Increase in acute malnutrition in children following the 2014–2015 Ebola outbreak in rural Sierra Leone
Bibliographic record
Abstract
Setting: All health facility and community malnutrition screening programmes in Tonkolili, a rural Ebola-affected district in Sierra Leone. Objectives: Before the Ebola disease outbreak, Sierra Leone had set a goal to reduce the prevalence of severe acute malnutrition (SAM) in children aged <5 years to <0.2%. We compared the number of children screened, diagnosed and treated for malnutrition before, during and after the outbreak (2013–2016). Design: This was a retrospective cross-sectional study. Results: Health facility screening declined from 16 805 children per month pre-outbreak to 13 510 during the outbreak ( P = 0.02), and returned to pre-outbreak levels after the outbreak. Community-based screening remained stable during the outbreak, and increased by 30% post-outbreak ( P < 0.001). The proportion diagnosed with moderate acute malnutrition using mid-upper arm circumference increased from respectively 3.6% and 5.1% pre-outbreak in the community and health facilities to 8.2% and 7.9% post-outbreak ( P < 0.001, P = 0.003). The proportion of children diagnosed with SAM using a weight-for-age ratio at health facilities increased from 1.5% pre-outbreak to 3.5% post-outbreak ( P = 0.003). On average, for every four children diagnosed with SAM per month, one child completed SAM treatment. Conclusion: After a decline in screening during the Ebola outbreak, diagnoses of acute malnutrition increased post-outbreak. Nutrition programmes need to be strengthened to pre-empt such effects in the event of future Ebola outbreaks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".