Evidence map›Paper›PMID 38589644›Full record

ArticleEuropean journal of epidemiology2024

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

Jing Tan, Yiquan Xiong, Chunrong Liu, Peng Zhao, Pei Gao, Guowei Li, Jin Guo, Mingxi Li, Wanqiang Wei, Guanhua Yao and 8 more

Abstract read
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In one paragraph

Article in European journal of epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Jing Tan *Institute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
Yiquan Xiong *Institute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
Chunrong LiuInstitute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
Peng ZhaoInstitute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
Pei GaoDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, China.
Guowei LiCenter for Clinical Epidemiology and Methodology (CCEM), Guangdong Second Provincial General Hospital, Guangzhou, 510317, China.
Jin GuoInstitute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
Mingxi LiInstitute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
Wanqiang WeiInstitute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
Guanhua YaoXiamen Health Commission, Xiamen, 361000, China.
Yongyao QianXiamen Health Commission, Xiamen, 361000, China.
Lishan YeXiamen Health and Medical Big Data Center, Xiamen, 361008, China.
Huanyang QiXiamen Health and Medical Big Data Center, Xiamen, 361008, China.
Hui LiuXiamen Health and Medical Big Data Center, Xiamen, 361008, China.
Moliang ChenXiamen Health and Medical Big Data Center, Xiamen, 361008, China.
Kang ZouInstitute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
Lehana ThabaneDepartment of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada.
Xin SunInstitute of Integrated Traditional Chinese and Western Medicine, Chinese Evidence-based Medicine Center, West China Hospital, Sichuan University, Chengdu, 610041, China. sunxin@wchscu.cn.ORCID http://orcid.org/0000-0002-6554-7088

Funding

China Medical Board CMB19-324National Key Research and Development Program of China 2021YFC2701503National Natural Science Foundation of China 72004148National Natural Science Foundation of China 72174132National Natural Science Foundation of China 82225049, 71974138Sichuan Youth Science and Technology Innovation Research Team 2020JDTD0015the Fundamental Research Funds for the Central public welfare research institutes 2020YJSZX-3
6 · The paper itself

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.

Indexed as

Pregnancy OutcomeAdultChinaCohort StudiesDatabases, FactualDrug-Related Side Effects and Adverse ReactionsFemaleHumansInfant, NewbornPregnancyPregnancy ComplicationsPremature BirthCohort studyCongenital malformationDesignDrug exposuresPopulation-basedPregnancy

Identifiers

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.