International Journal of Population Data Science最新文献

Introduction: Linkage of population-based administrative data is a powerful tool for studying important public issues. To overcome confidentiality and disclosure issues, records are de-identified and allocated a unique identifier. Within the Secure Anonymised Information Linkage (SAIL) Databank, these are known as Anonymised Linking Fields (ALFs). Assignment of an ALF enables linkage of individuals across multiple routinely collected datasets. Within the Children Looked After (CLA) Wales dataset, only 37% of the children have an ALF, limiting linkage to other datasets and, as a result, potential research. There are also other known data issues, including discrepancies with the week of births, duplicate identifiers and year-on-year changes in identifiers. Objectives To improve accuracy and availability of the ALFs in the CLA dataset, and overall research quality.

Methods: Using several datasets within the SAIL Databank, we developed a six-step CLA matching algorithm to improve the ALF matching rate and correct for data errors. To assess the performance of our algorithm, we benchmarked against routine ALFs already identified via the algorithm currently used by SAIL.

Results: Our algorithm increased ALF matching by 25%, assigning 61% of individuals an ALF. Inconsistent weeks of birth, and incorrect and duplicate identifiers were resolved. When benchmarking against the current ALF-assigning algorithm used by SAIL, our algorithm had an overall sensitivity of 90%.

Conclusion: We have developed an algorithm which demonstrates comparable ALF matching performance to the current algorithm used within SAIL, and which greatly improves the ALF matching in the CLA dataset. This algorithm may help to overcome potential bias due to missing data, and increases the potential for linkage to other datasets. Further development and refinement could result in the algorithm being applied to other datasets in SAIL.

Introduction: The Homecare Medicines (HCM) dataset is a national, patient-level dataset developed by Public Health Scotland (PHS) to capture the supply of specialist medicines delivered through homecare services in Scotland. These services are a critical component of outpatient treatment pathways, particularly for long-term conditions requiring specialist care, such as inflammatory arthritis, cancer, and immune-mediated diseases. Prior to 2019, data on homecare prescribing were fragmented and locally held, limiting national analyses.

Methods: The dataset was initially established during the COVID-19 pandemic to identify immunocompromised patients for vaccine prioritisation. Monthly supply-level data are submitted by homecare providers. Each record includes a pseudonymised unique patient identifier, derived through national health person-level data linkage processes and standardised medicine information mapped to the NHS dictionary of medicines and devices (dm+d), including medicine name (brand and/or generic), formulation and supply date, and, where provided, treatment indication. The presence of a unique patient identifier enables deterministic linkage with a range of national datasets, including community and hospital prescribing, hospital admissions, mortality, cancer registry, and demographic indicators.

Results: The HCM dataset is held securely within the PHS national data infrastructure and accessed via the National Safe Haven. As of April 2025, it includes data from five national providers and covers approximately 98% of the Scottish homecare market. The dataset comprises over 1.3 million supply records for more than 41,000 patients since 2019. Data quality is high for core fields, with missingness levels very low-almost all key variables have <1% missing values-and more than 99.9% of records are successfully indexed with the unique patient identifier. Indication data is partially complete and improving. Medicines are coded using standardised drug dictionaries.

Conclusion: Access to the HCM dataset is available through eDRIS subject to Public Benefit and Privacy Panel (HSC-PBPP) approval. The dataset is well suited for studies on medicine utilisation, equity in access, treatment outcomes, and service planning. Ongoing improvements include enhanced indication capture and integration with Scotland's wider digital prescribing infrastructure.

Introduction: The UK Longitudinal Linkage Collaboration (UK LLC) is the national Trusted Research Environment (TRE) for the UK's longitudinal research community, supporting the UK's unparalleled collection of Longitudinal Population Studies (LPS). Initially set up as a COVID-19 research resource, UK LLC is now a generic database for any research for the public good.

Objectives: UK LLC supports longitudinal research by providing record linkage and TRE services.

