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Acta Tropica 89 (2004) 215–225
A malaria risk analysis in an irrigated area in Sri Lanka
Eveline Klinkenberg∗, Wim van der Hoek, Felix P. Amerasinghe
International Water Management Institute (IWMI), P.O. Box 2075, Colombo, Sri Lanka
Abstract
MalariainSriLankaisunstableandepidemic,withlargespatialandtemporaldifferencesintransmissiondynamics.Thedisease
is of great public health significance and identification of underlying risk factors is important in order to use the limited resources
in a cost-effective way. The International Water Management Institute (IWMI) recently launched a project of GIS-based malaria
risk mapping in Sri Lanka, to investigate whether this tool could be used for epidemic forecasting and for the planning of malaria
controlactivities.This paper presents results forthe Uda Walaweregioninsouthern Sri Lanka, an irrigatedagricultural area where
malaria cases were mapped at the smallest administrative level for each month over a 10-year period. Malaria incidence rates
were related to land- and water-use patterns, socio-economic features, and data on malaria control interventions in a multivariate
analysis. Areas of high malaria risk were characterized by: (i) higher than average rainfall, (ii) greater forest coverage; (iii)
slash and burn cultivation as a predominant agricultural activity; (iv) presence of many abandoned irrigation reservoirs; and (v)
poor socio-economic status. Irrigated rice cultivation areas had a lower risk of malaria than non-irrigated areas. This difference
could be due to socio-economic factors related to irrigation development and/or transmission dynamics related to vector density
or species composition. Our findings call for malaria control strategies that are readily adapted to different ecological and
epidemiological settings. Malaria risk maps are a convenient tool for discussing targeted and cost-effective interventions with
disease control personnel.
© 2003 Elsevier B.V. All rights reserved.
Keywords: Irrigation; Malaria risk map; Sri Lanka; Spatial analysis; Risk factors; GIS
1. Introduction
Traditionally, malaria has been associated with ir-
rigation development and many have emphasized the
necessity to include health and environmental assess-
ments in irrigation development planning to prevent
adverse health effects (Tiffen, 1991; Birley, 1991;
Oomen et al., 1990; Jobin, 1999). Nevertheless, while
∗Corresponding author. Present address: International Water
Management Institute (IWMI), PMB CT 112 Cantonments, Accra,
Ghana. Tel.: +233-21-784753; fax: +233-21-784752.
E-mail address: e.klinkenberg@cgiar.org (E. Klinkenberg).
health issues are sometimes addressed in the planning
or assessment phase, they are often ignored during fi-
nal design and implementation, usually for economic
reasons (Service, 1997). Rice irrigation in particular
has been associated with breeding of malaria vec-
tors due to the permanently submerged rice fields.
However, although rice irrigation often leads to in-
creased breeding of vectors, epidemiological studies
have shown that this does not necessarily lead to an
increased malaria incidence. For an overview of this
so-called “paddies paradox” we refer to the review
by Ijumba and Lindsay (2001). They conclude that
irrigation can lead to an increase in malaria in areas
0001-706X/$ – see front matter © 2003 Elsevier B.V. All rights reserved.
doi:10.1016/j.actatropica.2003.08.007
216 E. Klinkenberg et al. / Acta Tropica 89 (2004) 215–225
of unstable transmission, but not in areas of stable
transmission. In Sri Lanka, malaria is unstable and
rice is the major crop, especially in irrigated areas.
Several studies were done during the large-scale irri-
gation development under the Mahaweli Project in Sri
Lanka and these have shown an association between
irrigation development and malaria (Amerasinghe
and Indrajith, 1994; Ramasamy et al., 1992;
Amerasinghe and Ariyasena, 1990; Amerasinghe
et al., 1991, 1992). While the main vector Anopheles
culicifacies decreased, potential secondary vectors,
like A. annularis (Ramasamy et al., 1992) and A.
subpictus (Amerasinghe et al., 1992) increased. The
major vector of malaria in Sri Lanka, A. culicifacies,
does not breed in rice fields but breeding sites can
be found in irrigation-associated surface water, other
than rice fields. Many irrigation systems in Sri Lanka
are cascade systems originally built in ancient times,
and consist of a series of connected reservoirs (known
as tanks or wewas). These small irrigation tanks can
be sources of malaria vector breeding in Sri Lanka
(Amerasinghe et al., 2001). The studies cited above
clearly show increases in malaria incidence concomi-
tant with irrigation development but do not clarify
the long term impacts of irrigation development on
malaria. This makes it difficult for the agriculture and
malaria control community to take informed decisions
with respect to land use planning and agricultural
practices.
