Data linkage: a powerful research tool with potential problems
Open accessAbstract
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.
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Data describing linkage methods and evaluating their success are critical to permit the reader to assess the impact of any linkage error and related bias.
“Data describing linkage methods and evaluating their success are critical to permit the reader to assess the impact of any linkage error and related bias [51].”
From The Reporting of Studies Conducted Using Observational Routinely-Collected Health Data (RECORD) statement. (2015) · cites this paper as [51]
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- Authors
- Bohensky, M. A., Jolley, D., Sundararajan, V., Evans, S., Pilcher, D. V., Scott, I., & Brand, C. A.
- Affiliations
- Monash University (AU)
- Australian and New Zealand Intensive Care Society (AU)
- Princess Alexandra Hospital (AU)
- Corresponding author
- Bohensky, Megan A
- Venue
- BMC Health Services Research
- Published
- 2010-12
- Volume
- 10
- Issue
- 1
- Pages
- 346-346
- ISSN
- 1472-6963 (ISSN-L)
- PMC
- PMC3271236
- OpenAlex
- W1975356361
- Cited by
- 234
- References
- 61
- Citations by year
- 2026: 8 · 2025: 19 · 2024: 24 · 2023: 16 · 2022: 27 · 2021: 25 · 2020: 18 · 2019: 16 · 2018: 20 · 2017: 16 · 2016: 13 · 2015: 7 · 2014: 10 · 2013: 4 · 2012: 5
- FWCI
- 5.65
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Health Services Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Monash University
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- data linkage, medical record linkage, systematic bias, patient characteristics, socioeconomic status, health status, clinical outcome measurement
- MeSH terms
- Data Collection, Health Services Research, Health Status, Hospitals, Humans, Outcome and Process Assessment, Health Care, Quality Assurance, Health Care, Research Design, Sensitivity and Specificity, Databases, Bibliographic, Information Storage and Retrieval, Empirical Research, Clinical Audit
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