title: Identifying Possible False Matches in Anonymized Hospital Administrative Data without Patient Identifiers.
abstract: OBJECTIVE
  To identify data linkage errors in the form of possible false matches, where two patients appear to share the same unique identification number.
  
  
  METHODS
  Hospital Episode Statistics (HES) in England, United Kingdom.
  
  
  METHODS
  Data on births and re-admissions for infants (April 1, 2011 to March 31, 2012; age 0-1 year) and adolescents (April 1, 2004 to March 31, 2011; age 10-19 years).
  
  
  METHODS
  Hospital records pseudo-anonymized using an algorithm designed to link multiple records belonging to the same person. Six implausible clinical scenarios were considered possible false matches: multiple births sharing HESID, re-admission after death, two birth episodes sharing HESID, simultaneous admission at different hospitals, infant episodes coded as deliveries, and adolescent episodes coded as births.
  
  
  RESULTS
  Among 507,778 infants, possible false matches were relatively rare (n = 433, 0.1 percent). The most common scenario (simultaneous admission at two hospitals, n = 324) was more likely for infants with missing data, those born preterm, and for Asian infants. Among adolescents, this scenario (n = 320) was more common for males, younger patients, the Mixed ethnic group, and those re-admitted more frequently.
  
  
  CONCLUSIONS
  Researchers can identify clinically implausible scenarios and patients affected, at the data cleaning stage, to mitigate the impact of possible linkage errors.
authors.affiliation: Centre for Paediatric Epidemiology and Biostatistics, UCL Institute of Child Health, Centre of Paediatric Epidemiology and Biostatistics, London, UK.
authors.forename: Gareth
authors.initials: G
authors.lastname: Hagger-Johnson
authors.affiliation: Institute of Health Informatics, Faculty of Pop Health Sciences, Centre for Paediatric Epidemiology and Biostatistics, UCL Institute of Child Health, Centre of Paediatric Epidemiology and Biostatistics, London, UK.
authors.forename: Katie
authors.initials: K
authors.lastname: Harron
authors.affiliation: Institute of Child Health, Faculty of Pop Health Sciences, Centre for Paediatric Epidemiology and Biostatistics, UCL Institute of Child Health, Centre of Paediatric Epidemiology and Biostatistics, London, UK.
authors.forename: Arturo
authors.initials: A
authors.lastname: Gonzalez-Izquierdo
authors.affiliation: Centre for Maternal and Child Health Research, School of Health Sciences, Centre for Paediatric Epidemiology and Biostatistics, UCL Institute of Child Health, Centre of Paediatric Epidemiology and Biostatistics, London, UK.
authors.forename: Mario
authors.initials: M
authors.lastname: Cortina-Borja
authors.affiliation: City University London, UK.
authors.forename: Nirupa
authors.initials: N
authors.lastname: Dattani
authors.affiliation: Public Health England, London, UK.
authors.forename: Berit
authors.initials: B
authors.lastname: Muller-Pebody
authors.affiliation: University of Leeds, Leeds, UK.
authors.forename: Roger
authors.initials: R
authors.lastname: Parslow
authors.affiliation: Public Health England, London, UK.
authors.forename: Ruth
authors.initials: R
authors.lastname: Gilbert
authors.affiliation: Centre for Paediatric Epidemiology and Biostatistics, UCL Institute of Child Health, Centre of Paediatric Epidemiology and Biostatistics, London, UK.|University of Bristol, Bristol, UK.
authors.forename: Harvey
authors.initials: H
authors.lastname: Goldstein
mesh_terms: D000293:Adolescent; D000367:Age Factors; D002648:Child; D003625:Data Collection / Q000592:standards* / Q000706:statistics & numerical data*; D005260:Female; D006302:Health Services Research; D006739:Hospital Administration / Q000706:statistics & numerical data*; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D015203:Reproducibility of Results; D012737:Sex Factors; D012959:Socioeconomic Factors; D006113:United Kingdom; D055815:Young Adult
keywords: Computerized patient medical records; data linkage; data quality; medical errors