Assessing data linkage quality in cohort studies. (17th February 2020)

• Record Type: Journal Article
• Authors: Harron, Katie; Doidge, James C.; Goldstein, Harvey
• Is Part Of: Annals of human biology. Volume 47:Number 2(2020)
• Page Start: 218
• Page End: 226
• Publication Date: 2020-02-17
• Subjects: Cohort studies -- data linkage -- measurement error -- administrative data -- selection bias; Human biology -- Periodicals; 612
• DOI: 10.1080/03014460.2020.1742379
• Languages: English
• ISSNs: 0301-4460

Abstract

Abstract: Background: Linkage of administrative data sources provides an efficient means of collecting detailed data on how individuals interact with cross-sectoral services, society, and the environment. These data can be used to supplement conventional cohort studies, or to create population-level electronic cohorts generated solely from administrative data. However, errors occurring during linkage (false matches/missed matches) can lead to bias in results from linked data. Aim: This paper provides guidance on evaluating linkage quality in cohort studies. Methods: We provide an overview of methods for linkage, describe mechanisms by which linkage error can introduce bias, and draw on real-world examples to demonstrate methods for evaluating linkage quality. Results: Methods for evaluating linkage quality described in this paper provide guidance on (i) estimating linkage error rates, (ii) understanding the mechanisms by which linkage error might bias results, and (iii) information that should be shared between data providers, linkers and users, so that approaches to handling linkage error in analysis can be implemented. Conclusion: Linked administrative data can enhance conventional cohorts and offers the ability to answer questions that require large sample sizes or hard-to-reach populations. Care needs to be taken to evaluate linkage quality in order to provide robust results.