Estimating protection afforded by prior infection in preventing reinfection: Applying the test-negative study design - Houssein H Ayoub (17704359) (2023)
The COVID-19 pandemic has highlighted the need to use infection testing databases to rapidly estimate effectiveness of prior infection in preventing reinfection (PEs) 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 PEs. Apart from the very early phase of an epidemic, the difference between the test-negative estimate for PEs and true value of PEs was minimal and became negligible as the epidemic progressed. The test-negative design provided robust estimation of PEs and its waning. Assuming that only 25% of prior infections are documented, misclassification of prior infection status underestimated PEs, 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 PEs. The test-negative design was applied to national-level testing data in Qatar to estimate PEs for SARS-CoV-2. PEs 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