Disparities in the prevalence of child undernutrition in Malawi – a crosssectional perspective
Bibliographic record
Abstract
Background. Child undernutrition is a significant public health problem in Malawi.Objective. To determine the localisation of underweight, stunting and wasting in three main agricultural development divisions (ADDs) in Malawi.Design. Descriptive population-based cross-sectional study.Setting. Rural subsistence farming communities in Mzuzu, Lilongwe and Blantyre ADDs.Subjects. Children aged 6 - 59.9 months.Outcome measures. Anthropometric measurements were taken to determine the mean weight-for-age z-scores (WAZ), height-for-age z-scores (HAZ) and weight-for-height z-scores (WHZ). The prevalences of underweight, stunting and wasting were also determined.Results. The mean WAZ of children from Mzuzu ADD was significantly higher than that of children from Lilongwe ADD (-1.04 v. -1.43, p = 0.001) and Blantyre ADD (-1.04 v. -1.32, p = 0.03). Similarly, children from Mzuzu ADD had significantly higher WHZ than their counterparts from Lilongwe (0.22 v. -0.04, p = 0.021) and Blantyre ADDs (0.22 v. -0.09, p = 0.003). There were no significant between-group differences in mean HAZ ( F = 2.73, p = 0.07). The prevalence of underweight was significantly lower in Mzuzu ADD (16.9%) than Blantyre (25.3%) and Lilongwe (31.3%) ADDs ( 2 = 11.95, p = 0.003). Likewise, stunting was significantly lower in Mzuzu ADD (46.6%) than Blantyre (53.8%) and Lilongwe (61.3%) ADDs ( 2 = 8.71, p = 0.013). There were no differences in the prevalence of either stunting or underweight between Lilongwe and Blantyre ADDs.Conclusion. The differences in prevalence of malnutrition among preschool children in the three agro-ecological zones may result from differences in ecological, demographic, social, economic and other pressures that these populations are exposed to. As Malawi decentralises most of its public services, there is a need for nutrition and health managers in specific areas to formulate uniquely localised programmes to deal effectively with the gravity and presumed diverse causes of nutrition problems. Some blanket national interventions are less likely to help in addressing local problems.
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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 | 0.001 |
| 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) | 0.001 | 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".