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Influence factors analysis of COVID-19 Prevention behavior of chinese Citizens: a path analysis based on the hypothetical model.
Li, Yun-Shan; Wang, Rui; Deng, Yu-Qian; Jia, Xiao-Rong; Li, Shan-Peng; Zhao, Li-Ping; Sun, Xin-Ying; Qi, Fei; Wu, Yi-Bo.
  • Li YS; School Of Public Health, Shandong University, 250012, Shandong, China.
  • Wang R; Center for Disease Control and Prevention of Qingdao, 175 Shandong Road, 266033, Shandong, China.
  • Deng YQ; Xiangya School of Nursing, Central South University, 410000, Changsha, China.
  • Jia XR; Center for Disease Control and Prevention of Qingdao, 175 Shandong Road, 266033, Shandong, China.
  • Li SP; Center for Disease Control and Prevention of Qingdao, 175 Shandong Road, 266033, Shandong, China.
  • Zhao LP; The Second Xiangya Hospital, Central South University, 410000, Changsha, China.
  • Sun XY; School of Public Health, Peking University, 38 Xueyuan Road, Haidian District, 100191, Beijing, China.
  • Qi F; Center for Disease Control and Prevention of Qingdao, 175 Shandong Road, 266033, Shandong, China. Qdqff@163.com.
  • Wu YB; School of Public Health, Peking University, 38 Xueyuan Road, Haidian District, 100191, Beijing, China. bjmuwuyibo@outlook.com.
BMC Public Health ; 22(1): 1098, 2022 06 01.
Article in English | MEDLINE | ID: covidwho-1869078
ABSTRACT

BACKGROUND:

Under the outbreak of Coronavirus disease 2019 (COVID-19), a structural equation model was established to determine the causality of important factors that affect Chinese citizens' COVID-19 prevention behavior.

METHODS:

The survey in Qingdao covered several communities in 10 districts and used the method of cluster random sampling. The research instrument used in this study is a self-compiled Chinese version of the questionnaire. Of the 1215 questionnaires, 1188 were included in our analysis. We use the rank sum test, which is a non-parametric test, to test the influence of citizens'basic sociodemographic variables on prevention behavior, and the rank correlation test to analyze the influencing factors of prevention behavior. IBM AMOS 24.0 was used for path analysis, including estimating regression coefficients and evaluating the statistical fits of the structural model, to further explore the causal relationships between variables.

RESULTS:

The result showed that the score in the prevention behavior of all citizens is a median of 5 and a quartile spacing of 0.31. The final structural equation model showed that the external support for fighting the epidemic, the demand level of health information, the cognition of (COVID-19) and the negative emotions after the outbreak had direct effects on the COVID-19 prevention behavior, and that negative emotions and information needs served as mediating variables.

CONCLUSIONS:

The study provided a basis for relevant departments to further adopt epidemic prevention and control strategies.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Humans Country/Region as subject: Asia Language: English Journal: BMC Public Health Journal subject: Public Health Year: 2022 Document Type: Article Affiliation country: S12889-022-13514-0

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Humans Country/Region as subject: Asia Language: English Journal: BMC Public Health Journal subject: Public Health Year: 2022 Document Type: Article Affiliation country: S12889-022-13514-0