Estimating protection afforded by prior infection in preventing reinfection: Applying the test-negative study design

The COVID-19 pandemic has highlighted the need to use infection testing databases to rapidly estimate effectiveness of prior infection in preventing reinfection (PE_s) by novel SARS-CoV-2 variants. Mathematical modeling was used to demonstrate a theoretical foundation for applicability of the test-negative, case-control study design to derive PE_s. Apart from the very early phase of an epidemic, the difference between the test-negative estimate for PE_s and true value of PE_s was minimal and became negligible as the epidemic progressed. The test-negative design provided robust estimation of PE_s and its waning. Assuming that only 25% of prior infections are documented, misclassification of prior infection status underestimated PE_s, but the underestimate was considerable only when >50% of the population was ever infected. Misclassification of latent infection, misclassification of current active infection, and scale-up of vaccination all resulted in negligible bias in estimated PE_s. The test-negative design was applied to national-level testing data in Qatar to estimate PE_s for SARS-CoV-2. PE_s against SARS-CoV-2 Alpha and Beta variants was estimated at 97.0% (95% CI: 93.6-98.6) and 85.5% (95% CI: 82.4-88.1), respectively. These estimates were validated using a cohort study design. The test-negative design offers a feasible, robust method to estimate protection from prior infection in preventing reinfection.

Other Information

Published in: American Journal of Epidemiology
License: https://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.1093/aje/kwad239