35 Estimating the Impact of Pregnancy and Childbirth Interventions Coverage on Neonatal Survival during the COVID-19 Pandemic in Nepal
Bibliographic record
Abstract
Abstract Background The indirect impact of the COVID-19 pandemic on neonatal survival, especially in a resource-constrained and yet previously well-performing nation and in terms of reductions in maternal and neonatal mortality rates, is of global health interest. Objectives We estimated additional newborn lives saved by pregnancy and childbirth interventions during the COVID-19 pandemic in Nepal. Design/Methods We used an open-access linear deterministic model, Lives Saved Tool (LiST), to estimate additional newborn lives saved in a year, based on the change in coverage of specified pregnancy and childbirth interventions from the previous year. We kept the rest of the interventions and the effectiveness values unchanged from the ‘default’ in the LiST model. We ran LiST projections based on coverage changes of three or more antenatal care visits and institutional delivery using: (i) ‘actual’ coverage rates reported by health facilities and published in the government’s annual reports; and (ii) ‘target’ coverage rates from the Nepal Every Newborn Action Plan. In those two scenarios, we compared the estimates of additional lives saved in the last three years. Results The number of additional newborn lives saved (with a 95% confidence interval) in a year based on ‘actual’ versus ‘target’ intervention coverage rates varied considerably during the three years of the COVID-19 pandemic: 124 (95% CI: 85,177) vs 115 (78,163) in the year 2020; 83 (57, 118) vs 226 (155,323) in 2021, and 228 (156,323) vs 333 (228,471) in 2022. The top four interventions during childbirth would contribute to 70% (304 of 435) of additional lives saved, based on ‘actual’ coverage rates, compared to 87% (588 of 674) based on the ‘target’ coverages. Neonatal resuscitation saved the most newborn lives (91 vs 178), followed by thermal protection (77 vs 148), clean cord care (72 vs 138), and assisted vaginal delivery (64 vs 124). Prematurity, birth asphyxia, and sepsis are the top three causes of lives lost, contributing to around 72% of neonatal mortality, with estimated neonatal mortality rates of 19.46, 19.53, and 19.28 per 1000 live births in the years 2020, 2021, and 2022 respectively. Conclusion A one-third reduction in estimated additional newborn lives saved in the year 2021 compared to 2020, and a significant difference noted that same year on estimates based on ‘actual’ versus ‘target’ coverage rates of antenatal care and institutional delivery in Nepal, may reflect an indirect impact of reduced coverage of pregnancy and childbirth interventions on neonatal mortality during the peak of the COVID-19 pandemic.
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 | 0.002 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".