Challenges in administrative data linkage for research. Issue 2 (December 2017)

• Record Type: Journal Article
• Authors: Harron, Katie; Dibben, Chris; Boyd, James; Hjern, Anders; Azimaee, Mahmoud; Barreto, Mauricio L; Goldstein, Harvey
• Is Part Of: Big data & society. Volume 4:Issue 2(2017)
• Publication Date: 2017-12
• Subjects: Data linkage -- record linkage -- epidemiological studies -- measurement error -- selection bias -- data accuracy -- administrative data; Big data -- Social aspects -- Periodicals; Social sciences -- Research -- Data processing -- Periodicals; Social sciences -- Research -- Methodology -- Periodicals; Data mining -- Periodicals; 300.28557
• DOI: 10.1177/2053951717745678
• Languages: English
• ISSNs: 2053-9517

Abstract

Linkage of population-based administrative data is a valuable tool for combining detailed individual-level information from different sources for research. While not a substitute for classical studies based on primary data collection, analyses of linked administrative data can answer questions that require large sample sizes or detailed data on hard-to-reach populations, and generate evidence with a high level of external validity and applicability for policy making. There are unique challenges in the appropriate research use of linked administrative data, for example with respect to bias from linkage errors where records cannot be linked or are linked together incorrectly. For confidentiality and other reasons, the separation of data linkage processes and analysis of linked data is generally regarded as best practice. However, the 'black box' of data linkage can make it difficult for researchers to judge the reliability of the resulting linked data for their required purposes. This article aims to provide an overview of challenges in linking administrative data for research. We aim to increase understanding of the implications of (i) the data linkage environment and privacy preservation; (ii) the linkage process itself (including data preparation, and deterministic and probabilistic linkage methods) and (iii) linkage quality and potential bias in linked data. We draw on examples from a number of countries to illustrate a range of approaches for data linkage in different contexts.