Inequities in childhood anaemia at provincial borders in Mozambique: cross-sectional study results from multilevel Bayesian analysis of 2018 National Malaria Indicator Survey
Bibliographic record
Abstract
Objectives This study aims to identify the child-level, maternal-level, household-level and community-level determinants of anaemia among children aged 6–59 months, and determine the inequities of anaemia prevalence across communities in Mozambique. Design Cross-sectional study. Setting Mozambique. Participants This study used data of a weighted population of 3946 children, 6–59 months, delivered by women between 15 and 49 years of age, from the 2018 Mozambique Malaria Indicator Survey. Primary outcome measure Child’s anaemic status, measured as altitude-adjusted haemoglobin concentration (in g/L); the severity of anaemia was categorised based on predefined threshold values. Multilevel Bayesian linear regressions identified key determinants of childhood anaemia. Based on data availability and policy implications, spatial analysis was used to determine geographical variation of anaemia at the community level and areas with higher risks. Results The mean prevalence of childhood anaemia was 77.7% (SD: 5.5%). Provincially, Cabo Delgado province (86.2%) had the highest prevalence, Maputo province (70.2%) the lowest. Children with excess risk were mostly found in communities that had proximity to provincial borders: Niassa-Cabo Delgado-Nampula triprovincial border, Gaza-Inhambane border, Zambezia-Nampula border and provinces of Manica and Inhambane. Children with anaemia tended to be younger, males and at risk of having malaria because they were not sleeping under mosquito nets. In addition, children from poor families relative to children from wealthier households and those living in female-headed households were prone to anaemia. Conclusion Findings from this study provide evidence that spatial inequities in childhood anaemia exist in Mozambique, mostly concentrated in the communities living close to the provincial borders. Anaemia among children could be effectively reduced through malaria prevention, for example, bed netting. Interventions are needed that generate income for households, increase community support for households headed by women, improve malaria control, build capacity of healthcare workers to manage severely anaemic children and health education for mothers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".