| Summary: | 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.
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