The road to hell is paved with good intentions: the experience of applying for national data for linkage and suggestions for improvement

Comparison of outcomes of the 50-year follow-up of a randomized trial assessed by study questionnaire and by data linkage: The CONCUR study

BACKGROUND/AIMS

Self-reported questionnaires on health status after randomized trials can be time-consuming, costly, and potentially unreliable. Administrative data sets may provide cost-effective, less biased information, but it is uncertain how administrative and self-reported data compare to identify chronic conditions in a New Zealand cohort. This study aimed to determine whether record linkage could replace self-reported questionnaires to identify chronic conditions that were the outcomes of interest for trial follow-up.

METHODS

Participants in 50-year follow-up of a randomized trial were asked to complete a questionnaire and to consent to accessing administrative data. The proportion of participants with diabetes, pre-diabetes, hyperlipidaemia, hypertension, mental health disorders, and asthma was calculated using each data source and agreement between data sources assessed.

RESULTS

Participants were aged 49 years (SD = 1, n = 424, 50% male). Agreement between questionnaire and administrative data was slight for pre-diabetes (kappa = 0.10), fair for hyperlipidaemia (kappa = 0.27), substantial for diabetes (kappa = 0.65), and moderate for other conditions (all kappa >0.42). Administrative data alone identified two to three times more cases than the questionnaire for all outcomes except hypertension and mental health disorders, where the questionnaire alone identified one to two times more cases than administrative data. Combining all sources increased case detection for all outcomes.

CONCLUSIONS

A combination of questionnaire, pharmaceutical, and laboratory data with expert panel review were required to identify participants with chronic conditions of interest in this follow-up of a clinical trial.

Evaluating the real-world implementation of the Family Nurse Partnership in England: a data linkage study

Background/objectives

The Family Nurse Partnership is an intensive home visiting programme for adolescent mothers. We aimed to evaluate the effectiveness of the Family Nurse Partnership on outcomes up to age 7 using national administrative data.

Design

We created a linked cohort of all mothers aged 13-19 using data from health, educational and children's social care and defined mothers enrolled in the Family Nurse Partnership or not using Family Nurse Partnership system data. Propensity scores were used to create matched groups for analysis.

Setting

One hundred and thirty-six local authorities in England with active Family Nurse Partnership sites between 2010 and 2017.

Participants

Mothers aged 13-19 at last menstrual period with live births between April 2010 and March 2019, living in a Family Nurse Partnership catchment area and their firstborn child(ren).

Interventions

The Family Nurse Partnership includes up to 64 home visits by a family nurse from early pregnancy until the child's second birthday and is combined with usual health and social care. Controls received usual health and social care.

Main outcome measures

Indicators of child maltreatment (hospital admissions for injury/maltreatment, referral to social care services); child health and development (hospital utilisation and education) outcomes and maternal hospital utilisation and educational outcomes up to 7 years following birth.

Data sources

Family Nurse Partnership Information System, Hospital Episode Statistics, National Pupil Database.

Results

Of 110,520 eligible mothers, 25,680 (23.2%) were enrolled in the Family Nurse Partnership. Enrolment rates varied across 122 sites (range: 11-68%). Areas with more eligible mothers had lower enrolment rates. Enrolment was higher among mothers aged 13-15 (52%), than 18-19 year-olds (21%). Indicators of child maltreatment: we found no evidence of an association between the Family Nurse Partnership and indicators of child maltreatment, except for an increased rate of unplanned admissions for maltreatment/injury-related diagnoses up to age 2 for children born to Family Nurse Partnership mothers (6.6% vs. 5.7%, relative risk 1.15; 95% confidence interval 1.07 to 1.24). Child health and developmental outcomes: there was weak evidence that children born to Family Nurse Partnership mothers were more likely to achieve a Good Level of Development at age 5 (57.5% vs. 55.4%, relative risk 1.05; 95% confidence interval 1.00 to 1.09). Maternal outcomes: There was some evidence that Family Nurse Partnership mothers were less likely to have a subsequent delivery within 18 months of the index birth (8.4% vs. 9.3%, relative risk 0.92; 95% confidence interval 0.88 to 0.97). Younger and more vulnerable mothers received higher numbers of visits and were more likely to achieve fidelity targets. Meeting the fidelity targets was associated with some outcomes.

