Skip to main content
SpringerLink
Log in

A population-based cohort of drug exposures and adverse pregnancy outcomes in China (DEEP): rationale, design, and baseline characteristics

  • COHORT PROFILE
  • Published:
European Journal of Epidemiology Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. 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.

    Article  Google Scholar 

  2. 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.

    Article  Google Scholar 

  3. 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.

    Article  PubMed  Google Scholar 

  4. 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.

    Article  PubMed  Google Scholar 

  5. 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.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. 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.

  7. 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.

    Article  CAS  PubMed  Google Scholar 

  8. 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.

    Article  PubMed  Google Scholar 

  9. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  10. 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.

  11. S. H-D. Pregnancy registries. In: AHRQ, ed. Registries for Evaluating Patient Outcomes: A User’s Guide: 3rd Edition 2014:135–168.

  12. 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.

  13. 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.

    Article  PubMed  Google Scholar 

  14. 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.

    Article  PubMed  Google Scholar 

  15. 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.

  16. 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.

    Article  PubMed  Google Scholar 

  17. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  18. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  19. 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.

  20. 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.

  21. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  22. 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.

    Article  Google Scholar 

  23. 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.

    Article  Google Scholar 

  24. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  25. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Statistics XBo. Xiamen City 2021 National Economic and Social Development Statistical Bulletin. http://tjj.xm.gov.cn/tjzl/ndgb/202203/t20220322_2636525.htm. 2022.

  27. National Health Committee of People‘s Republic of China. China Drug Supply Information Platform. 2018. http://cdsip.nhc.gov.cn/PriceBase/YPIDList.aspx? key¼ypid&value&#188.

  28. 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.

  29. 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.

  30. 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.

  31. March of Dimes Birth Defects Foundation of China. Disease list of congenital metabolic defects program, 2017. http://www.csqx.org.cn/content.aspx? id¼381707814440.

  32. Xie XKB, Duan T. Text book of Obstetrics and Gynecology (9th).Beijing: People’s medical publishing house2018.

  33. Deng QYQH. Normative definition of stillbirth and related registration procedures. Chin J Practical Obstet Gynecol. 2015;31(10):925–6. Chinese.

    Google Scholar 

  34. 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.

    Article  PubMed  Google Scholar 

  35. 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.

  36. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  37. 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.

    Article  PubMed  Google Scholar 

  38. 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.

    Article  PubMed  Google Scholar 

  39. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  40. 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.

Download references

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

Author notes

  1. Jing Tan and Yiquan Xiong contributed equally to this work.

Authors and Affiliations

  1. Institute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine Center, West China Hospital, Sichuan University, Chengdu, 610041, China

    Jing Tan, Yiquan Xiong, Chunrong Liu, Peng Zhao, Jin Guo, Mingxi Li, Wanqiang Wei, Kang Zou & Xin Sun

  2. NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu, 610041, China

    Jing Tan, Yiquan Xiong, Chunrong Liu, Peng Zhao, Jin Guo, Mingxi Li, Wanqiang Wei, Kang Zou & Xin Sun

  3. Sichuan Center of Technology Innovation for Real World Data, Chengdu, 610041, China

    Jing Tan, Yiquan Xiong, Chunrong Liu, Peng Zhao, Jin Guo, Mingxi Li, Wanqiang Wei, Kang Zou & Xin Sun

  4. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China

    Pei Gao

  5. Center for Clinical Epidemiology and Methodology (CCEM), Guangdong Second Provincial General Hospital, Guangzhou, 510317, China

    Guowei Li

  6. Xiamen Health Commission, Xiamen, 361000, China

    Guanhua Yao & Yongyao Qian

  7. Xiamen Health and Medical Big Data Center, Xiamen, 361008, China

    Lishan Ye, Huanyang Qi, Hui Liu & Moliang Chen

  8. Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada

    Lehana Thabane

  9. Biostatistics Unit, St Joseph’s Healthcare—Hamilton, Hamilton, Canada

    Lehana Thabane

Authors
  1. Jing Tan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yiquan Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chunrong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peng Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pei Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Guowei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jin Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Mingxi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Wanqiang Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Guanhua Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Yongyao Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Lishan Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Huanyang Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Hui Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Moliang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Kang Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Lehana Thabane
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Xin Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

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

Correspondence to Xin Sun.

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.

Supplementary Material 1

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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10654-024-01124-6

Keywords

Search

Navigation