International Journal of Population Data Science最新文献

{"title":"Comparing temperature exposure with time to birth using Bayesian joint models at population-scale; computationally tractable methods for exploring temporal associations incorporating full-parameter uncertainty.","authors":"James Rafferty, Amir Baniasadi, Wally Abdeldayem, Samantha Turner, Amy Mizen, Lucy Griffiths, Rhiannon Owen","doi":"10.23889/ijpds.v11i5.3662","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3662","url":null,"abstract":"<p><p>Climate change is affecting our world, and the impact of rising temperatures on health is not well understood. Prior work found exposure to heat was associated with reduced gestational age, increased prematurity and smaller birth weights. The goal of the Maternal and Pregnancy Health and Elevated Heat (MAGENTA) project is to determine if these patterns exist in a UK population. Utilising healthcare and environmental exposure data held in the Secure Anonymised Information Linkage (SAIL) Databank we developed a cohort of mothers who were pregnant between 2010 and 2023, and the associated daily maximum temperatures experienced during each pregnancy. Modelled Land Surface Temperature accounts for the effects of the built environment. The primary outcome was time to birth. We used a joint longitudinal and time-to-event model, constructed in a Bayesian framework to capture full parameter uncertainty and fit using the Integrated Nested Laplace Approximation (INLA). The longitudinal process modelled temperature experienced during the pregnancy with linear and quadratic terms for time. Time to birth was modelled using a Cox regression model with a spline baseline hazard, and smoothed at second order. Joint modelling is a flexible, powerful set of tools for understanding associations in healthcare research, and approximation methods such as INLA enable analysis in large-scale electronic health record datasets. Work is ongoing to produce fully adjusted models, which will be used in simulation studies in conjunction with climate change projections to explore the impact of future scenarios to inform mitigation and adaptation strategies.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3662"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426834/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654507","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":"Pilot Samples for Estimation of Linkage Error.","authors":"Gavin Thomson, Matt Wray","doi":"10.23889/ijpds.v11i5.3629","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3629","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3629"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426563/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654540","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":"Application of EMR data-based LLM-based Disease Identification Framework to improve ICD data-based risk adjustment algorithms.","authors":"Seungwon Lee, Elliot Martin, Kiarash Riazi, Noopur Swadas, Cathy Eastwood, Jinli Yao, Robin Walker, Danielle Southern, Bing Li, Jeffrey Bakal","doi":"10.23889/ijpds.v11i5.3481","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3481","url":null,"abstract":"<p><p>Risk adjustment is essential for comparisons across populations by integrating individual health status and demographic factors to evaluate healthcare outcomes. We hypothesized that risk adjustment based on Large Language Models (LLMs) for case definitions, using electronic medical records (EMR) data, would outperform international classification of diseases (ICD) based algorithms in predicting inpatient mortality. Study Design and Methods A retrospective chart review cohort (n=10,659) consisting of randomly selected patients aged 18 years or older who were discharged from acute care settings was used. The chart review data were deterministically linked to an ICD database and an EMR database. We developed and applied an LLM-based framework (i.e., Phi-4) to EMR data for large-scale disease identification and compared it with ICD-based algorithms. To predict inpatient mortality, we calculated C-statistics using logistic regression. Among the cohort, 717 patients experienced inpatient mortality. Across all disease categories, LLM-based identification outperformed the ICD-data-based method. The ICD data-based risk adjustment algorithm achieved a C-statistic of 0.66 (95% CI 0.64 to 0.68) for in-hospital mortality, while the EMR data-based algorithm achieved a 0.76 (95% CI: 0.74 to 0.78) C-statistic. The chart review had a C-statistic of 0.71 (95% CI: 0.68 to 0.73) Effective risk adjustment for predicting health outcomes requires accurate information on patient comorbidity and demographic profiles. EMR data-based case definitions can account for disease severity (e.g., disease subtypes) and other variables (e.g., social determinants of health) that may not be readily available with historical ICD-based methods.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3481"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426785/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654559","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":"Linking Geriatric Emergency Departments Using Deterministic and Machine Learning Methods.","authors":"Inessa Cohen, Cameron Gettel, Yuting Qian, Craig Rothenberg, Xi Chen, Ula Hwang","doi":"10.23889/ijpds.v11i5.3603","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3603","url":null,"abstract":"<p><strong>Objective: </strong>Evaluating geriatric emergency department (GED) interventions requires accurate longitudinal identification of GED sites and reliable linkage to hospital-level data, yet identifiers are often incomplete, inconsistent, or change over time. Our objective was to construct longitudinal GED identifiers by reconciling publicly available GED accreditation lists in the United States (US) linked to hospital characteristics in the American Hospital Association (AHA) survey.</p><p><strong>Approach: </strong>GED accreditation lists were first affirmed as GEDs and newly accredited GEDs using a two-stage fuzzy matching approach blocked by state. Sites were initially matched using hospital name similarity (distance ≤ 0.15), followed by city-based matching for remaining unmatched sites (distance ≤ 0.10). GEDs were then linked 1:1 to the AHA survey by manually assigning the AHAID using name and city. Discrepancies were resolved through multi-reviewer adjudication informed by geographic context, producing a curated reference linkage set of GEDs. In parallel, an XGBoost classifier paired GED sites with AHA hospitals in the same state using similarity features (name, city, teaching, rurality, and year difference) to independently reproduce curated linkages.