PubMed,
Год журнала:
2024,
Номер
65(1), С. 16 - 30
Опубликована: Июль 15, 2024
Depression
affects
individuals
across
all
age
groups,
genders,
and
socio-economic
backgrounds.
Socio-demographic
correlates
of
depression
may
include
factors
such
as
age,
gender,
education
level,
income,
marital
status.
These
factors,
including
the
presence
chronic
diseases,
have
been
shown
to
impact
prevalence
severity
depression.
This
study
assessed
depressive
symptoms
its
association
with
socio-demographic
co-morbid
medical
conditions
among
adult
patients
attending
a
National
Health
Insurance
Clinic
tertiary
health
facility
in
Southwest
Nigeria.
Journal of the American Geriatrics Society,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 8, 2025
ABSTRACT
Background
As
the
US
population
continues
to
age,
depression
and
other
mental
health
issues
have
become
a
significant
challenge
for
healthy
aging.
Few
studies,
however,
examined
prevalence
of
in
community‐dwelling
older
adults
United
States.
Methods
Baseline
data
from
Longitudinal
Research
on
Aging
Drivers
study
were
analyzed
examine
correlates
multisite
sample
aged
65–79
years
who
enrolled
assessed
between
July
2015
March
2017.
The
Patient‐Reported
Outcomes
Measurement
Information
System
(PROMIS)
scale
was
used
determine
status.
Results
Of
2990
participants,
186
(6.2%)
had
at
time
assessment.
Elevated
found
those
65–69
age
(7.9%);
women
(7.2%);
not
married
(8.1%);
attained
an
education
high
school
or
less
(8.3%);
annual
household
incomes
than
$50,000
(10.7%).
Older
with
positive
history
chronic
medical
conditions
(e.g.,
diabetes
mellitus
anxiety)
significantly
higher
whereas
engaged
volunteering
activities
lower
depression.
With
adjustment
demographic
characteristics
comorbidities,
associated
43%
reduction
odds
(adjusted
ratio:
0.57,
95%
confidence
interval
0.40–0.81).
Conclusions
point
this
States
6.2%,
which
varied
comorbid
conditions.
Engagement
might
help
reduce
their
risk
Frontiers in Psychiatry,
Год журнала:
2025,
Номер
15
Опубликована: Янв. 10, 2025
While
the
physical
health
effects
of
obesity
are
well-characterized,
an
emerging
branch
research
has
shown
that
additionally
plays
a
critical
role
in
one's
mental
health.
Young
adults,
pivotal
transition
phase
their
lives,
may
be
particularly
prone
to
concurrent
and
adverse
outcomes.
The
purpose
this
review
is
comprehensively
examine
existing
data
regarding
connection
between
two
widely
validated
measures
health:
psychological
distress
depression.
outcomes
mediated
by
complex
interplay
biological
sociocultural
factors,
which
explored
with
particular
focus
on
younger
adults
aged
20-39.
Further,
impact
several
demographic
factors
including
race/ethnicity,
gender,
immigration
status
examined
closely.
To
our
knowledge,
one
first
efforts
integrate
knowledge
health,
regard
for
young
other
key
sociodemographic
characteristics.
This
important
implications
at
interface
most
pressing
public
crises
United
States.
Frontiers in Psychology,
Год журнала:
2023,
Номер
13
Опубликована: Янв. 10, 2023
This
paper
uses
a
large-scale
nationally
representative
dataset,
the
Chinese
General
Social
Survey,
to
examine
relationship
between
subjective
well-being
and
depressive
disorders.
Statistical
results
indicate
that
higher
levels
of
help
decrease
perceived
depression.
Robustness
checks
are
carried
out
using
different
types
explanatory
dependent
variables,
various
regression
models,
penalized
machine
learning
methods,
instrumental
variable
approaches,
placebo
tests,
all
which
lend
further
credence
above
findings.
Based
on
it,
heterogeneities
in
self-rated
mental
disorders
explored.
In
respect
variations
age
cohorts,
it
is
found
absolute
values
happiness's
estimated
coefficients
smaller
20-30
30-40
groups,
while
40-50
group
increase
substantially.
older
estimates
remain
at
fluctuating
some
degree.
Furthermore,
significantly
negative
interaction
happiness
proves
amplifies
well-being's
effect
With
increasing,
impact
reducing
depression
tends
be
stronger.
