Evidence map›Paper›PMID 38061757›Full record

ArticleAmerican journal of epidemiology2024

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

Houssein H Ayoub, Milan Tomy, Hiam Chemaitelly, Heba N Altarawneh, Peter Coyle, Patrick Tang, Mohammad R Hasan, Zaina Al Kanaani, Einas Al Kuwari, Adeel A Butt and 15 more

Abstract read
In one paragraph

Article in American journal of epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

20 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

25 authors.

Houssein H AyoubMathematics Program, Department of Mathematics and Statistics, College of Arts and Sciences, Qatar University, Doha, Qatar.
Milan TomyMathematics Program, Department of Mathematics and Statistics, College of Arts and Sciences, Qatar University, Doha, Qatar.
Hiam ChemaitellyInfectious Disease Epidemiology Group, Weill Cornell Medicine-Qatar, Cornell University, Doha, Qatar.
Heba N AltarawnehInfectious Disease Epidemiology Group, Weill Cornell Medicine-Qatar, Cornell University, Doha, Qatar.
Peter CoyleHamad Medical Corporation, Doha, Qatar.
Patrick TangDepartment of Pathology, Sidra Medicine, Doha, Qatar.
Mohammad R HasanDepartment of Pathology, Sidra Medicine, Doha, Qatar.
Zaina Al KanaaniHamad Medical Corporation, Doha, Qatar.
Einas Al KuwariHamad Medical Corporation, Doha, Qatar.
Adeel A ButtDepartment of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY 10065, United States.
Andrew JeremijenkoHamad Medical Corporation, Doha, Qatar.
Anvar Hassan KaleeckalHamad Medical Corporation, Doha, Qatar.
Ali Nizar LatifHamad Medical Corporation, Doha, Qatar.
Riyazuddin Mohammad ShaikHamad Medical Corporation, Doha, Qatar.
Gheyath K NasrallahBiomedical Research Center, Member of QU Health, Qatar University, Doha, Qatar.
Fatiha M BenslimaneBiomedical Research Center, Member of QU Health, Qatar University, Doha, Qatar.
Hebah A Al KhatibDepartment of Biomedical Science, College of Health Sciences, Member of QU Health, Qatar University, Doha, Qatar.
Hadi M YassineBiomedical Research Center, Member of QU Health, Qatar University, Doha, Qatar.
Mohamed G Al KuwariPrimary Health Care Corporation, Doha, Qatar.
Hamad Eid Al RomaihiMinistry of Public Health, Doha, Qatar.
Hanan F Abdul-RahimDepartment of Public Health, College of Health Sciences, Member of QU Health, Qatar University, Doha, Qatar.
Mohamed H Al-ThaniMinistry of Public Health, Doha, Qatar.
Abdullatif Al KhalHamad Medical Corporation, Doha, Qatar.
Roberto BertolliniMinistry of Public Health, Doha, Qatar.
Laith J Abu-RaddadInfectious Disease Epidemiology Group, Weill Cornell Medicine-Qatar, Cornell University, Doha, Qatar.ORCID 0000-0003-0790-0506

Funding

Marubeni M-QJRC-2020-5NPRP 12S-0216-190094NPRP 9-040-3-008Qatar University collaborative QUCG-CAS-23/24-114
6 · The paper itself

Abstract

The COVID-19 pandemic has highlighted the need to use infection testing databases to rapidly estimate effectiveness of prior infection in preventing reinfection ($P{E}_S$) by novel severe acute respiratory syndrome coronavirus 2 (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 $P{E}_S$. Apart from the very early phase of an epidemic, the difference between the test-negative estimate for $P{E}_S$ and true value of $P{E}_S$ was minimal and became negligible as the epidemic progressed. The test-negative design provided robust estimation of $P{E}_S$ and its waning. Assuming that only 25% of prior infections are documented, misclassification of prior infection status underestimated $P{E}_S$, 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 $P{E}_S$. The test-negative design was applied to national-level testing data in Qatar to estimate $P{E}_S$ for SARS-CoV-2. $P{E}_S$ 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.

Indexed as

COVID-19ReinfectionSARS-CoV-2Case-Control StudiesCOVID-19 TestingHumansModels, TheoreticalCOVID-19effectivenessmathematical modelreinfectionSARS-CoV-2test-negative design

Identifiers

PMID38061757
PMCPMC11145912

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.