Analysis of sex disparities in under-five mortality rates in Ghana: Insights from vector autoregressive modeling
Bibliographic record
Abstract
ABSTRACT International monitoring organizations call for child mortality indicators to be disaggregated by gender. However, there remains a paucity of studies, especially, from the sub-Saharan region aimed at producing accurate forecasts of child mortality indicators with their sex variations. This study aims at investigating disparities in indicators of childhood mortality rates by sex in Ghana by employing vector autoregressive (VAR) model to analyze jointly annual recorded data on total, male and female under-five mortality rates (TU5MR, MU5MR, FU5MR, respectively). The results show gradual declining under-five mortality trends among sexes in both the historical and forecasted rates. The trivariate traditional and instantaneous Granger causality analyses found that any of the mortality indicators Granger causes the other two combinations, except TU5MR to MU5MR and FU5MR. The forecast error variance decomposition analyses revealed that FU5MR was the most exogenous variable while long-term impulse response function analyses indicated that unit shocks in FU5MR significantly increased TU5MR. The VAR(2) model forecast constructed revealed that contrary to recent predictions based on wider interval data derived from demographic health surveys, Ghana may meet the SDG 3.2.2 if ongoing efforts are sustained and that focusing policies and interventions on reducing FU5MR would largely contribute to reducing TU5MR in Ghana. Ethical considerations Not applicable. This study did not require ethics approval or consent for participation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".