Food insecurity—not having sufficient quantities of good-quality foods—is inversely related to physical and mental health and directly related to poor dietary intake. The objectives of this research were to (a) measure the prevalence of food insecurity among women in northern Jordan, (b) study the socioeconomic factors associated with an increased risk of food insecurity, and (c) investigate the relationship between household food insecurity and women's reported body-weight. This cross-sectional study was conducted using an interview-based questionnaire. In total, 500 women were interviewed in the waiting rooms of the outpatient clinics of two major public hospitals in northern Jordan. Food insecurity was assessed using the short form of the U.S. food security survey module. The prevalence of food insecurity was 32.4%. Income below the poverty-line, illiteracy, unemployment, rented housing, and woman heading the household were among the socioeconomic factors that increased the probability of food insecurity. No evidence was found to support the relationship between obesity and food insecurity. Except grains, food-insecure women with hunger had lower intake of all food-groups. This study demonstrated that the problem of food insecurity is present in Jordan. Food-insecure women with hunger are at a risk of malnutrition. Interventions that target reduction of the factors associated with food insecurity are necessary.

The present study examined differences in dietary habits and physical activity levels between students attending private and public high schools in Jordan. A total of 386 secondary-school males and 349 females aged 14-18 years were randomly recruited using a multistage, stratified, cluster sampling technique. Dietary habits and physical activity level were self-reported in a validated questionnaire. The prevalence of obesity was significantly higher among adolescents in private (26.0%) than in public schools (16.7%). The frequency of breakfast intake was significantly higher among adolescents in private schools, whereas French fries and sweets intake was significantly higher in public schools. Television viewing showed a significant interaction with school type by sex. A higher rate of inactivity was found among students attending private schools. Despite a slightly better overall dietary profile for students in private schools, they had a higher rate of overweight and obesity compared with those in public schools.

Consuming fruits and vegetables did not significantly correlate with a lowered incidence of CRC. However, a trend of protection was detected for several types of fruits and vegetables.

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Methods and Results:A convenient sample of 167 adults was obtained from students and employees in major hospital in Jordan. Serum concentrations of leptin, adiponectin, resistin and interleukine-6 were measured. Nutrients intakes were assessed using a validated quantitative food frequency questionnaire. Higher levels of leptin were associated with the highest consumption of energy from carbohydrate, insoluble, and soluble fiber (P=0.04). Lower levels of leptin were associated with highest consumption of energy from fat (P=0.04), monounsaturated fatty acids (P=0.04) and cholesterol (P=0.02). Lower levels of adiponectin were found among individuals with the highest consumption of carbohydrates (p=0.02) insoluble fibers (P=0.01); and copper (P=0.03). Higher levels of adiponectin were associated with higher consumption of cholesterol (P=0.03). Leptin/adiponectin ratio was positively associated with the intakes of carbohydrates (P=0.04), soluble-(P=0.01) and insoluble fibers (P=0.01) and copper (P=0.03), whereas the ratio was negatively associated with cholesterol (P=0.04), butyric acid (P=0.03) and omega-3 fatty acids (P=0.03). Levels of resistin were only associated with total fiber intake (P=0.04) and levels of interleukine-6 were only associated with cholesterol intake (P=0.01). Conclusion:Our findings suggest that intakes of carbohydrates, fat, cholesterol and fibers are the major dietary factors that may be associated with levels of leptin and adiponectin. Levels of resistin and interleukine-6 may be less associated with diet composition.

ObjectiveThis study aimed to detect possible associations between lung computed tomography (CT) findings in COVID-19 and patients' age, body weight, vital signs, and medical regimen in Jordan.MethodsThe present cross-sectional study enrolled 230 patients who tested positive for COVID-19 in Prince Hamza Hospital in Jordan. Demographic data, as well as major lung CT scan findings, were obtained from the hospital records of the COVID-19 patients.ResultsThe main observed major lung changes among the enrolled COVID-19 patients included ground-glass opacification in 47 (20.4%) patients and consolidation in 22 (9.6%) patients. A higher percentage of patients with major lung changes (24%) was observed among patients above 60 years old, while (50%) of patients with no changes in their lung findings were in the age group of 18–29 years old. Results obtained from the present study showed that only patients with major CT lung changes (9.7%) were prescribed more than three antibiotics. Additionally, 41.6 % of patients with major lung CT scan changes had either dry (31.0%) or productive (10.6%) cough at admission.ConclusionSeveral factors have been identified by this study for their ability to predict lung changes. Early assessment of these predictors could help provide a prompt intervention that may enhance health outcomes and reduce the risk for further lung changes.

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.