Prevalence and Predictors of Anxiety among Stable Hospitalized COVID-19 Patients in Malaysia.
Muhammad Azri Adam AdnanMohd Shaiful Azlan Bin KassimNorhafizah ShahrilMohamad Aznuddin Bin Abd RazakPublished in: International journal of environmental research and public health (2022)
The COVID-19 pandemic has created anxiety among hospitalized SARS-CoV-2 patients. Therefore, this study aimed to determine the prevalence of anxiety and its associated factors among stable inpatient COVID-19 patients in Malaysia. Method: A cross-sectional study was conducted using a web-based online survey involving 401 patients from Malaysia's leading COVID-19 hospitals from 15th April until 30th June 2020, who were chosen using quota sampling. The General Anxiety Disorders 7 items (GAD-7) scale, the Coping Orientation to Problems Experienced Inventory (Brief-COPE) and a socio-demographic profile questionnaire were used. Descriptive analysis and multiple logistic regression were performed using SPSS v23 to determine the prevalence of anxiety and its associated factors. Result: The results showed that the prevalence of anxiety was 7.0%. Multiple logistic regression analysis revealed that female gender ( p < 0.05), a fear of infection ( p < 0.05), a lack of information ( p < 0.05), a maladaptive coping mechanism of behavioral disengagement ( p < 0.001) and self-blame ( p < 0.001) were significantly associated with anxiety. Meanwhile, adaptive coping mechanisms via instrumental support ( p < 0.001) were a significant protective predictor of anxiety. Conclusions : COVID-19 infection has had a significant influence on the mental health of patients. Findings in our study provide baseline data on the prevalence of anxiety among stabilized COVID-19 inpatients in Malaysia. Despite the relatively low prevalence, the data have the potential to improve the present mental health monitoring system and the deployment of suitable treatments in dealing with similar circumstances.
Keyphrases
- sars cov
- mental health
- risk factors
- end stage renal disease
- sleep quality
- ejection fraction
- newly diagnosed
- peritoneal dialysis
- prognostic factors
- coronavirus disease
- physical activity
- machine learning
- climate change
- social media
- patient reported outcomes
- patient reported
- big data
- health information
- psychometric properties
- data analysis