Understanding inequities in child mortality in Egypt: Socioeconomic and proximate factors
Bibliographic record
Abstract
While there have been notable advancements in child health in Egypt, disparities in child mortality still exist. Understanding these disparities is crucial to addressing them. The objective of this study is to explore the factors linked to child mortality in Egypt, providing a comprehensive understanding of the disparities in child mortality rates. The study utilises cross-sectional data from Egypt's Demographic and Health Survey (EDHS) in 2014 to examine child mortality. The dataset consists of 15,848 observations from mothers with children born within five years prior to the survey. The choice of explanatory variables was guided by the Mosely and Chen Framework and logistic multivariate regression was used to conduct the analyses. The study finds lower education, early childbearing, insufficient birth spacing, lack of breastfeeding, and absence of improved toilet facilities (proxy for living conditions) were all significantly linked to an increased likelihood of child loss. Additionally, poorer people in rural settings experienced the worst child mortality. The findings align with the World Health Organization's Conceptual Framework for Action on the Social Determinants of Health (CSDH). Recommended policy interventions include targeting women in rural areas, improving living conditions and removing financial/other barriers to accessing care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".