Journal of Personalized Medicine,
Год журнала:
2023,
Номер
13(12), С. 1625 - 1625
Опубликована: Ноя. 21, 2023
Cardiovascular
disease
remains
a
leading
cause
of
morbidity
and
mortality
in
the
United
States
(US).
Although
high-quality
data
are
accessible
US
for
cardiovascular
research,
digital
literacy
(DL)
has
not
been
explored
as
potential
factor
influencing
mortality,
although
Social
Vulnerability
Index
(SVI)
used
previously
variable
predictive
modeling.
Utilizing
large
language
model,
ChatGPT4,
we
investigated
variability
CVD-specific
that
could
be
explained
by
DL
SVI
using
regression
We
fitted
two
models
to
calculate
crude
adjusted
CVD
rates.
Mortality
ICD-10
codes
were
retrieved
from
CDC
WONDER,
geographic
level
was
Department
Agriculture.
Both
datasets
merged
Federal
Information
Processing
Standards
code.
The
initial
exploration
involved
1999
through
2020
(n
=
65,791;
99.98%
complete
all
Counties)
(CCM).
Age-adjusted
(ACM)
had
3118
rows;
99%
Counties),
with
inclusion
model
(a
composite
internet
access).
By
leveraging
on
advanced
capabilities
ChatGPT4
linear
regression,
successfully
highlighted
importance
incorporating
predicting
mortality.
Our
findings
imply
just
availability
may
sufficient
without
significant
variables,
such
SVI,
predict
ACM.
Further,
our
approach
enable
future
researchers
consider
key
variables
study
other
health
outcomes
public-health
importance,
which
inform
clinical
practices
policies.
Research Square (Research Square),
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 24, 2025
Abstract
Climate
change
and
increased
human
activity
have
resulted
in
an
increase
the
frequency
intensity
of
wildfires.
Effective
wildfire
risk
assessment
is
essential
for
disaster
prevention,
resource
protection,
regional
stability.
Existing
studies
often
overlook
spatial
heterogeneity
temporal
patterns
wildfires,
with
limited
county-scale
quantitative
assessments.
To
address
these
gaps,
multidimensional
framework
Sichuan
Province
was
proposed,
combining
characterization
modeling.
Temporal
trends
mutation
wildfires
from
2001
to
2023
were
analyzed
using
Mann-Kendall
test.
Additionally,
model
constructed
by
hazard
vulnerability
Specifically,
assessed
Multiscale
Geographically
Weighted
Regression
(MGWR)
capturing
driving
factors.
Vulnerability
through
Multi-Criteria
Decision
Analysis
(MCDA)
approach
identify
areas
high
their
factor
importance.
The
results
indicated
a
significant
rise
particularly
during
winter
non-fire
prevention
periods.
MGWR
effectively
captured
heterogeneity,
identifying
highest
levels
southwestern
Sichuan,
Liangshan
Prefecture
Panzhihua
City.
High
scattered,
mainly
across
southwestern,
southern,
northern
Sichuan.
integrated
revealed
that
its
surrounding
counties
exhibited
significantly
higher
than
other
regions,
while
eastern
northeastern
regions
demonstrated
lowest
risk.
This
study
provides
scientific
foundation
targeted
management,
emergency
response
strategies
Province,
offering
valuable
insights
policymakers
managers.
Climate,
Год журнала:
2025,
Номер
13(5), С. 92 - 92
Опубликована: Апрель 30, 2025
Identifying
socio-spatial
inequalities
in
flood
resilience
is
crucial
for
effective
disaster
risk
management.
This
study
integrates
susceptibility
simulations
and
Weibo
activity
data
to
construct
a
index
incorporates
differentiation
represent
residents’
coping
capacities.
By
combining
awareness
capacity,
we
develop
comprehensive
response
capability
model
examine
the
spatial
patterns
of
inequality.
The
findings
reveal
that
(1)
high
concentrated
near
Yangtze
River
major
lakes
based
on
social
media
simulations;
(2)
capacity
floods
exhibits
central–periphery
pattern,
with
higher
urban
centers
gradually
decreases
suburban
exurban
areas;
(3)
communities
are
classified
into
four
types
combination
Multiple
linear
regression
analysis
indicates
both
natural
factors
significantly
influence
capacity.
research
provides
critical
insights
resilience,
offering
valuable
guidance
formulating
targeted
adaptation
strategies.
Environmental Science & Technology,
Год журнала:
2023,
Номер
57(5), С. 2019 - 2030
Опубликована: Янв. 24, 2023
Although
quantitative
environmental
(in)justice
research
demonstrates
a
disproportionate
burden
of
toxic
chemical
hazard
risks
among
racial/ethnic
minorities
and
people
in
low
socioeconomic
positions,
limited
knowledge
exists
on
how
groups
across
geographic
spaces
experience
hazards.
This
study
analyzed
the
spatial
non-stationarity
associations
between
risk
community
characteristics
census
block
Texas,
USA,
for
2017
using
multiscale
geographically
weighted
regression.
The
results
showed
that
percentage
Black
or
Asian
population
has
significant
positive
with
meaning
racial
suffered
more
from
wherever
they
are
located
state.
By
contrast,
Hispanic
Latino
relationship
risk,
varies
locally
is
only
eastern
areas
Texas.
Statistical
variables
not
stationary
state,
showing
sub-state
patterns
variation
terms
sign,
level,
magnitude
coefficient.
Income
negative
association
around
Dallas–Fort
Worth–Arlington
Metropolitan
Area.
Proportions
without
high
school
diploma
unemployment
rate
both
have
relationships
area
Our
findings
highlight
importance
identifying
at
group
level
addressing
inequality.
ISPRS International Journal of Geo-Information,
Год журнала:
2024,
Номер
13(9), С. 339 - 339
Опубликована: Сен. 22, 2024
This
study
investigates
the
spatial
disparities
in
flood
risk
and
social
vulnerability
across
66,543
census
tracts
Conterminous
United
States
(CONUS),
emphasizing
urban–rural
differences.
Utilizing
American
Community
Survey
(ACS)
2016–2020
data,
we
focused
on
16
factors
representing
socioeconomic
status,
household
composition,
racial
ethnic
minority
housing
transportation
access.
Principal
Component
Analysis
(PCA)
reduced
these
variables
into
five
principal
components:
Socioeconomic
Disadvantage,
Elderly
Disability,
Housing
Density
Vehicle
Access,
Youth
Mobile
Housing,
Group
Quarters
Unemployment.
An
additive
model
created
a
comprehensive
Social
Vulnerability
Index
(SVI).
Statistical
analysis,
including
Mann–Whitney
U
test,
indicated
significant
differences
between
urban
rural
areas.
Spatial
cluster
analysis
using
Local
Indicators
of
Association
(LISA)
revealed
high
clusters,
particularly
regions
along
Gulf
Coast,
Atlantic
Seaboard,
Mississippi
River.
Global
local
regression
models,
Ordinary
Least
Squares
(OLS)
Geographically
Weighted
Regression
(GWR),
highlighted
vulnerability’s
variability
localized
impacts
risk.
The
results
showed
substantial
regional
disparities,
with
areas
exhibiting
higher
risks
vulnerability,
especially
southeastern
centers.
also
that
Unemployment,
Access
are
closely
related
to
areas,
while
relationship
such
as
Disability
is
more
pronounced.
underscores
necessity
for
targeted,
region-specific
strategies
mitigate
enhance
resilience,
where
converge.
These
findings
provide
critical
insights
policymakers
planners
aiming
address
environmental
justice
promote
equitable
management
diverse
geographic
settings.