The Nutrition-Sensitive Food Environment Index: A comprehensive approach to assessing food environments in association with health risks for policy decision making
Food environment indices often focus on food affordability, overlooking public health aspects. This study introduces a Nutrition-Sensitive Food-Environment Index (N-FEI) that assesses the interplay between food diversity, accessibility, and water and sanitation facilities linked to malnutrition risk...
| Autores principales: | , , , , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/175055 |
| _version_ | 1855513923542843392 |
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| author | Akingbemisilu, Tosin Harold Jordan, Irmgard Asiimwe, Robert Bodjrenou, Sam Nabuuma, Deborah Odongo, Nicanor Onyango, Kevin Omondi Teferi, Ermias Tokeshi, Casey Lundy, Mark Termote, Celine |
| author_browse | Akingbemisilu, Tosin Harold Asiimwe, Robert Bodjrenou, Sam Jordan, Irmgard Lundy, Mark Nabuuma, Deborah Odongo, Nicanor Onyango, Kevin Omondi Teferi, Ermias Termote, Celine Tokeshi, Casey |
| author_facet | Akingbemisilu, Tosin Harold Jordan, Irmgard Asiimwe, Robert Bodjrenou, Sam Nabuuma, Deborah Odongo, Nicanor Onyango, Kevin Omondi Teferi, Ermias Tokeshi, Casey Lundy, Mark Termote, Celine |
| author_sort | Akingbemisilu, Tosin Harold |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Food environment indices often focus on food affordability, overlooking public health aspects. This study introduces a Nutrition-Sensitive Food-Environment Index (N-FEI) that assesses the interplay between food diversity, accessibility, and water and sanitation facilities linked to malnutrition risks.
Data from 17,294 food vendors collected between 2020 and 2023 in six countries were used. Sensitivity analyses, Monte Carlo simulations, and variance decomposition were conducted to validate the index’s robustness. The machine learning algorithm XGBoost was used to predict health risks from Demographic and Health Surveys (DHS) data, integrated into food environment data through geospatial techniques.
The index model is scalable and adaptable for global use. Integrating comprehensive food environment assessments at the administrative census level is recommended to reduce estimation biases and to enhance the policymaking process.
Future research should examine using the index for monitoring and evaluating food system transformations, tracking changes in food environments and related health outcomes. |
| format | Journal Article |
| id | CGSpace175055 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| record_format | dspace |
| spelling | CGSpace1750552025-12-11T21:34:53Z The Nutrition-Sensitive Food Environment Index: A comprehensive approach to assessing food environments in association with health risks for policy decision making Akingbemisilu, Tosin Harold Jordan, Irmgard Asiimwe, Robert Bodjrenou, Sam Nabuuma, Deborah Odongo, Nicanor Onyango, Kevin Omondi Teferi, Ermias Tokeshi, Casey Lundy, Mark Termote, Celine machine learning malnutrition dietary diversity spatial analysis food environment hygiene Food environment indices often focus on food affordability, overlooking public health aspects. This study introduces a Nutrition-Sensitive Food-Environment Index (N-FEI) that assesses the interplay between food diversity, accessibility, and water and sanitation facilities linked to malnutrition risks. Data from 17,294 food vendors collected between 2020 and 2023 in six countries were used. Sensitivity analyses, Monte Carlo simulations, and variance decomposition were conducted to validate the index’s robustness. The machine learning algorithm XGBoost was used to predict health risks from Demographic and Health Surveys (DHS) data, integrated into food environment data through geospatial techniques. The index model is scalable and adaptable for global use. Integrating comprehensive food environment assessments at the administrative census level is recommended to reduce estimation biases and to enhance the policymaking process. Future research should examine using the index for monitoring and evaluating food system transformations, tracking changes in food environments and related health outcomes. 2025-06-02 2025-06-11T09:41:51Z 2025-06-11T09:41:51Z Journal Article https://hdl.handle.net/10568/175055 en Open Access application/pdf Akingbemisilu, T.H.; Jordan, I.; Asiimwe, R.; Bodjrenou, S.; Nabuuma, D.; Odongo, N.; Onyango, K.O.; Teferi, E..; Tokeshi, C.; Lundy, M..; Termote, C. (2025) The Nutrition-Sensitive Food Environment Index: A comprehensive approach to assessing food environments in association with health risks for policy decision making. Journal of Sustainability 1(1). ISSN: 3052-3761 |
| spellingShingle | machine learning malnutrition dietary diversity spatial analysis food environment hygiene Akingbemisilu, Tosin Harold Jordan, Irmgard Asiimwe, Robert Bodjrenou, Sam Nabuuma, Deborah Odongo, Nicanor Onyango, Kevin Omondi Teferi, Ermias Tokeshi, Casey Lundy, Mark Termote, Celine The Nutrition-Sensitive Food Environment Index: A comprehensive approach to assessing food environments in association with health risks for policy decision making |
| title | The Nutrition-Sensitive Food Environment Index: A comprehensive approach to assessing food environments in association with health risks for policy decision making |
| title_full | The Nutrition-Sensitive Food Environment Index: A comprehensive approach to assessing food environments in association with health risks for policy decision making |
| title_fullStr | The Nutrition-Sensitive Food Environment Index: A comprehensive approach to assessing food environments in association with health risks for policy decision making |
| title_full_unstemmed | The Nutrition-Sensitive Food Environment Index: A comprehensive approach to assessing food environments in association with health risks for policy decision making |
| title_short | The Nutrition-Sensitive Food Environment Index: A comprehensive approach to assessing food environments in association with health risks for policy decision making |
| title_sort | nutrition sensitive food environment index a comprehensive approach to assessing food environments in association with health risks for policy decision making |
| topic | machine learning malnutrition dietary diversity spatial analysis food environment hygiene |
| url | https://hdl.handle.net/10568/175055 |
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