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A Segmented Logistic Regression Approach to Evaluating Change in Caesarean Section Rate with Reform of Birth Planning Policy in Two Regions in China from 2012 to 2016

Authors Kang L, Ye S, Jing K, Fan Y, Chen Q, Zhang N, Zhang B

Received 13 September 2019

Accepted for publication 11 March 2020

Published 19 March 2020 Volume 2020:13 Pages 245—253

DOI https://doi.org/10.2147/RMHP.S230923

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Kent Rondeau


Lili Kang, 1, 2,* Shangyuan Ye, 3,* Kangzhen Jing, 4 Yancun Fan, 2 Qihui Chen, 5 Ning Zhang, 6, 7 Bo Zhang 8

1Center for Health Policy and Management Studies, Nanjing University, Nanjing, Jiangsu, People’s Republic of China; 2School of Health Management, Inner Mongolia Medical University, Hohhot, Inner Mongolia, People’s Republic of China; 3Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, USA; 4Department of Medical Affair, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, Jiangsu, People’s Republic of China; 5Center for Food and Health Economic Research, College of Economics and Management, China Agricultural University, Beijing, People’s Republic of China; 6Department of Health Policy and Management, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, USA; 7Meyers Primary Care Institute, A Joint Endeavor of University of Massachusetts Medical School, Reliant Medical Group, and Fallon Health, Worcester, MA, USA; 8Department of Neurology and ICCTR Biostatistics and Research Design Center, Boston Children’s Hospital and Harvard Medical School, Boston, MA, USA

*These authors contributed equally to this work

Correspondence: Bo Zhang; Shangyuan Ye
Email bo.zhang@childrens.harvard.edu; shangyuan_ye@harvardpilgrim.org

Objective: This study evaluated change in caesarean section rate with reform of birth planning policy in China from one-child to two-child policy.
Methods: Study data were collected from patient-level hospital records of 59,668 pregnant women who visited three major urban hospitals in Jiangsu Province and Inner Mongolia Autonomous Region of China between January 2012 and December 2016. A segmented logistic regression approach was developed to evaluate the changes in caesarean section rate in these regions with the launch of China’s new partial and universal two-child policies in January 2014 and January 2016, respectively.
Results: Jiangsu Province had a significantly lower non-emergency caesarean rate (Jiangsu 8.15% vs Inner Mongolia 34.03%, p < 0.001) and a much lower percentage of minority population (Jiangsu 6.99% vs Inner Mongolia 21.76%, p < 0.001) than Inner Mongolia Autonomous Region. In Jiangsu Province, no change in caesarean section rate was detected with the two-child policies (all p-values > 0.05), although the unadjusted trend change (0.038, 95% confidence interval or CI: [0.016, 0.060], p < 0.001) in log odds after the implementation of the partial two-child policy was statistically significant. In Inner Mongolia Autonomous Region, an immediate jump in caesarean section rate was discovered by the segmented logistic regression with the implementation of both the partial (unadjusted level change 0.297, CI: [0.105, 0.489], p = 0.002) and universal two-child policies (unadjusted level change 1.945, CI: [1.277, 2.614], p < 0.001); but the rate reverted to the previous level thereafter. Ethnicity, maternal age, maternal reproduction history, insurance coverage type, infant weight, and infant gender were the significant factors associated with caesarean section rate (for odds ratios, all p-values < 0.05). However, the significance of infant gender may stem from the large sample size of the study and is not clinically meaningful.
Conclusion: Change in caesarean section rate was not observed with the launch of two-child policy in China.

Keywords: one-child policy, two-child policy, caesarean section, segmented logistic regression
 

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