Association of Household and Community Socioeconomic Position and Urbanicity with Underweight and Overweight among Women in Pakistan
Bibliographic record
Abstract
BACKGROUND: Similar to other developing countries, Pakistan is going through a rapid nutrition transition where shift from underweight to overweight and obesity is occurring. In this paper, we report on the relationship of household socioeconomic position (SEP), community SEP and urbanicity with under- and over-weight categories of BMI among Pakistani women. METHODS: We analyzed data on 4,767 women ages 15-49 years enrolled in a nationally representative Pakistan Demographic Health Survey (PDHS) conducted in 2012-13 that employed a multistage, stratified cluster sampling design. We assessed the association of urbanicity, household and community SEP derived from household assets and utilities, with categories of body mass index (BMI) using multinomial regression analysis where normal weight (BMI 18.6-22.5) was the reference category. RESULTS: Thirteen percent of women were underweight (BMI <18.5), 15% pre-overweight (BMI: 22.6-24.9), 25% overweight (BMI: 25.0-29.9) and 14% were obese (BMI≥30). Pre-overweight, overweight and obesity among women increased across household wealth quintiles (HWQs) in a graded fashion whereas there was no significant difference in underweight by household wealth. Women in urban areas were more likely to be obese. There was a pronounced increase in adjusted odds ratios (aORs) for overweight/obesity across HWQs within urban areas compared to rural areas. There was a steeper gradient in aORs for obesity from 1st to 5th HWQs in high income communities compared to the middle- and low income communities. In community-level analyses, communities in urban areas were more likely to have higher levels of obesity while in rural areas, especially in Sindh, more communities were more likely to have a higher level of underweight. CONCLUSION: A shift to higher overweight and obesity than underweight in Pakistan is associated with high household and community wealth as well as living in urban areas. Clustering of obesity and underweight in distinct communities afford opportunity for tailored intervention programs.
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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 | 0.001 |
| 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) | 0.001 | 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".