Dietary Diversity in the Eastern Mediterranean Region Before and During the COVID-19 Pandemic: Disparities, Challenges, and Mitigation Measures

Hoteit, Maha; Mortada, Hussein; Al-Jawaldeh, Ayoub; Mansour, Rania; Yazbeck, Batoul; Alkhalaf, Majid M.; Bookari, Khlood; Tayyem, Reema; Al-Awwad, Narmeen J.; Sabbah, Haleama Al; Ismail, Leila; Qasrawi, Radwan; Seir, Rania Abu; Kamel, Iman; Dashti, Somaia; Allehdan, Sabika; Al‐Mannai, Mariam; Bawadi, Hiba; Waly, Mostafa I.; Group, The Regional CORONA COOKING Survey; Backer, Charlotte De; Royen, Kathleen Van; Teunissen, Lauranna; Cuykx, Isabelle; Decorte, Paulien; Ouvrein, Gaëlle; Poels, Karolien; Vandebosch, Heidi; Maldoy, Katrien; Pabian, Sara; Matthys, Christophe; Smits, Tim; Vrinten, Jules; DeSmet, Ann; Teughels, Nelleke; Geuens, Maggie; Vermeir, Iris; Proesmans, Viktor; Hudders, Liselot; Alalwan, Tariq A.; Al-Bayyari, Nahla; Ibrahim, Mohammed O.; Hammouh, Fadwa; Dashti, Basma; Alkharaif, Dhuha; Alshatti, Amani; Mazedi, Maryam Al; Naim, Elissa; Ibrahim, Carla; Hamdan, Motasem; Halawa, Diala Abu Al; Agha, Hazem; Othman, Manal; Pakari, Jaafar; Farha, Allam Abu; Abu-El-Ruz, Rasha; Arrish, Jamila; Taha, Zainab; Aldhaheri, Ayesha

doi:10.3389/fnut.2022.813154

Cited by 30 publications

(6 citation statements)

References 30 publications

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“…Ensuring resilient food systems in the Arab world is challenging amidst the overwhelmed population growth, dwindling natural resources, and import dependency ( 7 ). Further, the COVID-19 crisis revealed how unprepared the region was to respond appropriately to the pandemic with food supply chains varying in complexity and vulnerable to disruption amongst sub-regions ( 8 ). Given these constraints, a multisector approach considering each policy’s situation in the larger food environment and supply chain is required.…”

Section: Introductionmentioning

confidence: 99%

Status and correlates of food and nutrition literacy among parents-adolescents’ dyads: findings from 10 Arab countries

Hoteit¹,

Mansour²,

Mohsen³

et al. 2023

Front. Nutr.

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BackgroundFood literacy is capturing the attention worldwide and gaining traction in the Arab countries. Strengthening food and nutrition literacy among Arab teenagers are important promising empowering tools which can protect them from malnutrition. This study aims to assess the nutrition literacy status of adolescents with the food literacy of their parents in 10 Arab countries.MethodsThis cross-sectional study involving a convenient sample of 5,401 adolescent-parent dyads (adolescents: mean age ± SD: 15.9 ± 3.0, females: 46.8%; parents: mean age ± SD: 45.0 ± 9.1, mothers: 67.8%) was launched between 29 April and 6 June 2022 in 10 Arab nations. The Adolescent Nutrition Literacy Scale (ANLS) and the Short Food Literacy Questionnaire (SFLQ) were used to meet the study aims.ResultsMore than one-quarter (28%) of adolescents had poor nutrition literacy, with 60% of their parents being food illiterate. The top three countries with nutritionally” less literate” adolescents were Qatar (44%), Lebanon (37.4%), and Saudi Arabia (34.9%). Adolescents’ age, gender, education level, primary caregivers, employment status, and the inclusion of nutrition education in the schools’ curriculum predicted the nutrition literacy levels of Arab adolescents. Besides, parental weight status, health status, parent’s food literacy level, and the number of children per household were significant determinants too. Adolescents studying at a university and having parents with adequate food literacy had the highest odds of being nutritionally literate (OR = 4.5, CI = 1.8–11.5, p = 0.001, OR = 1.8, CI = 1.6–2.1, p < 0.001, respectively).ConclusionNutrition literacy inadequacy among Arab adolescents is a prioritized challenge to be tackled.

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Section: Introductionmentioning

confidence: 99%

Status and correlates of food and nutrition literacy among parents-adolescents’ dyads: findings from 10 Arab countries

Hoteit¹,

Mansour²,

Mohsen³

et al. 2023

Front. Nutr.

