Household Poverty Does Not Correlate With Micronutrient Malnutrition: Preliminary Findings From A Cross‐sectional Survey in Madhya Pradesh
Bibliographic record
Abstract
Household poverty may be a useful indicator to identify micronutrient deficient populations. We aimed to determine the correlation between household poverty and prevalence of micronutrient deficiencies among children 6‐59 months of age and their mothers. A multistage stratified cross‐sectional survey conducted in Madhya Pradesh, India, was used. Household risk of poverty was assessed using the multidimensional poverty index (MPI), which measures deprivations in health and nutrition, education, and living standards. Hemoglobin <11‐12 g/dl, inflammation corrected serum retinol <0.70 umol/L, and 25‐hydroxyvitamin D concentration <20 ng/ml, were used to classify anaemia, vitamin A deficiency, and vitamin D deficiency, respectively. Chi‐square test and Spearman's correlation were used to evaluate associations. Micronutrient status of women and children were assessed in 583 households. Of these, 74 (13%) households were MPI‐poor. 471 (81%) children and 521 (89%) mothers were deficient in at least one micronutrient. Among MPI‐poor households, 78% (58/74) of children and 92% (68/74) of women were micronutrient deficient. MPI was not correlated with micronutrient deficiency among children (p=0.57) or mothers (p=0.62). In this study, children aged 6‐59 months and their mothers had a high burden of micronutrient deficiencies, regardless of household poverty. Further research is needed to adapt poverty indices to better identify populations vulnerable to micronutrient deficiencies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".