The effect of a new maternity unit on maternal outcomes in rural Haiti: an interrupted time series study
Bibliographic record
Abstract
BACKGROUND: In Haiti where there are high rates of maternal and neonatal mortality, efforts to reduce mortality and improve maternal newborn child health (MNCH) must be tracked and monitored to measure their success. At a rural Haitian hospital, local surveillance efforts allowed for the capture of MNCH indicators. In March 2018, a new stand-alone maternity unit was opened, with increased staff, personnel, and physical space. We aimed to determine if the new maternity unit brought about improvements in maternal and neonatal outcomes. METHODS: We conducted an interrupted time series analysis using data collected between July 2016 and October 2019 including 20 months before the opening of the maternity unit and 20 months after. We examined maternal-neonatal outcomes such as physiological (vaginal) births, caesarean birth, postpartum hemorrhage (PPH), maternal deaths, stillbirths and undesirable outcomes (eclampsia, PPH, perineal laceration, postpartum infection, maternal death or stillbirth). RESULTS: Immediately after the opening of the new maternity, the number of physiological births decreased by 7.0% (β = - 0.070; 95% CI: - 0.110 to - 0.029; p = 0.001) and there was an increase of 6.7% in caesarean births (β = 0.067; 95% CI: 0.026 to 0.107; p = 0.002). For all undesirable outcomes, preintervention there was an increasing trend of 1.8% (β = 0.018; 95% CI: 0.013 to 0.024; p < 0.001), an immediate 14.4% decrease after the intervention (β = - 0.144; 95% CI: - 0.255 to - 0.033; p = 0.012), and a decreasing trend of 1.8% through the postintervention period (β = - 0.018; 95% CI: - 0.026 to - 0.009; p < 0.001). No other significant level or trend changes were noted. CONCLUSIONS: The new maternity unit led to an upward trend in caesarean births yet an overall reduction in all undesirable maternal and neonatal outcomes. The new maternity unit at this rural Haitian hospital positively impacted and improved maternal and neonatal outcomes.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".