Prevalence and predictors of anemia among children under 5 years of age in Arusha District, Tanzania
Bibliographic record
Abstract
Abstract: Anemia is a global health problem affecting most developing countries. We examined the prevalence of anemia and its predictors among children under 5 years of age in Arusha District, Tanzania. Random sampling technique was used to identify 436 children aged 6–59 months. Anemia status was assessed by measuring hemoglobin concentration from blood sample obtained from a finger prick and HemoCue ® Hb 201+ photometer. Demographic information and dietary intake data were collected using a standardized questionnaire. Anemia cut-off points were defined according to World Health Organization standards for children aged 6–59 months. Logistic regression using backward procedure was used to estimate odds ratios (ORs) at 95% confidence intervals (CIs). Prevalence rate of anemia among under-fives was found to be 84.6% (n=369). Multivariable logistic regression identified the following predictors of anemia; low birth weight (adjusted OR (AOR): 2.1, 95% CI: 1.1–3.8), not consuming meat (AOR: 6.4, 95% CI: 3.2–12.9), not consuming vegetables (AOR: 2.1, 95% CI: 1.1–4.1), drinking milk (AOR: 2.5, 95% CI: 1.1–5.2), and drinking tea (AOR: 4.5, 95% CI: 1.5–13.7). It was concluded that low birth weight and dietary factors (ie, low or nonconsumption of iron-rich foods like meat, vegetables, and fruits) were predictors of anemia among under-five children living in this rural setting. Community education on exclusive breastfeeding and introduction of complementary foods should be improved. Mothers and caretakers should be educated about nutrition, in general, as well as potential use of micronutrient powder to improve the nutritional quality of complementary foods. Keywords: anemia, low birth weight, dietary intake, predictors, under-five children
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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 | 0.001 |
| 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 | 0.000 |
| 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".