Estimating protection afforded by prior infection in preventing reinfection: applying the test-negative study design
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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.
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SciScore for 10.1101/2022.01.02.22268622: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources 10, 13, 29-34 Modeling analyses were conducted in MATLAB R2019a (Boston/MA/USA).37 Effectiveness of prior infection against reinfection and impact of bias: Applying the test-negative, case-control study design, PES was derived as one minus the ratio of the odds of prior infection in subjects testing positive (such as by polymerase chain reaction (PCR) testing), to the odds of prior infection in subjects testing negative for the infection. MATLABsuggested: (MATLAB, RRID:SCR_001622)Matching of cases and controls was done to control for known differences in the risk of exposure to … SciScore for 10.1101/2022.01.02.22268622: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources 10, 13, 29-34 Modeling analyses were conducted in MATLAB R2019a (Boston/MA/USA).37 Effectiveness of prior infection against reinfection and impact of bias: Applying the test-negative, case-control study design, PES was derived as one minus the ratio of the odds of prior infection in subjects testing positive (such as by polymerase chain reaction (PCR) testing), to the odds of prior infection in subjects testing negative for the infection. MATLABsuggested: (MATLAB, RRID:SCR_001622)Matching of cases and controls was done to control for known differences in the risk of exposure to SARS-CoV-2 infection in Qatar.13, 43-46 Further description of Qatar’s databases and methods of analysis can be found in previous publications. Qatar’ssuggested: None13, 43-46, 48,49 PES and its associated 95% CI were calculated by applying the following equation: PES = 1™ odds ratio of prior infection among cases versus controls Statistical analyses were conducted in STATA/SE version 17.0.50 The study was approved by the Hamad Medical Corporation and Weill Cornell Medicine-Qatar Institutional Review Boards with waiver of informed consent. STATA/SEsuggested: NoneResults from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:In regard to limitations, specific forms of bias were investigated, but other sources of bias are possible, and these may depend on the database being analyzed.26 There is already a volume of literature investigating other forms of bias for the test-negative design in the context of vaccine effectiveness estimation,16, 17 some of which may apply in the context of PES estimation. While this study demonstrated use of the test-negative design to estimate PES, other factors need to be considered in actual application. For instance, the algorithm for matching needs to be developed with knowledge of the local epidemiology to ensure that matching can effectively control differences in the risk of exposure to the infection. In conclusion, the test-negative design offers a feasible and robust method to estimate protection of prior infection in preventing reinfection. This method should be considered to provide rapid, rigorous estimates of protection offered by prior infection for different variants of SARS-CoV-2, such as the Omicron that emerged recently.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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