Epidemiology Discussion: Mendelian Randomization Analysis

This discussion forum post provides an analysis of three articles discussing the use of Mendelian randomization and observational studies in the field of epidemiology. The post by Alam (2019) highlights how Mendelian randomization, with the integration of genetics, is revolutionizing epidemiology by differentiating causation from correlation and overcoming confounding variables. Gorman (2020) discusses the limitations of Mendelian randomization, focusing on its validity and its impact on understanding the relationship between health and lifestyle factors. Finally, Bond (2019) assesses the limitations of observational epidemiology, particularly cross-sectional studies, and their relevance in determining disease causation. The author favors observational epidemiology and provides a detailed analysis of the cross-sectional study design. The post offers insights into the strengths and weaknesses of different study designs, contributing to a more profound understanding of epidemiological research and its implications for public health.

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EPIDEMIOLOGY DISCUSSION
Trending Discussion on Epidemiology
Epidemiology is the branch of medical science that discusses the incidence of
diseases, its distribution, potential control methods, and other relevant health factors for the
diseases. The following section will discuss three current internet discussion posts on the
field of epidemiology and perform analysis of the posts to determine its relevancy and
importance in the public health sector.
Post Analysis
The first article by Adam (2019) on Nature.com discusses the gene-based Mendelian
randomization that has been used by scientists for too long now and how it is revolutionizing
the field of epidemiology. According to the author of the post, Mendelian randomization
offers a modest way of distinguishing between the causation from correlation currently.
However, before the advancement of genetics and its integration in the field of epidemiology,
scientists have used observation-based logical explanations for too long to determine and
differentiate between the terms ‘causation’ and ‘correlation’. With the integration of genetics,
the field of epidemiology has significantly improved, as genetic differences stand as proxies
for environmental exposure and remove confounding variables from analyses.
Mendelian randomization comes with several biases and the author picks several
methods including better data organization as a method to remove such biases. The article is
of great use to get a complete understanding of the use of Mendelian randomization in
epidemiology and relevant limitations to this study method, in addition to several solutions
for battling biases in the present time.
Another post on The-Scientist.com by Gorman (2020) discusses the same topic of
Mendelian Randomization in the field of epidemiology. However, the post has a greater focus
on the limitations of the study type and offers a critical analysis to determine the validity of
this genetic study type and its use in the field of epidemiology. Observational studies are

EPIDEMIOLOGY DISCUSSION
disfavored by the epidemiologists due to associated compounding factors and hinder the
epidemiologist’s ability to determine the cause of the disease. The reframing of observational
study during the 2000s required integration of genetic data to address the limitations. As per
the author’s statement, the inclusion of Mendelian Randomization in the field of
epidemiology has developed a more distinct understanding of the relationship between health
and lifestyle factors, critically appraising the importance of this type of study in influencing
public health.
The third post to be analyzed is by Bond (2019) published online in Science 2.0. In
this post, the author completely focuses on assessing the limitations of the observational
epidemiology type of research study used to determine disease causation. The author is in
favor of observational epidemiology evident by the statement of the author that this type of
study provides the most relevant scientific evidence and will continue to play a significant in
future studies. However, the cross-sectional study design is the weakest of all in
observational epidemiology study as it fails most of the time to publish accurate outcomes
with its potential limitations.
This post provides a detailed analysis of a cross-sectional type of observational
epidemiology study and can be useful for future studies to reduce the limitations or
completely avoid the use of such study design.

EPIDEMIOLOGY DISCUSSION
References
Alam, D. (2019) The gene-based hack that is revolutionizing epidemiology. Nature.com.
Retrieved 7 January 2020, from https://www.nature.com/articles/d41586-019-03754-3
Bond, G. (2020). Limitations Of Cross-Sectional Epidemiology Studies And What That
Means For Endocrine Disrupting Chemicals. Science 2.0. Retrieved 7 January 2020,
from
https://www.science20.com/gregory_bond/limitations_of_crosssectional_epidemiolog
y_studies_and_what_that_means_for_endocrine_disrupting_chemicals-243765
Gorman, R.M. (2020) A New Way to Establish Cause and Effect in Epidemiology?.
(2020). The Scientist Magazine®. Retrieved 7 January 2020, from https://www.the-
scientist.com/news-opinion/a-new-way-to-establish-cause-and-effect-in-
epidemiology--66871