Spatial analysis of under-five mortality in Africa using geographically weighted poisson regression
Bibliographic record
Abstract
Child mortality remains a significant public health challenge in developing countries despite the global decline in under-five deaths. The disparities in child mortality rates can be attributed to socioeconomic and environmental inequalities across nations. While several studies have examined geographic variations in under-five mortality in Africa using economic and health indicators, few have applied spatial analysis to characterize these patterns. This study employs Geographically Weighted Poisson Regression (GWPR) to uncover spatially varying in effects of global indicators on under-five mortality across Africa, offering a detailed understanding not captured by conventional global models. Data on under-five mortality rates and economic and health indicators were obtained from the World Bank’s World Development Indicators (WDI) for 2022 across 54 African countries. A Poisson regression model and GWPR were applied to examine the associations between under-five mortality and various socioeconomic and environmental factors. The results indicate substantial spatial heterogeneity in child mortality across countries. The GWPR model (AICc = 221.25, Pseudo R 2 = 86.5%) outperformed the conventional Poisson regression model (AICc = 360.733, Pseudo R 2 = 58.4%), highlighting the benefits of incorporating spatial variability. Key findings revealed that under-five mortality was positively associated with open defecation and negatively associated with literacy, health expenditure, access to electricity, and basic sanitation. Additionally, the relationship between under-five mortality, gross national expenditure, and access to basic drinking water varied across regions. These findings emphasize the need for localized, evidence-based interventions to address child mortality more effectively in Africa.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".