With the unstable and epidemic malaria pattern
prevailing in Sri Lanka, it is important to target
scarce resources at the time and place where it is
needed. Several risk factors have been suggested that
could help in defining high- and low-risk areas and
times. A. culicifacies mainly breeds in clear water
pools that have formed in drying riverbeds. There-
fore, rainfall and river flow velocities have an impact
on breeding habitats. Other malaria risk factors that
have been identified in Sri Lanka are, age and gender
(Mendis, 1990; van der Hoek et al., 1998), human
migration (Klinkenberg, 2001), type and location of
housing (Gamage-Mendis et al., 1991; Gunawardena
et al., 1998; Konradsen et al., 2003), and the type
and extent of personal protection measures (van der
Hoek et al., 1998). Although risk factors for malaria
have been identified at micro (i.e., household/village)
scale, there has been no attempt at spatial and tempo-
ral analysis at macro (i.e., regional) level to identify
high-risk areas and factors underlying the malaria
pattern. Such an approach could be a valuable tool to
assist the national Anti-Malaria Campaign (AMC) in
making better use of resources. Therefore, the Interna-
tional Water Management Institute (IWMI) launched
a risk-mapping project to investigate the malaria pat-
tern over time and space, correlate this with potential
risk factors within land use, socio-economic, and
meteorological parameters, and explore whether an
epidemic forecasting system could be developed. The
project started with a study in the Uda Walawe region
located in southern Sri Lanka. This paper presents the
main findings of the study. A more detailed account
of data and results is provided in Klinkenberg et al.
(2003).
2. Material and methods
2.1. Study area
The study was carried out within six Divisional
Secretary Divisions (DSDs)1in the Uda Walawe re-
gion in southeastern Sri Lanka (Fig. 1). The land area
covered by the six selected DSDs was 1820km with
a population of about 375,000. Each of these DSDs
is subdivided into so-called Grama Niladhari Divi-
sions (GND), which are the smallest administrative
units in Sri Lanka. The GND sub division is based
on population numbers and a GND typically consists
of two to three villages, with larger towns consist-
ing of several GNDs. The six DSDs together have
196 GNDs.
The region is situated in the dry climatic zone
(Fig. 1), characterized by nearly constant year-round
temperatures (26–28◦C) and less than 2000 mm of
annual rainfall. There is a north-south rainfall gra-
dient, with annual rainfall around the northern Uda
Walawe reservoir of 1500mm decreasing to 1000 mm
in the southern coastal areas (SL/JICA, 1993). About
70% of the rainfall is received in the Maha agricul-
tural season (October–April), mainly related to the
northeast monsoon, and about 30% in the Yala agri-
cultural season (May–September). A major irrigation
scheme, the Uda Walawe Irrigation and Extension
1A DSD is a subdivision of an administrative District, which
in turn is a subdivision of a Province.
E. Klinkenberg et al. / Acta Tropica 89 (2004) 215–225 217
Fig. 1. Study area and the three climatic zones of Sri Lanka. Note: Int. is intermediate zone.
Project (Fig. 1), was developed in the Walawe River
basin from 1967 onwards (SAPI, 2000). In 1993, the
Right Bank command area was about 12,000ha of
mainly irrigated paddy, but the cultivation of banana
and other field crops such as vegetables and pulses
has expanded within this area over the past decade.
