title: Data linkage errors in hospital administrative data when applying a pseudonymisation algorithm to paediatric intensive care records.
abstract: OBJECTIVE
  Our 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.
  
  
  METHODS
  The Paediatric Intensive Care Audit Network (PICANet) database, covering 33 paediatric intensive care units in England, Scotland and Wales.
  
  
  METHODS
  Data from infants and young people aged 0-19 years admitted between 1 January 2004 and 21 February 2014.
  
  
  METHODS
  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.
  
  
  RESULTS
  Of 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.
  
  
  CONCLUSIONS
  The 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.
authors: Centre for Paediatric Epidemiology and Biostatistics, UCL Institute of Child Health, London, UK Department of Epidemiology and Public Health, UCL, London, UK.
authors: Gareth
authors: http://orcid.org/0000-0002-7240-1597
authors: G
authors: Hagger-Johnson
authors: Centre for Paediatric Epidemiology and Biostatistics, UCL Institute of Child Health, London, UK.
authors: Katie
authors: K
authors: Harron
authors: Division of Epidemiology and Biostatistics, Leeds Institute of Cardiovascular and Metabolic Medicine, School of Medicine, University of Leeds, Leeds, UK.
authors: Tom
authors: T
authors: Fleming
authors: Centre for Paediatric Epidemiology and Biostatistics, UCL Institute of Child Health, London, UK.
authors: Ruth
authors: R
authors: Gilbert
authors: Centre for Paediatric Epidemiology and Biostatistics, UCL Institute of Child Health, London, UK Centre for Multilevel Modelling, University of Bristol, Bristol, UK.
authors: Harvey
authors: H
authors: Goldstein
authors: Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Queen Mary University of London.
authors: Rebecca
authors: R
authors: Landy
authors: Division of Epidemiology and Biostatistics, Leeds Institute of Cardiovascular and Metabolic Medicine, School of Medicine, University of Leeds, Leeds, UK.
authors: Roger C
authors: RC
authors: Parslow
mesh_terms: D000293:Adolescent; D000465:Algorithms; D015982:Bias; D002648:Child; D002675:Child, Preschool; D003422:Critical Care / Q000592:standards*; D000068598:Data Accuracy; D016208:Databases, Factual / Q000592:standards*; D005260:Female; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D015278:Intensive Care Units, Pediatric; D008297:Male; D008498:Medical Record Linkage / Q000592:standards*; D012042:Registries; D006113:United Kingdom
keywords: EPIDEMIOLOGY; STATISTICS & RESEARCH METHODS; data linkage