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 (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
Funding
Open Access funding provided by the Qatar National Library.
Qatar University collaborative grant (QUCG-CAS-23/24-114).
Marubeni grant (M-QJRC-2020-5).
Qatar National Research Fund (NPRP 9-040-3-008).
Qatar National Research Fund (NPRP 12S-0216-190094).
History
Language
- English
Publisher
Oxford University PressPublication Year
- 2023
License statement
This Item is licensed under the Creative Commons Attribution 4.0 International License.Institution affiliated with
- Qatar University
- College of Arts and Sciences - QU
- Biomedical Research Center - QU
- Qatar University Health - QU
- Weill Cornell Medicine - Qatar
- WHO Collaborating Centre for Disease Epidemiology Analytics on HIV/AIDS, Sexually Transmitted Infections, and Viral Hepatitis - WCM-Q
- Hamad Medical Corporation
- Hamad General Hospital - HMC