Vaccination
reduces
the
overall
burden
of
COVID-19,
while
its
allocation
procedure
may
introduce
additional
health
inequality,
since
populations
characterized
with
certain
social
vulnerabilities
have
received
less
vaccination
and
been
affected
more
by
COVID-19.
We
used
structural
equation
modeling
to
quantitatively
evaluate
extent
which
disparity
would
amplify
where
it
functioned
as
a
mediator
in
effect
pathways
from
COVID-19
mortality.We
USA
nationwide
county
(n
=
3112,
99%
total)
level
data
during
2021
an
ecological
study
design.
Theme-specific
rankings
vulnerability
index
published
CDC
(latest
2018,
including
socioeconomic
status,
household
composition
&
disability,
minority
status
language,
housing
type
transportation)
were
exposure
variables.
coverage
rate
(VCR)
was
variable,
case
fatality
(CFR)
John
Hopkinson
University,
outcome
variable.Greater
language
inversely
associated
VCR,
together
explaining
11.3%
variance
VCR.
Greater
disability
positively
CFR,
VCR
10.4%
CFR.
Our
mediation
analysis,
based
on
mid-year
(30th
June
2021),
found
that
37.6%
(mediation/total
effect,
0.0014/0.0037),
10%
(0.0003/0.0030)
100%
(0.0005/0.0005)
effects
involving
respectively,
mediated
As
whole,
significantly
counted
for
30.6%
CFR
disparity.
Such
seen
throughout
2021,
proportions
ranging
12
32%.Allocation
led
inequality
respect
mortality.
Viable
public
interventions
should
be
taken
guarantee
equitable
deployment
healthcare
recourses
across
different
population
groups.
Abstract
Background
Understanding
geographic
disparities
in
Coronavirus
Disease
2019
(COVID-19)
testing
and
outcomes
at
the
local
level
during
early
stages
of
pandemic
can
guide
policies,
inform
allocation
control
prevention
resources,
provide
valuable
baseline
data
to
evaluate
effectiveness
interventions
for
mitigating
health,
economic
social
impacts.
Therefore,
objective
this
study
was
identify
COVID-19
testing,
incidence,
hospitalizations,
deaths
first
five
months
Florida.
Methods
Florida
county-level
time
period
March-July
2020
were
used
compute
various
metrics
including
rates,
positivity
incidence
risks,
percent
hospitalized
cases,
hospitalization
case-fatality
mortality
risks.
High
or
low
risk
clusters
identified
using
either
Kulldorff’s
circular
spatial
scan
statistics
Tango’s
flexible
their
locations
visually
displayed
QGIS.
Results
Visual
examination
patterns
showed
high
estimates
all
Southern
Similar
patterns,
high-risk
rates
(i.e.
hospitalizations
deaths)
concentrated
The
distributions
these
other
parts
more
heterogeneous.
For
instance,
Northwest
well
below
state
median
(11,697
tests/100,000
persons)
but
they
above
North
Central
risks
equal
(878
cases/100,000
persons),
converse
true
Consequently,
a
cluster
Florida,
while
rate
1–3
case
fatality
had
low-rate
it
cases.
Conclusions
Substantial
distribution
exist
with
counties
generally
having
higher
severe
compared
Northern
These
findings
that
is
useful
assessing
preventive
interventions,
such
as
vaccinations,
state.
Future
studies
will
need
assess
changes
over
lower
geographical
scales
determinants
any
patterns.
Journal of Urban Health,
Год журнала:
2023,
Номер
100(1), С. 40 - 50
Опубликована: Янв. 12, 2023
COVID-19-related
health
outcomes
displayed
distinct
geographical
patterns
within
countries.
The
transmission
of
SARS-CoV-2
requires
close
spatial
proximity
people,
which
can
be
influenced
by
the
built
environment.
Only
few
studies
have
analysed
infections
related
to
environment
urban
areas
at
a
high
resolution.
This
study
examined
association
between
factors
and
in
metropolitan
area
Germany.
