Abstract
The DEEP cohort is the first population-based cohort of pregnant population in China that longitudinally documented drug uses throughout the pregnancy life course and adverse pregnancy outcomes. The main goal of the study aims to monitor and evaluate the safety of drug use through the pregnancy life course in the Chinese setting. The DEEP cohort is developed primarily based on the population-based data platforms in Xiamen, a municipal city of 5 million population in southeast China. Based on these data platforms, we developed a pregnancy database that documented health care services and outcomes in the maternal and other departments. For identifying drug uses, we developed a drug prescription database using electronic healthcare records documented in the platforms across the primary, secondary and tertiary hospitals. By linking these two databases, we developed the DEEP cohort. All the pregnant women and their offspring in Xiamen are provided with health care and followed up according to standard protocols, and the primary adverse outcomes – congenital malformations – are collected using a standardized Case Report Form. From January 2013 to December 2021, the DEEP cohort included 564,740 pregnancies among 470,137 mothers, and documented 526,276 live births, 14,090 miscarriages and 6,058 fetal deaths/stillbirths and 25,723 continuing pregnancies. In total, 13,284,982 prescriptions were documented, in which 2,096 chemicals drugs, 163 biological products, 847 Chinese patent medicines and 655 herbal medicines were prescribed. The overall incidence rate of congenital malformations was 2.0% (10,444/526,276), while there were 25,526 (4.9%) preterm births and 25,605 (4.9%) live births with low birth weight.
Similar content being viewed by others
References
Collaborative Group On Drug Use In Pregnancy C. An international survey on drug utilization during pregnancy. Int J risk Saf Med. 1991;2(6):345–9. https://doi.org/10.3233/jrs-1991-2606.
Mitchell AA, Gilboa SM, Werler MM, Kelley KE, Louik C, Hernández-Díaz S. Medication use during pregnancy, with particular focus on prescription drugs: 1976–2008. Am J Obstet Gynecol. 2011;205(1):e511–8. https://doi.org/10.1016/j.ajog.2011.02.029.
Emanuel M, Rawlins M, Duff G, Breckenridge A. Thalidomide and its sequelae. Lancet (London England). 2012;380(9844):781–3. https://doi.org/10.1016/s0140-6736(12)60468-1.
Bjørk MH, Zoega H, Leinonen MK, et al. Association of Prenatal Exposure to antiseizure medication with risk of Autism and Intellectual Disability. JAMA Neurol. 2022;79(7):672–81. https://doi.org/10.1001/jamaneurol.2022.1269.
Diav-Citrin O, Shechtman S, Weinbaum D, et al. Paroxetine and fluoxetine in pregnancy: a prospective, multicentre, controlled, observational study. Br J Clin Pharmacol. 2008;66(5):695–705. https://doi.org/10.1111/j.1365-2125.2008.03261.x.
Kmietowicz Z. Women are unaware of pregnancy risks linked with sodium valproate. BMJ (Clinical Res ed). 2016;355(i5829). https://doi.org/10.1136/bmj.i5829.
Mines D, Tennis P, Curkendall SM, et al. Topiramate use in pregnancy and the birth prevalence of oral clefts. Pharmacoepidemiol Drug Saf. 2014;23(10):1017–25. https://doi.org/10.1002/pds.3612.
Heyrana K, Byers HM, Stratton P. Increasing the participation of pregnant women in clinical trials. JAMA. 2018;320(20):2077–8. https://doi.org/10.1001/jama.2018.17716.
Huybrechts KF, Bateman BT, Hernández-Díaz S. Use of real-world evidence from healthcare utilization data to evaluate drug safety during pregnancy. Pharmacoepidemiol Drug Saf. 2019;28(7):906–22. https://doi.org/10.1002/pds.4789.
U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER). Center for Biologics Evaluation and Research (CBER). Postapproval pregnancy safety studies guidance for industry. (2019-05). https://www.fda.gov/media/124746/download.
S. H-D. Pregnancy registries. In: AHRQ, ed. Registries for Evaluating Patient Outcomes: A User’s Guide: 3rd Edition 2014:135–168.
