The influence of the food environment on diet quality: Insights from an extensive household survey in Ethiopia, focusing on women of reproductive age
Bibliographic record
Abstract
BACKGROUND: Undernutrition is a significant challenge in Ethiopia, where limited dietary diversity and widespread micronutrient deficiencies affect millions, especially women of reproductive age. Although much research has explored dietary gaps and practices, the impact of the food environment (FE) on diet quality remains understudied. This study examines how FE factors influence dietary quality, using a food systems approach to inform sustainable nutrition policies. METHODS: A cross-sectional survey of 1,828 households was utilized to assess women's dietary quality using Poisson regression, with food environment (FE) components and socioeconomic variables predicting four indicators: women's diet diversity score (WDDS), fruit and vegetable score (FVS), global dietary quality score (GDQS), and household diet diversity score (HDDS). RESULTS: Dietary diversity was low, with fewer than 20% of women meeting the minimum recommendation; however, improvement was observed with higher quality food environments (FE). Starchy staples dominated consumption across all FE, while pulses and dark green leafy vegetables increased in households with medium and high FE. Low household dietary diversity (average 6.23) and a GDQS of 20.7 (far below the maximum of 49) indicate widespread deficiency. Poisson regression (adjusted for socioeconomic covariates) showed that a high FE score significantly predicted better diet quality compared to a low FE score: 27% higher WDDS (exp(β) = 1.27, 95% CI: 1.22-1.32, p < 0.001), 43% higher FVS (exp(β) = 1.43, 95% CI: 1.33-1.54, p < 0.001), 5% higher GDQS (exp(β) = 1.05, 95% CI: 1.02-1.08, p < 0.001), and 48% higher HDDS (exp(β) = 1.48, 95% CI: 1.43-1.54, p < 0.001). The results demonstrate consistent and significant associations between higher food environment (FE) scores and improved diet quality across all four dietary metrics. CONCLUSIONS: This study demonstrates the critical influence of food environments-encompassing market food diversity, physical access (e.g., food availability), economic access (e.g., affordability), and supportive infrastructure (e.g., roads, transportation, financial services)-on improving dietary quality among Ethiopian women and household food security. High food prices, socioeconomic disparities, and regional variations limit access to nutrient-rich foods (e.g., fruits, vegetables, animal-sourced products), particularly for low-income households. To enhance dietary quality and health outcomes, policymakers should prioritize interventions that expand diverse food markets, strengthen rural infrastructure (roads, transportation), and improve affordability through targeted economic support and price stabilization, ensuring equitable access to nutritious foods. TRIAL REGISTRATION: Not applicable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".