Drivers of stunting and wasting across serial cross-sectional household surveys of children under 2 years of age in Pakistan: potential contribution of ecological factors
Bibliographic record
Abstract
BACKGROUND: The impact of direct and indirect drivers on linear growth and wasting in young children is of public health interest. Although the contributions of poverty, maternal education, empowerment, and birth weight to early childhood growth are well recognized, the contribution of environmental factors like heat, precipitation, agriculture outputs, and food security in comparable datasets is less well established. OBJECTIVES: This study aims to investigate the association of length-for-age z-score (LAZ) and weight-for-length z-score (WLZ) with various indicators among children aged under 2 y in Pakistan using representative household-level nutrition surveys and ecological datasets. METHODS: Using geo-tagged metadata from Pakistan's 2011 and 2018 National Nutrition Surveys, anthropometric data from 29,887 children (9231 from 2011 and 20,656 from 2018) were analyzed. Dietary intake and food security data for 140 districts were linked to gridded data on temperature, precipitation and soil moisture, and district measures of agriculture production of edible crops. Multiple linear regressions assessed factors associated with LAZ and WLZ in index children. RESULTS: LAZ was positively associated with improved socioeconomic conditions (β = 0.06), food security (β = 0.10), birth size (β = 0.26), maternal age (β = 0.02), body mass index (β = 0.02), height (β = 0.02), and dietary score (β = 0.03). Negative associations with LAZ were found for increased temperature, precipitation, diarrhea, household crowding, and parity. Similar patterns were observed with WLZ for higher surface temperatures and precipitation was associated with declines in linear growth, alongside increased diarrhea prevalence and higher maternal parity. CONCLUSIONS: Apart from recognized multifactorial drivers of stunting and wasting among children such as poverty, food insecurity, and maternal undernutrition, our analysis suggests the potential independent association with climatic factors such as heat and excess precipitation over time. These findings underscore the need for further research and the potential integration of climatic mitigation and adaptation with nutrition response strategies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".