Geo-spatial Patterns and Associated Risks of Iron Deficiency and Infection among Young Ghanaian Children: Implications for the Safety of Iron Supplementation in Malaria Endemic Areas
Bibliographic record
Abstract
Background: The safety and effectiveness of iron supplementation in malaria endemic areas may partly depend on host iron status; however, current methods for assessing iron deficiency risk tend to be confounded by infection and are infeasible to implement at a population level. Determining the geographical patterns of iron status and infection may provide a practical alternative means of identifying high risk populations for whom integrated anaemia and infection control programs are needed. Objective: Determine the geo-spatial factors associated with iron status and infection risk among 1943 Ghanaian children (6-35 months of age) before and after participating in a randomized iron home-fortification trial. Methods: Secondary spatial analyses of iron status and infection outcomes were conducted. Iron status was defined as serum ferritin concentration corrected for inflammation (C-reactive protein, CRP) using a regression-based method. Malaria and non-malaria infection outcomes were defined using four combinations of inflammation (CRP >5 mg/L) and malaria parasitaemia (with and without reported history of fever or concurrent axillary temperature >37.50 C). Analyses were performed using a geographical information system (GIS) and generalized linear geostatistical modelling with a Matern spatial correlation function. Results: After adjusting for demographic characteristics such as age, sex, and maternal education, none of the geo-spatial factors included in the iron status models (including elevation, and distance to a health facility) demonstrated associations at baseline or endline; however, there was significant residual spatial variation across the study area. Conversely, malaria parasitaemia at baseline was associated with greater distance to a health facility and lower elevation. These relationships did not remain at endline, nor when infection was defined using CRP only. Mapping the model outputs showed defined low-risk areas that tended to cluster around villages, particularly near the District centre. Conclusions: In a malaria endemic area, geographical location may play a role in the risk of iron deficiency and infection among children. Iron home-fortification likely alters the spatial risk profile of malaria and non-malaria infection in this setting, though additional research is needed to confirm the direction of these relationships.
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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 | 0.005 |
| 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) | 0.002 | 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".