Data linkage: a powerful research tool with potential problems

BMC Health Serv Res. 2010 Dec 22:10:346. doi: 10.1186/1472-6963-10-346.


Background: Policy makers, clinicians and researchers are demonstrating increasing interest in using data linked from multiple sources to support measurement of clinical performance and patient health outcomes. However, the utility of data linkage may be compromised by sub-optimal or incomplete linkage, leading to systematic bias. In this study, we synthesize the evidence identifying participant or population characteristics that can influence the validity and completeness of data linkage and may be associated with systematic bias in reported outcomes.

Methods: A narrative review, using structured search methods was undertaken. Key words "data linkage" and Mesh term "medical record linkage" were applied to Medline, EMBASE and CINAHL databases between 1991 and 2007. Abstract inclusion criteria were; the article attempted an empirical evaluation of methodological issues relating to data linkage and reported on patient characteristics, the study design included analysis of matched versus unmatched records, and the report was in English. Included articles were grouped thematically according to patient characteristics that were compared between matched and unmatched records.

Results: The search identified 1810 articles of which 33 (1.8%) met inclusion criteria. There was marked heterogeneity in study methods and factors investigated. Characteristics that were unevenly distributed among matched and unmatched records were; age (72% of studies), sex (50% of studies), race (64% of studies), geographical/hospital site (93% of studies), socio-economic status (82% of studies) and health status (72% of studies).

Conclusion: A number of relevant patient or population factors may be associated with incomplete data linkage resulting in systematic bias in reported clinical outcomes. Readers should consider these factors in interpreting the reported results of data linkage studies.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Clinical Audit
  • Data Collection / methods*
  • Databases, Bibliographic / statistics & numerical data
  • Empirical Research
  • Health Services Research
  • Health Status
  • Hospitals / statistics & numerical data
  • Humans
  • Information Storage and Retrieval / statistics & numerical data*
  • Outcome and Process Assessment, Health Care* / standards
  • Quality Assurance, Health Care
  • Research Design / standards*
  • Sensitivity and Specificity