Impact of an integrated nutrition intervention on nutrient intakes, morbidity and growth of rural Burkinabe preschool children
Bibliographic record
Abstract
Iron deficiency and undernutrition often co-exist among children and the prevalence in sub-Saharan African is high. In Burkina Faso, 92% of children under the age of five years were anaemic and 39% were stunted in 2003 according to Demographics and health survey. Risk factors include inadequate dietary intakes and infections that often occur concomitantly. Orphans and vulnerable children who are already atincreased risk for poor psycho-emotional development due to their social status are particularly susceptible to malnutrition. Therefore, there is a challenge to develop effective interventions that address the multiple risk factors of nutritional deficiencies in these children. An integrated nutrition intervention including dietary modification and changes in hygiene practices was implemented targeting a group of preschoolorphans and vulnerable children living in group foster homes. The amount of iron-rich foods particularly goat meat and condiments as well as citrus fruits were increased in the diet. The hygiene component of the intervention focused on handwashing, eating in individual plates, food storage and stool disposal. All members of the foster homesat large benefitted from the activities of the intervention; however, measurements and impact evaluation focused on preschool children (12 - 72 months). Energy and nutrient intakes, anthropometric indices and morbidity including diarrhoea, fever, vomiting and respiratory infections were assessed at baseline and after 18 weeks. Mean bioavailable iron intake was increased from 0.4 to 0.9 mg/d. The intervention resulted in a decrease in the prevalence of inadequate intakes for energy, proteins and most micronutrients. The intervention increased the meat-fish-poultry contribution to energy and iron intakes from 1.6 to 3.5% and from 5.2 to 7.9%, respectively. There was also a decrease in the incidence of infection (from 73 to 9%, p<0.01) and overall orbidity (from 39 to 15%, p<0.05) most likely due to deworming and improvementsin hygiene behaviours. Contrary to baseline, helminths such as Hymelolepis nana,Strongylo�des stercoralis and Necator americanus that often cause blood loss were rarely found at the end of the intervention. The overall changes resulted in improvements in height-for-age z-score (-1.63 to -1.27, p<0.01) and weight-for-age zscore (-1.22 to -1.06, p<0.05). In conclusion, an intervention that targets bioavailableiron and hygiene behaviours improved child growth, iron status and overall morbidity in areas of high incidence of infection.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".