7. Project Summary
Latinxs comprise a large and growing population in the US, but are typically underrepresented in study
samples, limiting statistical comparisons to only other racial or ethnic groups.1 Inadequate access to large,
diverse samples of Latinxs in public health research has led to treating Latinxs as a monolith, despite known
and important within-group differences.1–5 These conditions have led to inadequately tailored disease
prevention and control strategies1 and allowed for the persistence of health inequities such as those
experienced among Latinxs before and throughout the COVID-19 pandemic compared to non-Latinx Whites8–
12,14 and even to all other racial-ethnic-sex groups.13,92 Yearby's revised Social Determinants of Health (SDOH)
Framework explains that structural discrimination (e.g., racism, ethnocentrism, and sexism) is the underlying
cause of the inequitable distributions of economic, healthcare access, educational, social, and environmental
SDOH across populations, which ultimately results in disproportionate disease burden.19 Individuals hold
multiple identities (e.g., ethnic, racial, country of origin, gender, age, language) that interact with structural
discrimination in distinct ways,22–24 but how these identities interact with SDOH and SARS-CoV-2 testing and
vaccination outcomes within Latinxs is unknown. The present proposal harnesses Latinx-identifying participant
data (N = 31,372) from 10 purposively selected, geographically diverse Rapid Acceleration of Diagnostics for
Underserved Populations (RADx-UP) projects to overcome the limitations in prior research. Specifically, using
pooled Tier 1 common data elements for Aim 1, we will identify the relative importance of economic,
healthcare access, educational, and environmental SDOH on SARS-CoV-2 testing (e.g., engagement in
testing, testing access) and vaccination (e.g., vaccination status, reasons to/to not get vaccinated) outcomes
within a large, robust individual person data meta-analysis of Latinx US adults. Using the same Tier 1 data for
Aim 2, we will investigate how the Aim 1 model varies by race, country of origin, gender, age, and language.
Finally, for Aim 3, we will use Tier 2 data and employ scale equating data harmonization techniques to
examine additional identity-related moderators (e.g., immigration status), SDOH (e.g., racial discrimination,
food insecurity), and more robust measures of testing and vaccination outcomes that advance the Aim 1 and 2
models; and evaluate the degree to which the findings generalize to the national Latinx population. Study
findings will advance the empirical knowledge base necessary to design precise, culturally tailored prevention
and control strategies within Latinxs to reduce health inequities in COVID-19 and beyond.
