Harron, K;
Dibben, C;
Boyd, J;
Hjern, A;
Azimaee, M;
Barreto, ML;
Goldstein, H;
(2017)
Challenges in administrative data linkage for research.
Big Data & Society
, 4
(2)
pp. 1-12.
10.1177/2053951717745678.
Preview |
Text
Goldstein_Challenges in administrative data linkage for research_VoR.pdf - Published Version Download (162kB) | Preview |
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.
| Type: | Article |
|---|---|
| Title: | Challenges in administrative data linkage for research |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1177/2053951717745678 |
| Publisher version: | https://doi.org/10.1177%2F2053951717745678 |
| Language: | English |
| Additional information: | © The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/). |
| Keywords: | Data linkage, record linkage, epidemiological studies, measurement error, selection bias, data accuracy, administrative data |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Population, Policy and Practice Dept |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10060709 |
Archive Staff Only
 |
View Item |
