运用名义群体技术阐明针对母婴健康（MCH）人群的新冠肺炎研究议程。

Using Nominal Group Technique to Elucidate a COVID-19 Research Agenda for Maternal and Child Health (MCH) Populations.

作者信息

Ikedionwu Chioma A, Dongarwar Deepa, Yusuf Korede K, Maiyegun Sitratullah O, Ibrahimi Sahra, Salihu Hamisu M

机构信息

Center of Excellence in Health Equity, Training and Research, Baylor College of Medicine, Houston, Texas, USA.

Office of the Provost, Baylor College of Medicine, Houston, Texas, USA.

出版信息

Int J MCH AIDS. 2020;9(3):394-396. doi: 10.21106/ijma.410. Epub 2020 Sep 15.

DOI:10.21106/ijma.410

PMID:33014627

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7520884/

Abstract

As the global impact of the COVID-19 pandemic continues to evolve, robust data describing its effect on maternal and child health (MCH) remains limited. The aim of this study was to elucidate an agenda for COVID-19 research with particular focus on its impact within MCH populations. This was achieved using the Nominal Group Technique through which researchers identified and ranked 12 research topics across various disciplines relating to MCH in the setting of COVID-19. Proposed research topics included vaccine development, genomics, and artificial intelligence among others. The proposed research priorities could serve as a template for a vigorous COVID-19 research agenda by the NIH and other national funding agencies in the US.

摘要

随着新冠疫情的全球影响不断演变，描述其对母婴健康影响的有力数据仍然有限。本研究的目的是阐明一个针对新冠疫情的研究议程，特别关注其在母婴群体中的影响。这是通过名义群体技术实现的，研究人员通过该技术识别并对新冠疫情背景下与母婴健康相关的各学科领域的12个研究课题进行了排序。提出的研究课题包括疫苗研发、基因组学和人工智能等。提出的研究重点可以作为美国国立卫生研究院（NIH）和其他美国国家资助机构制定积极的新冠疫情研究议程的模板。