Does the reduction in obstetric hospitals result in an unintended decreased in-hospital delivery utilisation? A causal multilevel analysis in China
Bibliographic record
Abstract
Introduction: China's progress towards achieving Sustainable Development Goals for maternal health is largely attributed to a reduction in maternal mortality rates, driven by increased in-hospital delivery services utilisation. However, recent reductions in the number of obstetric hospitals have raised concerns about compromised access to these services. This study investigates the impact of reduced obstetric hospitals on spatial accessibility and the utilisation of in-hospital delivery services. Methods: Data from 2016 to 2020 were collected from a densely populated province with approximately 83 million residents. Directed Acyclic Graph was applied to identify a minimally sufficient set of confounders, including residential characteristics and transportation-related factors. Multilevel regression models were employed to analyse the causal effects, with sensitivity analysis using fixed effect and quantile regression models. Results: Between 2017 and 2020, the number of obstetric hospitals decreased by 21.3% (from 1209 to 951), leading to a decline in the proportion of pregnant women covered within a 2-hour driving radius (from 97.4% to 97.1%) and an increase in the maximum of shortest driving time within county (from 117.2 to 121.0 min). Multilevel regression models, adjusted for confounders, showed that a 1 percentage point increase in the proportion of pregnant women covered within a 2-hour driving radius was associated with a 13 percentage point (95% CI: 11.4 to 14.7) increase in in-hospital delivery rates, especially in areas with lower coverage and in-hospital delivery rates. Conclusions: The reduction in obstetric hospitals increased travel distances, negatively impacting in-hospital delivery utilisation. Expanding the proportion of pregnant women covered within a 2-hour driving radius may be more effective than reducing the maximum of shortest travel distance within a county when optimising obstetric hospital locations. These findings provide insights for optimising obstetric facility locations in similar low- and middle-income countries. While improving spatial accessibility is important, the potential quality gains from centralising obstetric resources should also be considered.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".