Factors Influencing Iron Levels Among Women of Reproductive Age and Children 6–48 Months in Livestock‐Keeping Communities in Narok County, Kenya
Bibliographic record
Abstract
ABSTRACT Proper nutrition is vital for maintaining good health for all people across their lifespan, especially children and mothers, who are especially vulnerable due to their specific nutrient needs. Despite the necessity of improved nutrition for these groups, some members do not fully meet their recommended daily micronutrient needs, a challenge exacerbated by different socioeconomic, cultural, and communal constraints resulting in malnutrition. Iron deficiency anaemia is a major concern among children and mothers, especially in pastoralist communities, due to poor nutrition and other related factors. Using a community‐based cross sectional study, this study investigated factors associated with hemoglobin levels among children and women in Narok County, Kenya. Anthropometrics were estimated using body mass index measurements for mothers, and the nutritional status of children was calculated using Z ‐score measurements. Haemoglobin was measured using a rapid test (Hemocue 301). Multiple logistic regression models were fitted to assess the association between child and maternal risk factors and anaemia. Anaemia in children was associated with age (OR = 1.99, p = 0.047), pastoralism (OR = 2.25, p = 0.002), educational of the mother (OR = 0.74, p = 0.008), severe and moderate undernourishment (OR = 1.14, p = 0.049 and OR = 1.10, p = 0.023), respectively, not meeting children dietary diversity (OR = 1.18, p = 0.027), number of people in a household (OR = 1.84, p = 0.003), and maternal age (OR = 0.30, p = 0.010). On the other hand, the occurrence of anaemia in women was associated with pastoralism (OR = 2.22, p = 0.001), having a primary school level of education (OR = 0.51, p = 0.028), pregnancy status (OR = 5.36, p = 0.002), not meeting maternal dietary diversity (OR = 1.39, p = 0.026), number of household members (OR = 1.93, p = 0.023), age of the mother (OR = 0.53, p = 0.018), and having animals infected with East Coast Fever ( Theileria parva ) within the household (OR = 1.10, p = 0.023). The results highlight the multifaceted nature of malnutrition, specifically anaemia in pastoral households, with interventions aimed at reducing disease infections in cattle, improved household dietary diversity, and community health education geared towards maternal and child nutrition being best placed to improve the overall household health outcomes relating to anaemia.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".