Increasing Levels of Urban Malnutrition with Rapid Urbanization in Informal Settlements of Katutura, Windhoek: Neighbourhood Differentials and the Effect of Socio-Economic Disadvantage
Bibliographic record
Abstract
Rapid urbanization and increasing urban poverty characterize much of Southern Africa, resulting in poor urban health. This study investigates inter-urban differences and determinants of undernutrition among marginalized communities. Using the 1992, 2000 and 2006/2007 Namibia Demographic and Health Survey data, we fitted hierarchical random intercept logit models, applied at 52 enumeration areas in the capital city (Windhoek), to estimate trends in undernutrition, and investigate risk factors associated with stunting and underweight. Findings demonstrate that undernutrition among children has risen (7.4% to 25.1%, p<0.001 for stunting; and 9.7% to 17.6%, p<0.001 for underweight, between 1992 and 2006/2007). The risk was pronounced for children from socioeconomically disadvantaged households (OR=1.53, 95% CI:[1.01, 2.31] for stunting and OR=2.16, 95% CI:[1.03, 4.89]for underweight). Evidence emerged of intra-urban variation in undernutrition. We argue that with increasing urbanization, comes the challenge of food insecurity and, consequently, malnutrition. For improved child health, urban planners should have targeted interventions for poor urban households and deprived neighbourhoods.
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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 | 0.001 | 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) | 0.002 | 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".