Prevalence of malnutrition, anemia, and soil-transmitted helminthiasis in preschool-age children living in peri-urban populations in the Peruvian Amazon
Bibliographic record
Abstract
Stunting, anemia, and soil-transmitted helminth (STH) infections are major health concerns for children in extremely poor regions of the world, especially rural and periurban ones. This study aimed to determine the prevalence of these three cooccurring conditions in preschool-age children in an extremely poor district on the outskirts of Iquitos, in the Peruvian Amazon, to inform public health actions. Malnutrition was assessed by standard World Health Organization-recommended metrics; anemia, by hemoglobin levels; and STH, by the Kato-Katz technique. Logistic regression analyses were performed to identify the risk factors for our three outcomes of interest. A total of 572 children aged 6-59 months were recruited in March 2019. We found a 31.3% stunting, 47.2% anemia, and 34.1% STH prevalence. Stunting and anemia figures exceeded both regional and national estimates for 2019. Having more children was a risk factor for stunting, whereas married mothers were associated with a lower risk. Risk factors for anemia included younger age and the male sex, whereas those for STH, older age, incomplete vaccination, and a lower socioeconomic status. Mothers' employment outside the home was also associated with a lower STH risk. This recent evidence highlights the need for prompt and integrated clinical attention and public health actions to address both short- and long-term health consequences in this vulnerable child age group. The integration of a monitoring and evaluation framework is important to effectively manage these conditions, optimize resources and accountability, and show their impact.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 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".