal and spatial distribution of dengue in an urban setting: evidence from Hanoi, Vietnam (2018-2022)

Abstract: Dengue fever remains a major mosquito-borne disease in Southeast Asia, with increasing incidence in rapidly urbanizing settings. In Vietnam, Hanoi has experienced recurrent outbreaks; however, comprehensive analyses of recent dengue epidemiology in northern regions remain limited. This study aimed to characterize the spatiotemporal dynamics of dengue in Hanoi from 2018 to 2022. A retrospective descriptive study was conducted using routine dengue surveillance data collected by the Hanoi Center for Disease Control. All clinically diagnosed and laboratory-confirmed dengue cases reported between January 2018 and December 2022 were included. Temporal trends were examined using descriptive analysis of monthly and annual incidence rates. District-level incidence rates were calculated and visualized using a geographic information system (GIS) mapping. Serotype distribution was examined using available virological data. A total of 46,650 dengue cases were reported during the study period. Dengue transmission occurred annually, with substantial inter-annual variability; the highest incidence was recorded in 2022 (236 per 100,000 population), while the lowest occurred in 2021 (41 per 100,000 population). A clear seasonal pattern was observed, with cases increasing from June and peaking between September and November. Spatial analysis revealed marked heterogeneity, with higher incidence consistently observed in peri-urban districts. All four dengue virus serotypes co-circulated, with DENV-1 and DENV-2 predominating and a shift toward DENV-2 dominance in later years. Dengue transmission in Hanoi exhibits pronounced seasonal and spatial patterns influenced by urbanization and ecological factors. Strengthening pre-season vector control activities and district-level surveillance may help reduce outbreak risk and maintaining integrated epidemiological and virological surveillance is essential to mitigate future outbreaks.

Bau VH
Nguyen HN
Le MD
Hoang NS
Nguyen VK
Vu KT
Nguyen VA

Vietnam
Humans
Dengue
Incidence
Retrospective Studies
Seasons
Dengue Virus
Spatio-Temporal Analysis
Urban Population
Serogroup
Disease Outbreaks
Geographic Information Systems


[Prescription patterns of Carthami Flos-containing compound formulas in intervention of diabetic peripheral neuropathy based on data mining]

Abstract: Based on a data science perspective, this study systematically elucidated the prescription patterns and core compatibility mechanisms of Carthami Flos-containing formulas in the treatment of diabetic peripheral neuropathy(DPN), aiming to provide scientific evidence for optimizing evidence-based TCM therapeutic strategies. A comprehensive literature search was conducted across CNKI, Wanfang, VIP, and PubMed databases, covering publications from database inception to September 18, 2025. Following rigorous screening, 297 clinical studies were included, all focusing on DPN as the primary condition, employing orally administered Chinese herbal medicines, and reporting effective clinical outcomes, encompassing a total of 12 783 cases. Frequency analysis was performed using R software, combined with association rule mining, network topology analysis, prescription similarity assessment, and cluster analysis, to clarify the structural composition and dosage distribution patterns of Carthami Flos-containing formulas. Data analysis showed that decoctions were the predominant dosage form in the clinical treatment of DPN. The core syndrome patterns were highly concentrated in Qi deficiency with blood stasis and combined Qi-Yin deficiency. Major outcome measures included nerve conduction velocity, blood glucose levels, and hemorheological parameters. Within the high-frequency herbal spectrum, Carthami Flos, Astragali Radix, Angelicae Sinensis Radix, Chuanxiong Rhizoma, and Paeoniae Radix Rubra occupied dominant positions. The core prescription network exhibited a compatibility structure centered on Astragali Radix, Chuanxiong Rhizoma, and Angelicae Sinensis Radix, with the "Astragali Radix-Angelicae Sinensis Radix" combination demonstrating the highest support in association rule analysis. The core network-derived combination of "Carthami Flos-Chuanxiong Rhizoma-Paeoniae Radix Rubra-Pheretima-Angelicae Sinensis Radix-Persicae Semen" closely corresponded to the classical formula Buyang Huanwu Decoction. Dosage analysis further revealed that the most common compatibility ratio of Astragali Radix to Carthami Flos was 3∶1, and that the dosages of Astragali Radix(mean 36 g) and Angelicae Sinensis Radix(mean 12 g) frequently exceeded standard pharmacopeial recommendations, reflecting a therapeutic strategy characterized by high-dose Qi supplementation to promote blood circulation. The findings suggest that, despite the complex and mixed deficiency-excess pathogenesis of DPN, blood stasis persists throughout the disease course. Carthami Flos-containing formulas primarily act through tonifying Qi, activating blood circulation, resolving stasis, and unblocking the collaterals, embodying a modern clinical strategy of simultaneous regulation of Qi and blood. This study not only quantitatively validates the guiding significance of classical TCM theories in modern DPN management and elucidates the dose-effect relationships of core prescriptions, but also provides a reproducible methodological framework for mining prescription patterns in complex diseases, offering substantial academic value for the modernization and internationalization of TCM.

Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica

Drugs, Chinese Herbal
Data Mining
Humans
Diabetic Neuropathies
Carthamus
Drug Prescriptions
Flowers


[Evaluating the taxonomy of Lacticaseibacillus casei in National Center for Biotechnology Information genome database based on whole-genome sequence analysis]

Abstract: Reverificate the taxonomic information of Lacticaseibacillus casei in the genome database of National Center for Biotechnology Information (NCBI) to afford accurate genomic information for L. casei species-specific sequence analysis. Based on the whole genome sequence downloaded from NCBI database, the taxonomic information of 45 L. casei was reverificated by average nucleotide identity (ANI) analysis and phylogenetic analysis based on 16S rRNA gene and 49 core genes. No 16S rRNA gene sequence was extracted from 4 genomes. Of the remaining 41 genomes, the analysis results of ANI were highly in accordance with that of core gene phylogenetic analysis. The results were 21 genomes identified as L. casei and 20 genomes as L. casei related species. Using 16S rRNA gene alone could not accurately identify L. casei and its related species. It is necessary to reverificate the taxonomic information of L. casei in NCBI genome database before using its genome information for further analysis.

Wei sheng yan jiu = Journal of hygiene research

Wang Q
Yang L
Wang W
Wei H
Wei Y
Li D
Zhang H

Phylogeny
Genome, Bacterial
Lacticaseibacillus casei
RNA, Ribosomal, 16S
Databases, Genetic
Whole Genome Sequencing
Sequence Analysis, DNA


Prenatal Exposure to Modafinil and the Risk of Major Congenital Anomalies and Other Adverse Neonatal and Pediatric Outcomes

Abstract: To assess the risk of major congenital anomalies (MCAs) and other adverse outcomes following prenatal exposure to modafinil. This retrospective cohort study used the French national healthcare database (SNDS) to include singleton children born between January 2009 and September 2024 of women aged 15-55 years. Prenatal exposure to psychostimulants was defined as maternal dispensation during pregnancy. Children prenatally exposed to modafinil were compared with two control groups: children prenatally exposed to methylphenidate and children prenatally unexposed to any psychostimulant matched to the exposed group using propensity-score matching (PSM). Outcomes included MCAs, neurodevelopmental disorders (NDs), and specialized consultations. Analyses included descriptive statistics, survival analysis, regression models, and dose-response analyses. Of 10 955 766 included children, 865 were prenatally exposed to modafinil and 950 to methylphenidate. Exposure to modafinil during the first trimester of pregnancy was statistically associated with increased risk of MCA compared to methylphenidate, although the confidence interval was wide and close to the null (aRR = 1.77 [1.01-3.07]). Comparison with PS-matched unexposed population indicated a possible modest increase in risk, albeit with a wide confidence interval crossing the null (aRR = 1.23 [0.76-1.99]), showing no clear association. Occurrence of NDs (aHR = 0.96 [0.56-1.65]), and specialized consultations (aHR = 1.08 [0.91-1.28]) were similar in the modafinil and PSM unexposed groups but were higher in the methylphenidate group (NDs: aHR = 0.48 [0.29-0.80]; specialized consultations: aHR = 0.71 [0.59-0.85]). This study shows no increase in neurodevelopmental risk, while a moderate MCA risk cannot be excluded.

