Socioeconomic Inequalities in Infant Mortality in Egypt: Analyzing Trends between 1995 and 2014
Bibliographic record
Abstract
This paper examines the trends in the socio-economic inequalities of infant mortality rates in Egypt during the period 1995-2014, using repeated cross sectional data from the National Demographic and Health Survey. A multivariate logistic regression model, concentration curves, and concentration indices are used to examine the demographic and socio-economic correlates of infant mortality, and how the degree of socio-economic disparities in child mortality rates has evolved over time. We find a significant drop in infant mortality rates from 63 deaths per 1000 live births in 1995 to 22 deaths per 1000 live births in 2014. Results show an inverse association between infant mortality rates and living standard measures, with the poor bearing the largest burden of early child mortality. Though the estimated concentration indices show a decline in the degree of socio-economic inequality in child mortality rates over time, infant mortality rate among the poor remains twice the rate of the richest wealth quintile. Nonetheless, this decline in the degree of socio-economic inequality in child mortality rates was not supported by the results of the multivariate logistic regression model. Results of the logistic model show higher odds of infant mortality among rural households, children who are twins, households with risky birth intervals. No statistically significant association was found between infant mortality and access to safe water, gender, and mothers' education. Infant mortality was negatively associated with household wealth, receiving a regular health care during pregnancy by mothers, having more than two under- five children. By identifying the correlates of child mortality, the findings of this paper inform intervention measures that aim at reducing child mortality rates and socio-economic inequalities in Egypt.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".