Limitations

Bias by indication and variation in the intervention and usual care over time and between areas may have limited our ability to detect effects. Multiple testing may have led to spurious, significant results.

Conclusions

This study supports findings from evaluations of the Family Nurse Partnership showing no evidence of benefit for maltreatment outcomes measured in administrative data. Amongst all the outcomes measured, we found weak evidence that the Family Nurse Partnership was associated with improvements in child development at school entry, a reduction in rapid repeat pregnancies and evidence of increased healthcare-seeking in the mother and child.

Future work

Future evaluations should capture better measures of Family Nurse Partnership interventions and usual care, more information on maternal risk factors and additional outcomes relating to maternal well-being.

Study registration

The study is registered as NIHR CRN Portfolio (42900).

Funding

This award was funded by the National Institute of Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: 17/99/19) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 11. See the NIHR Funding and Awards website for further award information.

Prehospital early warning scores for adults with suspected sepsis: the PHEWS observational cohort and decision-analytic modelling study

Background

Guidelines for sepsis recommend treating those at highest risk within 1 hour. The emergency care system can only achieve this if sepsis is recognised and prioritised. Ambulance services can use prehospital early warning scores alongside paramedic diagnostic impression to prioritise patients for treatment or early assessment in the emergency department.

Objectives

To determine the accuracy, impact and cost-effectiveness of using early warning scores alongside paramedic diagnostic impression to identify sepsis requiring urgent treatment.

Design

Retrospective diagnostic cohort study and decision-analytic modelling of operational consequences and cost-effectiveness.

Setting

Two ambulance services and four acute hospitals in England.

Participants

Adults transported to hospital by emergency ambulance, excluding episodes with injury, mental health problems, cardiac arrest, direct transfer to specialist services, or no vital signs recorded.

Interventions

Twenty-one early warning scores used alongside paramedic diagnostic impression, categorised as sepsis, infection, non-specific presentation, or other specific presentation.

Main outcome measures

Proportion of cases prioritised at the four hospitals; diagnostic accuracy for the sepsis-3 definition of sepsis and receiving urgent treatment (primary reference standard); daily number of cases with and without sepsis prioritised at a large and a small hospital; the minimum treatment effect associated with prioritisation at which each strategy would be cost-effective, compared to no prioritisation, assuming willingness to pay £20,000 per quality-adjusted life-year gained.

Results

Data from 95,022 episodes involving 71,204 patients across four hospitals showed that most early warning scores operating at their pre-specified thresholds would prioritise more than 10% of cases when applied to non-specific attendances or all attendances. Data from 12,870 episodes at one hospital identified 348 (2.7%) with the primary reference standard. The National Early Warning Score, version 2 (NEWS2), had the highest area under the receiver operating characteristic curve when applied only to patients with a paramedic diagnostic impression of sepsis or infection (0.756, 95% confidence interval 0.729 to 0.783) or sepsis alone (0.655, 95% confidence interval 0.63 to 0.68). None of the strategies provided high sensitivity (> 0.8) with acceptable positive predictive value (> 0.15). NEWS2 provided combinations of sensitivity and specificity that were similar or superior to all other early warning scores. Applying NEWS2 to paramedic diagnostic impression of sepsis or infection with thresholds of > 4, > 6 and > 8 respectively provided sensitivities and positive predictive values (95% confidence interval) of 0.522 (0.469 to 0.574) and 0.216 (0.189 to 0.245), 0.447 (0.395 to 0.499) and 0.274 (0.239 to 0.313), and 0.314 (0.268 to 0.365) and 0.333 (confidence interval 0.284 to 0.386). The mortality relative risk reduction from prioritisation at which each strategy would be cost-effective exceeded 0.975 for all strategies analysed.

