Shaw, Richard J;
Harron, Katie L;
Pescarini, Julia M;
Pinto Junior, Elzo Pereira;
Allik, Mirjam;
Siroky, Andressa N;
Campbell, Desmond;
... Katikireddi, Srinivasa Vittal; + view all
(2022)
Biases arising from linked administrative data for epidemiological research: a conceptual framework from registration to analyses.
European Journal of Epidemiology
, 37
(12)
pp. 1215-1224.
10.1007/s10654-022-00934-w.
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Abstract
Linked administrative data offer a rich source of information that can be harnessed to describe patterns of disease, understand their causes and evaluate interventions. However, administrative data are primarily collected for operational reasons such as recording vital events for legal purposes, and planning, provision and monitoring of services. The processes involved in generating and linking administrative datasets may generate sources of bias that are often not adequately considered by researchers. We provide a framework describing these biases, drawing on our experiences of using the 100 Million Brazilian Cohort (100MCohort) which contains records of more than 131 million people whose families applied for social assistance between 2001 and 2018. Datasets for epidemiological research were derived by linking the 100MCohort to health-related databases such as the Mortality Information System and the Hospital Information System. Using the framework, we demonstrate how selection and misclassification biases may be introduced in three different stages: registering and recording of people's life events and use of services, linkage across administrative databases, and cleaning and coding of variables from derived datasets. Finally, we suggest eight recommendations which may reduce biases when analysing data from administrative sources.
| Type: | Article |
|---|---|
| Title: | Biases arising from linked administrative data for epidemiological research: a conceptual framework from registration to analyses |
| Location: | Netherlands |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s10654-022-00934-w |
| Publisher version: | https://doi.org/10.1007/s10654-022-00934-w |
| Language: | English |
| Additional information: | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| Keywords: | Administrative data, Data linkage, Epidemiological biases, Linkage error, Record linkage |
| 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-pp.ucl.ac.uk/id/eprint/10159664 |
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