Inequities in childhood anaemia in Mozambique: results from multilevel Bayesian analysis of 2018 National Malaria Indicator Survey
Bibliographic record
Abstract
Abstract Introduction Childhood anaemia is a common public health problem worldwide. The geographical patterns and underlying factors of childhood anaemia have been understudied in Mozambique. The objectives of this study were to identify the child-, maternal-, household-, and community-level determinants of anaemia among children aged 6-59 months, and the contribution of these factors to the variation in childhood anaemia at the community level in Mozambique. Methods This is a cross-sectional study that utilized data of a weighted population of 4,141 children aged 6-59 months delivered by women between 15-49 years of age, from the 2018 Mozambique Malaria Indicator Survey. Multilevel Bayesian linear regressions identified key determinants of childhood anaemia. Spatial analysis was used to determine geographic variation of anaemia at the community level and areas with higher risks. Results The overall national prevalence of childhood anaemia was 78-80.3%. There was provincial variation with Cabo Delgado province (86.2%) having highest prevalence, and 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 tri-provincial 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 and those living in female-headed households were prone to anaemia. Conclusion This study provides evidence that anaemia among children aged 6-59 months is a severe public health threat across the provinces in Mozambique. It also identifies inequity in childhood anaemia—worse among communities living close to the provincial borders. We recommend interventions that would 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. More importantly, there is need to foster collaborations between communities, districts and provinces to strengthen maternal and child health programmes for the severely affected areas. What is already known? Nearly two billion people are anaemic, affecting mostly poor women and children. Anaemia, a co-morbidity with other major health conditions, frequently is less prioritized. Sustainable Development Goals 2 and 3, formulated to tackle hunger/food insecurity and attain optimal health/wellbeing, respectively, currently have no specific target for monitoring global progress for anaemia among children. What are the new findings? Twenty-four percent of children (6-59 months) had anaemia classified as mild, 50% moderate and 7% severe. Childhood anaemia showed spatial variation across the communities—especially in the provincial border regions--and provinces in Mozambique; they were younger, males, at risk of having malaria, from poor families and lived in female-headed household. What do the new findings imply? Anaemia among children could be effectively reduced through malaria prevention, e.g. bed netting. This report of anaemia at community and district level provides baseline data and can guide targeted implementation of the 2025 Mozambique National Development Plan. Interventions 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 | 0.002 | 0.007 |
| 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".