A Mixture Model for Estimating SARS-CoV-2 Seroprevalence in Chennai, India
Date:
09/01/2023
Locations:
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Citation:
Hitchings MDT, Patel EU, Khan R, Srikrishnan AK, Anderson M, Kumar KS, Wesolowski AP, Iqbal SH, Rodgers MA, Mehta SH, Cloherty G, Cummings DAT, Solomon SS. A Mixture Model for Estimating SARS-CoV-2 Seroprevalence in Chennai, India. Am J Epidemiol. 2023 Sep 1;192(9):1552-1561. doi: 10.1093/aje/kwad103. PMID: 37084085; PMCID: PMC10472327.
Abstract
Serological assays used to estimate the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) often rely on manufacturers' cutoffs established on the basis of severe cases. We conducted a household-based serosurvey of 4,677 individuals in Chennai, India, from January to May 2021. Samples were tested for SARS-CoV-2 immunoglobulin G (IgG) antibodies to the spike (S) and nucleocapsid (N) proteins. We calculated seroprevalence, defining seropositivity using manufacturer cutoffs and using a mixture model based on measured IgG level. Using manufacturer cutoffs, there was a 5-fold difference in seroprevalence estimated by each assay. This difference was largely reconciled using the mixture model, with estimated anti-S and anti-N IgG seroprevalence of 64.9% (95% credible interval (CrI): 63.8, 66.0) and 51.5% (95% CrI: 50.2, 52.9), respectively. Age and socioeconomic factors showed inconsistent relationships with anti-S and anti-N IgG seropositivity using manufacturer cutoffs. In the mixture model, age was not associated with seropositivity, and improved household ventilation was associated with lower seropositivity odds. 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. Estimates of SARS-CoV-2 seroprevalence using alternative targets must consider heterogeneity in seroresponse to ensure that seroprevalence is not underestimated and correlates are not misinterpreted.