ArticleBMJ open2015
Data linkage errors in hospital administrative data when applying a pseudonymisation algorithm to paediatric intensive care records.
Article in BMJ open, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Comparison of identifiable and non-identifiable data linkage: health technology assessment of MitraClip using registry, administrative and mortality datasets.BMJ health & care informatics · 2021Article
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- Assessing data linkage quality in cohort studies.Annals of human biology · 2020Article
- Prevalence of Down's Syndrome in England, 1998-2013: Comparison of linked surveillance data and electronic health records.International journal of population data science · 2020Article
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- Approach to record linkage of primary care data from Clinical Practice Research Datalink to other health-related patient data: overview and implications.European journal of epidemiology · 2019Article
- The impact of neurological disorders on hospital admissions for children and young people: a routine health data study.International journal of population data science · 2018Article
- Integrating electronic healthcare records of armed forces personnel: Developing a framework for evaluating health outcomes in England, Scotland and Wales.International journal of medical informatics · 2018Article
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- Challenges in administrative data linkage for research.Big data & society · 2017Article
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Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
objectivesOur aim was to estimate the rate of data linkage error in Hospital Episode Statistics (HES) by testing the HESID pseudoanonymisation algorithm against a reference standard, in a national registry of paediatric intensive care records.
settingThe Paediatric Intensive Care Audit Network (PICANet) database, covering 33 paediatric intensive care units in England, Scotland and Wales.
participantsData from infants and young people aged 0-19 years admitted between 1 January 2004 and 21 February 2014. PRIMARY AND SECONDARY OUTCOME MEASURES: PICANet admission records were classified as matches (records belonging to the same patient who had been readmitted) or non-matches (records belonging to different patients) after applying the HESID algorithm to PICANet records. False-match and missed-match rates were calculated by comparing results of the HESID algorithm with the reference standard PICANet ID. The effect of linkage errors on readmission rate was evaluated.
resultsOf 166,406 admissions, 88,596 were true matches (where the same patient had been readmitted). The HESID pseudonymisation algorithm produced few false matches (n=176/77,810; 0.2%) but a larger proportion of missed matches (n=3609/88,596; 4.1%). The true readmission rate was underestimated by 3.8% due to linkage errors. Patients who were younger, male, from Asian/Black/Other ethnic groups (vs White) were more likely to experience a false match. Missed matches were more common for younger patients, for Asian/Black/Other ethnic groups (vs White) and for patients whose records had missing data.
conclusionsThe deterministic algorithm used to link all episodes of hospital care for the same patient in England has a high missed match rate which underestimates the true readmission rate and will produce biased analyses. To reduce linkage error, pseudoanonymisation algorithms need to be validated against good quality reference standards. Pseudonymisation of data 'at source' does not itself address errors in patient identifiers and the impact these errors have on data linkage.
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