Prediction Model for COVID-19 Vaccination Intention among the Mobile Population in China: Validation and Stability

Since China’s launch of the COVID-19 vaccination, the situation of the public, especially the mobile population, has not been optimistic. We investigated 782 factory workers for whether they would get a COVID-19 vaccine within the next 6 months. The participants were divided into a training set and...

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Autores principales:	Fan Hu, Ruijie Gong, Yexin Chen, Jinxin Zhang, Tian Hu, Yaqi Chen, Kechun Zhang, Meili Shang, Yong Cai
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	COVID-19 vaccination intention nomogram prediction model model validation Medicine R
Acceso en línea:	https://doaj.org/article/cf0bfb3fcd4c4e8e80989fc6460d3b28
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spelling	oai:doaj.org-article:cf0bfb3fcd4c4e8e80989fc6460d3b282021-11-25T19:10:12ZPrediction Model for COVID-19 Vaccination Intention among the Mobile Population in China: Validation and Stability10.3390/vaccines91112212076-393Xhttps://doaj.org/article/cf0bfb3fcd4c4e8e80989fc6460d3b282021-10-01T00:00:00Zhttps://www.mdpi.com/2076-393X/9/11/1221https://doaj.org/toc/2076-393XSince China’s launch of the COVID-19 vaccination, the situation of the public, especially the mobile population, has not been optimistic. We investigated 782 factory workers for whether they would get a COVID-19 vaccine within the next 6 months. The participants were divided into a training set and a testing set for external validation conformed to a ratio of 3:1 with R software. The variables were screened by the Lead Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Then, the prediction model, including important variables, used a multivariate logistic regression analysis and presented as a nomogram. The Receiver Operating Characteristic (ROC) curve, Kolmogorov–Smirnov (K-S) test, Lift test and Population Stability Index (PSI) were performed to test the validity and stability of the model and summarize the validation results. Only 45.54% of the participants had vaccination intentions, while 339 (43.35%) were unsure. Four of the 16 screened variables—self-efficacy, risk perception, perceived support and capability—were included in the prediction model. The results indicated that the model has a high predictive power and is highly stable. The government should be in the leading position, and the whole society should be mobilized and also make full use of peer education during vaccination initiatives.Fan HuRuijie GongYexin ChenJinxin ZhangTian HuYaqi ChenKechun ZhangMeili ShangYong CaiMDPI AGarticleCOVID-19vaccination intentionnomogramprediction modelmodel validationMedicineRENVaccines, Vol 9, Iss 1221, p 1221 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	COVID-19 vaccination intention nomogram prediction model model validation Medicine R
spellingShingle	COVID-19 vaccination intention nomogram prediction model model validation Medicine R Fan Hu Ruijie Gong Yexin Chen Jinxin Zhang Tian Hu Yaqi Chen Kechun Zhang Meili Shang Yong Cai Prediction Model for COVID-19 Vaccination Intention among the Mobile Population in China: Validation and Stability
description	Since China’s launch of the COVID-19 vaccination, the situation of the public, especially the mobile population, has not been optimistic. We investigated 782 factory workers for whether they would get a COVID-19 vaccine within the next 6 months. The participants were divided into a training set and a testing set for external validation conformed to a ratio of 3:1 with R software. The variables were screened by the Lead Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Then, the prediction model, including important variables, used a multivariate logistic regression analysis and presented as a nomogram. The Receiver Operating Characteristic (ROC) curve, Kolmogorov–Smirnov (K-S) test, Lift test and Population Stability Index (PSI) were performed to test the validity and stability of the model and summarize the validation results. Only 45.54% of the participants had vaccination intentions, while 339 (43.35%) were unsure. Four of the 16 screened variables—self-efficacy, risk perception, perceived support and capability—were included in the prediction model. The results indicated that the model has a high predictive power and is highly stable. The government should be in the leading position, and the whole society should be mobilized and also make full use of peer education during vaccination initiatives.
format	article
author	Fan Hu Ruijie Gong Yexin Chen Jinxin Zhang Tian Hu Yaqi Chen Kechun Zhang Meili Shang Yong Cai
author_facet	Fan Hu Ruijie Gong Yexin Chen Jinxin Zhang Tian Hu Yaqi Chen Kechun Zhang Meili Shang Yong Cai
author_sort	Fan Hu
title	Prediction Model for COVID-19 Vaccination Intention among the Mobile Population in China: Validation and Stability
title_short	Prediction Model for COVID-19 Vaccination Intention among the Mobile Population in China: Validation and Stability
title_full	Prediction Model for COVID-19 Vaccination Intention among the Mobile Population in China: Validation and Stability
title_fullStr	Prediction Model for COVID-19 Vaccination Intention among the Mobile Population in China: Validation and Stability
title_full_unstemmed	Prediction Model for COVID-19 Vaccination Intention among the Mobile Population in China: Validation and Stability
title_sort	prediction model for covid-19 vaccination intention among the mobile population in china: validation and stability
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/cf0bfb3fcd4c4e8e80989fc6460d3b28
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Prediction Model for COVID-19 Vaccination Intention among the Mobile Population in China: Validation and Stability

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