Limitations

We estimated accuracy using a sample of older patients at one hospital. Reliable evidence was not available to estimate the effectiveness of prioritisation in the decision-analytic modelling.

Conclusions

No strategy is ideal but using NEWS2, in patients with a paramedic diagnostic impression of infection or sepsis could identify one-third to half of sepsis cases without prioritising unmanageable numbers. No other score provided clearly superior accuracy to NEWS2. Research is needed to develop better definition, diagnosis and treatments for sepsis.

Study registration

This study is registered as Research Registry (reference: researchregistry5268).

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 17/136/10) and is published in full in Health Technology Assessment; Vol. 28, No. 16. See the NIHR Funding and Awards website for further award information.

"In conferences, everyone goes 'health data is the future' ": an interview study on challenges in re-using EHR data for research in Clinical Data Warehouses

More and more hospital Clinical Data Warehouses (CDWs) are developed to gain access to EHR data. The rapid growth of investments in CDWs suggest a real potential for innovation in healthcare. However, it is still not confirmed that CDWs will deliver on their promises as researchers working with CDWs face many challenges. To gain a better understanding of these challenges and how to overcome them, we conducted a series of semi-structured interviews with EHR data experts. In this article, we share some initial results from the ongoing interview study. Two main themes emerged from the analysis of the transcripts of the interviews: the importance of infrastructures in terms of data and how it is generated, and the difficulty to make care, clinical research, and data science work together. Finally, based on the experts' experience, several recommendations were identified when using a CDW.

Federated learning for generating synthetic data: a scoping review

Introduction

Federated Learning (FL) is a decentralised approach to training statistical models, where training is performed across multiple clients, producing one global model. Since the training data remains with each local client and is not shared or exchanged with other clients the use of FL may reduce privacy and security risks (compared to methods where multiple data sources are pooled) and can also address data access and heterogeneity problems. Synthetic data is artificially generated data that has the same structure and statistical properties as the original but that does not contain any of the original data records, therefore minimising disclosure risk. Using FL to produce synthetic data (which we refer to as "federated synthesis") has the potential to combine data from multiple clients without compromising privacy, allowing access to data that may otherwise be inaccessible in its raw format.

Objectives

The objective was to review current research and practices for using FL to generate synthetic data and determine the extent to which research has been undertaken, the methods and evaluation practices used, and any research gaps.

Methods

A scoping review was conducted to systematically map and describe the published literature on the use of FL to generate synthetic data. Relevant studies were identified through online databases and the findings are described, grouped, and summarised. Information extracted included article characteristics, documenting the type of data that is synthesised, the model architecture and the methods (if any) used to evaluate utility and privacy risk.

Results

A total of 69 articles were included in the scoping review; all were published between 2018 and 2023 with two thirds (46) in 2022. 30% (21) were focussed on synthetic data generation as the main model output (with 6 of these generating tabular data), whereas 59% (41) focussed on data augmentation. Of the 21 performing federated synthesis, all used deep learning methods (predominantly Generative Adversarial Networks) to generate the synthetic data.

Conclusions

Federated synthesis is in its early days but shows promise as a method that can construct a global synthetic dataset without sharing any of the local client data. As a field in its infancy there are areas to explore in terms of the privacy risk associated with the various methods proposed, and more generally in how we measure those risks.

Towards a standardised cross-sectoral data access agreement template for research: a core set of principles for data access within trusted research environments

Introduction

Trusted Research Environments (TREs) are secure computing environments that provide access to data for approved researchers to use in studies that can save and improve lives. TREs rely on Data Access Agreements (DAAs) to bind researchers and their organisations to the terms and conditions of accessing the infrastructure and data use. However, DAAs can be overly lengthy, complex, and can contain outdated terms from historical data sharing agreements for physical exchange of data. This is often cited as a cause of significant delays to legal review and research projects starting.