Methods: The UK LLC partnership provides a secure analytics environment, a trusted third-party linkage processor and a comprehensive governance framework to minimise risks to participant confidentiality. UK LLC is ISO 27001 certified and accredited by the UK Statistics Authority as a processor under the Digital Economy Act. The active involvement by members of UK LLC's public involvement programme ensures UK LLC is acceptable to LPS participants and the wider public. All UK LPS are eligible for inclusion. Researchers can apply to access the TRE via an approach that fulfils the needs of the LPS, the linked data owners and includes a review by public contributors.

Results: Twenty-two LPS have so far joined UK LLC. Where permissions allow, participants are linked to their National Health Service (NHS) England, NHS Wales and place-based records, with work ongoing to link to NHS Scotland and non-health administrative records, including Department for Work and Pensions and His Majesty's (HM) Revenue and Customs. UK LLC Explore allows potential researchers to discover the breadth of data available in the TRE. All applications are listed on UK LLC's publicly accessible Data Access Register.

Conclusions: UK LLC enables researchers to interrogate pooled LPS participant data that are systematically linked to diverse records. UK LLC remains open to additional LPS joining the partnership and will increase the breadth of data to support the longitudinal research community and attract increasing numbers of researchers across multiple disciplines, government departments and industry.

Introduction: Environmental exposures are known to affect the health and well-being of populations throughout the life course. Children are particularly susceptible to environmental impacts on educational and health outcomes as they spend more time in their local environments compared to adults. In England, no national, longitudinal dataset linking information about the physical and social environment in and around homes and schools to children's health and education outcomes currently exists. This limits our understanding of how environments might impact the health and well-being of children as they grow up.

Objective: To establish the Kids' Environment and Health Cohort, a research-ready, de-identified and annually updated national birth cohort of all children born in England from 2006 onwards.

Methods: The Kids' Environment and Health Cohort will link birth and mortality records, health and educational attainment datasets, to maternal health (up to 12 months prior to their child's birth), and environmental data for all children born in England from 2006 - approximately 11 million children at first build. A subset of children born between 2010 and 2012, and between 2020 and 2022 will be linked to their mothers' 2011 or 2021 Census records, respectively. The cohort database will be held in, and accessed via, a trusted research environment (TRE) at the Office for National Statistics (ONS). All geographical identifiers in the cohort, allowing for linkage to further environmental data, will be securely held by the ONS, separately to the main cohort, and will be encrypted before being shared with researchers.

Conclusion: The Kids' Environment and Health Cohort will, for the first time, link administrative health and education data to longitudinal environmental exposures for children at national level in England. It will serve as a data resource to support research about the health and well-being of children via improved home and school environments.

Background: Children with major congenital anomalies (MCAs) disproportionately experience complex health problems requiring additional health and educational support.

Objectives: To describe survival to the start of school and recorded special educational needs (SEN) provision among children with and without administrative record-identified MCAs in England. We present results for 12 system-specific MCA subgroups and 25 conditions. We also describe the change of prevalence in recorded SEN provision before and after SEN reforms in 2014, which were implemented to improve and streamline SEN provision.

Methods: We created a birth cohort of 6,180,400 singleton children born in England between 1 September 2003 and 31 August 2013 using linked administrative records from the ECHILD database. MCAs were identified using hospital admission and mortality records during infancy. SEN provision in primary school was defined by one or more recording of SEN provision in state-school records during years 1 to 6 (ages 5/6 years to 10/11 years).

Results: Children with any MCA had a 5-year survival rate of 95.1% (95% confidence interval (CI) 95.0, 95.2) compared with 99.7% (95% CI 99.7, 99.7) among children without an MCA. 41.6% (75,381/181,324) of children with an MCA had any recorded SEN provision in primary school compared with 25.7% (1,285,572/5,008,598) of unaffected children. Of the 12 system-specific MCA subgroups, children with chromosomal, nervous system and eye anomalies had the highest prevalence of recorded SEN provision. The prevalence of recorded SEN provision decreased by 4.8% (99% CI -5.4, -4.3) for children with any MCA compared with a reduction of 4.2% (99% CI -4.3, -4.2) for unaffected children, when comparing pupils in year 1 before and after 2014.

