ArticleInternational journal of population data science2018
Challenges in accessing routinely collected data from multiple providers in the UK for primary studies: Managing the morass.
Article in International journal of population data science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Equitable inclusion of patients with cancer on the palliative care register: a systematic review in primary care.The British journal of general practice : the journal of the Royal College of General Practitioners · 2025Pooled it
- Access to routinely collected health data for clinical trials - review of successful data requests to UK registries.Trials · 2020Pooled it
- The Road to Hell Winds On: The High Administrative Burden of Maintaining Linked National Health Data.International journal of population data science · 2026Article
- Data-sharing practices in publications funded by the Canadian Institutes of Health Research: implications for health sciences librarians.The journal of the Canadian Health Libraries Association · 2025Article
- Using general practice data for chronic disease prevalence: the impact of record linkage on estimation accuracy.BMC medical informatics and decision making · 2025Article
- The Challenges and Lessons Learned Building a New UK Infrastructure for Finding and Accessing Population-Wide COVID-19 Data for Research and Public Health Analysis: The CO-CONNECT Project.Journal of medical Internet research · 2024Article
- Predictive Data Analytics in Telecare and Telehealth: Systematic Scoping Review.Online journal of public health informatics · 2024Article
- Now Is the Time to Strengthen Government-Academic Data Infrastructures to Jump-Start Future Public Health Crisis Response.JMIR public health and surveillance · 2024Article
- Coupling quantitative systems pharmacology modelling to machine learning and artificial intelligence for drug development: itsFrontiers in systems biology · 2024Article
- Mother and Infant Research Electronic Data Analysis (MIREDA): A protocol for creating a common data model for federated analysis of UK birth cohorts and the life course.International journal of population data science · 2024Article
- Healthcare systems data in the context of clinical trials - A comparison of cardiovascular data from a clinical trial dataset with routinely collected data.Contemporary clinical trials · 2023Article
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- Article
- Review
- Accessing routinely collected health data to improve clinical trials: recent experience of access.Trials · 2021Article
- Creation of the first national linked colorectal cancer dataset in Scotland: prospects for future research and a reflection on lessons learned.International journal of population data science · 2021Article
- Assessing the potential for prevention or earlier detection of on-site monitoring findings from randomised controlled trials: Further analyses of findings from the prospective TEMPER triggered monitoring study.Clinical trials (London, England) · 2021Article
- A study protocol for the development of a SPIRIT extension for trials conducted using cohorts and routinely collected data (SPIRIT-ROUTINE).HRB open research · 2021Article
- The SPOR-Canadian Data Platform: a national initiative to facilitate data rich multi-jurisdictional research.International journal of population data science · 2020Article
- Reliability of Supervised Machine Learning Using Synthetic Data in Health Care: Model to Preserve Privacy for Data Sharing.JMIR medical informatics · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
introductionResearchers are increasingly using routinely collected data in addition to, or instead of, other data collection methods. The UK government continues to invest in research centres to encourage use of these data, and trials and cohort studies utilise data linkage methods in the follow-up of participants. This does not come without its limitations and challenges, such as data access delays.
objectiveThis paper outlines the challenges faced by three projects utilising individual-level routinely-collected linked data for the longer-term follow-up of participants.
methodsThese studies are varied in design, study population and data providers. One researcher was common to the three studies and collated relevant study correspondence, formal documentary evidence such as data sharing agreements and, where relevant, meeting records to review. Key themes were identified and reviewed by other members of the research teams. Mitigating strategies were identified and discussed with a data provider representative and a broader group of researchers to finalise the recommendations presented.
resultsThe challenges discussed are grouped into five themes: Data application process; Project timelines; Dependencies and considerations related to consent; Information Governance; Contractual. In presenting our results descriptively we summarise each case study, identify the main cross-cutting themes and consider the potential for mitigation of challenges.
conclusionsWe make recommendations that identify responsibilities for both researchers and data providers for mitigating and managing data access challenges. A continued conversation within the research community and with data providers is needed to continue to enable researchers to access and utilise the wealth of routinely-collected data available. The suggestions made in this paper will help researchers be better prepared to deal with the challenges of applying for data from multiple data providers.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.