title: Evaluating bias due to data linkage error in electronic healthcare records.
abstract: BACKGROUND
  Linkage of electronic healthcare records is becoming increasingly important for research purposes. However, linkage error due to mis-recorded or missing identifiers can lead to biased results. We evaluated the impact of linkage error on estimated infection rates using two different methods for classifying links: highest-weight (HW) classification using probabilistic match weights and prior-informed imputation (PII) using match probabilities.
  
  
  METHODS
  A gold-standard dataset was created through deterministic linkage of unique identifiers in admission data from two hospitals and infection data recorded at the hospital laboratories (original data). Unique identifiers were then removed and data were re-linked by date of birth, sex and Soundex using two classification methods: i) HW classification - accepting the candidate record with the highest weight exceeding a threshold and ii) PII-imputing values from a match probability distribution. To evaluate methods for linking data with different error rates, non-random error and different match rates, we generated simulation data. Each set of simulated files was linked using both classification methods. Infection rates in the linked data were compared with those in the gold-standard data.
  
  
  RESULTS
  In the original gold-standard data, 1496/20924 admissions linked to an infection. In the linked original data, PII provided least biased results: 1481 and 1457 infections (upper/lower thresholds) compared with 1316 and 1287 (HW upper/lower thresholds). In the simulated data, substantial bias (up to 112%) was introduced when linkage error varied by hospital. Bias was also greater when the match rate was low or the identifier error rate was high and in these cases, PII performed better than HW classification at reducing bias due to false-matches.
  
  
  CONCLUSIONS
  This study highlights the importance of evaluating the potential impact of linkage error on results. PII can help incorporate linkage uncertainty into analysis and reduce bias due to linkage error, without requiring identifiers.
authors.affiliation: Institute of Child Health, University College London, 30 Guilford Street, London WC1 N 1EH, UK. katie.harron.10@ucl.ac.uk.
authors.forename: Katie
authors.initials: K
authors.lastname: Harron
authors.forename: Angie
authors.initials: A
authors.lastname: Wade
authors.forename: Ruth
authors.initials: R
authors.lastname: Gilbert
authors.forename: Berit
authors.initials: B
authors.lastname: Muller-Pebody
authors.forename: Harvey
authors.initials: H
authors.lastname: Goldstein
mesh_terms: D015982:Bias; D003625:Data Collection; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D008498:Medical Record Linkage / Q000379:methods*