Investigating Parkinson’s disease risk across farming activities using data mining and large-scale administrative health data
npj Parkinson s Disease,
Год журнала:
2025,
Номер
11(1)
Опубликована: Янв. 8, 2025
Abstract
The
risk
of
Parkinson’s
disease
(PD)
associated
with
farming
has
received
considerable
attention,
in
particular
for
pesticide
exposure.
However,
data
on
PD
specific
activities
is
lacking.
We
aimed
to
explore
whether
exhibited
a
higher
than
others
among
the
entire
French
farm
manager
(FM)
population.
A
secondary
analysis
real-world
administrative
insurance
claim
and
electronic
health/medical
records
(TRACTOR
project)
was
conducted
estimate
26
using
mining.
cases
were
identified
through
chronic
declarations
antiparkinsonian
drug
claims.
There
8845
1,088,561
FMs.
highest-risk
group
included
FMs
engaged
pig
farming,
cattle
truck
fruit
arboriculture,
crop
mean
hazard
ratios
(HRs)
ranging
from
1.22
1.67.
lowest-risk
all
involving
horses
small
animals,
as
well
gardening,
landscaping
reforestation
companies
(mean
HRs:
0.48–0.81).
Our
findings
represent
preliminary
work
that
suggests
potential
involvement
occupational
factors
related
onset
development.
Future
research
focusing
farmers
high-risk
will
allow
uncover
by
better
characterizing
exposome,
which
could
improve
surveillance
farmers.
Язык: Английский
Global research trends on the human exposome: a bibliometric analysis (2005–2024)
Environmental Science and Pollution Research,
Год журнала:
2025,
Номер
unknown
Опубликована: Март 8, 2025
Abstract
Exposome
represents
one
of
the
most
pressing
issues
in
environmental
science
research
field.
However,
a
comprehensive
summary
worldwide
human
exposome
is
lacking.
We
aimed
to
explore
bibliometric
characteristics
scientific
publications
on
exposome.
A
analysis
from
2005
December
2024
was
conducted
using
Web
Science
accordance
with
PRISMA
guidelines.
Trends/hotspots
were
investigated
keyword
frequency,
co-occurrence,
and
thematic
map.
Sex
disparities
terms
citations
examined.
From
2024,
931
published
363
journals
written
by
4529
authors
72
countries.
The
number
tripled
during
last
5
years.
Publications
females
(51%
as
first
34%
authors)
cited
fewer
times
(13,674)
than
males
(22,361).
Human
studies
mainly
focused
air
pollution,
metabolomics,
chemicals
(e.g.,
per-
polyfluoroalkyl
substances
(PFAS),
endocrine-disrupting
chemicals,
pesticides),
early-life
exposure,
biomarkers,
microbiome,
omics,
cancer,
reproductive
disorders.
Social
built
environment
factors,
occupational
multi-exposure,
digital
exposure
screen
use),
climate
change,
late-life
received
less
attention.
Our
results
uncovered
high-impact
countries,
institutions,
journals,
references,
authors,
key
trends/hotspots.
use
technologies
sensors,
wearables)
data
artificial
intelligence)
has
blossomed
overcome
challenges
could
provide
valuable
knowledge
toward
precision
prevention.
risk
scores
represent
promising
avenue.
Язык: Английский
Using Machine Learning and Nationwide Population‐Based Data to Unravel Predictors of Treated Depression in Farmers
Mental Illness,
Год журнала:
2025,
Номер
2025(1)
Опубликована: Янв. 1, 2025
Farmers
are
exposed
to
numerous
stressors
that
can
negatively
impact
their
mental
health,
leading
conditions
such
as
depression.
However,
most
studies
examining
depression
risk
in
farmers
limited
by
small
sample
sizes,
narrow
geographic
coverage,
and
a
focus
predominantly
on
male
general
agricultural
contexts.
To
complement
these
traditional
studies,
big
data
machine
learning
(ML)
advantageously
be
harnessed.
While
ML
algorithms
have
shown
high
accuracy
identifying
predictors
health
research,
no
study
has
yet
applied
farmers.
We
aimed
identify
key
of
among
the
entire
French
farmer
workforce
across
professional
categories,
activities,
sexes
using
(XGBoost).
A
secondary
analysis
large‐scale
administrative
databases
(TRACTOR
project)
was
conducted.
Potential
(
n
=
128
for
farm
managers
123
farmworkers)
included
broad
range
sociodemographic,
lifestyle,
occupational
variables.
The
predictor’s
importance
determined
Shapley’s
additive
explanation.
There
were
83,592
cases
1,088,561
149,285
5,831,302
farmworkers.
Models
performed
well,
with
F
1
scores
ranging
from
0.65
0.94.
noted
differences,
even
though
several
common
populations,
and/or
sexes.
top
working
year,
age,
sex,
experience,
job
security,
income,
preexisting
conditions.
which
reflects
cumulative
external
factors
(e.g.,
harsh
weather)
farmers’
emerged
important
predictor.
These
findings
highlight
potential
real‐world
modifiable
predictors,
thus
enhancing
early
detection
prevention
strategies.
By
differentiating
farming
groups,
our
results
suggest
tailored
interventions
could
developed
better
address
unique
needs
various
populations.
insights
inform
development
clinical
tools
calculators)
assist
decision‐making.
Язык: Английский