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Addressing misclassification bias in vaccine effectiveness studies with an application to Covid-19.

ABSTRACT: Safe and effective vaccines are crucial for the control of Covid-19 and to protect individuals at higher risk of severe disease. The test-negative design is a popular option for evaluating the effectiveness of Covid-19 vaccines. However, the findings could be biased by several factors, including imperfect sensitivity and/or specificity of the test used for diagnosing the SARS-Cov-2 infection. We propose a simple Bayesian modeling approach for estimating vaccine effectiveness that is robust even when the diagnostic test is imperfect. We use simulation studies to demonstrate the robustness of our method to misclassification bias and illustrate the utility of our approach using real-world examples.

SUBMITTER: Eusebi P

PROVIDER: S-EPMC9969950 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Addressing misclassification bias in vaccine effectiveness studies with an application to Covid-19.

Eusebi Paolo P Speybroeck Niko N Hartnack Sonja S Stærk-Østergaard Jacob J Denwood Matthew J MJ Kostoulas Polychronis P

BMC medical research methodology 20230227 1

Safe and effective vaccines are crucial for the control of Covid-19 and to protect individuals at higher risk of severe disease. The test-negative design is a popular option for evaluating the effectiveness of Covid-19 vaccines. However, the findings could be biased by several factors, including imperfect sensitivity and/or specificity of the test used for diagnosing the SARS-Cov-2 infection. We propose a simple Bayesian modeling approach for estimating vaccine effectiveness that is robust even ...[more]

PMID: 36849911

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Project description:In the last two years, the coronavirus disease 19 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been a scientific and social challenge worldwide. Vaccines have been the most effective intervention for reducing virus transmission and disease severity. However, virus genetic variants are still circulating among vaccinated individuals with different symptomatology disease cases. Understanding the protective or disease associated mechanisms in vaccinated individuals is relevant to advance in vaccine development and implementation. To address this objective, serum protein profiles were characterized by quantitative proteomics and data analysis algorithms in four cohorts of vaccinated individuals uninfected and SARS-CoV-2 infected with asymptomatic, nonsevere and severe disease symptomatology. The results showed that immunoglobulins were the most overrepresented proteins in infected cohorts when compared to PCR-negative individuals. The immunoglobulin profile varied between different infected cohorts and correlated with protective or disease associated capacity. Overrepresented immunoglobulins in PCR-positive individuals correlated with protective response against SARS-CoV-2, other viruses, and thrombosis in asymptomatic cases. In nonsevere cases, correlates of protection against SARS-CoV-2 and HBV together with risk of myasthenia gravis and allergy and autoantibodies were observed. Patients with severe symptoms presented risk for allergy, chronic idiopathic thrombocytopenic purpura, and autoantibodies. The analysis of underrepresented immunoglobulins in PCR-positive compared to PCR-negative individuals identified vaccine-induced protective epitopes in various coronavirus proteins including the Spike receptor-binding domain RBD. Non-immunoglobulin proteins were associated with COVID-19 symptoms and biological processes. These results evidence host-associated differences in response to vaccination and the possibility of improving vaccine efficacy against SARS-CoV-2.

Project description:Background and aimAs the rage of coronavirus disease 2019 (COVID-19) pandemic continues to spread globally, much effort is being directed to contain it through various efforts - genomic studies, drug discoveries, clinical trials, vaccine development, and the innovation of diagnostic techniques. However, some pertinent areas involving accurate and sensitive diagnostics, immunoglobulin specificity, evolution of mutant strains of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the drug combination strategy to combat it still require more attention.MethodsThis review critically examines the COVID-19 response and containment operations. It also addresses some standing challenges involving the areas of diagnostics, vaccine development and prospect, and treatment strategies in relation to antiviral drug treatment and immunotherapy. Designated set of keywords such as "SARS-CoV-2;" "coronavirus;" "severe acute respiratory syndrome coronavirus;" "repurposed;" "vaccination;" "containment;" "laboratory diagnostic;" "immunotherapy;" "antiviral;" "antiparasitic;" "antibiotic;" "antiprotozoal;" "antibody;" "anti-inflammatory;" "antitumor;" "corticosteroid;" "hypertensive drug;" "statin;" "supplement;" and "biological" along with "COVID-19" were inserted on electronic databases to retrieve articles and clinical trial information relevant to the study objectives. The search databases included ClinicalTrials.gov, NIH.gov, PubMed, Scinapse, CINAHL, Medline, Google Scholar, Academic Search Premier, SAGE, EBSCO Host, and Scopus.Relevance for patientsThe difficulties associated with SARS-CoV-2 rapid mutations are unceasingly evolving and re-evolving. These pose serious concerns and downplay the efficacy and effectiveness of the current pipeline antiviral drugs and vaccines. Entities encompassing immunotherapy, antiviral drug therapies, viral genomics, protein-protein interaction, and improved diagnostics as well as drug combination strategy against the emerging genetic variability of SARS-CoV-2 were critically appraised. This study suggests that robust collaborations in the development of more sensitive, rapid and accurate diagnostics, development of immunoglobulin specific agents and improved anti-viral treatment focus against multiple mutant genes of SARS-CoV-2 should be aggressively pursued for the overall benefits of COVID-19 patients.

Dataset Information

Addressing misclassification bias in vaccine effectiveness studies with an application to Covid-19.

Publications

Addressing misclassification bias in vaccine effectiveness studies with an application to Covid-19.

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