Kanoun M
Bouazza N
Treluyer JM
Collier M

Humans
Female
Modafinil
Pregnancy
Retrospective Studies
Prenatal Exposure Delayed Effects
Methylphenidate
Adolescent
Adult
Abnormalities, Drug-Induced
Central Nervous System Stimulants
Databases, Factual
Infant, Newborn
Young Adult
France
Middle Aged
Cohort Studies
Neurodevelopmental Disorders
Child
Dose-Response Relationship, Drug


Investigating demographic factors and geographical information mapping (GIS) of oral cancer in Golestan Province, Iran in 2014 to 2021

Abstract: Oral cancer is among the ten most common malignancies worldwide and can be highly lethal if not diagnosed and treated promptly. Mapping the spatial and geographic distribution of this cancer can assist health authorities in planning effective prevention, control, and treatment strategies by identifying its geographic and demographic patterns. This study aimed to investigate the demographic characteristics and geographic distribution of oral cancer in Golestan Province, Iran, during the period 2014-2021. This trend study used data from the National Cancer Registry System of Golestan Province, Iran, covering the period from 2014 to 2021. Geographic mapping of the cities in Golestan Province was performed using ArcGIS software. The annual incidence of oral cancer was calculated for each city and expressed as the number of cases per 100,000 population per year. A total of 390 patients with oral cancer were identified in Golestan Province between 2014 and 2021. Of these, 200 (51.3%) were male and 190 (48.7%) were female. The mean age of the patients was 56.18 ± 17.37 years, ranging from 2 to 96 years. The standardized annual incidence rate of oral cancer in Golestan Province ranged from 4.25 to 6.75 cases per 100,000 population during the study period. Among the cities of the Province, Maraveh Tappeh showed the highest incidence rate, with an average incidence of 12.35 cases per 100,000 population. The incidence of oral cancer in Golestan Province demonstrated temporal fluctuations during the study period, with higher rates observed in several northern cities. Geographic mapping of the disease may provide valuable insights into the potential influence of environmental, cultural, and lifestyle factors on the distribution of oral cancer and can support more targeted prevention and control strategies.

Humans
Iran
Female
Mouth Neoplasms
Male
Adolescent
Middle Aged
Aged
Child
Child, Preschool
Adult
Incidence
Aged, 80 and over
Geographic Mapping
Young Adult
Geographic Information Systems
Registries


Generation of High-Quality Succinyl Spectral Libraries for Improved Proteome-Wide Succinylome Analysis Using Data-Independent Acquisition

Abstract: Protein posttranslational modifications (PTMs) are crucial and dynamic modulators of protein functions, interactions, and localizations, as well as biological pathways and cellular signaling. Lysine succinylation analysis remains challenging, but sophisticated workflows combining succinylated peptide enrichments and quantitative mass spectrometry approaches have revolutionized proteome-wide succinylome analysis. The implementation of data-independent acquisition (DIA)-mass spectrometry has greatly advanced the detection of low-abundance succinylated peptides, succinylome coverage, identification reproducibility, and quantification accuracy. However, the complexity of DIA data requires dedicated data processing algorithms and tools, which typically rely on spectral libraries. These reference libraries can be generated from experimental data-dependent acquisition (DDA) acquisitions of representative study samples submitted to DDA database search engines for confident succinylated peptide identification and precise PTM site localization. Here, we describe how to build DDA PTM spectral libraries using various software tools, specifically Spectronaut, SpectroMine, and MSFragger. The generated libraries were imported into Skyline for the analysis of previously published DIA succinylome data of Sirtuin-5 knocked-out vs wild-type mouse brains in order to accurately quantify and visualize PTM-containing peptides.

Methods in molecular biology (Clifton, N.J.)

Protein Processing, Post-Translational
Proteome
Proteomics
Software
Animals
Succinic Acid
Mice
Peptide Library
Tandem Mass Spectrometry
Databases, Protein
Peptides


An Overview of Mass Spectrometry-Based Methods to Analyze Post-Translational Modifications

Abstract: Post-translational modifications (PTM) are the covalent modifications of proteins after their biosynthesis. PTMs change the structure and functions of proteins, which eventually regulate various biological processes that are closely related to the onset and development of diseases. Hundreds of different forms of PTMs have been discovered so far and mass spectrometry (MS) has become one of the most important tools to analyze PTMs. With the development of new methods in sample preparation, advances in MS instrumentation, and inventions of new bioinformatic tools, MS-based methods can identify most forms of PTMs, precisely locate the site of modification, sensitively and accurately quantify the changes of modification levels from various of biological samples. This chapter will summarize generic procedures to analyze PTMs with MS methods and provide an update with the latest development in sample preparation, MS analysis, and database search that advanced the way for PTM analysis, improving our knowledge of PTMs and understanding of their roles in various diseases.

Protein Processing, Post-Translational
Mass Spectrometry
Humans
Proteomics
Computational Biology
Proteins
Animals
Databases, Protein


A clinical decision support system for predicting drug-related adverse effects in pediatric patients with congenital heart disease: Development and retrospective evaluation

Abstract: ObjectiveChildren with congenital heart disease are highly vulnerable to drug-related adverse effects due to the use of complex polypharmacy. This study aimed to develop and retrospectively evaluate a hybrid clinical decision support system for predicting drug-related adverse effects in this population.MethodsThis two-phase study combined machine-learning techniques and expert clinical rules. Phase 1 included a retrospective analysis of 4651 pediatric congenital heart disease reports from the Food and Drug Administration Adverse Event Reporting System to train and compare five machine-learning models. The best-performing model, Random Forest, was selected. In Phase 2, a hybrid clinical decision support system integrating the Random Forest model with an expert-validated rule-based engine was developed and retrospectively evaluated using 330 inpatient records of pediatric patients with congenital heart disease.ResultsThe Random Forest model achieved a mean area under the receiver operating characteristic curve of 0.902. During clinical validation, the hybrid clinical decision support system demonstrated a mean accuracy of 0.85 across 11 common drug-adverse effect pairs, outperforming standalone machine-learning- and rule-based approaches. This study demonstrated the feasibility and clinical fidelity of using a hybrid clinical decision support system for predicting drug-related adverse effects in pediatric patients with congenital heart disease, supporting safer and more personalized pharmacotherapy.ConclusionsThis study demonstrated the feasibility and clinical fidelity of using a hybrid clinical decision support system for predicting drug-related adverse effects in pediatric patients with congenital heart disease, supporting safer and more personalized pharmacotherapy.

The Journal of international medical research

Toni E
Ayatollahi H
Abbaszadeh R
Fotuhi Siahpirani A

Drug-Related Side Effects and Adverse Reactions


Analysis of the diabetic arterial transcriptome to define novel biomarkers of macrovascular disease

Abstract: Diabetes mellitus (DM) approximately doubles the risk of atherosclerotic cardiovascular disease (ASCVD) events, but the molecular basis is poorly understood. Using RNA-sequencing data from two arterial sites in >90 people with DM and >330 controls, we defined differentially expressed genes (DEGs) associated with DM. UK Biobank (UKB) was then used to corroborate that DEGs in their plasma protein form were differentially abundant in people with DM and associated with ASCVD events. 619 and 356 DEGs were associated with DM in the thoracic aorta and tibial artery, respectively. Of these, 22 were common to both arteries, all of which were directionally concordant. Of these, 5 were included in the UKB plasma proteomics dataset and we corroborated 4 (ACP5, LEFTY2, LILRA5 and PSME2) as showing concordant differential abundance in people with DM; all were associated with a range of incident ASCVD events. Addition of the 4 proteins to the SCORE2 and SCORE2-Diabetes risk models (for people without and with DM, respectively) improved the population-level discrimination, classification and calibration of these models. These data show that DM is associated with a distinct arterial gene expression profile, hits from which are associated with ASCVD events and add value to risk prediction.

