International Journal of Population Data Science最新文献

{"title":"Linking population-level education and hospital data in England: who are we missing in ECHILD?","authors":"Vincent Nguyen, Farzan Ramzan, Milagros Ruiz Nishiki, Ruth Blackburn, Ruth Gilbert, Katie Harron","doi":"10.23889/ijpds.v11i5.3520","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3520","url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3520"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426679/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An intersectional multilevel analysis of mortality among minority groups in Northern Ireland.","authors":"Caitlyn Rawers, Emma Ross, Sarah McKenna, Jamie Murphy, Aideen Maguire","doi":"10.23889/ijpds.v11i5.3526","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3526","url":null,"abstract":"<p><p>Research consistently demonstrates substantial inequalities in physical and mental health outcomes among minority groups, yet the intersectionality of characteristics is often overlooked due to small sample sizes within these underserved populations. While health inequalities are known to contribute to elevated mortality risk, the intersectionality of multiple social identities and disadvantages remains unexplored. This study will use data from the Northern Ireland Mortality Study (NIMS), which links 2021 Census records to area-level measures and death registrations,) to examine intersectional patterns in mortality risk among the entire population of NI, approximately 1.9 million people. Using a novel approach called the intersectional multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA), we will undertake an in-depth investigation of inequalities in deaths by suicide, deaths of despair, preventable deaths, and all-cause mortality. The MAIHDA model will examine intersections of characteristics such as gender, ethnicity, religion, deprivation, and sexual orientation, and then estimate the average differences in mortality risk between these 'strata'. Overall, the aim is to determine which intersectional strata are at the highest risk of mortality outcomes in NI, providing essential evidence to guide targeted interventions and reduce health inequalities among these underserved populations.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3526"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426828/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Fully Automated End-to-End Framework for Scalable Record Linkage.","authors":"Barret A Monchka, Cole Chuchmach, Elizabeth Stoughton, Jingwen Li, Lisa M Lix","doi":"10.23889/ijpds.v11i5.3728","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3728","url":null,"abstract":"<p><strong>Background: </strong>Automated record linkage is often defined as applying a single threshold to classify candidate record pairs as matches or non-matches, in contrast to the Fellegi-Sunter two-threshold approach that relies on manual review for uncertain pairs. However, eliminating manual review alone does not constitute fully automated linkage, which also requires automation of data preprocessing, parameter selection, linkage quality evaluation, and report generation-steps that remain largely manual in practice and contribute substantially to the time and cost of data linkage projects.</p><p><strong>Objective: </strong>To develop an open-source, fully automated, record linkage framework that supports scalable, reproducible, and transparent workflows.</p><p><strong>Implementation: </strong>We developed three R packages, publicly available at https://github.com/CHIMB, that together form a fully automated linkage pipeline: datastan, autolink, and linkrep. The datastan package automates data standardization and preprocessing by transforming each data source into a consistent format prior to linkage, including variable naming, date formatting, variable categorization, standardization of name spelling variations, and handling of missing data. Autolink enables users to define and execute complex linkage algorithms, supporting deterministic, probabilistic, fuzzy, and phonetic matching. Linkrep automatically generates comprehensive linkage quality reports. Both the data standardization rules and linkage algorithms are easily editable through a graphical user interface and can be defined once, saved, and set to execute automatically for subsequent linkages (e.g., when data updates become available), without further user intervention.</p><p><strong>Implications: </strong>By automating the full linkage lifecycle, this framework reduces turnaround times while improving reproducibility and transparency-facilitating more efficient, equitable, and sustainable linkages for routinely updated datasets.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3728"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426702/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tackling undercoverage in educational attainment registers using large-scale survey data: Statistics Netherlands' Educational Attainment File.","authors":"Christian Fang, Karen van Hedel, Bram Hogendoorn, Johan van Rooijen","doi":"10.23889/ijpds.v11i5.3747","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3747","url":null,"abstract":"<p><p>Educational attainment is a key variable in social-scientific research. In the Netherlands, most information on educational attainment is obtained from various registers of the Ministry of Education. These cover student enrollments in nearly all Dutch education institutions from the 1980s onwards. However, some residents have attained (additional) education before these registers came into existence, abroad, or at institutions not included in these registers (e.g. private institutions). This means that for 29% of the population information on education is entirely absent, whereas for some of the observed 71%, educational attainment is underestimated. Here, we present a unique method developed to deal with these deficiencies. By combining and integrating administrative data and data from the Dutch Labour Force Survey in a unique way, Statistics Netherlands can make valid inference to the population level on the basis of incomplete coverage. We compute weights for individuals not observed in any register but who were observed in the survey, with these survey observations effectively being treated as though they are a random sample from the unobserved population. Furthermore, we impute scores for individuals observed in only registers to correct for the undercoverage of education. Finally, a bootstrap approach is applied to assess uncertainty of statistical outcomes. Taken together, this method allows valid inference to the population level, generating point estimates as well as estimates of uncertainty.