A cross-sectional study of knowledge, attitude, behaviour and preventive measures for COVID-19 infection in Lebanon

Background: The World Health Organization has often reiterated its recommendations for the prevention of COVID-19, however, the success of these measures largely depends on public knowledge and attitudes. Aims: This study assessed the relationship between knowledge, attitude, behaviour and preventive measures for COVID-19 infection in a Lebanese population. Methods: This cross-sectional study was conducted between September and October 2020 using the snowball sampling technique and an online self-administered questionnaire. The questionnaire had 4 parts targeting sociodemographic characteristics; medical history; knowledge, attitude and practices (preventive measures and behaviours related to COVID-19); and mental health variables such as psychological distress. Two models were derived using multivariable binomial logistic regression to optimize the picture of COVID-19 correlates. Results: Our sample comprised 1119 adults. Being older, female, a regular alcohol consumer, waterpipe smoker, having low level of education, low family income, and having contact with a COVID-19 patient correlated with increased odds of ever having been diagnosed with COVID-19. Participants who had ever been diagnosed with COVID-19 had a significantly better knowledge and a higher risky practice scale [adjusted odds ratio (ORa) = 1.49; 95% CI 1.27–1.74; P < 0.001; and ORa = 1.04; 95% CI 1.01–1.08; P = 0.024, respectively]. Conclusion: The most important predictors of COVID-19 infection appear to be generally well-known among the general population, however, their knowledge and adherence to preventive measures should be continuously re-evaluated. This study highlights the need for greater awareness to improve precautionary behaviours among the public.

A cross-sectional study of knowledge, attitude, behaviour and preventive measures for COVID-19 infection in Lebanon

Background: The World Health Organization has often reiterated its recommendations for the prevention of COVID-19, however, the success of these measures largely depends on public knowledge and attitudes. Aims: This study assessed the relationship between knowledge, attitude, behaviour and preventive measures for COVID-19 infection in a Lebanese population. Methods: This cross-sectional study was conducted between September and October 2020 using the snowball sampling technique and an online self-administered questionnaire. The questionnaire had 4 parts targeting sociodemographic characteristics; medical history; knowledge, attitude and practices (preventive measures and behaviours related to COVID-19); and mental health variables such as psychological distress. Two models were derived using multivariable binomial logistic regression to optimize the picture of COVID-19 correlates. Results: Our sample comprised 1119 adults. Being older, female, a regular alcohol consumer, waterpipe smoker, having low level of education, low family income, and having contact with a COVID-19 patient correlated with increased odds of ever having been diagnosed with COVID-19. Participants who had ever been diagnosed with COVID-19 had a significantly better knowledge and a higher risky practice scale [adjusted odds ratio (ORa) = 1.49; 95% CI 1.27–1.74; P < 0.001; and ORa = 1.04; 95% CI 1.01–1.08; P = 0.024, respectively]. Conclusion: The most important predictors of COVID-19 infection appear to be generally well-known among the general population, however, their knowledge and adherence to preventive measures should be continuously re-evaluated. This study highlights the need for greater awareness to improve precautionary behaviours among the public.

Machine Learning Techniques for the Identification of Risk Factors Associated with Food Insecurity among Adults in Arab countries during the COVID-19 Pandemic (preprint)

Background: A direct consequence of global warming, and strongly correlated with poor physical and mental health, food insecurity is a rising global concern associated with low dietary intake. The Coronavirus pandemic has further aggravated food insecurity among vulnerable communities, and thus has sparked the global conversation of equal food access, food distribution, and improvement of food support programs. This research was designed to identify the key features associated with food insecurity during the COVID-19 pandemic using Machine learning techniques. Seven machine learning algorithms were used in the model, which used a dataset of 32 features. The model was designed to predict food insecurity across ten Arab countries in the Gulf and Mediterranean regions. A total of 13,443 participants were extracted from the international Corona Cooking Survey conducted by 38 different countries during the COVID -19 pandemic. Results: The findings indicate that Jordanian, Palestinian, Lebanese, and Saudi Arabian respondents reported the highest rates of food insecurity in the region (15.4%,13.7%,13.7% and 11.3% respectively). On the other hand, Oman and Bahrain reported the lowest rates (5.4% and 5.5% respectively). Our model obtained accuracy levels of 70%-82% in all algorithms. Gradient Boosting and Random Forest techniques had the highest performance levels in predicting food insecurity (82% and 80% respectively). Place of residence, age, financial instability, difficulties in accessing food, and depression were found to be the most relevant features associated with food insecurity. Conclusions: Overall, ML algorithms seem to be an effective method in early detection and prediction of food insecurity. Future research would benefit from utilizing the proposed model in developing more complex and accurate models aiming to enhance granularity, with the ability to share data, to incorporate wide range of variables, and to make use of automation for effective prevention and intervention programs at the regional and individual levels.

The Effects of COVID-19 On The Quality of Life Among a Sample of The General Lebanese Population (preprint)

Background: Infectious illness outbreaks, such as COVID-19, have a devastating impact on physical health and social and psychological well-being. Therefore, the objective of this study was to assess the quality of life (QOL) after the COVID-19 outbreak in a sample of the Lebanese population and compare sociodemographic factors associated with QOL among COVID-19 patients and healthy controls. Methods: : A cross-sectional study conducted between January and March 2021 during the lockdown imposed by the Lebanese Government enrolled 2349 Lebanese adults. The major dependent variable was the 12-item Short Form Survey (SF-12), often used as a QOL measure for assessing the impact of health on an individual's everyday life. Results: : In participants with non-positive PCR, linear regression showed that higher income (Beta=2.224) is associated with a higher QOL score. Whereas higher household crowding index (Beta=-0.537), older age (Beta=-0.109), being married (Beta=-1.308), having hypertension (Beta=-2.479), and other chronic diseases (Beta=-3.704) were associated with a lower QOL score.In participants with positive PCR, linear regression showed that the female gender (Beta=2.416) and a higher income (Beta=4.856) were associated with a higher QOL score. Whereas shortness of breath (beta=-2.607), sore throat (Beta=-5.654), sneezing (Beta=-3.761), and having a chronic disease other than hypertension (Beta=-3.181) were associated with a lower QOL score. Conclusion: Overall, factors such as age, male gender, married status, crowded household, low monthly income, high BMI, the presence of chronic disease, and severe COVID-19 symptoms were related to lower QOL after the covid-19 pandemic.