Assessment of knowledge, attitudes, practices, and vaccine acceptance for coronavirus disease 2019 among the public in a MERS-CoV-endemic country

• Main Authors: Mazin Adnan Barry (Author), Bader A Zawawi (Author), Moath K AlGhusoon (Author), Abdulrahman S AlArifi (Author), Saud K AlHothaly (Author), Omar Abdulmohsen Fatani (Author)
• Format: Book
• Published: Wolters Kluwer Medknow Publications, 2021-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2589-627X; 2589-6288; 10.4103/jnsm.jnsm_51_21
• Keywords: adults; attitudes; coronavirus disease 2019; knowledge; practices; vaccine; Medicine; R; Public aspects of medicine; RA1-1270
• Source: Journal of Nature and Science of Medicine, Vol 4, Iss 4, Pp 317-323 (2021)

Objective: The objective of the study is to identify associations between the sociodemographic characteristics and the level of knowledge, scope of attitudes, and self-reported practices among adults during the coronavirus disease 2019 (COVID-19) pandemic in Riyadh. Methods: A quantitative, cross-sectional study of adults was conducted using a multistage participatory approach between May 31 and June 7, 2020, in Riyadh, Saudi Arabia. Results: Of 1364 study participants, 73.9% knew the descriptive type of COVID-19. Females showed a statistically significant higher knowledge composite (P < 0.0001), and the majority had higher sufficient knowledge scores than males. Only 24.9% agreed that individuals could be tested positive for influenza and COVID-19 at the same time. Negative attitudes were highly prevalent among younger, unmarried, non-Saudi, male participants (P < 0.05). 72.9% reported placing masks on infants or children under 2 years. Similarly, older age groups and Saudi females were better at practicing risk-based preventive controls for COVID-19 than younger age groups and non-Saudi males (P < 0.0001). 60% expressed that if a vaccine would be available this year, they would feel it was rushed, while 56% would consider receiving it but would wait to see what happens to others. Conclusion: These findings could be helpful in guiding machine learning models in groups more affected by knowledge insufficiency, gaps in attitudes, and behavioral compliance.