Journal Article Review 2
Abstract
Lester, D. G., Martani, A., Wangmo, T., & Elger, B. S. (2022). Precision public health and
structural racism in the United States: Promoting health equity in the COVID-19
pandemic response. JMIR Public Health and Surveillance, 8(3), e33277.
https://doi.org/10.2196/33277
The article “Precision Public Health and Structural Racism in the United States:
Promoting Health Equity in the COVID-19 Pandemic Response” examines the exposure of racial
and ethical equalities that were revealed during the Coronavirus outbreak. Also, this article
discusses the negative influence structural racism and defective data have on the establishment of
precision public health. The article informs public health policymakers of ethical precision public
health by using machine learning algorithms to anticipate public health risks.
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Journal Article Review
The article “Precision Public Health and Structural Racism in the United States:
Promoting Health Equity in the COVID-19 Pandemic Response” discuss the impact structural
racism has on healthcare during the Coronavirus pandemic. Structural racism is a system of
instructional, public, and cultural practices that reinforces ways to preserve racial inequities
(Lester, et al.,2022). Structural racism began more visible in healthcare during the Coronavirus
pandemic The social determinants of health and the negative effect of the pandemic on racial and
minority groups were recognized by the United States Center for Disease Control and Prevention
(Lester, et al.,2022). They realized the influence structural racism has on each one of these
determinants. Political and social influences caused structural vulnerabilities for several racial
and minority groups during the outbreak of Coronavirus (Lester, et al.,2022). These influences
allowed for the normalization of discrimination, which affected racial and minority groups from
receiving quality healthcare (Lester, et al.,2022). For example, Coronavirus testing among racial
and minority groups showed billing data that indicated disparities, because African American
was likely to receive a Coronavirus test compared to whites even if they are showing symptoms
(Lester, et al.,2022). Also, it was discovered that Coronavirus testing sites were mainly located
and wealthy and white neighborhoods. This limited access to quality care of people who resided
in poor neighborhoods. Therefore, particular attention is given to factors of structural racism that
can hinder approaches that can potentially achieve social justice and healthcare equity (Lester, et
al.,2022).
There are several elements that indicate how structural racism leads to healthcare
inequalities for racial and minority groups during the Coronavirus pandemic has increased. In a
large national study, showed higher seropositivity rates for Coronavirus antibodies in patients
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that lived in African American and Hispanic neighborhoods (Lester, et al.,2022). This study
argues that coronavirus is not homogeneous within the population and explains how a
manifestation of structural racism such an assumption can be. Also, the study highlights the
necessity for my data on the impact of the Coronavirus pandemic on racial and minority groups
specifically in low-income neighborhoods (Lester, et al.,2022). Furthermore, the data on racial
and minority groups relating to the impact of Coronavirus were systemically and consistently
collected across the United States. This resulted in a lack of understanding the spread of
coronavirus within low-income communities, which in turn these communities were unable to
receive timely and quality healthcare (Lester, et al.,2022). Also, the public health department did
not apply to guidelines for reporting data by August 1, 2020, which in detail reported data of
demographic data for Coronavirus. Healthcare professionals underreporting data on racial and
minority groups produce gaps that negatively affect the efficiency of public health institutions'
establishment of appropriate measures to prevent the spread of the Coronavirus in low-come
communities (Lester, et al.,2022). Therefore, researchers in this article examine structural racism
in public health and defective data collection of racial and minority groups' impact on healthcare
equity and the goal of precision public health interventions.
It is clear from the summary of the article that the issue is very complex. In fact, the
article states issues in healthcare equality that have no easy solution. The article explains how
poor communities are less like to receive quality care do to structural racism. Therefore, the
article explains in detail how underreporting of racial and minority groups can cause defective
data. What are ways to prevent healthcare professionals from underreporting? What could be the
future consequence of underreporting racial and minority groups?
Methods Used
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Researchers argue precision public health should be used as a method to fight the
Coronavirus pandemic. The precision public health is known as the applications and
collaboration of new and existing technologies that focus on preventing interventions for at-risk
communities and promote the overall improvement of healthcare for the population (Lester, et
al.,2022). There are two approaches to precision public health, which are the following: a
reduction of the version that focuses only on the use of genetic information in certain subgroups
of the population and a wider version that does not focus on genetic information and other
sources of data (Lester, et al.,2022). However, it is important to understand the issues regarding
Coronavirus before the deployment of precision public health interventions. The first issue is
examining how structural racism influences the use of data in public health practices. The second
issue is learning how to use data-driven technologies, which is an important component of
precision public health (Lester, et al.,2022). These technologies can lead to the original racial
and minority group discrimination in public health interventions. Therefore, researchers decided
to use a qualitative method to better understand structural racism in healthcare and how it can be
reconstructed.
Machine learning is one of the technologies that can enhance or decline ethical and racial
inequalities. Machine learning is known as a section of artificial intelligence that concentrates on
developing applications that learn from data and increase their efficiency over time without
being programs to perform a such task (Lester, et al.,2022). The classifications used in the
machine learning approach were unsupervised, supervised, and semi-supervised learning. While
using a machine learning approach for precision public health it is essential to consider which
data set to use to prepare the machine learning algorithms (Lester, et al.,2022). However, if data
is used from an underrepresented population or systemically disadvantaged because it will cause
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defective data. This is also known as data racism, which is multiple systems and technologies
that primarily focus on migrants and people of color (Lester, et al.,2022). Furthermore, in the
future data racism and structural racism can impact precision public health through machine
learning, because of the unfortunate experiences of data-driven predictive policing.
Machine learning is a great approach to removing defective data and structural racism in
healthcare, but it does not appear to have specific guidelines on how to prep the machine to work
on its own. Also, the author does not include any examples of work where other professionals
use machine learning to prevent defective data.
Findings
The precision public health goal is to project disease uprising and identify population
hotspots and subgroups for interventions focused on data predictive analytics. On the contrary,
precision public health may face issues such as the Coronavirus data crisis for ethical and racial
minorities. Some degree of discrimination will be replicated in the machine learning algorithms
unless appropriate steps are taken to correct the data (Lester, et al.,2022). One issue machine
learning can face is the low representation of racial and minority groups, which is caused by
structural racism. Also, structural racism has the potential to lead to biased data regarding racial
and minority groups (Lester, et al.,2022). Therefore, it is important for developers of the
learning machine to understand structural racism and real-world consequences through their
software. This will establish the machine learning algorithm to increase public health services
and encourage a fair circulation of resources for all racial and minority groups (Lester, et
al.,2022). The adjustment of machine learning only caters to the repercussions of structural
racism and cannot single-handedly reduce health inequalities among racial and minority groups
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(Lester, et al.,2022). To reduce health inequalities there needs to be a change at the individual
and instructional levels.
Conclusion
In conclusion, researchers in this article used quantitative methods to approach the
inequalities in healthcare regarding racial and minority groups. To effectively collect and analyze
data researchers suggested precision public health intervention technology known as machine
learning to enhance healthcare for the population. To such a complicated issue the author
suggests using machine learning algorithms to help prevent inequalities in healthcare, but the
defective data from the past can potentially affect the results of the machine. The machine only
can use data that healthcare professionals report. There can be a disadvantage to the machine if
reports are not reported in a timely manner and are accurate. From the article, researchers have
stated that there are issues in reporting such as data racism. How can we predict that data racism
will not continue to report if healthcare professionals are continuing to report how they choose
to? If machine learning technology is left to work on its own, will it result still lead to healthcare
inequalities?
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