Malnutrition in all its forms and socio-economic disparities in children under 5 years of age and women of reproductive age in Peru
Bibliographic record
Abstract
OBJECTIVE: To compare the distribution of malnutrition by socio-economic indicators (SEI) in Peruvian children under 5 years and women of reproductive age (WRA). DESIGN: We analysed data from the National Demographic and Family Health Survey. WHO criteria were used to define malnutrition indicators (overweight/obesity combined (OW); wasting/underweight; stunting/short stature; anaemia). Linear combination test was used to compare the prevalence of malnutrition by SEI (wealth index as a proxy of socio-economic status (SES); education; ethnicity). Prevalence ratio (PR) was used to describe disparities and associations between malnutrition and SEI. SETTING: Peru (2015). PARTICIPANTS: Children (n 22 833) under 5 years and WRA (n 33 503; 5008 adolescents and 28 495 adults). RESULTS: The most prevalent form of malnutrition was anaemia (32·0 %) in children and OW in adolescent and adult WRA (31·3 and 65·1 %, respectively). Adjusted models showed that stunting and anaemia were significantly lower among children with high SES (PR = 0·25, 0·67), high-educated mothers (PR = 0·26, 0·76) and higher in indigenous children (PR = 1·3, 1·2); conversely, OW was higher among those with high SES and high-educated mothers (PR = 1·8, 1·6) compared with their lowest counterparts. In WRA, stunting/short stature was lower among those with high SES, high education and higher in indigenous adult women. OW in adolescents and adults was higher in high SES (PR = 1·4, 1·1), lower in indigenous adult women (PR = 0·84) and lower in high-educated adult women (PR = 0·86). CONCLUSIONS: In the studied population, the distribution of malnutrition was associated with SEI disparities. Effective policies that integrate actions to overcome the double burden of malnutrition and reduce disparities are needed.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".