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Trend of caesarean section rate and puerpera characteristics: based on Robson classification / 中华流行病学杂志
Chinese Journal of Epidemiology ; (12): 963-967, 2017.
Article in Zh | WPRIM | ID: wpr-737756
Responsible library: WPRO
ABSTRACT
Objective To analyze the trend in caesarean section rate and puerpera characteristics in hospital,and provide valuable information for maternal and child health policy making and clinical practice.Methods A total of 12 041 women who delivered in the affiliated Chaohu Hospital of Anhui Medical University from October 1,2010 to September 30,2016 were selected.Based on Robson classification system,changes in the rate of caesarean delivery as well as its relationship with two-child policy and infant sex ratio were analyzed.Results The overall caesarean section rate gradually decreased from 66.9% to 44.2% during the past six years.Respectively,the caesarean section rate in primiparae with singleton term babies decreased to 32.1% and the rate in multiparas without uterine scar decreased to 14.2%,and the rate in premature delivery decreased to 22.9%,the differences were significant (P<0.01).The proportion of vaginal delivery (R1,R3),multiparas with uterine scar (R5) and twins pregnancy (R8) increased,the differences were significant (P<0.01).The annual overall newly-born sex ratio ranged from 110:100 to 128:100.In group R1,more babies were girls,the proportion was stable,more women with premature delivery and multiparas had boy babies,but the boy babies by multiparas without uterine scar obviously decreased in the last 2 years.Conclusions Primiparae with singleton head birth,multipara without uterine scar and women with premature deliveries are the key population in the effort of reduction of caesarean section rate.The caesarean section rate and proportion were unstable in multiparas with uterine scar,breech deliveries and twin deliveries.The application of Robson classification system can improve the comparability of the surveillance data.
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Full text: 1 Index: WPRIM Language: Zh Journal: Chinese Journal of Epidemiology Year: 2017 Type: Article
Full text: 1 Index: WPRIM Language: Zh Journal: Chinese Journal of Epidemiology Year: 2017 Type: Article