The determinants of severe food insecurity in Africa using the longitudinal generalized Poisson mixed model
Food insecurity is a multifaceted issue (challenge) that affects health care, policies, agriculture output leadership, the environment, the food system, and the politics of global commerce in the food industry. Our aim was to get the relevant components of food security and nutrition concerning Afr...
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| Format: | Article |
| Language: | English |
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Lietuvos statistikų sąjunga, Lietuvos statistikos departamentas
2024-01-01
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| Series: | Lithuanian Journal of Statistics |
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| Online Access: | https://www.zurnalai.vu.lt/statisticsjournal/article/view/33905 |
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| author | Adusei Bofa Temesgen Zewotir |
| author_facet | Adusei Bofa Temesgen Zewotir |
| author_sort | Adusei Bofa |
| collection | DOAJ |
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Food insecurity is a multifaceted issue (challenge) that affects health care, policies, agriculture output leadership, the environment, the food system, and the politics of global commerce in the food industry. Our aim was to get the relevant components of food security and nutrition concerning Africa holistically and use these identified components to discover the most informative correlates that affect the number of severe food insecure individuals in Africa with its population as an offset. Principal Component Analysis (PCA) was used to detect the relevant components of Africa’s food security and nutrition. The Poisson Generalized Linear Mixed Model (GLMM) was employed to identify the significant components. Generalized estimating equations were then applied to account for the overdispersion associated with the Poisson distribution. To make the interpretation of the results more meaningful, 10 PCA components were selected. They explained 74.6\% of the variation within the data. The GLMM analysis remarkably identified Nutrient Intake, Average Food Supplied, Child Care, Dietary Supply Adequacy, and Feeding Practices Among Infants to be significantly associated with the Rate of Severe Food Insecure Individuals (p-value < 0.05). A better improvement in the average food supply in Africa is likely to yield an improvement in food security and nutrition. Our findings provide insight concerning Africa which will help policymakers create targeted plans for Africa that will address issues with food security and nutrition, and this will fuel the achievement of Sustainable Development Goal 2.
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| format | Article |
| id | doaj-art-9c1db29b8c514d7e9c1a82b157a40340 |
| institution | Kabale University |
| issn | 1392-642X 2029-7262 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Lietuvos statistikų sąjunga, Lietuvos statistikos departamentas |
| record_format | Article |
| series | Lithuanian Journal of Statistics |
| spelling | doaj-art-9c1db29b8c514d7e9c1a82b157a403402025-02-11T18:12:00ZengLietuvos statistikų sąjunga, Lietuvos statistikos departamentasLithuanian Journal of Statistics1392-642X2029-72622024-01-016210.15388/LJS.2023.33905The determinants of severe food insecurity in Africa using the longitudinal generalized Poisson mixed modelAdusei Bofa0Temesgen Zewotir1University of KwaZulu Natal Westville CampusUniversity of KwaZulu Natal Westville Campus Food insecurity is a multifaceted issue (challenge) that affects health care, policies, agriculture output leadership, the environment, the food system, and the politics of global commerce in the food industry. Our aim was to get the relevant components of food security and nutrition concerning Africa holistically and use these identified components to discover the most informative correlates that affect the number of severe food insecure individuals in Africa with its population as an offset. Principal Component Analysis (PCA) was used to detect the relevant components of Africa’s food security and nutrition. The Poisson Generalized Linear Mixed Model (GLMM) was employed to identify the significant components. Generalized estimating equations were then applied to account for the overdispersion associated with the Poisson distribution. To make the interpretation of the results more meaningful, 10 PCA components were selected. They explained 74.6\% of the variation within the data. The GLMM analysis remarkably identified Nutrient Intake, Average Food Supplied, Child Care, Dietary Supply Adequacy, and Feeding Practices Among Infants to be significantly associated with the Rate of Severe Food Insecure Individuals (p-value < 0.05). A better improvement in the average food supply in Africa is likely to yield an improvement in food security and nutrition. Our findings provide insight concerning Africa which will help policymakers create targeted plans for Africa that will address issues with food security and nutrition, and this will fuel the achievement of Sustainable Development Goal 2. https://www.zurnalai.vu.lt/statisticsjournal/article/view/33905principal component analysisPoisson generalized linear mixed modelfood securitynutrition |
| spellingShingle | Adusei Bofa Temesgen Zewotir The determinants of severe food insecurity in Africa using the longitudinal generalized Poisson mixed model Lithuanian Journal of Statistics principal component analysis Poisson generalized linear mixed model food security nutrition |
| title | The determinants of severe food insecurity in Africa using the longitudinal generalized Poisson mixed model |
| title_full | The determinants of severe food insecurity in Africa using the longitudinal generalized Poisson mixed model |
| title_fullStr | The determinants of severe food insecurity in Africa using the longitudinal generalized Poisson mixed model |
| title_full_unstemmed | The determinants of severe food insecurity in Africa using the longitudinal generalized Poisson mixed model |
| title_short | The determinants of severe food insecurity in Africa using the longitudinal generalized Poisson mixed model |
| title_sort | determinants of severe food insecurity in africa using the longitudinal generalized poisson mixed model |
| topic | principal component analysis Poisson generalized linear mixed model food security nutrition |
| url | https://www.zurnalai.vu.lt/statisticsjournal/article/view/33905 |
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