Caesarean section in uninsured women in the USA: systematic review and meta-analysis
Bibliographic record
Abstract
Objective The aim of this study is to assess the odds of caesarean section (CS) for uninsured women in the USA and understand the underlying mechanisms as well as consequences of lower use. Study design Systematic review and meta-analysis. Data sources PubMed, Embase, the Cochrane Library and CINAHL from the first year of records to April 2018. Eligibility criteria We included studies that reported data to allow the calculation of ORs of CS of uninsured as compared with insured women. Outcomes The prespecified primary outcome was the adjusted OR of deliveries by CS of uninsured women as compared with privately or publicly insured women. The prespecified secondary outcome was the crude OR of deliveries by CS of uninsured women as compared with insured women. Results 12 articles describing 16 separate studies involving more than 8.8 million women were included in this study. We found: 0.70 times lower odds of CS in uninsured as compared with privately insured women (95% CI 0.63 to 0.78), with no relevant heterogeneity between studies (τ2=0.01); and 0.92 times lower odds for CS in uninsured as compared with publicly insured women (95% CI 0.80 to 1.07), with no relevant heterogeneity between studies (τ2=0.02). We found 0.70 times lower odds in uninsured as compared with privately and publicly insured women (95% CI 0.69 to 0.72). Conclusions CSs are less likely to be performed in uninsured women as compared with insured women. While the higher rates for CS among privately insured women can be explained with financial incentives associated with private insurance, the lower odds among uninsured women draw attention at barriers to access for delivery care. In many regions, the rates for uninsured women are above, close or below the benchmarks for appropriate CS rates and could imply both, underuse and overuse.
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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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".