Factors Predicting Preventive Behaviors of Coronavirus Disease 2019 Infection Among Older Adults in Rayong Province
Keywords:
Older adult, Coronavirus disease 2019, Preventive behaviors, Precede – proceed model, Predisposing factorAbstract
Coronavirus disease 2019 is characterized as a global pandemic that caused millions of infections and deaths worldwide. Particularly, the elderly with age-related deterioration and chronic disease are more vulnerable to be infected and death. This Cross-sectional study aimed to investigate the preventive behaviors of Coronavirus Disease 2019 and its predictive factors among older adults in Rayong province applying the PRECEDE – PROCEED model. The study samples were 420 elderly aged 60 and older recruited by using multistage sampling technique. Data were collected using interviewed questionnaire. Descriptive and inferential statistics such as Spearman Rank Correlation, Chi-square, and Multiple Logistic Regression analysis were applied for data analysis.
The results showed that the sample had a mean age of 66.9 years old. Most of them were female (62.9%), living in industrial area (59.0%), with good level of preventive behaviors of Coronavirus Disease 2019 (Mean = 34.05, SD. = 3.415). Factors significantly associated with preventive behaviors (p <0.05) were; Predisposing factors (sex, marital status, residential area, perceived susceptibility, perceived severity, perceived barriers, perceived benefits), enabling factors, and reinforcing factors. Multiple logistic regression revealed that the elderly who were female (p <0.05), living in industrial area (p <0.0001), having better perceived severity (p <0.0001), and receiving reinforcing factors (p <0.0001) were more likely to have appropriate preventive behaviors. All these factors could explain the variance of the preventive behavior by 11.8% with a predictive accuracy of 51.0%.
These findings can be used as a guideline for further developing a plan for surveillance, prevention, and control of Coronavirus disease 2019 among the elderly in the community especially for the elderly in the industrial area.
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