Like I’ve mentioned in a few of my previous posts, this pandemic has brought on a lot of changes for everyone– but there is variation in how COVID-19 has impacted different groups of people. The experience of racial and ethnic minorities during this pandemic has been very different from other groups and is reflective of health outcomes that often exist for minorities in the U.S.
COVID-19
In New York City, the death rates for Black/African American individuals is much higher due to the virus, than it is for other racial/ethnic groups (1). The chart below shows the disproportionality of the effects of COVID on the population of African American individuals in a few states. 
Compared to the total state population, the majority of patients affected by COVID are African American (2).
These disparities exist not only during this pandemic, but in healthcare in general:
Reasons for the Disparity
People who belong to racial or ethnic minority groups experience the health care system differently than others– many of the existing disparities in society lead to poorer health outcomes, as well as greater vulnerability to viruses and other illnesses. Many minority groups live in heavily populated areas or live in households with multiple generations, where social distancing might not be possible (1). Many work hourly, so health benefits are not part of the package and health services become difficult to access (2). There is also a much higher population of racial and ethnic minorities in jails, prisons, etc. which are again, places that increase the risk of spread of viruses and other illnesses (1).
Also, because of the social distancing measures that have to be taken because of the pandemic, there has been a decrease in quality of care for patients who don’t speak English (4). Before, interpreters/translators could be present in person during a patient’s visit, which makes it much easier to translate and understand the patient’s concerns and symptoms. Now, a lot of interpreters have to work over the phone and it’s possible that a lot of information is getting lost in translation because it’s just harder to have a conversation and understand each other that way.
While the pandemic has affected everyone in one way or another, it has not affected everyone equally. The social determinants of health that I talked about in my first blog, in addition to factors such as racism, geographic location, and poverty, play a large part in deciding how an individual is affected. Unfortunately, the same level and quality of healthcare are not available to every person. Moving towards a system that addresses these disparities and provides excellent health care to each patient will involve first resolving the inequalities that exist in health care delivery in general and access to healthier food, exercise, etc. Additionally, training healthcare providers to better understand cultural differences and barriers to care could improve health outcomes for minority groups (3).
181,878 thoughts on “Impact of COVID on Racial & Ethnic Minorities”
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These data indicate that SON insufficiency causes neuronal migration defects and dendritic spine abnormalities, which seem neuropathological bases of the neural symptoms of ZTTK syndrome. In addition, the results support that the neural abnormalities in ZTTK syndrome are caused by SON haploinsufficiency independent of the types of mutation that results in functional or dysfunctional proteins.Background Leprosy can be c
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The robot vacuum cleaner from German manufacturer Karcher is an impressive first appearance. Its innovative features, such as cliff-detection and AI mapping allowed it to effortlessly eat pet hair and biscuit crumbs however dust proved to be more difficult to clean.
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Increasing the coverage of community-based treatment of childhood pneumonia (CCM) is part of the strategy to improve child survival, increase life-expectancy at birth and promote equity in Ethiopia. However, full coverage of CCM has not been reached in any regions of the country. There are no sub-national cost-effectiveness analyses available to inform decision makers on the most equitable scale up strategy.
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In total, 1268 participants completed the FSS at both time points; approximately 62% had fatigue. Using doubly robust IPTW, high (>11/20) HLIS (odds ratio [OR]0.90, 95% confidence interval [CI] 0.81-0.98) and high (>3/5) SNAP (OR 0.82, 95% CI 0.73-0.90) were each associated with lower risk of fatigue at follow-up. Evaluating change in fatigue, a higher SNAP score was associated with a lower risk of fatigue
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There is a rising for cardiac biomarkers, including Pro-BNP, cTnI, MYO, CK-MB, as well as D-Dimer in COVID-19 cancer population, especially in deceased cancer subjects. The 30-day in hospital mortality in cancer group was dramatically raised than that in non-cancer group (12.9% vs. 4.0%, p<0.01). In multivariable COX regression models, fever, disease severity status, underlying diseases were risk factors for mortali
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The present study aims to design and fabricate a novel, versatile, and cost-effective Polymethyl Methacrylate (PMMA) head phantom for the dosimetric pretreatment verification of radiotherapy (RT) treatment plans.
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The comparison of simple finger motion data, collected by the OMS, to data recorded by a simple continuous wave radar suggests that radar is a promising option for the objective assessment of hand function. Overall, the broad scope of integrating two measurement technologies with traditional clinical tests shows promising potential for developing new pathways in understanding of the role of functional outcomes for the RA
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Recent advances in neuroscience suggest that a utility-like calculation is involved in how the brain makes choices, and that this calculation may use a computation known as divisive normalization. While this tells us how the brain makes choices, it is not immediately evident why the brain uses this computation or exactly what behavior is consistent with it. In this paper, we address both of these questions by provi
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Substance use disorder is a critical public health issue. Discovering the synergies among factors impacting treatment program success can help governments and treatment facilities develop effective policies. In this work, we propose a novel data analytics approach using machine learning models to discover interaction effects that might be neglected by traditional hypothesis-generating approaches.
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