Background COVID-19 vaccine coverage in the Latinx community depends on delivery systems that overcome barriers such as institutional distrust, misinformation, and access to care. We hypothesized that a community-centered vaccination strategy that included mobilization, vaccination, and “activation” components could successfully reach an underserved Latinx population, utilizing its social networks to boost vaccination coverage. Methods Our community-academic-public health partnership, “Unidos en Salud,” utilized a theory-informed approach to design our “Motivate, Vaccinate, and Activate” COVID-19 vaccination strategy. Our strategy’s design was guided by the PRECEDE Model and sought to address and overcome predisposing, enabling, and reinforcing barriers to COVID-19 vaccination faced by Latinx individuals in San Francisco. We evaluated our prototype outdoor, “neighborhood” vaccination program located in a central commercial and transport hub in the Mission District in San Francisco, using the Reach, Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) framework during a 16-week period from February 1, 2021 to May 19, 2021. Programmatic data, city-wide COVID-19 surveillance data, and a survey conducted between May 2, 2021 and May 19, 2021 among 997 vaccinated clients ≥16 years old were used in the evaluation. Results There were 20,792 COVID-19 vaccinations administered at the neighborhood site during the 16-week evaluation period. Vaccine recipients had a median age of 43 (IQR 32–56) years, 53.9% were male and 70.5% were Latinx, 14.1% white, 7.7% Asian, 2.4% Black, and 5.3% other. Latinx vaccinated clients were substantially more likely than non-Latinx clients to have an annual household income of less than $50,000 a year (76.1% vs. 33.5%), be a first-generation immigrant (60.2% vs. 30.1%), not have health insurance (47.3% vs. 16.0%), and not have access to primary care provider (62.4% vs. 36.2%). The most frequently reported reasons for choosing vaccination at the site were its neighborhood location (28.6%), easy and convenient scheduling (26.9%) and recommendation by someone they trusted (18.1%); approximately 99% reported having an overall positive experience, regardless of ethnicity. Notably, 58.3% of clients reported that they were able to get vaccinated earlier because of the neighborhood vaccination site, 98.4% of clients completed both vaccine doses, and 90.7% said that they were more likely to recommend COVID-19 vaccination to family and friends after their experience; these findings did not substantially differ according to ethnicity. There were 40.3% of vaccinated clients who said they still knew at least one unvaccinated person (64.6% knew ≥3). Among clients who received both vaccine doses (n = 729), 91.0% said that after their vaccination experience, they had personally reached out to at least one unvaccinated person they knew (61.6% reached out to ≥3) to recommend getting vaccinated; 83.0% of clients reported that one or more friends, and/or family members got vaccinated as a result of their outreach, including 18.9% who reported 6 or more persons got vaccinated as a result of their influence. Conclusions A multi-component, “Motivate, Vaccinate, and Activate” community-based strategy addressing barriers to COVID-19 vaccination for the Latinx population reached the intended population, and vaccinated individuals served as ambassadors to recruit other friends and family members to get vaccinated.

The genetic control of gene expression is a core component of human physiology. For the past several years, transcriptome-wide association studies have leveraged large datasets of linked genotype and RNA sequencing information to create a powerful gene-based test of association that has been used in dozens of studies. While numerous discoveries have been made, the populations in the training data are overwhelmingly of European descent, and little is known about the generalizability of these models to other populations. Here, we test for cross-population generalizability of gene expression prediction models using a dataset of African American individuals with RNA-Seq data in whole blood. We find that the default models trained in large datasets such as GTEx and DGN fare poorly in African Americans, with a notable reduction in prediction accuracy when compared to European Americans. We replicate these limitations in cross-population generalizability using the five populations in the GEUVADIS dataset. Via realistic simulations of both populations and gene expression, we show that accurate cross-population generalizability of transcriptome prediction only arises when eQTL architecture is substantially shared across populations. In contrast, models with non-identical eQTLs showed patterns similar to real-world data. Therefore, generating RNA-Seq data in diverse populations is a critical step towards multiethnic utility of gene expression prediction.

Background-Asthma, an inflammatory disorder of the airways, is the most common chronic disease of children worldwide. There are significant racial/ethnic disparities in asthma prevalence, morbidity and mortality among U.S. children. This trend is mirrored in obesity, which may share genetic and environmental risk factors with asthma. The majority of asthma biomedical research has been performed in populations of European decent.