Predictors and disparities in neonatal and under 5 mortality in rural Pakistan: cross sectional analysis
Bibliographic record
Abstract
Background: (UeN) or New Hope project is based on scaling up proven and effective Maternal and Newborn Child Health (MNCH) interventions in 8 of the highest burden districts of the country, using existing public sector platforms in Pakistan at both the community and facility level. The primary aim of the project is to reduce perinatal mortality in these districts by 20% from baseline. Methods: We report overall neonatal and post neonatal mortality rates for the two years preceding the UeN baseline household survey. Rates were calculated using the synthetic cohort probability method and predictors of neonatal and post neonatal mortality examined using Cox regression. To investigate spatial variations in the mortality rates, we calculated Moran's I at the district level using predicted probabilities of mortality. Finally, we create district level maps of predicted under 5 child mortality using a stochastic partial differentiation approach. Findings: A total of 26,258 children contributed to the analysis of mortality with 838 deaths in the neonatal period and 2236 under-5 deaths during the observation period from March 1, 2015 to March 17, 2017. Overall, we estimated the NMR to be 29.2 per 1000 live births (95% CI: 26.9-31.4) and the U5MR to be 86.1 per 1000 live births (95% CI: 85.5-86.8). We found evidence of within-district geospatial clustering of under 5 mortality (P < 0.0001) and that social factors (poverty, illiteracy, multiparity), poor coverage of community health workers and distance from health facilities were strongly associated with child mortality. Interpretation: Important factors associated with neonatal and post-neonatal mortality in our study population included maternal education, parity, household size and gender. Additionally, antenatal care coverage (at least 4 visits) was specifically associated with neonatal mortality only, whereas, LHW coverage and distance to health facility were strongly associated with post-neonatal mortality. These findings emphasise the need for comprehensive, multisectoral strategies to be implemented for future maternal and child health programs and outreach services in rural areas. Funding: The study was funded by an unrestricted grant from the Bill & Melinda Gates Foundation to the Aga Khan University (Grant OPP 1148892).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".