Analysis of predictors of COVID-19 vaccination intention: 2021 community health survey
Myeung Hee Han1
1School of Nursing, Dongyang University
Correspondence to: 1 Associate professor, School of Nursing, Dongyang University, Yeongju-si, 36040, Korea. E-mail: dewdrop54@daum.net
Received September 2, 2022; Revised December 21, 2022; Accepted December 22, 2022.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Abstract
- This study aim to build a model for predicting vaccination intention through decision tree analysis. This study used data from 41,146 people who were not vaccinated against COVID-19 from 2021 Community Health Survey. They were divided two group according to willingness of vaccination. In order to analysis, descriptive analysis, chi-squared test, independent t-test, decision tree analysis, cross validation, and split-sample validation were used. In the case of the youth group (19-34years), those who were married and had a bad subjective health status showed the highest rate (29.5%) of not getting vaccinated. Among the middle aged group (50-64years), those who do not do economic activities and had bad subjective health showed the highest rate (42.1%) of not getting vaccinated. In the group (35-49years), those who had the experience of cognitive impairment, and those with severe pain/discomfort had the highest rate (30.2%) of response not to vaccinate. In the elderly group (over 65 years), it was confirmed that the subjects with difficulty in understanding the written health information and underweight or normal BMI showed the highest percentage (56.3%) of not getting vaccinated. In order to manage and promote vaccination, factors related to the intention not to be vaccinated should be taken into account.
- Keywords : Correlation analysis, COVID-19, COVID-19 confirmed cases, weather factors.
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