Shouma A
Giannoudi M
Conning-Rowland M
Drozd M
Brown OI
Cheng C
Sukumar P
Bridge KI
Levelt E
Bailey MA
Griffin KJ
Kearney MT
Cubbon RM

Humans
Transcriptome
Diabetic Angiopathies
Gene Expression Profiling
Case-Control Studies
Proteomics
UK Biobank
Risk Assessment
Biomarkers
Genetic Markers
Predictive Value of Tests
Male
Atherosclerosis
Female
Prognosis
Risk Factors
Middle Aged


Development of A Novel Infection Risk Score for Prediction of Infections in Renal Transplant Recipients

Abstract: In renal transplant recipients, infections can have atypical outcomes due to immunosuppression therapy, and delayed diagnosis causes high mortality. We developed an infection risk score in renal transplant recipients to rapidly predict infection risk in emergency departments. Of 870 renal transplant recipients admitted to the emergency department, we included 608 patients and 262 control cases. Hospital record system data for renal transplant recipients were retrospectively investigated for the period January 2021 through December 2025. Laboratory and vital signs of patients suspected of infection were compared versus asymptomatic control cases admitted for routine check -ups. All of our patients metethical standards and were selected as related donors and recipients. Demographic characteristics and findings of 608 patients and 262 control cases were compared, and no significant difference was found between the 2 groups in terms of comorbidities (P > .05 ). The most frequent presenting symptom was diarrhea (20.1 % ); the least frequent symptom was sore throat (6.6 % ). Leukocytes, neutrophils, neutrophil -lymphocyte ratio, plasma -lymphocyte ratio, C -reactive protein, and sodium and potassium levels, as well as vital signs including fever, pulse, blood pressure, and peripheral oxygen saturation, were associated with infection. This novel infection risk score comprised 6 parameters, including C-reactive protein, neutrophil -lymphocyte ratio, sodium, fever, pulse, and systolic blood pressure, predicted infections with 89.1 % (area under the curve ) accuracy. Sensitivity, specificity, positive predictive value, and negative predictive value of the score were 0.734, 0.905, 0.947, and 0.594, respectively. The novel infection risk score developed in our study predicts infections in renal transplant recipients with high accuracy using only routine biochemical and vital parameters. This practical, rapid, and reliable system can contribute to early diagnosis processes in emergency departments. Future external validation through multicenter studies is needed to support its integration into clinical guidelines.

Experimental and clinical transplantation : official journal of the Middle East Society for Organ Transplantation

Muratoglu M
Bulut B
Manevi AR
Yıldırım HS
Kilicoglu Tanir S
Cheraqi MS
Safak A
Yildirim S
Haberal M

Humans
Kidney Transplantation
Female
Risk Factors
Risk Assessment
Predictive Value of Tests
Retrospective Studies
Male
Adult
Middle Aged
Immunosuppressive Agents
Decision Support Techniques
Immunocompromised Host
Treatment Outcome
Emergency Service, Hospital
Emergency Room Visits
Opportunistic Infections
Time Factors
Communicable Diseases


CBS Microdata

Abstract: CBS microdata is a valuable resource that provides researchers secure access to nearly all individual-level data available within Statistics Netherlands (CBS). The data is originated from administrative registers (e.g., Dutch Tax Authority, Personal records database (BRP)) and statistical surveys conducted by CBS. As such, CBS microdata includes information on individuals, households, businesses, and more, making it highly relevant for demographic, sociological and economic research. One of the key strengths of CBS microdata is its ability to link diverse datasets, enabling researchers to connect individuals to households, jobs to individuals, companies to jobs, and many other relationships. Furthermore, data can be linked across time, allowing for in-depth longitudinal studies. Researchers can also upload their own datasets, which can be pseudonymized and linked with CBS microdata to create even richer data analyses. CBS places a high priority on data security. Access to CBS microdata is strictly controlled and granted only to authorized institutions. Universities, scientific organizations, and public policy institutes in EEA countries can request authorization. All research is carried out within a secure CBS microdata environment, with privacy safeguards in place to ensure no individual-level data leaves this environment. Thanks to the vast amount of data, it's detailed level, the ability to link datasets, and robust data security, CBS microdata provides valuable insights into complex societal trends and relationships and therefore plays a vital role in supporting scientific or statistical research. More information can be found here: https://www.cbs.nl/en-gb/our-services/customised-services-microdata/microdata-conducting-your-own-research.

International journal of population data science

Netherlands
Humans
Databases, Factual
Computer Security
Registries


How Data Linkage Is Applied to Build Longitudinally Linked Data and Genealogy Data Based on Massive Sources of Administrative Data: A Two-decade Research on Linking Taiwan Household Registers

Abstract: My presentation uses data linkage to build data on Taiwan indigenous peoples (TIPs) as an example. TIPs used to be invisible and marginalized in Taiwan. In recent years, TIPs not only become very visible in Taiwan, but are also known globally. The most important contribution that helps TIPs become visible and are empowered in the real world is successfully building a number of big open data sets (see TIPD at https://osf.io/e4rvz/), based on open science, open data, data science, and scientific computing. Central to the processes of building open data are data linkage, including deterministic and probabilistic, that integrate Taiwan household registers and other administrative data from 2007 to 2024. Using data linkage, I have successfully built longitudinally linked register big data of population dynamics, with individual-level spatial information (point) and temporal information (monthly) being integrated in the linked data. In my presentation, I will demonstrate data linkage in building big complexed liked data (e.g., longitudinal and genealogy data), with a particular emphasis on the role of fine-tuning computing infrastructure (e.g., accelerating CPUs, DRAM, data transfer buses between CPUs-DRAM and between CPUs-PCIe lanes) to accelerate computing speed while conducting massive data linkage. In addition, I will demonstrate an automated geocoding method that allows us to parse spatial information from Google Map quickly and how legal and ethical issues are resolved. For reference, see https://link.springer.com/article/10.1007/s43545-025-01049-1.

Taiwan
Humans
Registries
Big Data
Longitudinal Studies
Information Storage and Retrieval


Indexing First Research: understanding the quality of generalised data linkage processes

Abstract: While data linkage has traditionally used bespoke, customer led methods, an increased demand for linked data has led to the development of indexes representing people, businesses and locations. Data are linked to indexes through generalised methods, and an index ID is appended to each statistical entity in the dataset. Users request relevant data and join them on index IDs. This process is called indexing. This approach has clear benefits, but there is still uncertainly about the quality of indexing and the quality of datasets linked through index IDs. The Indexing First Research programme provides evidence to better understand the quality outputs of these processes and give clear precedents to direct the future application of indexing. It takes existing bespoke linkages and compares them to an indexing approach on the same project. This provides evidence on the coverage of the indexes, precision and recall of different methods and bias in each linkage method. This paper will set out the aims of the research programme and the progress to date. It will discuss the outcomes of indexing hard to link populations (ie. homeless people and prisoners), of comparing bespoke linkages and linkages via the indexes (ie. births-deaths linkages), and the accuracy of indexing data through generalised methods (ie. nursing data linked to the persons index). This research has implications for the approach taken to data linkage and gives direction for when indexing is appropriate and when bespoke linkage is required.