Objectives

The aim was to develop a standardised DAA optimised for data science in TREs across the UK and framed around the 'Five Safes framework' for trustworthy data use. The DAA is underpinned by principles of data access in TREs, the development of which is described in this paper.

Methods

The Pan-UK Data Governance Steering Group of the UK Health Data Research Alliance led the development of a core set of data access principles. This was informed by a benchmarking exercise of DAAs used by established TREs and consultation with public members and stakeholders.

Results

We have defined a core set of principles for TRE data access that can be mapped to a common set of DAA terms for UK-based TREs. Flexibility will be ensured by including terms specific to TREs or specific data/data owners in customisable annexes. Public views obtained through public involvement and engagement (PIE) activities are also reported.

Conclusions

These principles provide the foundation for a standardised UK TRE DAA template, designed to support the growing ecosystem of TREs. By providing a familiar structure and terms, this template aims to build trust among data owners and the UK public and to provide clarity to researchers on their obligations to protect the data. Widespread adoption is intended to accelerate health data research by enabling faster approval of projects, ultimately enabling more timely and effective research.

Ethics and legal requirements for data linkage in 14 European countries for children with congenital anomalies

INTRODUCTION

Linking healthcare data sets can create valuable resources for research, particularly when investigating rare exposures or outcomes. However, across Europe, the permissions processes required to access data can be complex. This paper documents the processes required by the EUROlinkCAT study investigators to research the health and survival of children with congenital anomalies in Europe.

METHODS

Eighteen congenital anomaly registries in 14 countries provided information on all the permissions required to perform surveillance of congenital anomalies and to link their data on live births with available vital statistics and healthcare databases for research. Small number restrictions imposed by data providers were also documented.

RESULTS

The permissions requirements varied substantially, with certain registries able to conduct congenital anomaly surveillance as part of national or regional healthcare provision, while others were required to obtain ethics approvals or informed consent. Data linkage and analysis for research purposes added additional layers of complexity for registries, with some required to obtain several permissions, including ethics approvals to link the data. Restrictions relating to small numbers often resulted in a registry's data on specific congenital anomalies being unusable.

CONCLUSION

The permissions required to obtain and link data on children with congenital anomalies varied greatly across Europe. The variation and complexity present a significant obstacle to the use of such data, especially in large data linkage projects. Furthermore, small number restrictions severely limited the research that could be performed for children with specific rare congenital anomalies.

Time for a rethink in cardiogenic shock: the shock to survival framework document

Cardiogenic shock remains a time-critical, complex syndrome that continues to present challenges to clinicians and healthcare systems. Despite advances in the fields of cardiovascular and critical care medicine, mortality remains high. This article summarises the recent shock to survival document, which outlined the current and ideal future state of cardiogenic shock care nationally to improve patient outcomes. Shock to survival emphasises the need for education and training in the early recognition of the hypoperfusion that is pathognomomic of cardiogenic shock. Improved provision of focused cardiac ultrasound is essential to confirm a cardiac cause. Early identification of the patient with cardiogenic shock should be supported by access to defined pathways of care, including specialist shock centres and multiprofessional teams with domain expertise and the capability to manage the myriad of causative aetiologies. Given the absence of high-quality data to inform practice nationally, robust datasets are an unmet need to inform best practice, guide design of clinical services and pathways and drive innovation through research and clinical trials.

Lessons learned from using linked administrative data to evaluate the Family Nurse Partnership in England and Scotland

Introduction

"Big data" - including linked administrative data - can be exploited to evaluate interventions for maternal and child health, providing time- and cost-effective alternatives to randomised controlled trials. However, using these data to evaluate population-level interventions can be challenging.

Objectives

We aimed to inform future evaluations of complex interventions by describing sources of bias, lessons learned, and suggestions for improvements, based on two observational studies using linked administrative data from health, education and social care sectors to evaluate the Family Nurse Partnership (FNP) in England and Scotland.

