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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Gespeichert in:

Autor*in:	Fan Hu [verfasserIn] Ruijie Gong [verfasserIn] Yexin Chen [verfasserIn] Jinxin Zhang [verfasserIn] Tian Hu [verfasserIn] Yaqi Chen [verfasserIn] Kechun Zhang [verfasserIn] Meili Shang [verfasserIn] Yong Cai [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	COVID-19 vaccination intention nomogram prediction model model validation

Übergeordnetes Werk:	In: Vaccines - MDPI AG, 2013, 9(2021), 11, p 1221
Übergeordnetes Werk:	volume:9 ; year:2021 ; number:11, p 1221

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/vaccines9111221

Katalog-ID:	DOAJ025727842

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allfieldsGer	10.3390/vaccines9111221 doi (DE-627)DOAJ025727842 (DE-599)DOAJcf0bfb3fcd4c4e8e80989fc6460d3b28 DE-627 ger DE-627 rakwb eng Fan Hu verfasserin aut Prediction Model for COVID-19 Vaccination Intention among the Mobile Population in China: Validation and Stability 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. COVID-19 vaccination intention nomogram prediction model model validation Medicine R Ruijie Gong verfasserin aut Yexin Chen verfasserin aut Jinxin Zhang verfasserin aut Tian Hu verfasserin aut Yaqi Chen verfasserin aut Kechun Zhang verfasserin aut Meili Shang verfasserin aut Yong Cai verfasserin aut In Vaccines MDPI AG, 2013 9(2021), 11, p 1221 (DE-627)736559205 (DE-600)2703319-3 2076393X nnns volume:9 year:2021 number:11, p 1221 https://doi.org/10.3390/vaccines9111221 kostenfrei https://doaj.org/article/cf0bfb3fcd4c4e8e80989fc6460d3b28 kostenfrei https://www.mdpi.com/2076-393X/9/11/1221 kostenfrei https://doaj.org/toc/2076-393X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 11, p 1221
allfieldsSound	10.3390/vaccines9111221 doi (DE-627)DOAJ025727842 (DE-599)DOAJcf0bfb3fcd4c4e8e80989fc6460d3b28 DE-627 ger DE-627 rakwb eng Fan Hu verfasserin aut Prediction Model for COVID-19 Vaccination Intention among the Mobile Population in China: Validation and Stability 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. COVID-19 vaccination intention nomogram prediction model model validation Medicine R Ruijie Gong verfasserin aut Yexin Chen verfasserin aut Jinxin Zhang verfasserin aut Tian Hu verfasserin aut Yaqi Chen verfasserin aut Kechun Zhang verfasserin aut Meili Shang verfasserin aut Yong Cai verfasserin aut In Vaccines MDPI AG, 2013 9(2021), 11, p 1221 (DE-627)736559205 (DE-600)2703319-3 2076393X nnns volume:9 year:2021 number:11, p 1221 https://doi.org/10.3390/vaccines9111221 kostenfrei https://doaj.org/article/cf0bfb3fcd4c4e8e80989fc6460d3b28 kostenfrei https://www.mdpi.com/2076-393X/9/11/1221 kostenfrei https://doaj.org/toc/2076-393X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2021 11, p 1221
language	English
source	In Vaccines 9(2021), 11, p 1221 volume:9 year:2021 number:11, p 1221
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topic_facet	COVID-19 vaccination intention nomogram prediction model model validation Medicine R
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authorswithroles_txt_mv	Fan Hu @@aut@@ Ruijie Gong @@aut@@ Yexin Chen @@aut@@ Jinxin Zhang @@aut@@ Tian Hu @@aut@@ Yaqi Chen @@aut@@ Kechun Zhang @@aut@@ Meili Shang @@aut@@ Yong Cai @@aut@@
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author	Fan Hu
spellingShingle	Fan Hu misc COVID-19 misc vaccination intention misc nomogram misc prediction model misc model validation misc Medicine misc R Prediction Model for COVID-19 Vaccination Intention among the Mobile Population in China: Validation and Stability
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topic_title	Prediction Model for COVID-19 Vaccination Intention among the Mobile Population in China: Validation and Stability COVID-19 vaccination intention nomogram prediction model model validation
topic	misc COVID-19 misc vaccination intention misc nomogram misc prediction model misc model validation misc Medicine misc R
topic_unstemmed	misc COVID-19 misc vaccination intention misc nomogram misc prediction model misc model validation misc Medicine misc R
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title	Prediction Model for COVID-19 Vaccination Intention among the Mobile Population in China: Validation and Stability
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title_full	Prediction Model for COVID-19 Vaccination Intention among the Mobile Population in China: Validation and Stability
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author_browse	Fan Hu Ruijie Gong Yexin Chen Jinxin Zhang Tian Hu Yaqi Chen Kechun Zhang Meili Shang Yong Cai
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title_sort	prediction model for covid-19 vaccination intention among the mobile population in china: validation and stability
title_auth	Prediction Model for COVID-19 Vaccination Intention among the Mobile Population in China: Validation and Stability
abstract	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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Prediction Model for COVID-19 Vaccination Intention among the Mobile Population in China: Validation and Stability
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author2	Ruijie Gong Yexin Chen Jinxin Zhang Tian Hu Yaqi Chen Kechun Zhang Meili Shang Yong Cai
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up_date	2024-07-03T16:45:11.461Z
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