Polymerase
chain
reaction
(PCR)-confirmed
7866
citizens
Essen
March
2020
May
2021
were
analysed,
aggregated
neighbourhood
level.
We
performed
regression
analyses
investigate
associations
cumulative
number
per
1000
inhabitants
(cum.
infections)
up
31.05.2021
factors.
cum.
neighbourhoods
(median:
11.5,
IQR:
8.1-16.9)
followed
marked
socially
determined
north-south
gradient.
effect
estimates
adjusted
models
showed
negative
with
greenness,
i.e.
normalized
difference
vegetation
index
(NDVI)
(adjusted
β
=
-
35.36,
95%
CI:
57.68;
13.04),
rooms
person
(-
10.40,
13.79;
7.01),
living
space
0.51,
0.66;
0.36),
residential
0.07,
0.16;
0.01)
commercial
0.15,
0.25;
0.05).
Residential
multi-storey
buildings
0.03,
0.12;
0.06)
green
(0.03,
0.05;
0.11)
did
not
show
substantial
association.
Our
results
suggest
that
matters
for
spread
infections,
such
as
more
spacious
apartments
or
higher
levels
greenness
are
associated
lower
infection
rates
unequal
intra-urban
distribution
these
emphasizes
prevailing
environmental
inequalities
regarding
COVID-19
pandemic.
Circulation Cardiovascular Quality and Outcomes,
Год журнала:
2022,
Номер
15(8)
Опубликована: Июль 18, 2022
Background:
The
COVID-19
pandemic
has
disproportionately
affected
low-income
and
racial/ethnic
minority
populations
in
the
United
States.
However,
it
is
unknown
whether
hospitalized
patients
with
from
socially
vulnerable
communities
experience
higher
rates
of
death
and/or
major
adverse
cardiovascular
events
(MACEs).
Thus,
we
evaluated
association
between
county-level
social
vulnerability
in-hospital
mortality
MACE
a
national
cohort
patients.
Methods:
Our
study
population
included
American
Heart
Association
Cardiovascular
Disease
Registry
across
107
US
hospitals
January
14,
2020
to
November
30,
2020.
Social
Vulnerability
Index
(SVI),
composite
measure
community
developed
by
Centers
for
Control
Prevention,
was
used
classify
patients’
place
residence.
We
fit
hierarchical
logistic
regression
model
hospital-level
random
intercepts
evaluate
SVI
MACE.
Results:
Among
16
939
registry,
5065
(29.9%)
resided
most
(highest
quartile
SVI).
Compared
those
lowest
SVI,
highest
were
younger
(age
60.2
versus
62.3
years)
more
likely
be
Black
adults
(36.7%
12.2%)
Medicaid-insured
(31.1%
23.0%).
After
adjustment
demographics
(age,
sex,
race/ethnicity)
insurance
status,
(compared
lowest)
associated
likelihood
(OR,
1.25
[1.03–1.53];
P
=0.03)
1.26
[95%
CI,
1.05–1.50];
=0.01).
These
findings
not
attenuated
after
accounting
clinical
comorbidities
acuity
illness
on
admission.
Conclusions:
Patients
residing
experienced
MACE,
independent
race,
ethnicity,
several
factors.
Clinical
health
system
strategies
are
needed
improve
outcomes
This
study
summarizes
the
results
from
fitting
a
Bayesian
hierarchical
spatiotemporal
model
to
coronavirus
disease
2019
(COVID-19)
cases
and
deaths
at
county
level
in
United
States
for
year
2020.
Two
models
were
created,
one
deaths,
utilizing
scaled
Besag,
York,
Mollié
with
Type
I
spatial-temporal
interaction.
Each
accounts
16
social
vulnerability
7
environmental
variables
as
fixed
effects.
The
spatial
pattern
between
COVID-19
is
significantly
different
many
ways.
trend
of
pandemic
illustrates
shift
out
major
metropolitan
areas
into
Southeast
Southwest
during
summer
months
upper
Midwest
beginning
autumn.
Analysis
predictors
infection
death
found
that
counties
higher
percentages
those
not
having
high
school
diploma,
non-White
status
being
Age
65
over
be
significant.
