Analysis of uptake, effectiveness and safety of COVID-19 vaccinations in pregnancy using the QResearch® database: research protocol and statistical analysis plan

medrxiv(2022)

引用 0|浏览8
暂无评分
摘要
Background The COVID-19 pandemic has affected millions of people globally with major health, social and economic consequences, prompting development of vaccines for use in the general population. However, vaccination uptake is lower in some groups, including in pregnant women, because of concerns regarding vaccine safety. There is evidence of increased risk of adverse pregnancy and neonatal outcomes associated with SARS-CoV-2 infection, but fear of vaccine-associated adverse events on the baby both in short and longer term is one of the main drivers of low uptake for this group. Other vaccines commonly used in pregnancy include influenza and pertussis. These both have reportedly higher uptake compared with COVID-19 vaccination, which may be because they are perceived to be safer. In this study, we will undertake an independent evaluation of the uptake, effectiveness and safety of COVID-19 vaccinations in pregnant women using the QResearch primary care database in England. Objectives 1. To determine COVID-19 vaccine uptake in pregnant women compared to uptake of influenza and pertussis vaccinations. 2. To estimate COVID-19 vaccine effectiveness in pregnant women by evaluating the risk of severe COVID-19 outcomes following vaccination. 3. To assess the safety of COVID-19 vaccination in pregnancy by evaluating the risks of adverse pregnancy and perinatal outcomes and adverse events of special interest for vaccine safety after COVID-19 vaccination compared with influenza and pertussis vaccinations. Methods This population-based study uses the QResearch® database of primary health care records, linked to individual-level data on hospital admissions, mortality, COVID-19 vaccination, SARS-CoV-2 testing data and congenital anomalies. We will include women aged 16 to 49 years with at least one pregnancy during the study period of 30th December 2020 to the latest date available. Babies born during the study period will be identified and linked to the mother’s record, where possible. We will describe vaccine uptake in pregnant women by trimester and population subgroups defined by demographics and other characteristics. Cox proportional hazards multivariable regression will be used to identify factors associated with vaccine uptake. The effectiveness of COVID-19 vaccines in pregnant women will be assessed using time varying Royston-Palmar regression analyses to determine unadjusted and adjusted hazard ratios for the occurrence of severe COVID-19 outcomes after each vaccine dose compared with unvaccinated individuals. For the safety analysis, we will we use logistic regression analyses to determine unadjusted and adjusted odds ratios for the occurrence of maternal (e.g. miscarriage, ectopic pregnancy and gestational diabetes) and perinatal outcomes (e.g. stillbirth, small for gestational age and congenital anomalies) by vaccination status compared to unvaccinated individuals. For the adverse events of special interest for vaccine safety (e.g. venous thromboembolism, myocarditis and Guillain Barre syndrome), we will use time varying Royston-Palmar regression analyses to determine unadjusted and adjusted hazard ratios for the occurrence of each outcome by vaccination status to unvaccinated individuals. Ethics and dissemination QResearch is a Research Ethics Approved Research Database with ongoing approval from the East Midlands Multi-Centre Research Ethics Committee (Ref: 18/EM/0400). This study was approved by the QResearch Scientific Committee on 9th June 2022. This research protocol has been developed with support from a patient and public involvement panel, who will continue to provide input throughout the duration of the study. Research findings will be submitted to pre-print servers such as MedRxIv, academic publication and disseminated more broadly through media releases and community groups and conference presentations. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by NIHR School for Primary Care Research ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: QResearch obtained full approval from the Trent Multi-Centre Research Ethics Committee [Ref: 03/4/021]. Approval is now confirmed yearly from the East Midlands - Derby Research Ethics Committee. In 2018, an updated application was approved [Ref: 18/EM/0400] in readiness for the transfer of QResearch to the University of Oxford. All previous approval letters can be found in the Downloads section. Research studies which utilise QResearch data need to obtain approval from the QResearch Scientific Committee. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced in the present work are contained in the manuscript
更多
查看译文
关键词
pregnancy,uptake
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要