Risk factors for stillbirths: how much can a responsive health system prevent?
Bibliographic record
Abstract
BACKGROUND: The stillbirth rate is an indicator of quality of care during pregnancy and delivery. Good quality care is supported by a functional heath system. The objective of this study was to explore the risk factors for stillbirths, particularly those related to a health system. METHODS: This case-control study was conducted in two districts of Bihar, India. Information on cases (stillbirths) were obtained from facilities as reported by Health Management Information System; controls were consecutive live births from the same population as cases. Data were collected from 400 cases and 800 controls. The risk factors were compared using a hierarchical approach and expressed as odds ratio, attributable fractions and population attributable fractions. RESULTS: Of all the factors studied, 22 risk factors were independently associated with stillbirths. Health system-related factors were: administration of two or more doses of oxytocics to augment labour before reaching the facilities (OR 1.6; 95% CI 1.2-2.1), any complications during labour (OR 2.3;1.7-3.1), >30 min to reach a facility from home (OR 1.4;1.05-1.8), >10 min to attend to the pregnant woman after reaching the facility (OR 2.8;1.7-4.5). In the final regression model, modifiable health system-related risk factors included: >10 min taken to attend to women after they reach the facilities (AOR 3.6; 95% CI 2.5-5.1), untreated hypertension during pregnancy (AOR 2.9; 95% CI 1.5-5.6) and presence of any complication during labour, warranting treatment (AOR 1.7; 95% CI 1.2-2.4). Among mothers who reported complications during labour, time taken to reach the facility was significantly different between stillbirths and live births (2nd delay; 33.5 min v/s 25 min; p < 0.001). Attributable fraction for any complication during labour was 0.56 (95% CI 0.42-0.67), >30 min to reach the facility 0.48 (95% CI 0.31-0.60) and institution of management 10 min after reaching the facility 0.68 (95% CI 0.58-0.75). Reaching a facility within 30 min, initiation of management within 10 min of reaching the facility and timely management of complications during labour could have prevented 17%, 37% and 20% of stillbirths respectively. CONCLUSION: A pro-active health system with accessible, timely and quality obstetric services can prevent a considerable proportion of stillbirths in low and middle income countries.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".