COVID-19: The Ethno-Geographic Perspective of Differential Immunity.

Coronavirus disease 2019 (COVID-19), the agent behind the worst global pandemic of the 21st century (COVID-19), is primarily a respiratory-disease-causing virus called SARS-CoV-2 that is responsible for millions of new cases (incidence) and deaths (mortalities) worldwide. Many factors have played a role in the differential morbidity and mortality experienced by nations and ethnicities against SARS-CoV-2, such as the quality of primary medical health facilities or enabling economies. At the same time, the most important variable, i.e., the subsequent ability of individuals to be immunologically sensitive or resistant to the infection, has not been properly discussed before. Despite having excellent medical facilities, an astounding issue arose when some developed countries experienced higher morbidity and mortality compared with their relatively underdeveloped counterparts. Hence, this investigative review attempts to analyze the issue from an angle of previously undiscussed genetic, epigenetic, and molecular immune resistance mechanisms in correlation with the pathophysiology of SARS-CoV-2 and varied ethnicity-based immunological responses against it. The biological factors discussed here include the overall landscape of human microbiota, endogenous retroviral genes spliced into the human genome, and copy number variation, and how they could modulate the innate and adaptive immune systems that put a certain ethnic genetic architecture at a higher risk of SARS-CoV-2 infection than others. Considering an array of these factors in their entirety may help explain the geographic disparity of disease incidence, severity, and subsequent mortality associated with the disease while at the same time encouraging scientists to design new experimental approaches to investigation.

Racial and ethnic disparities in H1N1 vaccine uptake in Medicaid: Lessons learned for a COVID-19 vaccine rollout.

BACKGROUND: Racial and ethnic minorities in the US have been disproportionately affected by the COVID-19 pandemic and are at risk for disparities in COVID-19 vaccinations. The H1N1 flu vaccine experience provides lessons learned to address and prevent racial and ethnic disparities in COVID-19 vaccinations. We aim to identify racial/ethnic and geographic disparities in H1N1 vaccinations among Medicaid enrollees to inform equitable COVID-19 vaccination policies and strategies. METHODS: The study population included people under 65 who were continuously enrolled in Medicaid in 2009 and 2010 from 28 states and the District of Columbia. H1N1 vaccinations were identified from Medicaid outpatient claims. Vaccination rates were calculated for the overall sample and subpopulations by race/ethnicity and state. RESULTS: 3,708,894 (12.3%) Medicaid enrollees in the sample were vaccinated for H1N1 in 2009-2010. Race-specific vaccination rates ranged from 8.1% in American Indian/Alaska Native (AI/AN) to 19.8% in Asian/Pacific Islander Medicaid enrollees. NHB enrollees had lower vaccination rates than non-Hispanic White (NHW) enrollees in all states, with the exceptions of Maryland, Missouri, Ohio, and Washington. The largest disparity between NHB and NHW was in Pennsylvania (1.0% vs. 7.0%), while the largest absolute difference between NHB and NHW enrollees was in Georgia (17.4% vs. 30.7%). CONCLUSIONS: Our study found huge variation in H1N1 vaccinations across states and racial/ethnic disparities in H1N1 vaccinations within states. In most states, NHB and AI/AN Medicaid enrollees had lower vaccination rates than Whites. Hispanic and Asian/Pacific Islander Medicaid enrollees in most states had higher vaccination rates than Whites.

Location Matters: Geographic Disparities and Impact of Coronavirus Disease 2019.

The coronavirus disease 2019 (COVID-19) pandemic in the United States has revealed major disparities in the access to testing and messaging about the pandemic based on the geographic location of individuals, particularly in communities of color, rural areas, and areas of low income. This geographic disparity, in addition to deeply rooted structural inequities, have posed additional challenges to adequately diagnose and provide care for individuals of all ages living in these settings. We describe the impact that COVID-19 has had on geographically disparate populations in the United States and share our recommendations on what might be done to ameliorate the current situation.

Progression of COVID-19 From Urban to Rural Areas in the United States: A Spatiotemporal Analysis of Prevalence Rates.

PURPOSE: There are growing signs that the COVID-19 virus has started to spread to rural areas and can impact the rural health care system that is already stretched and lacks resources. To aid in the legislative decision process and proper channelizing of resources, we estimated and compared the county-level change in prevalence rates of COVID-19 by rural-urban status over 3 weeks. Additionally, we identified hotspots based on estimated prevalence rates. METHODS: We used crowdsourced data on COVID-19 and linked them to county-level demographics, smoking rates, and chronic diseases. We fitted a Bayesian hierarchical spatiotemporal model using the Markov Chain Monte Carlo algorithm in R-studio. We mapped the estimated prevalence rates using ArcGIS 10.8, and identified hotspots using Gettis-Ord local statistics. FINDINGS: In the rural counties, the mean prevalence of COVID-19 increased from 3.6 per 100,000 population to 43.6 per 100,000 within 3 weeks from April 3 to April 22, 2020. In the urban counties, the median prevalence of COVID-19 increased from 10.1 per 100,000 population to 107.6 per 100,000 within the same period. The COVID-19 adjusted prevalence rates in rural counties were substantially elevated in counties with higher black populations, smoking rates, and obesity rates. Counties with high rates of people aged 25-49 years had increased COVID-19 prevalence rates. CONCLUSIONS: Our findings show a rapid spread of COVID-19 across urban and rural areas in 21 days. Studies based on quality data are needed to explain further the role of social determinants of health on COVID-19 prevalence.