Utilising identifier error variation in linkage of large administrative data sources

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• 作者：Katie Harron ; Gareth Hagger-Johnson ; Ruth Gilbert…
• 关键词：Key wordsData linkage ; Record linkage ; Administrative data ; Linkage error ; Linkage evaluation ; Hospital admission
• 刊名：BMC Medical Research Methodology
• 出版年：2017
• 出版时间：December 2017
• 年：2017
• 卷：17
• 期：1
• 全文大小：800KB
• 刊物主题：Theory of Medicine/Bioethics; Statistical Theory and Methods; Statistics for Life Sciences, Medicine, Health Sciences;
• 出版者：BioMed Central
• ISSN：1471-2288
• 卷排序：17

文摘

BackgroundLinkage of administrative data sources often relies on probabilistic methods using a set of common identifiers (e.g. sex, date of birth, postcode). Variation in data quality on an individual or organisational level (e.g. by hospital) can result in clustering of identifier errors, violating the assumption of independence between identifiers required for traditional probabilistic match weight estimation. This potentially introduces selection bias to the resulting linked dataset. We aimed to measure variation in identifier error rates in a large English administrative data source (Hospital Episode Statistics; HES) and to incorporate this information into match weight calculation.