Food Security最新文献

This study characterizes the community food environment around households in Brazil with children < 5 years of age and analyzes its association with socioeconomic and demographic stratifiers. Data from 12,524 households from the Brazilian National Survey on Child Nutrition (ENANI-2019) were used. The community food environment was assessed through the perception of mothers or caregivers using three indicators: fruit and vegetable score (FV-S), ultra-processed foods score (UPF-S), and community food environment healthiness score (CFEH-S), all ranging from 0–10 points, categorized as low (worse), medium, and high (better). Unadjusted and adjusted multinomial logistic regression analyses were conducted to determine the independent associations between macro-region, household location, race/skin color, level of education of the mother or caregiver, and basic sanitation, and FV-S, UPF-S, and CFEH-S. The frequency of low and high categories was 21.8% and 64.1% for FV-S, 74.3% and 7.7% for UPF-S, and 34.1% and 9.8% for CFEH-S. Adjusted regressions showed that the North macro-region had the worst scenario for FV-S and CFEH-S. For UPF-S, the Northeast and South regions had the worst scenarios. Despite worse FV-S, rural households had better UPF-S and CFEH-S. Households with 0 or 1 basic sanitation service (sewage system, potable water, or garbage collection) had worse FV-S scores; those with 1 service had the worst CFEH-S scenario. We found that macro-region, place of residence, and access to sanitation services are associated with the healthiness of the community food environment around households with children < 5 years in Brazil.

Our research provides new evidence on how social safety net (SSN) programs contribute to household resilience through improved food security and child nutrition in Malawi. Using data from four waves of the Malawi Integrated Household Panel Survey (IHPS), we employ the Callaway and Sant’Anna difference-in-differences (CSDID) estimation technique, which accounts for staggered treatment timing, to investigate the combined and individual effects of various SSN programs, including social cash transfer (SCT), school feeding, supplementary feeding, food aid, and public work (PWP). Our findings suggest that households benefiting from multiple SSN programs demonstrate improved food security, particularly for Household Dietary Diversity Score (HDDS). Additionally, individual programs, such as school feeding, supplementary feeding, and food aid, significantly improve Food Consumption Score (FCS) and HDDS, while SCT program positively impacts child nutrition outcomes, reducing stunting. Heterogeneous effects reveal stronger impacts for female-headed and rural households on HDDS and stunting. These findings underscore the importance of combining programs for greater effectiveness among vulnerable populations and highlight the need for tailored interventions in rural and urban settings and for male- or female-headed households. Furthermore, these findings contribute to discussions on promoting SSN programs to achieve improved food security and nutrition while informing policy to support Sustainable Development Goals 1.3 and 2 in Malawi and other similar regions.

Undernutrition remains a critical issue among ethnic minorities in northern Vietnam, likely due to limited diet diversity. Our study examined the accessibility of retail seeds, its link to diet diversity, and the relative contribution of food and seed market access and income on diet diversity, specifically targeting diversity in vegetables and legumes. We utilized a comprehensive dataset, comprising a household survey conducted with nearly 500 respondents, a community survey in 38 villages, and a market survey of 14 marketplaces. Using the multi-level structural equation modelling (MSEM) method, we empirically validated a conceptual model linking diet diversity with food and seed market access and satisfaction with off-farm seed sources, where the latter two are aspects of accessibility of retail seeds. The linkages are mediated by crop diversity at both village and household levels, and household wealth. Results showed that retail seeds played a key role in enhancing crop diversity, while self-saved seeds remained the dominant seed source. Crop diversity influenced diets directly through the subsistence pathway and indirectly through the income pathway, though the latter effect was smaller. Household wealth also improved diet diversity directly and indirectly by promoting crop diversity, suggesting that income sources outside of vegetable and legume production are crucial for supporting diverse diets. Market access enhances diet diversity through food purchases and indirectly through crop diversity, yet this indirect effect is much smaller. Its overall impact on crop diversity was more nuanced due to multiple influences. Furthermore, farmers’ satisfaction with off-farm seed sources indirectly improved diet diversity by increasing crop diversity. Our findings suggest that promoting crop diversity and supporting food purchases are both essential for improving diets. Encouraging integrated seed systems that recognize the role of both farmer and formal seed systems could further enhance crop diversity and diet diversity.

This study integrates geospatial analysis with machine learning to examine the spatial dynamics of food insecurity among Syrian refugees in Lebanon. Using household survey data and novel geospatial indicators from 2018 to 2022 for 22,626 households, we investigate why certain food security measures are effective in specific contexts while others are not. Our findings reveal that geolocational factors play a significant role in shaping food insecurity outcomes, often overshadowing traditional factors like household sociodemographics and living conditions. This suggests a potential shift from labor-intensive socioeconomic survey methods toward more scalable, geospatially driven approaches. The study also highlights considerable variation in food insecurity across locations and subpopulations, raising concerns about the effectiveness of individual measures like the Food Consumption Score (FCS), Household Dietary Diversity Score (HDDS), and Reduced Coping Strategies Index (rCSI) in capturing localized needs. By disaggregating food insecurity dimensions and understanding their spatial distribution, humanitarian and development organizations can better tailor their strategies, directing resources to areas where refugees face the most severe food challenges. From a policy perspective, our insights call for a refined approach that improves the predictive power of food insecurity models, aiding organizations in efficiently targeting interventions.

Computational models are often used to explore the future of the global food system, including the implications for human nutrition, an essential aspect of sustainability. However, the confidence that can be placed in the outputs of these models is often poorly quantified. Here, a sensitivity analysis of the DELTA Model® - a linear mass balance model calculating global nutrient supply using global and regional food balance sheet, processing, waste, inedible portion, composition, and bioavailability datasets - is conducted. First, a one-at-a-time analysis, varying 4019 underpinning datapoints from the above datasets individually by ± 50% was conducted to identify those with the greatest impact on calculated global nutrient supply. The most influential values from this initial analysis were then carried forward into a multiple value sensitivity analysis, where all possible combinations of ± 50% variations were simulated. Values related to cereals supply, waste, and nutritional value were the most influential on model output, with selenium, cystine, and carbohydrate supply the most sensitive nutrients. When compared to global nutrient requirements, variations in the calculated supply of some nutrients led to qualitative changes from a sufficient global supply to an insufficient supply. These results, while indicative rather than precise estimates of uncertainty, emphasise the critical importance of accurate cereals data in food system models, provide insight on the degree of sensitivity of similar linear models, and should encourage broader application of sensitivity analysis in the field.

Unhealthy concentrations of food outlets near schools are a growing public health concern, as they may negatively influence the dietary habits and overall health of children and adolescents attending these institutions. This study investigates the spatial association between schools and food outlets selling ultra-processed foods (UPFs) in five Brazilian capital cities. Schools and ten types of UPF-selling outlets were georeferenced within neighborhoods across these cities. Density measures of schools and stores per 10,000 inhabitants were calculated for each neighborhood. Associations between school and food outlets densities were analyzed using negative binomial regression models for each city, as well as a multilevel negative binomial regression model for all cities combined. The results revealed that the densities of snack bars, bakeries, mini-markets, restaurants, and the total number of UPF-selling establishments were positively associated with school densities across all cities and collectively. This suggests that children and adolescents may be exposed to unhealthy food environments around their schools, emphasizing the need for targeted public health interventions.