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利用机器学习识别美国新冠疫苗犹豫的预测因素。

Using machine learning to identify COVID-19 vaccine-hesitancy predictors in the USA.

作者信息

Saldarriaga Enrique M

机构信息

University of Washington, Seattle, Washington, USA.

出版信息

BMJ Public Health. 2023 Dec 17;1(1):e000456. doi: 10.1136/bmjph-2023-000456. eCollection 2023 Nov.

DOI:10.1136/bmjph-2023-000456
PMID:40017853
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11812730/
Abstract

INTRODUCTION

Vaccine hesitancy is complex, multicausative phenomenon that undermines public health efforts to contain the spread of infectious diseases. Improving our understanding of the drivers of vaccine hesitancy might improve our capacity to address it.

METHODS

We used the results of the May 2021 Assistant Secretary for Planning and Evaluation's survey on COVID-19 vaccine hesitancy, which estimated the proportion of adults for every US county that felt either hesitant or unsure and strongly hesitant towards taking the COVID-19 vaccine when it becomes available. We developed a prediction model to identify the most important predictors of vaccine hesitancy. The potential predictors included demographic characteristics, the Centers for Disease Control and Prevention's Social Vulnerability Index and the Republican Party's voting share in the 2020 presidential election as a proxy of political affiliation, all at the county level.

RESULTS

We found that the main drivers of vaccine hesitancy are income level, marital status, poverty, income, schooling, race/ethnicity, age, health insurance status and political affiliation. While the drivers are shared by both levels of hesitancy, the order changes. Particularly, political affiliation is a more important predictor for strong hesitancy than for hesitancy or unsure.

CONCLUSIONS

These results deepen our understanding of the phenomenon and could help design more targeted interventions to reduce hesitancy in specific subgroups of the population.

摘要

引言

疫苗犹豫是一种复杂的、多因素导致的现象,它破坏了公共卫生部门控制传染病传播的努力。增进我们对疫苗犹豫驱动因素的理解可能会提高我们应对这一问题的能力。

方法

我们利用了2021年5月规划与评估助理部长关于新冠疫苗犹豫情况的调查结果,该调查估计了美国每个县中对新冠疫苗上市时接种感到犹豫或不确定以及强烈犹豫的成年人比例。我们开发了一个预测模型来确定疫苗犹豫最重要的预测因素。潜在的预测因素包括人口特征、疾病控制与预防中心的社会脆弱性指数以及作为政治归属代理指标的2020年总统选举中共和党投票份额,所有这些均在县一级。

结果

我们发现疫苗犹豫的主要驱动因素是收入水平、婚姻状况、贫困程度、收入、受教育程度、种族/族裔、年龄、医疗保险状况和政治归属。虽然这两种犹豫程度的驱动因素相同,但顺序有所变化。特别是,政治归属对于强烈犹豫而言是比犹豫或不确定更为重要的预测因素。

结论

这些结果加深了我们对这一现象的理解,并有助于设计更具针对性的干预措施,以减少特定人群亚组中的犹豫情况。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b52b/11812730/4a9c754191b1/bmjph-1-1-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b52b/11812730/121ccc87b7e6/bmjph-1-1-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b52b/11812730/50cc7bddb7e3/bmjph-1-1-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b52b/11812730/4a9c754191b1/bmjph-1-1-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b52b/11812730/121ccc87b7e6/bmjph-1-1-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b52b/11812730/50cc7bddb7e3/bmjph-1-1-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b52b/11812730/4a9c754191b1/bmjph-1-1-g003.jpg

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本文引用的文献

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新冠疫苗接种犹豫的全球流行率及潜在影响因素:一项荟萃分析
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Factors Contributing to SARS-CoV-2 Vaccine Hesitancy of Hispanic Population in Rio Grande Valley.导致里奥格兰德河谷西班牙裔人群对新冠病毒疫苗犹豫的因素
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Hum Vaccin Immunother. 2022 Nov 30;18(5):2074716. doi: 10.1080/21645515.2022.2074716. Epub 2022 Jun 17.
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BMJ. 2022 Apr 27;377:e069317. doi: 10.1136/bmj-2021-069317.
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