The effects of reproductive variables on child mortality in Ethiopia: evidence from demographic and health surveys from 2000 to 2016
Bibliographic record
Abstract
BACKGROUND: Child mortality is a crucial indicator reflecting a country's health and socioeconomic status. Despite significant global improvements in reducing early childhood deaths, Southern Asia and sub-Saharan Africa still bear the highest burden of newborn mortality. Ethiopia is one of five countries that account for half of new-born deaths worldwide. METHODS: This study examined the relationship between specific reproductive factors and under-five mortality in Ethiopia. A discrete-time survival model was applied to analyze data collected from four Ethiopian Demographic and Health Surveys (EDHS) conducted between 2000 and 2016. The study focused on investigating the individual and combined effects of three factors: preceding birth interval, maternal age at childbirth, and birth order, on child mortality. RESULTS: The study found that lengthening the preceding birth interval to 18-23, 24-35, 36-47, or 48+ months reduced the risk of under-five deaths by 30%, 46%, 56%, and 60% respectively, compared to very short birth intervals (less than 18 months). Giving birth between the ages 20-34 and 35+ reduced the risk by 34% and 8% respectively, compared to giving birth below the age of 20. The risk of under-five death was higher for a 7th-born child by 17% compared to a 2nd or 3rd-born child. The combined effect analysis showed that higher birth order at a young maternal age increased the risk. In comparison, lower birth order in older maternal age groups was associated with higher risk. Lastly, very short birth intervals posed a greater risk for children with higher birth orders. CONCLUSION: Not only does one reproductive health variable negatively affect child survival, but their combination has the strongest effect. It is therefore recommended that policies in Ethiopia should address short birth intervals, young age of childbearing, and order of birth through an integrated strategy.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".