Social Vulnerability and Rurality Associated With Higher Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection-Induced Seroprevalence: A Nationwide Blood Donor Study-United States, July 2020-June 2021.

BACKGROUND: Most studies on health disparities during the coronavirus disease 2019 (COVID-19) pandemic focused on reported cases and deaths, which are influenced by testing availability and access to care. This study aimed to examine severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody seroprevalence in the United States and its associations with race/ethnicity, rurality, and social vulnerability over time. METHODS: This repeated cross-sectional study used data from blood donations in 50 states and Washington, DC, from July 2020 through June 2021. Donor zip codes were matched to counties and linked with Social Vulnerability Index (SVI) and urban-rural classification. SARS-CoV-2 antibody seroprevalences induced by infection and infection-vaccination combined were estimated. Association of infection-induced seropositivity with demographics, rurality, SVI, and its 4 themes were quantified using multivariate regression models. RESULTS: Weighted seroprevalence differed significantly by race/ethnicity and rurality, and increased with increasing social vulnerability. During the study period, infection-induced seroprevalence increased from 1.6% to 27.2% and 3.7% to 20.0% in rural and urban counties, respectively, while rural counties had lower combined infection- and vaccination-induced seroprevalence (80.0% vs 88.1%) in June 2021. Infection-induced seropositivity was associated with being Hispanic, non-Hispanic Black, and living in rural or more socially vulnerable counties, after adjusting for demographic and geographic covariates. CONCLUSIONS: The findings demonstrated increasing SARS-CoV-2 seroprevalence in the United States across all geographic, demographic, and social sectors. The study illustrated disparities by race-ethnicity, rurality, and social vulnerability. The findings identified areas for targeted vaccination strategies and can inform efforts to reduce inequities and prepare for future outbreaks.

Framework for a Community Health Observing System for the Gulf of Mexico Region: Preparing for Future Disasters.

The Gulf of Mexico (GoM) region is prone to disasters, including recurrent oil spills, hurricanes, floods, industrial accidents, harmful algal blooms, and the current COVID-19 pandemic. The GoM and other regions of the U.S. lack sufficient baseline health information to identify, attribute, mitigate, and facilitate prevention of major health effects of disasters. Developing capacity to assess adverse human health consequences of future disasters requires establishment of a comprehensive, sustained community health observing system, similar to the extensive and well-established environmental observing systems. We propose a system that combines six levels of health data domains, beginning with three existing, national surveys and studies plus three new nested, longitudinal cohort studies. The latter are the unique and most important parts of the system and are focused on the coastal regions of the five GoM States. A statistically representative sample of participants is proposed for the new cohort studies, stratified to ensure proportional inclusion of urban and rural populations and with additional recruitment as necessary to enroll participants from particularly vulnerable or under-represented groups. Secondary data sources such as syndromic surveillance systems, electronic health records, national community surveys, environmental exposure databases, social media, and remote sensing will inform and augment the collection of primary data. Primary data sources will include participant-provided information via questionnaires, clinical measures of mental and physical health, acquisition of biological specimens, and wearable health monitoring devices. A suite of biomarkers may be derived from biological specimens for use in health assessments, including calculation of allostatic load, a measure of cumulative stress. The framework also addresses data management and sharing, participant retention, and system governance. The observing system is designed to continue indefinitely to ensure that essential pre-, during-, and post-disaster health data are collected and maintained. It could also provide a model/vehicle for effective health observation related to infectious disease pandemics such as COVID-19. To our knowledge, there is no comprehensive, disaster-focused health observing system such as the one proposed here currently in existence or planned elsewhere. Significant strengths of the GoM Community Health Observing System (CHOS) are its longitudinal cohorts and ability to adapt rapidly as needs arise and new technologies develop.