Back

Magnitude of Potential Biases in COVID-19 Vaccine Effectiveness Studies due to Differential Healthcare seeking following Home Testing: Implications for Test Negative Design Studies

Qasmieh, S. A.; Ferdinands, J. M.; Chung, J. R.; Wiegand, R. E.; Flannery, B. M.; Rane, M. S.; Nash, D.

2024-12-31 infectious diseases
10.1101/2024.12.30.24319700 medRxiv
Show abstract

The test-negative design (TND) is widely used to estimate COVID-19 vaccine effectiveness (VE). Biased estimates of VE may result from effects of at-home SARS-CoV-2 rapid diagnostic test (RDT) results on decisions to seek healthcare. To investigate magnitude of potential bias, we constructed decision trees with input probabilities obtained from longitudinal surveys of U.S. adults between March 2022 - October 2023. Prevalence of at-home RDT use and healthcare seeking following a positive or negative RDT result was estimated by participant vaccination status and socio-demographic characteristics. At true VE values ranging from 5% to 95%, we defined bias as the difference between the observed and true VE. Among 1,918 symptomatic adults, prevalence of at-home RDT use was higher among vaccinated (37%) versus unvaccinated (22%) participants. At-home RDT use was associated with seeking care, and participants reporting positive RDT were more likely than those reporting negative RDT to have sought care when ill. In primary analyses, we observed downward bias in VE estimates that increased in magnitude when true VE was low. Variations in proportions of vaccination, at-home RDT use and healthcare seeking by socio-demographic characteristics may impact VE estimates. Further evaluation of potential impact of at-home RDT use on VE estimates is warranted.

Published in American Journal of Epidemiology (predicted rank #1) · training set

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.