Evaluating bias due to data linkage error in electronic healthcare records

Katie Harron*, Angie Wade, Ruth Gilbert, Berit Muller-Pebody, Harvey Goldstein

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

72 Citations (Scopus)

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.

Original languageEnglish
Article number36
JournalBMC Medical Research Methodology
Volume14
Issue number1
DOIs
Publication statusPublished - 5 Mar 2014

Bibliographical note

Funding Information:
This work was supported by funding for the CATCH trial from the National Institute for Health Research Health Technology Assessment (NIHR HTA) programme (project number 08/13/47). The views and opinions expressed therein are those of the authors and do not necessarily reflect those of the HTA programme, NIHR, NHS or the Department of Health. The authors state no conflicts of interest. PICANet is funded by the National Clinical Audit and Patient Outcomes Programme via Healthcare Quality Improvement Partnership (HQIP), Health Commission Wales Specialised Services, NHS Lothian/National Service Division NHS Scotland, the Royal Belfast Hospital for Sick Children, Our Lady’s Children’s Hospital, Crumlin, Children’s University Hospital, Temple Street and The Harley Street Clinic, London.

Keywords

  • Bias
  • Data linkage
  • Electronic health records
  • Evaluation
  • Linkage quality
  • Routine data

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