Impact of Record-Linkage Errors in Covid-19 Vaccine-Safety Analyses using German Health-Care Data: A Simulation Study
Robin Denz, Katharina Meiszl, Peter Ihle, Doris Oberle, Ursula, Drechsel-B\"auerle, Katrin Scholz, Ingo Meyer, Nina Timmesfeld

TL;DR
This study uses simulation to evaluate how record-linkage errors affect Covid-19 vaccine safety analyses in German health data, finding that SCCS remains unbiased while Cox models are biased under linkage errors.
Contribution
It provides the first systematic quantification of record-linkage error impacts on vaccine safety analysis methods using realistic simulation.
Findings
SCCS method remains unbiased despite linkage errors.
Cox model underestimates true vaccine effects with linkage errors.
Linkage errors have limited impact on analysis power.
Abstract
With unprecedented speed, 192,248,678 doses of Covid-19 vaccines were administered in Germany by July 11, 2023 to combat the pandemic. Limitations of clinical trials imply that the safety profile of these vaccines is not fully known before marketing. However, routine health-care data can help address these issues. Despite the high proportion of insured people, the analysis of vaccination-related data is challenging in Germany. Generally, the Covid-19 vaccination status and other health-care data are stored in separate databases, without persistent and database-independent person identifiers. Error-prone record-linkage techniques must be used to merge these databases. Our aim was to quantify the impact of record-linkage errors on the power and bias of different analysis methods designed to assess Covid-19 vaccine safety when using German health-care data with a Monte-Carlo simulation…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsPneumonia and Respiratory Infections · Data-Driven Disease Surveillance · Chronic Disease Management Strategies
