This study aims to identify risk
factors for
malaria
related
to landscape,
environmental, and socio-economic and
human
behaviour
variables
in the
Red
River
basin area, Yunnan, China, to develop a predictive
model of malaria
spatial
distribution, and to utilise these to improve malaria
surveillance and control
in the
basin area.
Yunnan is one of the most endemic areas
for
malaria
in
China today,
particularly
in
the Red River basin and its border areas.
Chloroquine-resistant falciparum
malaria
is
continuing to increase, partly due to immigration
and
socio-economic
development
for agriculture in the region. Traditional intensive
surveillance systems
for
malaria are
becoming unreliable. The terrain shows considerable
variation
and some
of
it is
relatively inaccessible. The environment, particularly
its land
use pattern
keeps
changing. The present study has used geographical
information
systems
(GIS)
and
satellite imagery data to identify malaria risk
factors
related to landscape
and
environmental data and to develop a predictive model
in hope
of predicting the
risk
of
malaria transmission and outbreaks and guiding malaria
control
in the
Red River
basin area.
The work is in two phases:
Phase I was a retrospective ecological study.
It
used
basic
GIS techniques to
analyse
routine malaria data, and existing environmental and
ecological
data in the
Red River
basin area primarily to identify major
determinants
of malaria
spatial
distribution
related to landscape and environmental variables.
The
malaria
data
were those
were
reported from villages through health care
systems.
In
view
of their limitations
of
accuracy and coverage, phase II was undertaken.
However,
The
work
of the
phase
I
study helped to formulate the specific
hypotheses for
phase
II
study.
Phase II study was a prospective study.
Malaria incidence
data
were
collected
in
a
field survey of the whole study population
by
our
own
research team.
Malaria
incidence data were integrated with altitude
data derived from terrain
maps
and a
land
use map derived from SPOT 4 imagery data into the
GIS.
Multilevel
Poisson regression modelling were used to model
landscape
and
land
use
determinants
of
malaria.
The phase I study was carried out
in
one
county of the
Red River
basin
area,
Yuanyang County, Yunnan, China. Malaria
and population
data
at the level
of
administrative village were collected
in
131
administrative
villages of the
county.
Terrain maps, the land use map, soil map and
administrative
boundaries
of
Yuanyang
as well as malaria risk maps were integrated within
GIS.
The
data
were analysed
by
modelling the risk of malaria in the administrative villages
and their landscape
and
environmental variables generated from GIS.
The
results of the
analysis
revealed that
spatial distribution of malaria was determined by the landscape
and
land
use
patterns
in the administrative villages. Malaria was
negatively correlated
with
mean altitudes
of administrative villages, but more paddy and
forest
would
increase the
risk of
malaria in the villages.
Phase II study was carried out in the Feng Chun
Ling Township
of
Yuanyang
County
from May to December 1998. The entire population
of
24,280 in
5,007 households
was included in the study. Around 14%
of the
study
cohort,
mostly
from the
mountains, however, had a history of temporary
migration to the lowlands
where
malaria is highly endemic during the study
period.
A total
of
649
malaria
cases
(including 3 mixed infections) were identified in the
study cohort
during the
7-month
period. Of the 649 malaria cases, 400 cases were
from the
population
with
a
history
of
temporary migration during study period.
The
overall
risk
of people
with
and
without
temporary migration history were 118.6 and
12.1
per
1000
persons,
respectively,
during the 7-month study period. The relative
risk
of the
migrated
population
against
non-migrated population were 9.8, suggesting the
migrated
population
had
around
10-fold higher risk of malaria than those of
non-migrated
population.
Only
334
indigenous malaria cases out of all malaria cases
(649) in the
study
were
used
for
further analyses and model building. The risk
for
P. vivax
indigenous
malaria
was
17.8
per 1,000 person years at risk and for P. falciparum
was
6.9
per
1,000
person
years
at
risk. Malaria data were integrated with
household locations identified
by
Global
Position Systems (GPS), a land use map
derived from
a
SPOT
4 image
and terrain
maps into GIS. The results of multilevel Poisson regression modelling
in
phase
II
study revealed
that
indigenous malaria is negatively correlated with
altitude for P. vivax
and
P. falciparum. Paddy and forest would increase the
risk
of
malaria,
but the
effect
of
forest on malaria reached a plateau at certain
level
of
the
forest coverage.
The
mosquito
net is protective for indigenous malaria
in the
analysis.
The
protective efficacy
for
P.
vivax is 40% and P. falciparum 29%, respectively.
In conclusion, malaria transmission in the
study area
is
primarily
determined
by the
environmental variables particularly altitude, paddy
and
forest in the
Red
River
basin
area. But human behaviour such as temporary migration and
the
use of mosquito nets
play a very important role in determining the
malaria
spatial
pattern
in the
study
area.
Malaria control and surveillance should
focus
on the lower
altitude
areas
and the
mobile population in the Red River
basin
area.
The
overall temporarily
migrated
population plus population living
below
1,200
metres accounted
for
44.2%
(10734/24280) of total population, but accounted for
85.7% (559/652)
of
total
malaria
cases during the study period, while migrant
plus those living
under
800 metres accounted for 73.6% (480/652) of cases
in
only
21.2%
(5155/24280)
of the
population.
Future development of land should be aware of the
potential
for
malaria
and
other
vector borne disease risk arising from expansion
of paddy
field
and
deforestation,
which will provide breeding sites for mosquitoes, particularly
in the
middle
and
lower
altitude areas. Subsequently, they might result
in
malaria
and
other
vector
borne
disease outbreaks or epidemics. The first
priority
of
malaria
control
strategy
in the
immediate future is to encourage local residents
and
'downhill'
migrants to
use
mosquito nets and to ensure these are
regularly treated
with
insecticides.
Chemoprophylaxis and other control measures need to
be
explored
for the temporarily
migrating population in the Red River
basin
area.