</p><p><strong>Results: </strong>Among 545 US GEDs between 2018-2025, 482 had AHAIDs and 63 were missing. XGBoost reproduced 90% (95% CI: 87.8-92.5%) of curated linkages, with name similarity and city agreement accounting for over 85% of model importance by gain. Common challenges included name changes, system-level identifiers spanning multiple campuses, and accreditation turnover (sites gaining or losing accreditation).</p><p><strong>Conclusions: </strong>Accurate GED-AHA linkage is achievable, though sensitive to identifier instability, naming variation, and longitudinal changes.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3603"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426737/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654562","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":"Linking administrative benefits and child protection data to uncover poverty's hidden impact.","authors":"Natalia Valdebenito Contreras, Tamara Godoy Jalil, Rick Hood","doi":"10.23889/ijpds.v11i5.3590","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3590","url":null,"abstract":"<p><p>This study examines the relationship between household financial circumstances and children's social care (CSC) involvement using newly linked local administrative data. Household benefits data (SHBE and UCDS) were securely linked and anonymised with Children in Need (CIN) records from six local authorities in London and South England, covering 2019-2021. Financial precarity is defined as households living below the relative poverty line or experiencing a cash shortfall. Children living in financial precarity were not more likely to be initially referred to CSC. However, once referred, they were significantly more likely to experience higher-intensity statutory interventions, including having a Child Protection Plan (12% vs 9%) and being re-referred (32% vs 29%). We estimate that an additional 270 Child Protection Plans were made over the study period among children referred from households below the poverty line. The 2020-21 Universal Credit (UC) uplift provides a natural experiment to examine the role of income support. Households receiving the uplift were substantially less likely to be in financial precarity, with a 17.5 percentage-point relative improvement. Children in uplift-eligible households were more likely to be referred to CSC but less likely to receive subsequent statutory protective interventions, suggesting that improved financial stability reduces the need for high-intensity CSC involvement. These findings demonstrate the value of ethically governed administrative data linkage for evaluating social policy. They highlight the importance of policies that strengthen families' financial circumstances as part of a preventative approach to improving child wellbeing and reducing statutory demand.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3590"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426605/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654564","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":"Perinatal Opioid Use: Improving Insights through Federated Analysis of Linked Administrative Data.","authors":"Andi Camden, Hilary K Brown, Roxana Dragan, Tara Gomes, Jennifer A Hutcheon, Lauren Kelly, Hong Lu, Amy Metcalfe, Nazeem Muhajarine, Nathan Nickel","doi":"10.23889/ijpds.v11i5.3535","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3535","url":null,"abstract":"<p><strong>Objectives: </strong>Linked administrative data from 5 Canadian provinces were harmonized for the Canadian Perinatal Opioid Project, a nationally funded project. Using these data, we estimated opioid prescribing patterns during/after pregnancy at the national level and conducted cross-provincial comparisons.</p><p><strong>Approach: </strong>This population-based cohort study used linked administrative health records from Alberta, British Columbia, Manitoba, Ontario, and Saskatchewan. Included were all pregnancies to individuals aged 12-50 years, 2013-2023. Opioid use and prescribing patterns (type, duration, dose, timing) were measured from conception through to 1-year after the end of pregnancy. We conducted federated and province-specific analyses to produce pan-Canadian estimates of perinatal opioid use. Poisson models analyzed trends in perinatal opioid use and generated adjusted relative risks (aRR) of perinatal opioid use by socio-demographic and clinical factors. This research is being conducted in collaboration with people with lived/living experience.</p><p><strong>Results: </strong>Data from Alberta Health Services/Alberta Health, PopDataBC, Manitoba Centre for Health Policy, ICES, and Health Research Data Platform-Saskatchewan were harmonized. Federated data analyses are underway and will be completed by May 2026. We anticipate  3.2 million pregnancies (325,000 pregnancies/year). Preliminary analyses from Ontario show the prevalence of prenatal opioid exposure is 4.1% based on 1.6 million pregnancies.</p><p><strong>Conclusions/implications: </strong>Improving health system data access through multi-regional research using harmonized administrative data addresses important knowledge gaps and will generate the first pan-Canadian estimates of perinatal opioid use. This information is needed to inform prevention efforts, harm reduction, and tailored interventions for people who use opioids during/after pregnancy.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3535"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426528/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654569","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":"Assessing Deep Learning in Record Linkage: Recommendations Based on Dataset Characteristics and Computational Costs.","authors":"Eric Sartor, Jeremy Foxcroft, Luiza Antonie","doi":"10.23889/ijpds.v11i5.3716","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3716","url":null,"abstract":"<p><p>Deep learning systems are increasingly being applied to record linkage tasks, addressing either the matching stage or the entire linkage process. Although some deep learning methods appear to outperform classical approaches, they are typically evaluated on specific linkage problems, making wider applicability difficult to assess. Moreover, for any use in practical applications, it is critical to understand the trade-offs between the lift in linkage performance and the increase in computational complexity when using deep learning systems. When do the benefits of deep learning outweigh the costs compared to classical machine learning methods? Our investigations have three main objectives: increase our understanding of the