Trends in obesity by socioeconomic status among non-pregnant women aged 15–49 y: a cross-sectional, multi-dimensional equity analysis of demographic and health surveys in 11 sub-Saharan Africa countries, 1994–2015
Bibliographic record
Abstract
BACKGROUND: Global obesity estimates show a steadily increasing pattern across socioeconomic and geographical divides, especially among women. Our analysis tracked and described obesity trends across multiple equity dimensions among women of reproductive age (15-49 y) in 11 sub-Saharan African (SSA) countries during 1994-2015. METHODS: This study consisted of a cross-sectional series analysis using nationally representative demographic and health surveys (DHS) data. The countries included were Cameroon, Comoros, Congo, Cote d'Ivoire, Ghana, Kenya, Lesotho, Nigeria, Senegal, Zambia and Zimbabwe. The data reported are from a reanalysis conducted using the WHO Health Equity Assessment Toolkit that assesses inter- and intra-country health inequalities across socioeconomic and geographical dimensions. We generated equiplots to display intra- and inter-country equity gaps. RESULTS: There was an increasing trend in obesity among women of reproductive age across all 11 SSA countries. Obesity increased unequally across wealth categories, place of residence and educational measures of inequality. The wealthiest, most educated and urban dwellers in most countries had a higher prevalence of obesity. However, in Comoros, obesity did not increase consistently with increasing wealth or education compared with other countries. The most educated and wealthiest women in Comoros had lower obesity rates compared with their less wealthy and less well-educated counterparts. CONCLUSION: A window of opportunity is presented to governments to act structurally and at policy level to reduce obesity generally and prevent a greater burden on disadvantaged subpopulation groups in sub-Saharan Africa.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".