ArticleInternational journal of epidemiology2017
A guide to evaluating linkage quality for the analysis of linked data.
Article in International journal of epidemiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers, 1 of them a synthesis that pooled it.
What it found
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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
79 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Quality assessment of the forensic surveillance system: A data-quality monitoring system for substance-abuse detection in unnatural deaths in Thailand.Forensic science international. Synergy · 2026Article
- Identifying and mitigating bias in multiple aspects of modern clinical research.Communications medicine · 2026Review
- Data linkage studies on veteran suicidality: a scoping review of the field, knowledge gaps and evidence appraisal.BMC psychiatry · 2026Article
- Enhancing PRAMS with Administrative Data Linkages: A Generalized Framework.Maternal and child health journal · 2026Article
- Evaluation of the Accuracy of Probabilistic Record Linkage Across Sociodemographic Categories in 4 Databases: Exploratory Study.JMIR formative research · 2026Article
- Probabilistic data linkage to study the epidemiology of unintentional fatal drowning in a large metropolitan region.Injury epidemiology · 2025Article
- Article
- Five ways to enhance the diversity and quality of health data.Nature medicine · 2025Article
- Validation of influenza vaccination status using health administrative databases by integrating pharmacy claims and medical billing databases in Ontario, Canada.BMC infectious diseases · 2025Article
- Improving opportunities for data linkage within Children Looked After administrative records in Wales.International journal of population data science · 2025Article
- Effects of the Family Nurse Partnership on all eligible mothers: a data linkage cohort study in England.PloS one · 2025Article
- The nature and extent of the literature on linked reproductive health datasets in the UK: a scoping review.International journal of population data science · 2025Article
- The application of population data linkage to capture sibling health outcomes among children and young adults with neurodevelopmental conditions. A scoping review.International journal of population data science · 2025Article
- NHS national data opt-outs: trends and potential consequences for health data research.BJGP open · 2024Article
- A theoretical framework for linking hospitals longitudinally: demonstrated using German Hospital Quality Reports 2016-2020.BMC medical research methodology · 2024Article
- Intensive home visiting for adolescent mothers in the Family Nurse Partnership in England 2010-2019: a population-based data linkage cohort study using propensity score matching.BMJ public health · 2024Article
- Assessing Real-World Data From Electronic Health Records for Health Technology Assessment: The SUITABILITY Checklist: A Good Practices Report of an ISPOR Task Force.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2024Article
- Manual Evaluation of Record Linkage Algorithm Performance in Four Real-World Datasets.Applied clinical informatics · 2024Article
- Evaluating Linkage Quality of Population-Based Administrative Data for Health Service Research.Journal of Korean medical science · 2024Article
19 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Linked datasets are an important resource for epidemiological and clinical studies, but linkage error can lead to biased results. For data security reasons, linkage of personal identifiers is often performed by a third party, making it difficult for researchers to assess the quality of the linked dataset in the context of specific research questions. This is compounded by a lack of guidance on how to determine the potential impact of linkage error. We describe how linkage quality can be evaluated and provide widely applicable guidance for both data providers and researchers. Using an illustrative example of a linked dataset of maternal and baby hospital records, we demonstrate three approaches for evaluating linkage quality: applying the linkage algorithm to a subset of gold standard data to quantify linkage error; comparing characteristics of linked and unlinked data to identify potential sources of bias; and evaluating the sensitivity of results to changes in the linkage procedure. These approaches can inform our understanding of the potential impact of linkage error and provide an opportunity to select the most appropriate linkage procedure for a specific analysis. Evaluating linkage quality in this way will improve the quality and transparency of epidemiological and clinical research using linked data.
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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.