The objective of this study was to define the threatened perception types of pregnant women during the COVID-19 pandemic and determine the correlations between the perception types and their demographic factors, their preventive knowledge of COVID-19 and their mental status in order to provide suggestions for pregnant women during pandemic. Methods Latent class analysis were used to explore the optimal numbers of clusters. Multinomial logistic regression and multiple correspondence analysis were used to analyze the demographic variables of the latent categories. MANOVA was used to analyze the difference of knowledge of COVID-19 obtained among clusters and their psychological status, and chisquare test was used determine the relationship between the latent clusters and the participant's COVID-19 worry level. Results Five clusters were found: the first cluster (n = 120, 39%) was unthreatened and confident. Cluster 2(n = 84, 28%) was unthreatened but not confident. Cluster 3 (n = 49, 17%) was threatened but confident. Cluster 4 (n = 25, 9%) was threaten, not confident and knowledgeable, and Cluster 5 (n = 20, 7%) was threatened, not confident and lacking knowledge. Three demographic variables were shown an effect on the classification, they were support from work, family support and intrapartum and postpartum complications. Conclusion This study can help assess the mental health risks of pregnant women during an epidemic. The results could be helpful for families, work units, communities and medical institutions to make targeted intervention decisions for pregnant women.
Background
The World Health Organization (WHO) proposed COVID-19 vaccination as an emergent and important method to end the COVID-19 pandemic. Since China started vaccination programs in December 2020, vaccination has spread to provinces and municipalities nationwide. Previous research has focused on people's vaccination willingness and its influencing factors but has not examined vaccination behavior. We examine the effectiveness of psychosocial factors in predicting vaccination behavior.
Methods
A cross-sectional online survey was performed among Chinese adults on 8 May and 4 June 2021. The statistical analysis of the data included univariate analysis, receiver operator characteristics (ROC) analysis and ordinal multiclassification logistic regression model analysis.
Results
Of the 1300 respondents, 761 (58.5%) were vaccinated. Univariate analysis showed that a high education level and good subjective health status were protective factors for vaccination behavior, while suffering from chronic diseases was a risk factor. ROC analysis showed that subjective health status (AUC = 0.625, 95% CI: 0.594–0.656, P < 0.001) was the best predictor of vaccination behavior. Logistic regression analysis with subjective health status as a dependent variable indicated that older age, female sex, depression, neurasthenia, obsession, hypochondriasis and chronic disease were significant risk factors, while positive coping tendencies were a significant protective factor.
Conclusion
Our study found a simple and effective marker, subjective health status, that can predict vaccination behavior. This finding can guide future epidemic prevention work.
Background: The World Health Organization (WHO) proposed COVID-19 vaccination as an emergent and important method to end the COVID-19 pandemic. Since China started vaccination programs in December 2020, vaccination has spread to provinces and municipalities nationwide. Previous research has focused on people's vaccination willingness and its influencing factors but has not examined vaccination behavior. We examine the effectiveness of psychosocial factors in predicting vaccination behavior. Methods: A cross-sectional online survey was performed among Chinese adults on 8 May and 4 June 2021. The statistical analysis of the data included univariate analysis, receiver operator characteristics (ROC) analysis and ordinal multiclassification logistic regression model analysis. Results: Of the 1300 respondents, 761 (58.5%) were vaccinated. Univariate analysis showed that a high education level and good subjective health status were protective factors for vaccination behavior, while suffering from chronic diseases was a risk factor. ROC analysis showed that subjective health status (AUC = 0.625, 95% CI: 0.594–0.656, P < 0.001) was the best predictor of vaccination behavior. Logistic regression analysis with subjective health status as a dependent variable indicated that older age, female sex, depression, neurasthenia, obsession, hypochondriasis and chronic disease were significant risk factors, while positive coping tendencies were a significant protective factor. Conclusion: Our study found a simple and effective marker, subjective health status, that can predict vaccination behavior. This finding can guide future epidemic prevention work.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.