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

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C-sections and hospital characteristics: a long term analysis on low-risk deliveries. / Joaquim, Inês; Pereira, Luís Nobre; Nunes, Carla et al.
In: Research in Health Services & Regions, Vol. 1, No. 1, 14.12.2022.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Joaquim, I, Pereira, LN, Nunes, C & Mateus, C 2022, 'C-sections and hospital characteristics: a long term analysis on low-risk deliveries', Research in Health Services & Regions, vol. 1, no. 1. https://doi.org/10.1007/s43999-022-00014-2

APA

Joaquim, I., Pereira, L. N., Nunes, C., & Mateus, C. (2022). C-sections and hospital characteristics: a long term analysis on low-risk deliveries. Research in Health Services & Regions, 1(1). Advance online publication. https://doi.org/10.1007/s43999-022-00014-2

Vancouver

Joaquim I, Pereira LN, Nunes C, Mateus C. C-sections and hospital characteristics: a long term analysis on low-risk deliveries. Research in Health Services & Regions. 2022 Dec 14;1(1). Epub 2022 Dec 14. doi: 10.1007/s43999-022-00014-2

Author

Joaquim, Inês ; Pereira, Luís Nobre ; Nunes, Carla et al. / C-sections and hospital characteristics : a long term analysis on low-risk deliveries. In: Research in Health Services & Regions. 2022 ; Vol. 1, No. 1.

Bibtex

@article{780d58acb6cb417da859f7fb97a9c42d,
title = "C-sections and hospital characteristics: a long term analysis on low-risk deliveries",
abstract = "Background: Policymakers 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). Methods: CSR-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. Results: CSR-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. Conclusion: Hospital 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.",
keywords = "Original Paper, Caesarean sections, Hospital resources, Low-risk, Fractional response models",
author = "In{\^e}s Joaquim and Pereira, {Lu{\'i}s Nobre} and Carla Nunes and C{\'e}u Mateus",
year = "2022",
month = dec,
day = "14",
doi = "10.1007/s43999-022-00014-2",
language = "English",
volume = "1",
journal = "Research in Health Services & Regions",
issn = "2730-9827",
publisher = "Springer Berlin / Heidelberg",
number = "1",

}

RIS

TY - JOUR

T1 - C-sections and hospital characteristics

T2 - a long term analysis on low-risk deliveries

AU - Joaquim, Inês

AU - Pereira, Luís Nobre

AU - Nunes, Carla

AU - Mateus, Céu

PY - 2022/12/14

Y1 - 2022/12/14

N2 - Background: Policymakers 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). Methods: CSR-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. Results: CSR-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. Conclusion: Hospital 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.

AB - Background: Policymakers 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). Methods: CSR-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. Results: CSR-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. Conclusion: Hospital 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.

KW - Original Paper

KW - Caesarean sections

KW - Hospital resources

KW - Low-risk

KW - Fractional response models

U2 - 10.1007/s43999-022-00014-2

DO - 10.1007/s43999-022-00014-2

M3 - Journal article

VL - 1

JO - Research in Health Services & Regions

JF - Research in Health Services & Regions

SN - 2730-9827

IS - 1

ER -