Equity dimensions of the decline in under‐five mortality in Ghana: a joinpoint regression analysis
Bibliographic record
Abstract
BACKGROUND: There has been a global rise in interest and efforts to improve under-five mortality rates, especially in low- and middle-income countries. Ghana has made some progress in improving this outcome; however, the extent of such progress and its equity implications remains understudied. METHODS: This study used a joinpoint regression analysis to assess the significance of changes in trends of under-five mortality rates in Ghana between 1988 and 2017 using data from seven rounds of the Ghana Demographic and Health Survey. Annual percentage change (APC) was estimated. The APCs of different dimensions of equity (residence, administrative region, maternal education and wealth quintile) were compared by coincidence test - to determine similarity in joinpoint regression functions via 10 000 Monte Carlo resampling. RESULTS: There has been progress in reduction of under-five mortality in Ghana between 1988 and 2017 with an annual percentage change of -3.49%. Disaggregation of the trends showed that the most rapid improvement in under-five mortality rates occurred in the Upper East Region (APC = -5.0%). The closing of under-five mortality equity gaps in the study period has been uneven in the country. The gap between rural and urban rates has closed the most, followed by regional gaps (between Upper East and Ashanti Region), while the most persistent gaps remain in maternal education and wealth quintile. CONCLUSION: The findings suggest that programmatic interventions have been more successful in reducing geographic (rural-urban and by administrative region) than non-geographic (maternal education and wealth quintile) inequities in under-five mortality in Ghana. To accelerate reduction and bridge the inequities in under-five mortality, Ghana may need to pursue more social policies aimed at redistribution.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".