WeChat Mini Program
Old Version Features

A Summary of the Advisory Committee for Immunization Practices (ACIP) Use of a Benefit-Risk Assessment Framework During the First Year of COVID-19 Vaccine Administration in the United States.

Vaccine(2023)

1600 Clifton Rd NE

Cited 6|Views39
Abstract
To inform Advisory Committee for Immunization Practices (ACIP) COVID-19 vaccine policy decisions, we developed a benefit-risk assessment framework that directly compared the estimated benefits of COVID-19 vaccination to individuals (e.g., prevention of COVID-19-associated hospitalization) with risks associated with COVID-19 vaccines. This assessment framework originated following the identification of thrombosis with thrombocytopenia syndrome (TTS) after Janssen COVID-19 vaccination in April 2021. We adapted the benefit-risk assessment framework for use in subsequent policy decisions, including the adverse events of myocarditis and Guillain-Barre syndrome (GBS) following mRNA and Janssen COVID-19 vaccination respectively, expansion of COVID-19 vaccine approvals or authorizations to new age groups, and use of booster doses. Over the first year of COVID-19 vaccine administration in the United States (December 2020-December 2021), we used the benefit-risk assessment framework to inform seven different ACIP policy decisions. This framework allowed for rapid and direct comparison of the benefits and potential harms of vaccination, which may be helpful in informing other vaccine policy decisions. The assessments were a useful tool for decision-making but required reliable and granular data to stratify analyses and appropriately focus on populations most at risk for a specific adverse event. Additionally, careful decision-making was needed on parameters for data inputs. Sensitivity analyses were used where data were limited or uncertain; adjustments in the methodology were made over time to ensure the assessments remained relevant and applicable to the policy questions under consideration.
More
Translated text
Key words
COVID-19 vaccination,Benefits,Risks,ACIP,Vaccine policy
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined