Household Expenditures on Fruit and Vegetables are Associated with Significant Increases in zHFA at ages 5, 8 and 12 in Ethiopia, India, Peru and Vietnam
Bibliographic record
Abstract
Household expenditure surveys, covering fifteen days of household expenditures and including key food groups, are routinely conducted in low and middle income countries. This data may help identify patterns of food expenditure that influence child growth. Objectives We investigated the relationship between household food expenditures and child growth using four different analytic approaches. Methods We used data on 6,993 children from Ethiopia, India, Peru and Vietnam at ages 5, 8 and 12 yrs from the Young Lives cohort. We compared associations between household food expenditures and child growth (zhfa) using (a) total household food expenditures, (b) principal components, (c) factor analysis and (d) cluster analysis extracted from household expenditures on 13 different food groups, controlling for total food expenditures, round of data collection, rural/urban residence and child sex. Results Factors, principal components and indicators of clusters added significant information about zhfa over and above that provided by total food expenditures. Factors and principal components with loading on animal source foods and/or fruits and vegetables were significantly associated with higher zhfa in all countries except Ethiopia. Factors and principal components with loading on whole grains and pulses were significantly associated with lower zhfa in Peru, and higher zhfa in Ethiopia. Conclusion Household food expenditure data provides important insights into household food purchasing patterns that are significant predictors of zhfa. Including food expenditure data in analyses may yield important information about linear growth. Support or Funding Information This work was supported by the Bill & Melinda Gates Foundation [Global Health Grant OPP1032713]; Eunice Shriver Kennedy National Institute of Child Health and Development [grant number R01 HD070993]; and Grand Challenges Canada [grant number 0072‐03]. The data used in this study come from Young Lives, a 15‐year survey investigating the changing nature of childhood poverty in Ethiopia, India (Andhra Pradesh), Peru, and Vietnam ( www.younglives.org.uk ). Young Lives is core‐funded by UK Aid from the Department for International Development (DFID) and was co‐funded from 2010 to 2014 by the Netherlands Ministry of Foreign Affairs. The findings and conclusions contained herein are those of the authors and do not necessarily reflect positions or policies of the Bill & Melinda Gates Foundation, the Eunice Shriver Kennedy National Institute of Child Health and Development, Grand Challenges Canada, Young Lives, DFID, or other funders.
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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 | 0.000 |
| 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) | 0.002 | 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".