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Validation of de-identified record linkage to ascertain hospital admissions in a cohort study

Beauchamp, Alison, Tonkin, Andrew M., Kelsall, Helen, Sundararajan, Vijaya, English, Dallas R., Sundaresan, Lalitha, Wolfe, Rory, Turrell, Gavin, Giles, Graham G. and Peeters, Anna 2011, Validation of de-identified record linkage to ascertain hospital admissions in a cohort study, BMC medical research methodology, vol. 11, no. 42, pp. 1-8.

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Title Validation of de-identified record linkage to ascertain hospital admissions in a cohort study
Author(s) Beauchamp, Alison
Tonkin, Andrew M.
Kelsall, Helen
Sundararajan, Vijaya
English, Dallas R.
Sundaresan, Lalitha
Wolfe, Rory
Turrell, Gavin
Giles, Graham G.
Peeters, Anna
Journal name BMC medical research methodology
Volume number 11
Issue number 42
Start page 1
End page 8
Total pages 8
Publisher BioMed Central
Place of publication London, England
Publication date 2011-04-08
ISSN 1471-2288
Keyword(s) hospitalisation dataset
variables
cohort studies
health outcomes
data
Summary Background Cohort studies can provide valuable evidence of cause and effect relationships but are subject to loss of participants over time, limiting the validity of findings. Computerised record linkage offers a passive and ongoing method of obtaining health outcomes from existing routinely collected data sources. However, the quality of record linkage is reliant upon the availability and accuracy of common identifying variables. We sought to develop and validate a method for linking a cohort study to a state-wide hospital admissions dataset with limited availability of unique identifying variables.

Methods A sample of 2000 participants from a cohort study (n = 41 514) was linked to a state-wide hospitalisations dataset in Victoria, Australia using the national health insurance (Medicare) number and demographic data as identifying variables. Availability of the health insurance number was limited in both datasets; therefore linkage was undertaken both with and without use of this number and agreement tested between both algorithms. Sensitivity was calculated for a sub-sample of 101 participants with a hospital admission confirmed by medical record review.

Results Of the 2000 study participants, 85% were found to have a record in the hospitalisations dataset when the national health insurance number and sex were used as linkage variables and 92% when demographic details only were used. When agreement between the two methods was tested the disagreement fraction was 9%, mainly due to "false positive" links when demographic details only were used. A final algorithm that used multiple combinations of identifying variables resulted in a match proportion of 87%. Sensitivity of this final linkage was 95%.

Conclusions High quality record linkage of cohort data with a hospitalisations dataset that has limited identifiers can be achieved using combinations of a national health insurance number and demographic data as identifying variables.
Language eng
Field of Research 119999 Medical and Health Sciences not elsewhere classified
Socio Economic Objective 970111 Expanding Knowledge in the Medical and Health Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2011, BioMed Central
Persistent URL http://hdl.handle.net/10536/DRO/DU:30046465

Document type: Journal Article
Collections: Population Health
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.