Spatial Heterogeneity and Factors Influencing Anemia and Severe Anemia among Under-5 Children in Ethiopia: Spatial and Multilevel Analysis
Bibliographic record
Abstract
Abstract Background: Anemia is recognized as a significant public health problem in Ethiopia. Method: This secondary analysis, sampling technique and procedures done by DHS. A total of 8482 children aged 6–59 months were included in the study from EDHS 2016. Used tools for spatial heterogeneity analysis are Morans I, Local G*, Heat map, and Kulldorff’s scan statistic, those carried out open source software (QGIS, GeoDa, SaTScan). Multilevel logistic regression analysis was used to identify both individual and household level factors associated with anemia and severe anemia, which measures between household variability using IHHC of the null model and generates 4 models PCV, AIC, and log-likelihood ratio used model selection for a report, which is carried out Stata 14. Result: The highest risk of both anemia and severe anemia regions are Somalia, Afar, DireDawa, and Harari. Specifically, all zones of Somalia, Afar (zone1, and 3), DireDawa, Harari, and Oromia higher risk of anemia and some of them are high risk for severe anemia. The high concentration of the disease showed in DireDawa, Harari, Jigjiga some part of Gambela and Benishangul Afar at the boundary of Amhara and Tigray and Eastern part of Tigray for anemia. Harari, DireDawa, and Afar at the border of Djibouti had high concentrated(density) severe anemia. Children (Younger age, lower preceding birth interval, stunted, underweight); mothers (younger age and anemic); households (poor and denser family number); and children from the highest and the lowest ecological zone are high risk for anemia. Additionally, children from the younger, work less, uneducated, poor, and anemic mother were high risk of a severe anemia Conclusion: Across the country, anemia is high, especially rift value areas highly affected by both anemia and severe anemia like Somalia regions and its neighbors. Those regions are not productive areas, lack of health facility, malaria region, backward for any access. So the concerned body must be tackled to minimize this childhood series problem. Before starting any intervention first must be prioritized according to risk, concentration, and characteristics.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.010 |
| 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; both teacher heads agree on what is shown here.
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".