Administration FD. Guidance for industry: establishing pregnancy exposure registries. 2002. https://www.fda.gov/ucm/groups/fdagov-public/@fdagov-drugs-gen/documents/document/ucm071639.pdf.
Langhoff-Roos J, Krebs L, Klungsøyr K, et al. The nordic medical birth registers–a potential goldmine for clinical research. Acta Obstet Gynecol Scand. 2014;93(2):132–7. https://doi.org/10.1111/aogs.12302.
Charlton R, Snowball J, Sammon C, de Vries C. The Clinical Practice Research Datalink for drug safety in pregnancy research: an overview. Therapie. 2014;69(1):83–9. https://doi.org/10.2515/therapie/2014007.
Ailes EC, Simeone RM, Dawson AL, Petersen EE, Gilboa SM. Using insurance claims data to identify and estimate critical periods in pregnancy: an application to antidepressants. Birth defects research. Part A, clinical and molecular teratology. 2016;106(11):927–34. https://doi.org/10.1002/bdra.23573.
Bliddal M, Broe A, Pottegård A, Olsen J, Langhoff-Roos J. The Danish Medical Birth Register. Eur J Epidemiol. 2018;33(1):27–36. https://doi.org/10.1007/s10654-018-0356-1.
Pedersen LH, Petersen OB, Nørgaard M, et al. Linkage between the Danish National Health Service prescription database, the Danish fetal medicine database, and other Danish registries as a tool for the study of drug safety in pregnancy. Clin Epidemiol. 2016;8:91–5. https://doi.org/10.2147/clep.S98139.
Zhu C, Yan L, Wang Y, Ji S, Zhang Y, Zhang J. Fertility intention and related factors for having a second or third child among childbearing couples in Shanghai, China. Front Public Health. 2022;10:879672. https://doi.org/10.3389/fpubh.2022.879672.
Zhou Y, Mao X, Zhou H et al. Epidemiology of birth defects based on a birth defect surveillance system in Southern Jiangsu, China, 2014–2018. The journal of maternal-fetal & neonatal medicine: the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstet. 2022;35(4):745 – 51. https://doi.org/10.1080/14767058.2020.1731459.
Zhiwen Li JD, China CDC, Weekly, Foreword. Prevention and Control of Birth Defects in China: Achievements and Challenges. https://weekly.chinacdc.cn/en/article/doi/https://doi.org/10.46234/ccdcw2021.1912021.
Zhang X, Chen L, Wang X, et al. Changes in maternal age and prevalence of congenital anomalies during the enactment of China’s universal two-child policy (2013–2017) in Zhejiang Province, China: an observational study. PLoS Med. 2020;17(2):e1003047. https://doi.org/10.1371/journal.pmed.1003047.
Liang J, Mu Y, Li X, et al. Relaxation of the one child policy and trends in caesarean section rates and birth outcomes in China between 2012 and 2016: observational study of nearly seven million health facility births. BMJ (Clinical Res ed). 2018;360:k817. https://doi.org/10.1136/bmj.k817.
Dai L, Zhu J, Liang J, Wang YP, Wang H, Mao M. Birth defects surveillance in China. World J Pediatrics: WJP. 2011;7(4):302–10. https://doi.org/10.1007/s12519-011-0326-0.
Yue W, Zhang E, Liu R, et al. The China birth cohort study (CBCS). Eur J Epidemiol. 2022;37(3):295–304. https://doi.org/10.1007/s10654-021-00831-8.
Zhou Y, Tao J, Wang K, et al. Protocol of a prospective and multicentre China Teratology Birth Cohort (CTBC): association of maternal drug exposure during pregnancy with adverse pregnancy outcomes. BMC Pregnancy Childbirth. 2021;21(1):593. https://doi.org/10.1186/s12884-021-04073-0.
Statistics XBo. Xiamen City 2021 National Economic and Social Development Statistical Bulletin. http://tjj.xm.gov.cn/tjzl/ndgb/202203/t20220322_2636525.htm. 2022.
National Health Committee of People‘s Republic of China. China Drug Supply Information Platform. 2018. http://cdsip.nhc.gov.cn/PriceBase/YPIDList.aspx? key¼ypid&value¼.