Methods

We first considered how different sources of potential bias within the administrative data could affect results of the evaluations. We explored how each study design addressed these sources of bias using maternal confounders captured in the data. We then determined what additional information could be captured at each step of the complex intervention to enable analysts to minimise bias and maximise comparability between intervention and usual care groups, so that any observed differences can be attributed to the intervention.

Results

Lessons learned include the need for i) detailed data on intervention activity (dates/geography) and usual care; ii) improved information on data linkage quality to accurately characterise control groups; iii) more efficient provision of linked data to ensure timeliness of results; iv) better measurement of confounding characteristics affecting who is eligible, approached and enrolled.

Conclusions

Linked administrative data are a valuable resource for evaluations of the FNP national programme and other complex population-level interventions. However, information on local programme delivery and usual care are required to account for biases that characterise those who receive the intervention, and to inform understanding of mechanisms of effect. National, ongoing, robust evaluations of complex public health evaluations would be more achievable if programme implementation was integrated with improved national and local data collection, and robust quasi-experimental designs.

A profile of the Grampian Data Safe Haven, a regional Scottish safe haven for health and population data research

There has been a recent emphasis to establish and codify large-scale or national Trusted Research Environments (TREs) in the United Kingdom, with a view to limit smaller, local TREs. The basis for this argument is that it avoids duplication of infrastructure, information governance, privacy risks, monopolies and will promote innovation, particularly with commercial partners. However, the work around establishing TREs in the UK largely ignores the long-established local TRE landscape in Scotland, and the way in which local TREs can actually improve data quality, solve technical architecture challenges, promote information governance and risk minimisation, and encourage innovation and collaboration (both academic and commercial). This data centre profile focuses on the Grampian Data Safe Haven (DaSH), a secure, virtual healthcare data analysis and storage centre located in Aberdeen, Scotland. DaSH was co-established by the NHS Grampian Health Board and University of Aberdeen to allow for the secure processing and linking of health data for the Grampian and Scottish population when it is not practicable to obtain consent from individual patients. As an established trusted research environment now in its 10th operating year, DaSH technology ensures healthcare, social care data and other types of sensitive data, routinely collected and used without individual patient consent, are made accessible for both academic research and clinical service evaluation and improvements whilst protecting individuals' privacy at the local, national and international levels. DaSH has registered almost 600 projects and facilitated over 200 distinct research projects with data hosting, extraction, and novel linkages to completion. Ongoing innovation and collaboration between DaSH and the NHS Grampian Health Board continues to expand researcher access to new types of data and data linkages, introduce new technologies for advanced statistical research methods, and supports interdisciplinary research using population health and social care data for research, clinical and commercial advancements, and real-world practitioner applications. The purpose of this paper is to present DaSH's data population, operating model, architecture and information technology, governance, legislation and management, privacy-by-design principles and data access, data linkage methods, data sources, noteworthy research outputs, and further developments in order to demonstrate the value of local TREs within the data management and access debate.

Leveraging Administrative Data to Better Understand and Address Child Maltreatment: A Scoping Review of Data Linkage Studies

BACKGROUND

This scoping review aimed to overview studies that used administrative data linkage in the context of child maltreatment to improve our understanding of the value that data linkage may confer for policy, practice, and research.

METHODS

We searched MEDLINE, Embase, PsycINFO, CINAHL, and ERIC electronic databases in June 2019 and May 2020 for studies that linked two or more datasets (at least one of which was administrative in nature) to study child maltreatment. We report findings with numerical and narrative summary.

RESULTS

We included 121 studies, mainly from the United States or Australia and published in the past decade. Data came primarily from social services and health sectors, and linkage processes and data quality were often not described in sufficient detail to align with current reporting guidelines. Most studies were descriptive in nature and research questions addressed fell under eight themes: descriptive epidemiology, risk factors, outcomes, intergenerational transmission, predictive modelling, intervention/service evaluation, multi-sector involvement, and methodological considerations/advancements.

