Are Household Expenditures on Food Groups Associated with Children’s Future Heights in Ethiopia, India, Peru, and Vietnam?
Bibliographic record
Abstract
Household expenditure surveys, routinely conducted in low-and middle-income countries (LMICs), usually include questions pertaining to recent household expenditures on key food groups. When child anthropometrics are also available, such expenditure data can provide insights into household food purchasing patterns that are associated with subsequent child growth measures. We used data from 6993 children, born around 2001, from Ethiopia, India, Peru, and Vietnam, from the Young Lives younger cohort. We compared associations between two weeks of household food expenditures (in PPP-Purchasing Power Parity adjusted dollars) on food groups and child height-for-age-Z score (HAZ) at subsequent time points to assess longitudinal associations. Total food expenditures, rural/urban residence, maternal and paternal schooling, and child sex were included in our adjusted models because they may affect the relations between household food group expenditures and future child HAZ. In Ethiopia, India, and Peru every extra PPP$ spent on fats was associated with 0.02-0.07 higher future HAZ. In Vietnam every extra PPP$ spent on starches, was significantly associated with a 0.01 lower future HAZ. Across countries, different patterns of food expenditure and procurement may be differentially critical for predicting child HAZ. Our results demonstrate how expenditures on specific food groups can be associated with children's linear growth. This study provides additional evidence of the utility of longitudinal household food expenditure data in understanding child nutritional status.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".