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3747"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426663/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying bias due to linkage error: methods and application to South Africa's NHLS National HIV Cohort.","authors":"Evelyn Lauren, Dickman Gareta, Khumbo Shumba, Mazvita Muchengeti, William MacLeod, Matthew Fox, Wendy Stevens, Tshepo Molapo, Musa Manganye, Koleka Mlisana","doi":"10.23889/ijpds.v11i5.3534","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3534","url":null,"abstract":"<p><strong>Background: </strong>Linkage error can bias downstream estimates. South Africa's National Health Laboratory Service (NHLS) National HIV Cohort, created through probabilistic linkage of routine laboratory records, offers a rare opportunity to examine viral suppression durability at scale. We propose a sensitivity analysis approach to assess robustness of findings to linkage error.</p><p><strong>Methods: </strong>People living with HIV (PLHIV) aged 15-59 years in the NHLS cohort were followed for 18 months from their first viral load (VL) <200 copies/mL between July 2021-June 2022. We assessed: 1) 12-month monitoring (any VL 6-18 months post-baseline), and 2) 12-month transmission risk using WHO thresholds: <200 copies (zero risk), 200-999 copies (minimal risk), and ≥1000 copies (elevated risk). To assess robustness to linkage error, we repeated analyses in a subset of high-confidence patient clusters - clusters stable across linkage thresholds. We applied inverse probability weighting using demographics for representativeness. Assuming cluster selection is independent of outcomes, this yields minimal-error estimates for comparison with naïve estimates.</p><p><strong>Results: </strong>The cohort was linked with 90.4% sensitivity (Sen) and 91.9% positive predictive value (PPV). We identified 3,584,204 PLHIV with baseline VL<200 copies, of which 80% were monitored in 12 months; of these, 91% had no transmission risk, 5% minimal risk, and 4% elevated risk. In the high-confidence subset (3,072,758 individuals), 78% were monitored in 12 months with 92% having no transmission risk, 5% minimal risk, and 3% elevated risk.</p><p><strong>Conclusion: </strong>Viral suppression is highly durable among PLHIV in South Africa. Sensitivity analyses confirmed findings were robust despite non-trivial linkage error rates.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3534"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426721/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Publishing Linked Data with Correction Weights.","authors":"An-Chiao Liu, Peter Lugtig, Sander Scholtus, Ton de Waal","doi":"10.23889/ijpds.v11i5.3531","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3531","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3531"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426566/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methodological Challenges and Advances in Census-to-Census Linkage in Brazil.","authors":"VInicius Prado, Luiz Felipe Barros","doi":"10.23889/ijpds.v11i5.3758","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3758","url":null,"abstract":"<p><p>This study examines the methodological challenges and early advances involved in linking individual records from Brazil's 2010 and 2022 Demographic Censuses. Although Brazil has a long tradition of innovation in census operations, the linkage between consecutive census rounds remains underexplored, as observed in many developing and middle-income countries. The main goal is to identify the potential and limitations of probabilistic linkage between census datasets, particularly those related to data quality, variable availability, and structural changes across census rounds, and to document the methodological progress. Our approach comprised five stages: (1) mapping and assessing the structure, availability, and quality of identifying variables in both censuses; (2) preprocessing and harmonizing personal identifiers, household attributes, and address-related information; (3) integrating individual and household attributes to define the information hierarchy for linkage; (4) designing a preliminary probabilistic linkage strategy with alternative blocking rules; and (5) conducting exploratory tests to assess feasibility and inform refinements. The Brazilian context presents specific challenges, including naming conventions, variability in information quality, and changes in household and address structures, which require tailored harmonization and linkage solutions. To assess feasibility, we implemented small-scale exploratory tests that combine territorial references with household and individual characteristics. This approach enabled us to identify practical constraints and evaluate preliminary blocking and matching rules under real data conditions. The emerging lessons and methodological insights from this work may inform the refinement of linkage strategies using data from Brazil. They may also inform data integration efforts in other developing countries facing similar structural and operational challenges.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3758"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426602/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privacy-Preserving Record Linkage in Population Data Systems: Leveraging Synthetic HDSS Data and Deterministic Identifier Masking for Machine Learning Models.","authors":"Tathagata Bhattacharjee, Emma Slaymaker, Chodziwadziwa Kabudula, Jim Todd","doi":"10.23889/ijpds.v11i5.3673","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3673","url":null,"abstract":"<p><strong>Background: </strong>Linking population-based datasets requires identifiable attributes that raise privacy and governance concerns. In low- and middle-income countries (LMIC), Health and Demographic Surveillance Systems (HDSS) are key sources of longitudinal population data. Synthetic data offers a privacy-preserving route for developing and evaluating record linkage (RL) methodologies without exposing sensitive microdata. This study introduces a framework that leverages Conditional Tabular Generative Adversarial Network (CTGAN)-generated synthetic HDSS data and deterministic name masking to support ethical RL and the development of supervised and ensemble machine-learning linkage models.