The left bank command area was about 5000ha, di-
vided into 2000ha of sugar cane around Sevenegala
(Fig. 1) and 3000ha of paddy. The Uda Walawe irri-
gation system presently has several reservoirs (tanks
or wewas) that are interconnected. The main reservoir
is the Uda Walawe tank with a capacity of 268 mil-
lion cubic meters. The irrigated area is fed through
two main canals on the left and right banks of the
Walawe Ganga (river). These canals flow through
several smaller tanks that contribute to the project’s
overall water resources (SAPI, 2000). Currently, ir-
rigation development is about to start in the lower
part of the left bank extension area (Fig. 1). Many
small tanks with command areas ranging from 10 to
70ha are located in this extension area; some of them
will be upgraded and the area will be developed as
a small tank cascade system, in contrast to the al-
ready developed area which is based on a few large
tanks.
In addition to irrigated agriculture, people have
home gardens planted with coconut palms and other
fruit trees. Outside of the Uda Walawe irrigation
scheme, the main activities are chena (slash and burn)
cultivation, mainly in the northeastern part of the area
(Thanamalvilla DSD, see Fig. 1) and in the areas bor-
dering the irrigation scheme. Within Thanamalvilla
division there are also several remote, difficult to ac-
cess areas where illegal Ganja (cannabis) cultivation
takes place. The northern part of the Embilipitiya
division is more mountainous with small tea plan-
tations but also small rice paddies in the valleys.
In the Sooriyawewa and Embilipitya DSDs at some
places gem mining takes place and the gem pits are
abandoned after use.
218 E. Klinkenberg et al. / Acta Tropica 89 (2004) 215–225
2.2. Data collection and processing
The GND level was used for data analysis follow-
ing Abeysekera et al. (1997) who argued that this is
the most appropriate level to use for malaria data anal-
ysis in Sri Lanka, and all data were transformed to
the GND level with the aid of a GIS system. Depend-
ing on the level at which the data were available for a
particular variable, this was done by either aggregat-
ing village-level data to GND level or disaggregating
higher level data by overlaying GND boundaries. For
a detailed description of the GIS procedures, data col-
lection and data processing considerations we refer to
Klinkenberg et al. (2003).
Malaria case data were collected from the records
of all 14 government health facilities located in the
study area. The records contain the place of residence
of patients and, therefore, each new malaria case could
be assigned to a particular GND. The number of new
cases and the population figure was then used to calcu-
late malaria incidence (number of cases per 1000 pop-
ulation) for each GND. All the case data collected refer
to laboratory confirmed cases of malaria. In Sri Lanka,
in contrast to Africa and much of Asia, the hospital
recorded cases are considered a good representation of
the actual malaria incidence as people in general pre-
fer Western-type diagnosis and treatment for malaria
(Konradsen et al., 1997, 2000a,b). Malaria data from
private clinics were not available, as private clinics do
not systematically record the data, and could there-
fore not be included. Data from mobile clinics2were
included when available. Basic geographic features
(e.g. roads and streams) and land and water use pat-
terns for the study area were obtained from 1:50,000
scale maps of the Survey Department of Sri Lanka,
with permission from the Ministry of Defence. As
the main malaria vector in Sri Lanka prefers to breed
in pools in riverbeds and slow moving streams, the
length of natural waterways (hereafter referred to as
streams for convenience) per km2in each GND was in-
cluded as a covariate. Proximity of houses to a river or
stream has been suggested as a risk factor for malaria
(Gunawardena et al., 1998; van der Hoek et al., 2003).
Using GIS techniques a buffer layer was created for
the area within 250m of a stream. The percentage of
2Village-level clinics conducted periodically by the AMC, es-
pecially in the remote areas.
buffer area of the total area of the GND was used as a
covariate representing proximity to streams. A distinc-
tion was made between natural streams and irrigation
canals and these were included as two separate co-
variates. Socio-economic data at GND level consisted
of number of houses, number of families, number of
families receiving food subsidies, number of landless
families, number of families having electricity, and
number of livestock per family, and these were ob-
tained from the Department of Census and Statistics
(1993 data). Data on indoor insecticide spraying ac-
tivities for malaria control in the area were obtained
from the Anti-Malaria Campaign. For the purpose of
our analysis, each GND was categorized as sprayed or
unsprayed because the limited data available did not
allow for a more detailed categorization. Rainfall and
soil moisture data were available on a monthly basis
by GND for the period June 1999 to May 2000 from
a previous IWMI, Meteorological Department of Sri
Lanka project.
