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Nash D, Srivastava A, Shen Y, Penrose K, Kulkarni SG, Zimba R, You W, Berry A, Mirzayi C, Maroko A, Parcesepe AM, Grov C, Robertson MM. Seroincidence of SARS-CoV-2 infection prior to and during the rollout of vaccines in a community-based prospective cohort of U.S. adults. Sci Rep 2024; 14:644. [PMID: 38182731 PMCID: PMC10770061 DOI: 10.1038/s41598-023-51029-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 12/29/2023] [Indexed: 01/07/2024] Open
Abstract
This study used repeat serologic testing to estimate infection rates and risk factors in two overlapping cohorts of SARS-CoV-2 N protein seronegative U.S. adults. One mostly unvaccinated sub-cohort was tracked from April 2020 to March 2021 (pre-vaccine/wild-type era, n = 3421), and the other, mostly vaccinated cohort, from March 2021 to June 2022 (vaccine/variant era, n = 2735). Vaccine uptake was 0.53% and 91.3% in the pre-vaccine and vaccine/variant cohorts, respectively. Corresponding seroconversion rates were 9.6 and 25.7 per 100 person-years. In both cohorts, sociodemographic and epidemiologic risk factors for infection were similar, though new risk factors emerged in the vaccine/variant era, such as having a child in the household. Despite higher incidence rates in the vaccine/variant cohort, vaccine boosters, masking, and social distancing were associated with substantially reduced infection risk, even through major variant surges.
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Affiliation(s)
- Denis Nash
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA.
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York (CUNY), New York, NY, USA.
- CUNY Graduate School of Public Health and Health Policy, 55 W. 125th St., 6th Floor, New York, NY, 10027, USA.
| | - Avantika Srivastava
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York (CUNY), New York, NY, USA
| | - Yanhan Shen
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York (CUNY), New York, NY, USA
| | - Kate Penrose
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
| | - Sarah G Kulkarni
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
| | - Rebecca Zimba
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York (CUNY), New York, NY, USA
| | - William You
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
| | - Amanda Berry
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
| | - Chloe Mirzayi
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York (CUNY), New York, NY, USA
| | - Andrew Maroko
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
- Department of Environmental, Occupational, and Geospatial Health Sciences, Graduate School of Public Health and Health Policy, City University of New York (CUNY), New York, NY, USA
| | - Angela M Parcesepe
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
- Department of Maternal and Child Health, Gillings School of Public Health, University of North Carolina, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Christian Grov
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
- Department of Community Health and Social Sciences, Graduate School of Public Health and Health Policy, City University of New York (CUNY), New York, NY, USA
| | - McKaylee M Robertson
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY), New York, NY, USA
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Nash D, Srivastava A, Shen J, Penrose K, Kulkarni SG, Zimba R, You W, Berry A, Mirzayi C, Maroko A, Parcesepe AM, Grov C, Robertson MM. Seroincidence of SARS-CoV-2 infection prior to and during the rollout of vaccines in a community-based prospective cohort of U.S. adults. medRxiv 2023:2023.09.29.23296142. [PMID: 37873066 PMCID: PMC10593054 DOI: 10.1101/2023.09.29.23296142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Background Infectious disease surveillance systems, which largely rely on diagnosed cases, underestimate the true incidence of SARS-CoV-2 infection, due to under-ascertainment and underreporting. We used repeat serologic testing to measure N-protein seroconversion in a well-characterized cohort of U.S. adults with no serologic evidence of SARS-CoV-2 infection to estimate the incidence of SARS-CoV-2 infection and characterize risk factors, with comparisons before and after the start of the SARS-CoV-2 vaccine and variant eras. Methods We assessed the incidence rate of infection and risk factors in two sub-groups (cohorts) that were SARS-CoV-2 N-protein seronegative at the start of each follow-up period: 1) the pre-vaccine/wild-type era cohort (n=3,421), followed from April to November 2020; and 2) the vaccine/variant era cohort (n=2,735), followed from November 2020 to June 2022. Both cohorts underwent repeat serologic testing with an assay for antibodies to the SARS-CoV-2 N protein (Bio-Rad Platelia SARS-CoV-2 total Ab). We estimated crude incidence and sociodemographic/epidemiologic risk factors in both cohorts. We used multivariate Poisson models to compare the risk of SARS-CoV-2 infection in the pre-vaccine/wild-type era cohort (referent group) to that in the vaccine/variant era cohort, within strata of vaccination status and epidemiologic risk factors (essential worker status, child in the household, case in the household, social distancing). Findings In the pre-vaccine/wild-type era cohort, only 18 of the 3,421 participants (0.53%) had ≥1 vaccine dose by the end of follow-up, compared with 2,497/2,735 (91.3%) in the vaccine/variant era cohort. We observed 323 and 815 seroconversions in the pre-vaccine/wild-type era and the vaccine/variant era and cohorts, respectively, with corresponding incidence rates of 9.6 (95% CI: 8.3-11.5) and 25.7 (95% CI: 24.2-27.3) per 100 person-years. Associations of sociodemographic and epidemiologic risk factors with SARS-CoV-2 incidence were largely similar in the pre-vaccine/wild-type and vaccine/variant era cohorts. However, some new epidemiologic risk factors emerged in the vaccine/variant era cohort, including having a child in the household, and never wearing a mask while using public transit. Adjusted incidence rate ratios (aIRR), with the entire pre-vaccine/wild-type era cohort as the referent group, showed markedly higher incidence in the vaccine/variant era cohort, but with more vaccine doses associated with lower incidence: aIRRun/undervaccinated=5.3 (95% CI: 4.2-6.7); aIRRprimary series only=5.1 (95% CI: 4.2-7.3); aIRRboosted once=2.5 (95% CI: 2.1-3.0), and aIRRboosted twice=1.65 (95% CI: 1.3-2.1). These associations were essentially unchanged in risk factor-stratified models. Interpretation In SARS-CoV-2 N protein seronegative individuals, large increases in incidence and newly emerging epidemiologic risk factors in the vaccine/variant era likely resulted from multiple co-occurring factors, including policy changes, behavior changes, surges in transmission, and changes in SARS-CoV-2 variant properties. While SARS-CoV-2 incidence increased markedly in most groups in the vaccine/variant era, being up to date on vaccines and the use of non-pharmaceutical interventions (NPIs), such as masking and social distancing, remained reliable strategies to mitigate the risk of SARS-CoV-2 infection, even through major surges due to immune evasive variants. Repeat serologic testing in cohort studies is a useful and complementary strategy to characterize SARS-CoV-2 incidence and risk factors.
