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. 2021 Oct 7;5(4):1651.
doi: 10.23889/ijpds.v5i4.1651. eCollection 2020.

Developmental impacts of the COVID-19 pandemic on young children: a conceptual model for research with integrated administrative data systems

Heather L Rouse  1 , Rebecca J Bulotsky Shearer  2 , Sydney S Idzikowski  3 , Amy Hawn Nelson  4 , Mark Needle  2 , Matthew F Katz  4 , Jhonelle Bailey  2 , Justin T Lane  3 , Emily Berkowitz  4 , Sharon Zanti  4 , Astrid Pena  2 , Maggie Reeves  5
Affiliations

Developmental impacts of the COVID-19 pandemic on young children: a conceptual model for research with integrated administrative data systems

Heather L Rouse et al. Int J Popul Data Sci. .

Abstract

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.

Keywords: COVID-19; child development; data model; integrated administrative data systems.

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Conflict of interest statement

Statement on conflicts of interest: The authors declare they have no conflicts of interest. NameCredit Author StatementHeather Rouseconceptualization, methodology, investigation, writing-original draft, writing-review and editing, project administrationRebecca Bulotsky Shearerconceptualization, methodology, investigation, writing-original draft, writing-review and editing, project administrationSydney Idzikowskiconceptualization, methodology, investigation, visualisation, writing-original draft, writing-review and editing, project administrationAmy Hawn Nelsonconceptualization, methodology, investigation, visualisation, writing-original draft, writing-review and editing, project administrationMark Needleconceptualization, visualisation, writing-review and editingMatthew Katzconceptualization, investigation, writing-original draft, writing-review and editingJhonelle Baileyconceptualization, writing-original draft, writing-review and editingJustin Laneconceptualization, investigation, methodology, writing-review and editingEmily Berkowitzconceptualization, investigation, validation, writing-original draftSharon Zanticonceptualization, investigation, writing-original draft, writing-review and editingAstrid Pena Perezconceptualization, writing-original draft, writing-review and editingMaggie Reevesconceptualization, writing-original draft, writing-review and editing

Figures

Figure 1
Figure 1
Conceptual data model of developmental resilience pathways [22]
See this image and copyright information in PMC

References

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