Therefore,
for
people,
plays
more
important
role
suppressing
Heterogeneities
subgroups
with
demographic
characteristics
also
investigated.
It
correlation
stronger
among
those
educational
levels,
living
urban
areas,
being
members
Communist
Party
China,
having
pensions,
owning
housing
assets.
However,
gender,
ethnic
identity,
religious
belief,
marital
status
exert
no
significant
moderating
effects.
Previous
studies
have
suggested
a
significant
association
between
diet
quality
and
mental
health.
However,
limited
number
of
utilized
the
Prime
Diet
Quality
Score
(PDQS)
to
examine
this
association.
Additionally,
no
study
has
yet
compared
PDQS
Healthy
Eating
Index-2015
(HEI-2015)
in
terms
their
with
depression
anxiety.
This
cross-sectional
aimed
investigate
quality,
measured
by
HEI-2015,
odds
anxiety
adults.
data
from
LIPOKAP
study,
which
was
conducted
February
2018
July
2019
five
cities
Iran.
We
included
1994
adults
aged
18
above
who
were
selected
through
multistage
cluster
sampling
method.
Participants
completed
validated
semiquantitative
food
frequency
questionnaire
(FFQ)
evaluate
dietary
intake.
The
FFQ
used
calculate
HEI-2015.
Depression
levels
determined
using
Hospital
Anxiety
Scale
(HADS).
participants
had
mean
age
39.79
±
13.87
years,
females
accounting
for
1,041
(52.2%)
total
population.
showed
inverse
(OR
=
0.45,
95%
CI:
0.28–0.71)
0.40,
0.25–0.62)
fully
adjusted
model.
Similarly,
highest
quartile
HEI-2015
significantly
lower
0.60,
0.40–0.90)
0.62,
0.42–0.92)
lowest
quartile.
Both
associated
reduced
risk
demonstrated
stronger
these
risks
suggests
that
could
be
more
beneficial
pattern
preventing
Further
large-scale
are
required
confirm
findings.
PLoS ONE,
Год журнала:
2025,
Номер
20(2), С. e0314930 - e0314930
Опубликована: Фев. 4, 2025
This
study
investigates
the
influence
of
changes
in
physical
activity
(PA)
patterns
on
depression
risk
across
different
socioeconomic
statuses
(SES)
Korea.
Utilizing
National
Health
Insurance
Data
(NHID)
from
over
1.2
million
individuals
during
2013–2016,
we
matched
medical
aid
beneficiaries
with
health
insurance
beneficiaries,
excluding
those
prior
or
incomplete
PA
data.
Changes
moderate-to-vigorous
(MVPA)
were
categorized
into
16
groups,
and
incidence
was
tracked
2019
to
2021.
After
adjustment,
consistently
showed
higher
risks
compared
enrollees
same
change
patterns.
For
inactive,
1.68
times
(aOR,
1.68;
95%
CI,
1.37–2.05).
Those
who
increased
inactivity
3–4
per
week
had
a
3.33
3.33;
1.72–6.43).
Additionally,
2.64
for
increasing
1–2
≥5
2.64;
1.35–5.15),
2.83
engaging
2.83;
1.35–5.94).
Across
overall
patterns,
faced
risks,
increases
1.80
activity,
continuous
inactivity,
1.34
decreased
However,
very
active
group,
no
significant
difference
observed
between
two
groups.
Limitations
include
potential
bias
self-reported
NHIS
data
not
fully
capturing
severity.
The
findings
underscore
impact
SES
mental
health,
high
levels
potentially
mitigating
SES-related
risk.
BMC Medical Informatics and Decision Making,
Год журнала:
2025,
Номер
25(1)
Опубликована: Фев. 17, 2025
Depressive
disorder,
particularly
major
depressive
disorder
(MDD),
significantly
impact
individuals
and
society.
Traditional
analysis
methods
often
suffer
from
subjectivity
may
not
capture
complex,
non-linear
relationships
between
risk
factors.
Machine
learning
(ML)
offers
a
data-driven
approach
to
predict
diagnose
depression
more
accurately
by
analyzing
large
complex
datasets.
This
study
utilized
data
the
National
Health
Nutrition
Examination
Survey
(NHANES)
2013–2014
using
six
supervised
ML
models:
Logistic
Regression,
Random
Forest,
Naive
Bayes,
Support
Vector
(SVM),
Extreme
Gradient
Boost
(XGBoost),
Light
Boosting
(LightGBM).
