Geographic disparities in the time to under-five mortality in Ghana
Bibliographic record
Abstract
Globally, there has been tremendous advancement in medicine and child healthcare with increased life expectancy. That notwithstanding, the risk of under-five mortality ─ children dying before their fifth birthday remains relatively high in countries in Sub-Saharan Africa such as Ghana. In Ghana, under-five mortality remains a major public health problem that requires significant policy interventions. Using data from the 2017 Maternal Health Survey (n = 4785), we examined the geographic disparities in the time to under-five mortality in Ghana. The Kaplan Meier estimator showed significant (Log-rank: p< 0.001) rural-urban differences in the time to under-five mortality in Ghana. A disaggregated cox proportional hazards analysis showed that despite wide consensus that children in urban areas have a high survival rate, children in urban areas in northern regions of Ghana, especially the Upper West (HR = 4.40, p < 0.05) and Upper East (HR = 5.37, p < 0.01) Regions were significantly at increased risk of dying before the age of five compared to children in urban areas in the Greater Accra Region. A rural-urban comparison showed that children born in rural areas in all the other regions of Ghana were at a higher risk of dying before the age of five when compared to their counterparts in the rural areas of Greater Accra Region. Other factors such as sex of child, mothers' age and use of the internet, number of household members, ethnicity and household wealth were significantly associated with the timing of under-five mortality in Ghana. Healthcare policies and programs such as immunizations and affordable child healthcare services should be prioritized especially in rural areas of regions with a high risk of child mortality. Also, there is a need to improve healthcare delivery in urban areas, particularly in northern Ghana, where deplorable healthcare service infrastructure and delivery coupled with high poverty rates put children at risk of dying before their fifth birthday.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".