Validation of de-identified record linkage to ascertain hospital admissions in a cohort study
Beauchamp, Alison , Tonkin, Andrew , Kelsall, Helen , Sundararajan, Vijaya , English, Dallas , Sundaresan, Lalitha , Wolfe, Rory , Turrell, Gavin, Giles, Graham , & Peeters, Anna (2011) Validation of de-identified record linkage to ascertain hospital admissions in a cohort study. BMC Medical Research Methodology, 11(42), pp. 1-8.
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.
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|Item Type:||Journal Article|
|Keywords:||Record Linkage, Australia, Cohort Studies, Hospital Admissions|
|Subjects:||Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000)|
Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000) > PUBLIC HEALTH AND HEALTH SERVICES (111700) > Epidemiology (111706)
Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000) > PUBLIC HEALTH AND HEALTH SERVICES (111700) > Health Information Systems (incl. Surveillance) (111711)
Australian and New Zealand Standard Research Classification > MEDICAL AND HEALTH SCIENCES (110000) > PUBLIC HEALTH AND HEALTH SERVICES (111700) > Public Health and Health Services not elsewhere classified (111799)
|Divisions:||Current > QUT Faculties and Divisions > Faculty of Health|
Current > Institutes > Institute of Health and Biomedical Innovation
|Copyright Owner:||© 2011 Beauchamp et al; licensee BioMed Central Ltd.|
|Copyright Statement:||This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.|
|Deposited On:||19 Jul 2012 16:30|
|Last Modified:||09 Apr 2014 22:21|
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