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BJOG ; 128(5): 798-806, 2021 04.
Article in English | MEDLINE | ID: mdl-32929868

ABSTRACT

BACKGROUND: Caesarean delivery on maternal request (CDMR) is considered a significant contributor to the unprecedented increase in caesarean deliveries (CDs) for nonclinical reasons. Current literature lacks a reliable assessment of the rate of CDMR, which hinders the planning and delivery of appropriate interventions for reducing CDMR rates. OBJECTIVES: To conduct a systematic review of the literature and meta-regression to explore the global incidence of CDMR. SEARCH STRATEGY: PubMed, Embase, CINAHL, Medline, Google scholar and grey literature were searched from January 1985 to May 2019. SELECTION CRITERIA: Observational studies that report CDMR data were included. We excluded non-English articles, case notes, editorial reviews and articles reporting elective CDs from pregnancy risk factors. DATA COLLECTION AND ANALYSIS: Two reviewers independently conducted the screening and quality appraisal using a validated tool. The weighted average of CDMR over total deliveries (absolute proportion) and by total CDs (relative proportion) were generated. Quality-effects meta-regression was used to explain the variability of the CDMR estimates by moderators, including study methodology and demography of study participants. MAIN RESULTS: We identified 31 articles from 14 countries that include 5 million total births. The absolute proportion of CDMR varies between 0.2 and 42.0%, with significant variations across studies and subgroups. The economic status of the country and study year together explained 84% of the absolute and 76% of the relative proportion of CDMR variation. CONCLUSIONS: An appropriate reporting of CDMR should be a key priority in maternal health policies and practices. TWEETABLE ABSTRACT: Globally, the proportion of maternal requested caesarean delivery has mostly been influenced by the economic status of the country.


Subject(s)
Cesarean Section/statistics & numerical data , Elective Surgical Procedures/statistics & numerical data , Patient Preference/statistics & numerical data , Female , Global Health , Humans , Pregnancy , Regression Analysis
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