Linkage of multiple electronic health record datasets using a ‘spine linkage’ approach compared with all ‘pairwise linkages’

Abstract

Background Methods for linking records between two datasets are well established. However, guidance is needed for linking more than two datasets. Using all ‘pairwise linkages’—linking each dataset to every other dataset—is the most inclusive, but resource-intensive, approach. The ‘spine’ approach links each dataset to a designated ‘spine dataset’, reducing the number of linkages, but potentially reducing linkage quality. Methods We compared the pairwise and spine linkage approaches using real-world data on patients undergoing emergency bowel cancer surgery between 31 October 2013 and 30 April 2018. We linked an administrative hospital dataset (Hospital Episode Statistics; HES) capturing patients admitted to hospitals in England, and two clinical datasets comprising patients diagnosed with bowel cancer and patients undergoing emergency bowel surgery. Results The spine linkage approach, with HES as the spine dataset, created an analysis cohort of 15 826 patients, equating to 98.3% of the 16 100 patients identified using the pairwise linkage approach. There were no systematic differences in patient characteristics between these analysis cohorts. Associations of patient and tumour characteristics with mortality, complications and length of stay were not sensitive to the linkage approach. When eligibility criteria were applied before linkage, spine linkage included 14 509 patients (90.0% compared with pairwise linkage). Conclusion Spine linkage can be used as an efficient alternative to pairwise linkage if case ascertainment in the spine dataset and data quality of linkage variables are high. These aspects should be systematically evaluated in the nominated spine dataset before spine linkage is used to create the analysis cohort.

What a power-with looks like and why we should choose it

“…An illustration of the basic model of a data assemblage is the long-established practice in Australia and New Zealand of creating large linked research data assets using government or academia generated administrative and survey data (though this approach is not limited to these countries, for example see Harron et al, 2017, p. 2, andHolman et al, 2008, p. 767) Minister and Cabinet, 2021, p. 46-51;Young and Flack, 2018, p. 584-5). Hosted by data integrating authorities on behalf of data custodians, 4 generally these assemblages involve the establishment of a data integration 'spine' or 'master linkage key' that provides the correspondence between records across a broad range of data sets often across policy domains (Australian Bureau of Statistics, 2022; Australian Government Department of the Prime Minister and Cabinet, 2021, p. 49;Blake et al, 2022;Harron et al, 2017, p. 2). Trusted users apply for the creation and access to a linked subset of data relevant to their research project, which is often hosted in secure virtual data laboratories (Australian Government Department of the Prime Minister and Cabinet, 2021;Parker, 2017, p. 479;Sax Institute, 2020).…”