Except for the meteorological covariates, all co-
variates were only available on annual basis and
therefore the annual malaria incidence was used as
outcome measure in the data analysis. Annual data
analysis was run for the years 1991–1999. For the
year 2000, data were only available for January till
August, therefore this year was excluded from the
annual analysis. For all covariates except land use,
there were data only for 1 year. We assumed that
the malaria case data were independent between the
years and that the geographic variation in the covari-
ates did not change substantially between the years.
Therefore, the same covariate dataset was related to
each year’s malaria incidence data.
2.3. Statistical analysis
The annual dataset consisted of data for 196 GNDs
during the period 1991–1999. Malaria incidence rates
and incidence rate ratios were calculated for “high”
and “low” values of each covariate. The values of each
covariate measured on a continuous scale were coded
as 1 and 0 with the median value as cut-off point to
represent a high or low value of that covariate. The
median value was used in the absence of a better justi-
fication for placing the cut-off at any other value. The
value 1 was assigned to the category that was expected
to pose the highest risk for malaria. For example,
E. Klinkenberg et al. / Acta Tropica 89 (2004) 215–225 219
GNDs with more than median land area covered by
paddy were assigned the value 1, whereas GNDs with
less than median paddy area were assigned the value
0, based on the expectation that more paddy in a
GND would be associated with a higher malaria risk
in that GND. Confidence intervals for the incidence
rate ratios were calculated after Rothman (1986).On
the same dataset, after categorizing the incidence lev-
els, logistic regression analysis was carried out, with
SPSS software package (version 8.0), to investigate
the association between the outcome variable—the
malaria score—and the categorized covariates at the
GND level. The outcome variable was derived by re-
coding case count data as 1 or 0 based on a cut-off
value of annual parasite incidence (API). Three cut-off
values were investigated to represent low, moderate
and high-risk scenarios. In Sri Lanka, the AMC con-
siders malaria under control if the API is lower than 10
cases per 1000 inhabitants per annum. This value was
adopted for the low-risk scenario. An API of 30 was
utilized as cut-off value for the moderate-risk scenario
and an API of 100 for the high-risk scenario. In the lo-
gistic regression analysis, the malaria score was coded
as 1 for values above the cut-off and 0 for values below.
3. Results
The 10-year (1991–2000) dataset showed that
malaria incidence was consistently higher in the
Thanamalvilla area than in other areas. In Fig. 2,as
an example, the distribution of malaria incidence is
shown for 4 different years. The data showed a slight
overall decrease in malaria incidence over the 10-year
period. In the lower incidence areas the decrease on
average was from 10–50 to <10 cases per 1000 inhab-
itants per year and in the Thanamalvilla area the inci-
dence decreased from more than 500 cases per 1000
inhabitants per year to between 200 and 500 cases per
1000 inhabitants per year. Even at the lower incidence
level, Thanamalvilla would be considered a high-risk
area according to national guidelines of the Anti-
Malaria Campaign, that classify high-risk areas on the
basis of an annual parasite incidence (API) of 100 or
more cases per 1000 population and low risk on the
basis of an API below 10 cases per 1000 population.
Overlaying the malaria data with the land use data
revealed that the high incidence area consisted mainly
of non-irrigated land (compare Figs. 1 and 2), which
was mostly covered by forest and where chena (slash
and burn) cultivation was practiced. Malaria incidence
was much lower in the irrigated rice cultivation areas,
which would largely be classified as low risk (API <
10). Another salient land use feature of the high-risk
area was the presence of so-called “abandoned tanks”.
These are tanks that were formerly part of an ancient
irrigation system but were classified on land use maps
as “abandoned”. However, a field survey in the area
(Klinkenberg, 2001) revealed that these tanks were in
fact often used by local communities for small-scale
agriculture and other water uses.