Conclusion: We observed that approximately two fifths of children with MCAs have some type of SEN provision recorded during primary school, but this proportion varied according to condition and declined following the 2014 SEN reforms, similar to children unaffected by MCAs.

Introduction: We present a prototype solution for improving transparency and quality assurance of the data linkage process through data provenance tracking designed to assist Data Analysts, researchers and information governance teams in authenticating and auditing data workflows within a Trusted Research Environment (TRE).

Methods: Using a participatory design process with Data Analysts, researchers and information governance teams, we undertook a contextual inquiry, user requirements interviews, co-design workshops, low-fidelity prototype evaluations. Public Engagement and Involvement activities underpinned the methods to ensure the project and approaches met the public's trust for semi-automating data processing. These helped inform methods for technical implementation, applying the PROV-O ontology to create a derived ontology following the four-step Linked Open Terms methodology and development of automated scripts to collect provenance information for the data processing workflow.

Results: The resulting Provenance Explorer for Trusted Research Environments (PE-TRE) interactive tool displays the data linkage information extracted from a knowledge graph described using the derived SHP ontology and results of rule-based validation checks. User evaluations confirmed PE-TRE would contribute to better quality data linkage and reduce data processing errors.

Conclusion: This project demonstrates the next stage in advancing transparency and quality assurance within TREs by semi-automating and systematising data tracking in a single tool throughout the data processing lifecycle, improving transparency, openness and quality assurance.

Introduction: Determining risk factors and consequences of serious violence requires accurate measures of violence. Self-reported and police-recorded offending are subject to different sources of bias.

Objectives: To compare risk of self-reported and police-recorded serious violence perpetration in late adolescence and early adulthood using linked UK birth cohort and police data, to examine the association between cohort participation and police-recorded violence, and to use police-records to impute missing self-reported data on violence.

Methods: We included individuals in the Avon Longitudinal Study of Parents and Children (ALSPAC) who had been informed about the study's use of their linked data and had not opted out of linkage to police records (n = 12,662). We used descriptive statistics and logistic regression to address our objectives. Multiple imputation using chained equations was used to impute self-reported violence data to examine the likely impact of missing data on estimates of prevalence.

Results: Self-reported violence perpetration in the past year ranged from 5.3% (at 25 years) to 12.9% (at 20 years) among males and 3.2% (at 17, 22, 24 and 25 years) to 6.4% (at 18 years) among females. Police-recorded serious violence was lower at all ages, peaking at 17-18 years (1.7% among males, 0.5% among females). Study participation was lower among people who had or went on to have a police record for serious violence; as a result, the prevalence of self-reported violence in the imputed data was higher (compared to observed data) at all ages.

Conclusions: Overall, our study demonstrates the difficulties in measuring violence. While we have shown that a key advantage of linkage to police records is it enables outcomes to be measured irrespective of study participation, police data undercounts serious violence. Further, observational studies may also underestimate violence perpetration as individuals with police-recorded serious violence are less likely to participate in research. Therefore, while record linkage allows the advantages of both official police records and self-reported measures to be exploited, it does not negate their limitations.

Introduction: Each year, children's social care (CSC) recognises around 3% of all children as children in need (CiN) of intervention, including those who receive a child protection plan due to risks of substantial harm and those who become looked after in state care. A previous cumulative estimate of the incidence of becoming CiN of 14% to age 5 indicates that the childhood lifetime incidence is likely very high.

Objectives: We aimed to estimate the cumulative incidence of referrals, social work assessments, being recognised as a CiN or made subject to a child protection plan (CPP) before age 18 in England.

Methods: The annual CiN census contains all-of-England longitudinal records of CSC referrals. Data collection began in 2008, meaning there is no cohort that can be followed up from birth to age 17 (i.e., before 18^th birthday). Analyses revealed data quality issues before 2011/12. We estimated the above cumulative incidences in three cohorts and combined them, adjusting numerators to account for left-censoring. The three cohorts were children born in: (a) 2012/13, followed to age 5; (b) 2005/06, followed from age 6 age to 12; and (c) 2000/01, followed from age 13 to 17. We carried out sensitivity analyses to address possible bias induced by linkage error using one of two encrypted identifiers in the dataset.

