Measuring and Forecasting the Healthy Eating Index in Iran: 1991–2027
Bibliographic record
Abstract
Introduction: Compliance with food-based dietary guidelines, as measured by the Healthy Eating Index (HEI-2015), and an enhancement in its score have been linked to a reduction in mortality risk and the prevalence of chronic diseases. The objective of this study was to compute and scrutinize the HEI for Iran over the preceding 30 years and to project the anticipated index for the forthcoming 7 years. Material and Methods: This research is a repeated cross-sectional study on 665254 Household. Through the application of an array of statistical and nutritional methodologies, we have successfully transformed the Household Income-Expenditure Survey (HIES) questionnaire data, spanning from 1991 to 2020 for the Iranian population, into meaningful household food consumption data. The data analysis was executed using STATA v.17. Results: We found a growing trend in HEIs until 2017, which dropped sharply in the following years. The average HEI at the national level was estimated to be 48.39 ± 10.25 over 30 years. The lowest and highest indices were in Sistan and Baluchistan Province (41.23 ± 7.56) and Qom Province (57.73 ± 11.27). The highest average energy intake was in the year 2005 (2695.33 ± 808.31 Kcal). Conclusion: The utilization of HIES data in this study serves as a proxy for household consumption values. We observed a commendable trend in the HEI up until 2017. However, post 2018, the trend exhibited a decline, potentially attributable to multiple factors. We strongly recommend a regular and strategic review of food and nutrition policies, particularly during crises, to circumvent undesirable outcomes.
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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 | 0.003 | 0.002 |
| 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 | 0.001 |
| 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".