The influence of travel time to health facilities on stillbirths: A geospatial case-control analysis of facility-based data in Gombe, Nigeria
Bibliographic record
Abstract
Access to quality emergency obstetric and newborn care (EmONC); having a skilled attendant at birth (SBA); adequate antenatal care; and efficient referral systems are considered the most effective interventions in preventing stillbirths. We determined the influence of travel time from mother's area of residence to a tertiary health facility where women sought care on the likelihood of delivering a stillbirth. We carried out a prospective matched case-control study between 1st January 2019 and 31st December 2019 at the Federal Teaching Hospital Gombe (FTHG), Nigeria. All women who experienced a stillbirth after hospital admission during the study period were included as cases while controls were consecutive age-matched (ratio 1:1) women who experienced a live birth. We modelled travel time to health facilities. To determine how travel time to the nearest health facility and the FTHG were predictive of the likelihood of stillbirths, we fitted a conditional logistic regression model. A total of 318 women, including 159 who had stillborn babies (cases) and 159 age-matched women who had live births (controls) were included. We did not observe any significant difference in the mean travel time to the nearest government health facility for women who had experienced a stillbirth compared to those who had a live birth [9.3 mins (SD 7.3, 11.2) vs 6.9 mins (SD 5.1, 8.7) respectively, p = 0.077]. However, women who experienced a stillbirth had twice the mean travel time of women who had a live birth (26.3 vs 14.5 mins) when measured from their area of residence to the FTHG where deliveries occurred. Women who lived farther than 60 minutes were 12 times more likely of having a stillborn [OR = 12 (1.8, 24.3), p = 0.011] compared to those who lived within 15 minutes travel time to the FTHG. We have shown for the first time, the influence of travel time to a major tertiary referral health facility on the occurrence of stillbirths in an urban city in, northeast Nigeria.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".