</p><p><strong>Methods: </strong>A CTGAN-based pipeline was applied to clean and harmonise Kisesa HDSS data to generate a statistically comparable synthetic dataset, while sex-specific deterministic name masking ensured full replacement of identifiers. Synthetic data quality was evaluated using distributional similarity, relationship preservation, and privacy-risk assessment. Synthetic adult clinic datasets were derived from the synthetic HDSS by introducing graded, field-specific error patterns via controlled probabilistic perturbations, creating conditions to evaluate RL models. Supervised models (logistic regression, random forest, gradient boosting, support vector machines) and an ensemble meta-classifier were trained and compared on these synthetic linkage tasks.</p><p><strong>Results: </strong>The synthetic HDSS dataset preserved key demographic structures and multivariate dependencies, while deterministic masking eliminated direct identifiers. Across increasingly noisy error scenarios, supervised and ensemble models maintained strong match-nonmatch discrimination, with ensemble methods exhibiting the highest robustness as data quality deteriorated.</p><p><strong>Conclusion: </strong>CTGAN-derived synthetic HDSS data combined with deterministic name masking provides a secure testbed for machine-learning-based RL, enabling ethical, reproducible, and governance-aligned population data linkage research in LMICs.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3673"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426587/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Growing our Own Epidemiologists: our Journey towards Data Sovereignty. The RECLAIM project and Mobile Training Institutes for First Nations in Manitoba, Canada.","authors":"Sidney Leggett, Dr Wanda Phillips-Beck, Leona Star, Dr Jillian Waruk, Mabel Horton, Sherry Copenace, Doris Young, Stephanie Sinclair","doi":"10.23889/ijpds.v11i5.3573","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3573","url":null,"abstract":"<p><p>To advance First Nations (FN) data sovereignty in Manitoba by developing and implementing FN-specific training initiatives that build community capacity in epidemiology, biostatistics, data analysis, and data utilization. The First Nations Health and Social Secretariat of Manitoba (FNHSSM) established a regional training institute, youth training programs, and a mobile training lab designed to support FN leaders and community members in developing data skills grounded in FN methods and analytic frameworks. All training is FN-specific, recognizing the distinct policy and data contexts of First Nations in Canada. Development and facilitation have been guided by FNHSSM's Grandmother's Circle, with curriculum tailored through collaboration with Knowledge Keepers and local guest speakers to ensure cultural relevance and community alignment. In 2026, the mobile training lab will be piloted in FN communities to further enhance access and adaptability. These training initiatives strengthen community-level capacity, reduce reliance on external entities to conduct FN analyses, and support communities in asserting control over their data through the use of FN tools and frameworks. Equipped with these skills, trained individuals can participate meaningfully in data interpretation and decision-making and collaborate with external partners on their own terms. By building robust, community-driven data capacity, FNHSSM's training initiatives promote FN data sovereignty and support communities in accessing and applying their own data to inform resource planning, respond to outbreaks and pandemics, and advocate for social justice where data already exists. This work reflects a transformative pathway for FN self-determination in data governance and public health.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3573"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426840/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148653757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond Randomised Trials: Harnessing CARELINK Wales for Synthetic Control Evaluation in Child Healthcare.","authors":"Jonathan Kennedy, Richmond Opoku, Evangelos Kontopantelis, Michaela James, Jayne Noyes, Karen Hughes, Dan Bristow, Sinead Brophy, Natasha Kennedy","doi":"10.23889/ijpds.v11i5.3569","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3569","url":null,"abstract":"<p><p>Synthetic control methods have emerged as a powerful tool for evaluating interventions in healthcare by constructing counterfactual scenarios that closely approximate outcomes in the absence of treatment. In the context of children in care, the application of synthetic controls offers a novel and robust approach to assessing the impact of policies and programs designed to improve the well-being of this vulnerable population. Given the complex and heterogeneous nature of child welfare systems, randomised controlled trials are often challenging or ethically unfeasible. Synthetic control methods allow for the construction of weighted combinations of control units that best replicate the pre-intervention characteristics of the treatment group, thereby providing a more accurate comparison for outcomes such as health, educational attainment, and overall life satisfaction. This approach is particularly valuable for assessing outcomes across diverse care environments while accounting for confounding factors such as socio-economic background, prior health conditions, and regional differences. In addition, synthetic control methods enable the integration of data from multiple sources, helping to bridge gaps in evidence where traditional evaluation methods may fall short. By providing a rigorous and transparent means of estimating causal effects, these methods can help inform policy decisions aimed at improving the long-term prospects of children in care and guide the design of more effective and equitable child welfare interventions. Applying this approach to population-level data from Wales, synthetic controls indicate that placing at-risk children into care by age five is associated with a reduction in emergency department visits by age seven.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3569"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426648/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148653978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}