Univariate and Multivariate Analyses in Early Diagnosis of Depression

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Univariate and Multivariate Analyses in Early
Diagnosis of Depression There are different
tools, methods, and types of analyses in modern
clinical studies that can be utilized to obtain
important data and achieve positive outcomes. For
example, univariate and multivariate analyses can
be constructive in various treatment stages of
severe illnesses, such as depression. Depression
is a severe mental illness that can lead to many
negative consequences if left untreated, which is
why early diagnosis of this condition is critical
in medical terms. Thereby, this paper presents
the use of univariate and multivariate analyses
in clinical studies related to the early
diagnosis of depression among young adults
between 18 and 40 years old in primary care
practice. Univariate and multivariate analyses
can be used in the early diagnosis of depression
among adults to reach consistent and accurate
results. Diagnosis is the most crucial stage in
treating depression, as the sooner the illness
has been diagnosed, the sooner it can be
adequately treated, reducing the number of
potential negative consequences for the patient.
In this sense, one of the most effective
strategies is screening, which is usually used to
identify conditions or risk matters that have not
been recognized yet (Maurer et al., 2018). The
procedure is appropriate for treating the general
adult population. However, the American Academy
of Family Physicians and the United States
Preventive Services Task Force (USPSTF) emphasize
that screening is recommended for all people
older than 18, no matter what risk factors are in
place (Sauerbrei et al., 2020). It is essential
that screening is implemented in association with
proper systems to ensure accurate diagnosis,
which is why the usage of analyses under
discussion can be efficient. The first method of
analysis to be discussed is univariate analysis,
which is especially popular nowadays in
high-dimensional studies. According to Sauerbrei
et al. (2020), univariate analysis stands for the
"selection of variables based on significance in
univariable regression models" (p. 5). This
method can be implemented in the early diagnosis
of
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depression among adults to collect detailed data.
For instance, Almohammed et al. (2022) report
that it is possible to use univariate analysis to
determine differences in changes in the physical
component summaries (PCS) and mental component
summaries (MCS). Analyzing each piece of data
individually can lead to consistent results. The
second method that has the potential to be
utilized in the early diagnosis of depression is
multivariate analysis. That form of analysis
presents "a pragmatic procedure to create a
multivariable model with the parallel aims of
selecting important variables and determining a
suitable functional form for continuous
predictors" (Sauerbrei, 2020, p. 10). For
example, some outstanding factors may
considerably impact the early diagnosis of
depression among adults. Almohammed et al. (2022)
report that the results of their research differ
based on the differences related to socioeconomic
and demographical variables. In other words,
patients' socioeconomic and demographical
conditions should be considered simultaneously in
the early diagnosis stage of depression to make
accurate conclusions. Overall, the usage of
univariate and multivariate analyses can help
achieve consistent and accurate results in the
early diagnosis of depression among adults
between 18 and 40 years old. The univariate
analysis allows the evaluation of each variable
individually in accordance with its significance,
while multivariate analysis allows assessing
several variables to determine how they can
affect a patient's condition together. Using both
analyses in the practice of depression treatment
can help collect the necessary data to achieve
the best possible outcomes.
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https//doi.org/10.1186/s41512-020-00074-3