Evidence map›Paper›PMID 31399068›Full record

SynthesisBMC public health2019

The effectiveness of financial intervention strategies for reducing caesarean section rates: a systematic review.

Yushan Yu, Feili Lin, Weizhen Dong, Haohan Li, Xiangyang Zhang, Chun Chen

Abstract readSystematic Review
In one paragraph

Synthesis in BMC public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Factors Associated with the Type of Cesarean Section in Greece and Their Correlation with International Guidelines.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2021
    Article
  10. 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

6 authors.

Yushan YuSchool of Public Health and Management, Wenzhou Medical University, Tongren, Building 7B304, Wenzhou Medical University Chashan campus, Wenzhou, 325035, China.
Feili LinSchool of Public Health and Management, Wenzhou Medical University, Tongren, Building 7B304, Wenzhou Medical University Chashan campus, Wenzhou, 325035, China.
Weizhen DongDepartment of Sociology and Legal Studies, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, N2L 3G1, Canada.
Haohan LiZhongshan Hospital Fudan University, Shanghai, 200032, China.
Xiangyang ZhangFirst Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China. zxyanghero@126.com.
Chun ChenSchool of Public Health and Management, Wenzhou Medical University, Tongren, Building 7B304, Wenzhou Medical University Chashan campus, Wenzhou, 325035, China. chenchun408@126.com.ORCID http://orcid.org/0000-0002-5149-988X

Funding

National Natural Science Foundation of China 71603187the Key Social Science Project for University Teachers of Zhejiang, China 2014QN005Zhejiang Provincial Natural Science Foundation LQ16G030011
6 · The paper itself

Abstract

backgroundThe increasing trend of Caesarean section (CS) in childbirth has become a global public health challenge. Previous studies have proposed financial intervention strategies for reducing CS rates by limiting caesarean delivery on maternal request (CDMR). This study synthesizes such strategies while evaluating their effectiveness.

methodsThe sources of data for this study are Cochrane Library, PubMed, EMBASE, and CINAHL. The publication period included in this study is from January 1991 to November 2018. The financial intervention strategies are divide into two categories: healthcare provider interventions and patient interventions. Risk of Bias in Non-randomized Studies - of Interventions (ROBINS-I) was employed to assess the risk of bias of included studies. The outcome of each study was evaluated with Grades of Recommendation, Assessment, Development and Evaluation (GRADE) through the GRADEpro Guideline Development Tool software.

resultsNine studies were included in this systematic review: five with high certainty evidence (HCE), three with moderate certainty evidence (MCE), and one with low certainty evidence (LCE). Of the nine studies, seven are centered on the effect of provider-side interventions. Three of the HCE studies found that the diagnosis-related group payment system, risk-adjusted capitation, and equalizing fee for both facilities and physicians were effective intervention strategies. One HCE and one MCE study showed that only equalizing facility fees between vaginal and CS deliveries in healthcare service settings had no significant effect on reducing the CS rate. The MCE study showed that case payment had a negative effect on reducing the CS rates. One LCE study revealed that the effect of a global budget system was uncertain, and one HCE and one MCE study focused on combining both provider and patient-side interventions. However, equalizing fees for vaginal and CS deliveries and a co-payment policy for CDMRs failed to reduce the CS rate.

conclusionsThe effectiveness of risk-adjusted payment methods appears promising and should be the subject of further research. Financial interventions should consider stakeholders' characteristics, especially the personal interests of doctors. Finally, high-quality randomized control trials and comparative studies on different financial intervention methods are needed to confirm or refute previous studies' outcomes.

Indexed as

Cesarean SectionControlled Clinical Trials as TopicFemaleHumansPregnancyRandomized Controlled Trials as TopicCaesarean section rateCDMRFinancial intervention

Identifiers

PMID31399068
PMCPMC6688325

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Registered trials

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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.