title: Validation of de-identified record linkage to ascertain hospital admissions in a cohort study.
abstract: 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.
authors.affiliation: Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia. Alison.Beauchamp@monash.edu
authors.forename: Alison
authors.initials: A
authors.lastname: Beauchamp
authors.forename: Andrew M
authors.initials: AM
authors.lastname: Tonkin
authors.forename: Helen
authors.initials: H
authors.lastname: Kelsall
authors.forename: Vijaya
authors.initials: V
authors.lastname: Sundararajan
authors.forename: Dallas R
authors.initials: DR
authors.lastname: English
authors.forename: Lalitha
authors.initials: L
authors.lastname: Sundaresan
authors.forename: Rory
authors.initials: R
authors.lastname: Wolfe
authors.forename: Gavin
authors.initials: G
authors.lastname: Turrell
authors.forename: Graham G
authors.initials: GG
authors.lastname: Giles
authors.forename: Anna
authors.initials: A
authors.lastname: Peeters
mesh_terms: D000465:Algorithms; D015331:Cohort Studies; D006761:Hospitals; D006801:Humans; D007348:Insurance, Health; D008297:Male; D008498:Medical Record Linkage / Q000379:methods*; D009313:National Health Programs; D010343:Patient Admission; D010355:Patient Identification Systems / Q000379:methods*; D016896:Treatment Outcome