Prevalence and determinants of undernutrition among children of local farmers in central Malawi
Bibliographic record
Abstract
Abstract Background: Child undernutrition is a public health problem in Malawi. In 2015, about 23% of child mortality was linked to the phenomenon. Child undernutrition is more prevalent in rural areas and there is limited data to inform rural-specific programs. The aims of this study are to examine the prevalence and risk factors of undernutrition among 0-59 months-old children in rural central Malawi. Methods: A cross-sectional study design was used. A total of 538 child/mother dyads were systematically selected from postnatal clinics. Anthropometric assessment techniques and socio-demographic questionnaire was used to collect data. Child Z-scores for anthropometric data were calculated using World Health Organization’s (WHO) anthro v3.2.2. Logistic regressions were used to determine correlates of undernutrition. Results: The rates of stunting, underweight, and wasting were 42%, 11%, and 3%, respectively. In multivariable logistic regression models, household limited access to safe water, and maternal exposure to intimate partner violence (IPV) were risk factors of child stunting (OR=1.72, CI: 1.13-2.61) and (OR=1.505, CI: 1.001-2.261) respectively. Child deworming, born at a low weight, and household food insecurity were some of risk factors of child underweight (OR= 2.14, CI: 1.18-3.89), (OR= 2.41, CI: 1.23-4.71), and (OR= 1.89, CI: 1.01-3.51) respectively. Households that were near domestic water supply had low risk of registering wasted children than households that were far from water supply (OR= 0.18, CI: 0.41-0.79). Conclusions: Only the prevalence of child stunting is greater in central Malawi compared to national level. This study suggest that programs that can promote access to potable water, food security, child deworming, improving child birth weight, and reduction of IPV against mothers can reduce the risk of child undernutrition. Keywords: Undernutrition, Risk factors, Child health, Central Malawi
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".