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Record W7117236924 · doi:10.1016/j.ejogrb.2025.114921

Caesarean section rates in public vs private hospitals in Europe: a systematic review and meta-analysis using the Robson ten group classification system

2025· article· en· W7117236924 on OpenAlexaboutno aff
Sara Ebadi, Viktoria El Radaf, Tahir Mahmood, Charles Savona-Ventura, Mehreen Zaigham

Bibliographic record

VenueEuropean Journal of Obstetrics & Gynecology and Reproductive Biology · 2025
Typearticle
Languageen
FieldMedicine
TopicMaternal and Perinatal Health Interventions
Canadian institutionsnot available
FundersEuropean Board and College of Obstetrics and GynaecologyLunds UniversitetSweden-America Foundation
KeywordsCaesarean sectionPsychological interventionCaesarean deliveryPublic healthPublic hospitalMEDLINE

Abstract

fetched live from OpenAlex

INTRODUCTION: Since the last two decades, there has been a dramatic rise in caesarean sections (CS) throughout the world. This increase has been seen even in Europe, where rates vary significantly from 17% in Northern Europe to 56% in the South. Although, CS can be a lifesaving intervention when medically necessary, non-essential CS are associated with short- and long-term complications for both the mother and newborn. To curb this rising trend, it is important to understand underlying causes behind regional disparities, including differences between public and private hospitals. OBJECTIVE: To investigate variations in CS rates between public and private hospitals across European regions and at a country level using the Robson Ten Group Classification. METHODS: A systemic review of studies published between 1st January 2000 and 12th March 2025 was conducted using MEDLINE/PubMed, CINAHL, EMBASE, Global Index Medicus, Web of Science and Cochrane library, analysing CS rates in 25 European countries. All studies reporting births in Europe, Robson group, written in English or Swedish were included. The developed protocol was prospectively registered in PROSPERO (Registration number 513579). Meta-analysis using absolute numbers and percentages was conducted to compare the birth rates at country and regional levels. To assess the risk of bias, two reviewers independently evaluated the quality of the studies included using a modified Newcastle-Ottawa Scale adapted for cohort studies. RESULTS: Of 1385 articles, 46 were eligible for inclusion in the final analysis. A total of 12 505939 births were analysed, with 8 543803 (68.3%) occurring in public hospitals and 3 962136 (31.7%) in private hospitals. Overall, Southern Europe illustrated the highest CS rate (54.9% of all births) as compared to Northern Europe (16.9%). There was a lack of reporting from private hospitals, with data only for Southern Europe, where CS rates were significantly higher in private (73.1%) as compared to public (40.9%) hospitals. The largest differences were seen for low-risk women Robson Group 1, 2, 3 and 4 (private vs public: 67.8 vs 28%, 67.6 vs 39.7, 26.9 vs 9.1% and 38 vs 18% respectively). CONCLUSION: High CS rates were observed across Europe, with Southern Europe reporting the highest levels. Rates were consistently higher in private compared to public hospitals. In both settings, Group 5 (women with a previous CS) was the largest contributor to the overall CS rate. However, low-risk women in private hospitals (Groups 1 and 2) had twice the CS rates compared with public hospitals. These findings highlight that the excess CS burden in private hospitals is largely driven by unnecessary procedures in low-risk groups. There is an urgent need for interventions that promote evidence-based care and reduce unnecessary CS especially among low-risk women.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.021
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.043
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0210.031
Bibliometrics0.0160.018
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.076
GPT teacher head0.344
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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