CONCLUSIONS

Included studies demonstrated the wide variety of ways in which data linkage can contribute to the public health response to child maltreatment. However, how research using linked data can be translated into effective service development and monitoring, or targeting of interventions, is underexplored in terms of privacy protection, ethics and governance, data quality, and evidence of effectiveness.

A Hybrid Architecture (CO-CONNECT) to Facilitate Rapid Discovery and Access to Data Across the United Kingdom in Response to the COVID-19 Pandemic: Development Study

BACKGROUND

COVID-19 data have been generated across the United Kingdom as a by-product of clinical care and public health provision, as well as numerous bespoke and repurposed research endeavors. Analysis of these data has underpinned the United Kingdom's response to the pandemic, and informed public health policies and clinical guidelines. However, these data are held by different organizations, and this fragmented landscape has presented challenges for public health agencies and researchers as they struggle to find relevant data to access and interrogate the data they need to inform the pandemic response at pace.

OBJECTIVE

We aimed to transform UK COVID-19 diagnostic data sets to be findable, accessible, interoperable, and reusable (FAIR).

METHODS

A federated infrastructure model (COVID - Curated and Open Analysis and Research Platform [CO-CONNECT]) was rapidly built to enable the automated and reproducible mapping of health data partners' pseudonymized data to the Observational Medical Outcomes Partnership Common Data Model without the need for any data to leave the data controllers' secure environments, and to support federated cohort discovery queries and meta-analysis.

RESULTS

A total of 56 data sets from 19 organizations are being connected to the federated network. The data include research cohorts and COVID-19 data collected through routine health care provision linked to longitudinal health care records and demographics. The infrastructure is live, supporting aggregate-level querying of data across the United Kingdom.

CONCLUSIONS

CO-CONNECT was developed by a multidisciplinary team. It enables rapid COVID-19 data discovery and instantaneous meta-analysis across data sources, and it is researching streamlined data extraction for use in a Trusted Research Environment for research and public health analysis. CO-CONNECT has the potential to make UK health data more interconnected and better able to answer national-level research questions while maintaining patient confidentiality and local governance procedures.

Rapid systematic review to identify key barriers to access, linkage, and use of local authority administrative data for population health research, practice, and policy in the United Kingdom

BACKGROUND

Improving data access, sharing, and linkage across local authorities and other agencies can contribute to improvements in population health. Whilst progress is being made to achieve linkage and integration of health and social care data, issues still exist in creating such a system. As part of wider work to create the Cambridge Child Health Informatics and Linked Data (Cam-CHILD) database, we wanted to examine barriers to the access, linkage, and use of local authority data.

METHODS

A systematic literature search was conducted of scientific databases and the grey literature. Any publications reporting original research related to barriers or enablers of data linkage of or with local authority data in the United Kingdom were included. Barriers relating to the following issues were extracted from each paper: funding, fragmentation, legal and ethical frameworks, cultural issues, geographical boundaries, technical capability, capacity, data quality, security, and patient and public trust.

RESULTS

Twenty eight articles were identified for inclusion in this review. Issues relating to technical capacity and data quality were cited most often. This was followed by those relating to legal and ethical frameworks. Issue relating to public and patient trust were cited the least, however, there is considerable overlap between this topic and issues relating to legal and ethical frameworks.

CONCLUSIONS

This rapid review is the first step to an in-depth exploration of the barriers to data access, linkage and use; a better understanding of which can aid in creating and implementing effective solutions. These barriers are not novel although they pose specific challenges in the context of local authority data.

An overview of synthetic administrative data for research

Use of administrative data for research and for planning services has increased over recent decades due to the value of the large, rich information available. However, concerns about the release of sensitive or personal data and the associated disclosure risk can lead to lengthy approval processes and restricted data access. This can delay or prevent the production of timely evidence. A promising solution to facilitate more efficient data access is to create synthetic versions of the original datasets which are less likely to hold confidential information and can minimise disclosure risk. Such data may be used as an interim solution, allowing researchers to develop their analysis plans on non-disclosive data, whilst waiting for access to the real data. We aim to provide an overview of the background and uses of synthetic data and describe common methods used to generate synthetic data in the context of UK administrative research. We propose a simplified terminology for categories of synthetic data (univariate, multivariate, and complex modality synthetic data) as well as a more comprehensive description of the terminology used in the existing literature and illustrate challenges and future directions for research.

