Factors contributing to neonatal mortality reduction in three regions in Ghana: a mixed-methods study using the Lives Saved (LiST) modelling tool
Bibliographic record
Abstract
Background Ghana had significant reductions in neonatal mortality during the Millennium Development Goal (MDG) era. Few studies have attempted to identify the interventions, programs and policies that contributed to this progress. To understand the underlying factors which contributed to the reduction in neonatal mortality and to inform strategies to reach the Sustainable Development Goal (SDG) target to end preventable neonatal deaths, we conducted a mixed-methods study in the Northern, Upper West, and Volta Regions of Ghana. Methods The quantitative component consisted of an analysis of trends in coverage of key neonatal health interventions using subnational data from the Ghana Demographic and Health Surveys (DHS) and the Multiple Indicator Cluster Surveys (MICS). The Lives Saved Tool (LiST) was used to assess the contribution of various neonatal health interventions which resulted in reductions in neonatal mortality. Qualitative in-depth interviews (N=38) were conducted with government health officials, health workers, staff from non-government organizations, and community leaders, including traditional birth attendants and local chiefs from the Northern Region of Ghana. Findings Multiple interventions and contextual factors contributed to declines in the neonatal mortality rate (NMR) in all three regions. The Volta and Upper West Regions improved labor and delivery management practices which accounted for approximately one-third of the decline in NMR. The Northern Region had a substantial decline in NMR; the greatest proportion of the decline was attributed to control and management of infections in newborns (sepsis and pneumonia). Qualitative interviews suggest that improvements in neonatal survival were achieved through the prioritization of neonatal health at community-based health planning (CHPS) compounds and the development of a national-level strategic plan, coupled with neonatal health-related training for health workers. The establishment of neonatal intensive care units and strong partnership with local leaders and women’s groups to increase facility births and promote clean birth practices were also considered important. Conclusions The strategies that supported the reductions span from national-level policy intervention to facility-based quality improvement, to community level engagement. These strategies can be continued, strengthened, and scaled to improve neonatal survival in the SDG era.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".