The visual spatial pattern was confirmed by the
statistical analysis. A higher malaria incidence was
associated with more chena and less paddy cultiva-
tion, higher forest coverage, more families receiving
food subsidies, a larger area covered by abandoned
tanks, and more than median rainfall (Table 1). Fur-
thermore, high incidence was associated with areas
where residual insecticide spraying by the AMC
took place. As pointed out previously only limited
spray data were available (GNDs were categorized as
sprayed or not-sprayed, and details of frequency and
extent of coverage within a GND were not available),
and no conclusions regarding the effectiveness of the
strategy can be drawn from the statistical analysis.
The variables entered in the logistic regression
model explained about 40% of the variation in malaria
incidence at the GND level. Therefore, these vari-
ables were insufficient to build a predictive model, as
a large part of the variation in malaria incidence rates
remained unexplained. Different parameters became
more or less important with the scenario changing
from low to high risk. Average rainfall of >1200mm
and >1% forest coverage were the most important pa-
rameters that were significant at all levels (low, mod-
erate and high) of malaria risk (Table 2). Abandoned
tanks were clearly more important in the high-risk
scenario, as was the percentage of families receiving
food subsidies.
4. Discussion
One of the major concerns in a study of this na-
ture is the quality of the malaria data recorded at
government facilities. There is a high acceptance of
220 E. Klinkenberg et al. / Acta Tropica 89 (2004) 215–225
Fig. 2. Malaria incidence in 4 different years at GN level for the Uda Walawe region, Sri Lanka.
E. Klinkenberg et al. / Acta Tropica 89 (2004) 215–225 221
Fig. 2. (Continued).
222 E. Klinkenberg et al. / Acta Tropica 89 (2004) 215–225
Table 1
Risk factors for malaria in Uda Walawe at the level of the GND with incidence rates (IR), incidence rate ratios (IRRs), and 95% confidence
intervals for the IRR (95% CI)
Risk factors Categories nIR IRR 95% CI
Land use
Area covered by grass, scrubland and barren land <3% 868 16.6 1.00
≥3% 725 34.6 2.09 (2.07–2.11)
Area covered by forest <1% 1341 14.8 1.00
≥1% 252 78.1 5.29 (5.23–5.34)
Area covered by paddy ≤30% 816 36.3 1.00
>30% 777 7.5 0.21 (0.20–0.21)
Area covered by chena <5% 881 9.4 1.00
≥5% 712 35.9 3.83 (3.80–3.86)
Area covered by abandoned tanks ≤1% 1332 16.4 1.00
>1% 261 65.0 3.96 (3.92–4.00)
Land area at less than 250m of a natural stream ≥25% 810 21.3 1.00
>25% 783 27.4 1.29 (1.27–1.30)
Socio-economic status
Average number of livestock per family <1 1035 20.3 1.00
≥1 558 36.0 1.77 (1.75–1.80)
Families receiving food subsidies ≤65% 810 15.2 1.00
>65% 783 34.8 2.28 (2.26–2.30)
Families having electricity ≥5% 774 18.8 1.00
<5% 819 30.6 1.63 (1.61–1.64)
Meteorological data
Annual rainfall ≤1200mm 783 5.3 1.00
>1200mm 810 36.0 6.76 (6.71–6.81)
Mosquito control
Spraying activities Sprayed 414 52.2 1.00
Not sprayed 1179 10.3 0.20 (0.19–0.20)
n: number of Grama Niladhari Divisions (GNDs) whereby the unit of observation was a GND in a particular year between 1991 and 1999;
IR: malaria incidence per 1000 inhabitants; IRR: incidence rate ratio or relative risk with 1.00 for the reference category.
blood-filming by the population in Sri Lanka and
microscopists are generally available at government
health centers, resulting in about 70% of reported
cases being microscopically confirmed. Diagnosis
and treatment at private health facilities (whose data
are not included in national statistics), and the less
common phenomenon of self-diagnosis and treatment
are sources of underestimation of real incidence. Case
data from health facilities are not corrected for re-
crudescence of P. falciparum or relapse of P. vivax,
and this is a source of overestimation of incidence
(Briet et al., 2003). There are also the limitations of
the data used as covariates, which, apart from land
use (available for more than 1 year) and meteorology
(available monthly for the 10 years), were available
only for 1 year. Thus, statistical analyses had to be
based on assumptions that malaria data were inde-
pendent between years, and that geographic variation
among covariates did not change from year to year.