Linkage of National Congenital Heart Disease Audit data to hospital, critical care and mortality national data sets to enable research focused on quality improvement

OBJECTIVES

To link five national data sets (three registries, two administrative) and create longitudinal healthcare trajectories for patients with congenital heart disease (CHD), describing the quality and the summary statistics of the linked data set.

DESIGN

Bespoke linkage of record-level patient identifiers across five national data sets. Generation of spells of care defined as periods of time-overlapping events across the data sets.

SETTING

National Congenital Heart Disease Audit (NCHDA) procedures in public (National Health Service; NHS) hospitals in England and Wales, paediatric and adult intensive care data sets (Paediatric Intensive Care Audit Network; PICANet and the Case Mix Programme from the Intensive Care National Audit & Research Centre; ICNARC-CMP), administrative hospital episodes (hospital episode statistics; HES inpatient, outpatient, accident and emergency; A&E) and mortality registry data.

PARTICIPANTS

Patients with any CHD procedure recorded in NCHDA between April 2000 and March 2017 from public hospitals.

PRIMARY AND SECONDARY OUTCOME MEASURES

Primary: number of linked records, number of unique patients and number of generated spells of care. Secondary: quality and completeness of linkage.

RESULTS

There were 143 862 records in NCHDA relating to 96 041 unique patients. We identified 65 797 linked PICANet patient admissions, 4664 linked ICNARC-CMP admissions and over 6 million linked HES episodes of care (1.1M inpatient, 4.7M outpatient). The linked data set had 4 908 153 spells of care after quality checks, with a median (IQR) of 3.4 (1.8-6.3) spells per patient-year. Where linkage was feasible (in terms of year and centre), 95.6% surgical procedure records were linked to a corresponding HES record, 93.9% paediatric (cardiac) surgery procedure records to a corresponding PICANet admission and 76.8% adult surgery procedure records to a corresponding ICNARC-CMP record.

CONCLUSIONS

We successfully linked four national data sets to the core data set of all CHD procedures performed between 2000 and 2017. This will enable a much richer analysis of longitudinal patient journeys and outcomes. We hope that our detailed description of the linkage process will be useful to others looking to link national data sets to address important research priorities.

What makes administrative data "research-ready"? A systematic review and thematic analysis of published literature

Introduction

Administrative data are a valuable research resource, but are under-utilised in the UK due to governance, technical and other barriers (e.g., the time and effort taken to gain secure data access). In recent years, there has been considerable government investment in making administrative data "research-ready", but there is no definition of what this term means. A common understanding of what constitutes research-ready administrative data is needed to establish clear principles and frameworks for their development and the realisation of their full research potential.

Objective

To define the characteristics of research-ready administrative data based on a systematic review and synthesis of existing literature.

Methods

On 29th June 2021, we systematically searched seven electronic databases for (1) peer-reviewed literature (2) related to research-ready administrative data (3) written in the English language. Following supplementary searches and snowball screening, we conducted a thematic analysis of the identified relevant literature.

Results

Overall, we screened 2,375 records and identified 38 relevant studies published between 2012 and 2021. Most related to administrative data from the UK and US and particularly to health data. The term research-ready was used inconsistently in the literature and there was some conflation with the concept of data being ready for statistical analysis. From the thematic analysis, we identified five defining characteristics of research-ready administrative data: (a) accessible, (b) broad, (c) curated, (d) documented and (e) enhanced for research purposes.

Conclusions

Our proposed characteristics of research-ready administrative data could act as a starting point to help data owners and researchers develop common principles and standards. In the more immediate term, the proposed characteristics are a useful framework for cataloguing existing research-ready administrative databases and relevant resources that can support their development.

