title: Optimal strategy for linkage of datasets containing a statistical linkage key and datasets with full personal identifiers.
abstract: BACKGROUND
  Linkage of aged care and hospitalisation data provides valuable information on patterns of health service utilisation among aged care service recipients. Many aged care datasets in Australia contain a Statistical Linkage Key (SLK-581) instead of full personal identifiers. We linked hospital and death records using a full probabilistic strategy, the SLK-581, and three combined strategies; and compared results for each strategy.
  
  
  METHODS
  Linkage of Admitted Patient Data for 2000-01 to 2008-09 and Registry of Births, Deaths and Marriages death registration data for 2008-09 for New South Wales, Australia, was carried out using probabilistic methods and compared to links created using four strategies incorporating a SLK-581. The Basic SLK-581 strategy used the SLK-581 alone. The Most Recent SLK-581, Most Frequent SLK-581, and Any Match SLK-581 strategies leveraged probabilistic links between hospital records drawn from the Centre for Health Record Linkage Master Linkage Key. Rates of hospitalisations among people who died were calculated for each strategy and a range of health conditions.
  
  
  RESULTS
  Compared to full probabilistic linkage, the basic SLK-581 strategy produced substantial rates of missed links that increased over the study period and produced underestimates of hospitalisation rates that varied by health condition. The Most Recent SLK-581, Most Frequent SLK-581, and Any Match SLK-581 strategies resulted in substantially lower rates of underestimation than the Basic SLK-581. The Any Match SLK-581 strategy gave results closest to full probabilistic linkage.
  
  
  CONCLUSIONS
  Hospitalisation rates prior to death are substantially underestimated by linkage using a SLK-581 alone. Linkage rates can be increased by combining deterministic methods with probabilistically created links across hospital records.
authors.affiliation: Centre for Epidemiology and Evidence, NSW Ministry of Health, Sydney, Australia. ltayl@doh.health.nsw.gov.au.
authors.forename: Lee K
authors.initials: LK
authors.lastname: Taylor
authors.forename: Katie
authors.initials: K
authors.lastname: Irvine
authors.forename: Renee
authors.initials: R
authors.lastname: Iannotti
authors.forename: Taylor
authors.initials: T
authors.lastname: Harchak
authors.forename: Kim
authors.initials: K
authors.lastname: Lim
mesh_terms: D006801:Humans; D008498:Medical Record Linkage / Q000379:methods* / Q000592:standards; D016347:Medical Records Systems, Computerized / Q000592:standards*; D009517:New South Wales; D012042:Registries / Q000592:standards*