Humans
Information Storage and Retrieval
Abstracting and Indexing
Data Accuracy


Handling missing data when using Goldstein et al.'s Scalelink method of data linkage

Abstract: Scalelink is an innovative probabilistic data linkage method based on multiple correspondence analysis. Unlike the current gold-standard probabilistic algorithm, Fellegi-Sunter, it does not assume linkage variable independence. All real-world linkage contains dependent linkage variables. Thus, avoiding this assumption has potential to improve data linkage quality. However, to date there are no recommendations regarding handling missing data values when using Scalelink. As all real-world data contains missingness, this means Scalelink cannot currently be used for real-world linkage. Seven candidate methods were identified from the literature as being used in multiple correspondence analysis. They were iteratively tested, first using tiny artificial datasets and subsequently using random samples of real-world census data. These tests were performed using a novel Python/PySpark implementation of Scalelink. Initial testing resulted in a three-method short-list: the Missing Single method (which treats missingness as an agreement state) and two variants of the Missing Insertion method (which removes missingness by imputing donor values). Following testing, results were analysed by comparing precision, recall and the F1 metric. From this, the Missing Single method was clearly unsuitable for use with Scalelink. However, both of the other methods worked well, although they should be used for different types of missingness. Despite imputation being highly counter-intuitive for linkage, this work demonstrates that it is the best way to handle missingness when using Scalelink. This finding supports further testing of Scalelink in more realistic scenarios, increasing its potential to improve probabilistic data linkage and facilitate more reliable decision-making based on linked datasets.

Algorithms
Humans
Information Storage and Retrieval
Models, Statistical


Real World Use of EHR data and Public Data Sets to Inform Public Health and Population Health Research and Surveillance

Abstract: 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.

Electronic Health Records
Humans
United States
Population Surveillance
Public Health
Population Health
Digital Health
Primary Health Care
Datasets as Topic


Health and educational outcomes among adolescents with neurodisability in England: a population-based linked data study using ECHILD

Abstract: Adolescents with neurodisability (neurological conditions causing functional limitations) have more complex needs than their peers, yet population-level evidence on secondary school outcomes remains limited. Using linked health and education records from the ECHILD database, we developed a national cohort of Year 6 pupils (final year of primary school, ages 10-11) in state schools between 2007/08-2016/17. Neurodisability was identified from hospital and school records before Year 6. We described the proportion of pupils with Special Educational Needs and Disabilities (SEND) provision in Year 6, and examined planned and unplanned hospital admission rates and school absence rates (% of school sessions missed) from Year 6 through secondary school (Years 6-11, ages 10-16). Of 5,291,458 adolescents in Year 6, 226,689 (4.3%) had recorded neurodisability, 78% of whom had any SEND provision (versus 20% without neurodisability). One-quarter had intensive local government-funded provision. Between Years 6-11, adolescents with neurodisability had 5 times higher planned admission rates (19.8 vs 3.7 per 100 person-years), 3 times higher unplanned rates (9.7 vs 3.8 per 100 person-years), and 2 times higher stress-related rates (2.5 vs 1.3 per 100 person-years) compared with peers. Overall absence rates were 7.2% versus 5.4% for peers, increasing over time and driven by authorised absences (5.8%, mostly health-related). Unauthorised rates were comparable (1.2-1.4%). Girls had consistently higher admission and absence rates than boys. Adolescents with neurodisability experience substantially greater health and educational challenges. Linked administrative datasets can inform targeted and integrated support from secondary school transition onward.

Macaulay L
Saxton J
Ford T
Logan S
Harron K
Gilbert R
Shumway J
Nguyen V
De Stavola B
Totsika V

Humans
Adolescent
Female
Male
England
Child
Education, Special
Educational Status
Databases, Factual
Nervous System Diseases
Health Status


The influence of ethnic name characteristics on string similarities when linking large heterogeneous population databases

Abstract: 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.

Dharmawimala Y
Christen P
Ziyad S
Vidanage A
Lam J
Schnell R

Names
Humans
Ethnicity
Databases, Factual


An Experience-Driven Framework for Overcoming Challenges in Cross-Agency Data Linkage

Abstract: Cross-agency data-sharing collaborations have proven crucial to ensure effective usage of often limited funds. Through, delivering these resources to the right people in the right circumstances at the right time. Despite the cumbersome and often time-consuming process of setting those collaborations up. The data linkage part of those collaborations does not need to be time consuming. If it is well planned. Here we present two vastly different data linkage experiences and lessons learned, from our Scottish Safe Haven, supporting cross-agency social-housing and health projects. Collaborations between health board, local authorities, universities and charities, that set out to improve the health and well-being of vulnerable groups while simultaneously reducing the pressure on the local health system. Both projects required data linkage between the partners datasets; an NHS identified group of vulnerable people and a variety of property details and metrics, to identify those most in need. These datasets are inherently not homogenised nor set up for cross-sector data linkage. One project required a time-intensive multi-stage mixed-method linkage approach combining direct matching across multiple variable fields with fuzzy-matching techniques and manual verification. Where a year later, the other project was able to utilise a unique property identifier, that only had recently been introduced into the data of the national health care provider. Whilst we recognise, that a single unifying unique identifier is not always available for data linkage across different agencies. We propose a framework of recommendations to consider before the data sharing that can facilitate a smooth data linkage process.

Scheliga B
Wilde K
Evans J
E Butler J
Berrocal-Martin R

Humans
Information Dissemination
Scotland
Information Storage and Retrieval
Cooperative Behavior


Hospital Electronic Health Records as a research resource: data linkage, national scale-up, and five-year Impact of the National Centre for Healthy Ageing Data Platform

Abstract: Established in 2020, the National Centre for Healthy Ageing (NCHA) Data Platform is Australia's first linked hospital-based electronic health record (EHR) research database covering all residents within a geographic region. Recent activities have expanded its scope, reach, and innovation. The platform houses internally linked data for >1 million people, 4 million clinical encounters (2015 onwards) from two acute hospitals, two sub-acute hospitals, and >10 community health services serving 300,000 residents. Automated weekly updates and data improvement processes enhance reliability. Semi-automated extraction and streamlined ethics/governance enable rapid data access within a Trusted Research Environment. Expansion efforts include an AI pipeline to extract concepts from clinical notes and linkage to local, state, and national datasets (e.g., primary care, medication, aged care, Medicare, cohort studies). National collaborations are exploring expansion to other Australian regions. The platform has overcome barriers to optimising EHR data for research, providing a test-bed for national and international priorities. Researchers have leveraged the platform for >60 projects, securing >$20M in grants on topics including dementia, aged care, medication use, homelessness, environmental health, and healthcare redesign. NLP models have been trained to detect dementia and opioid harms. Linked state and commonwealth data for 179,089 residents aged over 60 years (2010-2021) achieved 98.4% accuracy. Expansion feasibility is being explored through a national working group, and future inclusion in the National Person Level Integrated Data Asset. The NCHA Data Platform exemplifies how linked EHR data can advance population health research and underpin data-driven innovation across domains.

