LINKAGE: Factors in selecting a data linkage approach
Author(s) -
Kerina Jones,
David Ford
Publication year - 2019
Publication title -
international journal for population data science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.602
H-Index - 7
ISSN - 2399-4908
DOI - 10.23889/ijpds.v4i3.1175
Subject(s) - linkage (software) , government (linguistics) , confidentiality , data science , linked data , computer science , record linkage , service (business) , data set , set (abstract data type) , population , knowledge base , data mining , knowledge management , business , information retrieval , world wide web , computer security , artificial intelligence , marketing , sociology , philosophy , biochemistry , chemistry , linguistics , semantic web , demography , gene , programming language
Background The great benefits of linking health datasets for research in the public interest have long been demonstrated. More recently, we are seeing an increase in the availability of wider administrative data, such as employment, education and housing, to add new opportunities for population data science. However, there are challenges to be overcome in selecting a data linkage approach.
Main Aim We set out to examine various data linkage approaches, and to formulate some high level questions to inform decision-making.
Methods We used published literature to review various data linkage methods in theory and in practical settings. The study was commissioned by the UK Government Statistical Service and a key focus was privacy and confidentiality in data linkage.
Results The questions we formulated are based on: Legislative position; Information systems; Nature of datasets; Knowledge-base; Aims and purposes; Ground truth; and Environment.
Conclusion There are many factors influencing the selection of a data linkage approach. While not exhaustive, our set of questions covers some of the major ones. The findings of the study are being taken forward by UK Government Statistical Service and government departments to inform decision-making on options for data linkage research and the greater availability of their datasets.
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