Small area vulnerability, household food insecurity and child malnutrition in Medellin, Colombia: results from a repeated cross-sectional study
Bibliographic record
Abstract
Background: ) malnutrition in children in Medellin, Colombia, during the years 2017 and 2018. Methods: We obtained data from two different sources: the Living Standards Measurement Survey (LSMS) and the nutrition surveillance system of Medellin. The main outcomes were food insecurity in households with children and anthropometric indicators for children under five. The main predictor was area-level vulnerability. Mixed effects Poisson regression with robust standard errors models were conducted to test the association of quintiles of deprivation with each outcome. Findings: Households with children living in areas with the highest deprivation had 1.9 times the prevalence of food insecurity as compared to those living in areas with the lowest deprivation (PR 1.91, 95% CI 1.42-2.57). Similar results were observed for underweight/risk of underweight (PR 1.26, 95% CI 1.11-1.42), stunting/risk of stunting (PR 1.36, 95% CI 1.22-1.53) and stunting (PR 1.93 95% CI 1.55-2.39) among children under five. We found no consistent associations with wasting/risk of wasting or excess weight/risk of overweight across quintiles of deprivation. Interpretation: This study sheds light on the role of area-level vulnerability on malnutrition in children in Medellin, Colombia, showing a pattern of increasing prevalence of food insecurity, underweight and stunting by quintile of deprivation. Funding: Swiss School of Public Health (SSPH+) and Centre for Global Health Inequalities Research (CHAIN).
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".