performance of deep learning methods across a wide range of linking tasks, particularly transformer-based methods; measure computational costs; and make recommendations accounting for performance-cost trade-offs. We developed a common and consistent framework to evaluate these trade-offs, which we apply on a large number of deep learning methods tackling a variety of record linkage tasks. Our evaluation covers both structured and unstructured datasets. We present precision-recall curves to better understand the performance of the models. We discuss computational costs for these methods to inform practitioners. Deep learning systems perform very well on unstructured data, while classical machine learning methods with feature engineering remain competitive on structured data at a significantly lower computational cost. This research will provide practical guidance to the practitioners in the data linkage community on linkage method selection based on dataset characteristics and computational costs.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3716"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426801/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654572","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":"Longer effect of pupil exclusion from school on labour market outcomes.","authors":"Babak Jahanshahi, Duncan McVicar, Neil Rowland","doi":"10.23889/ijpds.v11i5.3527","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3527","url":null,"abstract":"<p><strong>Objectives: </strong>The literature shows that time invested in education generally improves long-term labour market outcomes, although the size of the effect varies across contexts and demographic groups. While most research has focused on the impacts of increased instructional time, far less is known about the consequences of lost classroom time. Existing studies examine interruptions from weather events, natural disasters, strikes, and school calendar changes, but evidence on disciplinary exclusions is limited. This paper addresses that gap by examining the long-term labour market effects of school exclusions in England, using the Longitudinal Education Outcomes (LEO) dataset to track pupils' exclusions, attainment, and socio-demographic characteristics, and is among the first to study the long-term consequences of exclusion beyond established short-term effects on test scores.</p><p><strong>Methodology: </strong>We estimate regression models to isolate the association between exclusion and later labour market outcomes, controlling for confounding factors like background, prior achievement, and school context. We further use mother fixed effects models, comparing siblings within the same family to reduce bias from unobserved family characteristics, offering a closer causal interpretation.</p><p><strong>Results: </strong>Our analysis shows that exclusion negatively affects both employment probability and wages at age 24. In the mother fixed effects model, this negative effect remains, though it is smaller, suggesting that family-level factors explain part of the relationship. However, exclusion still appears to have an independent detrimental impact on long-term labour market outcomes.</p><p><strong>Conclusion: </strong>Exclusion has negative long-term consequences, including poorer labour market outcomes. These findings suggest that exclusion policies may need careful reconsideration.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3527"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426530/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654575","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":"Ascertainment of Chronic Kidney Disease and Palliative Care using routine health administrative data consolidated in a Health Information Exchange, Western Cape South Africa.","authors":"Florence Phelanyane, Nicki Tiffin, Andrew Boulle","doi":"10.23889/ijpds.v11i5.3739","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3739","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3739"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426638/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654582","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":"Potential for epidemiology of pregnancy-associated mortality using administrative data in the UK.","authors":"Christopher Grollman, Lucy Carty, Rachel Plachcinski, Mario Cortina-Borja, Alison Macfarlane","doi":"10.23889/ijpds.v11i5.3563","DOIUrl":"https://doi.org/10.23889/ijpds.v11i5.3563","url":null,"abstract":"<p><p>In the UK, administrative data alone under-ascertain pregnancy-associated deaths. The confidential enquiries into maternal deaths use a notification system, supplemented by administrative data and other sources, for the surveillance of pregnancy-associated death. The confidential enquiry method - the gold standard for ascertainment - does not routinely include population-based adjusted epidemiology to explore differences between social groups. Linkage of health and vital statistics data offers a cost-effective option for population-based analyses, including regarding inequalities, if ascertainment is adequate to minimise bias. This study compared numbers of deaths during or within one year of the end of pregnancy, in the confidential enquiries, administrative records from the Office for National Statistics in England and Wales and National Records of Scotland, and the City Birth Cohort of linked administrative and health data. There is structural non-ascertainment in England and Wales of deaths associated with pregnancies for which no birth was registered. We estimated numbers of deaths in the confidential enquiries excluding those associated with pregnancies that ended before 24 weeks' gestation. Against this more comparable denominator, administrative ascertainment was around 90%, meaning administrative under-ascertainment was not generalised across the dataset. The City Birth Cohort ascertained 80% of all-cause pregnancy-associated deaths within a year of a registerable live/stillbirth, and 60% of maternal deaths. Confidential enquiries will always be the gold standard for detailed case review and surveillance of pregnancy-associated and maternal deaths. Supplementary to this, linkage of administrative and health datasets may allow robust population-based adjusted epidemiology, to enable further insight into health inequalities.</p>","PeriodicalId":36483,"journal":{"name":"International Journal of Population Data Science","volume":"11 5","pages":"3563"},"PeriodicalIF":2.2,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426736/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654607","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}