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Affiliation(s)
- Denis Nash
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, New York, USA
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York (CUNY); New York, New York, USA
| | - Avantika Srivastava
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, New York, USA
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York (CUNY); New York, New York, USA
| | - Jenny Shen
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, New York, USA
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York (CUNY); New York, New York, USA
| | - Kate Penrose
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, New York, USA
| | - Sarah Gorrell Kulkarni
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, New York, USA
| | - Rebecca Zimba
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, New York, USA
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York (CUNY); New York, New York, USA
| | - William You
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, New York, USA
| | - Amanda Berry
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, New York, USA
| | - Chloe Mirzayi
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, New York, USA
- Department of Epidemiology and Biostatistics, Graduate School of Public Health and Health Policy, City University of New York (CUNY); New York, New York, USA
| | - Andrew Maroko
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, New York, USA
- Department of Environmental, Occupational, and Geospatial Health Sciences, Graduate School of Public Health and Health Policy, City University of New York (CUNY); New York, New York, USA
| | - Angela M. Parcesepe
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, New York, USA
- Department of Maternal and Child Health, Gillings School of Public Health, University of North Carolina, Chapel Hill, North Carolina, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Christian Grov
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, New York, USA
- Department of Community Health and Social Sciences, Graduate School of Public Health and Health Policy, City University of New York (CUNY); New York, New York, USA
| | - McKaylee M. Robertson
- Institute for Implementation Science in Population Health (ISPH), City University of New York (CUNY); New York, New York, USA
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Li H, Arcalas C, Song J, Rahmati M, Park S, Koyanagi A, Lee SW, Yon DK, Shin JI, Smith L. Genetics, structure, transmission, epidemiology, immune response, and vaccine efficacies of the SARS‐CoV‐2 Delta variant: A comprehensive review. Rev Med Virol 2022; 33:e2408. [PMID: 36420676 DOI: 10.1002/rmv.2408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 10/29/2022] [Accepted: 11/02/2022] [Indexed: 11/25/2022]
Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Delta variant (B.1.617.2) was the predominant variant behind the surges of COVID-19 in the United States, Europe, and India in the second half of 2021. The information available regarding the defining mutations and their effects on the structure, transmission, and vaccine efficacy of SARS-CoV-2 is constantly evolving. With waning vaccine immunity and relaxation of social distancing policies across the globe driving the increased spread of the Delta variant, there is a great need for a resource aggregating the most recent information for clinicians and researchers concerning the Delta variant. Accordingly, this narrative review comprehensively reviews the genetics, structure, epidemiology, clinical course, and vaccine efficacy of the Delta variant. Comparison with the omicron variant is also discussed. The Delta variant is defined by 15 mutations in the Spike protein, most of which increase affinity for the ACE-2 receptor or enhance immune escape. The Delta variant causes similar symptoms to prototypical COVID-19, but it is more likely to be severe, with a greater inflammatory phenotype and viral load. The reproduction number is estimated to be approximately twice the prototypical strains present during the early pandemic, and numerous breakthrough infections have been reported. Despite studies demonstrating breakthrough infection and reduced antibody neutralisation, full vaccination effectively reduces the likelihood of severe illness and hospitalisation.
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Affiliation(s)
- Han Li
- University of Florida College of Medicine Gainesville Florida USA
| | | | - Junmin Song
- Keimyung University School of Medicine Daegu Republic of Korea
| | - Masoud Rahmati
- Department of Physical Education and Sport Sciences Faculty of Literature and Human Sciences Lorestan University Khoramabad Iran
| | - Seoyeon Park
- Yonsei University College of Medicine Seoul Republic of Korea
| | - Ai Koyanagi
- Parc Sanitari Sant Joan de Deu/CIBERSAM Fundacio Sant Joan de Deu Universitat de Barcelona Sant Boi de Llobregat, Barcelona Spain
- ICREA (Catalan Institution for Research and Advanced Studies) Barcelona Spain
| | - Seung Won Lee
- Department of Precision Medicine Sungkyunkwan University School of Medicine Suwon Republic of Korea
| | - Dong Keon Yon
- Center for Digital Health, Medical Science Research Institute Kyung Hee University College of Medicine Seoul Republic of Korea
- Department of Pediatrics Kyung Hee University Medical Center Kyung Hee University College of Medicine Seoul Republic of Korea
| | - Jae Il Shin
- Department of Pediatrics Yonsei University College of Medicine Seoul Republic of Korea
| | - Lee Smith
- Centre for Health, Performance, and Wellbeing Anglia Ruskin University Cambridge UK
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