Trends and inequalities in neonatal mortality rate in Bangladesh: Evidence from cross‐sectional surveys
Bibliographic record
Abstract
Abstract Background and Aims Given the significance of addressing neonatal mortality in pursuing the 2030 Sustainable Development Goal on child health, research focus on this area is crucial. Despite the persistent high rates of neonatal mortality rate (NMR) in Bangladesh, there remains a notable lack of robust evidence addressing inequalities in NMR in the country. Therefore, this study aims to fill the knowledge gap by comprehensively investigating inequalities in NMR in Bangladesh. Methods The Bangladesh Demographic and Health Survey (BDHS) data from 2000 to 2017 were analyzed. The equity stratifiers used to measure the inequalities were wealth status, mother's education, place of residence, and subnational region. Difference ( D ) and population attributable fraction (PAF) were absolute measures, whereas population attributable risk (PAR) and ratio ( R ) were relative measures of inequality. Statistical significance was considered by estimating 95% confidence intervals (CIs) for each estimate. Results A declining trend in NMR was found in Bangladesh, from 50.2 in 2000 to 31.9 deaths per 1000 live births in 2017. This study detected significant wealth‐driven (PAF: −20.6, 95% CI: −24.9, −16.3; PAR: −6.6, 95% CI: −7.9, −5.2), education‐related (PAF: −11.6, 95% CI: −13.4, −9.7; PAR: −3.7, 95% CI: −4.3, −3.1), and regional (PAF: −20.6, 95% CI: −27.0, −14.3; PAR: −6.6, 95% CI: −8.6, −4.6) disparities in NMR in all survey points. We also found a significant urban–rural inequality from 2000 to 2014, except in 2017. Both absolute and relative inequalities in NMR were observed; however, these inequalities decreased over time. Conclusion Significant variations in NMR across subgroups in Bangladesh highlight the need for comprehensive, and targeted interventions. Empowering women through improved access to economic resources and education may help address disparities in NMR in Bangladesh. Future research and policies should focus on developing strategies to address these disparities and promote equitable health outcomes for all newborns.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".