Among
variables,
above
ground
temperature
had
strongest
effect
on
relative
risk
both
deaths.
Hot
cold
spots,
statistically
significant
low
respectively,
derived
convolutional
show
probability
average
have
Social
Vulnerability
Index
composite
scores.
same
analysis
interaction
term
exemplifies
more
complex
relationship
vulnerability,
measurements,
cases,
ISPRS International Journal of Geo-Information,
Год журнала:
2024,
Номер
13(3), С. 97 - 97
Опубликована: Март 18, 2024
Spatial
epidemiology
investigates
the
patterns
and
determinants
of
health
outcomes
over
both
space
time.
Within
this
field,
Bayesian
spatiotemporal
models
have
gained
popularity
due
to
their
capacity
incorporate
spatial
temporal
dependencies,
uncertainties,
intricate
interactions.
However,
complexity
modelling
computations
associated
with
vary
across
different
diseases.
Presently,
there
is
a
limited
comprehensive
overview
applications
in
epidemiology.
This
article
aims
address
gap
through
thorough
review.
The
review
commences
by
delving
into
historical
development
concerning
disease
mapping,
prediction,
regression
analysis.
Subsequently,
compares
these
terms
data
distribution,
general
models,
environmental
covariates,
parameter
estimation
methods,
model
fitting
standards.
Following
this,
essential
preparatory
processes
are
outlined,
encompassing
acquisition,
preprocessing,
available
statistical
software.
further
categorizes
summarizes
application
Lastly,
critical
examination
advantages
disadvantages
along
considerations
for
application,
provided.
enhance
comprehension
dynamic
distribution
prediction
epidemics.
By
facilitating
effective
scrutiny,
especially
context
global
COVID-19
pandemic,
holds
significant
academic
merit
practical
value.
It
also
contribute
improved
ecological
epidemiological
prevention
control
strategies.
Multiple Sclerosis Journal,
Год журнала:
2023,
Номер
29(10), С. 1304 - 1315
Опубликована: Июль 12, 2023
Black
and
Hispanic
patients
with
multiple
sclerosis
(MS)
have
been
shown
to
accumulate
greater
sclerosis-associated
disability
(MSAD)
than
White
patients.
Disparities
in
social
determinants
of
health
(SDOH)
among
these
groups
also
reported.To
determine
the
extent
which
associations
race
ethnicity
MSAD
may
be
attributable
differences
SDOH.Retrospective
chart
analysis
at
an
academic
MS
center
grouped
by
self-identified
(n
=
95),
93),
98)
race/ethnicity.
Individual
patient
addresses
were
geocoded
matched
neighborhood-level
area
deprivation
index
(ADI)
vulnerability
(SVI).Average
Expanded
Disability
Status
Scale
(EDSS)
scores
last-recorded
evaluations
(1.7
±
2.0)
significantly
lower
(2.8
2.4,
p
0.001)
(2.6
2.6,
0.020)
Neither
nor
was
associated
EDSS
multivariable
linear
regression
models
that
included
individual-level
SDOH
indicators
either
ADI
or
SVI.Black
are
not
include
individual
indicators.
Further
research
should
elucidate
mechanisms
structural
inequities
affect
disease
course.
International Journal of Environmental Health Research,
Год журнала:
2025,
Номер
unknown, С. 1 - 10
Опубликована: Янв. 20, 2025
The
purpose
of
this
study
was
to
examine
the
relationship
between
social
vulnerability
and
COVID-19
mortality
rates
during
whole
outbreak
in
U.S.
counties.
deaths
were
gleaned
from
USA
Facts.
Independent
variables
CDC's
Social
Vulnerability
Index.
Spatial
autoregressive
models
used
for
data
analysis.
Results
show
that
counties
with
more
(socioeconomic)
positively
associated
higher
rates.
Counties
(household
composition
&
disability)
(minority
status
language)
negatively
(housing
type
transportation)
In
conclusion,
county-level
provides
an
useful
framework
identifying
unequal
distribution
United
States.