National Health and Family Planning Commission of of People‘s Republic of China. Women’s health basic data set - part 6: birth defect surveillance, 2013. http://www.nhc.gov.cn/ewebeditor/uploadfile/2018/09/20180921141035465.pdf.
China Maternal and Child Health Serveillance. China birth defects surveillance system: Guidelines for reporting complicated and micro malformations. 2012., http://www.mchscn.org/admin/wenjian/wxjxbgbz-2012.pdf.
National Health Committee of People‘s Republic of China. 1 minute to understand the Congenital Structural Defects Program. 2019. http://www.nhc.gov.cn/fys/s3590/201903/9e08496ee740468a93764c4afd3ff4d8.shtml.
March of Dimes Birth Defects Foundation of China. Disease list of congenital metabolic defects program, 2017. http://www.csqx.org.cn/content.aspx? id¼381707814440.
Xie XKB, Duan T. Text book of Obstetrics and Gynecology (9th).Beijing: People’s medical publishing house2018.
Deng QYQH. Normative definition of stillbirth and related registration procedures. Chin J Practical Obstet Gynecol. 2015;31(10):925–6. Chinese.
Goldenberg RLCJ, Iams JD, Romero R. Epidemiology and causes of preterm birth. Lancet (London England). 2008;371:75–84. https://doi.org/10.1016/S0140-6736(08)60074-4.
Organization WH. International statistical classification of diseases and related health problems, 10th revision. https://icd.who.int/browse10/Content/statichtml/ICD10Volume2_en_2010.pdf. 2016.
Zhu Z, Yuan L, Wang J, et al. Mortality and morbidity of infants born extremely Preterm at Tertiary Medical Centers in China from 2010 to 2019. JAMA Netw open. 2021;4(5):e219382. https://doi.org/10.1001/jamanetworkopen.2021.9382.
Marcus DM, Snodgrass WR. Do no harm: avoidance of herbal medicines during pregnancy. Obstet Gynecol. 2005;105(5 Pt 1):1119–22. https://doi.org/10.1097/01.AOG.0000158858.79134.ea.
Bruno LO, Simoes RS, de Jesus Simoes M, Girão M, Grundmann O. Pregnancy and herbal medicines: an unnecessary risk for women’s health-A narrative review. Phytother Res. 2018;32(5):796–810. https://doi.org/10.1002/ptr.6020.
Tazare J, Wyss R, Franklin JM, et al. Transparency of high-dimensional propensity score analyses: Guidance for diagnostics and reporting. Pharmacoepidemiol Drug Saf. 2022;31(4):411–23. https://doi.org/10.1002/pds.5412.
National Bureau of Statistics of China. Statistical Monitoring Report of the Program for the Development of Chinese Women (2021–2030), 2021. https://www.stats.gov.cn/sj/zxfb/202312/t20231229_1946062.html.
Acknowledgements
The authors are grateful to the participants in the DEEP for their support in the data collection.
Funding
This study was supported by grants from National Natural Science Foundation of China (72174132, 82225049, 71974138, 72004148), National Key Research and Development Program of China (2021YFC2701503), Sichuan Youth Science and Technology Innovation Research Team (2020JDTD0015), China Medical Board (CMB19-324), the Fundamental Research Funds for the Central public welfare research institutes (2020YJSZX-3).
Author information
Authors and Affiliations
Contributions
The conceptualization of this study was developed by XS and JT. Data analysis were performed by YQX, CRL and PZ; data collection, cleaning and standardization were performed by JG, MXL, WQW, GHY, YYQ, LSY, HYQ, HL, MLC and KZ; the first draft of the manuscript was written by JT; manuscript revision was mainly performed by PG, GWL, LT, JT and XS. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Ethical approval
This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of West China Hospital of Sichuan University in China (No. 2019 − 825, 2019/10/21).
Consent to participate
and publish.
Informed consent
is waived due to the nature of retrospective data.
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Tan, J., Xiong, Y., Liu, C. et al. A population-based cohort of drug exposures and adverse pregnancy outcomes in China (DEEP): rationale, design, and baseline characteristics. Eur J Epidemiol (2024). https://doi.org/10.1007/s10654-024-01124-6
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10654-024-01124-6