A mixture model to estimate SARS-CoV-2 seroprevalence in Chennai, India
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Abstract
Background
Serological assays used to estimate SARS-CoV-2 seroprevalence rely on manufacturer cut-offs established based on more severe early cases who tended to be older.
Methods
We conducted a household-based serosurvey of 4,677 individuals from 2,619 households in Chennai, India from January to May, 2021. Samples were tested for SARS-CoV-2 IgG antibodies to the spike (S) and nucelocapsid (N) proteins. We calculated seroprevalence using manufacturer cut-offs and using a mixture model in which individuals were assigned a probability of being seropositive based on their measured IgG, accounting for heterogeneous antibody response across individuals.
Results
The SARS-CoV-2 seroprevalence to anti-S and anti-N IgG was 62.0% (95% confidence interval [CI], 60.6 to 63.4) and 13.5% (95% CI, 12.6 to 14.5), respectively applying the manufacturer’s cut-offs, with low inter-assay agreement (Cohen’s kappa 0.15). With the mixture model, estimated anti-S IgG and anti-N IgG seroprevalence was 64.9% (95% Credible Interval [CrI], 63.8 to 66.0) and 51.5% (95% CrI, 50.2 to 52.9) respectively, with high inter-assay agreement (Cohen’s kappa 0.66). Age and socioeconomic factors showed inconsistent relationships with anti-S IgG and anti-N IgG seropositivity using manufacturer’s cut-offs, but the mixture model reconciled these differences. In the mixture model, age was not associated with seropositivity, and improved household ventilation was associated with lower seropositivity odds.
Conclusions
With global vaccine scale-up, the utility of the more stable anti-S IgG assay may be limited due to the inclusion of the S protein in several vaccines. SARS-CoV-2 seroprevalence estimates using alternative targets must consider heterogeneity in seroresponse to ensure seroprevalence is not underestimated and correlates not misinterpreted.
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SciScore for 10.1101/2022.02.24.22271002: (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
Antibodies Sentences Resources 580 samples that were collected in 2016-17 from clients seeking testing for HIV and related conditions that were available at the YRGCARE laboratory’s specimen repository were tested for antibodies to establish pre-pandemic anti-N and anti-S IgG response. anti-Nsuggested: Noneanti-S IgGsuggested: NoneResults from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Given these limitations, mixture models have been proposed19 and used previously to perform inference on serological data20–22. A major strength …
SciScore for 10.1101/2022.02.24.22271002: (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
Antibodies Sentences Resources 580 samples that were collected in 2016-17 from clients seeking testing for HIV and related conditions that were available at the YRGCARE laboratory’s specimen repository were tested for antibodies to establish pre-pandemic anti-N and anti-S IgG response. anti-Nsuggested: Noneanti-S IgGsuggested: NoneResults from OddPub: Thank you for sharing your code.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Given these limitations, mixture models have been proposed19 and used previously to perform inference on serological data20–22. A major strength of these approaches is that they infer seropositivity using the distribution of raw antibody values in the population, obviating the need for a strict, binary cut-off. In doing so, these models reduce measurement error from equivocal assay results, and explicitly account for the uncertainty arising from heterogeneity in antibody response between individuals. In this case, use of the mixture model markedly improved the agreement between the assays. In addition to identifying the burden of infection, serosurveys are also used to identify factors associated with infection to plan appropriate interventions to curb transmission. Due to the association between age, severity, and antibody response, as well as the association between demographic factors, the timing of infection and subsequent waning, a risk factor analysis based on manufacturer’s cut-offs produced erroneous results based on anti-N IgG seropositivity, including a positive association between improved ventilation and seropositivity and higher seropositivity among individuals aged ≥50 years. On the other hand, the mixture model identified the same risk factors for the two assays by explicitly accounting for varying antibody response by age and time of sample collection. Due to the waning of measured antibody levels, factors associated with seroprevalence could be associated wit...
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.
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- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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