Rouse Heather L, Shearer Rebecca J Bulotsky, Idzikowski Sydney S, Nelson Amy Hawn, Needle Mark, Katz Matthew F, Bailey Jhonelle, Lane Justin T, Berkowitz Emily, Zanti Sharon, Pena Astrid, Reeves Maggie
Department of Human Development and Family Studies, 2330 Palmer Building, 2222 Osborn Drive, Ames, Iowa 50011-1084.
Department of Psychology, University of Miami, P.O. Box 248185, Coral Gables, FL 33124.
Int J Popul Data Sci. 2021 Oct 7;5(4):1651. doi: 10.23889/ijpds.v5i4.1651. eCollection 2020.
The COVID-19 pandemic made its mark on the entire world, upending economies, shifting work and education, and exposing deeply rooted inequities. A particularly vulnerable, yet less studied population includes our youngest children, ages zero to five, whose proximal and distal contexts have been exponentially affected with unknown impacts on health, education, and social-emotional well-being. Integrated administrative data systems could be important tools for understanding these impacts. This article has three aims to guide research on the impacts of COVID-19 for this critical population using integrated data systems (IDS). First, it presents a conceptual data model informed by developmental-ecological theory and epidemiological frameworks to study young children. This data model presents five developmental resilience pathways (i.e. early learning, safe and nurturing families, health, housing, and financial/employment) that include direct and indirect influencers related to COVID-19 impacts and the contexts and community supports that can affect outcomes. Second, the article outlines administrative datasets with relevant indicators that are commonly collected, could be integrated at the individual level, and include relevant linkages between children and families to facilitate research using the conceptual data model. Third, this paper provides specific considerations for research using the conceptual data model that acknowledge the highly-localised political response to COVID-19 in the US. It concludes with a call to action for the population data science community to use and expand IDS capacities to better understand the intermediate and long-term impacts of this pandemic on young children.
新冠疫情给整个世界都留下了印记，颠覆了经济，改变了工作和教育模式，还暴露了根深蒂固的不平等现象。一个特别脆弱但研究较少的群体是我们年龄在零至五岁的幼儿，他们的直接和间接环境受到了成倍的影响，对其健康、教育和社会情感幸福产生了未知的影响。综合行政数据系统可能是理解这些影响的重要工具。本文有三个目标，旨在利用综合数据系统（IDS）指导关于新冠疫情对这一关键群体影响的研究。首先，它提出了一个基于发展生态理论和流行病学框架的概念性数据模型，用于研究幼儿。这个数据模型呈现了五条发展韧性途径（即早期学习、安全且有养育功能的家庭、健康、住房以及金融/就业），其中包括与新冠疫情影响相关的直接和间接影响因素，以及可能影响结果的环境和社区支持。其次，本文概述了通常收集的、可在个体层面整合的、带有相关指标的行政数据集，并且包括儿童与家庭之间的相关联系，以便利用概念性数据模型开展研究。第三，本文针对使用概念性数据模型的研究提供了具体考量，承认美国对新冠疫情的应对措施具有高度的地方化特点。文章最后呼吁人口数据科学界采取行动，利用并扩大综合数据系统的能力，以更好地理解这场疫情对幼儿的中期和长期影响。