Andrew N
Beare R
Mann E
Parikh S
Carver A
Collyer T
Ung D
Dhillon R
Srikanth V

Electronic Health Records
Humans
Australia
Medical Record Linkage
Databases, Factual
Healthy Aging
Hospitals
Digital Health


Capitalising on administrative data to innovate and drive evaluation excellence

Abstract: Administrative Data Research (ADR) is transforming evaluation practice by improving robustness, complementing more traditional methods, and enabling evidence-based decision-making. In Wales, ADR Wales has embedded administrative data linkage into all Welsh Government evaluability assessments as standard practice, ensuring evaluations consider these rich data sources. Through the Secure Anonymised Information Linkage (SAIL) Databank, more Welsh population-level administrative data is available in Trusted Research Environments (TREs) that can be linked together, creating opportunities for evaluators to innovate. ADR Wales-a partnership between Welsh Government, Swansea University Medical School, and WISERD at Cardiff University-has demonstrated the value of linked data to Ministers and policy colleagues by informing evaluations of major initiatives, including early years interventions, the 20mph speed limit legislation, the basic income pilot for care leavers, tertiary education reform, and much more. Working alongside the UK Evaluation Society and the UK Evaluation Taskforce, ADR Wales is helping to mainstream administrative data use across the ADR UK network. This work improves evaluation quality, delivers value for money, and has a direct impact on citizens' lives. Supported by UK Research and Innovation and the ESRC, our renewed five-year funding will strengthen collaboration and expand data linkage opportunities. This session will showcase Welsh innovations in linked ADR, highlight practical strategies for integrating administrative data into evaluation design, using administrative data to complement other evaluation methods, and share lessons on how to encourage data owners (e.g. police, local authorities, and the third sector) to share data in TREs to unlock future evaluation potential.

Databases, Factual
United Kingdom
Wales
Routinely Collected Health Data


Educational Outcomes for Children in Intergenerational Households with Substance Misuse: A National Data Linkage Study in Wales, UK

Abstract: Parental substance misuse is a known risk factor for adverse child outcomes, yet large-scale UK evidence linking household misuse across health and justice systems to education remains limited. This study leverages whole-population linked administrative data in Wales to address this gap, operationalising policy commitments for earlier intervention. This retrospective electronic cohort study utilised the Secure Anonymised Information Linkage (SAIL) Databank. The cohort included children aged 11-15. A novel linkage combined Magistrates Court data with health records to define household substance misuse (SMHH). Children were categorised by the adult's misuse source: health only (SMHH-H), justice only (SMHH-J), or both (SMHH-HJ). Multilevel models analysed associations with school attendance and exclusions, adjusting for deprivation and Special Educational Needs (SEN). Exposure to household substance misuse was significantly associated with reduced attendance and increased exclusion odds. A clear risk gradient emerged: children in the SMHH-HJ group (misuse identified in both health and justice systems) experienced the poorest educational outcomes: highest likelihood of exclusion, lower attendance rates. These associations remained significant after adjusting for SEN and mental health, with effects compounded by socioeconomic deprivation. Household substance misuse, particularly when interacting with the justice system, is a critical marker for educational disengagement. By linking cross-sectoral data at a population scale, this study highlights the necessity of integrated preventative strategies across health, justice, and education to break cycles of disadvantage.

Farr I
Turner Evans H
Bailey G
Davies G
James D
Smith J

Humans
Wales
Substance-Related Disorders
Retrospective Studies
Child
Adolescent
Female
Male
Educational Status
Family Characteristics
Risk Factors
Socioeconomic Factors
Information Storage and Retrieval


Linking population-level education and hospital data in England: who are we missing in ECHILD?

Abstract: Linkage of National Pupil Database (NPD) to national hospital data has the potential to provide almost universal population coverage of children and young people in England. However, quantifying who is missed is a key requirement for producing unbiased population estimates. Deterministic linkage was conducted between longitudinal data from the NPD and secondary healthcare services via the NHS Personal Demographic Service. National Data OptOuts were applied to the linked data. We assessed the proportion of individuals who linked and used modified Poisson regression with robust standard errors to evaluate the risk of non-linkage when accounting for a variety of social determinants. Of 25,286,870 individuals born between 1984-2020 included in NPD, 84% (21,287,176/25,286,870) linked to a secondary care dataset. Linkage rates improved from 66% (507,820/765,642) for those born in 1985/86 to 98% (306,165/311,121) in 2019/20 and ranged from 89% (908,998/1,026,575) for those with Black ethnicity to 95% (13,644,182/14,401,835) in White groups. Females (IRR: 1.08, 95%CI: 1.08- 1.09) and those living in more deprived areas or in London were more likely to be unlinked. Individuals with recorded Special Educational Needs provision (IRR: 0.81, 95%CI: 0.81-0.82) or Free School Meals (IRR: 0.67, 95%CI: 0.67-0.68) were less likely to be unlinked. Whilst linkage rates have improved over time, there remain important differences in sex, ethnicity, deprivation and geography, between those who are and are not included in the linked dataset. Given high overall linkage rates reported by data providers, these differences are primarily driven by the application of Opt-Outs.

Nguyen V
Ramzan F
Ruiz Nishiki M
Blackburn R
Gilbert R
Harron K

Humans
England
Female
Male
Child
Adolescent
Educational Status
Databases, Factual
Hospitals
Child, Preschool


Building the Longitudinal Education Outcomes Northern Ireland (LEO NI) Dataset: Linking Administrative Data for Policy Impact

Abstract: This paper introduces the Longitudinal Education Outcomes Northern Ireland (LEO NI) dataset-an anonymised, research-ready resource that exemplifies the transformative potential of administrative data linkage. LEO NI integrates school-level records for individuals aged 14+ across eight academic years with seven years of post-16 data from Apprenticeships, Training Programmes, Further Education (FE), and Higher Education (HE). The dataset is designed to advance population data science by enabling empirical studies on educational attainment and post-16 trajectories, with direct implications for policy and practice. We detail methodological innovations in linking diverse administrative sources from the Department of Education and Department for the Economy, including School Census (with attendance), School Leavers Survey, and training/apprenticeship records. Linkage was achieved using anonymised identifiers under robust governance frameworks, ensuring privacy and compliance with legal standards. Additional variables capture Special Educational Needs (SEN) and health conditions, supporting complex, multi-dimensional analyses. The resulting dataset comprises circa 0.5 million unique individuals and includes comprehensive metadata and a researcher guide. We outline quality assurance processes, linkage accuracy, and strategies for managing high-volume, heterogeneous data. These methodological insights contribute to best practices in large-scale data infrastructures. We highlight policy areas where this dataset can be used. Scheduled for release in Spring 2026 via secure research environments, LEO NI offers unprecedented opportunities for longitudinal analysis of education and employment outcomes. By translating methodological advances into actionable evidence, LEO NI demonstrates how linked administrative data can inform policy across sectors and jurisdictions, while addressing ethical and governance challenges.

Wilgar P
McCullough N
Norris E
Foley B
Ross J

Northern Ireland
Humans
Longitudinal Studies
Educational Status
Datasets as Topic


Can and should we? Use of maternal administrative data to identify children at risk of poor outcomes

Abstract: Millions of children in the UK experience early adversity, with many requiring children's social care (CSC) support. National longitudinal data on these children is essential to understand lifelong impacts on health and development, yet they are often excluded from the UK's leading child development studies. This study explored the feasibility of using linked administrative records for sampling, supported by discussions with data owners. We analysed populationlevel administrative data from the SAIL Databank (Wales) and the ECHILD database (England) to identify maternal psychosocial risk factors (such as mental health conditions, substance misuse and domestic violence) and CSC involvement before age six. A cohort was created of mothers with live births between April 2009 and March 2016, linked to secondary healthcare and CSC records. In parallel, we engaged with data providers across the UK to assess practical and governance considerations. In Wales, 15% of mothers whose child was involved in CSC had psychosocial risk factors. By contrast, only 4% of mothers without these risk factors had a child with CSC involvement. Further analyses are ongoing. Data providers confirmed that while administrative records have precedent for recruitment, ethical and sensitivity concerns pose barriers. Findings highlight links between maternal vulnerability and CSC involvement, alongside rising psychosocial adversities. Administrative data offers valuable insights but using it to identify children at risk remains complex. Differences in legislation, governance, and data access across UK nations hinder a unified sampling frame, especially given the sensitivity of maternal and social care records.

