INTRODUCTION: Health equity science examines underlying social determinants, or drivers, of health inequities by building an evidence base to guide action across programs, public health surveillance, policy, and communications efforts. A Social Vulnerability Index (SVI) was utilized during the COVID-19 response to identify areas where inequities exist and support communities with vaccination. We set out to assess COVID-19 vaccination coverage by two SVI themes, Racial and Ethnicity Minority Status and Housing Type and Transportation to examine disparities. METHODS: US county-level COVID-19 vaccine administration data among persons aged 5 years and older reported to the Centers for Disease Control and Prevention from December 14, 2020 to December 14, 2022, were analyzed. Counties were categorized 1) into tertiles (low, moderate, high) according to each SVI theme's level of vulnerability or 2) dichotomized by urban or rural classification. Primary series vaccination coverage per age group were assessed for SVI social factors by SVI theme tertiles or urbanicity. RESULTS: Older adults aged 65 years and older had the highest vaccination coverage across all vulnerability factors compared with children aged 5-17 years and adults aged 18-64 years. Overall, children and adults had higher vaccination coverage in counties of high vulnerability. Greater vaccination coverage differences were observed by urbanicity as rural counties had some of the lowest vaccination coverage for children and adults. CONCLUSION: COVID-19 vaccination efforts narrowed gaps in coverage for adults aged 65 years and older but larger vaccination coverage differences remained among younger populations. Moreover, greater disparities in coverage existed in rural counties. Health equity science approaches to analyses should extend beyond identifying differences by basic demographics such as race and ethnicity and include factors that provide context (housing, transportation, age, and geography) to assist with prioritization of vaccination efforts where true disparities in vaccination coverage exist.

The first three SARS-CoV-2 phylogenetic lineages classified as variants of concern (VOCs) in the United States (U.S.) from December 15, 2020 to February 28, 2021, Alpha (B.1.1.7), Beta (B.1.351), and Gamma (P.1) lineages, were initially detected internationally. This investigation examined available travel history of coronavirus disease 2019 (COVID-19) cases reported in the U.S. in whom laboratory testing showed one of these initial VOCs. Travel history, demographics, and health outcomes for a convenience sample of persons infected with a SARS-CoV-2 VOC from December 15, 2020 through February 28, 2021 were provided by 35 state and city health departments, and proportion reporting travel was calculated. Of 1,761 confirmed VOC cases analyzed, 1,368 had available data on travel history. Of those with data on travel history, 1,168 (85%) reported no travel preceding laboratory confirmation of SARS-CoV-2 and only 105 (8%) reported international travel during the 30 days preceding a positive SARS-CoV-2 test or symptom onset. International travel was reported by 92/1,304 (7%) of persons infected with the Alpha variant, 7/55 (22%) with Beta, and 5/9 (56%) with Gamma. Of the first three SARS-CoV-2 lineages designated as VOCs in the U.S., international travel was common only among the few Gamma cases. Most persons infected with Alpha and Beta variant reported no travel history, therefore, community transmission of these VOCs was likely common in the U.S. by March 2021. These findings underscore the importance of global surveillance using whole genome sequencing to detect and inform mitigation strategies for emerging SARS-CoV-2 VOCs.

In December 2020, the B.1.1.7 genetic variant of SARS-CoV-2, the virus that causes COVID-19, was first reported after emergence and rapid circulation in the United Kingdom (1). Evidence suggests that the B.1.1.7 variant is more efficiently transmitted than are other SARS-CoV-2 variants, and widespread circulation could thereby increase SARS-CoV-2 infection and hospitalization rates (1,2). The first reported SARS-CoV-2 B.1.1.7 variant case in the United States was confirmed by sequencing in Colorado on December 29, 2020.* This report describes a person who traveled from the United Kingdom to the United States after experiencing COVID-19-compatible symptoms(†) and was eventually confirmed to be infected with the B.1.1.7 variant.

Meta-analysis has been used to examine the effectiveness of childhood obesity prevention efforts, yet traditional conventional meta-analytic methods restrict the kinds of studies included, and either narrowly define mechanisms and agents of change, or examine the effectiveness of whole interventions as opposed to the specific actions that comprise interventions. Taxonomic meta-analytic methods widen the aperture of what can be included in a meta-analysis data set, allowing for inclusion of many types of interventions and study designs. The National Collaborative on Childhood Obesity Research Childhood Obesity Evidence Base (COEB) project focuses on interventions intended to prevent childhood obesity in children 2-5 years old who have an outcome measure of BMI. The COEB created taxonomies, anchored in the Social Ecological Model, which catalog specific outcomes, intervention components, intended recipients, and contexts of policies, initiatives, and interventions conducted at the individual, interpersonal, organizational, community, and societal level. Taxonomies were created by discovery from the literature itself using grounded theory. This article describes the process used for a novel taxonomic meta-analysis of childhood obesity prevention studies between the years 2010 and 2019. This method can be applied to other areas of research, including obesity prevention in additional populations.

Objective: To evaluate the efficacy of childhood obesity interventions and conduct a taxonomy of intervention components that are most effective in changing obesity-related health outcomes in children 2-5 years of age. Methods: Comprehensive searches located 51 studies from 18,335 unique records. Eligible studies: (1) assessed children aged 2-5, living in the United States; (2) evaluated an intervention to improve weight status; (3) identified a same-aged comparison group; (4) measured BMI; and (5) were available between January 2005 and August 2019. Coders extracted study, sample, and intervention characteristics. Effect sizes [ESs; and 95% confidence intervals (CIs)] were calculated by using random-effects models. Meta-regression was used to determine which intervention components explain variability in ESs. Results: Included were 51 studies evaluating 58 interventions (N = 29,085; mean age = 4 years; 50% girls). Relative to controls, children receiving an intervention had a lower BMI at the end of the intervention (g = 0.10, 95% CI = 0.02-0.18; k = 55) and at the last follow-up (g = 0.17, 95% CI = 0.04-0.30; k = 14; range = 18-143 weeks). Three intervention components moderated efficacy: engage caregivers in praise/encouragement for positive health-related behavior; provide education about the importance of screen time reduction to caregivers; and engage pediatricians/health care providers. Conclusions: Early childhood obesity interventions are effective in reducing BMI in preschool children. Our findings suggest that facilitating caregiver education about the importance of screen time reduction may be an important strategy in reducing early childhood obesity.

Obesity and type 2 diabetes disproportionately impact U.S. racial and ethnic minority communities and low-income populations. Improvements in implementing efficacious interventions to reduce the incidence of type 2 diabetes are underway (i.e., the National Diabetes Prevention Program), but challenges in effectively scaling-up successful interventions and reaching at-risk populations remain. In October 2017, the National Institutes of Health convened a workshop to understand how to (1) address socioeconomic and other environmental conditions that perpetuate disparities in the burden of obesity and type 2 diabetes; (2) design effective prevention and treatment strategies that are accessible, feasible, culturally relevant, and acceptable to diverse population groups; and (3) achieve sustainable health improvement approaches in communities with the greatest burden of these diseases. Common features of guiding frameworks to understand and address disparities and promote health equity were described. Promising research directions were identified in numerous areas, including study design, methodology, and core metrics; program implementation and scalability; the integration of medical care and social services; strategies to enhance patient empowerment; and understanding and addressing the impact of psychosocial stress on disease onset and progression in addition to factors that support resiliency and health.