These limitations could have been contributories to
the overall low reliability of the multivariate analysis
(only 40% of variation explained by the model).
This study showed a consistent pattern of malaria
incidence in the Uda Walawe area over the pe-
riod 1991–2000 with highest risk for malaria in the
non-irrigated areas that had a relatively large propor-
tion of land under forest cover and chena cultivation.
Irrigated rice cultivation areas had a generally low
E. Klinkenberg et al. / Acta Tropica 89 (2004) 215–225 223
Table 2
Results of logistic regression analysis for three different scenarios
Scenario variable Low risk OR (95% CI) Moderate risk OR (95% CI) High risk OR (95% CI)
>1% forest within a GN 1.95 (1.34–2.85) 2.52 (1.62–3.92) 4.15 (2.01–8.54)
<30% paddy in a GN 2.00 (1.26–3.16) 1.64 (0.87–3.13) 2.83 (0.70–11.36)
<30% other crops in a GN 0.65 (0.46–0.90) 0.66 (0.43–1.02) 0.51 (0.24–1.10)
>5% chena in a GN 1.63 (1.12–2.37) 1.91 (1.15–3.21) 1.57 (0.68–3.63)
>1% working tanks in a GN 1.55 (1.16–2.09) 1.97 (1.34–2.89) 2.06 (1.10–3.87)
>1% abandoned tanks in a GN 1.12 (0.74–1.70) 1.90 (1.16–3.12) 4.22 (2.03–8.77)
>25% of a GN within 250m of a natural stream 2.39 (1.76–3.24) 1.76 (1.20–2.58) 1.15 (0.64–2.08)
>15% of a GN within 250m of an irrigation canal 1.34 (0.91–1.98) 1.87 (1.10–3.15) 2.05 (0.74–5.71)
>1 livestock per family 0.66 (0.47–0.92) 0.83 (0.54–1.26) 0.62 (0.32–1.18)
>65% families receiving food subsidies in a GN 0.85 (0.61–1.18) 1.26 (0.83–1.91) 3.46 (1.56–7.66)
Average annual rainfall >1200 mm in a GN 2.08 (1.43–3.02) 5.84 (3.28–10.39) 9.98 (3.09–32.23)
No spraying in a GN 0.19 (0.14–0.27) 0.16 (0.10–0.24) 0.08 (0.03–0.16)
Low-risk scenario API >10 scored 1; moderate-risk scenario API >30 scored 1; high-risk scenario API >100 scored 1; OR: odds ratio:
category coded as 1 relative to category coded as 0. Table after Klinkenberg et al. (2003); only the most important parameters are shown,
for full details we refer to Klinkenberg et al. (2003).
incidence of malaria. Other important risk factors
were the presence of abandoned tanks and a higher
than median rainfall. The population in the areas with
chena cultivation and with many abandoned tanks had
a lower socio-economic status than the population in
the irrigated areas. However, poor socio-economic
status was also an independent risk factor for malaria.
The low malaria risk in irrigated areas is surpris-
ing at first sight. Although the major malaria vector
in Sri Lanka, A. culicifacies, is not a rice field breeder
it could still be expected that conditions within an ir-
rigation system are favorable for vector breeding due
to the presence of an extensive canal network, seep-
age areas of canals, and other permanent water bod-
ies. In contrast with the water rich irrigated areas,
the chena-scrub-forest areas are relatively dry and one
would expect malaria to be confined to the rainy season
but this was clearly not the case (Klinkenberg et al.,
2003).
While the data available at present cannot give a
conclusive answer, there are several factors that could
contribute to the low malaria risk in the irrigated ar-
eas and the high malaria risk in the forest/chena cul-
tivation areas. First, it could be that certain land use
types generate different vector species and abundance
in different areas. At the moment, there are no ento-
mological data available that can support this. A land
use feature that showed significant impact in the sta-
tistical analysis of the data was the presence of tanks
that were classified as abandoned by the Survey De-
partment. As stated before, many of these tanks were
used by the local population on an informal basis. A
preliminary larval survey found no breeding of A. culi-
cifacies in these tanks but several possible secondary
vectors, i.e. A. annularis and A. vagus (Klinkenberg,
2001). Further investigations to define the exact role
of these tanks are ongoing.