Jane Griffiths L
Raybould A
Dennison K
Harron K
Bailey G

Humans
Female
Risk Factors
Mothers
Child
Wales
Child, Preschool
Infant
Databases, Factual
United Kingdom
Substance-Related Disorders
England


Large-scale geocoding via iterative deduplication: Building a unified address database in Brazil with CIDACS-RL

Abstract: This work aims to construct a dataset of unique addresses from Brazilian administrative, socioeconomic, and health databases. We assess the potential of iterative deduplication to generate a parent-duplicate data structure that supports address management and downstream information retrieval tasks. Our approach followed three steps: local deduplication within each database to establish parent-duplicate relationships; cross-database deduplication to identify global parent records and propagate their identifiers; and iterative deduplication of global parent records, updating assignments across databases. Graph-based modeling was used to identify shared connected components, enhancing geocoding coverage. Deduplication accuracy was evaluated through manual review of state-stratified samples (N = 2,000), with independent assessment by at least two reviewers and disagreements resolved by a third. Five data sources from Northeastern Brazil were used: the National Register of Addresses from 2010 (CNEFE 2010; N = 17,839,562), CNEFE 2022 (N = 30,545,117), the 100 Million Brazilian Cohort baseline (CadÚnico; N = 54,985,455), Mortality (SIM; N = 6,431,199), and Live Birth (SINASC; N = 17,199,287). Local deduplication substantially reduced data volume, identifying 8.25M, 531 thousand, and 583 thousand parent records for the CadÚnico, SIM, and SINASC, corresponding to reductions of 77.84%, 91.74%, and 96.61%. After iterative cross-database deduplication. The resulting database comprised 28.6M records, including 24.8M unique parent addresses and 5.9M duplicates. Average deduplication precision across all steps exceeded 0.9. Iterative deduplication is an effective strategy for enhancing data management and quality across heterogeneous datasets, facilitating large-scale spatial analysis for public health and climate change research.

Pita R
Ichihara M
Rocha G
Santos C
Ribeiro L
Pita P
Sena S
Eustáquio F
Gomes J
Silva R

Brazil
Humans
Databases, Factual
Geographic Information Systems
Geographic Mapping


Clean Data, Breakthrough Insights: The Importance of Standardizing Your Research Warehouse

Abstract: Leveraging routinely collected health services data to conduct scientifically robust research requires a clear understanding and standardization of data from disparate clinical information systems which often change over time. Within Alberta Provincial Research Data Services (PRDS) a large team of analysts utilize a vast Enterprise Data Warehouse containing dozens of datasets with various update cycles and availability, generally spanning more than 20 years of data for a Canadian province of over 4 million people. We examine two recently developed standardized data marts for mortality and medical lab tests. These data marts are now updated daily using Snowflake Tasks to ensure timely data can be accessed as needed to support research studies. The mortality data are linked from three sources to ensure comprehensive coverage: Vital Statistics, Provincial Registry, and Connect Care (the provincial Epic based electronic medical record) and resolves inconsistencies in personal health numbers and discrepancies in dates of death. The Lab data mart uses test level data from several different historical lab systems and the current Connect Care system and consolidates coding and definitions over time to create a clean, easy to use data mart. Both use cases create a single, validated and linkable source of truth that is easy to use for research studies, negating the need for each analyst to re-engineer from several different source tables each time. The process used here will be applied to other subject matters internally and could be similarly applied data warehouse environments in other jurisdictions.

Youngson E
Armani N
Whitten T
Quan H
Li B
Wang T
Bakal J

Humans
Alberta
Data Warehousing
Electronic Health Records
Registries


What is needed to make administrative data 'research ready' for causal inference? A case study of the Education and Child Health Insights from Linked Data (ECHILD) database of 15 million children in England

Abstract: Many jurisdictions have linked education and health data throughout childhood to address policy questions relating to equity and effectiveness of services. We use the example of special educational needs and disability (SEND) policy to show that causal evidence of effectiveness, based on administrative data, can be limited and potentially misleading. We discuss what needs to change. We derived health phenotypes to group children likely to benefit from SEND provision and used the target trial emulation framework to define causal contrasts, reduce design biases and guide analytic methods. We estimated the effectiveness of SEND provision at age 5/6 on outcomes recorded in health and education data for two phenotypes (cerebral palsy and cleft lip/palate). SEND provision did not improve rates of unplanned hospital admissions or educational attainment but did reduce rates of unauthorised school absences for both phenotypes. These results are likely biased due to unmeasured confounding and imprecise measures of SEND provision and relevant outcomes. We failed to find instrumental variables to account for unmeasured confounding. Despite the detailed information on health and school characteristics in ECHILD, there is insufficient information on which children are selected for which SEND interventions or comparators. Causal evaluations of SEND provision using observational analyses of ECHILD need linkage to additional measures of need, interventions and outcomes. Adding similar measures to linked health-education data across jurisdictions, and wider use of experimental designs, would help to quantify effective and generalisable SEND practices. These lessons are likely relevant to other practitioner-based interventions.

Gilbert R
De Stavola B
Nguyen V
Harron K

Humans
England
Education, Special
Child Health
Child
Child, Preschool
Databases, Factual
Cerebral Palsy
Causality
Cleft Lip
Cleft Palate
Female
Educational Status
Male
Secondary Data Analysis


ODISSEI: the Dutch National Infrastructure for Social Science

Abstract: This presentation demonstrates how the ODISSEI research infrastructure enables interoperable, population-scale social science through the integration of data, compute, and services. At the core of the architecture is access to high-quality administrative microdata via Statistics Netherlands (CBS), combined with secure storage and linkage through the CBS Data Storage environment. Discovery and contextualisation of these data are facilitated through the ODISSEI Data Access Broker (DAB) and the ODISSEI Portal, which together streamline dataset findability, access requests, and metadata harmonisation across providers. The analysis leverages population-scale networks constructed within the Secure Analytics Environment (SANE), enabling computationally intensive network operations while complying with legal and ethical constraints. Interoperability across services is achieved through shared standards, coordinated identifiers, and integrated workflows across ODISSEI's service stack, including the ODISSEI Secure Supercomputer (OSSC) for scalable analysis and the ODISSEI Code Library for transparent documentation, versioning, and reuse of analytical pipelines. By linking administrative registers, derived network layers, and reproducible code within a single federated infrastructure, ODISSEI lowers barriers to complex, multi-source research while increasing robustness and replicability. The presentation focuses on the architectural design rather than the empirical results, showing how ODISSEI functions as an integrated ecosystem rather than a collection of standalone tools and illustrates how national research infrastructures can operationalize FAIR principles, support population-scale analytics, and provide a blueprint for interoperable social science infrastructures in Europe.

Netherlands
Social Sciences
Humans
Information Storage and Retrieval


Ascertainment of Chronic Kidney Disease and Palliative Care using routine health administrative data consolidated in a Health Information Exchange, Western Cape South Africa

Abstract: The Provincial Health Data Centre (PHDC) integrates person-level clinical and administrative data in the public health systems, using clinically-informed phenotypic algorithms to infer health conditions (episodes), from laboratory, medication and service-use data. Identifying patients living with CKD and requiring palliative care is essential for effective health service delivery, as existing data sources, such as the national kidney replacement therapy registry and morphine monitoring, underestimate true prevalence. To develop and apply a phenotypic algorithm (ascertainment) to define CKD and palliative care episodes, and to integrate end-stage renal disease (ESRD) patients into the palliative care episode. Ascertainment used a rules-based inference engine processing data via phenotype algorithms. CKD clinical data points (evidences) included longitudinal laboratory data defining CKD by KDIGO eGFR criteria, the South African Renal Registry, triangulated clinical data, and associated medications. Palliative care episodes were inferred using ICD-10 codes, morphine dispensing, referrals, and an ESRD disease-specific layer. Descriptive statistics characterised the epidemiology for both conditions in the public sector of the Western Cape. The PHDC enumerated 224 469 CKD patients (170 977 alive and 145 719 visited health facilities within the last 5 years) and 56 931 palliative care patients identified(26 397 alive and 18 561 last visited a facility within the last 5 years) from 2000 to 2024. The ascertainment algorithm and ESRD integration more effectively identified patients requiring palliative care than previously possible. These findings demonstrate effective use of routine, linked health data to identify CKD patients with palliative care requirements, supporting health service delivery and planning.

