Evidence map›Paper›PMID 39177693›Full record

ArticleResearch in health services & regions2022

C-sections and hospital characteristics: a long term analysis on low-risk deliveries.

Inês Joaquim, Luís Nobre Pereira, Carla Nunes, Céu Mateus

Abstract read
In one paragraph

Article in Research in health services & regions, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Inês JoaquimEscola Nacional de Saúde Pública, Universidade Nova de Lisboa, Avenida Padre Cruz, 1600-560, Lisboa, Portugal. ines.s.joaquim@gmail.com.
Luís Nobre PereiraResearch Centre for Spatial and Organizational Dynamics (CIEO) - University of Algarve, School of Management, Hospitality and Tourism, University of Algarve, Campus da Penha, 8005-139, Faro, Portugal.ORCID https://orcid.org/0000-0003-0917-7163
Carla NunesCentro de Investigação em Saúde Pública, Escola Nacional de Saúde Pública, Universidade Nova de Lisboa, Avenida Padre Cruz, 1600-560, Lisboa, Portugal.ORCID https://orcid.org/0000-0003-4562-1057
Céu MateusDivision of Health Research, HI One, Lancaster University, Sir John Fisher Drive, Lancaster, LA1 4AT, UK. c.mateus@lancaster.ac.uk.ORCID https://orcid.org/0000-0001-6219-219X

Funding

Seventh Framework Programme 242189
6 · The paper itself

Abstract

backgroundPolicymakers aim to reduce C-section (CS) rates, due to well documented overtreatment. However, little is known about how hospital characteristics relate to their c-section rates on low-risk deliveries (CSR-LRD).

methodsCSR-LRD were computed using inpatient data from all Portuguese National Health Service hospitals (2002-2011). Linear and Fractional Response Models were estimated to quantify the relationship between CSR-LRD and a set of hospital characteristics: hospital size, type (exclusively obstetrics or not), Neonatal Intensive Care Unit (NICU) availability, obstetrician-to-obstetric bed ratio, and teaching status.

resultsCSR-LRD increased from 11.7% (2002) to 14.1% (2008), declining to 12.5% in 2011. While larger hospitals and hospitals with NICU had higher CSR-LRD rates, teaching status and obstetrician-to-obstetric bed ratio had no significant effect. Adjusted estimates, controlling for those four characteristics, indicate 91% of the variation in the CSR-LRD is left unexplained.

conclusionHospital characteristics do not explain variation in CSR-LRD rates. Further studies considering medical practice, financial incentives to hospitals and/or physicians, and patient education are needed.

Indexed as

Caesarean sectionsFractional response modelsHospital resourcesLow-risk

Identifiers

PMID39177693
PMCPMC11281737

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.