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

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

<p dir="ltr">The COVID-19 pandemic has highlighted the need to use infection testing databases to rapidly estimate effectiveness of prior infection in preventing reinfection (PE<sub>s</sub>) by novel SARS-CoV-2 variants. Mathematical modeling was used to demonstrate a the...

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Main Author: Houssein H Ayoub (17704359) (author)
Other Authors: Milan Tomy (17704362) (author), Hiam Chemaitelly (439114) (author), Heba N Altarawneh (17704365) (author), Peter Coyle (787159) (author), Patrick Tang (239534) (author), Mohammad R Hasan (14634173) (author), Zaina Al Kanaani (5018198) (author), Einas Al Kuwari (12024471) (author), Adeel A Butt (17704368) (author), Andrew Jeremijenko (11506565) (author), Anvar Hassan Kaleeckal (11847034) (author), Ali Nizar Latif (11570540) (author), Riyazuddin Mohammad Shaik (11847037) (author), Gheyath K Nasrallah (17704371) (author), Fatiha M Benslimane (17704374) (author), Hebah A Al Khatib (17704377) (author), Hadi M Yassine (17704380) (author), Mohamed G Al Kuwari (17704383) (author), Hamad Eid Al Romaihi (12024477) (author), Hanan F Abdul-Rahim (17704386) (author), Mohamed H Al-Thani (17704389) (author), Abdullatif Al Khal (12024468) (author), Roberto Bertollini (9538620) (author), Laith J Abu-Raddad (11868161) (author)
Published: 2023
Subjects:
Biomedical and clinical sciences
Clinical sciences
Health sciences
Epidemiology
Public health
reinfection
test-negative design
effectiveness
mathematical model
SARS-CoV-2
COVID-19
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Summary:<p dir="ltr">The COVID-19 pandemic has highlighted the need to use infection testing databases to rapidly estimate effectiveness of prior infection in preventing reinfection (PE<sub>s</sub>) 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<sub>s</sub>. Apart from the very early phase of an epidemic, the difference between the test-negative estimate for PE<sub>s</sub> and true value of PE<sub>s</sub> was minimal and became negligible as the epidemic progressed. The test-negative design provided robust estimation of PE<sub>s</sub> and its waning. Assuming that only 25% of prior infections are documented, misclassification of prior infection status underestimated PE<sub>s</sub>, 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<sub>s</sub>. The test-negative design was applied to national-level testing data in Qatar to estimate PE<sub>s</sub> for SARS-CoV-2. PE<sub>s</sub> 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.</p><h2>Other Information</h2><p dir="ltr">Published in: American Journal of Epidemiology<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1093/aje/kwad239" target="_blank">https://dx.doi.org/10.1093/aje/kwad239</a></p>

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