Humans
Palliative Care
South Africa
Renal Insufficiency, Chronic
Female
Algorithms
Male
Health Information Exchange
Middle Aged
Registries
Aged
Adult
Kidney Failure, Chronic


Developing an Evaluation Toolkit for Data Linkage Pipelines

Abstract: The linkage evaluation toolkit project aims to develop tools and criteria that can be used to evaluate data linkage pipelines and methods. This toolkit will not only allow assessment of a single pipeline, but will also facilitate the comparison between pipelines; enabling analysts to identify their strengths and weaknesses. There are currently limited standardised evaluation metrics for linkage pipelines, except for the precision, recall, and bias of the linked datasets. Eight key evaluation areas were identified; accuracy, bias in linkage error, flexibility in input datasets, scalability, efficiency, platform suitability, ease of use, and the ease and transparency of quality assurance. Evaluation criteria for each area were identified and quantified. Tools are being developed to enable users to assess pipelines across these criteria. These include tools for stress-testing, testing the pipelines under different parameters, assessing bias, and capturing and quantifying qualitative feedback. We are liaising with quality teams for standardised tools to assess precision and recall. Once finalised, the toolkit will be used to compare linkage pipelines when linking the same administrative datasets, to identify which pipeline performs best across each criterion. Future use will include identifying suitable linkage methods to support the UK's 2031 Census. The evaluation toolkit will allow analysts to evaluate the strengths and weaknesses of linkage pipelines, in both their use and impact on linked outputs. The methods can be used to improve existing pipelines, and to support the development and evaluation of new methodologies in future.

Humans
Information Storage and Retrieval
Software


Investigating Biases in Seniors' Drug Data Coverage in Canada

Abstract: While no province in Canada covers the cost of prescriptions for all of its residents, all have some form of provincial supplementary coverage (PSC) to cover some costs for particular subgroups (based on age, economics, and/or diagnosis). This PSC data can provide insights into the drug dispensation patterns of the population, but they often have significant biases that are often ignored in the literature. The objective of this research is to explore the differences between Drug Information System (DIS) and PSC data in the province of Nova Scotia (NS). METHODS: Demographic data on all eligible Nova Scotians, and their prescriptions in both the DIS and NSSPP, were extracted from August 2016-December 2024. Residents were classified as recipients, non-recipients, and non-consumers, and their demographics and prescription patterns were explored. There were over 342 000 residents eligible for NSSPP during the study window. Over 61 million prescriptions for these individuals were dispensed across the two databases, 38 million of which were paid for by NSSPP (63%). NSSPP paid for prescriptions for 58% of the population, though very few citizens had their prescriptions exclusively paid for by NSSPP (8.2%). Those that didn't receive prescriptions through NSSPP tended to be younger and more female. The data from NS show a clear potential for biases in using PSC data as a representative sample of seniors, with biases based on both age and gender. Future research will explore how potential patient disease patterns may influence coverage within the system.

A Stewart S
Park J
von Maltzahn M
Black E
E Isenor J
Sketris I
Trenaman S

Humans
Nova Scotia
Female
Aged
Male
Bias
Drug Prescriptions
Databases, Factual
Drug Information Services
Aged, 80 and over


Publishing Linked Data with Correction Weights

Abstract: Data linkage is often used to make inferences from distinct data sources. The data may be linked by a set of variables with high discrimination, for example, a social security number or some personal information. A good linking variable often implies a high disclosure risk in identifying the corresponding person. To prevent the disclosure risk, the data linker may choose to remove the linking variables before publishing the linked datasets. The design of the original data sources, the response pattern, and the unlinked part of the original data are often also unknown to the secondary user. However, the quality of the linked data is constrained by the original data sources and the linking process. Without considering the potential error or bias in linked datasets, naïvely treating them as error-free in secondary analysis may result in biased inference. To indicate the quality of the linked dataset without sacrificing privacy, we propose publishing correction weights alongside the linked datasets. The weights are generated given the information in the original data sources and the quality of the linkage. Both selection issues of the sample and measurement issues of the linking variables are addressed in the constructed weights, and we allow the possibility of having multiple potential links for a record. Secondary users may apply design-based estimators for subsequent analyses based on the correction weights, or apply sensitivity analysis given different sample inclusion criteria. An example is presented, and the option of secondary analysis given the constructed weights is discussed.

Humans
Information Storage and Retrieval
Publishing
Bias


Pilot Samples for Estimation of Linkage Error

Abstract: Efficient estimation of linkage error in large linked datasets requires careful allocation of limited clerical review resources. We investigate the use of small stratified "pilot" samples as an initial, low-cost strategy for informing sample size calculation and allocation across strata when estimating binomial error proportions. Stratified candidate links and unlinked pairs exhibit heterogeneous error rates and variances; however, without prior variance information, optimal allocation - such as Neyman allocation - cannot be applied. We show that pilot samples of approximately nh = 30 per stratum provide sufficiently informative variance estimates at minimal cost, enabling efficient allocation of subsequent sampling effort. Using binomial absolute margin-of-error behaviour as a rough guide we calculate uncertainty for error proportions up to a maximum of p = 0.5, and use simulations to show that beyond nh ≈ 30 the marginal reduction in uncertainty grows negligible. Simulations illustrate also that these small pilots successfully differentiate high- and low-variance strata, supporting targeted sampling in the final round. When pilots are combined with Neyman allocation, the resulting stratum-level confidence intervals were shown to become more homogeneous, reducing overall population-level variance relative to proportional allocation. Empirical comparisons therefore show that, for equal total clerical review effort, Neyman allocation informed by pilots consistently yields narrower population-level confidence intervals than proportional allocation (0.85% vs 0.93% MoE). This confirms that even very small pilot samples can materially improve efficiency by enabling variance-driven sample size calculation and allocation. The method offers a practical, scalable approach for organisations conducting clerical review of linkage errors in large datasets.

Computer Simulation
Pilot Projects
Sample Size
Datasets as Topic


From Consultation to Innovation: A Data Access Pathways Tool to Navigate Australia's Linked Data Landscape

Abstract: Australia's administrative data linkage environment has evolved from state-based repositories used for internal reporting, to an ecosystem of national linked data assets alongside bespoke linkage services. This has increased complexity for researchers seeking linked data access. A 2024 national consultation identified a need for tools to support transparent exploration of researcher-appropriate access pathways. The Population Health Research Network led a researcher-centred, co-design process with data linkage units, Commonwealth organisations, and research end-users to develop the Data Access Pathways tool, an online decision-support tool that enables researchers to input their project parameters and discover access pathway options. Tool development involved specification of pathway logic, business rules, wireframes, and iterative user testing. Access pathways present different ethics, governance and operational requirements, and importantly variations in data breadth and granularity. Such complex challenges also exist in federated or multi-agency linkage systems internationally (e.g., Canada, UK). The Data Access Pathways tool demonstrates that structured decision logic embedded in a user-friendly tool can simplify the complexity by supplying pathway options, guidance and contact points - building researcher capacity, reducing planning overhead, and promoting equitable access. The Data Access Pathways tool translates national-level consultation and stakeholder collaboration into practical, scalable infrastructure. Although developed for Australia, the underlying principles - neutrality, transparency, co-design, and flexible decision logic - are broadly applicable to other systems with fragmented or evolving linkage landscapes. This work offers a model for strengthening national data infrastructure and facilitating population data research globally.