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“…The “Corona Cooking Survey April 2020” [ 35 ] conducted by 38 different countries during the COVID -19 pandemic was used for this study. The survey was distributed to over 300 participants per country in each of the following Arab countries: (Palestine, Lebanon, Jordan, Kuwait, Oman, Qatar, Saudi Arabia, United Arab Emirates, and Bahrain.…”

Section: Methodsmentioning

confidence: 99%

Machine learning techniques for the identification of risk factors associated with food insecurity among adults in Arab countries during the COVID-19 pandemic

Qasrawi,

Hoteit,

Tayyem

et al. 2023

BMC Public Health

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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 The ML algorithms seem to be an effective method in early detection and prediction of food insecurity and can profoundly aid policymaking. The integration of ML approaches in public health strategies could potentially improve the development of targeted and effective interventions to combat food insecurity in these regions and globally.

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“…Hanbazaza [172]; Hanbazaza and Wazzan [68]; Hariri et al [173]; Helal et al [88]; Hesham et al [174]; Hoteit et al [81]; Hoteit et al [69]; Hoteit et al [196]; Husain and Ashkanani [122]; Issa et al [113]; Jalal et al [175]; Kamaleddine et al [70]; Khabour and Hassanein [103]; Khamees et al [114]; Kilani et al [97]; Mertens and Peñalvo [209];…”

Section: Food Utilisationmentioning

confidence: 99%

“…Faour-Klingbeil et al [205]; Faour-Klingbeil et al [94]; Fiddian-Qasmiyeh [129]; Ftouhi et al [201]; Gedeon et al [67]; Ghali-Zinoubi [183]; Hammoudi et al [131]; Hanbazaza [172]; Hanbazaza and Wazzan [68]; Hariri et al [173]; Helal et al [88]; Hesham et al [174]; Hoteit et al [81]; Hoteit et al [69]; Hoteit et al [196];…”

Section: Sustainability Dimension * Documents Topics Addressedmentioning

confidence: 99%

“…Regarding the social dimension, most studies address the impacts of the COVID-19 pandemic on health and food (in)security/(mal)nutrition. For instance, Hoteit et al [196], in their analysis of the Eastern Mediterranean region, concluded that the COVID-19 crisis exposed the region's lack of preparedness for a pandemic. They noted that while the aggressive containment strategy adopted by most countries in the region was crucial in preventing the spread of the virus, it resulted in a significant nutritional cost by leading to poor dietary diversity.…”

Section: Policy and Governancementioning

confidence: 99%

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Regional Agriculture and Food Systems Amid the COVID-19 Pandemic: The Case of the Near East and North Africa Region

El Bilali,

Ben Hassen

2024

Foods

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The COVID-19 pandemic affected agri-food systems worldwide. However, while the impacts differed from one country/region to another, the scholarly literature seems to focus on developed countries in the Global North. Consequently, this review scrutinizes the literature on the pandemic’s impacts in the Near East and North Africa (NENA) region. A search on the Web of Science in March 2023 generated 334 documents, of which 151 were eligible for inclusion in the systematic review. According to the bibliometric analysis, the most active nations are Saudi Arabia, Egypt, Jordan, the United Arab Emirates, and Lebanon. In general, the coverage of studies is better in the Gulf region than in the less developed countries of North Africa and those suffering from wars (cf. Libya, Syria, and Yemen). Studies generally focus on crop production and the downstream food chain (cf. distribution and consumption). While the pandemic influenced every pillar of food security, this research concentrates on access and utilization. Meanwhile, the lion’s share of the literature deals with the pandemic’s socio-economic effects, especially those linked to food (in)security and health. The pandemic, which laid bare the agri-food system vulnerabilities, should be seized to foster the transition towards more resilient agri-food systems in the NENA region.

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Dietary Diversity in the Eastern Mediterranean Region Before and During the COVID-19 Pandemic: Disparities, Challenges, and Mitigation Measures

Cited by 30 publications

References 30 publications

Status and correlates of food and nutrition literacy among parents-adolescents’ dyads: findings from 10 Arab countries

Status and correlates of food and nutrition literacy among parents-adolescents’ dyads: findings from 10 Arab countries

Machine learning techniques for the identification of risk factors associated with food insecurity among adults in Arab countries during the COVID-19 pandemic

Regional Agriculture and Food Systems Amid the COVID-19 Pandemic: The Case of the Near East and North Africa Region

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