The practicing of chena cultivation can be a risk
factor because farmers and their families often stay in
temporary huts during the cultivation season. These
are usually in remote areas where no health facili-
ties are present (Gunawardena, 1998). The burning
of chena areas greatly reduces the resting places for
mosquitoes, which pushes them to rest in houses
(Konradsen et al., 2000a). The seasonal migration
of farmers can contribute to transmission if people
get infected in the chena areas and go back to their
hometown (Gunawardena, 1998).
Differences in socio-economic status could also
underlie the observed results. The very high malaria
incidence recorded in Thanamalvilla could partly be
explained by the lower farm income and the high
proportion of people receiving food subsidies in this
area (Klinkenberg et al., 2003). Although we had
no access to data on type of houses in the different
DSDs it can be assumed that in the areas with a lower
socio-economic status the type of housing construc-
tion is poor. Earlier studies in Sri Lanka revealed that
the risk of being infected with malaria was up to 2.5
times greater for people in poorly constructed houses,
224 E. Klinkenberg et al. / Acta Tropica 89 (2004) 215–225
with thatched roofs and mud walls than for people
in better constructed houses (Gamage-Mendis et al.,
1991; Gunawardena et al., 1998; Konradsen et al.,
2000a, 2003). The importance of socio-economic
status in malaria transmission in Sri Lanka was also
stressed by other authors (Pinikahana, 1992; van der
Hoek et al., 1998). The importance of socio-economic
status and housing in determining malaria risk con-
firms, once again, that with the economic develop-
ment of an area the malaria situation and the health
status in general also would improve.
The observed risk factors and differences in malaria
patterns can have important implications for the cur-
rent control strategy. If secondary vectors like A. va-
gus and A. annularis are confirmed to be important
from an epidemiological point of view, then the in-
door residual spraying would not be very effective
because these are outdoor biting and resting species.
At present, there is some circumstantial evidence that
A. vagus may be an epidemiologically important vec-
tor in southern Sri Lanka (Pathinayake, 1997). This
study, therefore, highlights the importance of collect-
ing entomological data on a regular basis to explain
the malaria pattern and thus assist in control.
The analysis of malaria data in the Uda Walawe area
showed a consistent spatial pattern over 10 years of
observation with high risk in non-irrigated areas that
were dominated by forest and slash and burn cultiva-
tion, and a much lower risk in irrigated paddy culti-
vation areas. Additionally, the data showed an over-
all temporal pattern of a small but steady decline in
malaria incidence during the 10 year study period.
Briet et al. (2003) have shown that the island-wide
trend during the 1995–2002 period was an increase in
incidence upto 2000 and a sharp decrease thereafter.
The island-wide increase during 1995–2000 was not
reflected in the Uda Walawe area. At present, it is only
possible to speculate that the decreased incidence at
Uda Walawe may have been due to local vector trans-
mission dynamics, more effective malaria control, or
improved socio-economic condition, or a combination
of all of these.
Acknowledgements
The authors wish to thank Gayathri Jayasinghe, Lal
Muthuwatta and Dissanayake M. Gunawardena for
their contribution to the study. The assistance in the
field of Ravi Kurunarathne, Indrajith Gamage, Sarath
Lionalratne and Chandini Deepika was indispensable
as through their persistence most villages could be lo-
cated. The maps were digitized by Sarath Gunasinghe
and A.D. Ranjith. Database management and sup-
port services were provided by Mala Ranawake and
Sepali Goonaratne. We appreciate the help with the
different coordinate systems of Mr. Perakum Shanta
of the Survey Department. We further acknowledge
the importance of discussions that were held with Mr.
Jayasinghe, director of the Land Use Policy Planning
Division, Ms. Priyanthi of the Mapping Division,
Ms. Chandra Liyanage, land use officer of Ratnapura
District, Ms. Ruchira Wickremaratne, land use officer
of Hambantota District, and Mr. Dayaratna, land use
officer of Moneragala District.
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