Geographical and socioeconomic inequalities in women and children's nutritional status in Pakistan in 2011: an analysis of data from a nationally representative survey
Bibliographic record
Abstract
BACKGROUND: Pakistan has one of the highest levels of child and maternal undernutrition worldwide, but little information about geographical and socioeconomic inequalities is available. We aimed to analyse anthropometric indicators for childhood and maternal nutrition at a district level in Pakistan and assess the association of nutritional status with food security and maternal and household socioeconomic factors. METHODS: We used data from the 2011 Pakistan National Nutrition Survey, which included anthropometric measurements for 33 638 children younger than 5 years and 24 826 women of childbearing age. We estimated the prevalences of stunting, wasting, and underweight among children and of underweight, overweight, and obesity in women for all 143 districts of Pakistan using a Bayesian spatial technique. We used a mixed-effect linear model to analyse the association of nutritional status with individual and household sociodemographic factors and food security. FINDINGS: Stunting prevalence in Pakistan's districts ranged between 22% (95% credible interval 19-26) and 76% (69-83); the lowest figures for wasting and underweight were both less than 2·5% and the highest were 42% (34-50) for wasting and 54% (49-59) for underweight. In 106 districts, more women were overweight than were underweight; in 49 of these districts more women were obese than were underweight. Children were better nourished if their mothers were taller or had higher weight, if they lived in wealthier households, and if their mothers had 10 or more years of education. Severe food insecurity was associated with worse nutritional outcomes for both children and women. INTERPRETATION: We noted large social and geographical inequalities in child and maternal nutrition in Pakistan, masked by national and provincial averages. Pakistan is also beginning to face the concurrent challenge of high burden of childhood undernutrition and overweight and obesity among women of reproductive age. Planning, implementation, and evaluation of programmes for food and nutrition should be based on district-level needs and outcomes. FUNDING: Bill & Melinda Gates Foundation, Grand Challenges Canada, UK Medical Research Council.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".