Proximate, intermediate, and distal predictors of under-five mortality in Chad: analysis of the 2014–15 Chad demographic and health survey data
Bibliographic record
Abstract
Under-five mortality in Chad reached a minimum value of 119 deaths per 1000 live births in 2018, compared with a maximum of 250 in 1972. Despite this decline in the mortality trend, for every six children in Chad, one dies before the age of five. This study, therefore, investigated the proximate, intermediate, and distal determinants of under-five mortality in Chad.We used data from the 2014-15 Chad's Demographic and Health Survey. Data of 7782 children below 5 years were used for the study. Both descriptive and multivariable hierarchical logistic regression analyses were performed. Statistical significance was declared at p < 0.05.Under-five mortality was found to be 130 deaths per 1000 live births in Chad, with variations across the various population sub-groups. For distal predictors, the likelihood of death was higher in children born in the FChari Baguirmi region (AOR = 3.83, 95% CI: 1.81-8.14). Children whose mothers belonged to the Baguirmi/Barma ethnic group (AOR = 8.04, 95% CI: 1.75-36.99) were more likely to die before the age of five. On the contrary, the likelihood of under-five mortality was low among children born in rural areas (AOR = 0.73, 95% CI: 0.55-0.97). With the intermediate predictors, the likelihood of under-five deaths was higher among children whose mothers had no formal education (AOR = 1.72, 95% CI: 1.06-2.77). Regarding the proximate predictors, the odds of under-five deaths was higher among male children (AOR = 1.03, 95% CI: 1.05-1.63) and first rank children (AOR = 1.58, 95% CI: 1.13-2.21).The study found that the determinants of under-five mortality in Chad are region of residence, place of residence, ethnicity, education, sex of child, and birth rank. These findings show that both socio-economic and proximate factors explain the disparities in under-five mortality in Chad. The identification of these factors can be pivotal towards the design of evidence-based interventions intended to improve child survival. Therefore, improving maternal education while refocusing and re-packaging existing strategies to target selected sub-regional populations with high under-five mortality is urgently required.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".