Proximate and socio-economic determinants of under-five mortality in Benin, 2017/2018
Bibliographic record
Abstract
BACKGROUND: Globally, under-five mortality has declined significantly, but still remains a critical public health problem in sub-Saharan African countries such as Benin. Yet, there is no empirical information in the country using a nationally representative data to explain this phenomenon. The aim of this study was to examine how proximate and socio-economic factors are associated with mortality in under-five children in Benin. METHODS: . Results were reported as adjusted odds ratios (aORs). All comparisons were considered to be statistically significant at p<0.05. RESULTS: The study revealed an under-five mortality rate of 96 deaths per 1000 live births in Benin. Regarding the socio-economic determinants, the risk of death was found to be higher in children born in the Plateau region (aOR=3.05; 95% CI: 1.29 to 7.64), in rural areas (aOR=1.45; 95% CI: 1.07 to 1.98) and children with ≥4 birth rank and >2 years of birth interval (aOR=1.52; 95% CI: 1.07 to 2.17). Among the proximate determinants, we found the probability of death to be higher in children whose mothers had no postnatal check-up (PNC) visits after delivery (aOR=1.79; 95% CI: 1.22 to 2.63), but there was no significant association between individual-level/household-level factors and under-five mortality. CONCLUSION: This study has established that socio-economic and proximate factors are important determinants of under-five mortality in Benin. Our findings have shown the need to implement both socio-economic and proximate interventions, particularly those related to PNC visits when planning on under-five mortality. To achieve this, a comprehensive, long-term public health interventions, which consider the disparity in the access and utilisation of healthcare services in Benin are key.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".