Early-life environmental exposures and anaemia among children under age five in Sub-Saharan Africa: An insight from the Demographic & Health Surveys
Bibliographic record
Abstract
BACKGROUND: Reports show that the majority (60%) of children under age five years in Sub-Saharan Africa are anaemic. Studies in the region have mainly focused on the effect of individual, maternal and household socioeconomic status on the prevalence of anaemia. Currently, there is limited understanding of the association between early-life environmental exposures and anaemia among children in Sub-Saharan Africa. OBJECTIVE: The study examines the association between early-life environmental exposures and anaemia among children under five in Sub-Saharan Africa. METHODS: The study used health and demographic data from the Demographic and Health Survey (DHS) program and environmental data from NASA's Geospatial Interactive Online Visualization ANd aNalysis Infrastructure (GIOVANNI) and Atmospheric Composition Analysis Group. Three exposure periods were defined for the study, namely: in-utero, post-utero and cumulative life exposures. Multilevel mixed-effect models were used to assess the associations between environmental exposures and anaemia in each exposure period. RESULTS: increase in in-utero, post-utero and cumulative PM 2.5 exposures was associated with 4% to 5% increase in the prevalence of anaemia among children. A 10ppbv increase in in-utero, post-utero and cumulative carbon monoxide exposures was associated with 1% increase in the prevalence of anaemia among children. The spatial risk distribution maps show that socioeconomic factors modify the spatial risk distribution pattern. CONCLUSION: The findings of the study suggest that early-life exposure to ambient air pollution is significantly associated with anaemia among children in Sub-Saharan Africa. Thus, policies aimed at addressing air quality should be incorporated into targeted interventions for anaemia among children in the region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".