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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.

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BackgroundPublic agencies hold important, yet largely unused, administrative data on the families and communities they serve. Integrated Data Systems (IDS) provide the governance process, legal framework, technology, and human capacity to connect these families and communities across data siloes. By securely linking administrative data across siloes, IDS are able to support data-informed decision making. IntroductionFor 10+ years, AISP has helped jurisdictions through the developmental process of building IDS. We operate a network of 22 U.S. states and counties with fully-functioning Integrated Data Systems, and provide technical assistance to 18 jurisdictions at various stages of IDS development. Objectives and ApproachThis session presents the outcomes of an independent evaluation of our Learning Community initiative (2019) and presents a new developmental framework that outlines key dimensions of quality and readiness for IDS building and implementation. ResultsAs of 2020, 20 sites have received formal 18-month cohort based technical assistance. This presentation will discuss site-based approaches to facilitate data sharing, including common challenges and solutions, and progress to date, including findings of an independent evaluation (2019). We will also present a framework developed based on the deep knowledge developed through technical assistance efforts, and findings from a national survey of data integration efforts conducted in 2020. The framework uses purpose, partnership structure, technical architecture, and organizational model—with respect to the strengths and challenges of each—to categorize and synthesize data integration efforts for social policy and program improvement. The developmental approach to our work emphasizes that we seek to understand methods for sustainability in diverse ways. Conclusion / ImplicationsWhile there is broad agreement in the value of integrating data across domains, developing the capacity and skills necessary to link administrative data for policy evaluation and research remains an elusive goal. Initial results indicate that an individualized yet collaborative technical assistance approach is successful in developing data integration capacity.

Objectives • Present a conceptual data model for understanding the impacts of COVID on children 0-5 • Outline administrative datasets with relevant indicators that are commonly collected, integrated at the individual level, and include relevant 2-generation linkages • Provide specific considerations for research using the conceptual model ApproachThis session presents findings from a paper that was collaboratively generated by a workgroup of five integrated administrative data systems (IDS) in communities across the US with expansive data holdings in early education. Contributors have decades of experience in utilizing administrative data for social policy planning and analysis. We started with a thorough literature review, used a national survey data of IDS data holdings collected in 2020, and co-created a conceptual data model for administrative data reuse based upon current practices. Specific care was taken to include community-level changes and adult factors that serve as mediators for children’s outcomes. ResultsThe COVID-19 pandemic made its mark on the entire world, yet little is known about children, aged 0-5, whose lives have been exponentially affected. This conceptual 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 child-level outcomes. We then overview commonly available administrative datasets with relevant indicators to be linked at the individual and household level, with discussion of data specific considerations (e.g. access, insufficiency, availability, quality, and linkage). The US response creates a natural experiment of policy and implementation decisions across boundaries, and while US-centric, this approach could be applied internationally. ConclusionThe development and use of IDS for research has great potential to inform solutions to our most pressing social problems. Cross-site research studies and maintaining sustainable capacities for long-term research is paramount to understanding the implications of response interventions and to study impacts on child development over time.