Klein Ari Z, O'Connor Karen, Gonzalez-Hernandez Graciela
Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
JMIR Form Res. 2022 Jan 6;6(1):e33792. doi: 10.2196/33792.
COVID-19 during pregnancy is associated with an increased risk of maternal death, intensive care unit admission, and preterm birth; however, many people who are pregnant refuse to receive COVID-19 vaccination because of a lack of safety data.
The objective of this preliminary study was to assess whether Twitter data could be used to identify a cohort for epidemiologic studies of COVID-19 vaccination in pregnancy. Specifically, we examined whether it is possible to identify users who have reported (1) that they received COVID-19 vaccination during pregnancy or the periconception period, and (2) their pregnancy outcomes.
We developed regular expressions to search for reports of COVID-19 vaccination in a large collection of tweets posted through the beginning of July 2021 by users who have announced their pregnancy on Twitter. To help determine if users were vaccinated during pregnancy, we drew upon a natural language processing (NLP) tool that estimates the timeframe of the prenatal period. For users who posted tweets with a timestamp indicating they were vaccinated during pregnancy, we drew upon additional NLP tools to help identify tweets that reported their pregnancy outcomes.
We manually verified the content of tweets detected automatically, identifying 150 users who reported on Twitter that they received at least one dose of COVID-19 vaccination during pregnancy or the periconception period. We manually verified at least one reported outcome for 45 of the 60 (75%) completed pregnancies.
Given the limited availability of data on COVID-19 vaccine safety in pregnancy, Twitter can be a complementary resource for potentially increasing the acceptance of COVID-19 vaccination in pregnant populations. The results of this preliminary study justify the development of scalable methods to identify a larger cohort for epidemiologic studies.
孕期感染新冠病毒与孕产妇死亡、入住重症监护病房及早产风险增加相关；然而，许多孕妇因缺乏安全性数据而拒绝接种新冠疫苗。
本初步研究的目的是评估推特数据是否可用于确定一个队列，以开展孕期新冠疫苗接种的流行病学研究。具体而言，我们研究了是否有可能识别出报告以下两点的用户：（1）他们在孕期或受孕前后接种了新冠疫苗；（2）他们的妊娠结局。
我们开发了正则表达式，在2021年7月初之前在推特上宣布怀孕的用户发布的大量推文集中搜索新冠疫苗接种报告。为帮助确定用户是否在孕期接种了疫苗，我们利用了一种自然语言处理（NLP）工具来估计孕期的时间范围。对于发布带有时间戳表明在孕期接种疫苗推文的用户，我们利用其他NLP工具来帮助识别报告其妊娠结局的推文。
我们人工核实了自动检测到的推文内容，确定了150名在推特上报告他们在孕期或受孕前后至少接种了一剂新冠疫苗的用户。我们人工核实了60例已完成妊娠中的45例（75%）至少一项报告的结局。
鉴于孕期新冠疫苗安全性数据有限，推特可作为一种补充资源，可能会提高孕妇群体对新冠疫苗接种的接受度。这项初步研究的结果证明开发可扩展方法以识别更大队列用于流行病学研究是合理的。