Depression
was
assessed
Patient
Questionnaire
(PHQ-9),
with
score
of
10
or
higher
indicating
moderate
severe
depression.
The
dataset
split
into
training
testing
sets
(80%
20%,
respectively),
model
performance
evaluated
accuracy,
sensitivity,
specificity,
precision,
AUC,
F1
score.
SHAP
(SHapley
Additive
exPlanations)
values
were
used
identify
critical
factors
interpret
contributions
each
feature
prediction.
XGBoost
identified
as
best-performing
model,
achieving
highest
highlighted
most
significant
predictors
depression:
ratio
family
income
poverty
(PIR),
sex,
hypertension,
serum
cotinine
hydroxycotine,
BMI,
education
level,
glucose
levels,
age,
marital
status,
renal
function
(eGFR).
We
developed
models
for
interpretation.
identifies
key
associated
depression,
encompassing
socioeconomic,
demographic,
health-related
aspects.
Healthcare,
Год журнала:
2025,
Номер
13(5), С. 519 - 519
Опубликована: Фев. 27, 2025
Background/Objectives:
This
study
examined
the
impact
of
COVID-19
pandemic
on
mental
health
among
U.S.
adults
during
its
first
year,
using
monthly
surveys
from
March
to
November
2020.
Methods:
The
primary
outcome
was
Patient
Health
Questionnaire
four-item
(PHQ-4)
measure
anxiety
and
depressive
symptoms.
Univarite
bivariate
analyses
were
used
provide
foundational
understanding
key
variables.
Parametric
non-parametric
correlation
conducted
observe
relationship
between
impacts
or
risk
factors
frequency
anxiety/depressive
A
series
regression
models
fit
assess
stressors
PHQ-4
scores.
Results:
There
a
statistically
significant
increase
in
mean
scores
proportion
respondents
with
moderate
severe
symptoms
(PHQ-4
=
6+)
March-June
July-November
Factors
such
as
fear
contracting
virus,
concerns,
lifestyle
disruptions
had
outcomes;
however,
these
effects
more
modest
than
estimates
reported
elsewhere.
Financial
strain,
particularly
lower-income
households
those
experiencing
job
loss,
showed
stronger
associations
increased
symptoms,
but
overall
population-level
limited
due
small
severely
affected
financially.
Using
models,
we
found
that
demographic
collectively
explained
about
21%
variance
Conclusions:
provides
nuanced
pandemic's
impact,
suggesting
while
certain
subgroups
affected,
population
level
depression
less
pronounced
previously
assumed.
Background
Cardiovascular
diseases
(CVD)
and
depression
are
growing
global
health
concerns
as
heart
attack
stroke
solely
account
for
around
85%
of
total
CVD
deaths
280
million
ie,
while
3.4%
the
world's
population
have
depression.
A
bi-directional
relationship
exists
between
disease:
about
one-fourth
disease
patients
experience
depression,
those
with
a
higher
risk
developing
compared
to
general
population.
This
study
aims
examine
association
dependent
variable,
focusing
on
demographic
behavioral
correlates
individuals
in
Tennessee.
Methods
We
performed
cross-sectional
analysis
using
2022
Behavior
Risk
Factor
Surveillance
System
(BRFSS)
data
Tennessee
(N
=
5266).
Our
analytical
approaches
involved
descriptive
multivariate
(logistic
regression
analysis)
assess
The
primary
variable
interest
was
self-reported
lifetime
independent
variables
included
no
exercise
past
30
days,
smoking
status,
race/ethnicity,
gender,
age
category.
Results
7.5%
731)
participants
reported
27.8%
828)
Depression
found
be
significantly
associated
odds
(AOR
1.36;
95%
CI,
1.06,
1.73),
p
<
0.001).
Similarly,
days
1.74;
1.39,
2.20,
0.001)
also
attack.
Furthermore,
low
income,
current
race/ethnicity
were
not
our
study.
Conclusion
reinforces
significant
link
highlighting
complex
interplay
factors
influencing
onset
cardiovascular
diseases.
findings
underscore
necessity
comprehensive
approach
that
integrates
mental
considerations
addresses
broader
social
determinants
health.