A Noninvasive Approach to Assess the Prevalence of and Factors Associated With Anemia Risk in Malaysian Children Under Three Years of Age: Cross-Sectional Study
Bibliographic record
Abstract
Background: Anemia remains a significant public health concern with adverse effects among children. Noninvasive screening assessments enable the early detection and prompt treatment of anemia. However, there is limited literature on the use of such screening assessments. Objective: The study aimed to assess the prevalence of and factors associated with being at risk of anemia among Malaysian children aged ≥6 months to ≤36 months by using a noninvasive hemoglobin assessment. Methods: This was a cross-sectional study (from July to December 2022) of outpatient Malaysian children, aged ≥6 months to ≤36 months, who were selected from five maternal-and-child health clinics by convenience sampling. At risk of anemia was defined as a total hemoglobin level of <12 g/dL, measured using the Masimo Rad-67, a noninvasive screening device for total hemoglobin levels. The χ2 and multiple logistic regression analyses were used to assess the prevalence and factors associated with being at risk of anemia, using R-Studio (version 4.0.0). Results: The study included 1201 participants, of whom 30% (95% CI 28-33) were at risk of anemia. Children aged 6-12 months (210/364, 57.7%, P<.001), those of Asian Malay race (238/364, 65.4%, P<.05), those residing in the Klang district (123/371, 33.9%, P<.05), those born via a normal vaginal delivery (275/364, 75.5%, P<.05), those without a family history of thalassemia (284/364, 78.0%, P<.05), and those with lower weight-for-age Z scores (P<.05) were associated with being at risk of anemia. Children aged 6-12 months (adjusted odds ratio=1.73; 95% CI 1.34-2.24) had higher odds of being at risk of anemia compared to children aged >12-36 months. However, weight-for-age (adjusted odds ratio=0.88; 95% CI 0.80-0.98) was associated with lower odds of being at risk of anemia. Conclusions: The current study revealed a substantial prevalence of Malaysian children being at risk of developing anemia. The study results therefore imply a need for more community education and awareness on anemia, including nutrition education, as well as targeted community screening to enable the early detection and prompt treatment of anemia cases. Anemia reduction strategies in Malaysia should consider the highlighted factors indicative of higher risk of anemia.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".