Socioeconomic determinants of the double burden of malnutrition among women of reproductive age in sub‐Saharan Africa: A cross‐sectional study
Bibliographic record
Abstract
Background and Aim: The positioning of eliminating all forms of malnutrition within the spirit of the Sustainable Development Goals and the adoption of the United Nations resolution for a Decade of Action on Nutrition are a testament to strong global commitment to combat the double burden of malnutrition (DBM). Yet, there is a knowledge gap in sub-Saharan Africa (SSA) regarding the influence of socioeconomic status on DBM. We investigated the associative effect of socioeconomic status on DBM in SSA. Methods: Data for the study were extracted from the most recent Demographic and Health Surveys (DHS) of 29 countries in SSA conducted from 2010 to 2020. Bivariate and multivariate logistic regression models were fitted to examine the association between socioeconomic status and DBM. The results were presented using adjusted odds ratio (aOR) and 95% confidence interval (CI). Results: Children of obese mothers were less likely to be stunted compared to those born to mothers who were not overweight/obese [aOR = 0.70; 95% CI = 0.66-0.77]. The odds of stunting increased with wealth index, with children born to poorest mothers having the highest odds compared to those born to richest mother [aOR = 1.79; 95% CI = 1.64-1.95]. The odds of stunting among children was highest among those born to mothers with no formal education compared to those whose mothers had higher education [aOR = 2.73; 95% CI = 2.34-3.18]. Conclusion: DBM among children in SSA is predicted by maternal level of education, and wealth status. These results underscore the urgency of tailored interventions and policies that address DBM among women of reproductive age, with a particular focus on the socioeconomic disparities in SSA. To effectively combat this pressing public health issue, it is imperative to direct efforts towards empowering women to attain higher levels of education and to implement strategies that consider the specific needs of women across varying socioeconomic statuses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".