Australia
Access to Information
Humans
Information Storage and Retrieval


Why Big Data Matters - Free schools resource to engage young people in health research

Abstract: In collaboration with the Schools Health Research Network in Scotland (SHINE), Generation Scotland (GS) developed a free schools resource to educate students about health data and the value of taking part in research, supporting the recruitment of young people to the GS cohort. A scoping exercise with SHINE helped identify needs, and teachers and young people were engaged through their national conference and online focus groups to shape core content. In April 2025, funding from Research Data Scotland enabled revision and expansion of the resource based on teacher feedback. The resulting pack comprises three 45-minute lessons focused on "What is Big Data?" and introduces key concepts in health research, including big data, data linkage and data ethics. The resource was distributed to more than 50 interested schools via various newsletters and partner organisations, reaching schools in 22 of Scotland's 32 local authorities. Three schools have provided detailed feedback after delivering the lessons to approximately 150-250 pupils each. Offering these lesson plans created a reciprocal relationship that enabled recruitment of young people through schools. Teachers reported that the resource filled a gap in the curriculum, and delivering the lessons alongside GS recruitment materials provided a scalable, sustainable approach by leveraging existing school infrastructure. This not only supported recruitment but also contributed to lasting improvements in students' health research literacy and understanding of the value of data in research. The resource was developed with input from the SHINE network, extensive teacher feedback and contributions from the GS Young Persons Advisory Group.

Robertson S
Milbourn H
Campbell A
Clark F
Flaig R
Kirby L
Xiao X
Whalley H
Sudlow C

Humans
Scotland
Big Data
Adolescent
Schools
Focus Groups


Strengthening Trusted Research Environment Interoperability: A Metadata Platform Enabling Population-Scale Data Linkage Research

Abstract: Trusted Research Environments (TREs) increasingly underpin cross-sectoral and population-scale data linkage research, yet no single product currently provides a flexible, interoperable way to store and expose the breadth of a TRE's data assets, let alone in a useful way. Existing systems often lack the adaptability needed to incorporate diverse TRE outputs or support an organisation's workflow requirements. We sought to develop a system that enhances interoperability, integration, and usability of diverse data sources to drive core TRE functions. SeRP created the Data Portal platform to address this gap, using an in-house development approach to ensure tight integration with its own TRE operations. The platform enables the capture, management, and dissemination of metadata through three core components: a configurable form builder to capture data first-hand within the system and import tool for data capture from external sources; a high-performance database to store and serve metadata at scale; and a template engine for a flexible user interface presentation. This architecture supports a wide array of TRE-relevant asset types-including dataset definitions, data linkage releases, data quality assessments, provisioning specifications, project and governance metadata, and project outputs and is designed to interoperate with SeRP's own TRE management tools and third party infrastructures. Harnessing this data from one central repository allows it to be used to drive essential TRE services such as project applications, dataset and variable selections for provisioning, governance management, project impact tracking, data cataloguing etc. which will ultimately improve research with linked population data.

Metadata
Humans
Information Storage and Retrieval
Databases, Factual
Systems Integration


Enhancing geocoding in Brazil through language model-based preprocessing

Abstract: Similarity-based geocoding pipelines are highly sensitive to lexical noise in address data. This work evaluates the potential of language models to enhance and standardize address inputs during preprocessing, thereby improving geocoding accuracy in CIDACS-RL, which relies on Jaro-Winkler similarity and is sensitive to string length and prefix agreement. We extended CIDACS-RL with a preprocessing step that removes street types (e.g., street, avenue, lane) and stop words and expands numeric and abbreviated address components. Two pre-trained language models were included in the experiments. Records from nine Northeastern Brazilian states in the cohort baseline (N = 54,985,455) were geocoded by linking addresses to census tracts or coordinates from the Brazilian National Register of Addresses (CNEFE; N = 30,545,117). After linkage, we manually assessed the accuracy of our extension against previous CIDACS-RL version using stratified samples (N = 2,000) from each run to determine the cutoff points. The geocoding was evaluated using accuracy and precision. Overall, the proposed extension achieved competitive performance, with higher mean precision (0.86, SD = 0.01) and accuracy (0.94, SD = 0.024) compared to the original CIDACS-RL, which achieved a mean precision of 0.84 (SD = 0.02) and a mean accuracy of 0.93 (SD = 0.024). The largest gains were observed in states with shorter average street name lengths, such as Alagoas and Pernambuco, where precision increased by approximately 3% to 10%. Our findings support that geocoding tasks may benefit from pre-trained language models for preprocessing steps, addressing the limitation of well-posed similarity measures.

Pita R
Rocha G
Ichihara M
Harron K
Brito P
Carreiro R
Almeida B
Ramos P
Barreto M
Barreto M

Brazil
Geographic Mapping
Humans
Geographic Information Systems
Large Language Models


The automated generation of data linkage Information: A reusable, scalable metadata framework for Trusted Research Environments

Abstract: Using the correct linkage keys to join tables is essential when combining data from multiple sources. Although Trusted Research Environments (TREs) provide metadata to support data linkage, to the best of our knowledge, none currently automate the creation of detailed table-level linkage information. Researchers often conduct linkages, and complex linkages exist at many levels. However, this manual approach may misuse available linkages. As the number of data sources and datasets increases in complexity, ensuring correct linkages within and across data sources becomes difficult, reducing efficiency and reproducibility. The framework provides an end-to-end solution for data in the Secure Anonymised Information Linkage (SAIL) Databank by generating a full linkage matrix and documentation directly from common metadata fields (e.g., table_name, column_name, datatype) using configurable pattern-matching rules (e.g., column_name ending in '_E' denotes an encrypted ID field). The algorithm then compares shared keys across all tables to identify every combination of internal and external linkages, producing linkage matrix with structured natural-language statements for users, which can subsequently be used to generate linkage diagrams. The framework has been validated to ensure accuracy, including verifying that all linkage keys and information are detected. To assess performance, the framework calculates accuracy, precision, and recall by comparing results against a manually generated standard reference, which is validated by the SAIL User-and-Data-Support-Services (UDSS) team. Our approach offers major benefits for TREs and researchers by standardising scalable and reusable linkage documentation. We improved the consistency of linkage metadata production for complex, multi-table datasets within SAIL.

Mhereeg M
Rawlings A
Davies G
Howell-Wright O
Hughes L
Akbari A
Evans H

Metadata
Information Storage and Retrieval
Algorithms
Databases, Factual
Humans


Splink: recent developments and realtime linkage

Abstract: Splink is the award-winning open-source data-linkage software developed by the UK's Ministry of Justice (MoJ). It implements the probabilistic Fellegi-Sunter model in SQL, allowing users to employ their execution backend of choice, whether that be with DuckDB, Spark, or another engine of preference. Splink's focus on performance, accuracy, scalability, and rich feature set have enabled it to accrue a wide user base across the public sector, academia, and beyond. Splink is in continuous development, motivated by the ever-developing needs for record linkage within the MoJ, as well as the challenges faced by our wide range of users across the globe. We will discuss some of the latest developments within Splink, as well as a look at features that are on the near horizon. Recent focus includes work to allow scaling of linkages to ever-larger datasets, and to provide much greater flexibility for users to control the individual portions of their data-linking workflow. A particular focus has been our work on near-realtime record linkage. We have developed a system for operational record-linkage that keeps in sync with continuous updates to source data using incremental linkage, which brings unique challenges for performance and scaling compared to batch-processed linkage. We will discuss some of the issues around this, how it informed the design of our system in order to make its operation a reality, and how we have then folded these ideas back into Splink to make it more straightforward for other users to do likewise.

Software
Humans
United Kingdom
Information Storage and Retrieval