Results: Of all children living in England, before turning 18, 35.4% were referred, 32.3% were assessed, 25.3% were recorded as CiN and 6.9% were subject to a CPP (37.5%, 34.6%, 26.0% and 7.1%, respectively, in sensitivity analyses).

Conclusions: By age 18, an estimated 1 in 4 children are identified by CSC as needing support at some point. Government should monitor the cumulative incidence of ever receiving CSC support with a view to addressing upstream health and social determinants.

Background: Research and surveillance using routinely collected health data rely on algorithms or definitions to ascertain disease cases or health measures. Whenever algorithm validation studies are not possible due to the unavailability of a reference standard, algorithm feasibility studies can be used to create and assess algorithms for use in more than one population or jurisdiction. Publication of the methods used to conduct feasibility studies is critical for reproducibility and transparency. Existing guidelines applicable to feasibility studies include the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) and REporting of studies Conducted using Observational Routinely collected health Data (RECORD) guidelines. These guidelines may benefit from additional elements that capture aspects particular to multi-jurisdiction algorithm feasibility studies and ensure their reproducibility. The aim of this paper is to identify the minimum elements for reporting feasibility studies to ensure reproducibility and transparency.

Methods: A subcommittee of four individuals with expertise in routinely collected health data, multi-jurisdiction health research, and algorithm development and implementation was formed from Health Data Research Network (HDRN) Canada's Algorithms and Harmonized Data Working Group (AHD-WG). The subcommittee reviewed items within the STROBE and RECORD guidelines and evaluated these items against published feasibility studies. Items to ensure transparent reporting of feasibility studies not contained within STROBE or RECORD guidelines were identified through consensus by subcommittee members using the Nominal Group Technique. The AHD-WG reviewed and approved these additional recommended elements.

Results: Eleven new recommended elements were identified: one element for the title and abstract, one for the introduction, five for the methods, and four for the results sections. Recommended elements primarily addressed reporting jurisdictional data variabilities, data harmonization methods, and algorithm implementation techniques.

Significance: Implementation of these recommended elements, alongside the RECORD guidelines, is intended to encourage consistent publication of methods that support reproducibility, as well as increase comparability of algorithms and their use in national and international studies.

Introduction: Household overcrowding is an important determinant of health and is associated with adverse child health, educational and social outcomes.

Objectives: We aimed to determine whether households with dependent children were more likely to be overcrowded after taking into account household ethnicity and housing tenure in an urban, ethnically diverse, and disadvantaged London population by pseudonymously linking health and property data.

Methods: We used pseudonymised Unique Property Reference Numbers to link electronic health records to Energy Performance Certificate property data in north-east London and identified 332,473 households comprising 1,093,047 people. Our primary outcomes were overcrowding measures based on a bedroom standard and a space standard (space per person; m²). We examined household level associations of overcrowding with presence of children in the household before and after adjusting for household ethnicity and tenure. We used multivariable logistic regression to estimate the adjusted odds (aOR) and 95% Confidence Intervals (CI) of bedroom standard overcrowding and linear regression to estimate effects (95% CI) on space per person.

Results: Overall, 42.8% (142,401/332,473) of households included children, 54.5% were of White household ethnicity, and 58.4% in private or social rented accommodation. 22.5% (32,075/142,401) and 45.9% (65,388/142,401) of households with children were overcrowded by the bedroom and space standards respectively compared with 4.7% (8,953/190,072) and 9.6% (18,229/190,072) without children. After adjusting for household ethnicity and housing tenure, households with children were more likely to be overcrowded (aOR [95% CI] 5.54 [5.40-5.68] and had 22.61m² (95%CI: -22.75,-22.46) less space per person than those without children.

Conclusions: Up-to-date estimates of household overcrowding measured by bedroom and space standards can be derived from linked housing and health records. Our findings highlight the inequalities in overcrowding experienced by households with children and enable future work using linked data to evaluate impacts of overcrowding on children's health.