Childhood Nutrition and Service Delivery Indicators across and between two Epidemics (Ebola and COVID-19) in Sierra Leone: A Descriptive Study using Serial Cross-Sectional Surveys
Bibliographic record
Abstract
Abstract Introduction Sierra Leone is one of the worst affected by hunger and food insecurity, but data to understand the impact of public health emergencies on nutrition indicators is limited. In this study, we sought to describe nutrition service delivery and nutritional health among children under age five before and during the Ebola epidemic, the inter-epidemic period, and during the COVID-19 pandemic (2021) in Sierra Leone. Methods We conducted a descriptive study using secondary data from five serial cross-sectional surveys conducted using representative sampling as part of programmatic monitoring and evaluation: 2010 (N=14027, before Ebola); 2014 (N=10,975, during Ebola); 2017 and 2019 (N=9059, N=4,870, respectively, inter-pandemic period); 2021 (N=10,165, during COVID-19). We described and compared the prevalence of each of the following indicator at each time-point: shunting, global acute malnutrition, breastfeeding and underweight. Results The prevalence of stunting was 34.1% before the Ebola epidemic, 28.8% during the Ebola epidemic, 31.3% after the Ebola epidemic, 25.9% before the COVID-19 epidemic, and 26.2% during the COVID-19 epidemic. The prevalence of global acute malnutrition was: 6.9% before the Ebola epidemic, 4.7% during the Ebola epidemic, 5.1% after the Ebola epidemic, 5.0% before the COVID-19 epidemic, and 5.2% during the COVID-19 epidemic. Finally, the report showed that the proportion of breastfeeding for up to 23 months was 84.0% (before the Ebola epidemic), 86.0% (during the Ebola epidemic), 85.0% (after the Ebola epidemic), 61.8% (before the COVID-19 epidemic), and 53.1% (during the COVID-19 epidemic). Conclusion We found a variable effect of the Ebola epidemic and COVID-19 on nutrition health and nutrition indicators. Findings highlight the importance of continuing to strengthen the implementation of nutrition programs during and after public health emergencies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".