# The longitudinal impact of the COVID-19 pandemic and related multi-level mitigation and contextual factors on health and socioeconomic outcomes of individuals and families from a vulnerable population

> **NIH NIH U01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $594,081

## Abstract

The COVID-19 pandemic, transmission mitigation strategies and policies implemented across the United States
led to major disruption in day-to-day life, producing immediate effects on individuals, families and
communities. We hypothesize that the pandemic will have broadly negative health and economic consequences,
but that mitigation strategies and policies will affect participants differently in the short-, medium- and long-term
based on work, family, social, and health circumstances, which can only be thoroughly examined with
longitudinal analyses of data prior to and throughout the pandemic. We propose a “shovel ready” project
leveraging the existing longitudinal data collected in the Fragile Families and Childhood Wellbeing Study
(FFCWS), the longest running population-based US birth cohort, collected before and throughout the pandemic
linked with multilevel COVID-19 related health, social and economic measures, and prospective COVID-19
impact assessments to examine these issues. FFCWS follows a representative sample of 4,898 children born
1998-2000 in large US cities and their parents over 7 waves (3,580 families participated at the last wave),
providing a deep history of social support, economic factors, and health at the individual, family, neighborhood,
city, and state levels. Due to the unique sampling strategy and COVID-19 transmission patterns, over 87%
FFCWS families are considered underserved or COVID-19 vulnerable. Focal children are entering early
adulthood, a period when major adversity may derail that transition and parents are entering middle age where
the impacts of COVID-19 on health and financial resources are likely to have particularly large consequences.
We will also reinterview 1,400 respondents (700 parent-adult child pairs) over two additional waves on health,
economic, and behavior related to COVID-19. In collaboration with the COVID consortium, our aims are to first
compare essential workers (50% of FFCWS parents and 36% of young adults), non-essential workers, and
individuals who lost their jobs (over 20%) during the pandemic on short-term health, economic social and
behavioral outcomes. Second, to investigate immediate and downstream effects of COVID mitigation efforts on
interpersonal, intergenerational and family relationships and health resources. Third, to analyze immediate and
downstream effects of COVID transmission, mitigation efforts, and COVID-related policies on health using
contextual data (i.e., national, state, and local policy implementation and behavior) linked to measures of
individual health in young adults and their parents collected before, and multiple times throughout the pandemic.
We will also examine how these macro-level effects on individuals differ by other contextual measures such as
indicators of structural racism. This project will: 1) share macro-level data on COVID-19 burden and mitigation
strategies that can be linked to other studies, 2) share FFCWS COVID-19 impact survey data, 3) pr...

## Key facts

- **NIH application ID:** 10890122
- **Project number:** 5U01HD110063-03
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** COLTER M.S. MITCHELL
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $594,081
- **Award type:** 5
- **Project period:** 2022-09-15 → 2027-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10890122

## Citation

> US National Institutes of Health, RePORTER application 10890122, The longitudinal impact of the COVID-19 pandemic and related multi-level mitigation and contextual factors on health and socioeconomic outcomes of individuals and families from a vulnerable population (5U01HD110063-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10890122. Licensed CC0.

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