Geographic and Socioeconomic Disparities in Nutritional Status of Women in Pakistan: Secondary Analysis from Pakistan National Nutrition Survey
Bibliographic record
Abstract
Abstract Background: Pakistan is experiencing a rapid nutrition transition with a shift from underweight to overweight and obesity. This paper will examine the role of household socioeconomic position (SEP), community SEP and urbanicity on the nutritional status (underweight, overweight and obesity) of Pakistani women. Methods: We analysed data on 34,391 women aged ≥20 years enrolled in 2011 National Nutritional Survey of Pakistan (NNS). The NNS is a nationally representative survey employing a multistage stratified cluster sampling design. We assessed household SEP through a wealth index constructed using items from household possessions, utilities and housing conditions. We assessed the relationship of urbanicity, household and community SEP with categories of body mass index (BMI) using multinomial logistic regression where normal BMI (18.6-22.5 kg/m 2 ) was the reference category. Results: Overall, 15% of women were underweight (BMI<18.5 kg/m 2 ), 14% were pre-overweight (BMI 23.00-24.9 kg/m 2 ), 22% were overweight (BMI 25.0-29.99 kg/m 2 ) and 12% were obese (BMI≥30.0 kg/m 2 ). Households with higher SEP were associated with increased levels of overweight-1 (aOR: 2.91; 95%CI: 2.41-3.50), overweight-2 (aOR: 4.15; 95%CI: 3.31-5.19) and obesity (aOR: 6.20; 95%CI: 4.92-7.81) among women. Women were more likely to be obese in major urban (aOR: 2.34; 95%CI: 2.02-2.71) and urban (aOR: 1.84; 95%CI: 1.62-2.09) areas compared to rural areas. At the community level, communities in rural areas were more likely to have higher levels of underweight, while communities in urban areas were more likely to have higher levels of obesity. Furthermore, the likelihood of underweight and overweight women coexisting within the same community was low in major urban (r=0.67), urban (r=0.55) and rural (r=0.54) areas. Conclusions: In Pakistan, overweight and obesity among women is associated with urbanicity and household and community SEP. Women living in urban areas with high household and community SEP were associated with higher levels of overweight and obesity. Our findings suggest the importance of interventions targeting undernutrition in rural areas and overnutrition in urban areas.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".