Evaluation of the reported data linkage process and associated quality issues for linked routinely collected healthcare data in multimorbidity research: a systematic methodology review

Maria Elstad, Saiam Ahmed, Jo Røislien, Abdel Douiri

Research output: Contribution to journalArticlepeer-review

Abstract

OBJECTIVE: The objective of this systematic review was to examine how the record linkage process is reported in multimorbidity research.

METHODS: A systematic search was conducted in Medline, Web of Science and Embase using predefined search terms, and inclusion and exclusion criteria. Published studies from 2010 to 2020 using linked routinely collected data for multimorbidity research were included. Information was extracted on how the linkage process was reported, which conditions were studied together, which data sources were used, as well as challenges encountered during the linkage process or with the linked dataset.

RESULTS: Twenty studies were included. Fourteen studies received the linked dataset from a trusted third party. Eight studies reported variables used for the data linkage, while only two studies reported conducting prelinkage checks. The quality of the linkage was only reported by three studies, where two reported linkage rate and one raw linkage figures. Only one study checked for bias by comparing patient characteristics of linked and non-linked records.

CONCLUSIONS: The linkage process was poorly reported in multimorbidity research, even though this might introduce bias and potentially lead to inaccurate inferences drawn from the results. There is therefore a need for increased awareness of linkage bias and transparency of the linkage processes, which could be achieved through better adherence to reporting guidelines.

PROSPERO REGISTRATION NUMBER: CRD42021243188.

Original languageEnglish
Article numbere069212
Pages (from-to)e069212
JournalBMJ Open
Volume13
Issue number5
DOIs
Publication statusPublished - 8 May 2023

Keywords

  • Humans
  • Multimorbidity
  • Routinely Collected Health Data
  • Information Storage and Retrieval
  • Bias
  • Delivery of Health Care

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