Geospatial distribution of under-five mortality in Alexandria, Egypt: a cross-sectional survey
Bibliographic record
Abstract
Abstract Background Globally, infectious diseases, including pneumonia, diarrhea, and malaria, along with pre-term birth complications, birth asphyxia and trauma, and congenital anomalies remain the leading causes of death for under-five mortality (U5M). This study aimed to identify the geospatial pattern of U5M in Alexandria and its key determinants. Methodology We analyzed the geospatial distribution of 3064 deaths registered at 24 health offices reported from January 1, 2018 to June 30, 2019. We adopted two methods of analysis: geospatial analysis and the structural equation model (SEM). Result Neonates represented 58.7% of U5M, while post-neonates and children were 31.1%, 10.2%respectively. Male deaths were significantly higher compared to females (P = 0.036). The main leading causes of U5M were prematurity (28.32%), pneumonia (11.01%), cardiac arrest (10.57%), congenital malformation (9.95%), and childhood cardiovascular diseases (9.20%). The spatial distribution of U5M (including the most common three causes) tends to be clustered in western parts of Alexandria (El Hawaria, Bahig, Hamlis, and Ketaa Maryiut). SEM showed the total effects of exogenous and intermediate variables on U5M. The U5M proportionately increased by living in rural areas (8.48), followed by crowding rate (8.35), household size (1.36), population size (0.52), and illiteracy average (0.06). On the contrary, the U5M decreased with increasing access to sanitation (-0.17) and access to drinking water (-4.55). Conclusion Illiteracy, and poor locality characteristics (household size, population density, and access to water supply and sanitation) were statistically significant predictors of U5M.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".