Pingray Veronica, Belizán María, Matthews Sarah, Zaraa Sabra, Berrueta Mabel, Noguchi Lisa M, Xiong Xu, Gurtman Alejandra, Absalon Judith, Nelson Jennifer C, Panagiotakopoulos Lakshmi, Sevene Esperanca, Munoz Flor M, Althabe Fernando, Mwamwitwa Kissa W, Rodriguez Cairoli Federico, Anderson Steven A, McClure Elizabeth M, Guillard Christine, Nakimuli Annettee, Stergachis Andy, Buekens Pierre
Institute for Clinical Effectiveness and Health Policy (IECS-CONICET), Ciudad de Buenos Aires, Buenos Aires, 1414, Argentina.
School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana, 70112, USA.
Gates Open Res. 2021 Jul 12;5:99. doi: 10.12688/gatesopenres.13305.1. eCollection 2021.
Given that pregnant women are now included among those for receipt coronavirus disease 2019 (COVID-19) vaccines, it is important to ensure that information systems can be used (or available) for active safety surveillance, especially in low- and middle-income countries (LMICs). The aim of this study was to build consensus about the use of existing maternal and neonatal data collection systems in LMICs for COVID-19 vaccines active safety surveillance, a basic set of variables, and the suitability and feasibility of including pregnant women and LMIC research networks in COVID-19 vaccines pre-licensure activities. A three-stage modified Delphi study was conducted over three months in 2020. An international multidisciplinary panel of 16 experts participated. Ratings distributions and consensus were assessed, and ratings' rationale was analyzed. The panel recommended using maternal and neonatal data collection systems for active safety surveillance in LMICs (median 9; disagreement index [DI] -0.92), but there was no consensus (median 6; DI 1.79) on the feasibility of adapting these systems. A basic set of 14 maternal, neonatal, and vaccination-related variables. Out of 16 experts, 11 supported a basic set of 14 maternal, neonatal, and vaccination-related variables for active safety surveillance. Seven experts agreed on a broader set of 26 variables. The inclusion of pregnant women for COVID-19 vaccines research (median 8; DI -0.61) was found appropriate, although there was uncertainty on its feasibility in terms of decision-makers' acceptability (median 7; DI 10.00) and regulatory requirements (median 6; DI 0.51). There was no consensus (median 6; DI 2.35) on the feasibility of including research networks in LMICs for conducting clinical trials amongst pregnant women. Although there was some uncertainty regarding feasibility, experts recommended using maternal and neonatal data collection systems and agreed on a common set of variables for COVID-19 vaccines active safety surveillance in LMICs.
鉴于孕妇现已被纳入接种2019冠状病毒病（COVID-19）疫苗的人群之中，确保信息系统可用于（或具备）主动安全监测非常重要，尤其是在低收入和中等收入国家（LMICs）。本研究的目的是就低收入和中等收入国家现有孕产妇和新生儿数据收集系统用于COVID-19疫苗主动安全监测的使用情况、一组基本变量，以及将孕妇和低收入和中等收入国家研究网络纳入COVID-19疫苗上市前活动的适用性和可行性达成共识。2020年在三个月内开展了一项分三个阶段的改良德尔菲研究。一个由16名专家组成的国际多学科小组参与其中。评估了评分分布和共识情况，并分析了评分依据。该小组建议在低收入和中等收入国家使用孕产妇和新生儿数据收集系统进行主动安全监测（中位数为9；分歧指数[DI] -0.92），但对于调整这些系统的可行性未达成共识（中位数为6；DI 1.79）。一组包含14个与孕产妇、新生儿及疫苗接种相关的基本变量。在16名专家中，11名支持一组包含14个与孕产妇、新生儿及疫苗接种相关的基本变量用于主动安全监测。7名专家同意采用更广泛的一组26个变量。将孕妇纳入COVID-19疫苗研究（中位数为8；DI -0.61）被认为是合适的，尽管在决策者的接受度（中位数为7；DI 10.00）和监管要求（中位数为6；DI 0.51）方面其可行性存在不确定性。对于将低收入和中等收入国家的研究网络纳入针对孕妇开展临床试验的可行性未达成共识（中位数为6；DI 2.35）。尽管在可行性方面存在一些不确定性，但专家们建议使用孕产妇和新生儿数据收集系统，并就低收入和中等收入国家COVID-19疫苗主动安全监测的一组通用变量达成了一致。