Development and implementation of a national online application system for cross-jurisdictional linked data

The Population Health Research Network (PHRN) is an Australian national data linkage infrastructure that links a wide range of health and human services data in privacy-preserving ways. The data linkage infrastructure enables researchers to apply for access to routinely collected, linked, administrative data from the six states and two territories which make up the Commonwealth of Australia, as well as data collected by the Australian Government. The PHRN is a distributed network where data is collected and managed at the respective jurisdictional and/or cross-jurisdictional levels. As a result, access to linked data from multiple jurisdictions requires complex approval processes. This paper describes Australia's approach to enabling access to linked data from multiple jurisdictions. It covers the identification of, and agreement to, a minimum set of data items to be included in a unified national application form, the development and implementation of a national online application system and the harmonisation of business processes for cross-jurisdictional research projects. Utilisation of the online application system and the ongoing challenges of data linkage across jurisdictions are discussed. Changes to the data custodian and ethics committee approval criteria were out of scope for this project.

Establishing risk factors and outcomes for congenital hypothyroidism with gland in situ using population-based data linkage methods: study protocol

INTRODUCTION

There has been an increase in the birth prevalence of congenital hypothyroidism (CH) since the introduction of newborn screening, both globally and in the UK. This increase can be accounted for by an increase in CH with gland in situ (CH-GIS). It is not known why CH-GIS is becoming more common, nor how it affects the health, development and learning of children over the long term. Our study will use linked administrative health, education and clinical data to determine risk factors for CH-GIS and describe long-term health and education outcomes for affected children.

METHODS AND ANALYSIS

We will construct a birth cohort study based on linked, administrative data to determine what factors have contributed to the increase in the birth prevalence of CH-GIS in the UK. We will also set up a follow-up study of cases and controls to determine the health and education outcomes of children with and without CH-GIS. We will use logistic/multinomial regression models to establish risk factors for CH-GIS. Changes in the prevalence of risk factors over time will help to explain the increase in birth prevalence of CH-GIS. Multivariable generalised linear models or Cox proportional hazards regression models will be used to assess the association between type of CH and school performance or health outcomes.

ETHICS AND DISSEMINATION

This study has been approved by the London Queen Square Research Ethics Committee and the Health Research Authority's Confidentiality Advisory Group CAG. Approvals are also being sought from each data provider. Obtaining approvals from CAG, data providers and information governance bodies have caused considerable delays to the project. Our methods and findings will be published in peer-reviewed journals and presented at academic conferences.

Identifying researcher learning needs to develop online training for UK researchers working with administrative data: CENTRIC training

BACKGROUND

The use of administrative data in health and social science research continues to expand, with increased availability of data and interest from funders. Researchers, however, continue to experience delays in access, storage and sharing of administrative data. Training opportunities are limited and typically specific to individual data providers or focussed on the analytical aspects of working with administrative data. The CENTRIC study was funded by the Information Commissioners Office, with the aim of developing a broader training curriculum for researchers working with administrative data in the UK.

METHODS

A mixed-methods design informed curriculum content, including surveys with researchers, focus group discussions with data providers and workshops with members of the public. Researchers were identified from relevant administrative data networks and invited to participate in an online survey identifying training needs. Data providers were approached with a request to input to a face-to-face or online meeting with two members of the research team about their experiences of working with researchers. Data were analysed within the broad framework of the interview schedule, free text responses in the survey were analysed thematically.

RESULTS

107 researchers responded to the online survey and four data providers participated in the focus groups. We identified five main themes, relating to research training needs for UK researchers working with administrative data: communication; timelines; changes & amendments; future-proofing applications; and, the availability of training and support. Data providers either provided additional evidence on these learning needs or ways to address identified challenges. Six modules were developed addressing these training needs. Quotes from the survey and focus groups are used anonymously in the online training modules.

CONCLUSION

The CENTRIC online training curriculum was launched in September 2020 and is available, free of charge for UK researchers. CENTRIC specifically addresses commonly identified training needs of researchers working with administrative data.